Smart Home Control Method, Device, Electronic Device and Readable Storage Medium

Abstract

The present application relates to a smart home control method, device, electronic device, and readable storage medium. The method includes the steps of: obtaining current environmental data, and obtaining target control parameters based on the current environmental data; obtaining control groups including single or multiple controllable devices in the smart home system that can reach the target control parameters, and the operating energy consumption of each control group when reaching the target control parameters, and generating a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption; generating a control instruction according to the minimum energy consumption control strategy, and sending the control instruction to the controllable device corresponding to the control group with the minimum operating energy consumption. By obtaining target control parameters based on the current environmental data, and then determining the control group with the minimum operating energy consumption among multiple control groups, it is possible to generate a minimum energy consumption control strategy with the lowest energy consumption, thereby reducing power consumption.

Description

Technical Field

[0001] This application relates to the field of smart home, and in particular to a smart home control method, device, electronic device and readable storage medium. Background Art

[0002] With the development of society, smart homes have become popular in more and more families, and intelligent usage scenario recommendation has become an indispensable requirement in the process of using smart homes; in the prior art, most intelligent usage scenarios are preset, and the preset target control parameters are achieved by controlling smart homes. However, since the intelligent usage scenarios do not consider the operating energy consumption of smart homes, the electric energy consumed through the intelligent usage scenarios is often excessive. Summary of the Invention

[0003] This application provides a smart home control method, device, electronic device and readable storage medium, aiming to solve the technical problem of excessive electric energy consumption in intelligent usage scenarios in the prior art.

[0004] To solve the above technical problem or at least partially solve the above technical problem, this application provides a smart home control method, and the method includes the steps of:

[0005] Obtain current environmental data, and obtain target control parameters based on the current environmental data;

[0006] Obtain a control group including single or multiple controllable devices in the smart home system that can reach the target control parameters, and the operating energy consumption of each control group when reaching the target control parameters, and generate a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption;

[0007] Generate a control instruction according to the minimum energy consumption control strategy, and send the control instruction to the controllable device corresponding to the control group with the minimum operating energy consumption.

[0008] Optionally, the obtaining target control parameters based on the current environmental data includes:

[0009] Obtain target environmental data corresponding to the current environmental data;

[0010] Use the difference between the current environmental data and the target environmental data as the target control parameter.

[0011] Optionally, the obtaining target environmental data corresponding to the current environmental data includes:

[0012] Obtain a trained optimal environmental data calculation network;

[0013] Input the current environmental data into the optimal environmental data calculation network to obtain the target environmental data.

[0014] Optionally, before obtaining the trained optimal environmental data calculation network, it further includes:

[0015] Obtain historical control logs, which include user-set parameters corresponding to different environmental data;

[0016] Obtain preset optimal parameters corresponding to different environmental data, and generate a parameter training sample library based on the user-set parameters and the preset optimal parameters corresponding to different environmental data;

[0017] Train the initial optimal environmental data calculation network through the parameter training sample library to obtain the trained optimal environmental data calculation network.

[0018] Optionally, the generating a parameter training sample library based on the user-set parameters and the preset optimal parameters corresponding to different environmental data includes:

[0019] Obtain a preset user weight and a preset optimal weight;

[0020] For each set of user-set parameters and preset optimal parameters, calculate the first product of the user-set parameters and the preset user weight and the second product of the preset optimal parameters and the preset optimal weight;

[0021] Take the sum of the first product and the second product, the user-set parameters, the preset optimal parameters, and the environmental data as a parameter training sample;

[0022] Generate the parameter training sample library by aggregating the obtained parameter training samples.

[0023] Optionally, obtaining a control group including one or more controllable devices in the smart home system that can achieve the target control parameters, and the operating energy consumption of each control group when achieving the target control parameters, and generating a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption includes:

[0024] Obtain the trained minimum energy consumption strategy network, and input the target control parameters into the trained minimum energy consumption strategy network to obtain the operating energy consumption of each control group when achieving the target control parameters;

[0025] Generate a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption.

[0026] Optionally, before obtaining the trained minimum energy consumption strategy network, it includes:

[0027] Obtain the running duration and running energy consumption corresponding to different control groups when implementing different preset target control parameters;

[0028] Generate a policy training sample library based on the corresponding control group, the preset target control parameter, the running duration, and the running energy consumption;

[0029] Train the initial minimum energy consumption policy network through the policy training sample library to obtain a trained minimum energy consumption policy network.

[0030] To achieve the above object, the present invention further provides a smart home control device, and the smart home control device includes:

[0031] A first acquisition module, configured to acquire current environmental data and obtain a target control parameter based on the current environmental data;

[0032] A second acquisition module, configured to acquire a control group including one or more controllable devices in the smart home system that can reach the target control parameter, and the running energy consumption of each control group when reaching the target control parameter, and generate a minimum energy consumption control strategy based on the control group with the minimum running energy consumption;

[0033] A first generation module, configured to generate a control instruction according to the minimum energy consumption control strategy and send the control instruction to the controllable device corresponding to the control group with the minimum running energy consumption.

[0034] Optionally, the first acquisition module includes:

[0035] A first acquisition sub-module, configured to acquire target environmental data corresponding to the current environmental data;

[0036] A first execution sub-module, configured to use the difference between the current environmental data and the target environmental data as the target control parameter.

[0037] Optionally, the first acquisition sub-module includes:

[0038] A first acquisition unit, configured to acquire a trained optimal environmental data calculation network;

[0039] A first execution unit, configured to input the current environmental data into the optimal environmental data calculation network to obtain the target environmental data.

[0040] Optionally, the first acquisition sub-module further includes:

[0041] A second acquisition unit, configured to acquire a historical control log, and the historical control log includes user-set parameters corresponding to different environmental data;

[0042] A third acquisition unit, configured to acquire preset optimal parameters corresponding to different environmental data, and generate a parameter training sample library based on the user-set parameters and the preset optimal parameters corresponding to different environmental data;

[0043] A first training unit, configured to train an initial optimal environmental data calculation network through the parameter training sample library to obtain a trained optimal environmental data calculation network.

[0044] Optionally, the third acquisition unit includes:

[0045] A first acquisition subunit, configured to acquire a preset user weight and a preset optimal weight;

[0046] A first calculation subunit, configured to calculate a first product of the user-set parameter and the preset user weight and a second product of the preset optimal parameter and the preset optimal weight for each set of user-set parameters and preset optimal parameters;

[0047] A first execution subunit, configured to use the sum of the first product and the second product, the user-set parameter, the preset optimal parameter, and the environmental data as a parameter training sample;

[0048] A first generation subunit, configured to generate the parameter training sample library from the obtained parameter training samples.

[0049] Optionally, the second acquisition module includes:

[0050] A first matching submodule, configured to acquire a trained minimum energy consumption policy network, input the target control parameter into the trained minimum energy consumption policy network, and obtain the operating energy consumption of each control group when reaching the target control parameter;

[0051] A second execution submodule, configured to generate a minimum energy consumption control policy based on the control group with the minimum operating energy consumption.

[0052] Optionally, the second acquisition module further includes:

[0053] A second acquisition submodule, configured to acquire the operating duration and operating energy consumption corresponding to different control groups when achieving different preset target control parameters;

[0054] A third execution submodule, configured to generate a policy training sample library based on the corresponding control group, the preset target control parameter, the operating duration, and the operating energy consumption;

[0055] A first training submodule, configured to train an initial minimum energy consumption policy network through the policy training sample library to obtain a trained minimum energy consumption policy network.

[0056] To achieve the above object, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the smart home control method described above are implemented.

[0057] To achieve the above object, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the smart home control method described above are implemented.

[0058] A smart home control method, device, electronic device and readable storage medium provided by the present invention obtain current environmental data, and obtain target control parameters based on the current environmental data; obtain control groups including single or multiple controllable devices in the smart home system that can reach the target control parameters, and the operating energy consumption of each control group when reaching the target control parameters, and generate a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption; generate a control instruction according to the minimum energy consumption control strategy, and send the control instruction to the controllable device corresponding to the control group with the minimum operating energy consumption. By obtaining target control parameters based on the current environmental data, and then determining the control group with the minimum operating energy consumption among multiple control groups, it is possible to generate a minimum energy consumption control strategy with the minimum energy consumption, thereby reducing power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0061] Figure 1 It is a flowchart of the first embodiment of the smart home control method of the present invention;

[0062] Figure 2 It is a flowchart of the calculation algorithm for the target control parameters of the smart home control method of the present invention;

[0063] Figure 3 It is a flowchart of the generation algorithm for the minimum energy consumption control strategy of the smart home control method of the present invention;

[0064] Figure 4Schematic diagram of the overall process of the smart home control method of the present invention;

[0065] Figure 5 Schematic diagram of the module structure of the electronic device of the present invention. Detailed implementation manners

[0066] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0067] The present invention provides a smart home control method, which is applied to a smart home control device. The smart home control device can be, but is not limited to, a mobile terminal, a home management terminal, or a home device with data processing functions.

[0068] Referring to Figure 1 , Figure 1 which is a schematic diagram of the process of the first embodiment of the smart home control method of the present invention. The method includes the following steps:

[0069] Step S10: Obtain the current environmental data and obtain the target control parameter based on the current environmental data;

[0070] The current environmental data is used to characterize the environmental state of the current scene; the current environmental data includes, but is not limited to, temperature and humidity. The temperature can also be the outdoor temperature and the indoor temperature. The current environmental data can be obtained through the detection function of the controllable device, and can also be obtained by additionally setting detection devices such as thermometers and hygrometers; specifically, the controllable device can upload the detected current environmental data to the server, and the smart home control device directly obtains the current environmental data from the server; when the smart home control device establishes a connection with the controllable device, such as through the same local area network, Bluetooth or other connection methods, etc., the controllable device can directly send the detected current environmental data to the smart home control device.

[0071] The target control parameter is used to characterize the degree of control intervention required; taking temperature as an example, the target control parameter can be the target temperature, or the difference between the target temperature and the current temperature; the same applies to other types of environmental parameters and will not be elaborated here.

[0072] Step S20: Obtain control groups in the smart home system that can achieve the target control parameters and contain single or multiple controllable devices, as well as the operating energy consumption of each control group when achieving the target control parameters, and generate a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption;

[0073] The controllable devices are included in the current smart home system. Different controllable devices require different energy consumptions to achieve the same target control parameters. For different controllable devices that achieve the same function, they can be combined and operated together to achieve the target control parameters. The controllable devices that jointly achieve the target control parameters form a control group, and the control group can include one or more controllable devices; there are certain differences in the energy consumption required when using different control group solutions to achieve the target control parameters; therefore, selecting the solution with the minimum operating energy consumption can achieve the purpose of reducing power consumption; the control strategy is mainly reflected in setting the operating duration and power of different controllable devices; after obtaining the control group with the minimum operating energy consumption, the operating duration and power of the controllable devices in the control group can be set based on the target control parameters and the operating energy consumption, so as to obtain the minimum energy consumption control strategy.

[0074] Step S30: Generate a control instruction according to the minimum energy consumption control strategy, and send the control instruction to the controllable devices corresponding to the control group with the minimum operating energy consumption.

[0075] It can be understood that the corresponding number of control instructions is generated based on the number of controllable devices to be controlled. For example, if it is necessary to control the air conditioner and the humidifier at the same time, the air conditioner control instruction and the humidifier control instruction are generated respectively through the minimum energy consumption control strategy, and the air conditioner control instruction is sent to the air conditioner, and the humidifier control instruction is sent to the humidifier. It should be noted that when sending the control instruction to the controllable device, the control instruction can be sent to the server, and the server forwards the control instruction to the controllable device. In the case of establishing a connection with the controllable device, the control instruction can be directly sent to the controllable device.

[0076] In this embodiment, the target control parameters are obtained based on the current environmental data, and then the control group with the minimum operating energy consumption is determined among multiple control groups, so that the minimum energy consumption control strategy with the minimum energy consumption can be generated, thereby reducing power consumption.

[0077] Further, in the second embodiment of the smart home control method of the present invention proposed based on the first embodiment of the present invention, the step S10 includes the steps:

[0078] Step S11: Obtain the target environmental data corresponding to the current environmental data;

[0079] Step S12: Use the difference between the current environmental data and the target environmental data as the target control parameter.

[0080] The target environmental data is the environmental data that best meets the user's needs under the current environmental data. The target environmental data can be the environmental data set by the user in an environment identical or similar to the current environmental data, or it can be set by the manufacturer in advance for different environmental data. The difference between the current environmental data and the target environmental data can represent the degree of control required currently. Therefore, the difference is used as the target control parameter.

[0081] The step S11 includes the following steps:

[0082] Step S111: Obtain the trained optimal environmental data calculation network.

[0083] Step S112: Input the current environmental data into the optimal environmental data calculation network to obtain the target environmental data.

[0084] The type of the optimal environmental data calculation network can be selected according to the actual application scenario and requirements, such as a deep learning network or a neural network, etc., which is not limited here. The specific setting and execution method of the optimal environmental data calculation network can be based on the actual design and will not be elaborated here.

[0085] Before the step S111, it includes the following steps:

[0086] Step S113: Obtain the historical control log, which includes the user-set parameters corresponding to different environmental data.

[0087] Step S114: Obtain the preset optimal parameters corresponding to different environmental data, and generate a parameter training sample library based on the user-set parameters and the preset optimal parameters corresponding to different environmental data.

[0088] Step S115: Train the initial optimal environmental data calculation network through the parameter training sample library to obtain the trained optimal environmental data calculation network.

[0089] The historical control log records the operation records of the controllable devices. In addition to the environmental data and the user-set parameters, it also includes, but is not limited to, time, the controllable devices under control, etc. The user-set parameters are the control parameters set by the user according to their usage requirements; the preset optimal parameters are the control parameters set by the manufacturer in advance for different environmental data that are most suitable for the corresponding environmental data.

[0090] Based on different requirements, in some embodiments, the environmental parameters and the corresponding user-set parameters can be used as a parameter training sample; the environmental parameters and the corresponding preset optimal parameters can also be used as a parameter training sample; in some cases, such as when there is a large amount of user-set parameter data, the environmental parameters and the corresponding user-set parameters can be used as a parameter training sample, and in other cases, such as when there is a small amount of user-set parameter data, the environmental parameters and the corresponding preset optimal parameters can be used as a parameter training sample; the combination of the user-set parameters and the preset optimal parameters and the environmental parameters can also be used as a parameter training sample.

[0091] The step S114 includes the following steps:

[0092] Step S1141, obtaining a preset user weight and a preset optimal weight;

[0093] Step S1142, for each set of user-set parameters and preset optimal parameters, calculating a first product of the user-set parameters and the preset user weight and a second product of the preset optimal parameters and the preset optimal weight;

[0094] Step S1143, using the sum of the first product and the second product, the user-set parameters, the preset optimal parameters, and the environmental data as a parameter training sample;

[0095] Step S1144, generating the parameter training sample library from the obtained parameter training samples.

[0096] It can be understood that the sum of the preset user weight and the preset optimal weight is 1; the specific values of the preset user weight and the preset optimal weight can be set according to the actual application scenario and are not limited here. Specifically, the sum of the first product and the second product is the target control parameter corresponding to the environmental data. For example, the target control parameter includes a temperature target control parameter and a humidity target control parameter. The temperature target control parameter is:

[0097] t = a×t1+(1 - a)×t2

[0098] where t is the temperature target control parameter, a is the preset user weight, t1 is the user-set temperature, and t2 is the preset optimal temperature;

[0099] The humidity target control parameter is:

[0100] w = a×w1+(1 - a)×w2

[0101] where w is the humidity target control parameter, w1 is the user-set humidity, and w2 is the preset optimal humidity;

[0102] It should be noted that the specific steps, conditions, loss functions, etc. of network training can be set according to the actual application scenario, and will not be elaborated here.

[0103] This embodiment can accurately obtain the target control parameter.

[0104] Further, in the third embodiment of the smart home control method of the present invention proposed based on the first embodiment of the present invention, the step S20 includes the steps:

[0105] Step S21, obtain the trained minimum energy consumption policy network, and input the target control parameter into the trained minimum energy consumption policy network to obtain the operating energy consumption of each control group when reaching the target control parameter;

[0106] Step S22, generate a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption.

[0107] In practical applications, corresponding minimum energy consumption policy networks can be set for different control groups respectively, and the target control parameter is input into each minimum energy consumption policy network to obtain the operating energy consumption of each control group, and then the control group corresponding to the minimum operating energy consumption is selected from them; this method can make the calculation of a single minimum energy consumption policy network faster, but more resources need to be invested in building multiple minimum energy consumption policy networks. In other embodiments, only one minimum energy consumption policy network can be set. After the target control parameter is input into the minimum energy consumption policy network, the minimum energy consumption policy network directly outputs the control group with the minimum operating energy consumption; this method can reduce the construction resources of the minimum energy consumption policy network, but more calculations are required during application. The minimum energy consumption policy network can be specifically set according to the actual application scenario, and will not be elaborated here.

[0108] Before the step S21, it includes the steps:

[0109] Step S23, obtain the corresponding operating duration and operating energy consumption of different control groups when achieving different preset target control parameters;

[0110] Step S24, generate a policy training sample library based on the corresponding control group, the preset target control parameter, the operating duration, and the operating energy consumption;

[0111] Step S25, train the initial minimum energy consumption policy network through the policy training sample library to obtain the trained minimum energy consumption policy network.

[0112] Specifically, the running duration can be obtained through empirical induction or actual measurement. The running duration is the time required for the controllable device to operate at different powers to reach the preset target control parameters. After obtaining the running duration, the energy consumption can be calculated based on the running duration and power.

[0113] Take the corresponding control group, preset target control parameters, running duration, and running energy consumption as a policy training sample. The set of all policy training samples forms a policy training sample library.

[0114] It should be noted that the specific steps, conditions, loss functions, etc. of network training can be set according to the actual application scenario and will not be elaborated here.

[0115] This embodiment can accurately obtain the minimum controllable strategy.

[0116] The following combines Figures 2 - 4 to illustrate the overall solution of the present invention:

[0117] Refer to Figure 2 , first obtain the current environmental data in the current scenario from the cloud server, including but not limited to temperature, humidity, outdoor temperature, time, etc. Then, obtain the user's historical temperature setting values in the current situation, and at the same time obtain the most suitable indoor temperature and indoor humidity in the current environment through expert experience. By using the weighted average method and selecting appropriate weights through empirical values, calculate the most suitable temperature and humidity values. Integrate all the obtained information to establish a parameter training sample library. Use the current environmental data as the network input, and combine with the deep neural network model to construct a network for calculating the most suitable environmental parameters. The network outputs the most suitable temperature and humidity values in the current environment. In actual use, obtain the various environmental parameters of the user in the current situation through the server, and input them into the network for calculation. The network calculates the most suitable temperature and humidity values in the current situation.

[0118] Refer to Figure 3 , first, obtain the temperature and humidity in the current environment, and then calculate the difference in combination with the suitable environmental temperature and humidity values. Obtain the information of the controllable devices in the current environment. According to the temperature and humidity difference, obtain the running duration and energy consumption of each control group under this difference respectively. Through comparison, obtain the running strategy with the lowest energy consumption, and save this information to establish an energy consumption generation strategy training sample library for different temperature and humidity change targets. Use the temperature difference and humidity difference as the network input, and combine with the deep neural network model to construct a minimum energy consumption strategy network. The network outputs the running duration and related running parameters of each controllable device in the current environment. In actual use, input the calculated temperature difference and humidity difference into the network, and the network calculates the most suitable running parameters of each controllable device in the current situation, providing technical support for subsequent scenario generation and also improving the intelligence level of the method.

[0119] See Figure 4 , first, obtain all current environmental data in the user scenario from the server, then input the current environmental data into the optimal environmental parameter calculation network to calculate the optimal temperature and humidity values in the current environment. Next, calculate the temperature difference and humidity difference based on the current temperature and humidity values, and input this information into the minimum energy consumption strategy network to obtain the smart home energy consumption control strategy with the lowest energy consumption in the current environment. Finally, save the energy consumption control strategy, generate a usage scenario, and recommend this scenario to the user to complete the generation of the intelligent scenario.

[0120] Next, the solution of the present invention will be described in combination with specific parameters:

[0121] Taking the current environmental data of 30 degrees Celsius as an example, the preset optimal parameter corresponding to 30 degrees Celsius is matched to be 26 degrees Celsius, the user-set parameter corresponding to 30 degrees Celsius is obtained by matching the historical control log to be 25 degrees Celsius, the preset user weight is 0.8, and the preset optimal weight is 0.2. It can be obtained that the target environmental data is 25×0.8 + 26×0.2 = 25.2 degrees Celsius; furthermore, the target control parameter can be obtained as 30 - 25.2 = 4.8 degrees Celsius.

[0122] Taking the smart home system including an air conditioner and a fan as an example, the corresponding minimum energy consumption network is pre-trained based on the operating parameters of the air conditioner and the fan; taking 4.8 degrees Celsius as the input of the minimum energy consumption network, running the minimum energy consumption network to obtain the minimum energy consumption control strategy. The obtained minimum energy consumption control strategy is that the fan runs continuously at a power of 20 watts, and the air conditioner runs at a power of 5 kW for 10 minutes and then adjusts to a power of 1 kW and runs continuously; at this time, a control instruction of 20 watts of power is generated and sent to the fan, a control instruction of 5 kW of power is generated and sent to the air conditioner, and after 10 minutes, a control instruction of 1 kW of power is sent to the air conditioner; or a control instruction of 20 watts of power is generated and sent to the fan, and a planned control instruction is generated and sent to the air conditioner. The planned control instruction includes an immediately effective control instruction of 5 kW of power and a control instruction of 1 kW of power that takes effect after a delay of 10 minutes.

[0123] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0124] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0125] The present application also provides a smart home control device for implementing the above smart home control method. The smart home control device includes:

[0126] A first acquisition module, configured to acquire current environmental data and obtain target control parameters based on the current environmental data;

[0127] A second acquisition module, configured to acquire a control group including one or more controllable devices in the smart home system that can reach the target control parameters, and the operating energy consumption of each control group when reaching the target control parameters, and generate a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption;

[0128] A first generation module, configured to generate a control instruction according to the minimum energy consumption control strategy and send the control instruction to the controllable device corresponding to the control group with the minimum operating energy consumption.

[0129] This smart home control device obtains target control parameters based on current environmental data, and then determines the control group with the minimum operating energy consumption among multiple control groups, so that a minimum energy consumption control strategy with the minimum energy consumption can be generated, thereby reducing power consumption.

[0130] It should be noted that the first acquisition module in this embodiment can be used to execute step S10 in the embodiment of the present application, the second acquisition module in this embodiment can be used to execute step S20 in the embodiment of the present application, and the first generation module in this embodiment can be used to execute step S30 in the embodiment of the present application.

[0131] Further, the first acquisition module includes:

[0132] A first acquisition sub-module, configured to acquire target environmental data corresponding to the current environmental data;

[0133] A first execution sub-module, configured to use the difference between the current environmental data and the target environmental data as the target control parameter.

[0134] Further, the first acquisition sub-module includes:

[0135] A first acquisition unit, configured to acquire a trained optimal environment data calculation network;

[0136] A first execution unit, configured to input the current environment data into the optimal environment data calculation network to obtain the target environment data.

[0137] Further, the first acquisition sub-module further includes:

[0138] A second acquisition unit, configured to acquire a historical control log, where the historical control log includes user-set parameters corresponding to different environment data;

[0139] A third acquisition unit, configured to acquire preset optimal parameters corresponding to different environment data, and generate a parameter training sample library based on the user-set parameters and the preset optimal parameters corresponding to different environment data;

[0140] A first training unit, configured to train an initial optimal environment data calculation network through the parameter training sample library to obtain a trained optimal environment data calculation network.

[0141] Further, the third acquisition unit includes:

[0142] A first acquisition subunit, configured to acquire a preset user weight and a preset optimal weight;

[0143] A first calculation subunit, configured to calculate a first product of the user-set parameter and the preset user weight and a second product of the preset optimal parameter and the preset optimal weight for each group of user-set parameters and preset optimal parameters;

[0144] A first execution subunit, configured to use the sum of the first product and the second product, the user-set parameter, the preset optimal parameter, and the environment data as a parameter training sample;

[0145] A first generation subunit, configured to generate the parameter training sample library by aggregating the obtained parameter training samples.

[0146] Further, the second acquisition module includes:

[0147] A first matching sub-module, configured to acquire a trained minimum energy consumption policy network, and input the target control parameter into the trained minimum energy consumption policy network to obtain the operating energy consumption of each control group when reaching the target control parameter;

[0148] A second execution sub-module, configured to generate a minimum energy consumption control policy based on the control group with the minimum operating energy consumption.

[0149] Further, the second acquisition module further includes:

[0150] A second acquisition sub-module, configured to acquire the running duration and running energy consumption corresponding to different control groups when implementing different preset target control parameters;

[0151] A third execution sub-module, configured to generate a policy training sample library based on the corresponding control group, the preset target control parameters, the running duration, and the running energy consumption;

[0152] A first training sub-module, configured to train an initial minimum energy consumption policy network through the policy training sample library to obtain a trained minimum energy consumption policy network.

[0153] It should be noted here that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can be implemented by software or hardware, where the hardware environment includes a network environment.

[0154] Referring to Figure 5 , in terms of the hardware structure, the electronic device may include components such as a communication module 10, a memory 20, and a processor 30. In the electronic device, the processor 30 is respectively connected to the memory 20 and the communication module 10. A computer program is stored on the memory 20, and the computer program is simultaneously executed by the processor 30. When the computer program is executed, the steps of the above method embodiment are implemented.

[0155] The communication module 10 can be connected to an external communication device through a network. The communication module 10 can receive requests sent by the external communication device, and can also send requests, instructions, and information to the external communication device. The external communication device can be other electronic devices, servers, or Internet of Things devices, such as a TV, etc.

[0156] The memory 20 can be used to store software programs and various data. The memory 20 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as obtaining the current environment data of the current scene), etc.; the data storage area can include a database, and the data storage area can store data or information created according to the use of the system. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0157] The processor 30 is the control center of the electronic device. It connects various parts of the entire electronic device using various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 20, and by invoking the data stored in the memory 20, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 30 may include one or more processing units; optionally, the processor 30 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor may not be integrated into the processor 30 either.

[0158] Although Figure 5 not shown, the above-mentioned electronic device may further include a circuit control module, which is used to connect to the power supply to ensure the normal operation of other components. Those skilled in the art can understand that Figure 5 the structure of the electronic device shown in

[0159] does not constitute a limitation on the electronic device, and it may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. Figure 5 The present invention also proposes a computer-readable storage medium, on which a computer program is stored. The computer-readable storage medium may be the memory 20 in the

[0160] electronic device, or at least one of ROM (Read-Only Memory), RAM (Random Access Memory), magnetic disk, and optical disk. The computer-readable storage medium includes several instructions for causing a terminal device having a processor (which may be a TV, a car, a mobile phone, a computer, a server, a terminal, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0161] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0162] Although the embodiments of the present invention have been shown and described above, the scope of protection of the present invention is not limited thereto. It can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, and substitutions to the above embodiments within the scope of the present invention, and these changes, modifications, and substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be subject to the scope of protection of the claims.

Claims

1. A smart home control method, characterized in that, The method includes: Obtaining current environmental data and obtaining target control parameters based on the current environmental data; Obtaining control groups in the smart home system that can achieve the target control parameters and include single or multiple controllable devices, and the operating energy consumption of each control group when achieving the target control parameters, and generating a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption. Controllable devices that jointly achieve the same target control parameters form a control group; Generating a control instruction according to the minimum energy consumption control strategy and sending the control instruction to the controllable devices corresponding to the control group with the minimum operating energy consumption; The obtaining of the target control parameters based on the current environmental data includes: Obtaining target environmental data corresponding to the current environmental data, where the target environmental data is the sum of the product of the user-set parameters corresponding to the current environmental data and the preset user weight and the product of the preset optimal parameters and the preset optimal weight; Taking the difference between the current environmental data and the target environmental data as the target control parameter.

2. The smart home control method according to claim 1, wherein The obtaining of the target environmental data corresponding to the current environmental data includes: Obtaining a trained optimal environmental data calculation network; Inputting the current environmental data into the trained optimal environmental data calculation network to obtain the target environmental data.

3. The smart home control method according to claim 2, wherein, Before obtaining the trained optimal environmental data calculation network, it further includes: Obtaining historical control logs, where the historical control logs include user-set parameters corresponding to different environmental data; Obtaining preset optimal parameters corresponding to different environmental data and generating a parameter training sample library based on the user-set parameters and the preset optimal parameters corresponding to different environmental data; Training an initial optimal environmental data calculation network through the parameter training sample library to obtain a trained optimal environmental data calculation network.

4. The smart home control method according to claim 3, wherein, The generating of the parameter training sample library based on the user-set parameters and the preset optimal parameters corresponding to different environmental data includes: Obtaining a preset user weight and a preset optimal weight; For each set of user-set parameters and preset optimal parameters, calculating a first product of the user-set parameters and the preset user weight and a second product of the preset optimal parameters and the preset optimal weight; Taking the sum of the first product and the second product, the user-set parameters, the preset optimal parameters, and the environmental data as a parameter training sample; Generating the parameter training sample library by aggregating the obtained parameter training samples.

5. The smart home control method according to claim 1, wherein The obtaining of the control groups in the smart home system that can achieve the target control parameters and include single or multiple controllable devices, and the operating energy consumption of each control group when achieving the target control parameters, and generating a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption includes: Obtaining a trained minimum energy consumption strategy network and inputting the target control parameters into the trained minimum energy consumption strategy network to obtain the operating energy consumption of each control group when achieving the target control parameters; Generating a minimum energy consumption control strategy based on the control group with the minimum operating energy consumption.

6. The smart home control method according to claim 5, wherein, Before obtaining the trained minimum energy consumption policy network, the following steps are included: Obtain the running duration and running energy consumption corresponding to different control groups when implementing different preset target control parameters; Generate a policy training sample library based on the corresponding control groups, the preset target control parameters, the running duration, and the running energy consumption; Train the initial minimum energy consumption policy network through the policy training sample library to obtain the trained minimum energy consumption policy network.

7. A smart home control device, characterized in that, The smart home control device includes: A first acquisition module, configured to acquire current environmental data and obtain target control parameters based on the current environmental data; A second acquisition module, configured to acquire control groups including single or multiple controllable devices in the smart home system that can reach the target control parameters, and the running energy consumption of each control group when reaching the target control parameters, and generate a minimum energy consumption control policy based on the control group with the minimum running energy consumption. Controllable devices that jointly achieve the same target control parameter form a control group; A first generation module, configured to generate a control instruction according to the minimum energy consumption control policy and send the control instruction to the controllable devices corresponding to the control group with the minimum running energy consumption; The first acquisition module includes: A first acquisition sub-module, configured to acquire target environmental data corresponding to the current environmental data; A first execution sub-module, configured to use the difference between the current environmental data and the target environmental data as the target control parameter.

8. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the smart home control method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, the steps of the smart home control method according to any one of claims 1 to 6 are implemented.

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