Smart home control method and device, equipment, storage medium and program product

Abstract

The invention relates to a smart home control method and device, equipment, a storage medium and a program product. The method comprises the steps of obtaining current running state information of a target smart home and user preference information of a target user for the target smart home; obtaining an initial control strategy for the target smart home based on the operation state information and the user preference information; and obtaining the user satisfaction of the target user on the initial control strategy, and adjusting the initial control strategy at least based on the user satisfaction to obtain a target control strategy for controlling the target smart home. According to the embodiment of the invention, the control strategy of the target smart home is obtained through the operation state of the target smart home and the user preference information, and the control strategy is adjusted according to the user satisfaction, so that the requirements of the user can be met to a great extent; the situation that real requirements of users cannot be recognized and met due to environmental changes and the like can be avoided.

Description

Technical Field

[0001] The present disclosure relates to the field of Internet of Things technology, and in particular to smart home control methods, devices, equipment, storage media and program products. Background Art

[0002] With the continuous development of technology, smart homes are recognized by more and more users. At present, many smart homes use artificial intelligence and machine learning technologies, and use situational awareness and automation technologies to intelligently identify home environments and user needs, realizing automatic triggering of smart scenes.

[0003] In the related art, the smart home system based on contextual perception may produce misidentification due to environmental changes or sensor errors, making it difficult to cope with complex home environments, resulting in the smart home in the related art being unable to identify the user's real needs to a large extent. Summary of the invention

[0004] The present disclosure provides a smart home control method, device, equipment, storage medium and program product.

[0005] According to a first aspect of the present disclosure, a smart home control method is provided, the method comprising:

[0006] Acquire current operating status information of a target smart home and user preference information of a target user for the target smart home;

[0007] Based on the operating status information and the user preference information, acquiring an initial control strategy for the target smart home;

[0008] The user satisfaction of the target user with respect to the initial control strategy is obtained, and the initial control strategy is adjusted at least based on the user satisfaction to obtain a target control strategy for controlling the target smart home.

[0009] According to a second aspect of the present disclosure, a smart home control device is provided, the device comprising:

[0010] An information acquisition module, used to acquire current operating status information of a target smart home and user preference information of a target user for the target smart home;

[0011] An initial control strategy acquisition module, used to acquire an initial control strategy for the target smart home based on the operating state information and the user preference information;

[0012] The target control strategy acquisition module is used to obtain the user satisfaction of the target user to the initial control strategy, and adjust the initial control strategy based on at least the user satisfaction to obtain the target control strategy for controlling the target smart home.

[0013] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0014] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method of the present disclosure is implemented.

[0015] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the above method of the present disclosure when executed by a processor.

[0016] The smart home control method, device, equipment, storage medium and program product provided by the embodiments of the present disclosure obtain the current operating status information of the target smart home and the user preference information of the target user for the target smart home, and obtain the initial control strategy for the target smart home based on the operating status information and user preference information. In this way, by obtaining the user satisfaction of the target user for the initial control strategy, and adjusting the initial control strategy based on the user satisfaction, the target control strategy for controlling the target smart home can be seen. The embodiments obtain the control strategy for the target smart home through the operating status and user preference information of the target smart home, and adjust the control strategy according to the user satisfaction, which can meet the needs of the user to a large extent and avoid the situation where the real needs of the user cannot be identified and met due to environmental changes and other reasons. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0018] Figure 1 A schematic diagram of a framework of a user preference model provided by an exemplary embodiment of the present disclosure;

[0019] Figure 2 A flowchart of a smart home control method provided by an exemplary embodiment of the present disclosure;

[0020] Figure 3 A schematic block diagram of functional modules of a smart home control device provided by an exemplary embodiment of the present disclosure;

[0021] Figure 4A structural block diagram of an electronic device provided for an exemplary embodiment of the present disclosure;

[0022] Figure 5 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0024] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0025] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0026] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0027] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0028] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0029] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0030] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0031] In the related technologies, existing smart home systems usually use artificial intelligence and machine learning technologies to intelligently identify the home environment and user needs through contextual awareness technology and automation technology, and realize automatic triggering of smart scenes. Mainly including:

[0032] (1) Smart home systems based on contextual awareness: The home system obtains the current home environment information in real time through information such as sensors and user input, and conducts comprehensive analysis through technologies such as data fusion and machine learning. Based on the analysis results, the system automatically triggers the corresponding smart devices and services to adjust the home system to adapt to the current environment. However, smart home systems based on contextual awareness may produce misidentification due to environmental changes or sensor errors.

[0033] (2) Smart home systems based on preset rules. This system requires a series of trigger conditions, execution actions, constraints, and scene modes to be preset in advance. When external environmental information triggers a certain condition, the system automatically executes a certain rule. Smart home systems based on preset rules require the system or the user to preset a series of rules in advance. Simple preset rules cannot fully cover all scenes and are difficult to cope with complex home environments and user needs.

[0034] The above technical problems in the related art have not been solved. The embodiments of the present disclosure establish a user preference model, update the user preference model and dynamic strategy in real time, continuously learn and adapt to the changes of users, and continuously optimize its dynamic strategy. The application proposal can continuously learn and adapt to the changes of users, continuously optimize its dynamic strategy, and can more accurately identify the real needs of users and better meet the personalized needs of users. In addition, by combining user preferences, current environmental information and current behavior information, the embodiments can more accurately identify the real needs of users and reduce misoperations caused by environmental changes.

[0035] Specifically, the embodiment of the present disclosure provides an adaptive smart home control system based on user preference learning, which establishes a user preference model by collecting and analyzing user behavior data, and dynamically adjusts the operation settings of the smart home system according to the user's preferences to meet the user's personalized needs. The system may include a user data collection module, a user data analysis module, a user preference learning module, and a dynamic strategy adjustment module.

[0036] 1) The user data collection module collects the interaction data between users and devices in real time through the sensors and interactive interfaces of smart home devices.

[0037] The user data collection module is responsible for collecting various data related to the user experience, including but not limited to residential environment data, user's daily behavior data or interaction information between the user and the smart home system, etc. These data can be collected through sensors, wearable devices and other devices and means. Specifically, they include:

[0038] (1) Collection of residential environment data: Using sensors in the smart home system, the residential environment is comprehensively monitored and data is collected, including but not limited to indoor temperature information, indoor humidity information, indoor lighting information, or indoor volume information. Features related to user interaction behaviors, such as changes in temperature or light intensity, are extracted from the environmental information data as environmental information features.

[0039] (2) User behavior data collection: record the operations performed by the user on the smart home system, including but not limited to input voice commands, etc., so as to collect the user's interaction data information as user interaction features. In view of the fact that directly processing text data or voice data may affect the accuracy of text processing, the embodiment may use the change of environmental information data as an alternative means to more accurately record and analyze the user's interaction behavior. For example, when the user feels that the indoor ambient temperature is too high, he inputs the command of "lowering the temperature", and the final temperature after the reduction is used as the substitute data of the user's voice command intention. For example, the current air-conditioning temperature is 28°C. After receiving the "lower the temperature" command input by the user, the command can be converted into adjusting the air-conditioning temperature to 25°C.

[0040] 2) The user data analysis module can be used to receive the data collected by the user data collection module, quantify, clean and analyze the data, extract valuable data, and transmit the valuable data to the user preference learning module.

[0041] The user data analysis module processes and analyzes the collected information. Its main functions include: ① identifying the user's daily behavior habits; ② analyzing the user's preferred residential environment conditions when the user is in different behavior activities. Specifically including:

[0042] (1) Data quantification: Identify the collected data and quantify qualitative data, such as user gender (1-male, 2-female), etc. Different data represent different identification objects.

[0043] (2) Data preprocessing: The collected data is preprocessed, including data deduplication, unified format, filling or deleting missing values, standardization, etc., to ensure data quality and consistency. For duplicate data, strict deduplication is required to ensure data uniqueness and accuracy; for data with inconsistent formats, the format needs to be unified, such as date format, text format, temperature unit, etc., to ensure data consistency; for missing values, it is necessary to delete or fill them according to the importance of the data; for outliers, first perform normalization processing and identification. If the normalized value of a data is greater than 3, the value is judged as an outlier and processed with reference to the missing value. Specifically, it can be processed by the following formula (1).

[0044] z=(x-μ) / σ (1)

[0045] Among them, z represents the normalized value; x represents the original data value; μ represents the mean of the original data set; σ represents the population standard deviation of the original data set.

[0046] (3) Feature extraction and data dimensionality reduction: Extract valid data from preprocessed data, which can effectively remove data noise, discover hidden relationships between data, and better distinguish useful data from useless data.

[0047] Assume that after the above data cleaning preprocessing, a data set X consisting of n groups and m data is obtained, and is processed by the following formulas (2), (3) and (4).

[0048]

[0049] λ i , v i =eig(Cov(X)) (3)

[0050]

[0051] Among them, Cov(X) is the covariance matrix of the data set X, which is used to reflect the relationship between different indicators in the data set. i , v i are the corresponding eigenvalues ​​and eigenvectors respectively. is the cumulative contribution rate. When it is greater than 85%, the first k data are taken as the output data of the user data analysis module.

[0052] 3) The user preference learning module can use deep algorithms to train the data transmitted by the user data analysis module to establish a user preference model. It can also continuously iterate and optimize based on the user's historical preferences and current behavior habits, and update the user preference model in real time, so that it can more accurately reflect the user's behavior habits and preferences and meet the user's personalized needs.

[0053] Specifically, Figure 1 As shown, Figure 1 The framework diagram of the user preference model provided in the embodiment of the present disclosure may specifically be an LSTM (Long Short-Term Memory) model, and may specifically include the following equations (5) to (8).

[0054] G t =σ(W g *[ht-1,xt]+b g ) (5)

[0055]

[0056] H t =O t ⊙tanh(C t ) (8)

[0057] Among them, x t is the input data at a certain moment, usually the data passed by the user data analysis module; σ is the activation function, which controls the output within the range of [0,1] and is used to describe the user's preference for a certain condition; tanh is the activation function, which controls the output within the range of [-1,1] and is used to describe the continuous change process of user preference, such as the gradual increase or decrease of temperature; W is the weight matrix, such as W c and W g , which is used to store the user's behavior preferences in different time periods. The initial value is a randomly generated value, which will be continuously adjusted and optimized according to the user's actual behavior preferences during model training; C is a memory cell, C t-1 is the input at time t-1, C tis the output at time t, which is used to store the user's long-term preferences and behavior models; h is the hidden state at a certain moment, such as the hidden state at time t corresponds to h t , the hidden state at time t-1 corresponds to h t-1 , which is used to predict the user's current preference and is updated based on the long-term preference in the memory cell, the user's current behavior or environmental information. By continuously updating the hidden state, the model can track the user's preference changes in real time and respond accordingly; b is the bias vector, including b c and b g , which is used to adjust the input of the activation function to ensure that certain settings that users are more concerned about but not used frequently are not ignored; G t There are three gate control units (G t The value of is I / F / O, I is the input gate, F is the forget gate, and O is the output gate. Correspondingly, the value of g is i / f / o).

[0058] 4) Dynamic strategy adjustment module, which can adjust the control strategy of the device in real time according to the user preferences in the user preference learning module and the operating status of the smart home device to meet the personalized needs of the user.

[0059] In the embodiment, the dynamic strategy adjustment module can receive the user preference model transmitted by the user preference learning module, and convert the data in the user preference model into instructions that can be understood and executed by the smart home device by processing it. During the operation and control process of the smart home device, the module will also monitor the operation status of the device in real time, and dynamically adjust it according to the user's preferences and needs, and adjust the control strategy of the smart home device in real time to meet the user's personalized needs.

[0060] (1) Data preprocessing. The environmental parameter information collected by the data collection module and the user preference information transmitted by the user preference learning module can be used to generate environmental state information S and user preference information P.

[0061] S=[S1,S2,...,S n ],S i It is the current environmental status information, including one or more of temperature, humidity, light and sound.

[0062] P = [P1, P2, ..., P n ], P i is the user preference information, which indicates the user's preference for different environmental data S i The personalized preferences are used to represent the target environment state for smart home system strategy adjustment.

[0063] U=[U1,U2,...,U n ],U iRepresents the user satisfaction score, and its value range is [0,1], where 1 means the user is very satisfied and 0 means the user is very dissatisfied.

[0064] (2) Strategy selection. According to different residential environments, the smart home system will adjust the smart home devices according to the expected utility Q(s, a) achieved after adopting different strategies to maximize the satisfaction of user needs. The system will randomly select a strategy from the experience set with a probability of ε, and will choose the strategy a that best meets the user's expected utility (i.e., the strategy with the maximum Q value) with a probability of 1-ε.

[0065] Therefore, the strategy ∈ chosen at time t+1 t+1 It can be determined by the following formula (9):

[0066]

[0067] Among them, the initial value of ε can be 0.1, u t is the user's satisfaction with the current strategy, u θ is the satisfaction threshold, ∈ t is the strategy at time t, and u is the user satisfaction evaluation. The strategy in the embodiment can be understood as the control strategy for the smart home. It should be noted that ε can be a random function used to balance the random strategy of the experience pool with the current optimal strategy. The embodiment can calculate the probability of generating a random strategy based on the user's satisfaction, which is used to balance the random strategy of the experience pool with the current optimal strategy, and self-adjust and learn based on user behavior and feedback, and continuously learn and adapt to user changes.

[0068] In an embodiment, the current environmental status information of the smart home can be input into the trained LSTM model to obtain user preference information. For example, if the user is accustomed to an air-conditioning temperature of 22°C, the temperature can be used as the final goal to adjust the smart home. For example, if the current temperature is 25°C, it can be regulated by increasing the air volume and / or lowering the temperature in a coordinated manner. In this way, a variety of regulation strategies can be output, and strategies can also be selected from the control strategies in the experience set; by evaluating the satisfaction of these strategies, the final control strategy is determined.

[0069] (3) Dynamic adjustment. In the embodiment, after the initial strategy selection, the current strategy needs to be adjusted according to the current state s, user preference information p, real-time effect r of the home system and user satisfaction score U. The final Q is the maximum expected utility after the smart home system adopts a certain strategy a, and Q(s t , p, a t ;s t+1) is put into the experience set as an experience tuple. By continuously updating the strategy, we can adapt to the long-term changes of the system and improve the accuracy of decision-making. It can be determined by the following formulas (10) and (11).

[0070]

[0071] Q new (s t , p, a t )←Q t (s t , p, a t )+α·ΔQ (11)

[0072] Among them, ΔQ represents the error between the utility of the optimal strategy in historical experience and the utility of the actual strategy; r is the reward function, which represents the user's satisfaction feedback and the effect of environmental adjustment after taking action a. If the temperature is adjusted to be closer to the user's preference, a positive reward will be given; γ is a discount factor, which is used to balance immediate rewards and future rewards. Its value range is between [0,1]. The closer γ is to 1, the more the system pays attention to future rewards; otherwise, it means that the system pays more attention to immediate rewards; α is the learning rate, which represents the importance of controlling new information. Its value range is between [0,1]. The closer α is to 1, the greater the impact of new experience on Q value, otherwise, it means that the impact of old experience on Q value is more lasting.

[0073] (4) Feedback and optimization. After the smart home system executes strategy a, it updates the user preference model and dynamic strategy in real time, so that it can continuously learn and adapt to user changes, continuously optimize its dynamic strategy, and better meet the personalized needs of users.

[0074] P = [P1, P2, ..., P n ], P is the user's satisfaction evaluation. P = 0, 1, 2...10, where 0 means very dissatisfied and 10 means very satisfied.

[0075] In the embodiment, the ratio of the predicted strategy change amount to the optimal strategy change amount can be obtained by the following formula (12).

[0076]

[0077] Where E=[E1,E2,...,E n ], E is the ratio of the change in the predicted strategy to the change in the optimal strategy, a Q is the optimal strategy obtained based on the current user preference model and environment state information, a max is the optimal strategy in the historical experience set, a tIndicates the current strategy. The closer the E value is to 1, the more consistent the predicted strategy is with the historical optimal strategy; conversely, it means that the predicted strategy is significantly different from the historical optimal strategy.

[0078] The user satisfaction score can be obtained by the following formula (13):

[0079]

[0080] Among them, U is the user satisfaction score, and the user satisfaction parameters of the user preference model and the dynamic policy adjustment model can be updated according to the score to generate a new model. P is the user satisfaction evaluation, which can be the subjective evaluation given by the user, and E is the ratio of the predicted policy change to the optimal policy change in the above.

[0081] Based on the above embodiments, the present disclosure also provides a smart home control method, such as Figure 2 As shown, the method may include the following steps:

[0082] In step S210, the current operating status information of the target smart home and the user preference information of the target user for the target smart home are obtained.

[0083] In an embodiment, the current operating status information of the target smart home can be obtained, for example, the target smart home may include at least one of an air conditioner, a television, curtains, lights, and kitchen appliances, etc. The operating status may be the operating status information of each home in the target smart home, such as the current temperature of the air conditioner, the raised status of the curtains, the brightness of the lights, and other information.

[0084] The user preference information of the target user can be obtained by obtaining the historical settings of the target smart home by the target user in the past period of time and the historical operating status information of the target smart home, such as the temperature set by the air conditioner at each time period of the day, the state of the curtains being drawn, and the intensity of the lights, etc. The user preference information can reflect the habits of the target user so as to better meet the needs of the user.

[0085] In step S220, an initial control strategy for the target smart home is obtained based on the operating status information and the user preference information.

[0086] In an embodiment, by obtaining user preference information of a target user, the current operating status of the target smart home can be compared with the user preference information, and it can be determined whether the current operating status information of the target smart home matches the user preference information of the target user. If they do not match, the operating status of the target smart home can be adjusted according to the user preference information of the target user to generate an initial control strategy for the target smart home.

[0087] In step S230, the user satisfaction of the target user with respect to the initial control strategy is obtained, and the initial control strategy is adjusted based on the user satisfaction to obtain a target control strategy for controlling the target smart home.

[0088] In an embodiment, the target smart home is controlled by the above-mentioned initial control strategy, and the user satisfaction of the target user with respect to the initial control strategy can be obtained. For example, after the smart home is adjusted by the initial control strategy, the voice information of the target user can be collected in real time, and the user satisfaction can be obtained by analyzing the user feedback in the voice information. For example, the collected voice information contains "Why is it so hot now?", "The air is dry", etc., indicating that the current indoor temperature is relatively high and the air humidity is relatively dry, indicating that the user satisfaction of the target user is low, and the initial control strategy can be adjusted, and the smart home can be controlled by the adjusted control strategy until the user satisfaction corresponding to the adjusted control strategy meets the user requirements, so that the target control strategy for controlling the target smart home can be obtained.

[0089] The smart home control method provided by the embodiment of the present disclosure obtains the current operating status information of the target smart home and the user preference information of the target user for the target smart home, and obtains the initial control strategy for the target smart home based on the operating status information and the user preference information. In this way, by obtaining the user satisfaction of the target user for the initial control strategy, and adjusting the initial control strategy based on the user satisfaction, the target control strategy for controlling the target smart home can be seen. The embodiment obtains the control strategy for the target smart home through the operating status and user preference information of the target smart home, and adjusts the control strategy according to the user satisfaction, which can meet the needs of the user to a large extent and avoid the situation where the real needs of the user cannot be identified and met due to environmental changes and other reasons.

[0090] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S210 may further include the following steps:

[0091] In step S211, historical behavior data and historical environment data of the target user for the target smart home are obtained.

[0092] In step S212, the preset model is trained based on the historical behavior data and the historical environment data to obtain a user preference model, and user preference information of the target user is obtained based on the user preference model.

[0093] In the embodiment, the historical behavior data and historical environment data of the target user for the target smart home can be obtained, such as the target user's habit of adjusting the air conditioning temperature, controlling the lights, or controlling the curtain opening state in the past period of time. And at the same time as obtaining the user's historical behavior data, the historical environment data corresponding to the target smart home can be obtained, such as the actual indoor temperature when the target user adjusts the air conditioning temperature, and the indoor light intensity when the target user adjusts the light intensity.

[0094] By acquiring historical behavior data and historical environment data, training samples can be generated based on the historical behavior data and historical environment data, and the preset model can be trained through the training samples, so that the preset model can learn the user preferences of the target user, and then the user preference information of the target user can be obtained. Among them, the preset model can specifically be an LSTM model corresponding to the above embodiment. Exemplary user preference information, for example, in summer, the target user can adjust the air conditioning temperature to 26°C when sleeping at night, turn off the air conditioning when getting up in the morning, etc., and the embodiment is not limited to this.

[0095] The embodiment obtains the historical behavior data and historical environment data of the target user in the past period of time, and uses these data as training samples to train the model, so as to accurately obtain the user preference information of the target user, which makes it easier to control the target smart home based on the user's preference information, thereby meeting the user's needs.

[0096] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S230 may further include the following steps:

[0097] In step S231, the user satisfaction evaluation of the target user on the initial control strategy is obtained, and the change between the initial control strategy and the historical optimal strategy is obtained.

[0098] In step S232, based on the user satisfaction evaluation and the change amount, the user satisfaction of the target user with respect to the initial control strategy is determined.

[0099] In an embodiment, the user satisfaction evaluation can be a subjective evaluation made by the target user, such as comfort, heat or dryness, etc. The user satisfaction evaluation can be quantified and represented by a value between 0 and 10. The larger the value, the higher the user's satisfaction, and the lower the value.

[0100] In the embodiment, the variation between the initial control strategy and the historical optimal strategy can be obtained in combination with formula (12) in the above embodiment. If the variation approaches 1, it means that the initial control strategy is closer to the historical optimal strategy. In this way, the user satisfaction score can be calculated by using the user satisfaction evaluation and the variation, using formula (13) in the above embodiment, and the user satisfaction score is used as the user satisfaction of the target user with the initial control strategy. Since the user satisfaction evaluation belongs to the user's subjective evaluation, the variation is the comparison between the initial control strategy and the historical optimal strategy, reflecting the objective evaluation of the initial control strategy. In this way, the user satisfaction obtained by combining the user's subjective evaluation and the objective evaluation will be more accurate.

[0101] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S220 may further include the following steps:

[0102] In step S221 , the selection probability of the control strategy is selected from the prediction strategy set or the experience strategy set.

[0103] The control strategies included in the prediction strategy set are determined based on the operating state information and the user preference information.

[0104] In step S222 , an initial control strategy is determined from a prediction strategy set or an experience strategy set based on the selection probability.

[0105] In an embodiment, a prediction strategy set may be generated according to current operating status information of a target smart home and user preference information of a target user for the target smart home. The prediction strategy set may include multiple control strategies.

[0106] The experience strategy set in the embodiment may include multiple control strategies, and the control strategies that the user is more satisfied with can be put into the experience strategy set through historical events. Therefore, the control strategies in the prediction strategy set are control strategies predicted based on user preference information and the current operating status information of the target smart home, while the control strategies in the experience strategy set are control strategies obtained based on historical experience.

[0107] The probability of determining the initial control strategy from the prediction strategy set or the experience strategy set can be determined, and the selected initial control strategy can be determined by the random function of the above formula (9). For example, the initial state of the probability can be set, and the probability of determining the initial control strategy from the experience strategy set is set to ε, such as 0.1, and the probability of determining the initial control strategy from the prediction strategy set is set to 1-ε, such as 0.9. For example, if the user satisfaction corresponding to the initial control strategy determined from the prediction strategy set is less than the user satisfaction threshold, the probability of determining the initial control strategy from the prediction strategy set can be reduced, for example, to 0.8, and the probability of determining the initial control strategy from the experience strategy set can also be set to 0.2.

[0108] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S220 may further include the following steps:

[0109] In step S223, a first control strategy is selected from the prediction strategy set.

[0110] The control strategies included in the prediction strategy set are determined based on the operating state information and the user preference information.

[0111] In step S224, when the user satisfaction of the first control strategy is less than the user satisfaction threshold, an initial control strategy is determined from the experience strategy set; or, when the user satisfaction of the first control strategy is not less than the user satisfaction threshold, the first control strategy is used as the initial control strategy.

[0112] In the embodiment, the first control strategy can be selected from the prediction strategy set. The control strategy of the prediction strategy set is a strategy generated according to the current operating state information of the target smart home and the user preference information of the target user for the target smart home. Therefore, the first control strategy can be selected from the prediction strategy set. If the user satisfaction of the first control strategy is less than the user satisfaction threshold, the initial control strategy is determined from the experience strategy set. If the user satisfaction of the first control strategy is not less than the user satisfaction threshold, the first control strategy is used as the initial control strategy.

[0113] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S230 may further include the following steps:

[0114] In step S233, the user satisfaction of the target user with respect to the initial control strategy is obtained.

[0115] In step S234, the initial control strategy is adjusted based on user satisfaction, current operating status information of the target smart home, user preference information, and target effect of the target smart home.

[0116] In the embodiment, the initial control strategy is adjusted by obtaining the user satisfaction of the target user with respect to the initial control strategy, obtaining the current operating state and user preference information of the target smart home, and obtaining the target effect obtained by the target user for controlling the target smart home through the initial control strategy. The target effect can be obtained by comparing the obtained effect with the expected effect. For example, according to the user preference information, the user wants the indoor temperature to be 25°C, while the actual indoor temperature after adjustment through the initial control strategy is 22°C, indicating that the obtained target effect is not very good. The target effect can be quantified by a value between 0 and 10, for example, the larger the value, the better the effect. The embodiment is not limited thereto.

[0117] The embodiment adjusts the initial control strategy through user satisfaction, current operating status information of the target smart home, user preference information and target effect of the target smart home to obtain a target control strategy. The target smart home is controlled through the target control strategy to better meet user needs.

[0118] In the case of dividing each functional module according to each function, an embodiment of the present disclosure provides a smart home control device, which may be a server, a terminal, or a chip applied to a server. Figure 3 This is a schematic block diagram of the functional modules of a smart home control device provided by an exemplary embodiment of the present disclosure. Figure 3 As shown, the smart home control device includes:

[0119] An information acquisition module 10 is used to acquire the current operating status information of the target smart home and the user preference information of the target user for the target smart home;

[0120] An initial control strategy acquisition module 20, configured to acquire an initial control strategy for the target smart home based on the operating state information and the user preference information;

[0121] The target control strategy acquisition module 30 is used to obtain the user satisfaction of the target user to the initial control strategy, and adjust the initial control strategy based on at least the user satisfaction to obtain the target control strategy for controlling the target smart home.

[0122] In another embodiment provided by the present disclosure, the information acquisition module is specifically used to:

[0123] Acquire historical behavior data and historical environment data of the target user for the target smart home;

[0124] A preset model is trained based on the historical behavior data and the historical environment data to obtain a user preference model, and user preference information of the target user is obtained based on the user preference model.

[0125] In another embodiment provided by the present disclosure, the target control strategy acquisition module is specifically used to:

[0126] Obtaining a user satisfaction evaluation of the target user on the initial control strategy, and obtaining a change between the initial control strategy and a historical optimal strategy;

[0127] Based on the user satisfaction evaluation and the change amount, the user satisfaction of the target user with respect to the initial control strategy is determined.

[0128] In another embodiment provided by the present disclosure, the initial control strategy acquisition module is specifically used to:

[0129] Selecting a selection probability of a control strategy from the prediction strategy set or the experience strategy set respectively; wherein the control strategy included in the prediction strategy set is determined based on the operation state information and the user preference information;

[0130] An initial control strategy is determined from the prediction strategy set or the experience strategy set based on the selection probability.

[0131] In another embodiment provided by the present disclosure, the initial control strategy acquisition module is further used to:

[0132] Selecting a first control strategy from the prediction strategy set; wherein the control strategies included in the prediction strategy set are determined based on the operating state information and the user preference information;

[0133] When the user satisfaction of the first control strategy is less than a user satisfaction threshold, determining an initial control strategy from the experience strategy set;

[0134] Alternatively, when the user satisfaction of the first control strategy is not less than a user satisfaction threshold, the first control strategy is used as the initial control strategy.

[0135] In another embodiment provided by the present disclosure, the target control strategy acquisition module is specifically used to:

[0136] Obtaining user satisfaction of the target user with respect to the initial control strategy;

[0137] The initial control strategy is adjusted based on the user satisfaction, the current operating status information of the target smart home, the user preference information and the target effect of the target smart home.

[0138] The smart home control device provided by the embodiment of the present disclosure obtains the current operating status information of the target smart home and the user preference information of the target user for the target smart home, and obtains the initial control strategy for the target smart home based on the operating status information and the user preference information. In this way, by obtaining the user satisfaction of the target user for the initial control strategy, and adjusting the initial control strategy based on the user satisfaction, the target control strategy for controlling the target smart home can be seen. The embodiment obtains the control strategy for the target smart home through the operating status and user preference information of the target smart home, and adjusts the control strategy according to the user satisfaction, which can meet the needs of the user to a large extent and avoid the situation where the real needs of the user cannot be identified and met due to environmental changes and other reasons.

[0139] An embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.

[0140] Figure 4 The structure diagram of an electronic device provided by an exemplary embodiment of the present disclosure is shown in FIG. Figure 4 As shown, the electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801 , and the processor 1801 can execute corresponding steps in the above method disclosed in the embodiment of the present disclosure.

[0141] The processor 1801 may also be referred to as a central processing unit (CPU), which may be an integrated circuit chip having signal processing capabilities. Each step in the method disclosed in the embodiment of the present disclosure may be completed by an integrated logic circuit of hardware in the processor 1801 or by instructions in the form of software. The processor 1801 may be a general-purpose processor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a memory 1802, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, or other mature storage media in the art. The processor 1801 reads the information in the memory 1802 and completes the steps of the method in combination with its hardware.

[0142] In addition, when various operations / processes according to the present disclosure are implemented by software and / or firmware, they can be transmitted from a storage medium or a network to a computer system having a dedicated hardware structure, such as Figure 5 The computer system 1900 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can perform various functions, including the functions described above. Figure 5 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure.

[0143] Computer system 1900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0144] like Figure 5As shown, the computer system 1900 includes a computing unit 1901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 to a random access memory (RAM) 1903. In the RAM 1903, various programs and data required for the operation of the computer system 1900 can also be stored. The computing unit 1901, the ROM 1902, and the RAM 1903 are connected to each other via a bus 1904. An input / output (I / O) interface 1905 is also connected to the bus 1904.

[0145] A plurality of components in the computer system 1900 are connected to the I / O interface 1905, including: an input unit 1906, an output unit 1907, a storage unit 1908, and a communication unit 1909. The input unit 1906 may be any type of device capable of inputting information to the computer system 1900, and the input unit 1906 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1907 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1908 may include, but is not limited to, a disk, an optical disk. The communication unit 1909 allows the computer system 1900 to exchange information / data with other devices over a network such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0146] The computing unit 1901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1901 performs the various methods and processes described above. For example, in some embodiments, the above methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1908. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 1902 and / or communication unit 1909. In some embodiments, the computing unit 1901 may be configured to perform the above methods disclosed in the embodiments of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0147] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.

[0148] The computer-readable storage medium in the disclosed embodiments may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specifically, the computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0149] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0150] The embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor, the above method disclosed in the embodiments of the present disclosure is implemented.

[0151] In embodiments of the present disclosure, computer program codes for performing the operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0152] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0153] The modules, components or units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the names of the modules, components or units do not, in some cases, limit the modules, components or units themselves.

[0154] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0155] The above descriptions are only some embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, a technical solution formed by replacing the above features with the technical features with similar functions disclosed in the present disclosure (but not limited to).

[0156] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A smart home control method, characterized in that: The method comprises: Acquire current operating status information of a target smart home and user preference information of a target user for the target smart home; Based on the operating status information and the user preference information, acquiring an initial control strategy for the target smart home; The user satisfaction of the target user with respect to the initial control strategy is obtained, and the initial control strategy is adjusted at least based on the user satisfaction to obtain a target control strategy for controlling the target smart home.

2. The method according to claim 1, characterized in that Obtaining user preference information of the target user for the target smart home, including: Acquire historical behavior data and historical environment data of the target user for the target smart home; A preset model is trained based on the historical behavior data and the historical environment data to obtain a user preference model, and user preference information of the target user is obtained based on the user preference model.

3. The method according to claim 1, characterized in that: The obtaining of user satisfaction of the target users with respect to the initial control strategy includes: Obtaining a user satisfaction evaluation of the target user on the initial control strategy, and obtaining a change between the initial control strategy and a historical optimal strategy; Based on the user satisfaction evaluation and the change amount, the user satisfaction of the target user with respect to the initial control strategy is determined.

4. The method according to claim 1, characterized in that: The obtaining of the initial control strategy for the target smart home comprises: Selecting a selection probability of a control strategy from a prediction strategy set or an experience strategy set respectively; wherein the control strategy included in the prediction strategy set is determined based on the operation state information and the user preference information; An initial control strategy is determined from the prediction strategy set or the experience strategy set based on the selection probability.

5. The method according to claim 1, characterized in that The obtaining of the initial control strategy for the target smart home comprises: Selecting a first control strategy from a set of prediction strategies; wherein the control strategies included in the set of prediction strategies are determined based on the operating state information and the user preference information; When the user satisfaction of the first control strategy is less than a user satisfaction threshold, determining an initial control strategy from the experience strategy set; Alternatively, when the user satisfaction of the first control strategy is not less than a user satisfaction threshold, the first control strategy is used as the initial control strategy.

6. The method according to claim 1, characterized in that The adjusting the initial control strategy at least based on the user satisfaction comprises: Obtaining user satisfaction of the target user with respect to the initial control strategy; The initial control strategy is adjusted based on the user satisfaction, the current operating status information of the target smart home, the user preference information and the target effect of the target smart home.

7. A smart home control device, characterized in that: The device comprises: An information acquisition module, used to acquire current operating status information of a target smart home and user preference information of a target user for the target smart home; An initial control strategy acquisition module, used to acquire an initial control strategy for the target smart home based on the operating state information and the user preference information; The target control strategy acquisition module is used to obtain the user satisfaction of the target user to the initial control strategy, and adjust the initial control strategy based on at least the user satisfaction to obtain the target control strategy for controlling the target smart home.

8. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as claimed in any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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