Interaction method and device of intelligent equipment, equipment, storage medium and program product

Abstract

The invention discloses an interaction method and device of intelligent equipment, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the steps of collecting activity state data of a first object through an intelligent device, and determining a state prediction result corresponding to the first object in combination with relevance between activity plan data preset for the first object and the activity state data, therefore, the intelligent device is controlled to execute the target interaction action to interact with the first object according to the state prediction result of the first object, that is, the intelligent device is utilized to automatically collect the activity state of the first object in real time, and the interaction action needing to be executed is autonomously determined. The situation that the first object manually operates the intelligent device to achieve the interaction function is avoided, the man-machine interaction efficiency is improved, and meanwhile the product function diversity of the intelligent device is enriched.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to an interaction method, apparatus, device, storage medium, and program product for a smart device. Background Technology

[0002] With the development of technology, the functions of smart devices are becoming more and more diversified, such as making calls, playing videos, and playing voice messages, allowing different users to interact with each other through smart devices.

[0003] In related technologies, users can achieve different functions of smart devices by triggering designated controls installed on the smart device or by inputting instructions (e.g., voice commands).

[0004] However, in related technologies, the functionality of smart devices relies on manual user-triggered commands, resulting in low efficiency of human-computer interaction and limited functionality of smart devices. Summary of the Invention

[0005] This application provides an interaction method, apparatus, device, storage medium, and program product for intelligent devices. It enables the automatic execution of various actions by setting up multiple intelligent agents for data collection and interaction, thereby improving the human-computer interaction efficiency of intelligent devices. The technical solution is as follows.

[0006] On the one hand, an interaction method for a smart device is provided, the method being applied to the smart device, the method comprising: Collect activity status data corresponding to the first object, and the activity status data is used to indicate the activity status of the first object; Obtain the activity plan data corresponding to the first object that has been preset; The state prediction result of the first object is determined based on the correlation between the activity plan data and the activity status data, and the state prediction result is used to determine the interactive action corresponding to the smart device. If the state prediction result meets the first interaction condition, a target interaction action is executed based on the state prediction result. The target interaction action is used to instruct the smart device to interact with the first object based on the activity plan data.

[0007] In some implementations of the first aspect, the smart device includes a target interaction component for performing the target interaction action; The execution of the target interaction action based on the state prediction result includes: A target interaction instruction is generated based on the state prediction result, and the target interaction instruction corresponds to the target interaction action. Based on the target interaction instruction, control the target interaction component to execute the target interaction action.

[0008] In some implementations of the first aspect, the smart device includes multiple candidate interaction components, each of which corresponds to a different component function; The generation of target interaction instructions based on the state prediction result includes: Obtain the state type and state content corresponding to the state prediction result. The state type is used to determine the target interaction component from the plurality of candidate interaction components. The state content is used to determine the action type corresponding to the target interaction action. The target interaction instruction is generated based on the state type and the state content.

[0009] In some implementations of the first aspect, the activity state data includes multiple state sub-data, which respectively correspond to the activity state of the first object in different modalities; Determining the state prediction result of the first object based on the correlation between the activity plan data and the activity status data includes: Based on the activity plan data, target sub-data is determined from the plurality of status sub-data, wherein the target sub-data is related to the activity type corresponding to the activity plan data; If the target sub-data meets the first state condition, the first object is scored based on the target sub-data to obtain the scoring result corresponding to the first object, which is used as the state prediction result.

[0010] In some implementations of the first aspect, the smart device establishes a communication connection with the terminal device; The step is to obtain the activity plan data corresponding to the first object that has been preset in advance; Receive object information and activity content data corresponding to the first object sent by the terminal device; The activity plan data is generated based on the object information and the activity content data.

[0011] In some implementations of the first aspect, a data modification instruction sent by the terminal device is received, the data modification instruction including plan modification data; the activity plan data is adjusted based on the plan modification data to obtain adjusted plan data.

[0012] In some implementations of the first aspect, after executing the target interaction action based on the state prediction result when the state prediction result meets the first interaction condition, the method further includes: After the target interaction action is completed, obtain the interaction status corresponding to the first object; Based on the interaction details and the activity status data, generate activity summary data corresponding to the first object; The activity summary data is sent to the terminal device.

[0013] On the other hand, an interaction device for a smart device is also provided, the device comprising: The acquisition module is used to acquire activity status data corresponding to the first object, and the activity status data is used to indicate the activity status of the first object. The acquisition module is used to acquire the activity plan data corresponding to the first object that has been preset in advance; The determination module is used to determine the state prediction result of the first object based on the correlation between the activity plan data and the activity status data, and the state prediction result is used to determine the interactive action corresponding to the smart device; An execution module is configured to execute a target interaction action based on the state prediction result when the state prediction result meets the first interaction condition. The target interaction action is used to instruct the smart device to interact with the first object based on the activity plan data.

[0014] On the other hand, a smart device is provided, the smart device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the control method of any of the smart devices described in the embodiments of this application above.

[0015] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the control method of the smart device as described in any of the embodiments of this application above.

[0016] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the control method for the intelligent device described in any of the above embodiments.

[0017] The beneficial effects of the technical solutions provided in this application include at least the following: By collecting activity status data of the first object through smart devices and combining the correlation between the activity plan data and the activity status data pre-set for the first object, the corresponding state prediction result of the first object is determined. Based on the state prediction result of the first object, the smart device is controlled to execute the target interactive action to interact with the first object. In other words, the smart device automatically collects the activity status of the first object in real time and autonomously determines the required interactive action, avoiding the first object from manually operating the smart device to achieve the interactive function. This improves the efficiency of human-computer interaction and also enriches the product function diversity of smart devices. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an implementation environment provided by an exemplary embodiment of this application; Figure 2 This is a flowchart of an interaction method for a smart device provided in an exemplary embodiment of this application; Figure 3 This is a flowchart of an interaction method for a smart device provided in another exemplary embodiment of this application; Figure 4 This is a structural block diagram of the interaction device of a smart device provided in another exemplary embodiment of this application; Figure 5 This is a schematic diagram of the structure of a server provided in an exemplary embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0021] First, the terms used in the embodiments of this application will be explained: An intelligent agent is an entity or system capable of perceiving information or collecting data in a specific environment, autonomously generating decisions, and taking actions to achieve a specific goal. In other words, an intelligent agent can be implemented as a software program or a hardware facility.

[0022] In this embodiment, the implementation of an intelligent agent as program content running in an intelligent device is used as an example for explanation.

[0023] Application Programming Interface (API): refers to a set of predefined functions that enable applications or developers to access a set of routines based on software or hardware, without needing to access the source code or understand the details of its internal workings.

[0024] Secondly, the implementation environment corresponding to this application will be described. In one case, the implementation environment includes a smart device; in another case, it includes both a smart device and a terminal device; and in yet another case, it includes a smart device, a terminal device, and a server. This embodiment will be illustrated using one of these cases as an example for illustrative purposes. Please refer to [the original text]. Figure 1 The illustration shows an implementation environment provided by an exemplary embodiment of this application, which includes a smart device 110 and a terminal device 120. The smart device 110 and the terminal device 120 are connected through a communication network 130. Alternatively, the smart device 110 and the terminal device 120 are connected through a physical interface, such as a Universal Serial Bus (USB).

[0025] In some embodiments, the smart device 110 collects activity status data corresponding to the first object 11, wherein the activity status data refers to the activity status corresponding to the first object 11.

[0026] In some embodiments, the smart device 110 stores pre-defined activity plan data corresponding to the first object 11 in its cache; therefore, the smart device 110 can directly read the activity plan data from the cache; or, as... Figure 1 As shown, the activity plan data of the first object 11 is generated by the terminal device 120. The smart device 110 sends a data acquisition request to the terminal device 120. The data acquisition request is used to request the terminal device 120 to send the activity plan data. After receiving the data acquisition request, the terminal device 120 reads the activity plan data corresponding to the first object 11 from the cached data and sends it to the smart device 110. Alternatively, the terminal device 120 sends the relevant parameters (e.g., activity type, object data of the first object, etc.) corresponding to the first object 11 to the smart device 110. After receiving the relevant parameters of the first object 11, the smart device 110 generates the activity plan data of the first object 11 based on the relevant parameters. This embodiment of the application does not limit this.

[0027] In some embodiments, the smart device 110 predicts the state of the first object 11 based on the correlation between activity plan data and activity status data, and obtains the state prediction result corresponding to the first object 11. The state prediction result is used to determine the interactive action corresponding to the smart device 110. For example, if the state prediction result is "the first object 11 is in a reading state", then the corresponding interactive action can be implemented as "starting the robotic arm corresponding to the smart device 110".

[0028] In some embodiments, if the state prediction result meets the first interaction condition, the smart device 110 is controlled to perform a target interaction action with the first object 11 based on the activity plan data, according to the state prediction result. For example, if the state prediction result is "the first object 11 is in a reading state" and the reading state lasts for more than 20 minutes (meets the first interaction condition), the target interaction action is "the smart device 110 raises its robotic arm to contact the head of the first object 11 and reminds it to rest for 5 minutes".

[0029] The terminal device 120 can be optional and can be at least one of the following: desktop computer, laptop computer, mobile phone, tablet computer, e-book reader, MP3 (Moving Picture Experts Group Audio Layer III) player, MP4 (Moving Picture Experts Group Audio Layer IV) player, smart TV, smart vehicle. This application embodiment does not limit it in this way.

[0030] The smart device 110 is optional and can be at least one of the following: smart lamps, smartwatches, smart glasses, and robots. This application does not limit this type of smart device.

[0031] It is worth noting that the aforementioned communication network can be implemented as a wired network or a wireless network, and the communication network can be implemented as any one of a local area network, a metropolitan area network, or a wide area network. This application embodiment does not limit this.

[0032] The server includes at least one of a single server, multiple servers, a cloud computing platform, and a virtualization center. Optionally, the server undertakes the main computing work, and the terminal device 120 undertakes the secondary computing work; or, the server undertakes the secondary computing work, and the terminal device 120 undertakes the main computing work; or, the server and the terminal device 120 collaborate on computing using a distributed computing architecture.

[0033] It is worth noting that the aforementioned servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0034] It is worth noting that the aforementioned communication network can be implemented as a wired network or a wireless network, and the communication network can be implemented as any one of a local area network, a metropolitan area network, or a wide area network. This application embodiment does not limit this.

[0035] It should be noted that this application may display prompt interfaces, pop-ups, or output voice prompts before and during the collection of user data. These prompt interfaces, pop-ups, or voice prompts are used to inform the user that their data is being collected. This ensures that the application only begins the steps for collecting user data after receiving confirmation from the user regarding the prompt interface or pop-up; otherwise (i.e., without user confirmation), the steps for collecting user data end, meaning no user data is collected. In other words, all user data collected in this application is collected with the user's consent and authorization, and the collection, use, and processing of related user data must comply with the relevant laws, regulations, and standards of the relevant regions.

[0036] Based on the above-described terminology and implementation environment, the interaction method of the smart device provided in this application embodiment will be described. The method is illustrated by example, with the smart device executing the method. Alternatively, the method can be completed collaboratively by the smart device and the terminal device. For illustrative purposes, please refer to [reference needed]. Figure 2 The diagram illustrates a flowchart of an interaction method for a smart device provided in an exemplary embodiment of this application, the method comprising the following steps.

[0037] Step 210: Collect the activity status data corresponding to the first object.

[0038] The activity status data is used to indicate the activity status of the first object.

[0039] In illustrative terms, the first object refers to an object that meets the first positional condition with respect to the smart device. For example, the lighting range of the smart device is a circular area with a radius of 25 centimeters centered on the smart device, and the first object is located within this circular area.

[0040] The first object is an object using a smart device, such as a student, teacher, or other object; or, the first object is simply an object within the lighting range corresponding to the smart device. This application embodiment does not limit this.

[0041] Illustratively, the first object is a single object, or the first object is a collection of objects.

[0042] Indicatively, when the positional relationship between the first object and the smart device meets the pre-set first positional conditions, it means that the smart device can interact with the first object, and that the smart device can collect the activity status data of the first object.

[0043] In one example, when the distance between the first object and the smart device is less than a preset distance threshold, it means that the first object and the smart device meet the first location condition. In another example, the smart device has a preset location range (a range centered on the smart device or a range centered on a specified location). If the smart device is within the location range, it means that the first object and the smart device meet the first location condition.

[0044] Indicatively, activity state data refers to the data collected by the intelligent device when the first object performs an activity, used to characterize the activity state of the first object at the time of collection.

[0045] Among them, the activity state refers to the content, type, and angle of the action when the first object performs the relevant action.

[0046] Optionally, the activity status data can be of a single data type or multiple data types, such as text data, image data, coordinate data, audio data, video data, force / torque data, etc. The data type can also be referred to as a modality, with different data types corresponding to different modalities.

[0047] As an illustration, the smart device has multiple components with data acquisition capabilities. For example, video and image data are acquired by the camera in the smart device, audio data is acquired by the microphone in the smart device, and text data is acquired by the scanner in the smart device after scanning the text content. The scanner can be an Optical Character Recognition (ORC) scanner. Coordinate data can be acquired by hardware sensors in the smart device, such as torque sensors or angle sensors installed at the joints of the robotic arm to acquire the force / torque or angle at the joints of the robotic arm.

[0048] In illustrative terms, activity state data refers to the action state of the first object in a specified modality. For example, if the first object performs the action of reading a book, then in the audio modality, the collected audio data is the audio of the first object turning pages; in the image modality, the collected image data is the image of the first object's reading posture, the image of the book, the image of the sitting posture, etc.; in the video modality, the collected video content includes the process of the first object reading the book.

[0049] The type of modality is related to the acquisition components used by the smart device.

[0050] Optionally, the activity state data is data for different modalities corresponding to the same action of the first object, or the activity state data includes data corresponding to different actions performed by the first object. For example, data a collects the audio content of the first object writing when writing, data b collects the audio content of the first object turning pages when reading a book, and data c collects the sleeping posture image corresponding to the first object sleeping.

[0051] As an illustration, when a smart device is equipped with a robotic arm, the smart device will also collect the corresponding state data of the robotic arm, such as the angle data of the corresponding joints of the robotic arm, force / torque data, etc.

[0052] In a schematic representation, a first intelligent agent is provided in the intelligent device. The activity status data is collected by at least one component in the intelligent device. The first intelligent agent is used to control at least one component to collect the activity status data of a first object.

[0053] Optionally, the first intelligent agent is a single intelligent agent, that is, the first intelligent agent can control the above-mentioned different components to collect activity state data of different modalities; or, the first intelligent agent includes multiple sub-intelligent agents, each of which is used to control at least one component to collect activity state data. For example, the first intelligent agent includes a robotic arm intelligent agent, a text intelligent agent, an audio intelligent agent, and a vision intelligent agent, which are respectively used to collect the force / torque and angle corresponding to the robotic arm joint, text data, audio data, video data, and image data.

[0054] In this embodiment, multiple modal data received are converted into structured data as activity state data. For example, a visual agent collects video content of the first object's head-down reading posture (the video content is 10 minutes long), and an audio agent collects audio of the first object continuously turning pages within 10 minutes. Therefore, the head-down reading posture video content data and the page-turning audio are fused in a multimodal manner to obtain the structured data "the first object continuously lowered its head for 10 minutes".

[0055] Step 220: Obtain the activity plan data corresponding to the pre-defined first object.

[0056] Indicatively, activity plan data refers to pre-defined activity content for a first target.

[0057] Optionally, the activity plan data may include at least one of the following data types: The first type is activity type data. For example, in a learning scenario, activity type data can be divided into homework type, review type, extension type, entertainment type, etc. The second type is activity content data. For example, in a learning scenario, if it's homework, the activity content data can be divided into memorization content, homework tutoring content, dictation supervision content, etc. Another example: The third type is activity duration data. For example, in a learning scenario, if it is an entertainment type, the activity duration is 10 minutes.

[0058] It is worth noting that the above-mentioned activity plan data is merely an illustrative example, and the embodiments of this application do not limit it.

[0059] To illustrate with an example, the activity plan data can be implemented as "From 7:30 to 8:00 tonight, the first person needs to do a memorization assignment, the content of which is text A".

[0060] Optionally, the activity plan data is pre-generated and stored in the smart device's cache; or, the activity plan data is generated by a terminal device that establishes a communication connection with the smart device, and then sent to the smart device for storage after being generated by the terminal device; or, the activity plan data is generated by a terminal device that establishes a communication connection with the smart device, and then sent to the smart device by the terminal device after the smart device collects the activity status data of the first object. This embodiment does not limit this.

[0061] Optionally, the activity plan data can be generated in at least one of the following ways: The first type of activity plan data is generated based on the object information of the first object and the pre-set activity content data. For example, taking students as the first object, the student information (e.g., grade, class, etc.) and homework content (e.g., memorization content) of the first object are combined, and the execution time is allocated accordingly to generate the memorization plan data. The second type of activity plan data is generated based on the activity status data of the first object within a historical time period. For example, taking the first object as a student, the activity plan data for tonight (completing math homework) is generated based on the first object's historical activities from 7 pm to 8 pm in the past week (e.g., performing math homework).

[0062] It is worth noting that the above-described method for generating activity plan data is merely an illustrative example, and the embodiments of this application do not limit it.

[0063] Optionally, the activity plan data for different objects may be the same or different; this embodiment does not limit this.

[0064] In an optional scenario, after collecting the activity plan data of the first object, the smart device needs to establish a detection task. The detection task refers to the smart device's requirement to detect the action status of the first object within a target time period. It can also be understood as the object's corresponding requirements. For example, if the activity plan data is "the first object continuously lowers its head for 10 minutes", then the detection task can be implemented as at least one of the following task types: eye use detection task, object sitting posture detection task, rest detection task, etc.

[0065] Among them, the eye use detection task refers to detecting the eye use of the first subject during the target time period; the subject sitting posture detection task refers to detecting the sitting posture of the first subject during the target time period; and the rest detection task refers to detecting the duration of the first subject's actions during the target time period (to ultimately determine whether the first subject needs to rest).

[0066] To illustrate, after receiving activity plan data, the main agent generates corresponding detection tasks based on the modality type of the activity plan data. For example, it generates an eye use detection task based on eye images, an object's sitting posture detection task based on sitting posture video content, and a rest detection task based on audio content (e.g., audio of turning pages, writing, or reading aloud).

[0067] Optionally, a single modality of data may be used to generate a detection task, or at least two modalities of data may be used to generate a detection task.

[0068] In this embodiment, taking a job scenario as an example, the main intelligent agent obtains pre-stored candidate task records and pre-stored job plan data. If a specified task record that matches the job plan data exists among multiple candidate task records, it is used as a reference task record (or preset task data). If the reference task record matches the modal data, the reference task record is used as the corresponding detection task.

[0069] In other words, the detection task is used to determine whether the smart device needs to acquire the activity state data specified by the first object. Unlike the purpose of the initial collection, the activity state data collected this time is used to determine whether the smart device needs to perform an interactive action, and what kind of interactive action to perform. That is to say, after the initial collection of activity state data, further activity state data will be collected according to the detection task. The activity state data at this time can be the same as the activity state data collected initially (directly using the data collected initially) or different (re-collecting).

[0070] In a schematic manner, the master agent is used to assign tasks to the slave agents and call different slave agents to perform different functions (for example, taking multiple slave agents including a first agent and a second agent as an example, the first agent collects data and the second agent performs data analysis). Therefore, after the master agent generates multiple detection tasks, it sends the multiple detection tasks and activity plan data to the second agent.

[0071] In a schematic manner, the second intelligent agent analyzes the received modal data and detection tasks to determine the corresponding action execution strategy for the intelligent device. Therefore, the second intelligent agent can also be called a detection intelligent agent or a supervisory intelligent agent.

[0072] To illustrate, taking the activity plan data including the nth modal data as an example (n is a positive integer), the nth modal data represents the first object's action during the target time period (for example, the nth modal data is the first object's sitting posture data, indicating that the first object performs the "sitting" action during the target time period). The first detection task is included among multiple detection tasks (for example, the object sitting posture detection task). Therefore, there is an analytical matching relationship between the nth modal data and the first detection task. The analytical matching relationship means that the detection parameters required by the first detection task are related to the nth modal data.

[0073] Step 230: Determine the state prediction result of the first object based on the correlation between the activity plan data and the activity status data.

[0074] The state prediction results are used to determine the corresponding interactive actions of the smart device.

[0075] Indicatively, the state prediction result refers to the activity state of the first object determined after analyzing the activity state data based on the activity plan data.

[0076] Among them, the state prediction result is used to determine whether the smart device needs to perform an interactive action, and / or the type of interactive action that the smart device performs.

[0077] Optionally, the determination of the state prediction result includes at least one of the following methods: The first method is to use the activity plan data as a benchmark, score the activity status data, and use the score results corresponding to the activity status data as the status prediction results. The second approach is to use activity plan data as a benchmark, analyze the similarity between activity status data and activity plan data, and use the similarity results as the status prediction results.

[0078] It is worth noting that the above-described method for determining the state prediction result is merely an illustrative example, and the embodiments of this application do not limit it.

[0079] Step 240: If the state prediction result meets the first interaction condition, execute the target interaction action based on the state prediction result.

[0080] Among them, the target interaction action is used to instruct the smart device to interact with the first object based on the activity plan data.

[0081] Indicative, target interactive action refers to the operational result achieved by the intelligent device controlling the built-in interactive components to run related functions.

[0082] Optionally, the interactive components include speaker components, projection components, motor components, robotic arm components, lighting components, etc.

[0083] For illustrative purposes, if the state prediction result meets the first interaction condition, the first interaction condition is used to determine whether the smart device performs the target interaction action, or the first interaction condition is used to determine the action type (target interaction action) of the interaction action performed by the smart device.

[0084] In summary, the interaction method of the smart device provided in this application collects the activity status data of a first object by the smart device, and determines the corresponding state prediction result of the first object by combining the correlation between the activity plan data and the activity status data pre-set for the first object. Then, the smart device is controlled to execute the target interactive action to interact with the first object according to the state prediction result of the first object. In other words, the smart device automatically collects the activity status of the first object in real time and autonomously determines the required interactive action, avoiding the first object from manually operating the smart device to achieve the interactive function. This improves the efficiency of human-computer interaction and also enriches the product function diversity of the smart device.

[0085] This is illustrative; please refer to it. Figure 3 This illustrates a flowchart of an interaction method for a smart device provided in an exemplary embodiment of this application. Specifically, step 240 includes steps 241 and 242, as follows: Figure 3 As shown, the method includes the following steps.

[0086] Step 241: Generate target interaction instructions based on the state prediction results.

[0087] The smart device includes a target interaction component, which is used to execute target interaction actions. The target interaction instructions correspond to the target interaction actions.

[0088] In illustrative terms, the target interaction command is used to control the target interaction component to perform the target interaction action. For example, if the target interaction component is a robotic arm component, the target interaction command is "control the robotic arm to raise to a height level with the head of the first object".

[0089] In some embodiments, the smart device includes multiple candidate interaction components, each corresponding to a different component function; obtains the state type and state content corresponding to the state prediction result, whereby the state type is used to determine the target interaction component from the multiple candidate interaction components, and the state content is used to determine the action type corresponding to the target interaction action; and generates a target interaction instruction based on the state type and state content.

[0090] Optionally, candidate interactive components include speaker components, projection components, motor components, robotic arm components, lighting components, etc.

[0091] Therefore, based on the state type and state content corresponding to the state prediction results, the target interaction component is determined from multiple candidate interaction components, and the target interaction instruction corresponding to the target interaction component is generated.

[0092] In a schematic way, the action type of the target interactive component is determined by the state type and state content corresponding to the state prediction result, so that the action execution of the target interactive component matches the state prediction result better and the action execution effect is better.

[0093] Step 242: Control the target interaction component to execute the target interaction action based on the target interaction command.

[0094] In a schematic representation, the intelligent device includes a second intelligent agent, which generates a first action task. The first action task refers to the device action that the intelligent device needs to perform, as determined by the second intelligent agent based on the state prediction result.

[0095] As an illustration, the smart device has a third intelligent agent that controls the device's actions.

[0096] To illustrate, when the third intelligent agent receives the first action task, it determines the action type and action trajectory corresponding to the intelligent device, and then executes the action corresponding to the action type based on the action trajectory.

[0097] As an illustration, the actions of the intelligent device are performed by the robotic arm within the device.

[0098] In this embodiment, the third intelligent agent includes a first sub-intelligent agent (also known as an action library intelligent agent) and a second sub-intelligent agent (also known as a motion trajectory intelligent agent) as an example. The first sub-intelligent agent pre-stores multiple candidate action types to determine the action type corresponding to the robotic arm in the intelligent device. The second sub-intelligent agent pre-stores multiple candidate action types and their corresponding candidate trajectories. If the action type corresponding to the robotic arm is determined, the candidate trajectory corresponding to the robotic arm is also determined.

[0099] In this embodiment, after determining the action type and candidate trajectory of the robotic arm, the parameters of the candidate trajectory are corrected according to the positional relationship between the smart device and the first object to obtain the corrected action trajectory. Finally, the robotic arm is controlled to execute the corresponding device action based on the action trajectory.

[0100] In some embodiments, a first object and a smart device are located within a first area, which also includes a second object. A first action task is used to instruct the adjustment of the position of the second object within the first area to adjust the positional relationship between the first object and the second object. A second smart agent is further used to generate a first acquisition task based on the first action task, the first acquisition task being used to acquire the position of the second object within the first area; send the first acquisition task to the first smart agent; the first smart agent is further used to receive the first acquisition task; acquire the position of the second object based on the first acquisition task to obtain first acquisition data; send the first acquisition data to a second sub-smart agent; the second sub-smart agent is further used to receive the first acquisition data; determine a third position corresponding to the second object based on the first acquisition data; determine a first position corresponding to the first object based on the nth modal data; acquire a second position corresponding to the smart device; and correct the first candidate trajectory based on the positional relationship between the first position, the second position, and the third position to obtain a first action trajectory.

[0101] To illustrate, if the first area also includes a second object (e.g., a teacup), the device action of the smart device can also be implemented as adjusting the positional relationship between the first object and the second object. Therefore, taking the first action task as adjusting the position of the second object in the first area as an example, the second smart agent generates a first collection task corresponding to the second object according to the first action task and sends it to the first smart agent so that the first smart agent collects the position of the second object in the first area as the first collection data.

[0102] In a schematic manner, after the first intelligent agent obtains the first collected data, it forwards it to the second sub-intelligent agent. Thus, the second sub-intelligent agent determines the third position of the second object, takes the first position as the end position, the third position as the waypoint position, and the second position as the starting position, adjusts the first candidate trajectory, and generates the first action trajectory. For example, the robotic arm extends to the second position where the teacup is located and touches the teacup, pushing the teacup to the first position.

[0103] The above method enables smart devices to autonomously infer user needs and then take corresponding actions on other objects in the environment to achieve interactive functions with users, thereby improving the interaction effect between smart devices and users.

[0104] In some embodiments, the activity state data includes multiple state sub-data, each corresponding to the activity state of the first object in different modalities; target sub-data is determined from the multiple state sub-data based on the activity plan data, and the target sub-data is related to the activity type corresponding to the activity plan data; if the target sub-data meets the first state condition, the first object is scored based on the target sub-data to obtain the scoring result corresponding to the first object, which is used as the state prediction result.

[0105] For illustrative purposes, target sub-data is a specific state sub-data related to activity plan data among multiple state sub-data. For example, if the activity plan data is to detect whether the sitting posture is standard, then the target sub-data is the sitting posture data of the first object.

[0106] For illustrative purposes, the first state condition is a condition pre-set based on the state type corresponding to the target sub-data. For example, if the target sub-data is sitting posture data, then the first state condition is the duration threshold of the sitting posture data.

[0107] To illustrate, when the second agent receives multiple detection tasks and activity plan data, it performs data analysis on the action of the first object based on the first detection task and the data of the nth modality, and obtains the state prediction result corresponding to the action of the first object.

[0108] Indicatively, the state prediction result is used to indicate whether the first object's action meets the preset action conditions. For example, if the first object is in a sitting position and the sitting time exceeds 15 minutes, the posture needs to be changed to protect the lower back. Therefore, if the first object's action is a sitting position, the state prediction result is whether the duration of the first object's sitting position reaches the preset duration threshold.

[0109] In illustrative terms, the first score result is used to determine the action status of the first object action in numerical form. For example, if the first score result is 60 points, which is lower than the preset first score threshold (e.g., 70 points), it means that the first action task needs to be established. If the first score result is 80 points, which is higher than the first score threshold, it means that the first action task is not established, or a second action task is established. The device action type corresponding to the second action task is different from the device action type corresponding to the first action task.

[0110] That is, the first score result is used to determine the suggestion of the action task corresponding to the second agent, including whether to establish the action task, the type of the action task, the start time of the action task, and the end time of the action task.

[0111] In illustrative terms, by pre-storing candidate reference scores and candidate reference actions with corresponding relationships, after obtaining the first object action, the system can select the specified score result corresponding to the first object reference score from multiple candidate reference scores, thereby improving the efficiency and accuracy of obtaining the score result.

[0112] In a schematic way, by pre-setting different candidate reference scores corresponding to different candidate reference actions, the target reference action that matches the first object action is determined based on the action matching degree between the first object action and multiple candidate reference actions, and then the candidate reference score corresponding to the target reference action is used as the first score result.

[0113] In some embodiments, the smart device establishes a communication connection with the terminal device; receives object information and activity content data corresponding to a first object sent by the terminal device; and generates activity plan data based on the object information and activity content data.

[0114] To illustrate, taking the student as the first example, the terminal device can be a device held by the parent, or it can be a device held by the teacher.

[0115] In this embodiment, the terminal device stores object information and activity content data corresponding to the first object, and sends them to the smart device, which then automatically generates activity plan data based on the object information and activity content data.

[0116] By using the above method, activity plan data is generated based on the relevant object information and activity content data of the first object sent by the terminal device, which enables the activity plan data to meet the activity needs of the first object.

[0117] In some embodiments, a data modification instruction sent by a terminal device is received, the data modification instruction including planned modification data; the activity plan data is adjusted based on the planned modification data to obtain adjusted plan data.

[0118] In illustrative terms, the planned modification data refers to the modified data generated after the terminal device receives the modification operation. It is used to adjust the activity plan data to obtain the modified activity plan data, which serves as the adjusted plan data. For example, if the activity plan data is "30-minute reading task" and the planned modification data is "50 minutes", then the "30 minutes" in the activity plan data will be modified to "50 minutes", resulting in the adjusted plan data "50-minute reading task".

[0119] As an illustration, after the smart device automatically generates activity plan data based on object information and activity content data, the terminal device also has the authority to modify the activity plan data. Therefore, if the terminal device receives a modification operation, it generates a data modification instruction and sends it to the smart device. The data modification instruction includes the plan modification data. The smart device can adjust the content of the activity plan data based on the plan modification data to obtain the adjusted plan data, which is then used as the final activity plan data.

[0120] As an example, adding the ability for terminal devices to adjust activity plan data can improve the flexibility of modifying activity plan data and facilitate timely adjustments to activity content.

[0121] In some embodiments, after the target interactive action is completed, the interaction status corresponding to the first object is obtained; based on the interaction status and activity state data, activity summary data corresponding to the first object is generated; and the activity summary data is sent to the terminal device.

[0122] Indicatively, the interaction refers to the interactive data between the smart device and the first object, for example: the smart device extends its robotic arm and taps the first object's head three times.

[0123] In a schematic manner, based on the interaction between the smart device and the first object, activity summary data corresponding to the first object is generated, and the terminal device sends the activity summary data for summary display.

[0124] For illustrative purposes, the activity summary data refers to the data integration of the above-mentioned interaction situation and the corresponding activity status data of the first object itself, which is obtained as the activity summary data of the first object. That is, the activity summary data includes both the activity situation of the first object and the interaction situation between the first object and the smart device.

[0125] The activity summary data can be implemented as at least one of video data, chart data, or text data.

[0126] By using the above methods, and combining the activities of the first object with the interactions between the first object and the smart device, the data summary is made richer and more comprehensive.

[0127] The following is a detailed description of several application scenarios that can be achieved by this application, as illustrative purposes.

[0128] The first type is the learning supervision scenario.

[0129] Step 1: The cloud system uses integrated sensing devices on the control lamp, such as a camera module (capturing user posture and emotions), a sound module (recognizing student speech, page turning sounds, and writing sounds), and a touch module, to acquire image, sound, and touch-related sensory information. Image preprocessing results in information uploaded to cloud storage, sound preprocessing results in text information, and touch preprocessing results in command information.

[0130] Step 2: The cloud transmits the perceived information to the backend service via API calls. The backend service starts the Little Bookworm Agent (central control), overlays the perceived information with the learning plan information and historical information stored in the backend, and calls the remote image understanding model to infer the student's potential behavior, such as whether the student has entered the state of completing homework.

[0131] Step 3: The Agent determines that the student has entered a learning state and, based on the cue word rules, determines whether the student needs to enter a supervised state. The Agent creates a task, uses a front-facing photo of the student as a parameter to call the supervised Agent. The supervised Agent passes the photo to the image understanding model, scores the student's posture and emotions, and determines whether direct intervention in the student's behavior is needed. If so, it generates command information.

[0132] Step 4: After the monitoring agent determines whether to directly intervene in the student's behavior, it generates a command message and sends it to the client via WebSocket. Upon receiving the command message, the client program invokes the appropriate module to remind the student based on the message type and content. For example, it might remind the student to correct their posture via a loudspeaker.

[0133] The second type: Learning tutoring scenario: Step 1: Obtain and input the student's personal information and select the textbook to be used by the student from the parent's end; Step 2: Parents upload homework to the cloud and set completion time requirements. The corresponding model in the cloud will generate today's learning plan, which can be edited and modified by the parent's device to generate the final learning plan. Step 3: The model generates voice and action commands to remind the child to start doing homework; Step 4: Once the camera recognizes that the task has been completed, it captures the task image and uploads it to the cloud for intelligent task grading. Step 5: Conduct one-on-one tutoring and analysis of incorrect answers and knowledge points through projection and voice; Step 6: Summarize the learning progress, generate visual charts and reports, and send them to parents.

[0134] The third scenario is memorization tutoring: Step 1: Parents upload the memorization content to the cloud and set the completion time requirement. The corresponding model in the cloud generates today's plan, which can be edited and modified by the parents to generate the final memorization plan. Step 2: Generate voice and action instructions to remind the child to start writing and reciting; Step 3: Guide the child to read aloud and recite through actions / voices. After the audio sensor collects the child's recitation voice, the data is uploaded to the server. Step 4: The server converts the speech to text and compares it with the textbook to check for errors; Step 5: After memorization, the cloud generates action / voice encouragement commands to reward the child and provide emotional value. Step 6: Summarize the memorization progress, generate visual charts and reports, and send them to the parents.

[0135] The fourth type is the learning supervision scenario: Step 1: The camera collects the child's facial expressions and behavioral data, uploads it to the cloud, and the corresponding model determines whether the child has entered the learning stage; Step 2: Infer the child's emotions and intentions, and record the child's emotional fluctuations; Step 3: When a child's low mood is detected, a parenting decision is generated and promptly fed back to the parents; Step 4: When the robot detects that the child is not focused (unfocused postures: such as shaking the head, dozing off, playing with the phone, etc.; how to judge: draw conclusions by identifying images uploaded to the storage via cloud AI), the robot generates voice or gesture reminders to encourage the child to study attentively and provides feedback to the parents. Step 5: When the robot detects that the child is not focused while sitting, it generates voice or gesture reminders to encourage the child to study attentively and then sends feedback to the parents.

[0136] The fifth scenario is reading companionship: Step 1: Parents upload a reading plan and set a completion time requirement. The model generates today's plan, which parents can edit and modify to generate the final reading plan. Step 2: Using data collected by sensors, identify the questions the child asks while reading and infer the child's intentions.

[0137] Step 3: Generate voice commands or projection commands for the robot to execute and answer the child's questions.

[0138] The sixth type is English dialogue scenarios: Step 1: Parents upload the English learning plan to the cloud and set the completion time requirement. The system generates today's plan, which parents can edit and modify. Step 2: Sensors collect the child's voice data, and the model generates corresponding English responses to communicate with the child through voice and projection.

[0139] In summary, the interaction method of the smart device provided in this application collects the activity status data of a first object by the smart device, and determines the corresponding state prediction result of the first object by combining the correlation between the activity plan data and the activity status data pre-set for the first object. Then, the smart device is controlled to execute the target interactive action to interact with the first object according to the state prediction result of the first object. In other words, the smart device automatically collects the activity status of the first object in real time and autonomously determines the required interactive action, avoiding the first object from manually operating the smart device to achieve the interactive function. This improves the efficiency of human-computer interaction and also enriches the product function diversity of the smart device.

[0140] Figure 4 This is a structural block diagram of the interaction device of a smart device provided in an exemplary embodiment of this application, such as... Figure 4 As shown, the device includes: The acquisition module 410 is used to acquire activity status data corresponding to the first object, and the activity status data is used to indicate the activity status corresponding to the first object; The acquisition module 420 is used to acquire the activity plan data corresponding to the first object that has been preset in advance; The determining module 430 is used to determine the state prediction result of the first object based on the correlation between the activity plan data and the activity status data, and the state prediction result is used to determine the interactive action corresponding to the smart device; The execution module 440 is configured to execute a target interaction action based on the state prediction result when the state prediction result meets the first interaction condition. The target interaction action is used to instruct the smart device to interact with the first object based on the activity plan data.

[0141] Optionally, the smart device includes a target interaction component, which is used to perform the target interaction action; The execution module 440 is further configured to generate a target interaction instruction based on the state prediction result, the target interaction instruction corresponding to the target interaction action; and control the target interaction component to execute the target interaction action based on the target interaction instruction.

[0142] Optionally, the smart device includes multiple candidate interaction components, each corresponding to a different component function; The execution module 440 is further configured to obtain the state type and state content corresponding to the state prediction result, wherein the state type is used to determine the target interaction component from the plurality of candidate interaction components, and the state content is used to determine the action type corresponding to the target interaction action; and to generate the target interaction instruction based on the state type and the state content.

[0143] Optionally, the activity state data includes multiple state sub-data, which respectively correspond to the activity state of the first object in different modalities; The determining module 430 is further configured to determine target sub-data from the plurality of state sub-data based on the activity plan data, wherein the target sub-data is related to the activity type corresponding to the activity plan data; and, if the target sub-data meets the first state condition, to score the state of the first object based on the target sub-data to obtain the scoring result corresponding to the first object, which is used as the state prediction result.

[0144] Optionally, the smart device establishes a communication connection with the terminal device; The acquisition module 410 is further configured to receive object information and activity content data corresponding to the first object sent by the terminal device; and generate the activity plan data based on the object information and the activity content data.

[0145] Optionally, the acquisition module 410 is further configured to receive a data modification instruction sent by the terminal device, the data modification instruction including plan modification data; and adjust the content of the activity plan data based on the plan modification data to obtain adjusted plan data.

[0146] Optionally, the acquisition module 410 is further configured to acquire the interaction status corresponding to the first object after the target interaction action has been completed; generate activity summary data corresponding to the first object based on the interaction status and the activity status data; and send the activity summary data to the terminal device.

[0147] In summary, the interactive device for intelligent devices provided in this application collects activity status data of a first object and, by combining the correlation between the activity plan data and the activity status data pre-set for the first object, determines the corresponding state prediction result of the first object. Based on the state prediction result of the first object, the intelligent device is controlled to execute a target interactive action to interact with the first object. In other words, by automatically collecting the activity status of the first object in real time and autonomously determining the required interactive action, the first object avoids manually operating the intelligent device to achieve the interactive function, thus improving the efficiency of human-computer interaction and enriching the product functionality diversity of the intelligent device.

[0148] It should be noted that the control device for the intelligent device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the control device for the intelligent device provided in the above embodiments and the control method embodiments for the intelligent device belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0149] Figure 5 A schematic diagram of the structure of a server provided in an exemplary embodiment of this application is shown. Specifically: See Figure 5 This illustration shows a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 5 As shown, the computer device 1000 of this embodiment includes: at least one processor 1010 ( Figure 5 (Only one is shown in the image) a processor, a memory 1020, and a computer program 1021 stored in the memory 1020 and executable on at least one processor 1010, wherein the processor 1010 executes the computer program 1021 to implement the steps in the above-described location hinting method embodiment.

[0150] Computer device 1000 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. This terminal device may include, but is not limited to, processor 1010 and memory 1020. Those skilled in the art will understand that... Figure 5 This is merely an example of computer device 1000 and does not constitute a limitation on computer device 1000. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0151] The processor 1010 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0152] In some embodiments, memory 1020 may be an internal storage unit of computer device 1000, such as a hard disk or memory of computer device 1000. In other embodiments, memory 1020 may be an external storage device of computer device 1000, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on computer device 1000. Furthermore, memory 1020 may include both internal and external storage units of computer device 1000. Memory 1020 is used to store operating system, program content, boot loader, data, and other programs, such as program code of computer programs. Memory 1020 may also be used to temporarily store data that has been output or will be output.

[0153] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0154] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0155] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0156] In the embodiments provided in this application, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0157] The unit described as a separate component may or may not be physically separate. The displayed components may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0158] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0159] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, swivel hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0160] The implementation of all or part of the processes in the methods of the above embodiments can also be accomplished by a computer program product. When the computer program product is run on a computer device, the computer device can implement the steps in the various method embodiments described above.

[0161] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An interaction method for a smart device, characterized in that, The method is applied to a smart device, and the method includes: Collect activity status data corresponding to the first object, and the activity status data is used to indicate the activity status of the first object; Obtain the activity plan data corresponding to the first object; The state prediction result of the first object is determined based on the correlation between the activity plan data and the activity status data, and the state prediction result is used to determine the interactive action corresponding to the smart device. If the state prediction result meets the first interaction condition, a target interaction action is executed based on the state prediction result. The target interaction action is used to instruct the smart device to interact with the first object based on the activity plan data.

2. The method according to claim 1, characterized in that, The smart device includes a target interaction component, which is used to perform the target interaction action; The execution of the target interaction action based on the state prediction result includes: A target interaction instruction is generated based on the state prediction result, and the target interaction instruction corresponds to the target interaction action. Based on the target interaction instruction, control the target interaction component to execute the target interaction action.

3. The method according to claim 2, characterized in that, The smart device includes multiple candidate interaction components, each of which corresponds to a different component function. The generation of target interaction instructions based on the state prediction result includes: Obtain the state type and state content corresponding to the state prediction result. The state type is used to determine the target interaction component from the plurality of candidate interaction components. The state content is used to determine the action type corresponding to the target interaction action. The target interaction instruction is generated based on the state type and the state content.

4. The method according to any one of claims 1 to 3, characterized in that, The activity status data includes multiple state sub-data, which respectively correspond to the activity status of the first object in different modalities; Determining the state prediction result of the first object based on the correlation between the activity plan data and the activity status data includes: Based on the activity plan data, target sub-data is determined from the plurality of status sub-data, wherein the target sub-data is related to the activity type corresponding to the activity plan data; If the target sub-data meets the first state condition, the first object is scored based on the target sub-data to obtain the scoring result corresponding to the first object, which is used as the state prediction result.

5. The method according to any one of claims 1 to 4, characterized in that, The intelligent device establishes a communication connection with the terminal device; The step of obtaining the pre-defined activity plan data corresponding to the first object includes: Receive object information and activity content data corresponding to the first object sent by the terminal device; The activity plan data is generated based on the object information and the activity content data.

6. The method according to claim 5, characterized in that, The method further includes: Receive a data modification instruction sent by the terminal device, wherein the data modification instruction includes a plan to modify data; Based on the plan modification data, the activity plan data is adjusted to obtain the adjusted plan data.

7. The method according to claim 5, characterized in that, After executing the target interaction action based on the state prediction result when the state prediction result meets the first interaction condition, the method further includes: After the target interaction action is completed, obtain the interaction status corresponding to the first object; Based on the interaction details and the activity status data, generate activity summary data corresponding to the first object; The activity summary data is sent to the terminal device.

8. An interactive device for a smart device, characterized in that, The device includes: The acquisition module is used to acquire activity status data corresponding to the first object, and the activity status data is used to indicate the activity status of the first object. The acquisition module is used to acquire the activity plan data corresponding to the first object; The determination module is used to determine the state prediction result of the first object based on the correlation between the activity plan data and the activity status data, and the state prediction result is used to determine the interactive action corresponding to the smart device; An execution module is configured to execute a target interaction action based on the state prediction result when the state prediction result meets the first interaction condition. The target interaction action is used to instruct the smart device to interact with the first object based on the activity plan data.

9. A smart device, characterized in that, The smart device includes a processor and a memory, the memory storing at least one program, which is loaded and executed by the processor to implement the interaction method of the smart device as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one program segment, which is loaded and executed by a processor to implement the interaction method of the smart device as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the interaction method of the smart device as described in any one of claims 1 to 7.

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