Smart home equipment control method, device and system, equipment and medium

Abstract

The invention provides a smart home equipment control method, device and system, equipment and a medium. The method comprises the following steps: acquiring control information for smart home equipment, and inputting the control information into a control model; processing control information through a first processing layer of the control model to generate a first result; the first result represents a dependency relationship between the control information and time; processing the first result through a second processing layer of the control model to obtain a second result; the second result represents a future prediction result of the control information; and under the condition that the second result meets a preset condition, controlling the smart home equipment according to the second result. By adopting the method provided by the embodiment of the invention, the control accuracy of the smart home equipment on the home environment can be improved.

Description

Technical Field

[0001] The present application belongs to the field of smart home devices, and specifically relates to a smart home device control method, system, device, equipment and medium. Background Art

[0002] With the development of Internet of Things technology, smart home equipment systems are becoming increasingly popular.

[0003] Smart home devices can receive instructions from users and control the home environment according to the user's instructions to improve the comfort and safety of the home environment.

[0004] However, the factors affecting the home environment are complex, and the control strategies for the home environment in related technologies are generally based on preset rules, which are difficult to adjust dynamically, resulting in inaccurate control results for the home environment. Summary of the invention

[0005] The present application aims to provide a smart home device control method, apparatus, system, equipment and medium, at least to solve the problem in the related art that smart home devices are based on preset rules, resulting in inaccurate control of the home environment.

[0006] In a first aspect, an embodiment of the present application discloses a method for controlling a smart home device, the method comprising:

[0007] Acquire control information for smart home devices and input the control information into a control model;

[0008] Processing the control information through the first processing layer of the control model to generate a first result; the first result represents the dependency relationship between the control information and time;

[0009] Processing the first result through the second processing layer of the control model to obtain a second result; the second result represents a future prediction result of the control information;

[0010] When the second result meets a preset condition, the smart home device is controlled according to the second result.

[0011] In a second aspect, an embodiment of the present application discloses a smart home device control device, the device comprising:

[0012] Acquisition module: used to acquire control information for smart home devices and input the control information into the control model;

[0013] A first processing module: used for processing the control information through the first processing layer of the control model to generate a first result; the first result represents the dependency relationship between the control information and time;

[0014] A second processing module: used for processing the first result through the second processing layer of the control model to obtain a second result; the second result represents a future prediction result of the control information;

[0015] Implementation module: used for controlling the smart home device according to the second result when the second result meets a preset condition.

[0016] In a third aspect, the present application also provides a smart home device system, the system comprising a smart home device control device, and the smart home device control device is used to implement the smart home device control method as described in any one of claims 1 to 9.

[0017] In a fourth aspect, an embodiment of the present application further discloses an electronic device, comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0018] In a fifth aspect, an embodiment of the present application further discloses a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0019] In summary, in the embodiments of the present application, control information for smart home devices is collected, and the time dependency of the control information is captured through the first processing layer of the control model. The control information is predicted on the basis of the first processing layer through the second processing layer of the control model, and the prediction results are tested according to preset conditions, thereby ensuring the logical consistency of the prediction results and improving the accuracy of the prediction of the home environment state. Finally, the tested prediction results are used to control the smart home devices, giving the smart home devices the ability to dynamically adjust their own states, enriching the control strategies of the smart home devices for the home environment. Compared with the control strategies based on preset rules in the related art, the present application improves the intelligence level of smart home devices and improves the accuracy of smart home devices in controlling the home environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In the attached picture:

[0021] Figure 1 It is a flowchart of a method for controlling a smart home device provided by an embodiment of the present application;

[0022] Figure 2 is a flowchart of another smart home device control method provided by an embodiment of the present application;

[0023] Figure 3is a flowchart of another method for controlling a smart home device provided by an embodiment of the present application;

[0024] Figure 4 is a block diagram of a smart home device control device provided in an embodiment of the present application;

[0025] Figure 5 is a block diagram of an electronic device according to an embodiment of the present application;

[0026] Figure 6 It is a block diagram of an electronic device of another embodiment provided by the embodiments of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0029] Before introducing the specific implementation methods, it is necessary to explain the application scenarios and related technologies of this application in detail:

[0030] Smart home devices generally have environmental perception functions, which can monitor the home environment in real time and control the home environment according to preset rules. However, in related technologies, smart homes control the home environment based on preset rules, which makes it difficult to capture the complexity and long-term dependence of changes in the home environment. This is reflected in smart home devices, often resulting in inaccurate control instructions for the home environment from smart home devices.

[0031] The complexity of changes in the home environment is reflected in the fact that changes in the home environment are affected by a variety of real-time factors. Taking the temperature in the home environment as an example, the temperature change in the home environment is a sequence data that changes over time. It is affected by many factors such as the weather outside the home, the user's indoor activities, and equipment operation: external weather factors such as outdoor temperature, humidity, and wind speed will affect the temperature of the indoor home environment. For example, the temperature of the home environment may be higher on a sunny day, and the temperature of the home environment may be lower on a cloudy day; user indoor activities such as cooking and washing will affect the temperature of the home environment. For example, when cooking, the temperature in the kitchen will increase, and when washing, the temperature in the bathroom will increase; equipment operation, especially the operation of temperature control equipment, will directly affect the temperature of the home environment. For example, when the air conditioner is turned on for cooling, the temperature of the home environment will decrease, and when the heating equipment is running, the temperature of the home environment will increase.

[0032] The long-term dependence of changes in the home environment is reflected in the fact that changes in the home environment will be affected by time accumulation factors. Taking the temperature in the home environment as an example, the temperature change in the home environment is not only affected by real-time factors, but also by the accumulation of past temperature changes over time. For example, if the home environment temperature gradually rises in the morning, then in the next few hours, the temperature may continue to rise; the temperature change of the home environment is also affected by the season. For example, the temperature of the home environment is generally higher for a longer period of time in summer, and the temperature of the home environment is generally lower for a longer period of time in winter.

[0033] In the related art, smart home devices generally control their own states based on preset rules, which leads to deviations between the results of some control operations and user expectations. Taking the temperature changes in the home environment as an example, when the user just uses the kitchen in winter, the temperature of the home environment will rise briefly. After collecting the above changes, the related technology may issue a false alarm based on certain preset rules, or, based on other preset rules, predict that the temperature of the home environment will continue to rise in the future, and then perform cooling operations. Obviously, in the cold winter, lowering the temperature of the home environment is extremely bad for the user experience.

[0034] In addition, for abnormal situations in changes in the home environment, the relevant technology relies on threshold judgment of the collected environmental information, and often cannot discover and handle abnormal situations in a timely manner; the above problems lead to the fact that in the related technology, the control of the home environment by smart home devices is not accurate enough. In order to improve the intelligence level of smart home devices, improve the accuracy of smart home devices in predicting the future state of the home environment, and optimize the user experience, the embodiments of the present application propose a control method, device, electronic device and storage medium for smart home devices to at least partially solve the problems encountered in the above usage scenarios.

[0035] In a first aspect, the present application embodiment first provides a smart home device control method. Figure 1 is a flowchart of a method for controlling a smart home device provided in this embodiment, referring to Figure 1 , the method may include the following steps:

[0036] Step 101: Acquire control information for smart home devices and input the control information into a control model.

[0037] In order to construct the input of the control model, control information for smart home devices needs to be collected.

[0038] Exemplarily, smart home devices may include smart air conditioners, smart heating devices, smart humidifiers, smart lighting control devices, smart curtain control devices, smart home fire protection equipment, and other home devices that can directly or indirectly control the home environment.

[0039] Exemplarily, the control information may include environmental information that directly represents the home environment, user behavior information generated by users actively controlling smart home devices in the past, log information generated by controlling smart home devices according to control models in the past, forecast information generated by external devices predicting environmental information, etc. The environmental information may be collected and obtained in the form of a numerical set or in the form of a text set, and user behavior information, log information, forecast information, etc. may be collected and obtained in the form of a text set.

[0040] Exemplarily, the control model can be built based on conditional recurrent neural fields (Conditional Recurrent Neural Fields, CRNF), which is a model that combines recurrent neural networks (RNN) and conditional random fields (CRF), which can effectively process dependencies in sequence data and capture long-term temporal dependencies of sequence data; in this embodiment, the first processing layer in the control model can be equivalent to a recurrent neural network, and the second processing layer of the control model can be equivalent to a conditional random field.

[0041] Step 102: Process the control information through the first processing layer of the control model to generate a first result, where the first result represents the dependency relationship between the control information and time.

[0042] After the control information is input into the model, it is first converted into the first result in the first processing layer of the control model. The first result is an intermediate result representing the relationship between the control information and time dependency. The first processing layer can accurately capture the long-term dependency of the control information, so that the first result can more accurately represent the historical changes of the control information when it is input into the subsequent second processing layer, making the result of the second processing layer more in line with future trends.

[0043] Exemplarily, the first processing layer of the control model can be implemented by model training. For example, for the first processing layer, historical data can be collected first. The historical data here refers to the historical data of control information, which can include environmental information, such as indoor and outdoor temperature, humidity, wind speed, and user behavior information, such as temperature preferences set by users and user usage of smart home devices. Then, the first processing layer is trained using the collected historical data. During the training process, the first processing layer learns how to analyze the changing trend of control information based on historical data. Finally, in the actual control process of smart home devices, the control information collected in real time is input into the trained first processing layer, and the first processing layer obtains a first result representing the changing trend of the control information based on the input control information.

[0044] Step 103: Process the first result through the second processing layer of the control model to obtain a second result, where the second result represents a future prediction result of the control information.

[0045] By processing the first result, the second processing layer of the control model can predict the control information and obtain a second result representing the predicted result of the control information. The second processing layer can predict the control information and verify the control information to ensure that the predicted control information result is logical, thereby improving the accuracy of the prediction result.

[0046] Exemplarily, the second result may be environmental information within a period of time in the future, such as temperature information, humidity information, and light information. For example, for the temperature information of the environmental information in the control information, the second result may be a prediction of the change in temperature information every 5 minutes in the next half hour, where the length of the future period and the time of each prediction can be confirmed based on the first result.

[0047] Exemplarily, the second processing layer can be implemented by model training. For example, for the second processing layer, the input data can be preprocessed into a time-based series, and the data can be smoothed by commonly used analysis methods in time series, such as moving average method, difference method, etc., and then the characteristics of the time series can be extracted. The characteristics of the time series can be characterized by statistical characteristics, such as mean, variance, autocorrelation coefficient, etc. The characteristics of the time series are key features that can reflect the control information content in the time series. Then, the second processing layer is trained using the extracted features. After the training is completed, a time series can be generated based on the control information collected in real time, and features in the time series data can be extracted. The extracted features are input into the second processing layer, and the second processing layer outputs possible control information based on the characteristics of the time series data.

[0048] Step 104: When the second result meets a preset condition, control the smart home device according to the second result.

[0049] The result predicted by the second processing layer must pass the test before it can be used to control the smart home device. Therefore, before controlling the smart home device based on the second result, the situation in which the second result meets the preset conditions is obtained. The preset conditions characterize the degree to which the second result conforms to the prediction logic. In other words, when the second result does not meet the preset conditions, it can be considered that an abnormality has occurred in the control information predicted in the second result. The establishment of the test ensures the logical consistency of the predicted control information.

[0050] Exemplarily, logical consistency may be that the predicted control information maintains reasonable continuity over time to avoid sudden changes or unreasonable fluctuations; for example, for environmental information in the control information, if the predicted temperature information represents that the predicted temperature value rises from 22°C to 30°C in a short period of time, the predicted temperature information is logically less consistent.

[0051] For example, an abnormal situation may be an unacceptable change in the predicted environmental information, such as a significant change in temperature in a short period of time, a significant increase in smoke concentration in a short period of time, etc.

[0052] The second result that meets the preset conditions can be considered as the predicted result of the control information that meets the prediction logic. At this time, the state of the smart home device can be adjusted according to the second result, that is, the predicted control information, to realize the control of the home environment by the smart home device.

[0053] Exemplarily, if the predicted control information exceeds the range set by the user, the smart home device can be adjusted so that the smart home device ultimately conforms to the range set by the user. For example, with respect to the environmental information in the control information, if the predicted temperature information indicates that the predicted temperature exceeds the comfort range set by the user, the method provided in the present application will control the air conditioner to adjust the temperature so that the temperature of the home environment is closer to the comfort range set by the user; if the predicted lighting information indicates that the predicted lighting intensity exceeds the normal range of lighting intensity, the method provided in the present application will control the curtain control device to adjust the opening and closing state of the curtain so that the lighting intensity of the home environment is closer to the normal range of lighting intensity.

[0054] In summary, in this embodiment, control information for smart home devices is collected, and the time dependency of the control information is captured through the first processing layer of the control model. The control information is predicted on the basis of the first processing layer through the second processing layer of the control model, and the prediction result is tested according to preset conditions, thereby ensuring the logical consistency of the prediction result and improving the accuracy of the prediction of the home environment state. Finally, the tested prediction result is used to control the smart home devices, giving the smart home devices the ability to dynamically adjust their own state, enriching the control strategy of the smart home devices for the home environment. Compared with the control strategy based on preset rules in the related art, the present application improves the intelligence level of smart home devices and improves the accuracy of smart home devices in controlling the home environment.

[0055] Figure 2 A flowchart of another method for controlling smart home devices is provided. Figure 2 , the method may include the following steps:

[0056] Step 201: Acquire various environmental information through the sensor module of the smart home device.

[0057] The smart home devices have been explained in step 101 and will not be repeated here. The environmental information can be determined by the home environment factors controlled by the smart home devices. For example, the air conditioner can control the temperature or humidity in the home environment, the smart heating equipment can control the temperature or humidity in the home environment, the smart humidifier can control the humidity in the home environment, the smart lighting equipment and the smart curtain control equipment can control the light intensity in the home environment, and the smart home fire protection equipment can control the smoke concentration in the home environment. The environmental information may include temperature information, humidity information, lighting information, and smoke information.

[0058] Environmental information can be represented by numerical values ​​obtained by the sensor module. For example, the temperature value obtained by the temperature sensor represents temperature information. Similarly, humidity information can be represented by humidity value, light information can be represented by light intensity, and smoke information can be represented by smoke concentration.

[0059] Step 202: preprocess the environmental information to obtain a preprocessing result, and input the preprocessing result into a control model;

[0060] The environmental information obtained through the sensor module cannot be directly input into the control model. The environmental information needs to be preprocessed to make it conform to the input specifications of the control model.

[0061] Exemplarily, preprocessing may include data cleaning, the purpose of which is to remove noise from environmental information acquired by the sensor module and ensure the representativeness of data input into the model.

[0062] Optionally, step 202 may include:

[0063] Sub-step 2021: Divide the environmental information into multiple first sequences according to time nodes, and use the first sequences as the preprocessing results; each of the first sequences includes multiple types of environmental information obtained at the same time node.

[0064] Furthermore, in order to ensure that the control model can predict the environmental information according to the time series, the environmental information can be divided into multiple first sequences according to time nodes. The time nodes can be divided according to the period for the sensor module of the smart home device to obtain different environmental information, and the acquisition period of different sensors can be consistent.

[0065] For example, in one embodiment, it is necessary to predict the environmental information on October 1 and control smart home devices based on the predicted environmental information, where the environmental information includes indoor temperature information, humidity information, lighting information, and smoke information.

[0066] Then the environmental information obtained by the following sensor modules can be collected and processed: from 10-1.8:00:00, the temperature sensor collects the indoor temperature every 5 minutes; the humidity sensor collects the indoor humidity every 5 minutes; the light sensor collects the indoor light intensity every 5 minutes; the smoke sensor collects the indoor smoke concentration every 5 minutes. If the above acquired data is noise-free, the acquired data can be divided in the form shown in Table 1, where each timestamp represents a time node, and each timestamp and the temperature, humidity, light, and smoke collected at the timestamp will be taken as a first sequence.

[0067]

[0068] Table 1

[0069] Step 203: The environmental information is divided into a plurality of first sequences according to time nodes, each of the first sequences including a plurality of the environmental information acquired at the same time node; and feature information of the plurality of the environmental information is extracted from the first sequences through the first processing layer of the control model.

[0070] After preprocessing to obtain a plurality of first sequences, feature information of the environmental information in the first sequences may be extracted first. Compared with the data obtained by the original sensor module, the feature information may better represent the change of the environmental information.

[0071] For example, for the multiple first sequences shown in Table 1, characteristic information such as temperature change rate, humidity change rate, light change rate, and smoke change rate can be extracted, and the results are shown in Table 2:

[0072]

[0073] Table 2

[0074] Step 204: Generate state information of each time node according to the characteristic information through the first processing layer, and use the state information of each time node as the first result; the state information represents the dependency between the characteristic information of the environmental information and the time change.

[0075] After extracting the characteristic information of the environmental information, a first result may be generated according to the characteristic information in the first sequence, and the first result may be a hidden state of each time node;

[0076] For example, in one embodiment, it is necessary to obtain and predict environmental information, and control smart home devices according to the predicted results. The environmental information includes: temperature information, humidity information, light information, and smoke information. Through step 201 and step 202, there are multiple first sequences as shown in Table 3:

[0077]

[0078] Table 3

[0079] The data in Table 3 can be expressed in a hidden state according to the first result obtained in step 204 as shown in Table 4:

[0080]

[0081] Table 4

[0082] Step 205: Generate, through the second processing layer, a second sequence corresponding to a plurality of future time nodes according to the first result, and use the second sequence as the second result; each second sequence includes control information for the smart home predicted at the same future time node, and the second sequence represents the prediction result of the control information at the future time node.

[0083] The control information can be predicted by the second processing layer, and the future time nodes can be divided according to the time nodes of the first sequence. For example, taking the data shown in Table 4 as an example, the data in Table 4 can be predicted through step 205 as shown in multiple second sequences in Table 5:

[0084]

[0085] Table 5

[0086] Among them, the first sequence of environmental information in Table 4 is collected through the sensor module, and the timestamps in Table 5 are future time nodes. Correspondingly, each timestamp and the predicted temperature, predicted humidity, predicted light (intensity), and predicted smoke (concentration) at the timestamp constitute a sequence of environmental information at a future time node, that is, the second sequence.

[0087] The control information in the second sequence may also include control instructions for smart devices. The control instructions correspond to each type of environmental information and characterize the control status of the smart home devices. For example, for temperature information, the control instructions may include adjusting the air conditioner or not adjusting the air conditioner. Furthermore, adjusting the air conditioner may include controlling the air conditioner to heat up or cool down.

[0088] For example, according to the data in Table 2, the second sequence predicted from 08:50:00 to 09:35:00 in the future can be shown in Table 6:

[0089]

[0090] Table 6

[0091] Table 1, Table 2, and Table 6 can show the overall process from the collected environmental information to the second result.

[0092] Step 206: Each control information in the second sequence has its own probability distribution; when the probability distributions of the control information in the second sequence are all within their respective preset ranges, the smart home device is controlled according to the second result.

[0093] Step 207: Obtain a feedback result in the target device, wherein the feedback result represents the user's feedback on the execution result of the smart home device control method; adjust the second result according to the feedback result, and control the smart home device according to the adjusted second result.

[0094] After the smart home device is controlled according to the second result, the control strategy can be continuously optimized according to the user's feedback, and the user's feedback can be obtained through the target device, which can include the user's mobile phone, a mobile terminal matching the smart home device, or a touch operation panel on the main structure of the smart home device. For example, the user can feedback the comfort level after controlling the smart home device according to the second result through a smart speaker or a mobile phone application (Application, APP). After obtaining the feedback result, the second result will be adjusted according to the feedback result, and the smart home device will be controlled again through the adjusted second result.

[0095] Optionally, after step 205, the following may also be included:

[0096] Step 208: Each control information in the second sequence has its own probability distribution; when the probability distribution of the control information in the second sequence deviates from a preset range, an alarm is issued for the control information in the second sequence that deviates from the preset range.

[0097] Step 206 and step 208 show the verification process for the second result. The second processing layer outputs not only the predicted environmental information, but also the probability distribution of each environmental information. By analyzing the probability distribution, if the probability distribution of a certain environmental information deviates significantly from the normal range, it can be judged as an abnormal situation. At this time, an alarm can be issued based on the environmental information.

[0098] The alarm may be for environmental information. For example, when the probability distribution of smoke concentration in a second sequence is abnormal, a fire alarm for the smoke concentration may be issued.

[0099] In one embodiment, it is necessary to obtain and predict environmental information, and control smart home devices according to the predicted results. The environmental information includes: temperature information, humidity information, lighting information, and smoke information.

[0100] The set of the first sequence and the second sequence obtained according to step 202 and step 205 is shown in Table 7, wherein the sequence before and before the timestamp 2023 / 10 / 1 8:20 is the first sequence, and the sequence after the timestamp 2023 / 10 / 1 8:20 is the second sequence. In the second sequence corresponding to the timestamp 2023 / 10 / 1 8:45, the predicted smoke (concentration) is 10ppm:

[0101]

[0102]

[0103] Table 7 According to step 208, the second sequence can be tested, and the test results are shown in Table 8:

[0104]

[0105] Table 8

[0106] Among them, for the second sequence corresponding to the timestamp 2023 / 10 / 1 8:25 to the timestamp 2023 / 10 / 1 8:40, the predicted smoke (concentration) is 0ppm, and its probability is close to 1, which is in line with the normal range. The smoke concentration prediction result of the timestamp 2023-10-01 08:45:00 is 10ppm, but its probability is 0.01. Under normal circumstances, the normal value of the probability that the smoke concentration prediction result is 10ppm is 0. Therefore, it can be considered that the second sequence represented by the timestamp 2023-10-01 08:45:00 has an abnormal situation. At this time, a fire alarm can be issued for the smoke information.

[0107] In summary, in the embodiments of the present application, control information for smart home devices is collected, and the time dependency of the control information is captured through the first processing layer of the control model. The control information is predicted on the basis of the first processing layer through the second processing layer of the control model, and the prediction results are tested according to preset conditions, thereby ensuring the logical consistency of the prediction results and improving the accuracy of the prediction of the home environment state. Finally, the tested prediction results are used to control the smart home devices, giving the smart home devices the ability to dynamically adjust their own states, enriching the control strategies of the smart home devices for the home environment. Compared with the control strategies based on preset rules in the related art, the present application improves the intelligence level of smart home devices and improves the accuracy of smart home devices in controlling the home environment.

[0108] Figure 3 This is a flowchart of another method for controlling smart home devices provided in an embodiment of the present application. Figure 3 , the method may include the following steps:

[0109] Step S1, data collection.

[0110] Obtaining raw data. The raw data can be collected by the sensor module according to a set period. This step is equivalent to the above step 201.

[0111] Step S2: data feature processing.

[0112] Useful features are extracted from the raw data, and the raw data and features are preprocessed to convert them into a model input format. This step is equivalent to step 202 above.

[0113] Step S3: environmental modeling prediction.

[0114] The constructed control model is used to predict the environmental state in the future, and the probability distribution of the model output is calculated by the second processing layer in the control model to detect abnormal conditions. This step is equivalent to the steps 203 to 205 above, and the steps of probability distribution detection in steps 206 and 208 above.

[0115] Step S4, control strategy implementation.

[0116] According to the predicted environmental state, the state of the home appliance is adjusted. The predicted environmental state is equivalent to the second result or the second sequence mentioned above. This step is equivalent to controlling the smart home device according to the second result in step 206 above and the step of issuing an alarm for the control information in the second sequence that deviates from the preset range in step 208.

[0117] Step S5: user feedback.

[0118] The control strategy is adjusted and optimized in combination with the user's behavior data and feedback information. This step is equivalent to step 207 in the above text.

[0119] Second, refer to Figure 4 , which shows a smart home device control device 30 provided in an embodiment of the present application, the device 30 includes:

[0120] An acquisition module 301 is used to acquire control information for smart home devices and input the control information into a control model;

[0121] The first processing module 302 is used to process the control information through the first processing layer of the control model to generate a first result; the first result represents the dependency relationship between the control information and time;

[0122] The second processing module 303 is used to process the first result through the second processing layer of the control model to obtain a second result; the second result represents a future prediction result of the control information;

[0123] Implementation module 304: configured to control the smart home device according to the second result when the second result meets a preset condition.

[0124] Optionally, the acquisition module 301 may include:

[0125] The environmental information submodule is used to obtain a variety of different environmental information through the sensor module of the smart home device.

[0126] Optionally, the device 30 may further include:

[0127] Preprocessing module: used to preprocess the environmental information to obtain preprocessing results.

[0128] Input module: used for inputting the preprocessing results into the control model.

[0129] Optionally, the preprocessing module may include:

[0130] The first sequence submodule is used to divide the environmental information into multiple first sequences according to time nodes, and use the first sequences as the preprocessing results; each of the first sequences includes multiple types of environmental information obtained at the same time node.

[0131] Optionally, the control information includes environmental information, and the environmental information is divided into multiple first sequences according to time nodes, and each of the first sequences includes multiple types of environmental information obtained at the same time node; the first processing module 302 may include:

[0132] Feature extraction submodule: used for extracting feature information of a plurality of environmental information from the first sequence through the first processing layer of the control model.

[0133] State information submodule: used to generate state information of each time node according to the characteristic information through the first processing layer, and use the state information of each time node as the first result; the state information characterizes the dependency between the characteristic information of the environmental information and the time change.

[0134] Optionally, the second processing module 302 may include:

[0135] The second sequence submodule is used to generate, through the second processing layer, a second sequence corresponding to a plurality of future time nodes according to the first result, and use the second sequence as the second result; each second sequence includes control information for the smart home predicted at the same future time node, and the second sequence represents the prediction result of the control information at the future time node.

[0136] Optionally, each control information in the second sequence has its own probability distribution; the implementation module 304 may include:

[0137] The first implementation submodule is configured to control the smart home device according to the second result when the probability distribution of the control information in the second sequence is within the respective preset ranges.

[0138] Optionally, each control information in the second sequence has its own probability distribution; the device 30 may further include:

[0139] Alarm module: used for issuing an alarm for control information in the second sequence that deviates from a preset range when the probability distribution of the control information in the second sequence deviates from a preset range.

[0140] Optionally, the device 30 may further include:

[0141] Feedback acquisition module: used to obtain feedback results in the target device, and the feedback results represent the user's feedback on the execution results of the smart home device control method.

[0142] Feedback adjustment module: used to adjust the second result according to the feedback result, and control the smart home device according to the adjusted second result.

[0143] In summary, in the embodiments of the present application, control information for smart home devices is collected, and the time dependency of the control information is captured through the first processing layer of the control model. The control information is predicted on the basis of the first processing layer through the second processing layer of the control model, and the prediction results are tested according to preset conditions, thereby ensuring the logical consistency of the prediction results and improving the accuracy of the prediction of the home environment state. Finally, the tested prediction results are used to control the smart home devices, giving the smart home devices the ability to dynamically adjust their own states, enriching the control strategies of the smart home devices for the home environment. Compared with the control strategies based on preset rules in the related art, the present application improves the intelligence level of smart home devices and improves the accuracy of smart home devices in controlling the home environment.

[0144] In a third aspect, based on the same inventive concept, the present application also provides a smart home device system, the system comprising a smart home device control device as described in the second aspect, the smart home device control device being used to implement a smart home device control method as described in any one of the first aspects.

[0145] In a fourth aspect, an embodiment of the present application provides an electronic device, referring to Figure 5 , the electronic device 500 may include one or more of the following components: a processing component 502 , a memory 504 , a power component 506 , a multimedia component 508 , an audio component 510 , an input / output (I / O) interface 512 , a sensor component 514 , and a communication component 516 .

[0146] The processing component 502 generally controls the overall operation of the electronic device 500, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 502 may include one or more processors 520 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 502 may include one or more modules to facilitate the interaction between the processing component 502 and other components. For example, the processing component 502 may include a multimedia module to facilitate the interaction between the multimedia component 508 and the processing component 502.

[0147] The memory 504 is used to store various types of data to support the operation of the electronic device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phone book data, messages, pictures, multimedia, etc. The memory 504 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0148] The power supply component 506 provides power to the various components of the electronic device 500. The power supply component 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 500.

[0149] The multimedia component 508 includes an interface that provides an output interface between the electronic device 500 and the user. In some embodiments, the interface may include a liquid crystal display (LCD) and a touch panel (TP). If the interface includes a touch panel, the interface may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 508 includes a front camera and / or a rear camera. When the electronic device 500 is in an operating mode, such as a shooting mode or a multimedia mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0150] The audio component 510 is used to output and / or input audio signals. For example, the audio component 510 includes a microphone (MIC), and when the electronic device 500 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is used to receive an external audio signal. The received audio signal can be further stored in the memory 504 or sent via the communication component 516. In some embodiments, the audio component 510 also includes a speaker for outputting audio signals.

[0151] The input / output I / O interface 512 provides an interface between the processing component 502 and the peripheral interface module, which may be a keyboard, a click wheel, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0152] The sensor assembly 514 includes one or more sensors for providing various aspects of status assessment for the electronic device 500. For example, the sensor assembly 514 can detect the open / closed state of the electronic device 500, the relative positioning of components, such as the display and keypad of the electronic device 500, and the sensor assembly 514 can also detect the position change of the electronic device 500 or a component of the electronic device 500, the presence or absence of user contact with the electronic device 500, the orientation or acceleration / deceleration of the electronic device 500, and the temperature change of the electronic device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 514 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0153] The communication component 516 is used to facilitate wired or wireless communication between the electronic device 500 and other devices. The electronic device 500 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 516 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0154] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to implement a smart home device control method provided in an embodiment of the present application.

[0155] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 504 including instructions, and the instructions can be executed by a processor 520 of an electronic device 500 to perform the above method. For example, the non-transitory storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0156] Figure 6 600 is a block diagram of an electronic device 600 according to another embodiment of the present invention. For example, the electronic device 600 may be provided as a server. Figure 6 The electronic device 600 includes a processing component 622, which further includes one or more processors, and a memory resource represented by a memory 632 for storing instructions executable by the processing component 622, such as an application. The application stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 622 is configured to execute instructions to execute a water flow control method for a water supply device provided in an embodiment of the present application.

[0157] The electronic device 600 may also include a power supply component 626 configured to perform power management of the electronic device 600, a wired or wireless network interface 650 configured to connect the electronic device 600 to a network, and an input / output (I / O) interface 658. The electronic device 600 may operate based on an operating system stored in the memory 632, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0158] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0159] Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referenced to each other.

[0160] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0161] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing terminal device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0163] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0164] Finally, it should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0165] The above is a detailed introduction to the provided smart home device control method, device, system, equipment, and storage medium. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0166] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A smart home device control method, characterized in that: The method comprises: Acquire control information for smart home devices and input the control information into a control model; Processing the control information through the first processing layer of the control model to generate a first result; the first result represents the dependency relationship between the control information and time; Processing the first result through the second processing layer of the control model to obtain a second result; the second result represents a future prediction result of the control information; When the second result meets a preset condition, the smart home device is controlled according to the second result.

2. The method according to claim 1, characterized in that The control information includes environmental information; the acquiring control information includes: Acquire various environmental information through the sensor module of the smart home device; Before inputting the control information into the control model, the method further includes: Preprocessing the environmental information to obtain a preprocessing result; The step of inputting the control information into a control model comprises: The preprocessing results are input into the control model.

3. The method according to claim 2, characterized in that The environmental information includes: temperature information, humidity information, lighting information, and smoke information.

4. The method according to claim 2, characterized in that: The preprocessing of the environmental information to obtain a preprocessing result includes: The environmental information is divided into a plurality of first sequences according to time nodes, and the first sequences are used as the preprocessing results; each of the first sequences includes a plurality of the environmental information acquired at the same time node.

5. The method according to claim 1, characterized in that: The control information includes environment information, and the environment information is divided into a plurality of first sequences according to time nodes, each of the first sequences includes a plurality of the environment information acquired at the same time node, and the first processing layer of the control model processes the control information to generate a first result, including: Extracting characteristic information of a plurality of types of the environmental information from the first sequence through a first processing layer of the control model; Through the first processing layer, state information of each time node is generated according to the characteristic information, and the state information of each time node is used as the first result; the state information represents the dependency between the characteristic information of the environmental information and the time change.

6. The method according to claim 1, characterized in that The step of processing the first result by the second processing layer of the control model to obtain a second result of the control information includes: Through the second processing layer, a second sequence corresponding to multiple future time nodes is generated according to the first result, and the second sequence is used as the second result; each second sequence includes control information for the smart home predicted at the same future time node, and the second sequence represents the prediction result of the control information at the future time node.

7. The method according to claim 6, characterized in that: Each control information in the second sequence has its own probability distribution, and when the second result satisfies a preset condition, controlling the smart home device according to the second result includes: When the probability distributions of the control information in the second sequence are all within respective preset ranges, the smart home device is controlled according to the second result.

8. The method according to claim 6, characterized in that: Each control information in the second sequence has its own probability distribution. After the step of processing the first result by the second processing layer of the control model to obtain the second result of the control information, the method further includes: When the probability distribution of the control information in the second sequence deviates from a preset range, an alarm is issued for the control information in the second sequence that deviates from the preset range.

9. The method according to claim 1, characterized in that: After the step of controlling the smart home device according to the second result, the method further includes: Acquire a feedback result in the target device, wherein the feedback result represents the user's feedback on the execution result of the smart home device control method; The second result is adjusted according to the feedback result, and the smart home device is controlled according to the adjusted second result.

10. A smart home equipment control device, characterized in that: The smart home equipment control device comprises: Acquisition module: used to acquire control information for smart home devices and input the control information into the control model; A first processing module: used for processing the control information through the first processing layer of the control model to generate a first result; the first result represents the dependency relationship between the control information and time; A second processing module: used for processing the first result through the second processing layer of the control model to obtain a second result; the second result represents a future prediction result of the control information; Implementation module: used for controlling the smart home device according to the second result when the second result meets a preset condition.

11. A smart home device system, characterized in that: It comprises a smart home device control device, and the smart home device control device is used to implement the smart home device control method as described in any one of claims 1 to 9.

12. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method according to any one of claims 1 to 9 are implemented.

13. A readable storage medium, characterized in that: The readable storage medium stores a program or an instruction, and when the program or the instruction is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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