WO2020252703A1 - 设备控制方法、装置、服务器及存储介质 - Google Patents
设备控制方法、装置、服务器及存储介质 Download PDFInfo
- Publication number
- WO2020252703A1 WO2020252703A1 PCT/CN2019/091945 CN2019091945W WO2020252703A1 WO 2020252703 A1 WO2020252703 A1 WO 2020252703A1 CN 2019091945 W CN2019091945 W CN 2019091945W WO 2020252703 A1 WO2020252703 A1 WO 2020252703A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- data
- control
- household
- household device
- condition data
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 64
- 238000012549 training Methods 0.000 claims abstract description 124
- 230000007613 environmental effect Effects 0.000 claims abstract description 25
- 238000012216 screening Methods 0.000 claims description 8
- 230000004044 response Effects 0.000 claims description 5
- 239000013598 vector Substances 0.000 description 11
- 238000010586 diagram Methods 0.000 description 9
- 238000005516 engineering process Methods 0.000 description 9
- 238000012545 processing Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 6
- 238000003062 neural network model Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 230000003993 interaction Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 235000019640 taste Nutrition 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000010411 cooking Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000003066 decision tree Methods 0.000 description 2
- 230000036651 mood Effects 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000006996 mental state Effects 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000005406 washing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Definitions
- This application relates to the field of smart home technology, and more specifically, to a device control method, device, server, and storage medium.
- this application proposes a device control method, device, electronic device, and storage medium to improve the degree of intelligence of household device control.
- an embodiment of the present application provides a device control method.
- the method includes: acquiring current condition data, where the current condition data is used to control a first household device; When the trigger condition is triggered, the trained training model is called, and the training model is obtained by training based on the historical condition data and device data when the first household device is operated.
- the condition data includes at least the user's personal data and environmental data
- the device data includes at least a control instruction for the first household device; inputting the current condition data into the training model to obtain control of the first household device Instruction; according to the control instruction, the first household device is controlled.
- an embodiment of the present application provides a device control device, the device includes: a data acquisition module, a model acquisition module, an instruction acquisition module, and a control execution module, wherein the data acquisition module is used to acquire current condition data , The current condition data is used to control the first home appliance; the model acquisition module is used to call the trained training model when the current condition data meets the set trigger condition, and the training model is based on the The historical condition data and equipment data training obtained when the first household device is operated, the condition data includes at least the user's personal data, environmental data and the status data of the second household device, and the device data includes at least The control instruction of the first household device; the instruction acquisition module is used to input the current condition data into the training model to obtain the control instruction of the first household device; the control execution module is used to The control instruction controls the first household equipment.
- an embodiment of the present application provides a server, including: one or more processors; a memory; one or more application programs, wherein the one or more application programs are stored in the memory and are It is configured to be executed by the one or more processors, and the one or more programs are configured to execute the device control method provided in the first aspect described above.
- an embodiment of the present application provides a computer-readable storage medium.
- the computer-readable storage medium stores program code, and the program code can be called by a processor to execute the device provided in the first aspect. Control Method.
- the solution provided by this application obtains the current condition data used to control the first household device, and when the current condition data meets the set control trigger condition, call the historical condition data and device data when the first household device is operated
- the training model obtained by training the historical condition data includes the user's personal data, environmental data, and the status data of the second home device, and the device data includes control instructions for the first home device, and then the current condition data is input to the training model, Obtain the control instruction for the first household device, and control the first household device according to the control instruction, so as to realize the prediction of the operation instruction on the household device according to the user's historical operation of the household device, and realize the automatic control of the household device, and improve
- the degree of intelligence of home equipment control and high relevance to users meets user needs and improves user experience.
- Fig. 1 shows a schematic diagram of an application scenario proposed by an embodiment of the present application.
- Fig. 2 shows a schematic diagram of another application scenario proposed by an embodiment of the present application.
- Fig. 3 shows a flow chart of a device control method according to an embodiment of the present application.
- Fig. 4 shows a flowchart of a device control method according to another embodiment of the present application.
- Fig. 5 shows a flow chart of a device control method according to another embodiment of the present application.
- Fig. 6 shows a block diagram of a device control apparatus according to an embodiment of the present application.
- Fig. 7 is a block diagram of an electronic device used to execute the device control method according to the embodiment of the present application.
- Fig. 8 is a storage unit for storing or carrying program codes for implementing the device control method according to the embodiment of the present application.
- smart homes have penetrated into various families and are loved by many families because of the convenience it brings.
- the so-called smart home is based on the housing platform, using integrated wiring technology, network communication technology, security technology, automatic control technology, audio and video technology to integrate facilities related to home life to build an efficient management system for residential facilities and family schedule affairs , Improve home safety, convenience, comfort, artistry, and realize an environmentally friendly and energy-saving living environment.
- smart home connects various home appliances (such as smart air conditioners, smart lights, smart refrigerators, smart washing machines, etc.) in the home through the Internet of Things technology to provide home appliance control, lighting control, telephone remote control, indoor and outdoor remote control, and anti-theft Various controls such as alarm, environmental monitoring, HVAC control, infrared forwarding and programmable timing control.
- home appliances such as smart air conditioners, smart lights, smart refrigerators, smart washing machines, etc.
- Various controls such as alarm, environmental monitoring, HVAC control, infrared forwarding and programmable timing control.
- the traditional way of controlling home equipment is mostly through the user manually control on the spot, or through the visual interface on the mobile terminal (for example, mobile phone, tablet, etc.) to remotely control the home equipment, or Delay control, or use a set scene to control, of course, there are also ways to control home equipment through language control.
- the inventor proposes the device control method, device, electronic device, and storage medium provided by the embodiments of the application.
- the current condition data is used to control the first household device, and when the current condition data meets
- Control the first home device according to the control instruction so as to realize the prediction of the operation instruction of the home device according to the user's historical operation of the home device, and realize the automatic control of the home device, improve the intelligent degree of the control of the home device, and It has high relevance to users, meets user needs, and improves user experience.
- FIG. 1 shows a schematic diagram of an application scenario of the device control method provided by an embodiment of the present application.
- the application scenario includes a smart home system.
- the smart home system may include a server 100, a home device 200, a gateway device 300, and a mobile device.
- Terminal 400 The gateway device 300 and the mobile terminal 400 can communicate with the server 100, and the home device 200 establishes a wireless connection with the gateway device 300, so that the mobile terminal 400 can interact with the home device 100 through the server 100 and the gateway device 300.
- the mobile terminal 400 may also establish a wireless connection with the gateway device 300 to realize data interaction between the mobile terminal 400 and the household equipment 200.
- FIG. 2 shows another schematic diagram of an application scenario of the device control method provided by an embodiment of the present application.
- the smart home system of the application scenario includes a first cloud server 101, a second cloud server 102, and household equipment 200, Gateway device 300 and mobile terminal 400.
- the mobile terminal 400 may be in communication connection with the first cloud server 101 to realize data interaction with the first cloud server 101.
- the household device 200 communicates with the second cloud server 102 through the gateway device 300 to realize data interaction between the second and the server 102.
- data exchange can be performed between the first cloud server 101 and the second cloud server 102, so that the mobile terminal 400 can communicate with the home device 200 through the first cloud server 101, the second cloud server 102, and the gateway device 300. Data interaction.
- an embodiment of the present application provides a device control method, which can be applied to a server, and the device control method may include:
- Step S110 Obtain current condition data, where the current condition data is used to control the household equipment.
- the server may obtain current condition data, and the current condition data is used to control the first household device.
- the current condition data is used by the server to input into the setting model to obtain data predicting the user's control instruction for the first household device.
- the current condition data may include the user's personal data, environmental data, and status data of the second household device.
- the personal data may be personal information data of the user, and may include the user's current status, gender, taste, temperament, educational background, physical data, etc.
- the current state of the user may be whether the user is at work, the user's mental state, the user's mood state, and so on.
- the environmental data may be data of the environment in which the user and/or the aforementioned first household device is located.
- the environmental data may include location data, time data, weather data, conditions of people at home, and so on.
- the second household device may be another household device, and the state data of the second household device may be data representing the current state of the second household device, for example, whether it is in a working state, working parameters, etc.
- the specific current condition data may not be limited in the embodiments of the present application.
- it may also include instructions sent by the user through the mobile terminal.
- the server may obtain current condition data from the user's mobile terminal, environment collection device, the aforementioned first household equipment, second household equipment, and other servers.
- the server can communicate with the user's mobile terminal, and the mobile terminal can send the collected condition data to the server.
- the condition data collected by the mobile terminal can include the location of the user located, the user status analyzed based on the collected user image, etc.
- the condition data collected by the specific mobile terminal may not be regarded as a limitation.
- the environment collection device may be an environment collection device in the environment where the first household equipment is located (for example, a camera at home, various sensors, etc.), and the server may obtain the environment data collected by the environment collection device from the environment collection device.
- the server can obtain the detected state data from the first household device and the second household device.
- the server can obtain the corresponding data in the current condition data from other servers (for example, a third-party server).
- a third-party server For example, it can obtain weather data from a server used for weather prediction, and it can also obtain the user’s current tastes and current preferences from a catering service server. Time to eat etc.
- the specific way the server obtains the current condition data may not be a limitation.
- the server may obtain the current condition data for controlling the first household device in real time, or may obtain the current condition data every set time interval, and the specific interval for obtaining the current condition data may not be limited.
- Step S120 When the current condition data satisfies the set trigger condition, call the trained training model, which is trained based on the historical condition data and device data when the first household device is operated, so The condition data includes at least the user's personal data, environmental data, and status data of the second household device, and the device data includes at least a control instruction for the first household device.
- the server after acquiring the current condition data used to control the first household device, the server can determine whether the current condition data meets the set trigger condition, so as to determine whether to follow the current condition data to the user according to the current condition data.
- a control command for controlling a household device is predicted.
- the server may determine whether the setting type data in the current condition data is consistent with the setting data corresponding to the setting type.
- the server can obtain the data of the setting type in the current condition data and the setting data corresponding to the setting type, and then determine whether the data of the setting type is consistent with the setting data, and it can specifically determine Whether the fields, byte length, and byte size of the two data are consistent. For example, it can be judged whether the time data in the current condition data is set time data to determine whether the current condition data meets the set trigger condition.
- the specific setting data may not be a limitation, and the setting data may be a single value, or a numerical range composed of multiple values.
- the trigger condition can be used as a trigger condition for predicting that the user will operate the smart device. If the above current condition data meets the set trigger condition, it can indicate that the user will operate the smart device. When the condition data does not meet the set trigger condition, it can indicate that the user will not operate the smart device.
- the trained training model can be obtained to predict the control instruction of the user to control the first household device according to the current condition data.
- the trained training model can be stored in the server, and the server can read and call the training model when it needs to calculate the control instruction according to the current condition data.
- the trained training model can be obtained by training based on historical condition data and device data when the first household device is operated.
- the historical condition data may include at least the user's personal data, environmental data, and status data of the second household device
- the device data may include at least a control instruction for the first household device. That is to say, the historical condition data is consistent with the data content contained in the current condition data, so the training model can calculate a control instruction that conforms to the control habits of the first household device other than the user based on the current condition data.
- the aforementioned historical condition data corresponds to device data.
- Each historical condition data can form a pair of data with each device data.
- the historical condition data is used as the input data input to the initial model for training, and the device data is used as the output data input to the initial model for training. Therefore, a data set for model training can be constructed based on historical condition data and device data during the control operation of the first household device by the user in the past.
- the personal data in the aforementioned historical condition data may include the user's current state, taste, temperament, education, physical data, and so on.
- the environmental data may be data of the user's mobile terminal and/or the environment where the first household device is located.
- the environmental data may include location data, time data, weather data, etc., and the location data may be specific to the location where the user is often, for example, Home location, work location, etc., time data can be specific to dates, time points, holidays, etc., which can reflect the user's work and rest habits.
- the status data of the other first household equipment may be data representing the current status of the second household equipment, for example, whether it is in a working state, working parameters, and so on.
- the specific current condition data and device data may not be limited in the embodiment of this application.
- the device data may also include device information and device status of the device.
- the aforementioned historical condition data can be obtained from the mobile terminal, the first household device, the second household device, the environment collection device, and a third-party server, so that the personal information related to the user and the environment when the first household device is controlled can be obtained.
- the training model trained based on the historical condition data and the device data will be more in line with the user's operating habits, and the training model will be more accurate.
- the acquired historical condition data can be quantized into an input vector corresponding to the initial model, and the device data can be quantized into an output vector corresponding to the initial model. Then, the above-mentioned data set is quantized into a vector set corresponding to the initial model, and the vector set is input to the initial model for training, so as to obtain a trained training model, that is, a model for predicting control commands for controlling the first home appliance.
- the initial model may be a neural network model, for example, a BP (back propagation) neural network model.
- the specific initial model may not be a limitation.
- the initial model may also be a decision tree model or other neural network models.
- Step S130 Input the current condition data into the training model to obtain a control instruction for the first household device.
- the server After the server obtains the trained training model, it can use the above current condition data as input data and input it to the training model.
- the training model can calculate the control instruction corresponding to the current condition data according to the current condition data, that is, to A control instruction for controlling the first household device.
- the current condition data when the current condition data is input to the training model, the current condition data can be quantified into a vector corresponding to the training model, that is, a vector that can be used for training model calculations, and then the vector corresponding to the current condition data is input To train the model to calculate the control instruction for controlling the first household device.
- Step S140 Control the first household device according to the control instruction.
- the server after the server obtains the control instruction for controlling the first household device, it can control the first household device matching the control instruction according to the control instruction, so as to realize the control of the first household device. Automatic control of household equipment.
- the server may issue the control instruction to the first household device via the network, for example, send it to the router in the network where the first household device is located via the network, and the control instruction is forwarded by the router to the gateway device, and the gateway device The control instruction is forwarded to the first household device, and finally the first household device performs a processing operation matching the control instruction according to the control instruction, thereby realizing control of the first household device.
- the device control method provided by the embodiments of the application obtains current condition data, which is used to control the first household device.
- the current condition data meets the set trigger condition
- it uses historical condition data and device data training
- the latter training model calculates the control instruction that matches the current condition data, and then controls the first household device according to the control instruction, so as to realize the calculation of subsequent user operation instructions on the household device based on the historical data of the user's operation on the household device ,
- realize the automatic control of home equipment improve the intelligent degree of home equipment control
- the historical condition data is related to the user, environment and other home equipment, so that the control instructions calculated by the training model conform to the user’s operating habits of the home equipment. Meet user needs and improve user experience.
- another embodiment of the present application provides a device control method, which can be applied to a server, and the device control method may include:
- Step S210 Obtain historical condition data and device data when the first household device is operated, where the historical condition data corresponds to the device data.
- the server may obtain historical condition data and device data when the first household device is operated, so as to train the initial model according to the historical condition data and device data.
- the historical condition data corresponds to device data
- each piece of historical condition data corresponds to each piece of device data.
- the historical condition data includes at least the user's personal data, environmental data, and status data of the second household device.
- personal data is data about the user, which may include the user's physical state, mood state, height, weight, taste, occupation, etc.
- the specific personal data may not be limited, and the personal data is used to reflect the user's habits and preferences.
- the environmental data may include time data, weather data, location data, air data, traffic data, etc.
- the environmental data is related to the environment where the user is located and/or the environment where the first household device is located.
- the second household device may be other household devices other than the first household device, and the state data of the second household device may include the on state, off state, and working parameters of the second household device.
- the device data may include at least the control instruction corresponding to the current condition data, that is, the control instruction when the first household device is operated.
- the status data may also include device information, parameters, type, etc., so that training can be performed for different devices.
- the specific historical condition data and device data may not be regarded as a limitation.
- the above-mentioned control commands can be specific to the control of the working parameters of the first home appliance.
- the control commands corresponding to the lamps can be specific to the light-emitting color and brightness, etc., and the TV can be specific to the broadcast channel, etc., so that the training model can be trained.
- the control instruction for specifically controlling the first household device can be predicted.
- the current condition data and device data when controlling the first household device can be obtained from the user's mobile terminal, environment collection device, first household device, second household device, and the server of a third-party platform .
- it can also be obtained from a local database. Therefore, it is possible to obtain data related to the user and the device every time the user controls the first household device, and the data covers a wide range and types, so as to subsequently train an accurate training model.
- the server may also filter the historical condition data and device data, and compare the filtered history Condition data and equipment data are classified.
- the server can filter valid data from historical condition data and device data according to the set filtering rules to remove invalid data, ensure the correctness of the data, and thereby improve the accuracy of the trained training model. For example, the server can filter according to the number of times the first household device is controlled. If the number of times the first household device is controlled is less than the set number in the same time period, it can determine the time period when the first household device is controlled. The current condition data and device data are invalid.
- the specific screening rules are not limited in the embodiments of this application.
- the server when the server classifies the current condition data and device data, it can classify the user’s personal data and the environment data of the user’s environment as user-related data, and classify the first The environmental data of the environment in which the household device is located, the state data of the second household device, and the device data of the first household device are used as device-related data.
- device types can also be divided, for example, into home appliances, gateways, sensors, and smart cars.
- the above classification is only an example, for example, it is also possible to classify the control instructions in the device data.
- the training model can be made more accurate.
- Step S220 Input the historical condition data and the device data into an initial model, and train the initial model to obtain a trained training model.
- the server can construct an initial model, and of course, the initial model can also be stored locally in advance.
- historical condition data and equipment data can be input to the initial model to train the initial model.
- the current condition data corresponds to the device data one-to-one.
- the current condition data can be quantified as an input vector, and the device data can be quantified as an output vector.
- multiple input vectors and output vectors corresponding to each other are input to the initial model for training.
- Obtain a trained training model that is, a model for predicting control instructions for controlling the first home appliance.
- the accuracy of the trained training model can also be verified, and it can be judged whether the output information of the trained target detection model based on the input data meets the preset requirements.
- the aforementioned initial model may be a neural network model or a decision tree model.
- the specific method of training the initial model is not limited.
- the neural network model can be trained based on the ssd algorithm, the faster-rcnn algorithm, the yolo algorithm, etc., which will not be repeated here.
- Step S230 Acquire current condition data used to control the first household device.
- step S230 may refer to the content of the foregoing embodiment, which will not be repeated here.
- Step S240 Obtain a trained training model when the current condition data meets the set trigger condition, and the training model is obtained by training based on the historical condition data and device data when the first household device is operated, so
- the historical condition data includes at least the user's personal data, environmental data, and state data of the second household device
- the device data includes at least a control instruction for the first household device.
- the set trigger conditions may include: time data is set time data, location data is set location data, weather data is set weather data, the second household device is in a set state, and conditions
- the data includes at least one of the trigger instructions input by the user.
- the trigger condition is used as a trigger condition for determining that the user will operate the smart device. If the current condition data meets the trigger condition, the current condition data needs to be input to the training model to calculate the control instruction corresponding to the current condition data. Control the first household device. For example, in a scenario, the user usually gets off work at six o’clock, then six o’clock can trigger the calculation of the control instructions for the smart car, so that the user can experience the in-vehicle environment with the parameters set after getting in the car, and set the parameters with the current The condition data corresponds.
- the calculation of the control instructions for the smart curtain can be triggered to make the degree of curtain merging meet the conditions corresponding to the current condition data and the user’s Control habits.
- the smart bath heater in the bathroom when the smart bath heater in the bathroom is turned on, it can trigger the calculation of the control instructions for the water heater, so that the working parameters of the water heater correspond to the current condition data and conform to the user's control habits of the water heater.
- the above scenario is only an example.
- the above condition data includes the trigger instruction input by the user, which can be the user input the trigger instruction through the mobile terminal, so that the server calculates the control instruction corresponding to the first household device corresponding to the trigger instruction, and subsequently performs the calculation of the first household device according to the control instruction.
- Home equipment is controlled.
- the server may control the first household device according to the control instruction at a time point corresponding to the trigger instruction. For example, the user can send the trigger instruction of "eat tomorrow at six o'clock" to the server through the mobile terminal, and the server can calculate the control instructions for the cooking robot according to the current condition data, and control the cooking robot according to the control instructions at 5:30 tomorrow .
- the above trigger conditions can also be used in combination, and can be specifically set according to different devices and scenarios. For example, when the simultaneous time data is the set time data, the location data is the set location data, the weather data is the set weather data, and the second household device is in the set state, it is determined that the current condition data meets the trigger condition.
- the above-mentioned trained training model can be obtained to calculate the control instruction for the first household device according to the current condition data.
- Step S250 Input the current condition data into the training model to obtain a control instruction for the first household device.
- step S250 can refer to the content of the foregoing embodiment, which will not be repeated here.
- Step S260 Control the first household equipment according to the control instruction.
- the server after the server obtains the control instruction by using the training model and the current condition data, it can control the first household device according to the control instruction.
- controlling the first household device according to the control instruction may include:
- the control instruction is sent to the gateway device in the network where the first household device is located.
- the gateway device is used to issue the control instruction to the first household device, and the control instruction is used to instruct the first household device to perform operations corresponding to the control instruction.
- the server can send a control instruction to the gateway device, so that the gateway device can issue the control instruction to the first household device, and the first household device can The operation corresponding to the control instruction is performed according to the control instruction, so as to realize the control of the first household device.
- the server when the server is not the vendor server corresponding to the first household device, the server may communicate with the vendor server corresponding to the first household device.
- the vendor server By sending the control instruction to the vendor server corresponding to the first household device, the vendor server sends the control instruction to the above-mentioned gateway device, and the gateway device sends the control instruction to the first household device, thereby realizing the A control of household equipment.
- the manner in which the above server transmits control instructions to the first household device is not limited.
- the server may also directly send the control instruction to the first household device. Home equipment.
- the server can also continuously correct the above-mentioned training model to make the control instructions calculated by the training model more accurate.
- correcting the training model by the server may include: after controlling the first household device according to the control instruction, judging whether the control instruction is wrong; if it is wrong, correcting the training model.
- the server can detect whether the user's control operation on the first household device is obtained within the set time after controlling the first household device; if the user's control operation on the first household device is obtained, it is determined that the control instruction is incorrect ; If the user's control operation of the first household device is not obtained, it is determined that the control instruction is correct.
- the control instruction is wrong, the user will know that the control of the first household device is wrong, and the user will control the first household device. Therefore, after the first household device is controlled according to the control instruction, it can be detected whether the user's control of the first household device is acquired within a set time period, and specifically the first household device can be detected or used to control the first household device Whether the mobile terminal of has received the user's operation on the first household device.
- the specific setting time length and the manner of detecting whether the control command is incorrect are not limited, for example, the setting time length may be 5 minutes, or 10 minutes, etc.
- the training model is corrected. Specifically, the control instruction corresponding to the user's control operation of the first home appliance and the condition data during the control operation can be obtained, and then the control instruction corresponding to the control operation and the condition data during the control operation can be input to the training model , To train the training model. Among them, the specific training method will not be repeated here. Of course, when it is determined that the control instruction is correct, there is no need to correct the training model. In the embodiment of the present application, the manner of correcting the training model may not be limited.
- the control instruction corresponding to the control operation can be generated and based on the control operation
- the corresponding control instruction controls the first household device.
- the method of controlling the first household device according to the calculated control instruction can be referred to, which will not be repeated here.
- the first household device is also required The device stops the operation previously performed according to the calculated control instruction. Therefore, it is also possible to send an operation stop instruction to the first household device to control the first household device to stop the previous operation and avoid conflicts caused by different control commands.
- the device control method uses historical condition data and device data when the first household device is operated to train the initial training model to obtain the trained training model, which is used to control the first household device
- the training model is used to calculate the control instruction that matches the current condition data, and then the first household device is controlled according to the control instruction, so as to realize the first home appliance according to the user
- the historical data of the operation of a household device is calculated, and the subsequent user's operation instruction to the first household device is calculated, and the automatic control of the first household device is realized, and the degree of intelligence of the household device control is improved, and the historical condition data is related to the user, environment and Other household equipment is related, so that the control instructions calculated by the training model conform to the user's operating habits of the household equipment and meet the user's needs.
- the training model is continuously corrected to improve the accuracy of the training model's calculation of control instructions and enhance the user experience.
- another embodiment of the present application provides a device control method, which can be applied to a server, and the device control method may include:
- Step S310 Receive an automatic control request sent by the mobile terminal.
- the first household device may be in an automatic control mode, and the automatic control mode of the first household device may be selected by the user according to requirements.
- the server may control whether the automatic control mode of the first household device is turned on according to a request sent by the user's mobile terminal.
- the mobile terminal may send an automatic control request or a control shutdown request.
- the automatic control request may instruct the server to turn on the automatic control mode
- the control shutdown request may instruct the server to turn off the automatic control mode.
- the mobile terminal may display an interface for controlling the application program of the first household device, and the interface may include a setting page of the automatic control mode, so that the user can send an automatic control request or a control shutdown request to the server through the setting page.
- the automatic control mode is used by the server to automatically send a control instruction to the first household device according to a corresponding strategy to control the first household device.
- the first household device When the first household device is in the automatic control mode, the first household device can execute the corresponding control instruction sent by the server according to the corresponding strategy without the user's manual control on site or remote control through the mobile terminal to complete the corresponding control instruction. Control operation, thereby realizing automatic control of the first household equipment.
- the automatic control mode of the first household device may be turned on or off by a request or instruction sent by the user's mobile terminal.
- the server can automatically execute the corresponding strategy and issue a control instruction to the first household device.
- the first home furnishing device can also be controlled manually by the user. For example, the user can still control the first home furnishing device through buttons on the first home furnishing device on site, or through a mobile terminal The running application program controls the first household device.
- Step S320 In response to the automatic control request, control the first household device to be in an automatic control mode.
- the server can respond to the automatic control request and control the first household device to be in the automatic control mode.
- the first household device is in the automatic control mode, and the server continuously collects condition data to determine whether to use the training model to calculate control instructions to control the first household device.
- the server does not use the condition data and the training model to calculate the control instruction to control the first household device.
- Step S330 When the first household device is in the automatic control mode, obtain current condition data for controlling the first household device.
- Step S340 Obtain a trained training model when the current condition data meets the set trigger condition, and the training model is obtained by training based on historical condition data and device data when the first household device is operated.
- the historical condition data includes at least the user's personal data, environmental data, and state data of the second household device
- the device data includes at least a control instruction for the first household device.
- Step S350 Input the current condition data into the training model to obtain a control instruction for the first household device.
- step S330, step S340, and step S350 can refer to the content of the above-mentioned embodiment, which will not be repeated here.
- Step S360 Send prompt content to the user's mobile terminal, where the prompt content is used to prompt whether to perform the operation corresponding to the control instruction on the first household device.
- the user may also be prompted whether to perform the control corresponding to the control instruction on the first household device.
- the prompt content may be sent to the user's mobile terminal, and the prompt content is used to prompt whether to perform an operation corresponding to the control instruction on the first household device.
- the server may obtain the number of control operations of the user on the first household device within a set time period between the current time, and determine whether to send the prompt content to the user's mobile terminal according to the number of times, and prompt the user .
- the current time is the time when the control instruction is obtained using the training model. It is understandable that if the user has more control operations on the first household device in the set time period between the current time, it means that the user controls the first household device more by using manual control.
- the control instructions calculated by the training model to control the first household device may not meet the needs of the user.
- the server may determine whether the number of acquisitions is greater than the set number of times. If it is greater than the set number of times, it means that the user has more control operations on the first household device in the set time period between the current time. If it is not greater than the set number of times, it means that the number of control operations of the first household device by the user in the set time period before the current time is not many. Therefore, when it is determined that the number of acquisitions is greater than the set number of times, the prompt content can be sent to the user's mobile terminal.
- the specific set times and set time period are not limited, for example, the set times can be 5 times, 10 times, etc.
- the set time period can be within 2 days before the current time, and within 3 days before the current time Time period etc.
- Step S370 Upon receiving the determination instruction sent by the mobile terminal, control the first household device according to the control instruction.
- the server after sending the prompt content to the user's mobile terminal, the server can detect whether the determination instruction sent by the mobile terminal is received. If the determination instruction sent by the mobile terminal is received, the server can control the first household device according to the above control instruction. The determining instruction instructs the user to determine that the first household device needs to be controlled according to the control instruction.
- the determination instruction may be a determination instruction detected by the prompt interface after the mobile terminal receives the prompt content and displays the prompt interface according to the prompt content. If the server does not receive the confirmation instruction returned by the mobile terminal, it does not execute the control of the first household device according to the control instruction.
- the first household device in the automatic control mode obtains current condition data for controlling the first household device, and When the current condition data meets the set trigger condition, the training model trained based on the historical condition data and the device data is used to calculate the control instruction that matches the current condition data, and then the first home device is controlled according to the control instruction to achieve According to the historical data of the user's operation on the first household device, calculate the subsequent user's operation instruction on the first household device, and when the user has manually controlled more frequently recently, the prompt content is sent to the user's mobile terminal to prompt the user whether to A household device executes the operation corresponding to the control instruction, and when the definite instruction is received, the first household device is controlled according to the control instruction, so as to realize the automatic control of the household device, improve the degree of intelligent control of the household device, and the historical conditions
- the data is related to the user, the environment and other household equipment, so that the control instructions calculated by the training model conform to the user
- FIG. 6 shows a block diagram of a device control apparatus 500 according to an embodiment of the present application.
- the device control device 500 is applied to a server.
- the device shown in FIG. 6 will be described below.
- the device control device 500 includes: a data acquisition module 510, a model acquisition module 520, an instruction acquisition module 530, and a control execution module 540.
- the data acquisition module 510 is used to obtain current condition data
- the current condition data is used to control the first household equipment
- the model acquisition module 520 is used to obtain the current condition data when the set trigger condition is satisfied.
- the training model is obtained by training based on the historical condition data and device data when the first home appliance is operated, and the condition data includes at least the user's personal data, environmental data and the second State data of the household equipment, the device data includes at least a control instruction for the first household device; the instruction acquisition module 530 is used to input the current condition data into the training model to obtain A control instruction of a household device; the control execution module 540 is used to control the first household device according to the control instruction.
- the environmental data includes at least time data, date data, location data, and weather data
- the set trigger condition includes: the time data is set time data, and the date data is set At least one of date data, the location data is set location data, the weather data is set weather data, and the second household device is in a set state.
- the device control apparatus 500 may further include: a model training module.
- the above-mentioned data acquisition module 510 is also used to acquire historical condition data and device data when the first household device is operated, and the historical condition data corresponds to the device data.
- the model training module is used to input the historical condition data and the device data into an initial model, and train the initial model to obtain the trained training model.
- the device control device 500 may further include: a data screening module and a data classification module.
- the data screening module is used to screen the historical condition data and the device data; the data classification module is used to classify the filtered historical condition data and the device data.
- the aforementioned data acquisition module 510 acquires historical condition data and device data when the household device is operated, which may include: from a mobile terminal, an environment collection device, the first household device, and the second household device.
- the server of the household device and the third-party platform obtains historical condition data and device data when the first household device is operated.
- control execution module 540 may be specifically configured to: send the control instruction to a gateway device in the network where the first household device is located, and the gateway device is used to issue the control instruction to all In the first household device, the control instruction is used to instruct the first household device to perform an operation corresponding to the control instruction.
- control execution module 540 sending the control instruction to the gateway device in the network where the household device is located may include: sending the control instruction to the vendor server corresponding to the first household device, and the vendor server It is used to send the control instruction to the gateway device in the network where the first household device is located.
- the device control apparatus 500 may further include: a model correction module.
- the model correction module may be used to determine whether the control instruction is incorrect after the first household device is controlled according to the control instruction; if it is incorrect, correct the training model.
- determining whether the control instruction is wrong by the model correction module may include: detecting whether the user's control operation on the first household device is acquired within a set time period after the first household device is controlled; if If the user's control operation on the first household device is acquired, it is determined that the control instruction is incorrect.
- the model calibration module calibrating the training model may include: acquiring the control instruction corresponding to the control operation and condition data when performing the control operation; and performing the control instruction corresponding to the control operation and performing the control operation.
- the condition data during the control operation is input to the training model, and the training model is trained.
- control execution module 550 may be specifically configured to: send prompt content to the user's mobile terminal, the prompt content being used to prompt whether to perform the operation corresponding to the control instruction on the first household device; When the determination instruction is sent by the mobile terminal, the first household device is controlled according to the control instruction.
- control execution module 550 sending the prompt content to the user's mobile terminal may include: acquiring the number of control operations of the user on the first household device in a set period of time before the current time, and the current time is obtained The time when the control instruction is described; if the number of times is greater than the set number of times, the prompt content is sent to the user's mobile terminal.
- the device control apparatus 500 may further include a request receiving module and a request response module.
- the request receiving module is used to receive an automatic control request sent by the mobile terminal; the request response module is used to respond to the automatic control request and control the first household device to be in an automatic control mode.
- the coupling between the modules may be electrical, mechanical or other forms of coupling.
- each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
- the above-mentioned integrated modules can be implemented in the form of hardware or software functional modules.
- the solution provided by this application acquires current condition data used to control the first household device, and when the current condition data meets the set control trigger condition, call the history based on when the first household device is operated Condition data and the training model obtained through the training of equipment data.
- the historical condition data includes the user’s personal data, environmental data, and the status data of the second home device.
- the equipment data includes the control instructions for the first home device, and then the current condition data Input to the training model to obtain control instructions for the first home equipment, and control the first home equipment according to the control instructions, so as to realize the prediction of the operation instructions on the home equipment according to the user's historical operations on the home equipment, and realize the control of the home equipment
- the automatic control of home appliances enhances the degree of intelligence of home equipment control, and is highly relevant to users, meeting user needs, and improving user experience.
- FIG. 7 shows a structural block diagram of a server provided by an embodiment of the present application.
- the server 100 in this application may include one or more of the following components: a processor 110, a memory 120, and one or more application programs, where one or more application programs may be stored in the memory 120 and configured to be operated by one or Multiple processors 110 execute, and one or more programs are configured to execute the method described in the foregoing method embodiment.
- the processor 110 may include one or more processing cores.
- the processor 110 uses various interfaces and lines to connect various parts of the entire server 100, and executes the server by running or executing instructions, programs, code sets, or instruction sets stored in the memory 120, and calling data stored in the memory 120. 100 various functions and processing data.
- the processor 110 may adopt at least one of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).
- DSP Digital Signal Processing
- FPGA Field-Programmable Gate Array
- PDA Programmable Logic Array
- the processor 110 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem.
- the CPU mainly processes the operating system, user interface, and application programs; the GPU is used for rendering and drawing of display content; the modem is used for processing wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 110, but may be implemented by a communication chip alone.
- the memory 120 may include random access memory (RAM) or read-only memory (Read-Only Memory).
- the memory 120 may be used to store instructions, programs, codes, code sets or instruction sets.
- the memory 120 may include a program storage area and a data storage area, where the program storage area may store instructions for implementing the operating system and instructions for implementing at least one function (such as touch function, sound playback function, image playback function, etc.) , Instructions for implementing the following method embodiments, etc.
- the data storage area can also store data (such as phone book, audio and video data, chat record data) created by the terminal 100 during use.
- FIG. 8 shows a structural block diagram of a computer-readable storage medium provided by an embodiment of the present application.
- the computer-readable medium 800 stores program code, and the program code can be invoked by a processor to execute the method described in the foregoing method embodiment.
- the computer-readable storage medium 800 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), EPROM, hard disk, or ROM.
- the computer-readable storage medium 800 includes a non-transitory computer-readable storage medium.
- the computer-readable storage medium 800 has storage space for the program code 810 for executing any method steps in the above-mentioned methods. These program codes can be read out from or written into one or more computer program products.
- the program code 810 may be compressed in a suitable form, for example.
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Telephonic Communication Services (AREA)
- Selective Calling Equipment (AREA)
Abstract
Description
Claims (20)
- 一种设备控制方法,其特征在于,所述方法包括:获取当前条件数据,所述当前条件数据用于对第一家居设备进行控制;在所述当前条件数据满足设定的触发条件时,调用已训练的训练模型,所述训练模型根据所述第一家居设备被执行操作时的历史条件数据以及设备数据训练得到,所述条件数据至少包括用户的个人数据、环境数据以及第二家居设备的状态数据,所述设备数据至少包括对所述第一家居设备的控制指令;将所述当前条件数据输入至所述训练模型中,得到对所述第一家居设备的控制指令;根据所述控制指令,对所述第一家居设备进行控制。
- 根据权利要求1所述的方法,其特征在于,所述环境数据至少包括时间数据、位置数据以及天气数据,所述设定的触发条件包括:所述时间数据为设定时间数据、所述位置数据为设定位置数据、所述天气数据为设定天气数据、所述第二家居设备为设定状态、以及所述当前条件数据包括用户输入的触发指令中的至少一种。
- 根据权利要求1所述的方法,其特征在于,在所述当前条件数据满足设定的触发条件时,调用已训练的训练模型之前,所述方法还包括:获取所述第一家居设备被执行操作时的历史条件数据以及设备数据,所述历史条件数据与所述设备数据对应;将所述历史条件数据以及所述设备数据输入初始模型,对所述初始模型进行训练,得到所述已训练的训练模型。
- 根据权利要求3所述的方法,其特征在于,在所述将所述历史条件数据以及将所述设备数据输入初始模型之前,所述方法还包括:对所述历史条件数据以及所述设备数据进行筛选;对筛选后的所述历史条件数据以及所述设备数据进行分类。
- 根据权利要求3所述的方法,其特征在于,所述获取所述第一家居设备被执行操作时的历史条件数据以及设备数据,包括:从移动终端、环境采集装置、所述第一家居设备、所述第二家居设备以及第三方平台的服务器获取所述第一家居设备被执行操作时的历史条件数据以及设备数据。
- 根据权利要求1-5任一项所述的方法,其特征在于,所述根据所述控制指令,对所述第一家居设备进行控制,包括:将所述控制指令发送至所述第一家居设备所在网络中的网关设备,所述网关设备用于将所述控制指令下发至所述第一家居设备,所述控制指令用于指示所述第一家居设备进行所述控制指令对应的操作。
- 根据权利要求6所述的方法,其特征在于,所述将所述控制指令发送至所述第一家居设备所在网络中的网关设备,包括:将所述控制指令发送至所述第一家居设备对应的厂商服务器,所述厂商服务器用于将所述控制指令发送至所述第一家居设备所在网络中的网关设备。
- 根据权利要求1-7任一项所述的方法,其特征在于,所述方法还包括:在所述根据所述控制指令,对所述第一家居设备进行控制后,判断所述控制指令是否有误;如果有误,则对所述训练模型进行校正。
- 根据权利要求8所述的方法,其特征在于,所述判断所述控制指令是否有误,包括:检测对所述第一家居设备进行控制后的设定时长内是否获取到用户对所述第一家居设备的控制操作;如果获取到用户对所述第一家居设备的控制操作,则确定所述控制指令有误。
- 根据权利要求9所述的方法,其特征在于,所述对所述训练模型进行校正,包括:获取所述控制操作对应的控制指令以及进行所述控制操作时的条件数据;将所述控制操作对应的控制指令以及进行所述控制操作时的条件数据输入至所述训练模型,对所述训练模型进行训练。
- 根据权利要求1-10任一项所述的方法,其特征在于,所述根据所述控制指令,对所述第一家居设备进行控制,包括:发送提示内容至用户的移动终端,所述提示内容用于提示是否对所述第一家居设备进行所述控制指令对应的操作;在接收到所述移动终端发送的确定指令时,根据所述控制指令,对所述第一家居设备进行控制。
- 根据权利要求11所述的方法,其特征在于,所述发送提示内容至用户的移动终端,包括:获取当前时间之前的设定时间段内用户对所述第一家居设备的控制操作的次数,所述当前时间为得到所述控制指令时的时间;如果所述次数大于设定次数,则发送提示内容至用户的移动终端。
- 根据权利要求1-12任一项所述的方法,其特征在于,在所述获取当前条件数据之前,所述方法还包括:接收移动终端发送的自动控制请求;响应所述自动控制请求,控制所述第一家居设备处于自动控制的模式。
- 一种设备控制装置,其特征在于,所述装置包括:数据获取模块、模型获取模块、指令获取模块以及控制执行模块,其中,所述数据获取模块用于获取当前条件数据,所述当前条件数据用于对第一家居设备进行控制;所述模型获取模块用于在所述当前条件数据满足设定的触发条件时,调用已训练的训练模型,所述训练模型根据所述第一家居设备被执行操作时的历史条件数据以及设备数据训练得到,所述条件数据至少包括用户的个人数据、环境数据及第二家居设备的状态数据,所述设备数据至少包括对所述第一家居设备的控制指令;所述指令获取模块用于将所述当前条件数据输入至所述训练模型中,得到对所述第一家居设备的控制指令;所述控制执行模块用于根据所述控制指令,对所述第一家居设备进行控制。
- 根据权利要求14所述的装置,其特征在于,所述环境数据至少包括时间数据、日期数据、位置数据以及天气数据,所述设定的触发条件包括:所述时间数据为设定时间数据、所述位置数据为设定位置数据、所述天气数据为设定天气数据、所述第二家居设备为设定状态、以及所述当前条件数据包括用户输入的触发指令中的至少一种。
- 根据权利要求14所述的装置,其特征在于,所述装置还包括:模型训练模块,所述模型训练模块用于:在所述当前条件数据满足设定的触发条件时,获取已训练的训练模型之前,获取所述第一家居设备被执行操作时的历史条件数据以及设备数据,所述历史条件数据与所述设备数据对应;将所述历史条件数据以及所述设备数据输入初始模型,对所述初始模型进行训练,得到所述已训练的训练模型。
- 根据权利要求16所述的装置,其特征在于,所述装置还包括:数据筛选模块以及数据分类模块,其中,所述数据筛选模块用于在将所述历史条件数据以及将所述设备数据输入初始模型之前,对所述历史条件数据以及所述设备数据进行筛选;所述数据分类模块用于对筛选后的所述历史条件数据以及所述设备数据进行分类。
- 根据权利要求16所述的装置,其特征在于,所述模型训练模块获取所述第一家居设备被执行操作时的历史条件数据以及设备数据,包括:从移动终端、环境采集装置、所述第一家居设备、所述第二家居设备以及第三方平台的服务器获取所述第一家居设备被执行操作时的历史条件数据以及设备数据。
- 一种服务器,其特征在于,包括:一个或多个处理器;存储器;一个或多个应用程序,其中所述一个或多个应用程序被存储在所述存储器中并被配置为由所述一个或多个处理器执行,所述一个或多个程序配置用于执行如权利要求1-13任一项所述的方法。
- 一种计算机可读取存储介质,其特征在于,所述计算机可读取存储介质中存储有程序代码,所述程序代码可被处理器调用执行如权利要求1-13任一项所述的方法。
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201980091634.3A CN113412609B (zh) | 2019-06-19 | 2019-06-19 | 设备控制方法、装置、服务器及存储介质 |
PCT/CN2019/091945 WO2020252703A1 (zh) | 2019-06-19 | 2019-06-19 | 设备控制方法、装置、服务器及存储介质 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2019/091945 WO2020252703A1 (zh) | 2019-06-19 | 2019-06-19 | 设备控制方法、装置、服务器及存储介质 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2020252703A1 true WO2020252703A1 (zh) | 2020-12-24 |
Family
ID=74036915
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2019/091945 WO2020252703A1 (zh) | 2019-06-19 | 2019-06-19 | 设备控制方法、装置、服务器及存储介质 |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN113412609B (zh) |
WO (1) | WO2020252703A1 (zh) |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113405134A (zh) * | 2021-05-31 | 2021-09-17 | 青岛海尔科技有限公司 | 用于自动控制烟机的方法、装置、存储介质及服务器 |
CN114237059A (zh) * | 2021-11-24 | 2022-03-25 | 深圳市龙慧网络技术有限公司 | 自动控制智能家居设备的方法、装置、系统和存储介质 |
CN114599097A (zh) * | 2022-02-24 | 2022-06-07 | 深圳市海洋王照明工程有限公司 | 一种终端设备的控制方法、装置、集中控制器和存储介质 |
CN114879523A (zh) * | 2022-05-16 | 2022-08-09 | 青岛海尔科技有限公司 | 设备控制方法和相关装置 |
CN115277281A (zh) * | 2022-09-30 | 2022-11-01 | 南昌华飞物联技术有限公司 | 智能家居控制方法、装置、计算机设备及可读存储介质 |
CN115576215A (zh) * | 2022-10-14 | 2023-01-06 | 清华大学 | 智能家居系统以及基于智能家居系统的联动控制方法 |
CN117527399A (zh) * | 2023-11-28 | 2024-02-06 | 广州视声智能股份有限公司 | 用于智能家居的信息安全加密方法及系统 |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115174299B (zh) * | 2022-06-13 | 2024-04-09 | 海信集团控股股份有限公司 | 家居设备的绑定方法及电子设备 |
CN116998142A (zh) * | 2023-03-15 | 2023-11-03 | 康佳集团股份有限公司 | 基于物联网的智能家居控制系统、方法及装置 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104914833A (zh) * | 2015-05-18 | 2015-09-16 | 百度在线网络技术(北京)有限公司 | 一种智能设备的控制方法及系统 |
US20170085691A1 (en) * | 2015-09-23 | 2017-03-23 | Samsung Electronics Co., Ltd | Apparatus and method for generating group profile |
CN108181819A (zh) * | 2017-11-28 | 2018-06-19 | 珠海格力电器股份有限公司 | 家电设备的联动控制方法、装置、系统及家电设备 |
CN108919669A (zh) * | 2018-09-11 | 2018-11-30 | 深圳和而泰数据资源与云技术有限公司 | 一种智能家居动态决策方法、装置和服务终端 |
CN109358515A (zh) * | 2018-10-09 | 2019-02-19 | 珠海格力电器股份有限公司 | 一种家居设备控制方法、装置、控制设备及可读存储介质 |
CN109818839A (zh) * | 2019-02-03 | 2019-05-28 | 三星电子(中国)研发中心 | 应用于智能家居的个性化行为预测方法、装置和系统 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109298638B (zh) * | 2017-07-25 | 2022-07-29 | 美的智慧家居科技有限公司 | 智能家居的控制方法、系统、智能脚垫及智能网关 |
-
2019
- 2019-06-19 CN CN201980091634.3A patent/CN113412609B/zh active Active
- 2019-06-19 WO PCT/CN2019/091945 patent/WO2020252703A1/zh active Application Filing
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104914833A (zh) * | 2015-05-18 | 2015-09-16 | 百度在线网络技术(北京)有限公司 | 一种智能设备的控制方法及系统 |
US20170085691A1 (en) * | 2015-09-23 | 2017-03-23 | Samsung Electronics Co., Ltd | Apparatus and method for generating group profile |
CN108181819A (zh) * | 2017-11-28 | 2018-06-19 | 珠海格力电器股份有限公司 | 家电设备的联动控制方法、装置、系统及家电设备 |
CN108919669A (zh) * | 2018-09-11 | 2018-11-30 | 深圳和而泰数据资源与云技术有限公司 | 一种智能家居动态决策方法、装置和服务终端 |
CN109358515A (zh) * | 2018-10-09 | 2019-02-19 | 珠海格力电器股份有限公司 | 一种家居设备控制方法、装置、控制设备及可读存储介质 |
CN109818839A (zh) * | 2019-02-03 | 2019-05-28 | 三星电子(中国)研发中心 | 应用于智能家居的个性化行为预测方法、装置和系统 |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113405134A (zh) * | 2021-05-31 | 2021-09-17 | 青岛海尔科技有限公司 | 用于自动控制烟机的方法、装置、存储介质及服务器 |
CN114237059A (zh) * | 2021-11-24 | 2022-03-25 | 深圳市龙慧网络技术有限公司 | 自动控制智能家居设备的方法、装置、系统和存储介质 |
CN114599097A (zh) * | 2022-02-24 | 2022-06-07 | 深圳市海洋王照明工程有限公司 | 一种终端设备的控制方法、装置、集中控制器和存储介质 |
CN114879523A (zh) * | 2022-05-16 | 2022-08-09 | 青岛海尔科技有限公司 | 设备控制方法和相关装置 |
CN115277281A (zh) * | 2022-09-30 | 2022-11-01 | 南昌华飞物联技术有限公司 | 智能家居控制方法、装置、计算机设备及可读存储介质 |
CN115277281B (zh) * | 2022-09-30 | 2022-12-20 | 南昌华飞物联技术有限公司 | 智能家居控制方法、装置、计算机设备及可读存储介质 |
CN115576215A (zh) * | 2022-10-14 | 2023-01-06 | 清华大学 | 智能家居系统以及基于智能家居系统的联动控制方法 |
CN117527399A (zh) * | 2023-11-28 | 2024-02-06 | 广州视声智能股份有限公司 | 用于智能家居的信息安全加密方法及系统 |
CN117527399B (zh) * | 2023-11-28 | 2024-05-17 | 广州视声智能股份有限公司 | 用于智能家居的信息安全加密方法及系统 |
Also Published As
Publication number | Publication date |
---|---|
CN113412609B (zh) | 2023-06-20 |
CN113412609A (zh) | 2021-09-17 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2020252703A1 (zh) | 设备控制方法、装置、服务器及存储介质 | |
CN113412457B (zh) | 场景推送方法、装置、系统、电子设备以及存储介质 | |
CN108919669B (zh) | 一种智能家居动态决策方法、装置和服务终端 | |
US20220018567A1 (en) | Data learning server and method for generating and using learning model thereof | |
US11137161B2 (en) | Data learning server and method for generating and using learning model thereof | |
US10584892B2 (en) | Air-conditioning control method, air-conditioning control apparatus, and storage medium | |
CN105446162B (zh) | 一种智能家居系统以及机器人的智能家居控制方法 | |
US20200341436A1 (en) | Interactive environmental controller | |
US11782590B2 (en) | Scene-operation method, electronic device, and non-transitory computer readable medium | |
CN112255928A (zh) | 智能家居的控制方法、装置、系统及电子设备 | |
US10755730B1 (en) | System and method for beep detection and interpretation | |
CN112740640B (zh) | 用于物联网设备的消歧的系统和方法 | |
CN109951363B (zh) | 数据处理方法、装置及系统 | |
CN110578994A (zh) | 一种运行方法及装置 | |
CN110131846B (zh) | 一种智能空调控制方法及空调 | |
CN111965991B (zh) | 智能控制开关的权限调节方法、装置、智能控制开关以及存储介质 | |
CN111965985B (zh) | 智能家居设备控制方法、装置、电子设备以及存储介质 | |
WO2015003377A1 (zh) | 智能住宅系统及其运作方法 | |
CN112785802B (zh) | 智能家居安防系统控制方法、装置、电子设备及介质 | |
CN112015099B (zh) | 智能开关的权限调节方法、装置、智能开关以及存储介质 | |
CN114608152B (zh) | 一种加湿器控制方法、装置、系统、电子设备及存储介质 | |
US11818820B2 (en) | Adapting a lighting control interface based on an analysis of conversational input | |
CN114114936A (zh) | 智能灯具的分组方法、分组装置、智能设备及存储介质 | |
CN114659248B (zh) | 空调器与抽油烟机的互联控制方法与装置 | |
CN115221400A (zh) | 房屋状态信息推送方法、装置、设备及存储介质 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 19933286 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19933286 Country of ref document: EP Kind code of ref document: A1 |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 27/05/2022) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19933286 Country of ref document: EP Kind code of ref document: A1 |