WO2021179148A1

WO2021179148A1 - Setting method and device

Info

Publication number: WO2021179148A1
Application number: PCT/CN2020/078485
Authority: WO
Inventors: 茹昭
Original assignee: Oppo广东移动通信有限公司
Priority date: 2020-03-09
Filing date: 2020-03-09
Publication date: 2021-09-16
Also published as: CN115004641A

Abstract

Embodiments of the present application provide a setting method and device. The method comprises: obtaining instruction information; and generating corresponding setting information according to the instruction information, the setting information being used for setting scenario resources and/or rule resources. The embodiments of the present application can reduce the operation difficulty in setting the scenario resources and/or rule resources.

Description

A setting method and equipment

Technical field

This application relates to the field of Internet of Things, and more specifically, to a setting method and device.

Background technique

In the Internet of Things system, smart home systems including equipment, networks, platforms, and applications are usually used to build specific equipment automation and equipment linkage to achieve specific applications and services.

In the Open Connectivity Foundation (OCF, Open Connectivity Foundation) protocol, the automatic control of equipment and equipment is generally realized by creating and operating a scene resource (Scene Resource), and the linkage of equipment is realized by creating a rule resource (Rule Resource).

When setting scene resources and rule resources, users need to select or input multiple pieces of information, which is cumbersome and inconvenient to use. Moreover, the application is more difficult to use, because users need to have an understanding of IoT scenarios/rules and their structure.

Summary of the invention

The embodiments of the present application provide a setting method and device, which can automatically generate setting information for setting scene resources and/or rule resources according to user instruction information, without the user selecting or inputting multiple pieces of information, thereby reducing operation difficulty.

The embodiment of the application proposes a setting method, including:

Get instruction information;

The corresponding setting information is generated according to the instruction information, and the setting information is used to set scene resources and/or rule resources.

The embodiment of the application proposes a setting device, including:

Obtaining module for obtaining instruction information;

The setting information generating module is configured to generate corresponding setting information according to the instruction information, and the setting information is used to set scene resources and/or rule resources.

An embodiment of the present application proposes a setting device, including a processor and a memory, the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the setting method described above.

The embodiment of the present application proposes a chip, which includes a processor, configured to call and run a computer program from a memory, so that a device installed with the chip executes the above-mentioned setting method.

The embodiment of the present application proposes a computer-readable storage medium for storing a computer program, and the computer program enables a computer to execute the above-mentioned setting method.

The embodiment of the present application proposes a computer program product, which includes computer program instructions, and the computer program instructions cause a computer to execute the above-mentioned setting method.

The embodiment of the present application proposes a computer program that enables a computer to execute the above-mentioned setting method.

In the embodiment of the present application, when the setting device obtains the instruction information, it generates corresponding setting information according to the instruction information, which is used to set the scene resource and/or the rule resource, avoiding the user to select or input to create and modify the scene resource and / Or various information required when the resource is ruled, thus reducing the difficulty of operation.

Description of the drawings

Fig. 1 is a flow chart for implementing a setting method 100 according to an embodiment of the present application.

Fig. 2 is a flowchart for implementing step S120 in a setting method according to an embodiment of the present application.

Fig. 3 is an implementation flowchart of Embodiment 1 of this application.

Fig. 4 is a schematic structural diagram of a setting device 400 according to an embodiment of the present application.

Fig. 5 is a schematic structural diagram of a setting information generating submodule 520 according to an embodiment of the present application.

Fig. 6 is a schematic structural diagram of a setting device 600 according to an embodiment of the present application.

FIG. 7 is a schematic structural diagram of a setting device 700 according to an embodiment of the present application.

FIG. 8 is a schematic structural diagram of a chip 800 according to an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

The technical solutions of the embodiments of this application can be applied to various communication systems, such as: Global System of Mobile Communication (GSM) system, Code Division Multiple Access (CDMA) system, and Wideband Code Division Multiple Access (Wideband Code Division Multiple Access, WCDMA) system, General Packet Radio Service (GPRS), Long Term Evolution (LTE) system, Advanced Long Term Evolution (LTE-A) system , New Radio (NR) system, evolution system of NR system, LTE (LTE-based access to unlicensed spectrum, LTE-U) system on unlicensed spectrum, NR (NR-based access to unlicensed spectrum) unlicensed spectrum, NR-U) system, universal mobile telecommunication system (UMTS), wireless local area network (Wireless Local Area Networks, WLAN), wireless fidelity (Wireless Fidelity, WiFi), next-generation communications (5th-Generation) , 5G) system or other communication systems, etc.

Generally speaking, traditional communication systems support a limited number of connections and are easy to implement. However, with the development of communication technology, mobile communication systems will not only support traditional communication, but also support, for example, Device to Device (Device to Device, D2D) communication, machine to machine (Machine to Machine, M2M) communication, machine type communication (MTC), and vehicle to vehicle (V2V) communication, etc. The embodiments of this application can also be applied to these communications system.

Optionally, the communication system in the embodiments of the present application can be applied to a carrier aggregation (Carrier Aggregation, CA) scenario, can also be applied to a dual connectivity (DC) scenario, and can also be applied to a standalone (SA) deployment. Network scene.

The embodiment of the application does not limit the applied frequency spectrum. For example, the embodiments of this application can be applied to licensed spectrum or unlicensed spectrum.

The embodiments of this application describe various embodiments in conjunction with setting equipment, where the setting equipment can also be User Equipment (UE), access terminal, subscriber unit, subscriber station, mobile station, mobile station, remote station, and remote terminal. , Mobile equipment, user terminal, terminal, wireless communication equipment, user agent or user device, etc. The setting device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a wireless local loop (Wireless Local Loop, WLL) station, personal digital processing (Personal Digital Assistant, PDA) devices, handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, vehicle-mounted devices, wearable devices, and next-generation communication systems, such as terminal devices in the NR network or Terminal equipment in the public land mobile network (PLMN) network that will evolve in the future. The setting device can also be a smart terminal, a personal computer, a tablet computer and other devices. A tablet computer may also be called a portable computer (Tablet Personal Computer, Tablet PC), which uses a touch screen, a camera, a microphone, etc. as basic input devices.

As an example and not a limitation, in the embodiment of the present application, the setting device may also be a wearable device. Wearable devices can also be called wearable smart devices. It is a general term for using wearable technology to intelligently design everyday wear and develop wearable devices, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is directly worn on the body or integrated into the user's clothes or accessories. Wearable devices are not only a kind of hardware device, but also realize powerful functions through software support, data interaction, and cloud interaction. In a broad sense, wearable smart devices include full-featured, large-sized, complete or partial functions that can be achieved without relying on smart phones, such as smart watches or smart glasses, and only focus on a certain type of application function, and need to cooperate with other devices such as smart phones. Use, such as all kinds of smart bracelets and smart jewelry for physical sign monitoring.

It should be understood that the terms "system" and "network" in this article are often used interchangeably in this article. The term "and/or" in this article is only an association relationship describing the associated objects, which means that there can be three relationships, for example, A and/or B, which can mean: A alone exists, A and B exist at the same time, exist alone B these three situations. In addition, the character "/" in this text generally indicates that the associated objects before and after are in an "or" relationship.

The embodiments of the present application can be applied to the field of Internet of Things. Through various information sensors, radio frequency identification technology, global positioning system, infrared sensors, laser scanners and other devices and technologies, the Internet of Things collects any equipment or process that needs to be monitored, connected, and interacted in real time, and collects its sound, light, Various required information such as heat, electricity, mechanics, chemistry, biology, location, etc., can be accessed through various possible networks to realize a wide range of connections between things and people, and realize intelligent perception of objects and processes , Identification and management. The Internet of Things technology can be applied to the field of smart homes. The smart home system connects various devices in the home (such as audio and video equipment, lighting system, curtain control, air conditioning control, security system, digital cinema system, audio-visual server, network home appliances, etc.) through the Internet of Things technology to provide home appliance control, Various functions and methods such as lighting control, telephone remote control, indoor and outdoor remote control, anti-theft alarm, environmental monitoring, HVAC control, infrared forwarding, and programmable timing control.

In the OCF protocol, automatic device control is achieved by operating a scene resource that has been created (created by the user or pre-defined by the system). A scene resource is a collective resource that references one or more scene member resources (Scene Member Resources). Each scene member resource is also a collective resource, which references a local or remote device resource. At the same time, in the OCF protocol, device linkage is realized by defining rule resources. A rule resource is a collection resource, which refers to a rule input collection resource (Rule Input Collection Resource), which refers to one or more resources (these resources are generally the functional resources of the device, which can generate the status data of the device) notify). The rule resource also references a rule expression resource (Rule Expression Resource). The rule resource also references a rule action collection resource (Rule Actions Collection Resource), and the rule action collection resource references one or more scene resources. Currently, in order to create or modify scene resources and/or rule resources, users need to select or input multiple pieces of information, which is cumbersome and inconvenient to use. In order to reduce the difficulty of operation, an embodiment of the present application proposes a setting method. FIG. 1 is a flowchart of implementing a setting method 100 according to an embodiment of the present application, including the following steps:

S110: Obtain instruction information;

S120: Generate corresponding setting information according to the instruction information, where the setting information is used to set scene resources and/or rule resources.

Optionally, the above method may further include: setting the scene resource and/or the rule resource according to the setting information.

Optionally, as shown in FIG. 2, the foregoing step S120 may include:

S210: Generate corresponding setting parameters according to the instruction information;

S220: Generate corresponding setting information according to the setting parameter.

In some embodiments, the instruction information obtained in step S110 may include at least one of voice information, image information, action information, and gesture information. The above-mentioned instruction information may refer to the control instruction issued by the user to the smart device, including the control effect that the user hopes to achieve. For example, the user utters instructions such as "turn on the living room lights and air conditioner when going home", "turn off the living room lights when turning on the TV", "turn on the air conditioner at 30 degrees", and "call me to watch a football game at 2 o'clock in the middle of the night." In the case where the user sends the instruction information in the form of voice, the embodiment of the present application may use a voice receiving device to obtain the instruction information sent by the user. For another example, the user draws an image of the desired effect, for example, the user draws a picture indicating "turn on the living room lights and air conditioner when going home", or the user writes the text "turn on the living room lights and air conditioner when going home". In the case of the aforementioned user sending instruction information in the form of an image, an image acquisition device may be used in the embodiment of the present application to obtain the instruction information issued by the user. For another example, if the user issues control instructions by using actions or gestures, an image acquisition device may be used in the embodiments of the present application to capture images containing the user's actions or gestures.

In the case where the instruction information includes voice information, the embodiment of the present application may use a voice semantic service to convert the instruction information into corresponding text information, and convert the text information into corresponding semantic information, and the semantic information may include setting parameters. Speech semantic services include speech recognition services and semantic recognition services. Semantic recognition services can also be called natural language processing services.

In the case where the instruction information includes image information, the embodiment of the present application may use an image recognition service to extract the image feature in the image information, and use the image feature to search for the preset first correspondence to obtain the corresponding image feature Setting parameters.

In the case where the instruction information includes action information or gesture information, the embodiments of the present application can capture images of user actions or gestures, and use image recognition services to identify the action features in the action information/gesture information, and use the action features to find presets The second corresponding relationship of, obtains the setting parameter corresponding to the action feature.

In some implementation manners, the manner of generating corresponding setting information according to the setting parameters in step S220 may include at least one of the following:

(1) Generate corresponding setting information according to the setting parameters and predetermined rules.

(2) The corresponding setting information is generated according to the setting parameters and the smart device information, the smart device information includes the operation record and/or status data of the smart device. For example, the temperature usually set when the air conditioner is turned on, and the brightness of the lighting lamp at different times.

(3) Generate corresponding setting information according to the setting parameters and user information, the user information including the user's preference information for the smart device. For example, the user's favorite brightness, humidity, temperature, TV channel, background music, and so on. These preference information can be formed by using an artificial intelligence (AI, Artificial Intelligence) system to analyze and process user behavior over a period of time.

In some implementation manners, the setting of the scene resource and/or the rule resource according to the setting information in the embodiment of the present application includes at least one of the following:

Create scene resources and/or rule resources according to the setting information;

Modify the existing scene resources and/or rule resources according to the setting information.

For example, when the setting information is converted into a scene or rule, if a scene or rule with the same name already exists, the new setting will be used to overwrite the existing setting; if it does not exist, a new scene resource and/or rule resource will be created.

Further, the embodiment of the present application may also convert the setting information into feedback information, and the feedback information includes voice information or image information; the feedback information is played or displayed.

Using the foregoing process, the embodiment of the present application can play the feedback information in the form of voice, or display the feedback information in the form of images, etc., so as to inform the user of the setting result and prompt the user to confirm the setting result or make further adjustments.

In the embodiment of the present application, multiple devices may be used to implement the above setting method, and each device shares different functions, and there is information interaction between each other. Specific examples are given below to introduce this application in detail.

In this embodiment, the application program of the smart device is used to receive the instruction information in the form of voice sent by the user, the voice semantic service is used to realize the conversion of the instruction information to the semantic information (the semantic information may include the setting parameters), and the AI system is used to realize the setting parameters For the conversion to setting information, the scene/rule management program is used to realize the setting of scene resources and/or rule resources using the setting information. The above-mentioned smart device may be a smart terminal, such as a smart phone. In the following embodiments, a smart phone (referred to as a mobile phone) is taken as an example for description. The setting device in the embodiment of the present application is not limited to a smart phone, and the embodiment of the present application is also applicable to other forms of terminal devices.

Among them, the voice semantic service can run on the server, and provide voice for the application through the HyperText Transfer Protocol/HyperText Transfer Protocol (HTTP/HTTPS, HyperText Transfer Protocol/Hyper Text Transfer Protocol/Hyper Text Transfer Protocol/over Secure Socket Layer) interface on the server. Recognition, semantic analysis, voice broadcast and other functions. Specifically, the speech recognition function can receive speech input and convert it into text output after processing; the semantic analysis function can extract semantic information such as the meaning, topic, category, and similarity of the text from the text according to a specific context, such as "what is it?" , Who, when, where, and why" information; the voice broadcast function can receive text input and convert it to voice output after processing.

The AI system analyzes and simulates user behavior by collecting user and environmental data. In particular, in the Internet of Things system, the AI system collects the user's operation records of the smart device, the status data of the device, etc., and can analyze the user's favorite temperature, humidity, brightness, TV channel and other user information. These user information includes User preference information for smart devices.

Figure 3 is an implementation flowchart of Embodiment 1 of this application, including:

S301: The user naturally has to say the control effect to be achieved, such as "turn on the living room lights and air conditioner when going home", or "turn off the lights in the living room when the TV is turned on", or "turn on the air conditioner at 30 degrees", and For example, "Tell me to watch a football game at 2 o'clock in the middle of the night."

S302: The mobile phone application sends the user's voice instruction information to the voice semantic service. Optionally, the mobile phone application sends the command information to the voice semantic service running on the server through the HTTP/HTTPS interface.

S303: The speech semantic service converts speech into text, and analyzes the semantic information in the text, or key semantics, such as "what, who, when, where, and why" information. The semantic information may include setting parameters used to generate the setting information.

In one embodiment, when the voice instruction information is "turn on the living room lights and air conditioner when going home", the semantic information analyzed by the voice semantic service includes: "go home, turn on the living room lights, turn on the living room air conditioner".

In another embodiment, the instruction information in the form of voice is "turn off the lights in the living room when the TV is turned on", and the semantic information analyzed by the voice semantic service includes: "turn off the lights in the living room when the TV is turned on".

In another embodiment, the instruction information in the form of voice is "turn on the air conditioner at 30 degrees", and the semantic information analyzed by the voice semantic service includes: "turn on the air conditioner at 30 degrees".

In another embodiment, the instruction information in the form of voice is "Call me to watch the football game at 2 in the middle of the night", and the semantic information analyzed by the voice semantic service includes: "Watch the football game at 2 in the middle of the night".

The semantic information in the foregoing several implementation manners includes at least one item of "what, who, when, where, and why" information.

S304: The voice semantic service feeds back semantic information to the mobile phone application. Optionally, the voice semantic service feeds back semantic information to the mobile phone application through the HTTP/HTTPS interface.

S305: The mobile phone application sends the semantic information to the AI system, and requests the AI system to process the user's semantic information.

S306: The AI system converts the setting parameters included in the semantic information into setting information that conforms to user habits.

In one embodiment, for user A, the semantic information includes: "go home, turn on the living room light, turn on the living room air conditioner", and the conversion into setting information includes:

{"Name":"Go home","desc":"Turn on the living room lights and air conditioner when you go home","trigger":{"datetime":{"season":"winter","date":"daily ","Time":"19:00"},…},"devices":[{"Living room lights":{"on":true,"brightness":"70"}},{"Living room air conditioning": {"On":true,"temperature":"25"}},…}]}

In the above setting information, the operation objects are living room lights and living room air conditioners. Among them, the trigger condition for the living room lamp is: the time is 19:00 in winter; the operation mode is: turn on, and set the brightness of the living room lamp to 70. The trigger condition for the living room air conditioner is: the time is 19:00 in winter; the operation mode is: turn on, and set the temperature of the living room air conditioner to 25 degrees.

In another implementation manner, for user B, the semantic information includes: "go home, turn on the living room light, turn on the living room air conditioner", and the conversion into setting information includes:

{"Name":"Go home","desc":"Turn on the living room lights and air conditioner when going home","trigger":{"Door sensor":{"on":"true"},…}," devices":[{"Living room lights":{"on":true,"brightness":"60"}},{"Living room air conditioning":{"on":true,"temperature":"25"}}, …}]}

In the above setting information, the operation objects are living room lights and living room air conditioners. Among them, the trigger condition for the living room lamp is: the door sensor is turned on; the operation mode is: open, and the brightness of the living room lamp is set to 60. The trigger condition for the living room air conditioner is: the door sensor is turned on; the operation mode is: open, and the temperature of the living room air conditioner is set to 25 degrees.

In another embodiment, for user C, the semantic information includes: "go home, turn on the living room light, turn on the living room air conditioner", and the conversion to the setting information includes:

{"Name":"Go home","desc":"Turn on the living room lights and air conditioner when going home","trigger":{"Human sensor":{"on":"true"},"datetime" :{"Season":"winter","date":"daily","time":"19:00"}},…},"devices":[{"Living room lights":{"on":true ,"Brightness":"80"}},{"Living room air conditioning":{"on":true,"temperature":"26"}},…}]}

In the above setting information, the operation objects are living room lights and living room air conditioners. Among them, the trigger condition for the living room lamp is: the time is 17:00 in winter and the human sensor is turned on; the operation mode is: turn on, and the brightness of the living room lamp is set to 80. The trigger condition for the living room air conditioner is: the time is 17:00 in winter, and the human sensor is turned on; the operation mode is: turn on, and the temperature of the living room air conditioner is set to 26 degrees.

In another implementation manner, for user D, the semantic information includes: "go home, turn on the living room light, turn on the living room air conditioner", and the conversion into setting information includes:

{"Name":"Go home","desc":"Turn on the living room lights and air conditioner when you go home","trigger":{"microphone":{"voice":"I'm back"},…}, "Devices":[{"Living room lights":{"on":true,"brightness":"80"}},{"Living room air conditioning":{"on":true, "mode":"cold"," temperature”:”26”}},…}]}

In the above setting information, the operation objects are living room lights and living room air conditioners. Among them, the trigger condition for the living room lamp is: the voice receiving device receives the voice of "I'm back"; the operation mode is: turn on, and set the brightness of the living room lamp to 80. The trigger condition for the living room air conditioner is: the voice receiving device receives the voice of "I'm back"; the operation mode is: turn on, and set the temperature of the living room air conditioner to 26 degrees.

In some embodiments, the setting information is described in a JS Object Notation (JSON, JavaScript Object Notation) manner.

It can be seen that although the semantic information is the same in the above-mentioned several embodiments, because users have different preferences for the setting of home equipment, for different users, the setting information is converted into setting information according to the setting parameters contained in the semantic information. different. These setting information is set by the AI system by collecting the user's operation records on the smart device, analyzing the user's preferences, and setting it according to the user's preferences. For example, user A usually goes home at 19:00 and is used to setting the brightness of the living room light to 70 and the living room air conditioner to 25 degrees. User B's entrance door is equipped with a door sensor. User B is used to setting the brightness of the living room lamp to 60 and the living room air conditioner to 25 degrees. User C has a human body sensing device installed in his home. User C is used to setting the brightness of the living room lamp to 80 and the living room air conditioner to 26 degrees. User D has a sound sensing device installed in his home. User D is used to setting the brightness of the living room light to 80, and the living room air conditioner to 26 degrees of cooling, and so on.

In the above embodiment, the AI system is used to generate the setting information. In other embodiments of the present application, predefined rules may also be used to convert the above-mentioned semantic information into setting information. The aforementioned predefined rules may include: the corresponding relationship between the setting parameter representing "what" and the operation of the device in the scene, the corresponding relationship between the setting parameter representing "when"/"why" and the name or input information in the rule, and many more. Using this predefined rule, the "what" can be converted into the operation on the device in the scene, and the "when"/"why" can be converted into the name and input in the rule, and the generated scene can be used as the set of operations in the rule. When there is no "when"/"why" information, only the scene is generated and the automatic name is used (for example, "scene 1").

In one embodiment, the semantic information includes: "go home, turn on the living room light, turn on the living room air conditioner", and converting the setting parameters included in the semantic information into setting information includes:

{"Name":"Go home","desc":"Turn on the living room lights and air conditioner when you go home","trigger":{"datetime":{"date":"daily","time":"19 :00”}},”devices”:[{”Living room lights”:{”on”:true}},{”Living room air conditioning”:{”on”:true}},…}]}.

In another embodiment, the semantic information includes: "when the TV is turned on, turn off the living room light", and converting the setting parameters contained in the semantic information into setting information includes:

{"Name":"Turn on the TV","desc":"Turn off the lights in the living room when turning on the TV","trigger":{"TV":{"on":"true"}},"devices":[{ "Living room lights": {"on":false},…}]}.

In another embodiment, the semantic information includes: "when 30 degrees, turn on the air conditioner", and converting the setting parameters contained in the semantic information into setting information includes:

{"Name":"30 degrees","desc":"Turn on the air conditioner at 30 degrees","trigger":{"temperature sensor":{"temperature":"30"}},"devices":[{ "Air conditioning":{"on":false,"mode":"cold","temperature":25},…}]}.

In another embodiment, the semantic information includes: "2 o'clock in the middle of the night, watching a football game", and converting the setting parameters contained in the semantic information into setting information includes:

{"Name":"2 o'clock in the middle of the night", "desc":"Call me to watch the game at 2 o'clock in the middle of the night", "trigger":{"datetime":{"date":"2020-02-20","time" :"2:00"}},"devices":[{"Alarm":{"time":"2:00","alarm":true},{"TV":{"on":true," channel”:”5”},…}]}.

S307: The AI system returns the setting information to the mobile phone application.

S308: The mobile phone application issues setting information to the scene/rule management program.

S309: The scene/rule management program sets the scene resource and/or the rule resource according to the setting information. Optionally, when converting the above setting information into scenes and rules, if a scene or rule with the same name already exists, the new setting is used to overwrite the existing setting. If it does not exist, create a new scenario or rule.

S310: The scene/rule management program returns a feedback message to the mobile phone application, for example, a setting success message, which carries the above setting information.

S311: The mobile phone application sends the setting information to the voice semantic service, and requests the voice semantic service to convert the setting information into voice.

S312: The voice semantic service converts the above setting information into voice information, and returns the voice information to the mobile phone application. For example: "When you say'I'm back', the living room light will be turned on and set to a brightness of 80, and the living room air conditioner will be turned on and set to cooling at 26 degrees."

S313: The mobile phone application broadcasts the voice information and informs the user as a result of the user setting, prompting the user to confirm the foregoing setting result or make further adjustments. If the user chooses to further adjust, then jump to step S301.

In other embodiments of the present application, the user may input instruction information in the form of an image. In view of this situation, an image recognition service can be used to identify image features in the image information, and obtain the setting parameters corresponding to the image features. Alternatively, the user may input instruction information including motion information or gesture information. In response to this situation, it is possible to take a picture containing the user's actions or gestures, use the image recognition service to recognize the action information in the image/the action feature in the gesture information, and obtain the setting parameters corresponding to the action feature. In addition, in other embodiments of the present application, predetermined rules may also be used to convert semantic information into corresponding setting information.

With the embodiments of this application, when creating/modifying scene resources and rule resources, users can input instruction information through natural language, images, actions, or gestures. The operation is simple, and the user only needs to naturally describe the control effect to be achieved. However, there is no need to understand the rules and structure of the Internet of Things scenario, and the application is less difficult to use.

The embodiment of the present application also proposes a setting device. FIG. 4 is a schematic structural diagram of a setting device 400 according to an embodiment of the present application, including:

The obtaining module 410 is used to obtain instruction information;

The setting information generating module 420 is configured to generate corresponding setting information according to the instruction information, and the setting information is used to set scene resources and/or rule resources.

FIG. 5 is a schematic structural diagram of a setting information generating module 420 in a setting device according to an embodiment of the present application, including:

The setting parameter generation sub-module 510 is configured to generate corresponding setting parameters according to the instruction information;

The setting information generating sub-module 520 is configured to generate corresponding setting information according to the setting parameters.

In some embodiments, the instruction information includes at least one of voice information, image information, motion information, and gesture information.

Optionally, when the instruction information includes voice information, the setting parameter generation submodule 510 is configured to generate corresponding text information according to the voice information; and generate corresponding setting parameters according to the text information.

Optionally, when the instruction information includes image information, a parameter generation sub-module 510 is set to extract image features in the image information; the image features are used to search for a preset first corresponding relationship to obtain the Setting parameters corresponding to image features.

Optionally, when the instruction information includes action information or gesture information, the parameter generation sub-module 510 is set to identify the action feature in the action information/gesture information; use the action feature to search for a preset second Corresponding relationship, the setting parameter corresponding to the action feature is obtained.

As shown in FIG. 5, optionally, the above-mentioned setting information generating submodule 520 includes:

The rule conversion unit 521 is configured to generate corresponding setting information according to the instruction information and predetermined rules; and/or,

The artificial intelligence conversion unit 522 is configured to generate corresponding setting information according to the instruction information and smart device information, where the smart device information includes operation records and/or status data of the smart device; and/or, according to the instruction information and The user information generates corresponding setting information, and the user information includes the user's preference information for the smart device.

FIG. 6 is a schematic structural diagram of a setting device 600 according to an embodiment of the present application. As shown in FIG. 6, the foregoing device further includes:

The feedback module 630 is used to convert the setting information into feedback information. The feedback information includes voice information or image information; and to play or display the feedback information.

It should be understood that the above-mentioned and other operations and/or functions of the modules in the setting device according to the embodiment of the present application are used to implement the corresponding process in the method 100 in FIG. 1, and are not repeated here for brevity.

The setting device proposed in the embodiment of the present application may be implemented by an independent device, or each module of the setting device may be set in multiple devices. For example, the acquisition module 410 is set in the smart terminal, the setting parameter generation sub-module 510 is set in the speech semantic server or the server that provides image recognition services and semantic recognition services, and the setting information generation sub-module 520 is set in the AI system. middle.

FIG. 7 is a schematic structural diagram of a setting device 700 according to an embodiment of the present application. The setting device 700 shown in FIG. 7 includes a processor 710, and the processor 710 can call and run a computer program from a memory to implement the method in the embodiment of the present application.

Optionally, as shown in FIG. 7, the setting device 700 may further include a memory 720. The processor 710 may call and run a computer program from the memory 720 to implement the method in the embodiment of the present application.

The memory 720 may be a separate device independent of the processor 710, or may be integrated in the processor 710.

Optionally, as shown in FIG. 7, the setting device 700 may further include a transceiver 730, and the processor 710 may control the transceiver 730 to communicate with other devices. Specifically, it may send information or data to other devices, or receive other devices. Information or data sent by the device.

Wherein, the transceiver 730 may include a transmitter and a receiver. The transceiver 730 may further include an antenna, and the number of antennas may be one or more.

Optionally, the setting device 700 can implement the corresponding processes implemented by the setting device in each method in the embodiments of the present application. For brevity, details are not described herein again.

FIG. 8 is a schematic structural diagram of a chip 800 according to an embodiment of the present application. The chip 800 shown in FIG. 8 includes a processor 810, and the processor 810 can call and run a computer program from the memory to implement the method in the embodiment of the present application.

Optionally, as shown in FIG. 8, the chip 800 may further include a memory 820. The processor 810 can call and run a computer program from the memory 820 to implement the method in the embodiment of the present application.

The memory 820 may be a separate device independent of the processor 810, or may be integrated in the processor 810.

Optionally, the chip 800 may further include an input interface 830. The processor 810 can control the input interface 830 to communicate with other devices or chips, and specifically, can obtain information or data sent by other devices or chips.

Optionally, the chip 800 may further include an output interface 840. The processor 810 can control the output interface 840 to communicate with other devices or chips, and specifically, can output information or data to other devices or chips.

Optionally, the chip can be applied to the setting device in the embodiment of the present application, and the chip can implement the corresponding process implemented by the setting device in each method of the embodiment of the present application. For the sake of brevity, details are not described herein again.

It should be understood that the chip mentioned in the embodiment of the present application may also be referred to as a system-level chip, a system-on-chip, a system-on-chip, or a system-on-chip, etc.

The aforementioned processor may be a general-purpose processor, a digital signal processor (digital signal processor, DSP), a ready-made programmable gate array (field programmable gate array, FPGA), an application specific integrated circuit (ASIC), or Other programmable logic devices, transistor logic devices, discrete hardware components, etc. Among them, the aforementioned general-purpose processor may be a microprocessor or any conventional processor.

The above-mentioned memory may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (programmable ROM, PROM), erasable programmable read-only memory (erasable PROM, EPROM), and electrically available Erase programmable read-only memory (electrically EPROM, EEPROM) or flash memory. The volatile memory may be random access memory (RAM).

It should be understood that the foregoing memory is exemplary but not restrictive. For example, the memory in the embodiment of the present application may also be static random access memory (static RAM, SRAM), dynamic random access memory (dynamic RAM, DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous connection Dynamic random access memory (synch link DRAM, SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DR RAM) and so on. That is to say, the memory in the embodiments of the present application is intended to include, but is not limited to, these and any other suitable types of memory.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, it can be implemented in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instruction may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instruction may be transmitted from a website, computer, server, or data center through a cable (Such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center integrated with one or more available media. The usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, and a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).

It should be understood that in the various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not correspond to the embodiments of the present application. The implementation process constitutes any limitation.

Those skilled in the art can clearly understand that, for convenience and concise description, the specific working process of the above-described system, device, and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

The above are only specific implementations of this application, but the protection scope of this application is not limited to this. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application. Covered in the scope of protection of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

A setting method includes:

Get instruction information;

The corresponding setting information is generated according to the instruction information, and the setting information is used to set scene resources and/or rule resources.
The method according to claim 1 or 2, wherein the generating corresponding setting information according to the instruction information comprises:

Generate corresponding setting parameters according to the instruction information;

The corresponding setting information is generated according to the setting parameters.
The method according to claim 1 or 2, wherein the instruction information includes at least one of voice information, image information, motion information, and gesture information.
The method according to claim 3, wherein, in the case that the instruction information includes voice information, the generating corresponding setting parameters according to the instruction information comprises:

Generate corresponding text information according to the voice information;

The corresponding setting parameters are generated according to the text information.
The method according to claim 3, wherein, when the instruction information includes image information, the generating corresponding setting parameters according to the instruction information comprises:

Extracting image features in the image information;

The image feature is used to search for the preset first corresponding relationship, and the setting parameter corresponding to the image feature is obtained.
The method according to claim 3, wherein, when the instruction information includes action information or gesture information, the generating corresponding setting parameters according to the instruction information comprises:

Identifying the action feature in the action information/gesture information;

Use the action feature to search for the preset second correspondence to obtain the setting parameter corresponding to the action feature.
The method according to any one of claims 2 to 6, wherein said generating corresponding setting information according to said setting parameters comprises:

Generate corresponding setting information according to the setting parameters and predetermined rules; and/or,

Generate corresponding setting information according to the setting parameters and smart device information, where the smart device information includes operation records and/or status data of the smart device; and/or,

The corresponding setting information is generated according to the setting parameters and user information, and the user information includes the user's preference information for the smart device.
The method according to any one of claims 1 to 7, further comprising:

Converting the setting information into feedback information, where the feedback information includes voice information or image information;

Play or display the feedback information.
A setting device, including:

Obtaining module for obtaining instruction information;

The setting information generating module is configured to generate corresponding setting information according to the instruction information, and the setting information is used to set scene resources and/or rule resources.
The device according to claim 9, wherein the setting information generating module comprises:

A setting parameter generation sub-module for generating corresponding setting parameters according to the instruction information;

The setting information generating sub-module is used to generate corresponding setting information according to the setting parameters.
The device according to claim 9 or 10, wherein the instruction information includes at least one of voice information, image information, motion information, and gesture information.
The device according to claim 11, wherein, when the instruction information includes voice information, the setting parameter generation sub-module is configured to generate corresponding text information according to the voice information; and generate corresponding text information according to the text information. Corresponding setting parameters.
The device according to claim 11, wherein, in a case where the instruction information includes image information, the setting parameter generation sub-module is configured to extract image features in the image information; Set the first corresponding relationship to obtain the setting parameter corresponding to the image feature.
The device according to claim 11, wherein, in a case where the instruction information includes motion information or gesture information, the setting parameter generation sub-module is used to identify the motion characteristics in the motion information/gesture information; The action feature searches for a preset second corresponding relationship, and obtains the setting parameter corresponding to the action feature.
The device according to any one of claims 10 to 14, wherein the setting information generating sub-module comprises:

The rule conversion unit is configured to generate corresponding setting information according to the instruction information and predetermined rules; and/or,

The artificial intelligence conversion unit is used to generate corresponding setting information according to the instruction information and smart device information, the smart device information including the operation record and/or status data of the smart device; and/or, according to the instruction information and the user The information generates corresponding setting information, and the user information includes the user's preference information for the smart device.
The device according to any one of claims 9 to 15, further comprising:

The feedback module is used to convert the setting information into feedback information. The feedback information includes voice information or image information; and to play or display the feedback information.
A setting device, comprising: a processor and a memory, the memory is used to store a computer program, the processor is used to call and run the computer program stored in the memory, and execute any one of claims 1 to 8 Methods.
A chip comprising: a processor, configured to call and run a computer program from a memory, so that a device installed with the chip executes the method according to any one of claims 1 to 8.
A computer-readable storage medium for storing a computer program that enables a computer to execute the method according to any one of claims 1 to 8.
A computer program product comprising computer program instructions that cause a computer to execute the method according to any one of claims 1 to 8.
A computer program that causes a computer to execute the method according to any one of claims 1 to 8.