CN115542755A - Method and device for predicting equipment control command, electronic equipment and storage medium - Google Patents
Method and device for predicting equipment control command, electronic equipment and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of digital twinning, and discloses a method for predicting a device control command, which comprises the following steps: acquiring current user data of a target user; the current user data comprises one or more of target user voice information, target user image information or target user physiological information; acquiring the coupling degree between the current user data and a preset target user model; and predicting the equipment control command according to the coupling degree. Therefore, by acquiring the current user data of the target user and matching the current user data with the preset target user model, the command which the target user wants to issue can be predicted according to the current user data of the target user, the user is not required to actively issue the command to control the intelligent household equipment, and the interaction experience between the user and the intelligent household equipment is improved. The application also discloses a device for predicting the equipment control command, electronic equipment and a storage medium.
Description
Technical Field
The present application relates to the field of digital twinning technologies, and for example, to a method and apparatus for predicting a device control command, an electronic device, and a storage medium.
Background
Along with the improvement of living standard, the intelligent household equipment in the user's house is more and more, and the user needs to control various intelligent household equipment. Often, a user needs to manually set a trigger condition and an operation logic of the smart home device, so as to control the smart home device by the user, for example: and controlling equipment or scenes according to the triggering of environmental parameters such as voice commands, air, weather and the like.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art:
in the related art, the control of the intelligent home equipment depends on the active triggering of a user, so that the interactive experience between the user and the intelligent home equipment is poor.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
The embodiment of the disclosure provides a method and a device for predicting a device control command, an electronic device and a storage medium, so as to improve the interaction experience of a user and an intelligent home device.
In some embodiments, the method for predicting device control commands, the method comprising: acquiring current user data of a target user; the current user data comprises one or more of target user voice information, target user image information or target user physiological information; acquiring the coupling degree between the current user data and a preset target user model; the target user model includes a plurality of data modalities of a target user; the data mode is used for representing the current user data of a target user when issuing a device control command; and predicting a device control command according to the coupling degree.
In some embodiments, the target user model is obtained by: a historical command data set is obtained, and the historical command data set comprises a plurality of pieces of historical command data. The historical command data comprises historical user data and historical command information corresponding to the historical user data. The historical command information comprises historical equipment control commands, historical equipment control command issuing time, historical equipment control command types and equipment identity identification numbers (ID) corresponding to the historical equipment control commands. And acquiring the data modality of the target user and historical command information corresponding to the data modality according to the historical command data set. And acquiring a target user model according to the data mode and the historical command information corresponding to the data mode by using a digital twinning technology.
In some embodiments, obtaining the data modality of the target user and the historical command information corresponding to the data modality according to the historical command data set includes: and under the condition that more than preset historical command data with the same number exist in the historical command data set, determining historical user data in the historical command data as a data modality of the target user, and determining historical command information in the historical command data as historical command information corresponding to the data modality.
In some embodiments, obtaining the target user model from the data modality and the historical command information corresponding to the data modality using a digital twinning technique comprises: and adding the data modality and historical command information corresponding to the data modality into a preset initial user model by using a digital twinning technology for learning, and obtaining a target user model. The initial user model is used to characterize personal information of the target user.
In some embodiments, the target user model includes a plurality of data modalities of a target user; acquiring the coupling degree between the current user data and a preset target user model, wherein the acquiring step comprises the following steps: respectively acquiring the similarity between the current user data and each data modality; and determining the similarity as the coupling degree between the current user data and a preset target user model.
In some embodiments, the obtaining the similarity between the current user data and each of the data modalities includes: and acquiring a first text feature vector corresponding to the current user data and a second text feature vector corresponding to each data mode. And acquiring the similarity between the first text feature vector and each second text feature vector.
In some embodiments, obtaining the similarity between the first text feature vector and each of the second text feature vectors includes: and calculating by using the first text characteristic vector and the second text characteristic vector according to a preset algorithm to obtain the similarity.
In some embodiments, predicting a device control command based on the degree of coupling comprises: and determining the historical equipment control command corresponding to the data modality in the target user model as an equipment control command under the condition that the coupling degree is greater than a preset threshold value.
In some embodiments, determining historical device control commands in the target user model that correspond to the data modality as device control commands comprises: and determining the second text feature vector corresponding to the coupling degree as a target feature vector. And determining the historical equipment control command in the data modality corresponding to the target feature vector as the equipment control command.
In some embodiments, predicting the device control command according to the coupling degree further comprises: matching out the equipment ID corresponding to the equipment control command from the target user model; and sending the equipment control command to equipment corresponding to the equipment ID, and triggering the equipment to execute the equipment control command.
In some embodiments, predicting the device control command according to the coupling degree further comprises: monitoring whether the target user adjusts the equipment control command; and under the condition that the target user adjusts the equipment control command, acquiring alternative user data of the target user and command information corresponding to the adjusted equipment control command. The alternative user data is used to characterize user data of the target user when adjusting the device control command. And updating the target user model by using the alternative user data and the command information corresponding to the adjusted equipment control command.
In some embodiments, updating the target user model with command information corresponding to the alternative user data and the adjusted device control command includes: and adding the alternative user data and the command information corresponding to the adjusted device control command into the target user model for learning so as to update the target user model.
In some embodiments, the apparatus for predicting device control commands comprises a processor and a memory storing program instructions, the processor being configured to, when executing the program instructions, perform the method for predicting device control commands as described above.
In some embodiments, the electronic device comprises the above-described apparatus for predicting device control commands.
In some embodiments, the storage medium stores program instructions that, when executed, perform the above-described method for predicting device control commands.
The method and the device for predicting the device control command, the electronic device and the storage medium provided by the embodiment of the disclosure can realize the following technical effects: acquiring current user data of a target user; the current user data comprises one or more of target user voice information, target user image information or target user physiological information; acquiring the coupling degree between the current user data and a preset target user model; the user model includes a plurality of data modalities of the target user; the data mode is used for representing the current user data of a target user when issuing a device control command; and predicting the equipment control command according to the coupling degree. Therefore, by acquiring the current user data of the target user and matching the acquired current user data with the preset target user model, the command which the target user wants to issue can be predicted according to the current user data of the target user, the user does not need to actively issue the command to control the intelligent household equipment, and the interaction experience between the user and the intelligent household equipment is improved.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:
FIG. 1 is a schematic diagram of a method for predicting device control commands provided by an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a method for obtaining a target user model according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of another method for predicting device control commands provided by an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of another method for predicting device control commands provided by embodiments of the present disclosure;
FIG. 5 is a schematic diagram of another method for predicting device control commands provided by an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an apparatus for predicting a device control command according to an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and advantages of the embodiments of the present disclosure can be understood in detail, a more particular description of the embodiments of the disclosure, briefly summarized above, may be had by reference to the appended drawings, which are included to illustrate, but are not intended to limit the embodiments of the disclosure. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged as appropriate for the embodiments of the disclosure described herein. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
The term "plurality" means two or more, unless otherwise specified.
In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
The term "correspond" may refer to an association or binding relationship, and a corresponds to B refers to an association or binding relationship between a and B.
The technical scheme in the embodiment of the invention can be applied to electronic equipment such as a computer, a tablet computer or a server.
In the embodiment of the invention, the electronic equipment acquires the current user data of the target user and matches the acquired current user data with a preset target user model, wherein the target user model comprises a plurality of data modalities of the target user; the data modality is used for representing the state information of the target user when the target user issues the device control command. Therefore, the device control command which the target user wants to issue at the current moment can be predicted according to the current user data of the target user, the intelligent household device is controlled without actively issuing the command by the user, and the interaction experience between the user and the intelligent household device is improved.
As shown in fig. 1, an embodiment of the present disclosure provides a method for predicting a device control command, where the method includes:
step S101, the electronic equipment acquires current user data of a target user; the current user data includes one or more of target user voice information, target user image information, or target user physiological information.
Step S102, the electronic equipment acquires the coupling degree between the current user data and a preset target user model; the target user model includes a plurality of data modalities of the target user; the data modality is used for representing the state information of the target user when the target user issues the device control command.
In step S103, the electronic device predicts a device control command according to the coupling degree.
By adopting the method for predicting the equipment control command provided by the embodiment of the disclosure, the equipment control command which the target user wants to issue at the current moment can be predicted according to the current user data of the target user by acquiring the current user data of the target user and matching the acquired current user data with the preset target user model, so that the intelligent home equipment is controlled without actively issuing the command by the user, and the interaction experience between the user and the intelligent home equipment is improved.
Optionally, the current user data is target user voice information. The method for acquiring the current user data of the target user by the electronic equipment comprises the following steps: the electronic equipment collects voice information of a target user through equipment with a recording device. The voice information includes short-time energy, short-time zero-crossing rate, fundamental tone frequency, formant feature, speech speed or Mel cepstrum coefficient and other voice features of the voice of the target user.
In some embodiments, the user's voice information can reflect the user's status information, such as: the state information is a sick state, a healthy state, an open state, and the like. The current state of the target user can be predicted to be a sick state or a healthy state according to the voice information of the target user, and the current state of the target user can also be predicted to be an open state or an open state.
Optionally, the current user data is target user image information. The electronic equipment acquires the current user data of a target user, and the method comprises the following steps: the electronic equipment collects the image information of the target user through equipment with a camera device. Wherein the image information comprises a photograph or a short video over a period of time of the target user.
In some embodiments, the image information of the user can reflect the status information of the user, such as: the status information is that the user is lying on a sofa, the user is lying in a bedroom, the user is sweating, etc.
Optionally, the current user data is target user physiological information. The electronic equipment acquires the current user data of a target user, and the method comprises the following steps: the electronic equipment monitors the physiological information of the target user through the intelligent wearable equipment. Wherein, intelligence wearing equipment includes equipment such as intelligent wrist-watch or intelligent bracelet. The physiological information includes: the physiological parameters of the target user such as blood pressure, heart rate or body temperature.
In some embodiments, the physiological information of the user can reflect the state information of the user, such as: the state information is high blood pressure, low blood pressure, normal blood pressure, high heart rate, low heart rate, normal heart rate, high body temperature, low body temperature, normal body temperature and the like.
Optionally, the current user data is environment information where the target user is located. The method for acquiring the current user data of the target user by the electronic equipment comprises the following steps: and monitoring the environmental parameters of the target user through the household appliance. The household appliances comprise air conditioners and other appliances. The indoor environmental parameters include environmental parameters such as indoor temperature and indoor humidity. The environment in which the target user is located is an indoor environment.
Further, the electronic device obtains the target user model by: the electronic device obtains a historical command data set. The historical command data set comprises a plurality of pieces of historical command data, and the historical command data comprises historical user data and historical command information corresponding to the historical user data. The historical command information comprises historical device control commands, historical device control command issuing time, historical device control command types and device IDs corresponding to the historical device control commands. And acquiring a data mode of the target user and historical command information corresponding to the data mode according to the historical command data set. And acquiring a target user model according to the data mode and the historical command information corresponding to the data mode by using a digital twinning technology. The historical device control command types comprise switch type commands or numerical value adjusting type commands and the like. For example: the switching class commands include turning on/off an air conditioner, turning on/off a television, and the like. The numerical adjustment class commands include: adjust the temperature of the air conditioner to 24 degrees celsius, the volume of the television to 30, etc. Wherein the preset number is 50.
Further, obtaining a data modality of a target user and historical command information corresponding to the data modality according to the historical command data set includes: when more than preset historical command data with the same number exist in the historical command data set, determining historical user data in the historical command data as a data modality of a target user, and determining historical command information in the historical command data as historical command information corresponding to the data modality.
In some embodiments, the historical command data includes historical user data and historical command information corresponding to the historical user data. The historical user data includes target user image information and target user physiological information. The target user image information is image information of the target user sweating, and the target user physiological information is high heart rate, high body temperature and high blood pressure. The historical command information corresponding to the historical user data is' historical device control command: starting an air conditioner, and issuing control commands of historical equipment for time: 18: device IDs corresponding to the switch class command and the historical device control command: KFR-22GW/HA ". In the case where there are more than 50 pieces of the same historical command data in the historical command data set, the historical user data "the target user is sweating, high heart rate, high body temperature, and high blood pressure" in the historical command data is determined as the data modality of the target user, for example: [ target user image information "target user is sweating", target user physiological information "high heart rate, high body temperature, high blood pressure" ]. And history command information "history device control command: starting an air conditioner, and issuing control commands of historical equipment for time: 18: device ID corresponding to switch class command and history device control command: KFR-22 GW/HA' is determined as the historical command information corresponding to the data modality.
In some embodiments, the historical command data includes historical user data and historical command information corresponding to the historical user data. The historical user data comprises target user voice information, target user image information and target user physiological information. And if the voice characteristics corresponding to the voice information of the target user are matched with the cold voice template, the voice information of the target user is in a sick state. The target user image information is the image information of the target user lying on the sofa. The target user physiological information is normal heart rate, high body temperature and normal blood pressure. The historical command information corresponding to the historical user data is as follows: "historical device control commands: adjusting the temperature of the air conditioner to 26 ℃, and issuing time of historical equipment control commands: 18: device IDs corresponding to the numerical adjustment command and the historical device control command: KFR-22GW/HA ". "historical device control commands: closing the curtain and issuing control commands of historical equipment for time: 18, historical device control command type: device ID corresponding to switch command and history command: ECL001". "historical apparatus control command: closing the television, and issuing control commands of historical equipment: 18, historical device control command type: device ID corresponding to switch class command and history device control command: EDC110". In the case that more than 50 pieces of the same historical command data exist in the historical command data set, determining the historical user data "voice feature is matched with the cold voice template, the target user is lying on a sofa, the heart rate is normal, the body temperature is high, and the blood pressure is normal" in the historical command data as the data modality of the target user, for example: [ voice information of the target user 'voice characteristics matched with a cold voice template', image information of the target user 'the target user lies down on a sofa', and physiological information of the target user 'normal heart rate, high body temperature and normal blood pressure' ]. And determining the historical command information in the historical command data as historical command information corresponding to data modalities [ voice information of the target user ' voice characteristic is matched with a cold voice template ], image information of the target user ' the target user lies on a sofa ], and physiological information of the target user ' normal heart rate, high body temperature and normal blood pressure ].
Further, the electronic device obtains the target user model according to the data modality and the historical command information corresponding to the data modality by using a digital twin technology, and the method includes the following steps: and adding the data mode and historical command information corresponding to the data mode into a preset initial user model by using a digital twin technology for learning to obtain a target user model. The initial user model is used to characterize the personal information of the target user. Wherein the personal information of the target user comprises: name, gender, birthday, hobby, work, voiceprint, etc. In this way, by collecting the historical command data set of the target user, the data of living habits, emotional states, physical states and the like of the target user can be learned, so that different data modalities can be obtained. Facilitating the generation of a variety of different device control commands and scene control commands. The device control command is a control command for a single device, and the scene control command is a control command for a plurality of devices.
As shown in fig. 2, an embodiment of the present disclosure provides a method for obtaining a target user model, where the method includes:
step S201, the electronic equipment acquires a historical command data set; the historical command data set comprises a plurality of pieces of historical command data; the historical command data comprises historical user data and historical command information corresponding to the historical user data; the historical command information comprises historical equipment control commands, historical equipment control command issuing time, historical equipment control command types and equipment identification numbers (ID) corresponding to the historical equipment control commands.
In step S202, when more than the preset number of pieces of historical command data are in the historical command data set, the electronic device determines historical user data in the historical command data as a data modality of the target user, and determines historical command information in the historical command data as historical command information corresponding to the data modality.
Step S203, the electronic equipment adds a data mode and historical command information corresponding to the data mode to a preset initial user model by using a digital twin technology for learning to obtain a target user model; the initial user model is used to characterize the personal information of the target user. Wherein the personal information of the target user comprises: name, gender, birthday, hobby, work, voiceprint, etc.
By adopting the method for predicting the control command of the equipment, provided by the embodiment of the disclosure, the historical command data set is mapped into an initial user model by a digital twinning technology for training and learning by acquiring the historical command data set. The method can learn the living habits, the eating habits, the work and rest rules, the emotional emotions, the physiological health and the like of the user, realizes the construction of a target user model by utilizing a digital twin technology, and learns the habits of the user so as to continuously perfect the user model. Therefore, the target user model can be used for predicting the command which the target user wants to issue, the user is not required to actively issue the command to control the intelligent household equipment, and the interaction experience of the user and the intelligent household equipment is improved.
Further, the target user model includes a plurality of data modalities of the target user, and the obtaining, by the electronic device, the coupling degree between the current user data and the preset target user model includes: the electronic equipment respectively obtains the similarity between the current user data and each data mode, and determines the similarity as the coupling degree between the current user data and a preset target user model.
Further, the electronic device respectively obtains the similarity between the current user data and each data modality, including: the electronic equipment acquires a first text feature vector corresponding to current user data and a second text feature vector corresponding to each data modality. And acquiring the similarity between the first text feature vector and each second text feature vector.
Further, obtaining the similarity between the first text feature vector and each second text feature vector respectively includes: and calculating by using the first text characteristic vector and the second text characteristic vector according to a preset algorithm to obtain the similarity. The preset algorithm is a cosine similarity algorithm.
Further, predicting the device control command based on the coupling level includes: and under the condition that the coupling degree is greater than a preset threshold value, determining the historical equipment control command corresponding to the data modality in the target user model as the equipment control command. Wherein the preset threshold is 95%. Therefore, the prediction of the equipment control instruction can be realized, the user does not need to actively trigger the equipment control, and the interaction experience of the user and the intelligent household equipment is improved. And the historical equipment control command corresponding to the data modality is the historical equipment control command in the historical command information corresponding to the data modality.
Further, determining, as the device control command, a historical device control command corresponding to the data modality in the target user model when the coupling degree is greater than the preset threshold, including: and under the condition that the coupling degree is greater than a preset threshold value, determining a second text feature vector corresponding to the coupling degree as a target feature vector. And determining the historical equipment control command in the data modality corresponding to the target feature vector as the equipment control command.
In some embodiments, the current user data of the target user is [ image information of the target user "the target user is sweating", physiological information of the target user "high heart rate, high body temperature, and high blood pressure" ], and the first text feature vector corresponding to the current user data is a. The data modalities include: [ target user image information "target user is sweating" and target user physiological information "high heart rate, high body temperature, high blood pressure" ], historical command information corresponding to the data modality is "historical device control command: starting an air conditioner, and issuing control commands of historical equipment for time: 18, historical device control command type: device IDs corresponding to the switch class command and the historical device control command: KFR-22GW/HA ". The data modalities include: [ target user image information "the target user lies on the sofa", and target user physiological information "normal heart rate, normal body temperature, normal blood pressure" ], the historical command information corresponding to the data modality is "historical device control command: starting a television, and issuing control commands of historical equipment: 18, historical device control command type: device IDs corresponding to the switch class command and the historical device control command: EDC110". The data modalities include: [ target user image information "the target user lies on a bedroom bed", and target user physiological information "heart rate is normal, body temperature is normal, and blood pressure is normal" ]. The historical command information corresponding to the data modality is' historical device control command: adjusting the temperature of the air conditioner to 26 ℃, and issuing time of historical equipment control commands: 18, historical device control command type: device IDs corresponding to the numerical adjustment command and the historical device control command: KFR-22GW/HA ". "historical device control commands: closing the curtain and issuing control commands of historical equipment for time: 18: device ID corresponding to switch command and history command: ECL001". "historical apparatus control command: closing the television, and issuing control commands of historical equipment: 18, historical device control command type: device IDs corresponding to the switch class command and the historical device control command: EDC110".
Data modality: the second text feature vector corresponding to [ target user image information "target user is sweating", target user physiological information "high heart rate, high body temperature, high blood pressure" ] is B. Data modality: the second text feature vector corresponding to the target user image information 'the target user lies on the sofa', and the target user physiological information 'normal heart rate, normal body temperature, and normal blood pressure' is C. Data modality: the second text feature vector corresponding to the target user image information 'the target user lies on a bed in a bedroom', and the target user physiological information 'heart rate is normal, body temperature is normal, and blood pressure is normal' is D. The similarity between the first text feature vector a and the second text feature vector B is 100%, i.e., the degree of coupling is 100%, and the similarity between the first text feature vector a and the second text feature vector C is 90%, i.e., the degree of coupling is 90%, and the similarity between the first text feature vector a and the second text feature vector D is 50%, i.e., the degree of coupling is 50%. The second text feature vector B corresponding to the coupling degree of 100% is determined as the target feature vector. And determining a historical equipment control command [ starting an air conditioner ] in the data modality corresponding to the target characteristic vector as an equipment control command.
As shown in fig. 3, an embodiment of the present disclosure provides a method for predicting a device control command, where the method includes:
step S301, the electronic equipment acquires current user data of a target user; the current user data includes one or more of target user voice information, target user image information, or target user physiological information.
Step S302, the electronic device respectively obtains similarities between the current user data and each data modality.
In step S303, the electronic device determines the similarity as a coupling degree between the current user data and a preset target user model. The target user model includes a plurality of data modalities of the target user; the data modality is used for representing the current user data of the target user when the target user issues the device control command.
Step S304, the electronic device determines the historical device control command corresponding to the data modality in the target user model as the device control command when the coupling degree is greater than the preset threshold.
By adopting the method for predicting the equipment control command provided by the embodiment of the disclosure, the current user data of the target user is acquired, and the acquired current user data is matched with the preset target user model, so that the command which the target user wants to issue can be predicted according to the current user data of the target user, the user is not required to actively issue the command to control the intelligent household equipment, and the interaction experience between the user and the intelligent household equipment is improved.
Optionally, after predicting the device control command according to the coupling degree, the method further includes: and matching the device ID corresponding to the device control command from the target user model, sending the device control command to the device corresponding to the device ID, and triggering the device to execute the device control command.
In some embodiments, the device control command is [ turn on air conditioner ], the device ID corresponding to the device control command [ turn on air conditioner ] is KFR-22GW/HA, and the device control command [ turn on air conditioner ] is sent to the device corresponding to the KFR-22GW/HA, and the device corresponding to the KFR-22GW/HA is triggered to execute the device control command [ turn on air conditioner ].
As shown in fig. 4, an embodiment of the present disclosure provides a method for predicting a device control command, where the method includes:
step S401, the electronic equipment acquires current user data of a target user; the current user data includes one or more of target user voice information, target user image information, or target user physiological information.
Step S402, the electronic equipment acquires the coupling degree between the current user data and a preset target user model; the user model includes a plurality of data modalities of the target user; the data modality is used for representing the current user data of the target user when the target user issues the device control command.
In step S403, the electronic device predicts a device control command according to the degree of coupling.
Step S404, the electronic device sends the device control command to the device corresponding to the device ID from the device ID corresponding to the device control command in the target user model, and triggers the device to execute the device control command.
By adopting the method for predicting the equipment control command provided by the embodiment of the disclosure, the current user data of the target user is acquired, and the acquired current user data is matched with the preset target user model, so that the command which the target user wants to issue can be predicted according to the current user data of the target user, the equipment control command is sent to the corresponding equipment, and the equipment is triggered to execute the equipment control command. The intelligent home equipment is controlled without actively issuing commands by the user, and the interaction experience of the user and the intelligent home equipment is improved.
Optionally, after predicting the device control command according to the coupling degree, the electronic device further includes: the electronic device monitors whether the target user adjusts the device control command. And under the condition that the target user adjusts the equipment control command, acquiring the alternative user data of the target user and the command information corresponding to the adjusted equipment control command. And updating the target user model by using the alternative user data and the command information corresponding to the adjusted equipment control command. The alternative user data is used to characterize the user data of the target user in adjusting the device control commands. In this way, in the event that the target user makes an adjustment to the device control command, the predicted device control command is deemed to not meet the current needs of the target user. Therefore, the user data of the target user and the command information corresponding to the adjusted equipment control command are collected, and the accurate command according with the current user data can be obtained. And the alternative user data and the adjusted equipment control command are used for continuously learning, so that the target user model can be updated. Thereby making the target user model predictive device control commands more accurate.
Further, the updating, by the electronic device, the target user model by using the alternative user data and the command information corresponding to the adjusted device control command includes: and the electronic equipment adds the alternative user data and the command information corresponding to the adjusted equipment control command into the target user model for learning so as to update the target user model.
In some embodiments, the electronic device predicts that the obtained device control command is "adjust the temperature of the air conditioner to 26 degrees celsius" and that the adjusted device control command is "adjust the temperature of the air conditioner to 25 degrees celsius". The command information corresponding to the adjusted device control command is' device control command: adjusting the temperature of the air conditioner to 25 ℃, and issuing a device control command for: 18, device control command type: device ID corresponding to numerical class command and device control command: KFR-22GW/HA ". The alternative user data is [ target user image information "target user lies on bed in bedroom", target user physiological information "heart rate is normal, body temperature is normal, blood pressure is normal" ]. Adding the alternative user data and the command information corresponding to the adjusted device control command into the target user model for learning so as to update the target user model.
As shown in fig. 5, an embodiment of the present disclosure provides a method for predicting a device control command, where the method includes:
step S501, the electronic equipment acquires current user data of a target user; the current user data includes one or more of target user voice information, target user image information, or target user physiological information.
Step S502, the electronic equipment acquires the coupling degree between the current user data and a preset target user model; the user model includes a plurality of data modalities of the target user; the data modality is used for representing the current user data of the target user when the target user issues the device control command.
In step S503, the electronic device predicts a device control command according to the degree of coupling.
Step S504, the electronic device monitors whether the target user adjusts the device control command. And under the condition that the target user adjusts the equipment control command, acquiring alternative user data of the target user and command information corresponding to the adjusted equipment control command.
And step S505, the electronic equipment updates the target user model by using the alternative user data and the command information corresponding to the adjusted equipment control command.
By adopting the method for predicting the equipment control command provided by the embodiment of the disclosure, the current user data of the target user is acquired, and the acquired current user data is matched with the preset target user model, so that the command which the target user wants to issue can be predicted according to the current user data of the target user, the user is not required to actively issue the command to control the intelligent household equipment, and the interaction experience between the user and the intelligent household equipment is improved. Meanwhile, the target user model is continuously updated, so that the target user model can predict the equipment control command more accurately.
As shown in fig. 6, an apparatus for predicting device control commands according to an embodiment of the present disclosure includes a processor (processor) 600 and a memory (memory) 601. Optionally, the apparatus may also include a Communication Interface 602 and a bus 603. The processor 600, the communication interface 602, and the memory 601 may communicate with each other via a bus 603. The communication interface 602 may be used for information transfer. The processor 600 may call logic instructions in the memory 601 to perform the method for predicting device control commands of the above embodiments.
By adopting the device for predicting the equipment control command provided by the embodiment of the disclosure, the current user data of the target user is collected, and the collected current user data is matched with the preset target user model, so that the command which the target user wants to issue can be predicted according to the current user data of the target user, the user does not need to actively issue the command to control the intelligent household equipment, and the interaction experience between the user and the intelligent household equipment is improved.
In addition, the logic instructions in the memory 601 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products.
The memory 601 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 600 executes functional applications and data processing, i.e., implements the method for predicting device control commands in the above-described embodiments, by executing program instructions/modules stored in the memory 601.
The memory 601 may include a storage program area and a storage data area, wherein the storage program area may store an application program required for operation, at least one function; the storage data area may store data created according to the use of the terminal device, and the like. In addition, memory 601 may include high speed random access memory and may also include non-volatile memory.
The embodiment of the disclosure provides an electronic device, which includes the above apparatus for predicting a device control command.
Further, the electronic device includes a computer, a tablet computer, a server, or the like.
By adopting the electronic equipment provided by the embodiment of the disclosure, the current user data of the target user is collected, and the collected current user data is matched with the preset target user model, so that the command which the target user wants to issue can be predicted according to the current user data of the target user, the user is not required to actively issue the command to control the intelligent home equipment, and the interaction experience between the user and the intelligent home equipment is improved.
The disclosed embodiments provide a storage medium storing program instructions that, when executed, perform the above-described method for predicting device control commands.
The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described method for predicting device control commands.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other media capable of storing program codes, and may also be a transient storage medium.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising a" \8230; "does not exclude the presence of additional like elements in a process, method or apparatus comprising the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the apparatuses and units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (15)
1. A method for predicting device control commands, comprising:
acquiring current user data of a target user; the current user data comprises one or more of target user voice information, target user image information or target user physiological information;
acquiring the coupling degree between the current user data and a preset target user model; the target user model includes a plurality of data modalities of a target user; the data mode is used for representing the current user data of a target user when issuing a device control command;
and predicting a device control command according to the coupling degree.
2. The method of claim 1, wherein the target user model is obtained by:
acquiring a historical command data set; the historical command data set comprises a plurality of pieces of historical command data; the historical command data comprises historical user data and historical command information corresponding to the historical user data; the historical command information comprises a historical device control command, historical device control command issuing time, historical device control command types and device identity identification numbers (ID) corresponding to the historical device control commands;
acquiring a data modality of the target user and historical command information corresponding to the data modality according to the historical command data set;
and acquiring a target user model according to the data modality and the historical command information corresponding to the data modality by utilizing a digital twin technology.
3. The method according to claim 2, wherein obtaining the data modality of the target user and the historical command information corresponding to the data modality according to the historical command data set comprises:
and under the condition that more than preset historical command data with the same number exist in the historical command data set, determining historical user data in the historical command data as a data modality of the target user, and determining historical command information in the historical command data as historical command information corresponding to the data modality.
4. The method of claim 2, wherein obtaining a target user model from the data modality and historical command information corresponding to the data modality using a digital twin technique comprises:
adding the data modality and historical command information corresponding to the data modality into a preset initial user model by using a digital twin technology for learning to obtain a target user model; the initial user model is used to characterize personal information of the target user.
5. The method of claim 2, wherein the target user model comprises a plurality of data modalities of a target user; acquiring the coupling degree between the current user data and a preset target user model, wherein the acquiring step comprises the following steps:
respectively acquiring the similarity between the current user data and each data modality;
and determining the similarity as the coupling degree between the current user data and a preset target user model.
6. The method of claim 5, wherein obtaining the similarity between the current user data and each of the data modalities comprises:
acquiring a first text characteristic vector corresponding to the current user data and a second text characteristic vector corresponding to each data modality;
and acquiring the similarity between the first text feature vector and each second text feature vector.
7. The method according to claim 6, wherein obtaining the similarity between the first text feature vector and each of the second text feature vectors comprises:
and calculating by using the first text characteristic vector and the second text characteristic vector according to a preset algorithm to obtain the similarity.
8. The method of claim 6, wherein predicting a device control command based on the degree of coupling comprises:
and determining the historical equipment control command corresponding to the data modality in the target user model as an equipment control command under the condition that the coupling degree is greater than a preset threshold value.
9. The method of claim 8, wherein determining historical device control commands in the target user model corresponding to the data modality as device control commands comprises:
determining a second text feature vector corresponding to the coupling degree as a target feature vector;
and determining the historical equipment control command in the data modality corresponding to the target characteristic vector as the equipment control command.
10. The method of claim 2, wherein predicting a device control command based on the degree of coupling further comprises:
matching out the equipment ID corresponding to the equipment control command from the target user model;
and sending the equipment control command to equipment corresponding to the equipment ID, and triggering the equipment to execute the equipment control command.
11. The method of claim 1, wherein predicting a device control command based on the degree of coupling further comprises:
monitoring whether the target user adjusts the equipment control command;
under the condition that the target user adjusts the equipment control command, acquiring alternative user data of the target user and command information corresponding to the adjusted equipment control command; the alternative user data is used for representing user data of a target user when the control command of the equipment is adjusted;
and updating the target user model by using the alternative user data and the command information corresponding to the adjusted equipment control command.
12. The method of claim 11, wherein updating the target user model with command information corresponding to the alternative user data and the adjusted device control command comprises:
and adding the alternative user data and the command information corresponding to the adjusted device control command into the target user model for learning so as to update the target user model.
13. An apparatus for predicting device control commands, comprising a processor and a memory having stored thereon program instructions, wherein the processor is configured to perform the method for predicting device control commands according to any one of claims 1 to 12 when executing the program instructions.
14. An electronic device, characterized in that it comprises means for predicting device control commands according to claim 13.
15. A storage medium storing program instructions which, when executed, perform a method for predicting device control commands according to any one of claims 1 to 12.
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| CN117742223A (en) * | 2024-02-20 | 2024-03-22 | 深圳市凯度电器有限公司 | Control method and device of embedded remote water purification system |
| CN117742223B (en) * | 2024-02-20 | 2024-04-26 | 深圳市凯度电器有限公司 | Control method and device of embedded remote water purification system |
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