A control method and device for intelligent toys and intelligent toys

Abstract

The present application provides a control method, device and smart toy for smart toys, which are applied to the field of control technology of smart toys. The method includes using a wearable device to detect the real-time vital sign data of the user in real time, and obtaining the real-time operation data of the target smart toy. According to the real-time vital sign data and the real-time operation data, the action intensity value corresponding to the user at the current moment is determined. According to the real-time feedback data, the physical parameter value of the target smart toy at the current moment is determined. According to the action intensity value and the physical parameter value, the target action intensity value of the user at the next moment is predicted. According to the target action intensity value, the control signal of the target smart toy is determined, and the target smart toy is controlled by the control signal, so that the target smart toy fits the user's experience at the next moment, which can solve the problems of traditional smart toys with single mode, simple interaction, lack of diversity and insufficient entertainment.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent toy control, and in particular to an intelligent toy control method, an intelligent toy control device and an intelligent toy. Background Art

[0002] With the development of technology and the diversification of consumer needs, the smart toy market is also evolving. Traditional smart toys mainly control their mode changes through physical buttons, and users can select several preset modes through buttons. However, this control method has problems such as single mode, simple interaction, lack of diversity and insufficient entertainment, and poor user experience. For example, traditional smart toys usually have only a few fixed preset modes, and users cannot customize them according to their personal preferences. The interaction between users and smart toys is limited to simple button operations, lacking a deeper interactive experience. The number of preset modes in smart toys is limited and cannot meet the needs of different users. Smart toys lack interaction with the external environment or the user's emotional state, which makes the fun and immersion during use insufficient. Summary of the invention

[0003] The purpose of the present application is to provide a control method, device and smart toy for a smart toy, so as to solve the problems of traditional smart toys having a single mode, simple interaction, lack of diversity and insufficient entertainment.

[0004] In the first aspect, an embodiment of the present application provides a control method for an intelligent toy, the method comprising: using a wearable device to detect the real-time vital sign data of a user in real time, and obtaining the real-time operation data of a target intelligent toy. The real-time operation data includes real-time operation data and real-time feedback data. The real-time operation data is used to represent the operation data of the target intelligent toy and the operation data of the operation program running in the target intelligent toy. The real-time feedback data is used to represent the motion state and physical changes of the target intelligent toy. According to the real-time vital sign data and the real-time operation data, the action intensity value corresponding to the user at the current moment is determined. According to the real-time feedback data, the physical parameter value of the target intelligent toy at the current moment is determined. According to the action intensity value and the physical parameter value, the target action intensity value of the user at the next moment is predicted. According to the target action intensity value, the control signal of the target intelligent toy is determined, and the target intelligent toy is controlled by the control signal so that the target intelligent toy fits the user's experience at the next moment.

[0005] The control method of the smart toy provided in the embodiment of the present application can dynamically adjust the action intensity value according to the real-time vital sign data of the user and the real-time operation data of the target smart toy to provide a personalized experience. At the same time, it is also possible to predict the action intensity value of the user at the next moment based on the current vital sign data and device operation data, and adjust the control signal of the device in advance so that the device can better meet the user's experience needs. The control method of the smart toy provided in the embodiment of the present application not only takes into account the user's vital sign data, but also combines the operation data and feedback data of the device, realizes multi-dimensional data fusion, and improves the overall performance and responsiveness of the system.

[0006] A possible implementation method is to determine the action intensity value corresponding to the user at the current moment based on real-time vital sign data and real-time operation data, including: based on the real-time vital sign data, from the correspondence between the action intensity coefficient and the real-time vital sign data stored in advance, the action intensity coefficient corresponding to the real-time vital sign data is obtained. Based on the real-time vital sign data and the action intensity coefficient, the vital sign data influence value is determined. Based on the real-time operation data, from the correspondence between the action intensity coefficient and the real-time operation data stored in advance, the operation data coefficient corresponding to the real-time operation data is obtained. Based on the real-time operation data and the operation data coefficient, the operation data influence value is determined. Based on the vital sign data influence value and the operation data influence value, the action intensity value is determined.

[0007] In a possible implementation, the real-time vital sign data includes one or more of the following: heart rate, brain wave voltage value, and character posture data. The heart rate includes a heart rate value and / or a pulse value. The brain wave voltage value is the average brain wave voltage value of the user in the preset period at the current moment. According to the real-time vital sign data and the action intensity coefficient, the influence value of the vital sign data is determined, including: according to the heart rate recognition coefficient in the action intensity coefficient corresponding to the heart rate, the heart rate influence value is determined. The historical brain wave voltage value of the user's brain wave in the previous period of the preset period is obtained. The absolute value of the difference between the brain wave voltage value and the historical brain wave voltage value is determined to obtain the brain wave vibration intensity value. According to the brain wave vibration intensity value and the brain wave coefficient in the action intensity coefficient corresponding to the brain wave vibration intensity value, the brain wave vibration intensity influence value is determined. According to the character posture data and the character posture coefficient in the action intensity coefficient corresponding to the character posture data, the character posture influence value is determined. According to the heart rate influence value, the brain wave vibration intensity influence value, and the character posture influence value, the vital sign data influence value is determined.

[0008] In a possible implementation, the real-time operation data includes: the audio frequency and pressure value of the running program. The pressure value is the pressure applied to the target smart toy by the user and received by the target smart toy. The operation data impact value is determined based on the real-time operation data and the operation data coefficient, including: determining the audio vibration intensity value corresponding to the audio frequency based on the audio frequency. Determining the audio vibration impact value based on the audio vibration intensity value and the audio vibration intensity coefficient in the operation data coefficient corresponding to the audio vibration intensity value. Determining the pressure impact value based on the pressure value and the pressure coefficient in the operation data coefficient corresponding to the pressure value. Determining the operation data impact value based on the audio vibration impact value and the pressure impact value.

[0009] In a possible implementation, the real-time operation data includes: a video screen of a running program. According to the influence value of the vital sign data and the influence value of the operation data, the action intensity value is determined, including: identifying the first person posture of the person in the video screen at the current moment from the video screen. Obtaining the historical video screen of the previous moment before the current moment. Identifying the second person posture of the task from the historical video screen. Determining the scene coefficient corresponding to the video screen according to the first person posture and / or the second person posture. Determining the action intensity value according to the vital sign data influence value, the operation data influence value and the scene coefficient.

[0010] In a possible implementation, the real-time feedback data includes mechanical motion data and physical state data. The mechanical motion data is used to represent the motion state of each component of the target intelligent toy at the current moment. The physical state data is used to represent the physical changes of the target intelligent toy in a preset time period before the current moment. According to the real-time feedback data, the physical parameter value of the target intelligent toy at the current moment is determined, including: determining a mechanical motion reference value according to the mechanical motion data and a mechanical motion intensity coefficient corresponding to the mechanical motion data. Determining a physical state reference value according to the physical state data and a physical state intensity coefficient corresponding to the physical state data. Determining a physical parameter value according to the mechanical motion reference value and the physical state reference value.

[0011] A possible implementation method predicts the target action intensity value of the user at the next moment based on the action intensity value and the physical parameter value, including: determining the scene coefficient corresponding to the current moment based on the action intensity value and the physical parameter value. Determine the current vibration value of the target smart toy based on the action intensity value, the physical parameter value and the scene coefficient. Obtain the historical vibration curve of the target smart toy. The historical vibration curve is generated based on a set of vibration values ​​of the historical vibration values ​​of the target smart toy. Generate the current vibration curve of the target smart toy based on the historical vibration curve and the current vibration value. Predict the target action intensity value based on the current vibration curve.

[0012] In a possible implementation, the control method of the smart toy further includes: determining the operating parameters of the operating program of the target smart toy according to the physical parameter value and the conversion coefficient corresponding to the physical parameter value. Correcting the real-time operating data of the operating program based on the operating parameters to obtain target operating data. The target operating data is used to reversely control the operating program of the smart toy. The operating program is driven by the target operating data to make the operating program fit the user's experience.

[0013] In a possible implementation, determining the control signal of the target intelligent toy according to the target action intensity value includes: determining a first control parameter of the target intelligent toy according to the target action intensity value. Based on the first control parameter, determining the control signal.

[0014] A possible implementation method is to determine a control signal based on a first control parameter, including: in response to a user's remote control instruction, sending the first control parameter to a first terminal device connected to a smart toy, so that the first terminal device sends the first control parameter to at least one second terminal device. The second terminal device is connected to the first terminal device via a network. At least one target control parameter forwarded by the first terminal device is received. The target control parameter is determined by the second terminal device based on the first control parameter and sent to the first terminal via a network connection. The at least one target control parameter is integrated into a second control parameter of the smart toy. The control signal is determined based on the second control parameter.

[0015] In a second aspect, an embodiment of the present application provides a control device for an intelligent toy, which includes an acquisition module, a determination module and a prediction module.

[0016] Among them, the acquisition module is used to use the wearable device to detect the real-time vital sign data of the user in real time, and to obtain the real-time operation data of the target smart toy. The real-time operation data includes real-time operation data and real-time feedback data. The real-time operation data is used to represent the operation data of the target smart toy and the operation data of the operation program running in the target smart toy. The real-time feedback data is used to represent the motion state and physical changes of the target smart toy.

[0017] The determination module is used to determine the action intensity value corresponding to the user at the current moment according to the real-time vital sign data and the real-time operation data, and to determine the physical parameter value of the target intelligent toy at the current moment according to the real-time feedback data.

[0018] The prediction module is used to predict the target action intensity value of the user at the next moment according to the action intensity value and the physical parameter value.

[0019] The determination module is also used to determine the control signal of the target smart toy according to the target action intensity value, and control the target smart toy through the control signal so that the target smart toy fits the user's experience at the next moment.

[0020] In a third aspect, an embodiment of the present application provides an intelligent toy, which has the function of implementing the control method of the intelligent toy of the first aspect or any possible implementation method. The function can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, the computer can execute the control method of the smart toy of the first aspect or any possible implementation method.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the method for controlling an intelligent toy according to the first aspect or any possible implementation method.

[0023] Among them, the technical effects brought about by any design method in the second to fifth aspects can refer to the technical effects brought about by different possible implementation methods in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 A schematic diagram of a structure of a control device of an intelligent toy provided in an embodiment of the present application;

[0026] Figure 2 A schematic flow chart of a control method of an intelligent toy provided in an embodiment of the present application;

[0027] Figure 3 A schematic diagram of the structure of a control device for an intelligent toy provided in an embodiment of the present application;

[0028] Figure 4 Another system architecture diagram of a control system of an intelligent toy provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0030] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0031] At present, traditional smart toys mainly control their mode changes through physical buttons, and users can select several preset modes through buttons. However, this control method has problems such as single mode, simple interaction, lack of diversity and insufficient entertainment, and poor user experience. For example, traditional smart toys usually have only a few fixed preset modes, and users cannot customize them according to their personal preferences. The interaction between users and smart toys is limited to simple button operations, lacking a deeper interactive experience. The number of preset modes in smart toys is limited and cannot meet the needs of different users. Smart toys lack interaction with the external environment or the user's emotional state, which makes the fun and immersion during use insufficient.

[0032] Based on this, the embodiment of the present application provides a control method, device and equipment for an intelligent toy, the method comprising: using a wearable device to detect the real-time vital sign data of a user in real time, and obtaining the real-time operation data of a target intelligent toy. The real-time operation data includes real-time operation data and real-time feedback data. The real-time operation data is used to represent the operation data of the target intelligent toy and the operation data of the operation program running in the target intelligent toy. The real-time feedback data is used to represent the motion state and physical changes of the target intelligent toy. According to the real-time vital sign data and the real-time operation data, the action intensity value corresponding to the user at the current moment is determined, and according to the real-time feedback data, the physical parameter value of the target intelligent toy at the current moment is determined. According to the action intensity value and the physical parameter value, the target action intensity value of the user at the next moment is predicted. According to the target action intensity value, the control signal of the target intelligent toy is determined, and the target intelligent toy is controlled by the control signal so that the target intelligent toy fits the user's experience at the next moment.

[0033] The control method of the smart toy provided in the embodiment of the present application can dynamically adjust the action intensity value according to the real-time vital sign data of the user and the real-time operation data of the target smart toy to provide a personalized experience. At the same time, it is also possible to predict the action intensity value of the user at the next moment based on the current vital sign data and device operation data, and adjust the control signal of the device in advance so that the device can better meet the user's experience needs. The control method of the smart toy provided in the embodiment of the present application not only takes into account the user's vital sign data, but also combines the operation data and feedback data of the device, realizes multi-dimensional data fusion, and improves the overall performance and responsiveness of the system.

[0034] The implementation of the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0035] On the one hand, the solution shown in the embodiment of the present application can be executed by a smart toy. Figure 1 As shown, the smart toy 100 may include: a processing module 101 , a detection module 102 , a sensor 103 , and a display module 104 .

[0036] The processing module 101 is used for data processing, control and coordination of the work of each module. The processing module 101 receives data from the detection module 102 and the sensor 103, analyzes and processes the data, generates corresponding instructions, and controls the display module 104 and other related functions.

[0037] For example, the processing module 101 can receive the brain wave voltage value sent by the detection module 102 and the real-time vital sign data (such as pressure, temperature, acceleration, etc.) sent by the sensor 103, and analyze the brain wave voltage value and the real-time vital sign data. According to the analysis results, a control instruction is generated to drive the display module 104 and other functional modules. The processing module 101 can also communicate with external devices (such as mobile phones) to receive and send data.

[0038] The detection module 102 is used to obtain the user's brain wave voltage value and real-time physical sign data, and transmit these data to the processing module 101 for further processing. The detection module 102 may include a head-mounted brain wave detector and a smart wearable device.

[0039] Among them, the head-mounted EEG detector can be used to detect the user's EEG voltage value, and judge the user's emotional state and psychological activities by analyzing the EEG voltage value. The probe can sense the weak potential changes under the brain, and after pre-amplification, filtering and data conversion, the signal is converted into digital form to obtain the EEG voltage value, thereby reflecting the user's excitement. Smart wearable devices can be bracelets, chest straps or other forms of wearable devices, which are used to monitor the user's heart rate, blood pressure, pressure, posture, spatial displacement, movement speed, acceleration and other real-time vital signs data.

[0040] The sensor 103 can be built into the smart toy, or can be made into an independent accessory, or directly call the sensor in the mobile phone to realize the pressure, temperature and posture sensing functions. The data of these sensors are transmitted to the processing module 101 for analysis and processing. The sensor 103 can include a pressure sensor, a gyroscope, a temperature sensor, and an acceleration sensor.

[0041] Among them, the pressure sensor is used to detect the pressure applied by the user on the smart toy 100. It can be built into the smart toy 100 or made into an independent accessory, such as a resistive strain pressure sensor, a piezoresistive pressure sensor, a piezoelectric pressure sensor, an inductive pressure sensor, a barometric pressure sensor, etc. The gyroscope is used to detect the posture change of the smart toy 100 and provide rotation information in three-dimensional space. The temperature sensor is used to detect the temperature of the smart toy 100 and can monitor the user's body temperature or the ambient temperature. The acceleration sensor is used to detect the acceleration change of the smart toy 100 and provide motion state information.

[0042] The display module 104 is used to display pictures, videos, animations, games, and sounds. The display module 104 can dynamically adjust the display content based on the instructions generated by the processing module 101 to respond to the user's physiological and psychological state. For example, the display module 104 can display corresponding pictures and video content according to the user's state and preferences. The display module 104 can also display dynamic animations and interactive games to enhance the user experience. The display module 104 can also play music, sound effects or other audio content to provide an auditory experience.

[0043] It should be noted that the function of the above-mentioned smart toy 100 is only an example, and the present application does not limit the functions of each component in the smart toy. At the same time, the present application does not limit the specific form of the smart toy. For example, the smart toy can also be other auxiliary tools, including auxiliary learning tools for the visually impaired, auxiliary medical tools for the sexually disabled, intelligent entertainment and medical auxiliary equipment, etc.

[0044] On the one hand, the embodiment of the present application provides a control method of an intelligent toy, which can be Figure 1 The intelligent toy 100 shown in FIG. Figure 2 As shown, the method may include the following steps.

[0045] S201, using the wearable device to detect the user's real-time vital sign data in real time, and obtain the real-time operation data of the target smart toy.

[0046] The real-time operation data includes real-time operation data and real-time feedback data. The real-time operation data is used to represent the operation data of the target intelligent toy and the operation data of the operation program running in the target intelligent toy. The real-time feedback data is used to represent the motion state and physical changes of the target intelligent toy.

[0047] For example, the wearable device is used to detect the user's heart rate and brain wave voltage value in real time, and the video screen, audio frequency and pressure value of the running program of the target smart toy are obtained.

[0048] The heart rate may include a heart rate value and / or a pulse value.

[0049] S202, determining the action intensity value corresponding to the user at the current moment according to the real-time vital sign data and the real-time running data.

[0050] A possible implementation method is to obtain the action intensity coefficient corresponding to the real-time vital sign data from the pre-stored correspondence between the action intensity coefficient and the real-time vital sign data based on the real-time vital sign data, and determine the vital sign data impact value according to the real-time vital sign data and the action intensity coefficient.

[0051] Specifically, the heart rate influence value is first determined according to the heart rate and the heart rate identification coefficient in the action intensity coefficient corresponding to the heart rate.

[0052] Exemplarily, the heart rate can reflect the user's state of excitement. When the user is in an excited or exercising state, the sympathetic nervous system is activated, resulting in an accelerated heart rate. Once the excited state subsides or the exercise stops, the heart rate tends to stabilize. The smart wearable device monitors the user's heart rate in real time. For example, the smart wearable device can obtain the user's heart rate value through the ultrasonic Doppler effect or a chest strap heart rate monitor, or the smart wearable device can monitor the user's pulse value through a photoelectric sensor. Set a reasonable range of heart rate, such as 50 to 180 times / minute. Filter out heart rates that exceed this range. Standardize the heart rate to obtain a standardized heart rate. The standardized heart rate is weighted and integrated into the action signal sequence to determine the heart rate impact value = standardized heart rate * heart rate recognition coefficient.

[0053] For example, the heart rate vibration intensity value is the normalized heart rate, and the heart rate identification coefficient in the action intensity coefficient corresponding to the heart rate is 0.3. Finally, the heart rate impact value = normalized heart rate × 0.3.

[0054] Among them, the heart rate recognition coefficient can be modified by the user according to his own needs.

[0055] Secondly, the historical brain wave voltage value of the user's brain wave in the previous cycle of the preset cycle is obtained. The absolute value of the difference between the brain wave voltage value and the historical brain wave voltage value is determined to obtain the brain wave vibration intensity value. According to the brain wave vibration intensity value and the brain wave coefficient in the action intensity coefficient corresponding to the brain wave vibration intensity value, the brain wave vibration intensity impact value is determined.

[0056] Among them, electroencephalogram (EEG) is an electrical signal generated by neuronal activity in the cerebral cortex. By monitoring the voltage value of brain waves, the degree of brain excitement can be reflected. Generally speaking, the higher the frequency of cerebral cortical activity, the more obvious the change in the potential difference between neurons. Frequent neuronal activity leads to more action potential generation, which in turn causes more frequent and greater changes in potential difference. Therefore, the potential difference value of brain waves reflects the degree of brain excitement. The greater the change in potential difference, the higher the degree of brain excitement.

[0057] Exemplarily, the brain wave voltage value is collected every 30 seconds. For example, the brain wave voltage value of the first cycle is (V1), and the brain wave voltage value of the second cycle is (V2). The absolute value of the brain wave voltage difference is calculated to obtain the brain wave vibration intensity value, and the brain wave vibration intensity value = |V1-V2|. The brain wave vibration intensity value is standardized. The standardized brain wave vibration intensity value is integrated into the action signal sequence to determine the brain wave vibration intensity impact value = brain wave vibration intensity value * brain wave coefficient.

[0058] For example, the brain wave vibration intensity value is the standardized brain wave vibration intensity value, and the brain wave recognition coefficient corresponding to the brain wave vibration intensity value is 0.5. Finally, the brain wave vibration intensity impact value is the brain wave vibration intensity value*0.5.

[0059] Then, the character posture influence value is determined according to the character posture data and the character posture coefficient in the action intensity coefficient corresponding to the character posture data.

[0060] Exemplarily, the character posture data is collected every 100 milliseconds. For example, a gyroscope, an accelerometer, or other device is used to capture the user's posture. The relative displacement of the user during the sampling period is calculated to obtain the character posture data. The character posture data is standardized so that it is between 0 and 1. The standardized character posture data is integrated into the motion signal sequence to determine the character posture influence value = standardized character posture data * character posture coefficient. For example, the character posture coefficient is 0.55, and the final character posture influence value = standardized character posture * 0.55.

[0061] Finally, the influence value of the vital sign data is determined according to the influence value of the heart rate, the influence value of the brain wave vibration intensity and the influence value of the character's posture.

[0062] Exemplarily, the influence value of physical sign data = the influence value of heart rate + the influence value of brain wave vibration intensity + the influence value of character posture.

[0063] This process detects heart rate and brain waves. By monitoring the user's physiological state, it can obtain the user's heart rate data and cerebral cortex activity in real time, and transmit this data to the smart toy. This process gives the smart toy a higher level of intelligence, enabling it to dynamically adjust the mode according to the user's physiological response, thereby providing a more personalized experience that is closer to the user's needs.

[0064] Another possible implementation is to obtain the operation data coefficient corresponding to the real-time operation data from the pre-stored correspondence between the action intensity coefficient and the real-time operation data based on the real-time operation data, and determine the operation data impact value according to the real-time operation data and the operation data coefficient.

[0065] The real-time operation data may include: the audio frequency of the running program and the pressure value. The pressure value is the pressure applied to the target smart toy by the user and received by the target smart toy.

[0066] Specifically, firstly, the audio vibration intensity value corresponding to the audio frequency is determined according to the audio frequency, and the audio vibration impact value is determined according to the audio vibration intensity value and the audio vibration intensity coefficient in the operation data coefficient corresponding to the audio vibration intensity value.

[0067] Exemplarily, the audio file in the application is collected every 100 milliseconds to extract the audio frequency. Audio frequencies between -60dB and 60dB are screened out from the audio frequencies to filter out abnormal audio frequencies. The audio frequencies are standardized to obtain standardized audio vibration intensity values, and then the standardized audio vibration intensity values ​​are integrated into the action signal sequence to determine the audio vibration intensity value impact value = standardized audio vibration intensity value * audio vibration intensity coefficient. For example, the intensity coefficient of audio recognition is 0.3, and the final audio vibration intensity value impact value = standardized audio vibration intensity value * 0.3.

[0068] The pressure impact value is then determined based on the pressure value and the pressure coefficient in the operating data coefficient corresponding to the pressure value.

[0069] Exemplarily, the pressure value in the pressure sensor is obtained in real time, the pressure value is standardized, and the standardized pressure value is weighted and integrated into the action signal sequence. Determine the pressure impact value = standardized pressure value * pressure coefficient. For example, if the pressure coefficient is 0.1, the final pressure impact value = standardized pressure value * 0.1.

[0070] Finally, the operating data impact value is determined based on the audio vibration impact value and the pressure impact value.

[0071] Specifically, the operating data impact value = audio vibration impact value + pressure impact value.

[0072] Furthermore, the action intensity value is determined according to the influence value of the vital sign data and the influence value of the operation data.

[0073] The real-time operation data may also include video images of the running program.

[0074] A possible implementation method is to identify a first person posture of a person in the video screen at the current moment from the video screen. Obtain a historical video screen at the previous moment of the current moment. Identify a second person posture of the task from the historical video screen. Determine a scene coefficient corresponding to the video screen according to the first person posture and / or the second person posture. Determine an action intensity value according to the vital sign data influence value, the operation data influence value and the scene coefficient.

[0075] Specifically, the video image is recognized by the video recognition system, and the scene information in the video image is automatically recognized. Based on the scene information, the scene coefficient corresponding to the scene information is determined. This process can guide users to express different emotions in different images, and the vibration effect of the target smart toy can be adjusted according to the scene information to meet user needs.

[0076] It should be noted that the scene coefficient may be determined based on the first character posture or the second character posture alone, or the scene coefficient may be determined by combining the first character posture and the second character posture.

[0077] In a possible implementation, after the scene information is determined, a scene coefficient corresponding to the video picture is determined according to a first character posture of a character in the current video picture or a second character posture of the character in a historical video picture at a previous moment.

[0078] Another possible implementation method is to determine the scene information, and then determine the relative displacement value of the character's posture in the previous and next frames according to the first character posture of the character in the current video screen and the second character posture of the character in the previous historical video screen. The relative displacement value indicates the movement of the character in the scene. Then, the scene coefficient corresponding to the video screen is determined according to the relative displacement value.

[0079] After determining the scene coefficient of the video image, determine the action intensity value = (vital sign data impact value + operation data impact value) * scene coefficient.

[0080] For example, motion intensity value = (standardized heart rate * heart rate recognition coefficient + standardized brain wave vibration intensity value * brain wave coefficient + standardized character posture data * character posture coefficient + standardized audio vibration intensity value * audio vibration intensity coefficient + pressure impact value + standardized pressure value * pressure coefficient) * scene coefficient.

[0081] This process allows the smart toy to automatically identify the scene when the user uses it to watch a video. The smart toy can obtain the corresponding scene coefficient based on the identified scene, and generate the corresponding vibration value in combination with the action recognition and sound recognition in the video. Users can adjust these coefficients by themselves to achieve personalized adjustment of the vibration curve. This intelligent feedback mechanism allows different users, different videos, and even different scenes in the same video to affect the vibration mode of the smart toy, providing users with a more personalized experience.

[0082] For example: the scene coefficient is 2, if the heart rate and brain wave values ​​do not change within a fixed period, the character posture coefficient is 0.55, and the audio vibration coefficient is 0.3. The action intensity value of the target smart toy = (character posture data × 0.55 + audio vibration data × 0.3) × 2.

[0083] If the heart rate and brain wave values ​​change within the fixed period, the character posture coefficient is 0.55, the audio vibration coefficient is 0.3, the brain wave coefficient is 0.5, the heart rate recognition coefficient is 0.3, and the pressure coefficient is 0.1. The action intensity value of the target smart toy = (human posture data × 0.3 + audio vibration intensity value × 0.1 + brain wave vibration intensity value × 0.5 + heart rate × 0.3 + pressure value × 0.1) × 2.

[0084] S203, determining the physical parameter value of the target intelligent toy at the current moment according to the real-time feedback data.

[0085] The real-time feedback data includes mechanical motion data and physical state data. The mechanical motion data is used to represent the motion state of each component of the target smart toy at the current moment. The physical state data is used to represent the physical changes of the target smart toy in a preset time period before the current moment.

[0086] In a possible implementation, a mechanical motion reference value is determined based on the mechanical motion data and the mechanical motion intensity coefficient corresponding to the mechanical motion data. A physical state reference value is determined based on the physical state data and the physical state intensity coefficient corresponding to the physical state data. A physical parameter value is determined based on the mechanical motion reference value and the physical state reference value.

[0087] In a possible implementation, after determining the physical parameter value, the operating parameters of the operating program of the target intelligent toy are determined according to the physical parameter value and the conversion coefficient corresponding to the physical parameter value. The real-time operating data of the operating program is corrected based on the operating parameters to obtain target operating data. The target operating data is used to reversely control the operating program of the intelligent toy. The operating program is driven by the target operating data to make the operating program fit the user's experience.

[0088] Specifically, operating parameter=physical parameter value*conversion coefficient corresponding to the physical parameter value.

[0089] For example, the target smart toy can reverse the content in the control software (including the APP and PC). The mechanical motion data of the target smart toy itself can include: displacement, speed, angular velocity, acceleration, angular acceleration. The physical state data of the target smart toy itself can include: deformation, temperature, pressure, vibration, etc.

[0090] The mechanical motion data and physical state data of the target smart toy can be transmitted to the software through communications such as Wi-Fi or Bluetooth, driving the content (including sounds, pictures, videos, animations, games, etc. produced manually or assisted by AI) to change in association with it. This interactive mode not only increases the humanization of the user experience, but also significantly improves the interactivity. This interactive method provides users with a more personalized, intelligent and immersive experience.

[0091] For example, the smart toy first extracts sound feature values, such as pitch, speaking speed, and timbre, from a short audio clip provided by the user. Using a deep learning model (such as Tacotron or WaveNet), the extracted sound features are modeled to generate a clone model that can imitate the sound features. The clone model can be a lightweight model that can run on a server or mobile terminal, and can also complete sound cloning in a very short time, usually with only about 3 seconds of audio material. When there is new text input, the smart toy uses the clone model to convert the text into an audio output similar to the target sound source. In this process, even support for different languages ​​can be seamlessly converted through a multilingual model. In this process, users can clone similar sounds by inputting audio clips of more than 3 seconds in the smart toy application, which can be used for various text and language playback in the smart toy, allowing the smart toy to say anything in any voice they want, greatly improving personalization and fun.

[0092] Users can also input requirements to smart toys in a variety of ways, including but not limited to specific scene descriptions, character features, desired art styles, etc. In addition, reference materials can be uploaded as guidance. The AIGC system uses deep learning models (such as GANs, Transformer, etc.) to analyze the information input by users, understand their intentions and extract key features. Based on the key features, the smart toy will call the corresponding generation model (such as image generator, text generator, video editor, etc.) to create preliminary generated content. In this process, the smart toy supports a real-time feedback mechanism, allowing users to make modifications at any time, such as changing the appearance of a character or adjusting the atmosphere of the scene. The final generated content not only meets the user's initial definition, but also meets the user's unique needs and style, thus providing a highly customized experience.

[0093] S204: predicting the target action intensity value of the user at the next moment according to the action intensity value and the physical parameter value.

[0094] A possible implementation method is to determine the scene coefficient corresponding to the current moment according to the action intensity value and the physical parameter value. According to the action intensity value, the physical parameter value and the scene coefficient, the current vibration value of the target smart toy is determined. The historical vibration curve of the target smart toy is obtained. The historical vibration curve is generated based on the vibration value set of the historical vibration value of the target smart toy. Based on the historical vibration curve and the current vibration value, the current vibration curve of the target smart toy is generated. Based on the current vibration curve, the target action intensity value is predicted.

[0095] S205, determining a control signal of the target smart toy according to the target action intensity value, and controlling the target smart toy through the control signal, so that the target smart toy fits the user's experience at the next moment.

[0096] In a possible implementation, a first control parameter of a target intelligent toy is determined according to a target action intensity value, and a control signal is determined based on the first control parameter.

[0097] Specifically, after predicting the target action intensity value of the user at the next moment, the smart toy can search a pre-set mapping table or use a dynamic adjustment algorithm to determine the first control parameter corresponding to the target action intensity value. Based on the first control parameter, a specific control signal format is constructed, and the signal can directly act on the smart toy to drive the smart toy to fit the user's experience at the next moment in an expected manner.

[0098] A possible implementation method is to send a first control parameter to a first terminal device connected to a smart toy in response to a user's remote control instruction, so that the first terminal device sends the first control parameter to at least one second terminal device. The second terminal device is connected to the first terminal device via a network. At least one target control parameter forwarded by the first terminal device is received. The target control parameter is determined by the second terminal device based on the first control parameter and sent to the first terminal via a network connection. The at least one target control parameter is integrated into a second control parameter of the smart toy. A control signal is determined based on the second control parameter.

[0099] Specifically, after determining the first control parameter, the user can input a remote control command in the interactive interface of the smart toy. The smart toy responds to the remote control command, combines the first control parameter calculated previously, packages the first control parameter and sends it to the first terminal device through the network.

[0100] The first terminal device may establish a connection with the smart toy by downloading an application corresponding to the smart toy, or may establish a connection with the smart toy in a wireless or wired manner.

[0101] It should be noted that the user can also input the remote control command in the interactive interface of the first terminal device. The interactive interface of the first terminal device can be provided by the application corresponding to the smart toy and displayed on the first terminal device.

[0102] In response to the remote control instruction, the first terminal device sends a first control parameter acquisition instruction to the smart toy. After receiving the first control parameter acquisition instruction, the smart toy packages the first control parameter and sends it to the first terminal device through the network.

[0103] After receiving the first control parameter, the first terminal device forwards the first control parameter to at least one second terminal device through an established network connection (such as Wi-Fi, Bluetooth, Zigbee, etc.) At this time, a multi-level communication link is formed between the first terminal and the second terminal.

[0104] After the second terminal device receives the first control parameter, the user of the first terminal device may perform necessary processing or optimization on the first control parameter according to requirements to obtain the target control parameter.

[0105] The second terminal device sends the target control parameter back to the first terminal device via the network connection. After receiving the target control parameters from all relevant second terminal devices, the first terminal device integrates at least one target control parameter to form a final second control parameter.

[0106] It should be noted that the integration process can be a simple merger or a complex coordination and decision-making process, and this application is not limited to this.

[0107] Finally, the smart toy generates a precise control signal based on the integrated second control parameter, ensuring that the smart toy can accurately respond to the user's real-time vital sign data and the real-time operation data of the target smart toy, while being adaptively adjusted by other users.

[0108] After receiving the control signal, the smart toy immediately performs the corresponding operation and continuously monitors its own status through the built-in sensor to ensure that the operation meets the expected effect. At the same time, the smart toy can also feedback the actual execution status to the first terminal device for subsequent adjustment or optimization.

[0109] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the working principle of the device. It is understandable that in order to realize the above functions, the control device of the intelligent toy includes a hardware structure and / or software module corresponding to each function. Those skilled in the art should easily realize that, in combination with the algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0110] The embodiment of the present application can divide the function modules of the control device of the intelligent toy according to the above method example. For example, each function module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or software function module.

[0111] It should be noted that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. Figure 3 FIG. 2 shows a possible schematic diagram of the composition of the control device of the intelligent toy involved in the above-mentioned embodiment. Figure 3 As shown, the control device 300 of the intelligent toy may include: an acquisition module 301 , a determination module 302 , and a prediction module 303 .

[0112] The acquisition module 301 is used to support the control device 300 of the smart toy to execute Figure 2 S201 in the illustrated intelligent toy control method.

[0113] Determining module 302, used to support the control device 300 of the intelligent toy to execute Figure 2 S202, S203 and S205 in the illustrated intelligent toy control method.

[0114] Prediction module 303, used to support the control device 300 of the intelligent toy to execute Figure 2 S204 in the illustrated intelligent toy control method.

[0115] A possible implementation method is that the determination module is specifically used to determine the action intensity value corresponding to the user at the current moment based on the real-time vital sign data and the real-time operation data, including: based on the real-time vital sign data, from the correspondence between the pre-stored action intensity coefficient and the real-time vital sign data, obtaining the action intensity coefficient corresponding to the real-time vital sign data. Based on the real-time vital sign data and the action intensity coefficient, determining the vital sign data influence value. Based on the real-time operation data, from the correspondence between the pre-stored action intensity coefficient and the real-time operation data, obtaining the operation data coefficient corresponding to the real-time operation data. Based on the real-time operation data and the operation data coefficient, determining the operation data influence value. Determine the action intensity value based on the vital sign data influence value and the operation data influence value.

[0116] In a possible implementation, the real-time vital sign data includes one or more of the following: heart rate, brain wave voltage value, and character posture data. The heart rate includes a heart rate value and / or a pulse value. The brain wave voltage value is the average brain wave voltage value of the user in the preset period at the current moment. The determination module is specifically used to determine the heart rate impact value according to the heart rate recognition coefficient in the action intensity coefficient corresponding to the heart rate. Obtain the historical brain wave voltage value of the user's brain wave in the previous period of the preset period. Determine the absolute value of the difference between the brain wave voltage value and the historical brain wave voltage value to obtain the brain wave vibration intensity value. Determine the character posture impact value according to the character posture data and the character posture coefficient in the action intensity coefficient corresponding to the character posture data. Determine the vital sign data impact value according to the heart rate impact value, the brain wave vibration intensity impact value, and the character posture impact value.

[0117] In a possible implementation, the real-time operation data includes: the audio frequency and pressure value of the running program. The pressure value is the pressure applied to the target smart toy by the user and received by the target smart toy. According to the audio frequency, the audio vibration intensity value corresponding to the audio frequency is determined. According to the audio vibration intensity value and the audio vibration intensity coefficient in the operation data coefficient corresponding to the audio vibration intensity value, the audio vibration impact value is determined. According to the pressure value and the pressure coefficient in the operation data coefficient corresponding to the pressure value, the pressure impact value is determined. According to the character posture impact value, the audio vibration impact value and the pressure impact value, the operation data impact value is determined.

[0118] In a possible implementation, the real-time operation data includes: a video screen of the running program. The determination module is specifically used to identify a first character posture of a character in the video screen at the current moment from the video screen. Obtain a historical video screen at the previous moment of the current moment. Identify a second character posture of the task from the historical video screen. Determine a scene coefficient corresponding to the video screen based on the first character posture and / or the second character posture. Determine an action intensity value based on the vital sign data influence value, the operation data influence value and the scene coefficient.

[0119] In a possible implementation, the real-time feedback data includes mechanical motion data and physical state data. The mechanical motion data is used to represent the motion state of each component of the target intelligent toy at the current moment. The physical state data is used to represent the physical changes of the target intelligent toy in a preset time period before the current moment. The determination module is specifically used to determine the mechanical motion reference value according to the mechanical motion data and the mechanical motion intensity coefficient corresponding to the mechanical motion data. Determine the physical state reference value according to the physical state data and the physical state intensity coefficient corresponding to the physical state data. Determine the physical parameter value according to the mechanical motion reference value and the physical state reference value.

[0120] In a possible implementation, the prediction module is specifically used to determine the scene coefficient corresponding to the current moment according to the action intensity value and the physical parameter value. According to the action intensity value, the physical parameter value and the scene coefficient, the current vibration value of the target smart toy is determined. The historical vibration curve of the target smart toy is obtained. The historical vibration curve is generated based on a set of vibration values ​​of the historical vibration values ​​of the target smart toy. Based on the historical vibration curve and the current vibration value, the current vibration curve of the target smart toy is generated. Based on the current vibration curve, the target action intensity value is predicted.

[0121] In a possible implementation, the device can also be used to determine the operating parameters of the operating program of the target intelligent toy according to the physical parameter value and the conversion coefficient corresponding to the physical parameter value. The real-time operating data of the operating program is corrected based on the operating parameters to obtain target operating data. The target operating data is used to drive the operating program so that the operating program fits the user's experience.

[0122] In a possible implementation, the determination module is specifically configured to determine a first control parameter of the target intelligent toy according to the target action intensity value, and to determine a control signal based on the first control parameter.

[0123] In a possible implementation, the determination module is specifically used to determine the control signal based on the first control parameter, including: in response to the user's remote control instruction, sending the first control parameter to the first terminal device connected to the smart toy, so that the first terminal device sends the first control parameter to at least one second terminal device. The second terminal device is connected to the first terminal device through a network. Receive at least one target control parameter forwarded by the first terminal device. The target control parameter is determined by the second terminal device based on the first control parameter and sent to the first terminal through the network connection. Integrate the at least one target control parameter into a second control parameter of the smart toy. Determine the control signal based on the second control parameter.

[0124] It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module and will not be repeated here.

[0125] The control device 300 of the smart toy provided in the embodiment of the present application is used to execute the above Figure 2 The control method of the smart toy shown can therefore achieve the same effect as the control method of the smart toy described above.

[0126] The present application also provides a control device for an intelligent toy. The control device for the intelligent toy can be a device that deploys the device described in the above embodiment. Figure 1 The smart toy 100 shown can execute the smart toy control method and related steps in the above method embodiment.

[0127] The embodiment of the present application also provides a computer-readable storage medium on which instructions are stored. When the instructions are executed, the control method and related steps of the smart toy in the above method embodiment are executed.

[0128] The embodiment of the present application also provides a computer program product. When the computer program product is run on a computer, the computer executes the control method and related steps of the intelligent toy in the above method embodiment.

[0129] In some embodiments, the methods described herein may be implemented as computer program instructions encoded in a machine-readable format on a computer-readable storage medium or encoded on other non-transitory media or articles of manufacture.

[0130] The present application also provides a control system 400 of an intelligent toy, such as Figure 4 As shown, the control system 400 of the intelligent toy includes at least one processor 401 and at least one interface circuit 402 .

[0131] As an example, when the control system 400 of the intelligent toy includes a processor and an interface circuit, the processor may be Figure 4 The processor 401 shown in the solid line frame (or the processor 401 shown in the dotted line frame) may be Figure 4 The interface circuit 402 shown in the solid line frame (or the interface circuit 402 shown in the dotted line frame). When the control system 400 of the intelligent toy includes two processors and two interface circuits, the two processors include Figure 4 The processor 401 shown in the solid line frame and the processor 401 shown in the dotted line frame, the two interface circuits include Figure 4 The interface circuit 402 shown in the solid line frame and the interface circuit 402 shown in the dotted line frame are not limited to this.

[0132] The processor 401 and the interface circuit 402 may be interconnected via a line. For example, the interface circuit 402 may be used to receive a signal. In another example, the interface circuit 402 may be used to send a signal to another device (e.g., the processor 401). For example, the interface circuit 402 may read a computer instruction stored in a memory and send the computer instruction to the processor 401. The processor 401 executes the instruction and implements the various steps in the above embodiments in combination with the input and output devices, such as implementing Figure 2 The various steps performed in the method embodiment shown. Of course, the control system of the intelligent toy may also include other discrete components, which are not specifically limited in the embodiment of the present application.

[0133] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned 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.

[0134] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0138] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for controlling an intelligent toy, characterized in that: The method comprises: The wearable device is used to detect the real-time vital sign data of the user in real time, and to obtain the real-time data of the target smart toy; the real-time data includes real-time operation data and real-time feedback data; the real-time operation data is used to represent the operation data of the target smart toy and the operation data of the operation program running in the target smart toy; the operation data of the target smart toy is used to represent the pressure value applied to the target smart toy by the user received by the target smart toy; the real-time feedback data includes mechanical motion data and physical state data; the mechanical motion data is used to represent the motion state of each component of the target smart toy at the current moment; the physical state data is used to represent the physical changes of the target smart toy in a preset time period before the current moment; Determine the action intensity value corresponding to the user at the current moment according to the real-time vital sign data and the real-time operation data; the action intensity value includes a vital sign data influence value and an operation data influence value; the vital sign data influence value is determined based on the real-time vital sign data and the action intensity coefficient corresponding to the real-time vital sign data; the operation data influence value is determined based on the real-time operation data and the operation data coefficient corresponding to the real-time operation data; Determine the physical parameter value of the target intelligent toy at the current moment according to the real-time feedback data; Predicting a target action intensity value of the user at a next moment according to the action intensity value and the physical parameter value; Determine a control signal of the target smart toy according to the target action intensity value, and control the target smart toy by the control signal so that the target smart toy fits the experience of the user at the next moment; The predicting, according to the action intensity value and the physical parameter value, a target action intensity value of the user at a next moment includes: Determine the scene coefficient corresponding to the current moment according to the action intensity value and the physical parameter value; determine the current vibration value of the target smart toy according to the action intensity value, the physical parameter value and the scene coefficient; obtain the historical vibration curve of the target smart toy; the historical vibration curve is generated based on a set of vibration values ​​of the historical vibration values ​​of the target smart toy; generate the current vibration curve of the target smart toy based on the historical vibration curve and the current vibration value; predict the target action intensity value based on the current vibration curve.

2. The method according to claim 1, characterized in that The step of determining the action intensity value corresponding to the user at the current moment according to the real-time vital sign data and the real-time operation data includes: Based on the real-time vital sign data, obtaining the action intensity coefficient corresponding to the real-time vital sign data from a pre-stored corresponding mathematical relationship between the action intensity coefficient and the real-time vital sign data; Determining a vital sign data impact value according to the real-time vital sign data and the action intensity coefficient; Based on the real-time operation data, obtaining an operation data coefficient corresponding to the real-time operation data from a corresponding mathematical relationship between a pre-stored operation data coefficient and the real-time operation data; Determining an operation data impact value according to the real-time operation data and the operation data coefficient; The action intensity value is determined according to the vital sign data influence value and the operation data influence value.

3. The method according to claim 2, characterized in that The real-time vital sign data includes one or more of the following: heart rate, brain wave voltage value, and character posture data; the heart rate includes heart rate value and / or pulse value; The brain wave voltage value is the average brain wave voltage value of the user in the preset period of the current moment; The step of determining the influence value of the physical sign data according to the real-time physical sign data and the action intensity coefficient includes: determining a heart rate influence value according to the heart rate and a heart rate identification coefficient in the action intensity coefficient corresponding to the heart rate; Obtaining historical brain wave voltage values ​​of the user's brain waves in the previous cycle of the preset cycle; Determine the absolute value of the difference between the brain wave voltage value and the historical brain wave voltage value to obtain a brain wave vibration intensity value; Determine a brain wave vibration intensity influence value according to the brain wave vibration intensity value and a brain wave coefficient in the action intensity coefficient corresponding to the brain wave vibration intensity value; Determining a character posture influence value according to the character posture data and a character posture coefficient in the action intensity coefficient corresponding to the character posture data; The vital sign data influence value is determined according to the heart rate influence value, the brain wave vibration intensity influence value and the character posture influence value.

4. The method according to claim 2, characterized in that The real-time operation data includes: the audio frequency of the operation program; and determining the operation data impact value according to the real-time operation data and the operation data coefficient includes: According to the audio frequency, determining the audio vibration intensity value corresponding to the audio frequency; determining an audio vibration impact value according to the audio vibration intensity value and an audio vibration intensity coefficient in the operation data coefficient corresponding to the audio vibration intensity value; determining a pressure influence value according to the pressure value and a pressure coefficient in the operation data coefficient corresponding to the pressure value; The operation data impact value is determined according to the audio vibration impact value and the pressure impact value.

5. The method according to claim 2, characterized in that: The real-time operation data includes: a video screen of the running program; the determining the action intensity value according to the vital sign data impact value and the operation data impact value includes: Recognize from the video picture a first person posture of a person in the video picture at the previous moment; Obtain the historical video image of the previous moment before the current moment; Recognizing a second person posture of the person from the historical video frame; Determining a scene coefficient corresponding to the video picture according to the first character posture and / or the second character posture; The action intensity value is determined according to the vital sign data influence value, the operation data influence value and the scene coefficient.

6. The method according to claim 1, characterized in that Determining the physical parameter value of the target intelligent toy at the current moment according to the real-time feedback data includes: determining a mechanical motion reference value according to the mechanical motion data and a mechanical motion intensity coefficient corresponding to the mechanical motion data; determining a physical state reference value according to the physical state data and a physical state intensity coefficient corresponding to the physical state data; The physical parameter value is determined according to the mechanical motion reference value and the physical state reference value.

7. The method according to claim 1, characterized in that The method further comprises: Determining the operating parameters of the operating program of the target intelligent toy according to the physical parameter value and the conversion coefficient corresponding to the physical parameter value; Based on the operating parameters, the real-time operating data of the operating program is modified to obtain target operating data; the target operating data is used to reversely control the operating program of the intelligent toy; The target operation data is used to drive the operation program, so that the operation program fits the user's experience.

8. The method according to claim 1, characterized in that: The step of determining the control signal of the target intelligent toy according to the target action intensity value includes: Determining a first control parameter of the target intelligent toy according to the target action intensity value; Based on the first control parameter, the control signal is determined.

9. The method according to claim 8, characterized in that The determining the control signal based on the first control parameter includes: In response to the user's remote control instruction, the first control parameter is sent to a first terminal device connected to the smart toy, so that the first terminal device sends the first control parameter to at least one second terminal device; the second terminal device is connected to the first terminal device through a network; receiving at least one target control parameter forwarded by the first terminal device; the target control parameter is determined by the second terminal device based on the first control parameter and sent to the first terminal through the network connection; Integrate at least one of the target control parameters into a second control parameter of the intelligent toy; The control signal is determined based on the second control parameter.

10. A control device for an intelligent toy, characterized in that: The device comprises: An acquisition module is used to detect the real-time vital sign data of the user in real time by using a wearable device, and to acquire the real-time data of the target smart toy; the real-time data includes real-time operation data and real-time feedback data; the real-time operation data is used to represent the operation data of the target smart toy and the operation data of the operation program running in the target smart toy; the operation data of the target smart toy is used to represent the pressure value applied to the target smart toy by the user received by the target smart toy; the real-time feedback data includes mechanical motion data and physical state data; the mechanical motion data is used to represent the motion state of each component of the target smart toy at the current moment; the physical state data is used to represent the physical changes of the target smart toy in a preset time period before the current moment; A determination module, used to determine the action intensity value corresponding to the user at the current moment according to the real-time vital sign data and the real-time operation data; the action intensity value includes a vital sign data influence value and an operation data influence value; the vital sign data influence value is determined based on the real-time vital sign data and the action intensity coefficient corresponding to the real-time vital sign data; the operation data influence value is determined based on the real-time operation data and the operation data coefficient corresponding to the real-time operation data; determine the physical parameter value of the target intelligent toy at the current moment according to the real-time feedback data; A prediction module, used for predicting a target action intensity value of the user at a next moment according to the action intensity value and the physical parameter value; The determination module is further used to determine a control signal of the target smart toy according to the target action intensity value, and control the target smart toy through the control signal so that the target smart toy fits the experience of the user at the next moment; The prediction module is specifically used to determine the scene coefficient corresponding to the current moment according to the action intensity value and the physical parameter value; determine the current vibration value of the target smart toy according to the action intensity value, the physical parameter value and the scene coefficient; obtain the historical vibration curve of the target smart toy; the historical vibration curve is generated based on a set of vibration values ​​of the historical vibration values ​​of the target smart toy; based on the historical vibration curve and the current vibration value, generate the current vibration curve of the target smart toy; based on the current vibration curve, predict the target action intensity value.

11. A smart toy, characterized in that: The smart toy comprises a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the control method of the smart toy according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the control method of the intelligent toy according to any one of claims 1 to 9.

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