WO2022033282A1

WO2022033282A1 - Data processing method and related device

Info

Publication number: WO2022033282A1
Application number: PCT/CN2021/107582
Authority: WO
Inventors: 许振强; 谢靖然
Original assignee: 华为技术有限公司
Priority date: 2020-08-14
Filing date: 2021-07-21
Publication date: 2022-02-17
Also published as: CN114077412A

Abstract

Disclosed in the present application is a data processing method, being applied to the field of artificial intelligence, and particularly to the field of multi-device coordination. The method comprises: a first device can determine, according to a first value and a weight coefficient, a second value used for playing a target signal on a second device. By determining, by means of the method of using the weight coefficient, the value used for the second device to play the target signal, and sending the second value to the second device, a switched second device can automatically adjust a first amount to the corresponding value (i.e., the second value) of the first device, thereby reducing the sensing of a user for the difference between the values, comfortably performing the device switching of the second device, and improving user experience.

Description

A data processing method and related equipment

This application claims the priority of the Chinese patent application with the application number 202010818113.8 and the invention titled "A data processing method and related equipment", which was filed with the China Patent Office on August 14, 2020, the entire contents of which are incorporated herein by reference. middle.

technical field

The embodiments of the present application relate to the field of terminal artificial intelligence, and in particular, to a data processing method and related equipment.

Background technique

Artificial intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that responds in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making. Research in the field of artificial intelligence includes robotics, natural language processing, computer vision, decision-making and reasoning, human-computer interaction, recommendation and search, and basic AI theory.

In the modern information society where the wireless communication network is flourishing, the mobile phone has become one of the most commonly used communication tools in the society. Mobile phones allow users to communicate wirelessly anytime, anywhere, and easily communicate by voice. Moreover, the applications provided by mobile phones are becoming more and more extensive, and users can use the mobile phones to realize multimedia functions such as watching movies, playing music, watching news, and video recording.

With the increasing number of sound output devices of the terminal, the mobile phone can also output audio through an external sound output device (power amplifier, Bluetooth device, USB audio, etc.). In this way, when the mobile phone switches from the speaker to another sound output device (eg, from the speaker to Bluetooth) for audio output, the mobile phone will still use the volume gain corresponding to the speaker to adjust the output volume of the switched sound output device.

However, since each sound output device has different device properties, it will have different effects on the same audio data. Therefore, when the mobile phone uses the same sound gain to adjust the output volume when different sound output devices output the same audio, then This will result in different output volumes of the same audio output by different sound output devices, which will cause the user to manually adjust the volume of the mobile phone to meet the user's volume requirements when switching the sound output device at the terminal, which brings great inconvenience to the user.

SUMMARY OF THE INVENTION

Embodiments of the present application provide a data processing method and related equipment. It can be applied in the field of AI, specifically in the field of sub-field multi-device collaboration, to determine appropriate playback parameters of the second device and improve user experience.

An exemplary embodiment of the present application provides an applicable application scenario for a data processing method: a user needs to play multimedia audio, such as music, video, and calls, on a mobile phone device. In the process of audio playback, the second device is switched through Bluetooth, one-touch transfer, and App screen projection. This embodiment of the present application can automatically adjust the volume of the switched second device to the relative volume of the first device, reducing the need for users to switch Perception of audio size differences, and comfortable second device switching.

Exemplary scenario:

1. The user needs to convert the music played on the mobile phone to the Bluetooth headset for playback.

2. The user needs to convert the music, video or image played on the mobile phone to a TV, computer or projection screen for playback.

3. The user needs to convert the music, text or images played on the mobile phone to the smart watch for playback.

It should be understood that the above three examples should not impose any restrictions on the application scenarios of the embodiments of the present application.

A first aspect of an embodiment of the present application provides a data processing method, the method includes: a first device obtains a first value used by a target signal to play on the first device and a device type of the second device; the first device determines the second The weight coefficient of the device; the first device determines the second value used by the target signal to play in the second device according to the weight coefficient and the first value; the first device sends the second value to the second device, so that the second device uses the second value Numerically plays the target signal.

In this embodiment of the present application, the first device may determine the second value used to play the target signal on the second device according to the first value and the weight coefficient, and determine the value used by the second device to play the target signal by using the weight coefficient method , and send the second value to the second device, so that the switched second device can automatically adjust the first volume to the relative volume of the first device (ie, the second value), reducing the user's concern about the difference in value during switching. Perceive, perform comfortable switching of the second device to improve the user experience.

Optionally, in a possible implementation manner of the first aspect, determining the weight coefficient of the second device by the first device in the above steps includes: the first device determining the weight coefficient of the second device according to the first mapping table, The first mapping table is used to represent the association relationship between the device type of the second device and the weight coefficient.

In this possible implementation manner, the weight coefficient of the second device is determined according to the first mapping table. Optionally, the first mapping table combines human factors engineering analysis to improve the user experience when switching audio playback.

Optionally, in a possible implementation manner of the first aspect, the above steps further include: the first device receives a third value sent by the second device, where the third value is a value obtained after the second value is adjusted; A device updates the first mapping table according to the third value to obtain a second mapping table, and the weight coefficient of the second device in the second mapping table is different from the weight coefficient of the second device in the first mapping table.

In this possible implementation manner, the first device updates the first mapping table according to the third value, that is, the first device can update the first mapping table through subsequent user operations, which is more in line with the switching usage scenario of the first device and the second device .

Optionally, in a possible implementation manner of the first aspect, the above steps further include: the first device acquires a target feature, where the target feature includes the first value, the playback location of the second device, the system time of the second device, and At least one of the third numerical values; the first device trains the model to be trained according to the target feature to obtain a prediction weight model.

In this possible implementation manner, the first device can perform model training according to the target characteristics such as the playback location of the second device, the third value adjusted by the user, and the system time of the second device when the user adjusts the third value, so as to obtain a model more suitable for the user. Use custom and predictive weight models for specific scenarios.

Optionally, in a possible implementation manner of the first aspect, the above steps further include: the first device obtains a fourth value according to the first value and the prediction weight model; and the first device sends the fourth value to the second device.

In this possible implementation manner, the first device obtains, according to the trained prediction weight model, a numerical requirement that meets the user in a specific scenario. The fourth value is sent to the second device, so that the second device uses the fourth value that better meets the user's needs to play the target signal, further reducing the user's perception of the difference in value during switching, and improving the user experience.

Optionally, in a possible implementation manner of the first aspect, the target signal in the above steps includes an audio signal, the first numerical value includes a volume value used by the audio signal to play on the first device, and the second numerical value includes an audio signal. The volume value used for the signal to be played on the second device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal in the second device. The volume value or brightness value used for playback on the device; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for playing the image on the first device, and the second value includes the image on the second device. The brightness value or the zoom ratio of the image used for playback; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the text playback on the second device. Playback speed value.

In this possible implementation manner, the user needs to play multimedia audio, such as music, video, and calls, on the mobile phone device. During the audio playback process, the media playback device is switched through Bluetooth, one-touch transfer, and App screen projection. This embodiment of the present application can automatically adjust the volume of the switched second device to the relative volume of the first device, reducing the need for users to switch The perception of audio size difference in the middle, make the second device switch comfortably.

A second aspect of an embodiment of the present application provides a data processing method, the method includes: a second device receives a target signal; the second device receives a second value sent by the first device, where the second value is the first device according to the first value , the device type of the second device and the first mapping table are obtained, the first numerical value is the numerical value used by the first device to play the target signal, and the first mapping table is used to represent the association relationship between the device type of the second device and the weight coefficient , the weight coefficient is used to obtain the second value according to the first value; the second device uses the second value to play the target signal.

In this embodiment of the present application, the second device may know the second value corresponding to the first value, that is, the first device determines the value used by the second device to play the target signal by using a weight coefficient method, and sends the second value to the second device. value, the second device after switching can automatically adjust the first volume to the relative volume of the first device (ie, the second value), reducing the user's perception of the difference in value during switching, and performing comfortable second device device switching , to improve the user experience.

Optionally, in a possible implementation manner of the second aspect, the above steps further include: the second device obtains a third numerical value, where the third numerical value is a value obtained after the second numerical value is adjusted; The device sends the third value, so that the first device updates the first mapping table to obtain a second mapping table, and the weight coefficient of the second device in the second mapping table is different from the weight coefficient of the second device in the first mapping table.

In this possible implementation manner, after receiving the user's adjustment value, the second device sends the user's adjustment value to the first device, so that the first device can update the first mapping table according to the user's usage habits, so that the second device can play The value used by the target signal is more in line with the switching usage scenario of the first device and the second device. It further reduces the user's perception of the difference in numerical value during switching, performs comfortable switching between the second device and the device, and improves the user experience.

Optionally, in a possible implementation manner of the second aspect, the target signal in the above steps includes an audio signal, the first numerical value includes a volume value used by the audio signal to play on the first device, and the second numerical value includes an audio signal. The volume value used for the signal to be played on the second device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal in the second device. The volume value or brightness value used for playback on the device; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for playing the image on the first device, and the second value includes the image on the second device. The brightness value or the zoom ratio of the image used for playback; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the text playback on the second device. Playback speed value.

A third aspect of an embodiment of the present application provides a data processing method, the method includes: a first device obtains a first value used by a target signal to play on the first device and a device type of the second device; the first device determines the second The weight coefficient of the device; the first device sends the first value and the weight coefficient to the second device, so that the second device determines the second value of the second device according to the first value and the weight coefficient.

In this embodiment of the present application, the second device may determine the second value used to play the target signal on the second device according to the first value and the weight coefficient, and determine the value used by the second device to play the target signal by using the weight coefficient method , on the one hand: the second device after switching can automatically adjust the first volume to the relative volume (ie, the second value) of the first device, reducing the user's perception of the difference in the value of the value during switching, and making a comfortable second Device device switching to improve user experience. On the other hand: reduce the power consumption problem caused when the first device calculates the second value.

Optionally, in a possible implementation manner of the third aspect, determining the weight coefficient of the second device by the first device in the above steps includes: the first device determining the weight coefficient of the second device according to the first mapping table, The first mapping table is used to represent the association relationship between the device type of the second device and the weight coefficient.

Optionally, in a possible implementation manner of the third aspect, the above steps further include: the first device receives a third value sent by the second device, where the third value is a value obtained after the second value is adjusted; A device updates the first mapping table according to the third value to obtain a second mapping table, and the weight coefficient of the second device in the second mapping table is different from the weight coefficient of the second device in the first mapping table.

In this possible implementation manner, the first device updates the first mapping table according to the third value, that is, the first device can update the first mapping table through user adjustment, which is more in line with the switching usage scenario of the first device and the second device.

Optionally, in a possible implementation manner of the third aspect, the above steps further include: the first device acquires a target feature, where the target feature includes the first value, the playback location of the second device, the system time of the second device, and At least one of the third numerical values; the first device trains the model to be trained according to the target feature to obtain a prediction weight model.

Optionally, in a possible implementation manner of the third aspect, the above steps further include: the first device obtains a fourth value according to the first value and the prediction weight model; the first device sends the fourth value to the second device.

In this possible implementation manner, the first device obtains a numerical value that meets the user's demand in a specific scenario according to the trained prediction weight model, and sends a fourth numerical value to the second device, so that the second device uses the first numerical value that better meets the user's demand. The target signal is played with four numerical values, which further reduces the user's perception of the difference in numerical value during switching, and improves the user experience.

Optionally, in a possible implementation of the third aspect, the target signal in the above steps includes an audio signal, the first numerical value includes a volume value used by the audio signal to play on the first device, and the second numerical value includes an audio signal. The volume value used for the signal to be played on the second device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal in the second device. The volume value or brightness value used for playback on the device; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for playing the image on the first device, and the second value includes the image on the second device. The brightness value or the zoom ratio of the image used for playback; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the text playback on the second device. Playback speed value.

A fourth aspect of an embodiment of the present application provides a data processing method, the method includes: a second device receives a target signal; the second device receives a first value and a weight coefficient sent by the first device, and the first value is played by the first device The value used for the target signal; the second device determines the second value according to the first value and the weight coefficient; the second device uses the second value to play the target signal.

Optionally, in a possible implementation manner of the fourth aspect, the second device in the above steps determines the second value according to the first value and the weight coefficient, including: the second device calculates the first value and the weight coefficient. to obtain the second value.

In this possible implementation manner, when the second device has computing capability, the second value is obtained by calculating the product of the first value and the weight coefficient, thereby reducing the power consumption problem caused by the first device calculating the second value .

Optionally, in a possible implementation manner of the fourth aspect, the above steps further include: the second device obtains a third value, where the third value is a value obtained after the second value is adjusted; the second device uses the third value. Playing the target signal numerically; the second device sends a third numerical value to the first device, so that the first device uses the third numerical value to update the weight coefficient of the second device in the first mapping table.

In this possible implementation manner, after acquiring the adjustment value of the user, the second device sends the adjustment value to the first device, so that the first device can update the first mapping table through the adjustment of the user, which is more in line with the first device and the second device. Switching usage scenarios of devices.

Optionally, in a possible implementation manner of the fourth aspect, the above steps further include: the second device acquires a target feature, and the target feature includes the playback location of the second device, the system time of the second device, and the third numerical value. At least one of the two; the second device trains the model to be trained according to the target feature to obtain a prediction weight model.

In this possible implementation manner, the second device may perform model training according to target features such as the playback location of the second device, the third value adjusted by the user, and the system time of the second device when the user adjusts the third value. Obtain a prediction weight model that is more in line with user habits and specific scenarios. On the other hand: reduce the power consumption problem caused by the training model of the first device.

Optionally, in a possible implementation manner of the fourth aspect, the above steps further include: the second device obtains a fourth numerical value according to the first numerical value and the prediction weight model; and the second device uses the fourth numerical value to play the target signal.

In this possible implementation manner, the second device obtains a numerical value that meets the user's demand in a specific scenario according to the trained prediction weight model, and uses a fourth numerical value that better meets the user's demand to play the target signal, further reducing the need for the user to switch between The perception of the difference in numerical value improves the user experience.

Optionally, in a possible implementation manner of the fourth aspect, the target signal in the above steps includes an audio signal, the first numerical value includes a volume value used by the audio signal to play on the first device, and the second numerical value includes an audio signal. The volume value used for the signal to be played on the second device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal in the second device. The volume value or brightness value used for playback on the device; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for playing the image on the first device, and the second value includes the image on the second device. The brightness value or the zoom ratio of the image used for playback; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the text playback on the second device. Playback speed value.

A fifth aspect of the embodiments of the present application provides a data processing method, the method includes: a second device receives a target signal; the second device receives a first value from the first device, where the first value is the target signal played by the first device. The second device determines the weight coefficient; the second device determines the second value according to the first value and the weight coefficient; the second device uses the second value to play the target signal.

In this embodiment of the present application, the second device may obtain the second value used to play the target signal on the second device according to the received first value and the determined weight coefficient, and determine the playback target of the second device by using the weight coefficient method. The numerical value used by the signal, on the one hand, enables the second device after switching to automatically adjust the first quantity to the relative volume (ie, the second numerical value) of the first device, reducing the user's perception of the difference in numerical value during switching, Perform comfortable second device switching to improve user experience. On the other hand: reduce the power consumption problem caused when the first device calculates the second value.

Optionally, in a possible implementation manner of the fifth aspect, determining the weight coefficient by the second device in the above steps includes: the second device determines the weight coefficient according to a first mapping table, and the first mapping table is used to represent the first mapping table. The relationship between the device type of the second device and the weight coefficient.

In this possible implementation, on the one hand, the second device determines the weight coefficient of the second device according to the first mapping table, optionally, the first mapping table combines human factors engineering analysis, which improves the user's ability to switch audio playback. user experience. On the other hand, the second device may store the first mapping table, which reduces the memory occupation caused by the first device storing the first mapping table and the power consumption problem caused by the first device calculating the second value.

Optionally, in a possible implementation manner of the fifth aspect, the above steps further include: the second device obtains a third value, where the third value is a value obtained after the second value is adjusted; the second device uses the third value. Numerically plays the target signal.

In this possible implementation manner, the second device may play the target signal with a value that conforms to the user's habit according to the user's adjustment, so as to improve the user experience.

Optionally, in a possible implementation manner of the fifth aspect, the above steps further include: the second device uses the third value to update the first mapping table to obtain a second mapping table, the weight of the second device in the second mapping table. The coefficients are different from the weighting coefficients of the second device in the first mapping table.

In this possible implementation manner, after receiving the adjustment value from the user, the second device updates the first mapping table according to the user's usage habits, so that the value used by the second device to play the target signal is more in line with the first device and the second device. switching usage scenarios. It further reduces the user's perception of the difference in numerical value during switching, performs comfortable switching between the second device and the device, and improves the user experience.

Optionally, in a possible implementation manner of the fifth aspect, the above steps further include: the second device acquires a target feature, and the target feature includes the playback location of the second device, the system time of the second device, and the third numerical value. At least one of; the second device trains the model to be trained according to the target feature, and obtains a prediction weight model.

Optionally, in a possible implementation manner of the fifth aspect, the above steps further include: the second device obtains a fourth numerical value according to the first numerical value and the prediction weight model; and the second device uses the fourth numerical value to play the target signal.

Optionally, in a possible implementation manner of the fifth aspect, the target signal in the above steps includes an audio signal, the first numerical value includes a volume value used by the audio signal to be played on the first device, and the second numerical value includes an audio signal. The volume value used for the signal to be played on the second device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal in the second device. The volume value or brightness value used for playback on the device; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for playing the image on the first device, and the second value includes the image on the second device. The brightness value or the zoom ratio of the image used for playback; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the text playback on the second device. Playback speed value.

A sixth aspect of the present application provides a first device, where the first device may be a terminal device. It can also be a component of a terminal device (such as a processor, a chip or a system of chips), the first device including:

a transceiver unit, used for acquiring the first numerical value used by the target signal to play in the first device and the device type of the second device;

a processing unit, configured to determine the weight coefficient of the second device;

The processing unit is also used to determine the second numerical value used by the target signal to play in the second device according to the weight coefficient and the first numerical value;

The transceiver unit is further configured to send the second value to the second device, so that the second device uses the second value to play the target signal.

Optionally, in a possible implementation manner of the sixth aspect, the processing unit is specifically configured to determine the weight coefficient of the second device according to a first mapping table, where the first mapping table is used to indicate that the device type of the second device is the same as the value of the second device. The relationship between the weight coefficients.

Optionally, in a possible implementation manner of the sixth aspect, the transceiver unit is further configured to receive a third numerical value sent by the second device, where the third numerical value is a numerical value obtained after the second numerical value is adjusted;

The processing unit is further configured to update the first mapping table according to the third value to obtain a second mapping table, where the weight coefficient of the second device in the second mapping table is different from the weight coefficient of the second device in the first mapping table.

Optionally, in a possible implementation manner of the sixth aspect, the transceiver unit is further configured to acquire target features, where the target features include the first numerical value, the playback location of the second device, the system time of the second device, and the third at least one of the values;

The processing unit is further used to train the model to be trained according to the target feature to obtain a prediction weight model.

Optionally, in a possible implementation manner of the sixth aspect, the processing unit is further configured to obtain the fourth numerical value according to the first numerical value and the prediction weight model;

The transceiver unit is further configured to send a fourth numerical value to the second device.

Optionally, in a possible implementation manner of the sixth aspect, the target signal includes an audio signal, the first numerical value includes a volume value used by the audio signal to be played on the first device, and the second numerical value includes the audio signal in the second. The volume value used for playback on the device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal played on the second device. The volume value or brightness value used; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for the image to be played on the first device, and the second value includes the image used to play on the second device. The brightness value or the zoom ratio of the image; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the playback speed value used for text playback on the second device.

A seventh aspect of the present application provides a second device, where the second device may be a smart watch, a TV, a computer, a projection screen, or the like. It can also be a component (such as a processor, chip or chip system) of a smart watch, a TV, a computer or a projection screen, and the second device includes:

a transceiver unit for receiving target signals;

The transceiver unit is further configured to receive a second value sent by the first device, where the second value is obtained by the first device according to the first value, the device type of the second device and the first mapping table, and the first value is the playback target of the first device The numerical value used by the signal, the first mapping table is used to represent the relationship between the device type of the second device and the weighting coefficient, and the weighting coefficient is used to obtain the second numerical value according to the first numerical value;

The processing unit is used for playing the target signal using the second value.

Optionally, in a possible implementation manner of the seventh aspect, the transceiver unit is further configured to obtain a third numerical value, where the third numerical value is a numerical value obtained after the second numerical value is adjusted;

The transceiver unit is further configured to send a third numerical value to the first device, so that the first device updates the first mapping table to obtain a second mapping table, where the weight coefficient of the second device in the second mapping table is the same as the first mapping table in the first mapping table. The weight coefficients of the two devices are different.

Optionally, in a possible implementation manner of the seventh aspect, the target signal includes an audio signal, the first numerical value includes a volume value used by the audio signal to be played on the first device, and the second numerical value includes the audio signal in the second. The volume value used for playback on the device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal played on the second device. The volume value or brightness value used; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for the image to be played on the first device, and the second value includes the image used to play on the second device. The brightness value or the zoom ratio of the image; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the playback speed value used for text playback on the second device.

An eighth aspect of the present application provides a first device, where the first device may be a terminal device. It can also be a component of a terminal device (such as a processor, a chip or a system of chips), the first device including:

Transceiver unit, for acquiring the first numerical value and the device type of the second device that the target signal is used for playing in the first device;

The transceiver unit is further configured to send the first numerical value and the weight coefficient to the second device, so that the second device determines the second numerical value of the second device according to the first numerical value and the weight coefficient.

Optionally, in a possible implementation manner of the eighth aspect, the processing unit is specifically configured to determine the weighting coefficient of the second device according to a first mapping table, where the first mapping table is used to indicate that the device type of the second device is the same as the value of the second device. The relationship between the weight coefficients.

Optionally, in a possible implementation manner of the eighth aspect,

The transceiver unit is further configured to receive a third value sent by the second device, where the third value is a value obtained after the second value is adjusted;

Optionally, in a possible implementation manner of the eighth aspect, the transceiver unit is further configured to acquire target features, where the target features include the first numerical value, the playback location of the second device, the system time of the second device, and the third at least one of the values;

The processing unit is also used to train the model to be trained according to the target feature to obtain a prediction weight model.

Optionally, in a possible implementation manner of the eighth aspect, the processing unit is further configured to obtain a fourth numerical value according to the first numerical value and the prediction weight model;

Optionally, in a possible implementation manner of the eighth aspect, the target signal includes an audio signal, the first numerical value includes a volume value used by the audio signal to be played on the first device, and the second numerical value includes the audio signal in the second. The volume value used for playback on the device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal played on the second device. The volume value or brightness value used; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for the image to be played on the first device, and the second value includes the image used to play on the second device. The brightness value or the zoom ratio of the image; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the playback speed value used for text playback on the second device.

A ninth aspect of the present application provides a second device, where the second device may be a smart watch, a TV, a computer, a projection screen, or the like. It can also be a component (such as a processor, chip or chip system) of a smart watch, a TV, a computer or a projection screen, and the second device includes:

a transceiver unit for receiving target signals;

The transceiver unit is further configured to receive the first numerical value and the weight coefficient sent by the first device, where the first numerical value is the numerical value used when the first device plays the target signal;

a processing unit, configured to determine the second value according to the first value and the weight coefficient;

The processing unit is further configured to play the target signal by using the second value.

Optionally, in a possible implementation manner of the ninth aspect, the processing unit is specifically configured to calculate the product of the first numerical value and the weight coefficient to obtain the second numerical value.

Optionally, in a possible implementation manner of the ninth aspect, the transceiver unit is further configured to obtain a third numerical value, and the third numerical value is a numerical value obtained after the second numerical value is adjusted;

The processing unit is also used to play the target signal by using the third numerical value;

The transceiver unit is further configured to send a third value to the first device, so that the first device uses the third value to update the weight coefficient of the second device in the first mapping table.

Optionally, in a possible implementation manner of the ninth aspect, the transceiver unit is used to obtain a target feature, and the target feature includes at least one of the playback location of the second device, the system time of the second device, and the third value. kind;

The processing unit is used to train the to-be-trained model according to the target feature to obtain the prediction weight model.

Optionally, in a possible implementation manner of the ninth aspect, the processing unit is further configured to obtain a fourth numerical value according to the first numerical value and the prediction weight model;

The processing unit is further configured to play the target signal by using the fourth numerical value.

Optionally, in a possible implementation manner of the ninth aspect, the target signal includes an audio signal, the first numerical value includes a volume value used by the audio signal to be played on the first device, and the second numerical value includes the audio signal in the second. The volume value used for playback on the device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal played on the second device. The volume value or brightness value used; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for the image to be played on the first device, and the second value includes the image used to play on the second device. The brightness value or the zoom ratio of the image; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the playback speed value used for text playback on the second device.

A tenth aspect of the present application provides a second device, where the second device may be a smart watch, a TV, a computer, a projection screen, or the like. It can also be a component (such as a processor, chip or chip system) of a smart watch, a TV, a computer or a projection screen, and the second device includes:

a transceiver unit for receiving target signals;

The transceiver unit is further configured to receive the first numerical value from the first device, where the first numerical value is the numerical value used by the first device to play the target signal;

a processing unit for determining the weight coefficient;

a processing unit, further configured to determine the second numerical value according to the first numerical value and the weight coefficient;

Optionally, in a possible implementation manner of the tenth aspect, the processing unit is specifically configured to determine the weight coefficient according to a first mapping table, where the first mapping table is used to indicate the relationship between the device type of the second device and the weight coefficient. association relationship.

Optionally, in a possible implementation manner of the tenth aspect, the transceiver unit is further configured to acquire a third numerical value, where the third numerical value is a numerical value obtained after the second numerical value is adjusted;

The processing unit is further configured to play the target signal by using the third value.

Optionally, in a possible implementation manner of the tenth aspect, the processing unit is further configured to update the first mapping table by using the third value to obtain a second mapping table, and the weight coefficient of the second device in the second mapping table is the same as that of the second mapping table. The weight coefficients of the second devices in the first mapping table are different.

Optionally, in a possible implementation manner of the tenth aspect, the transceiver unit is further configured to acquire a target feature, where the target feature includes at least one of the playback location of the second device, the system time of the second device, and the third value. A sort of;

Optionally, in a possible implementation manner of the tenth aspect, the processing unit is also used to obtain the fourth numerical value according to the first numerical value and the prediction weight model;

Optionally, in a possible implementation manner of the tenth aspect, the target signal includes an audio signal, the first numerical value includes a volume value used by the audio signal to be played on the first device, and the second numerical value includes the audio signal in the second. The volume value used for playback on the device; or the target signal includes a video signal, the first value includes the volume value or brightness value used for the video signal to be played on the first device, and the second value includes the video signal played on the second device. The volume value or brightness value used; or the target signal includes an image, the first value includes the brightness value or the zoom ratio of the image used for the image to be played on the first device, and the second value includes the image used to play on the second device. The brightness value or the zoom ratio of the image; or the target signal includes text, the first value includes the playback speed value used for text playback on the first device, and the second value includes the playback speed value used for text playback on the second device.

An eleventh aspect of the embodiments of the present application provides a first device, where the first device may be a terminal device. It can also be a component of a terminal device (such as a processor, a chip or a chip system), and the first device executes the aforementioned first aspect, the method in any possible implementation manner of the first aspect, or the third aspect or the third aspect. A method in any possible implementation.

A twelfth aspect of the embodiments of the present application provides a second device, where the second device may be a smart watch, a TV, a computer, or a projection screen, or the like. It can also be a component (such as a processor, chip or chip system) of a smart watch, a TV, a computer or a projection screen, and the second device executes the method or the first aspect in the second aspect, any possible implementation manner of the second aspect. The fourth aspect, the method in any possible implementation manner of the fourth aspect, or the fifth aspect, the method in any possible implementation manner of the fifth aspect.

A thirteenth aspect of the present application provides a first device, comprising: a processor, the processor is coupled to a memory, the memory is used for storing a program or an instruction, when the program or the instruction is executed by the processor , so that the first device implements the first aspect or the method in any possible implementation manner of the first aspect, or enables the first device to implement the third aspect or the method in any possible implementation manner of the third aspect.

A fourteenth aspect of the present application provides a second device, comprising: a processor coupled to a memory for storing a program or an instruction, when the program or instruction is executed by the processor , so that the second device implements the above-mentioned second aspect or the method in any possible implementation manner of the second aspect, or makes the first device implement the above-mentioned fourth aspect or the method in any possible implementation manner of the fourth aspect, Or make the first device implement the method in the fifth aspect or any possible implementation manner of the fifth aspect.

A fifteenth aspect of the embodiments of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, cause the computer to execute the foregoing first aspect or any of the first aspects. Possible implementations, the second aspect or any possible implementation of the second aspect, the third aspect or any possible implementation of the third aspect, the fourth aspect or any possible implementation of the fourth aspect, the fifth aspect or the method in any possible implementation manner of the fifth aspect.

A sixteenth aspect of the embodiments of the present application provides a computer program product, which, when executed on a computer, enables the computer to execute the foregoing first aspect or any possible implementation manner of the first aspect, the second aspect or the second aspect In any possible implementation of the aspect, the third aspect or any possible implementation of the third aspect, the fourth aspect or any possible implementation of the fourth aspect, the fifth aspect or any possible implementation of the fifth aspect Methods.

A seventeenth aspect of an embodiment of the present application provides a communication system, including the first device provided in the eleventh or thirteenth aspect, and the second device in the twelfth or fourteenth aspect.

Wherein, for the technical effects brought by the sixth, eleventh, thirteenth, fifteenth, sixteenth, seventeenth aspects or any of the possible implementations, please refer to the first aspect or the different possibilities of the first aspect The technical effects brought by the implementation manner will not be repeated here.

Wherein, for the technical effects brought by the seventh, twelfth, fourteenth, fifteenth, sixteenth, seventeenth aspects or any of the possible implementations, please refer to the second aspect or the different possibilities of the second aspect The technical effects brought by the implementation manner will not be repeated here.

Among them, for the technical effects brought by the eighth, eleventh, thirteenth, fifteenth, sixteenth, seventeenth aspects or any of the possible implementations, please refer to the third aspect or the third aspect for different possibilities The technical effects brought by the implementation manner will not be repeated here.

Among them, for the technical effects brought by the ninth, twelfth, fourteenth, fifteenth, sixteenth, seventeenth aspects or any of the possible implementations, please refer to the fourth aspect or the fourth aspect for different possibilities The technical effects brought by the implementation manner will not be repeated here.

Wherein, for the technical effects brought by the tenth, twelfth, fourteenth, fifteenth, sixteenth, seventeenth aspects or any of the possible implementations, please refer to the fifth aspect or the fifth aspect for different possibilities The technical effects brought by the implementation manner will not be repeated here.

It can be seen from the above technical solutions that the embodiment of the present application has the following advantages: in the embodiment of the present application, the first device can determine the second value used to play the target signal on the second device according to the first value and the weight coefficient, and The method of weight coefficient is used to determine the value used by the second device to play the target signal, and send the second value to the second device, so that the switched second device can automatically adjust the first amount to the relative volume of the first device ( That is, the second value), reduce the user's perception of the difference in the value of the value during switching, perform comfortable switching of the second device device, and improve the user experience.

Description of drawings

Fig. 1 is a kind of structural schematic diagram of artificial intelligence main frame;

2 is a schematic diagram of an application environment provided by an embodiment of the present invention;

3 is a schematic flowchart of a data processing method provided by an embodiment of the present application;

4 is another schematic flowchart of the data processing method provided by the embodiment of the present application;

FIG. 5 is another schematic flowchart of a data processing method provided by an embodiment of the present application;

FIG. 6 is another schematic flowchart of a data processing method provided by an embodiment of the present application;

7 is a schematic structural diagram of a first device in an embodiment of the present application;

8 is a schematic structural diagram of a second device in an embodiment of the present application;

FIG. 9 is a schematic structural diagram of a communication device in an embodiment of the application;

FIG. 10 is another schematic structural diagram of a communication device in an embodiment of the present application.

detailed description

Embodiments of the present application provide a data processing method and related equipment. It is used to determine suitable playback parameters of the second device to improve user experience.

The embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention. The terms used in the embodiments of the present invention are only used to explain specific embodiments of the present invention, and are not intended to limit the present invention.

The embodiments of the present application will be described below with reference to the accompanying drawings. Those of ordinary skill in the art know that with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

The terms "first", "second" and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances, and this is only a distinguishing manner adopted when describing objects with the same attributes in the embodiments of the present application. Furthermore, the terms "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, product or device comprising a series of elements is not necessarily limited to those elements, but may include no explicit or other units inherent to these processes, methods, products, or devices.

First, the overall workflow of the artificial intelligence system will be described. Please refer to Figure 1. Figure 1 is a structural schematic diagram of the main frame of artificial intelligence. The above-mentioned artificial intelligence theme framework is elaborated in two dimensions. Among them, the "intelligent information chain" reflects a series of processes from data acquisition to processing. For example, it can be the general process of intelligent information perception, intelligent information representation and formation, intelligent reasoning, intelligent decision-making, intelligent execution and output. In this process, data has gone through the process of "data-information-knowledge-wisdom". The "IT value chain" reflects the value brought by artificial intelligence to the information technology industry from the underlying infrastructure of human intelligence, information (providing and processing technology implementation) to the industrial ecological process of the system.

(1) Infrastructure

The infrastructure provides computing power support for artificial intelligence systems, realizes communication with the outside world, and supports through the basic platform. Communication with the outside world through sensors; computing power is provided by smart chips (hardware acceleration chips such as CPU, NPU, GPU, ASIC, FPGA); the basic platform includes distributed computing framework and network-related platform guarantee and support, which can include cloud storage and computing, interconnection networks, etc. For example, sensors communicate with external parties to obtain data, and these data are provided to the intelligent chips in the distributed computing system provided by the basic platform for calculation.

(2) Data

The data on the upper layer of the infrastructure is used to represent the data sources in the field of artificial intelligence. The data involves graphics, images, voice, and text, as well as IoT data from traditional devices, including business data from existing systems and sensory data such as force, displacement, liquid level, temperature, and humidity.

(3) Data processing

Data processing usually includes data training, machine learning, deep learning, search, reasoning, decision-making, etc.

Among them, machine learning and deep learning can perform symbolic and formalized intelligent information modeling, extraction, preprocessing, training, etc. on data.

Reasoning refers to the process of simulating human's intelligent reasoning method in a computer or intelligent system, using formalized information to carry out machine thinking and solving problems according to the reasoning control strategy, and the typical function is search and matching.

Decision-making refers to the process of making decisions after intelligent information is reasoned, usually providing functions such as classification, sorting, and prediction.

(4) General ability

After the above-mentioned data processing, some general capabilities can be formed based on the results of data processing, such as algorithms or a general system, such as translation, text analysis, computer vision processing, speech recognition, image identification, etc.

(5) Smart products and industry applications

Intelligent products and industry applications refer to the products and applications of artificial intelligence systems in various fields. They are the encapsulation of the overall solution of artificial intelligence, and the productization of intelligent information decision-making to achieve landing applications. Its application areas mainly include: intelligent terminals, intelligent transportation, Smart healthcare, autonomous driving, safe city, etc.

The following briefly introduces the application scenarios of the solutions provided in this application.

FIG. 2 is a schematic diagram of an example application scenario of the present application. As shown in FIG. 2, the user first uses the first device 201 to play the target signal (audio, video, image or music, etc.), if the user needs to play the target signal played by the first device 201 on the second device 202 (or the user When the target signal is planned to be played on the second device 202, that is, the target signal is not currently played on the first device 201), the second device 202 can use a suitable value (for example, volume) by using the data processing method provided in this application. , brightness or speed, etc.) to play the target signal to improve user experience.

An exemplary specific introduction to several application scenarios for which Figure 2 is applicable:

It should be understood that the example shown in FIG. 2 should not impose any restrictions on the application scenarios of the embodiments of the present application.

Because most of the audio played by the audio playback device comes from different audio operators. Different audio operators will have different volume levels when recording audio.

Therefore, for the same audio, under the condition that the playback device sets the same playback volume, there is a very obvious volume difference. The user performs an audio playback operation through the terminal device. Inevitably, in the process of playing audio, in order not to disturb other people who are in a public place, the user will adjust the audio from the speaker to the audio from the earphone. However, after adjusting the audio playback device, the audio playback volume will become too large or too small, especially if the audio playback volume suddenly becomes very large, the user's hearing effect will be seriously degraded, and the user may even be frightened.

In order to solve the above problems, the present application provides a data processing method, which can satisfy the user's need to play multimedia audio, such as music, video, and calls, on a mobile phone device. During the audio playback process, the media playback device is switched through Bluetooth, one-touch transmission, and App screen projection. This embodiment of the present application can automatically adjust the volume of the switched playback device to the relative volume of the mobile phone device, reducing the number of users in the switching process. Perceive the difference in audio size, and switch the playback device comfortably.

In the embodiments of this application, only the first device is a mobile phone as an example for description. It can be understood that the first device and the second device may be terminal devices (such as mobile phones, etc.) Personal Digital Assistant, PDA) and other devices that can display images, display text, play music or videos, etc. This embodiment of the present application does not limit the first device here.

The second device in the embodiment of the present application may be a device such as a headset, a computer, a TV, a speaker, a projection screen, or a watch, which is capable of displaying images, displaying text, and playing music or videos. This embodiment of the present application does not limit the second device here.

Exemplarily, when the second device is an earphone, a stereo or a display screen on a smart car, the first device and the second device may transmit data through the Institute of Electrical and Electronics Engineers (IEEE) 802.11 protocol. When the second device is a projection screen, the first device and the second device can transmit data through a hypertext transfer protocol (HTTP). When the second device is a projection screen, a display screen on a smart car, a tablet computer or a computer, the first device and the second device can transmit data through WIFI-Direct. The protocol or method used by the first device and the second device to transmit data is not limited here.

In this embodiment of the present application, the data processing method provided by the present application may be divided into various situations according to the content of the data sent by the first device to the second device:

The first type: the first device sends the second value to the second device.

A data processing method provided by the present application will be described below with reference to FIG. 2 . As shown in FIG. 3 , a flow of the data processing method provided by this application may include steps 301 to 311 . Each step in the method will be described in detail below with reference to FIG. 3 .

301. The first device acquires the first value and the device type of the second device.

The first device first obtains the first value, which may specifically be the first value used by the first device to record the target signal played by the first device, or the preset value (ie, the first value) of the target signal to be played by the first device.

It can be understood that there is no limitation on whether the target signal has been played on the first device, and the first value may be the value used for the target signal to be played on the first device.

The target signal in this embodiment of the present application may be music, lyrics of music, video, image, or text, etc., which is not specifically limited here.

The first value in the embodiment of the present application may be the volume value of playing music or video, the brightness value of playing music or video, or the playback speed value of music lyrics, novels or texts. Practical applications. The first numerical value may also be other numerical values, for example, the first numerical value may also be a scaling value of a video or a picture, which is not specifically limited here.

In this embodiment of the present application, the first device may acquire the device type of the second device by sending the device type of the second device to the first device through the second device, or by sending the device type of the second device from the third device (except for the first device and the first device). Obtain the device type of the second device on a device other than the second device), it can be understood that, in practical applications, the first device can also obtain the device type of the second device in other ways, for example: the first device scans the second device by scanning the second device. The device type of the second device is obtained by means of a device, which is not specifically limited here.

The device type of the second device in the embodiment of the present application may be a headset, a computer, a TV, a speaker, a projection screen (a screen or a display screen in a smart car), a watch, etc. For multiple second devices, the identification or numbering of the The second device is distinguished by the form, for example: 1, 2 or 0001, 0002, etc. The specific manner of the device type is not limited here.

Exemplarily, when a digital living network alliance (DLNA) or Miracast is connected between the first device and the second device, a type description field can be preset in the second device, and the mobile phone device can read the data in the network. The description file of each second device obtains the device type of the second device.

Exemplarily, if the mobile phone (ie, the first device) switches the playback device in the multimedia playback state, the device type of the second device is obtained. For example, the Bluetooth connection can return the type value of the device type of the second device through getBluetoothClass. Optionally, it can be further judged according to the value whether it is the device type recorded in the first mapping table.

302. The first device determines a weight coefficient of the second device.

In the embodiment of the present application, there may be various ways for the first device to determine the weight coefficient of the second device. The following only uses the first device to determine the weight coefficient of the second device according to the first mapping table as an example. It can be understood that, The first device may also determine the weight coefficient of the second device according to a preset rule, or the first device may determine the weight coefficient of the second device according to an instruction of the third device, which is not specifically limited here.

The weight coefficient in the embodiment of the present application is equivalent to the weight ratio of the volume between the first device and the second device under the same auditory comfort level.

Exemplarily, the first device stores a first mapping table, where the first mapping table is used to represent the association relationship between the device type of the second device and the weight coefficient.

For ease of understanding, for an example, please refer to Table 1:

Table 1

标识logo	设备类型Equipment type	音量权重volume weight	亮度权重Luminance weight	速度权重speed weight	缩放权重scaling weight
11	0000(投影屏)0000 (projection screen)	0.70.7	0.80.8	0.90.9	1.21.2
22	0001(耳机)0001(Headphone)	1.11.1	--	0.70.7	--
33	0002(音箱)0002 (speaker)	1.51.5	--	0.80.8	--
44	0003(手表)0003 (watch)	0.80.8	1.31.3	0.70.7	1.51.5
……	……	……	……	……	……

Among them, the device types and various weights in Table 1 are only examples, and the specific expression forms of the device types and the values of various weights are not limited here.

Exemplarily, when the first value is the volume value of playing music or video, the weight type of the second device may be volume weight. When the first numerical value is the displayed brightness value, the weight coefficient of the second device may be a brightness weight. When the first value is the speed value of the lyrics or text of the playing music, the weight coefficient of the second device may be the speed weight. When the first value is a scaling value of a video or a picture, the weight coefficient of the second device may be a scaling weight. It can be understood that the weight coefficient of the second device is not limited to the above examples, and may also be other forms of weight coefficients, which are not limited here.

The first mapping table in this embodiment of the present application may be preset manually, or obtained by a survey method or an experimental method. Of course, the first mapping table may also be adjusted to the second mapping table on the basis of the first numerical value according to pre-trained users. The habit of obtaining the numerical value, the obtaining method of the first mapping table is not limited here.

303. The first device determines the second numerical value according to the first numerical value and the weighting coefficient.

The first device acquires the weight coefficient of the second device according to the first mapping table, and then determines the second value according to the first value and the weight coefficient. Specifically, the first device may calculate the product of the first numerical value and the weight coefficient to obtain the second numerical value.

Exemplarily, when the device type of the second device is 0001, it is assumed that the user needs to transfer the music (that is, the target signal) played on the mobile phone (that is, the first device) to the headset (that is, the second device) for playback, and the current mobile phone If the volume value of the music played on the device is 85 (ie, the first value is 85), the volume played by the headset (ie, the second value) is 93.5 (ie, 85 times 1.1 is 93.5).

304. The first device sends the second value to the second device. Correspondingly, the second device receives the second value sent by the first device.

After the first device determines the second value, the first device sends the second value to the second device. After receiving the second value, the second device can use the second value to play the target signal.

Exemplarily, continuing the above example, the mobile phone determines that after the volume of the headset playing music is 93.5, the volume value transmitted by the mobile phone to the headset is 93.5; or the mobile phone sets the headset to play music with a volume value of 93.5.

Exemplarily, when the first value and the second value are volume values, the first device target signal and the second value are transmitted to the second device through Bluetooth, DLNA or Miracast protocol, and the second device uses the second value to play the target signal. Among them, DLNA transmission searches for available second devices in the local area network through the root device, and carries the volume and Url through the Body parameter for HTTP Post transmission. Miracast finds attached Miracast devices through WiFi-Direct and transmits data through real time streaming protocol (RTSP).

305. The second device obtains a third numerical value. This step is optional.

After the second device uses the second value to play the target signal, the second device obtains the third value by adjusting the second device to play the target signal according to the user.

Optionally, after the second device acquires the third value, the second device uses the third value to play the target signal.

306. The second device sends the third numerical value to the first device, and correspondingly, the first device receives the third numerical value sent by the second device. This step is optional.

After acquiring the third numerical value input by the user, the second device may send the third numerical value to the first device, so that the first device updates the first mapping table according to the third numerical value.

It can be understood that the above two ways for the first device to obtain the third numerical value are only examples, and in practical applications, the first device has other ways to obtain the third numerical value, which is not specifically limited here.

307. The first device updates the first mapping table according to the third value. This step is optional.

After acquiring the third value, the first device obtains a new weight coefficient by calculating the ratio of the third value to the first value, and updates the first mapping table with the new weight coefficient to obtain the second mapping table.

Exemplarily, the first value is 50, the initial weight coefficient is 1.1, and the second value is 55. When the second device uses the second value to play the target signal, and the user adjusts 55 to 60, the second device sends 60 to the first device, and the first device calculates a new weight coefficient based on 60 as 1.2 (that is, the value obtained by dividing 60 by 50 1.2). The first device replaces the previous weight coefficient with the new weight coefficient of the second device, that is, updates the weight coefficient 1.1 of the second device in the first mapping table to 1.2, and obtains the second mapping table (the weight of the second device in the second mapping table) The coefficient is 1.2).

308. The first device acquires the target feature. This step is optional.

The first device acquires the target feature, and the target feature includes the first value used by the first device to play the target signal, the device type of the second device, the playback location of the second device, the system time of the second device, and the first value adjusted by the user. at least one of the three values.

Optionally, the target feature may further include a user portrait when the user inputs the third numerical value, so that the first device can provide different fourth numerical values to different users of the first device, thereby more accurately judging the usage habits of different users.

309. The first device trains the model to be trained according to the target feature to obtain a prediction weight model. This step is optional.

After the user adjusts the third numerical value used by the second device to play the target signal each time, the second device sends the third numerical value to the first device, so that the first device knows the adjustment amount of the user each time.

The first device trains the model to be trained according to the target feature, which is equivalent to recording the adjustment value (ie the third value), device type, current time, current location and other target features when the user adjusts the second value. By taking the target features as training samples. Learn the user's usage habits and predict the weight coefficient that best fits the current user scenario. Obtain a subsequent prediction weight model that conforms to the user's usage habits.

The model to be trained in the embodiment of the present application may be selected from a machine learning model such as a LightGBM model, an SVM model, and a CNN model, or a model such as a neural network model.

Exemplarily, when the current time is late at night, when the video (that is, the target signal) on the mobile phone (that is, the first device) is transferred to the projection screen (that is, the second device) for playback, the second numerical value is used when the projection screen plays the video, The user may feel disturbed, and the user will turn down the volume of the video played on the projection screen (that is, the user will adjust the second value to the third value). After the user adjusts several times, record the value adjusted by the user and the current time (late night), and train to learn. Later, when the user plays a video on the projection screen late at night, the first device can select a volume value suitable for the user according to the previous training, thereby reducing the impact on other people.

The first device may adopt the method of training the model to be trained, so that the weight coefficient is more in line with the user's habits, and even conforms to the usage habits and needs of different users in different scenarios. For the implementation without the user-adjusted value in advance, the second value may be obtained first according to the preset first mapping table. If the user inputs the third numerical value multiple times to adjust the second numerical value, the second device can record multiple third numerical values and send the third numerical value to the first device, and the first device can update the weight model according to the multiple third numerical values. That is, the first device may acquire the adjustment value (ie, the third numerical value) input by the user each time, and update the weight coefficient according to the third numerical value and the first numerical value. That is, the first device acquires the user's adjustment value as a training sample, learns the user's usage habits, and subsequently provides a new weight coefficient that is more in line with the user's usage habits. In addition, when recording the adjustment value input by the user, the location, system time and user portrait of the second device may also be recorded when the user inputs the adjustment value. In order to make the new weight coefficient obtained by training more in line with user habits and meet different needs in different scenarios.

310. The first device obtains a fourth value according to the first value and the prediction weight model. This step is optional.

The first device may input the first numerical value into the prediction weight model to obtain the updated weighting coefficient, and specifically may calculate the product of the first numerical value and the updated weighting coefficient to obtain the fourth numerical value.

It can be understood that the prediction weight model can also directly output the fourth numerical value, which is not specifically limited here.

311. The first device sends a fourth numerical value to the second device. This step is optional.

After obtaining the fourth numerical value according to the prediction weight model, the first device sends the fourth numerical value to the second device, so that the second device uses the fourth numerical value to play the target signal, and can obtain the numerical value conforming to the user's habit without user adjustment.

This embodiment of the present application may include all or only part of the steps shown in FIG. 3 . For example, an implementation manner of the embodiment of the present application may include steps 301 to 304 . An implementation manner of this embodiment of the present application may include steps 301 to 305 . Another implementation manner of the embodiment of the present application may include steps 301 to 307 . Another implementation manner of this embodiment of the present application may include steps 301 to 310 . Another implementation manner of this embodiment of the present application may include steps 301 to 311 . There is no specific limitation here.

In this embodiment of the present application, the first device may determine the second value used for playing the target signal on the second device according to the first value and the weight coefficient. By using the weight coefficient method to determine the value used by the second device to play the target signal, the volume of the second device after switching is automatically adjusted to the relative volume of the first device, reducing the user's perception of the difference in audio size during switching, and making comfortable The second device can be switched to improve the user experience. Further, the first device may also update the first mapping table according to the third value used by the user to adjust the second value, so that the next calculation of the weight coefficient of the second device is more in line with the user's habit. In addition, the first device can also obtain the fourth value in the specific scenario according to the method of training the model to be trained, so as to meet the requirements in the specific scenario.

The second type: the first device sends the first value and the weight coefficient to the second device.

Referring to FIG. 4 , another flow of the data processing method provided by the present application may include steps 401 to 411 . Each step in the method will be described in detail below with reference to FIG. 4 .

401. The first device acquires a first numerical value.

402. The first device determines a weight coefficient of the second device.

Steps 401 and 402 in this embodiment are similar to steps 301 and 302 in the aforementioned embodiment shown in FIG. 3 , and details are not described herein again.

403. The first device sends the first numerical value and the weight coefficient to the second device. Correspondingly, the second device receives the first value and the weight coefficient sent by the first device.

After the first device acquires the first value and determines the weight coefficient of the second device, the first device sends the first value and the weight coefficient to the second device.

404. The second device determines the second value according to the first value and the weighting coefficient.

After the second device acquires the first value and the weight coefficient of the second device, the second device may determine the second value according to the first value and the weight coefficient. Specifically, the second value may be obtained by calculating the product of the first value and the weighting coefficient.

Exemplarily, when the first value is 85 and the weight coefficient of the second device is 1.1, the second value of the second device is 93.5 (ie, 85 times 1.1 is 93.5).

Optionally, after determining the second value, the second device uses the second value to play the target signal.

405. The second device obtains a third numerical value. This step is optional.

406. The second device sends a third numerical value to the first device. Correspondingly, the first device receives the third value sent by the second device. This step is optional.

407. The first device updates the first mapping table according to the third value. This step is optional.

408. The first device acquires the target feature. This step is optional.

409. The first device trains the model to be trained according to the target feature to obtain a prediction weight model. This step is optional.

410. The first device obtains a fourth value according to the first value and the prediction weight model. This step is optional.

411. The first device sends a fourth numerical value to the second device. This step is optional.

Steps 405 to 411 in this embodiment are similar to steps 305 to 311 in the aforementioned embodiment shown in FIG. 3 , and details are not repeated here.

This embodiment of the present application may include all or only part of the steps shown in FIG. 4 . For example, an implementation manner of the embodiment of the present application may include steps 401 to 404 . An implementation manner of this embodiment of the present application may include steps 401 to 405 . Another implementation manner of this embodiment of the present application may include steps 401 to 407 . Another implementation manner of this embodiment of the present application may include steps 401 to 410 . Another implementation manner of this embodiment of the present application may include steps 401 to 411 . There is no specific limitation here.

In this embodiment of the present application, the second device may determine the second value used to play the target signal on the second device according to the first value and the weight coefficient, and determine the value used by the second device to play the target signal by using the weight coefficient method , automatically adjust the volume of the second device after switching to the relative volume of the first device, reduce the user's perception of the difference in audio volume during switching, perform comfortable switching of the second device, and improve user experience. Further, the first device may also update the first mapping table according to the third value used by the user to adjust the second value, so that the next calculation of the weight coefficient of the second device is more in line with the user's habit. In addition, the first device can also obtain the fourth value in the specific scenario according to the method of training the model to be trained, so as to meet the requirements in the specific scenario.

The third type: the first device sends the first value to the second device.

Referring to FIG. 5 , another flow of the data processing method provided by the present application may include steps 501 to 508 . Each step in the method will be described in detail below with reference to FIG. 5 .

501. A first device sends a first value to a second device. Correspondingly, the second device receives the first value sent by the first device.

The manner in which the first device acquires the first numerical value is similar to the manner in which the first device acquires the first numerical value in step 301 of the aforementioned embodiment shown in FIG. 3 , and details are not repeated here.

After the first device acquires the first value, the first device sends the first value to the second device.

Exemplarily, the first device is a mobile phone, the second device is a watch, the target signal is text (for example, novels, news or lyrics), and the first value is the playback speed of the text on the mobile phone, assuming that the first value is 2x speed. or a velocity value of 120.

502. The second device determines a weight coefficient of the second device.

The second device may pre-store a first mapping table, wherein the related description of the first mapping table is similar to the first mapping table in the foregoing embodiment shown in FIG. 3 , and details are not repeated here.

The second device may determine the weight coefficient of the second device according to the first mapping table.

Optionally, the second device determines the weight type of the second device according to the device type of the second device and the first mapping table.

Exemplarily, as shown in Table 1, continuing the above example, the device type of the second device is 0003, that is, the second device is a watch, and the speed weight of the second device is 0.7.

503. The second device determines the second value according to the first value and the weighting coefficient.

After the second device acquires the first numerical value and determines the weighting coefficient, the second device may obtain the second numerical value according to the first numerical value and the weighting coefficient. Specifically, the second device may calculate the product of the first numerical value and the weight coefficient to obtain the second numerical value.

Exemplarily, continuing the above example, the first value is 2 times the speed or the speed value is 120, and the weight coefficient of the second device is 0.7, then the second value calculated by the second device is 1.4 times the speed (obtained by multiplying the 2 times speed by 0.7). 1.4x) or 84 (120 times 0.7 to get 84).

Optionally, the second device uses the second value to play the target signal.

Exemplarily, the watch plays text using a speed value of 1.4 times or 84.

504. The second device obtains a third numerical value. This step is optional.

505. The second device updates the first mapping table according to the third value. This step is optional.

Exemplarily, continuing the above example, the user adjusts the speed of playing the text to a speed of 1.2 times or 72 (that is, the third value is 1.2 or 80), and the second device calculates the quotient of the third value and the first value, The resulting new weight factor is 0.6 (ie 1.2 divided by 2 to get 0.6, and 72 divided by 120 to get 0.6). The second device replaces the previous weight coefficient with the new weight coefficient of the second device, that is, updates the weight coefficient of the second device in the first mapping table from 0.7 to 0.6, and obtains the second mapping table (the weight of the second device in the second mapping table The coefficient is 0.6).

506. The second device acquires the target feature. This step is optional.

The second device acquires the target feature, where the target feature includes the first value used by the first device to play the target signal, the device type of the second device, the playback location of the second device, the system time of the second device, and the first value adjusted by the user. at least one of the three values.

Optionally, the target feature may further include a user portrait when the user inputs the third numerical value, so that the second device can provide different fourth numerical values to different users of the second device, thereby more accurately judging the usage habits of different users.

507. The second device trains the model to be trained according to the target feature to obtain a prediction weight model. This step is optional.

The second device records the third value after each time the user adjusts the third value used by the second device to play the target signal. Optionally, the second device records the system time, playback location, and user portrait when the user adjusts the value.

The second device trains the model to be trained according to the target feature, which is equivalent to recording the adjustment value (ie the third value), device type, current time, current location and other target features when the user adjusts the second value. By taking the target features as training samples. Learn the user's usage habits and predict the weight coefficient that best fits the current user scenario. Obtain a subsequent prediction weight model that conforms to the user's usage habits.

Exemplarily, when the current time is late at night, when the video (that is, the target signal) on the mobile phone (that is, the first device) is transferred to the projection screen (that is, the second device) for playback, the second numerical value is used when the projection screen plays the video, The user may feel disturbed, and the user will turn down the volume of the video played on the projection screen (that is, the user will adjust the second value to the third value). After the user adjusts multiple times, the second device records the value adjusted by the user and the current time (late night), and trains to learn. Later, when the user plays a video on the projection screen late at night, the second device can select a volume value suitable for the user according to the previous training, thereby reducing the impact on others.

The second device may adopt the method of training the model to be trained, so that the weight coefficient is more in line with the user's habits, and even conforms to the usage habits and needs of different users in different scenarios. For the implementation without the user-adjusted value in advance, the second value may be obtained first according to the preset first mapping table. If the user inputs the third numerical value multiple times to adjust the second numerical value, the second device may record multiple third numerical values, and update the weight model according to the multiple third numerical values. That is, the second device may acquire the adjustment value (ie, the third value) input by the user each time, and update the weight coefficient according to the third value and the first value. That is, the second device acquires the user's adjustment value as a training sample, learns the user's usage habits, and subsequently provides a new weight coefficient that is more in line with the user's usage habits. In addition, when recording the adjustment value input by the user, the location, system time and user portrait of the second device may also be recorded when the user inputs the adjustment value. In order to make the new weight coefficient obtained by training more in line with user habits and meet different needs in different scenarios.

508. The second device obtains a fourth value according to the first value and the prediction weight model. This step is optional.

The second device inputs the first numerical value into the prediction weight model to obtain the updated weighting coefficient, and specifically may calculate the product of the first numerical value and the updated weighting coefficient to obtain the fourth numerical value.

Optionally, the second device uses the fourth value to play the target signal.

This embodiment of the present application may include all or only part of the steps shown in FIG. 5 . For example, an implementation manner of the embodiment of the present application may include steps 501 to 503 . An implementation manner of this embodiment of the present application may include steps 501 to 504 . Another implementation manner of this embodiment of the present application may include steps 501 to 505 . Another implementation manner of this embodiment of the present application may include steps 501 to 507 . Another implementation manner of this embodiment of the present application may include steps 501 to 508 . There is no specific limitation here.

In this embodiment of the present application, the second device may determine the second value used to play the target signal on the second device according to the first value and the weight coefficient, and determine the value used by the second device to play the target signal by using the weight coefficient method , automatically adjust the volume of the second device after switching to the relative volume of the first device, reduce the user's perception of the difference in audio volume during switching, perform comfortable switching of the second device, and improve user experience. Further, the second device may also update the first mapping table according to the third value used by the user to adjust the second value, so that the next calculation of the weight coefficient of the second device is more in line with the user's habit. In addition, the second device can also obtain the fourth value in the specific scenario according to the method of training the model to be trained, so as to meet the requirements in the specific scenario.

Please refer to FIG. 6 , another flow of the data processing method provided by the present application. The data processing method provided by the present application will be described below with reference to FIG. 6 .

In the embodiment of the present application, the data processing method shown in FIG. 6 is described only by taking the first device being a mobile phone device and the target signal being an audio signal as an example.

First, calculate the relative volume preset table among multiple devices through human factors engineering to assign the initial value to the mobile phone device. Human factors engineering studies the overall design of the human-machine system, and can use various research methods such as: survey method, experimental method, graphic model and other methods The weight ratio of the volume between the devices under the same auditory comfort level is measured, and a first mapping table is generated.

When the mobile device is playing multimedia, the Android system can judge the current audio playback status of the mobile device through the interface in the Audio Manager. If the mobile phone device switches the playback device in the multimedia playback state, the device type of the playback device (that is, the second device) is obtained. For example, the Bluetooth connection can return the type value of the corresponding type of the playback device through getBluetoothClass, and then judge whether it is the corresponding type according to the value. Valid types of equipment to which the invention relates. For the connection between DLNA and Miracast devices, the type description field can be preset in the corresponding device, and the device type can be obtained by reading the description file of each device in the network through the mobile phone device. Valid device types will enter the next step of weight table query, invalid device types (that is, invalid devices are not recorded in the first mapping table), and invalid devices use the same volume value as the mobile device to play audio signals.

The mapping table calculation module in the mobile phone first obtains the current media playback volume (ie, the first value) of the mobile phone device, and transmits it to the mapping table calculation module with the valid device type obtained through the connection protocol, and obtains the corresponding weight value through the device type (ie, the first value). weight coefficient), and calculate the volume value (ie, the second value) of the playback device (ie, the second device) in combination with the current volume of the mobile phone device.

The next step is to transmit the media audio data stream (that is, the audio signal) and the calculated volume of the playback device to the playback device through Bluetooth, DLNA and Miracast protocols to perform media playback with the calculated volume value. Bluetooth transmission converts volume and audio data into digital signals, decodes the data stream and sends it to the target device. DLNA transmission searches for available devices on the local area network through the root device, and carries the volume and Url through the Body parameter for HTTP Post transmission. Miracast finds attached Miracast devices via WiFi-Direct and transmits data via RTSP live streaming protocol.

When the playback device uses the calculated relative sound for media playback, if the user is dissatisfied with the current volume and adjusts it manually, the volume adjusted by the user (ie, the third value) will be returned to the mobile phone configuration through the volume adjustment dot. The value of the mapping weight table is refreshed in combination with the optimal volume perceived by the user (that is, the first mapping table is updated to obtain the second mapping table).

Corresponding to the methods given in the foregoing method embodiments, the embodiments of the present application further provide corresponding apparatuses, including corresponding modules for executing the foregoing embodiments. Modules can be software, hardware, or a combination of software and hardware.

Referring to FIG. 7 , an embodiment of the first device in the embodiment of the present application may also be an embodiment of a component (for example, a processor, a chip, or a chip system) of the first device.

In a possible implementation manner, the first device 700 includes:

Transceiver unit 701, used to obtain the first value used by the target signal to play in the first device and the device type of the second device;

a processing unit 702, configured to determine the weight coefficient of the second device;

The processing unit 702 is also used to determine the second numerical value used by the target signal to play in the second device according to the weight coefficient and the first numerical value;

The transceiver unit 701 is further configured to send the second value to the second device, so that the second device uses the second value to play the target signal.

Optionally, the processing unit 702 is specifically configured to determine the weight coefficient of the second device according to the first mapping table, where the first mapping table is used to represent the association relationship between the device type of the second device and the weight coefficient.

Optionally, the transceiver unit 701 is also used to receive the third numerical value sent by the second device, and the third numerical value is the numerical value obtained after the adjustment of the second numerical value;

The processing unit 702 is further configured to update the first mapping table according to the third value to obtain a second mapping table, where the weight coefficient of the second device in the second mapping table is different from the weight coefficient of the second device in the first mapping table.

Optionally, the transceiver unit 701 is further configured to acquire target features, where the target features include at least one of the first numerical value, the playback location of the second device, the system time of the second device, and the third numerical value;

The processing unit 702 is further configured to train the model to be trained according to the target feature to obtain a prediction weight model.

Optionally, the processing unit 702 is further configured to obtain the fourth numerical value according to the first numerical value and the prediction weight model;

The transceiver unit 701 is further configured to send a fourth numerical value to the second device.

Optionally, the target signal includes an audio signal, the first numerical value includes the volume value used by the audio signal to be played on the first device, and the second numerical value includes the volume value used by the audio signal to be played on the second device; or the target signal includes Video signal, the first value includes the volume value or brightness value used by the video signal to be played on the first device, and the second value includes the volume value or brightness value used by the video signal to be played on the second device; or the target signal includes an image , the first value includes the brightness value or the zoom ratio of the image used to play the image on the first device, the second value includes the brightness value or the zoom ratio of the image used to play the image on the second device; or the target signal includes text , the first value includes the playback speed value used for text playback on the first device, and the second value includes the playback speed value used for text playback on the second device.

In this embodiment, the operations performed by each unit in the first device are similar to the operations performed by the first device in the foregoing embodiments shown in FIG. 3 to FIG. 6 , and details are not repeated here.

In this embodiment, the processing unit 702 may determine the second value used for playing the target signal on the second device according to the first value and the weight coefficient. By using the weight coefficient method to determine the value used by the second device to play the target signal, the volume of the second device after switching is automatically adjusted to the relative volume of the first device, reducing the user's perception of the difference in audio size during switching, and making comfortable The second device can be switched to improve the user experience. Further, the processing unit 702 may also update the first mapping table according to the third value used by the user to adjust the second value, so that the next calculation of the weight coefficient of the second device is more in line with the user's habit. In addition, the processing unit 702 may also obtain the fourth numerical value in the specific scenario according to the method of training the model to be trained, so as to meet the requirements in the specific scenario.

In another possible implementation manner, the first device 700 includes:

The transceiver unit 701 is further configured to send the first numerical value and the weight coefficient to the second device, so that the second device determines the second numerical value of the second device according to the first numerical value and the weight coefficient.

Optionally, the transceiver unit 701 is further configured to receive a third numerical value sent by the second device, where the third numerical value is a numerical value obtained after the second numerical value is adjusted;

In this embodiment, the transceiver unit 701 sends the first numerical value and the weight coefficient to the second device, so that the second device can determine the second numerical value used to play the target signal on the second device according to the first numerical value and the weight coefficient, and automatically The volume of the switched second device is adjusted to the relative volume of the first device, so as to reduce the user's perception of the difference in audio size during switching, perform comfortable switching of the second device, and improve user experience. Further, the processing unit 702 may also update the first mapping table according to the third value used by the user to adjust the second value, so that the next calculation of the weight coefficient of the second device is more in line with the user's habit. In addition, the processing unit 702 may also obtain the fourth numerical value in the specific scenario according to the method of training the model to be trained, so as to meet the requirements in the specific scenario.

The second device in the embodiment of the present application will be described below. Referring to FIG. 8, an embodiment of the second device in the embodiment of the present application may also be a component of the second device (for example, a processor, a chip, or a chip system). an example.

In a possible implementation manner, the second device 800 includes:

a transceiver unit 801 for receiving a target signal;

The transceiver unit 801 is further configured to receive a second value sent by the first device, where the second value is obtained by the first device according to the first value, the device type of the second device and the first mapping table, and the first value is played by the first device The value used by the target signal, the first mapping table is used to represent the relationship between the device type of the second device and the weight coefficient, and the weight coefficient is used to obtain the second value according to the first value;

The processing unit 802 is configured to play the target signal by using the second value.

Optionally, the transceiver unit 801 is further configured to obtain a third numerical value, where the third numerical value is a numerical value obtained after the second numerical value is adjusted;

The transceiver unit 801 is further configured to send a third numerical value to the first device, so that the first device updates the first mapping table to obtain a second mapping table, and the weight coefficient of the second device in the second mapping table is the same as that in the first mapping table. The weight coefficients of the second devices are not the same.

In this embodiment, the operations performed by each unit in the second device are similar to the operations performed by the second device in the foregoing embodiments shown in FIG. 3 to FIG. 6 , and details are not described herein again.

In this embodiment, the processing unit 802 may receive the second value sent by the first device. The second value is the value used by the first device to determine the target signal played by the second device by adopting a weight coefficient method, and automatically adjusts the volume of the second device after switching to the relative volume of the first device, reducing the user's confusion in the switching process. The perception of the difference in audio size enables comfortable switching of the second device to improve the user experience. Further, the transceiver unit 801 can also acquire the adjustment value of the user and send the adjustment value (ie, the third value) to the second device, so that the first device updates the first mapping table according to the adjustment value, so that the second device calculates the second device next time. The weight coefficient is more in line with user habits. In addition, the processing unit 802 may also receive a fourth numerical value sent by the first device, where the fourth numerical value is obtained by the first device according to the method of training the model to be trained, so as to meet the requirements in a specific scenario.

In another possible implementation manner, the second device 800 includes:

a transceiver unit 801 for receiving a target signal;

The transceiver unit 801 is further configured to receive a first numerical value and a weight coefficient sent by the first device, where the first numerical value is a numerical value used when the first device plays the target signal;

a processing unit 802, configured to determine the second value according to the first value and the weight coefficient;

The processing unit 802 is further configured to play the target signal by using the second value.

Optionally, the processing unit 802 is specifically configured to calculate the product of the first value and the weight coefficient to obtain the second value.

The processing unit 802 is also used to use the third numerical value to play the target signal;

The transceiver unit 801 is further configured to send a third numerical value to the first device, so that the first device uses the third numerical value to update the weight coefficient of the second device in the first mapping table.

Optionally, the transceiver unit 801 is further configured to acquire target features, where the target features include at least one of the playback location of the second device, the system time of the second device, and a third value;

The processing unit 802 is further configured to train the model to be trained according to the target feature to obtain a prediction weight model.

Optionally, the processing unit 802 is further configured to obtain a fourth numerical value according to the first numerical value and the prediction weight model;

The processing unit 802 is further configured to use the fourth numerical value to play the target signal.

In this embodiment, the processing unit 802 may receive the first numerical value and the weight coefficient sent by the first device. The processing unit 802 can obtain the second numerical value according to the first numerical value and the weighting coefficient, and automatically adjust the volume of the second device after switching to the relative volume of the first device, so as to reduce the user's perception of the difference in audio volume during the switching, and perform a comfortable first step. Switch between two devices to improve user experience. Further, the transceiver unit 801 can also acquire the adjustment value of the user and send the adjustment value (ie, the third value) to the second device, so that the first device updates the first mapping table according to the adjustment value, so that the second device calculates the second device next time. The weight coefficient is more in line with user habits. In addition, the processing unit 802 can also obtain the fourth value according to the method of training the model to be trained, so as to meet the requirements in a specific scenario.

In another possible implementation manner, the second device 800 includes:

a transceiver unit 801, used for the second device to receive the target signal;

The transceiver unit 801 is further configured to receive a first numerical value from the first device, where the first numerical value is a numerical value used by the first device to play the target signal;

a processing unit 802, configured to determine a weight coefficient;

The processing unit 802 is further configured to determine the second numerical value according to the first numerical value and the weight coefficient;

Optionally, the processing unit 802 is specifically configured to determine the weight coefficient according to a first mapping table, where the first mapping table is used to represent the association relationship between the device type of the second device and the weight coefficient.

The processing unit 802 is further configured to play the target signal by using the third value.

Optionally, the processing unit 802 is further configured to update the first mapping table with the third value to obtain a second mapping table, and the weight coefficient of the second device in the second mapping table is different from the weight coefficient of the second device in the first mapping table. same.

In this embodiment, the processing unit 802 may receive the first value sent by the first device. The processing unit 802 can obtain the second numerical value according to the first numerical value and the weighting coefficient, and automatically adjust the volume of the second device after switching to the relative volume of the first device, so as to reduce the user's perception of the difference in audio volume during the switching, and perform a comfortable first step. Switch between two devices to improve user experience. Further, the transceiver unit 801 can also obtain the adjustment value of the user, and the processing unit 802 can update the first mapping table according to the adjustment value (ie, the third value), so that the next calculation of the weight coefficient of the second device is more in line with the user's habits. In addition, the processing unit 802 can also obtain the fourth value according to the method of training the model to be trained, so as to meet the requirements in a specific scenario.

Referring to FIG. 9 , an embodiment of the present application provides a communication device. Specifically, the communication device may be a first device or a second device. For the convenience of description, only the part related to the embodiment of the present application is shown, and the specific technical details are If not disclosed, please refer to the method section of the embodiments of the present application. The communication device can be any terminal device including a mobile phone, a tablet computer, a personal digital assistant (PDA), a point of sales (POS), a vehicle-mounted computer, etc. The communication device is a mobile phone as an example:

FIG. 9 is a block diagram showing a partial structure of a mobile phone related to the communication device provided by the embodiment of the present application. Referring to FIG. 9 , the mobile phone includes: a radio frequency (RF) circuit 910 , a memory 920 , an input unit 930 , a display unit 940 , a sensor 950 , an audio circuit 960 , a wireless fidelity (WiFi) module 970 , and a processor 980 , and power supply 990 and other components. Those skilled in the art can understand that the structure of the mobile phone shown in FIG. 9 does not constitute a limitation on the mobile phone, and may include more or less components than shown, or combine some components, or arrange different components.

Below in conjunction with Figure 9, each component of the mobile phone will be introduced in detail:

The RF circuit 910 can be used to send and receive information or to receive and send signals during a call. Typically, the RF circuit 910 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, RF circuitry 910 may communicate with networks and other devices via wireless communications. The above-mentioned wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile communication (GSM), General Packet Radio Service (General Packet Radio Service, GPRS), Code Division Multiple Access (Code Division) Multiple Access, CDMA), Wideband Code Division Multiple Access (Wideband Code Division Multiple Access, WCDMA), Long Term Evolution (Long Term Evolution, LTE), email, Short Messaging Service (Short Messaging Service, SMS), etc.

The memory 920 can be used to store software programs and modules, and the processor 980 executes various functional applications and data processing of the mobile phone by running the software programs and modules stored in the memory 920 . The memory 920 may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; Data created by the use of the mobile phone (such as audio data, phone book, etc.), etc. Additionally, memory 920 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

Optionally, the memory 920 may store the first mapping table.

The input unit 930 may be used to receive inputted numerical or character information, and generate key signal input related to user setting and function control of the mobile phone. Specifically, the input unit 930 may include a touch panel 931 and other input devices 932 . The touch panel 931, also referred to as a touch screen, can collect touch operations made by the user on or near it (such as the user's finger, stylus, etc., any suitable object or accessory on or near the touch panel 931). operation), and drive the corresponding connection device according to the preset program. Optionally, the touch panel 931 may include two parts, a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the touch controller. To the processor 980, and can receive the command sent by the processor 980 and execute it. In addition, the touch panel 931 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch panel 931, the input unit 930 may also include other input devices 932. Specifically, other input devices 932 may include, but are not limited to, one or more of physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, joysticks, and the like.

The display unit 940 may be used to display information input by the user or information provided to the user and various menus of the mobile phone. The display unit 940 may include a display panel 941. Optionally, the display panel 941 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an organic light-emitting diode (Organic Light-Emitting Diode, OLED), and the like. Further, the touch panel 931 can cover the display panel 941. When the touch panel 931 detects a touch operation on or near it, it transmits it to the processor 980 to determine the type of the touch event, and then the processor 980 determines the type of the touch event according to the touch event. Type provides corresponding visual output on display panel 941 . Although in FIG. 9, the touch panel 931 and the display panel 941 are used as two independent components to realize the input and input functions of the mobile phone, in some embodiments, the touch panel 931 and the display panel 941 can be integrated to form Realize the input and output functions of the mobile phone.

The cell phone may also include at least one sensor 950, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor can include an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 941 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 941 and/or when the mobile phone is moved to the ear. or backlight. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in all directions (usually three axes), and can detect the magnitude and direction of gravity when it is stationary. games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc. Repeat.

The audio circuit 960, the speaker 961, and the microphone 962 can provide an audio interface between the user and the mobile phone. The audio circuit 960 can convert the received audio data into an electrical signal, and transmit it to the speaker 961, and the speaker 961 converts it into a sound signal for output; on the other hand, the microphone 962 converts the collected sound signal into an electrical signal, which is converted by the audio circuit 960 After receiving, it is converted into audio data, and then the audio data is output to the processor 980 for processing, and then sent to, for example, another mobile phone through the RF circuit 910, or the audio data is output to the memory 920 for further processing.

WiFi is a short-distance wireless transmission technology. The mobile phone can help users to send and receive emails, browse web pages, and access streaming media through the WiFi module 970. It provides users with wireless broadband Internet access. Although FIG. 9 shows the WiFi module 970, it can be understood that it is not a necessary component of the mobile phone.

The processor 980 is the control center of the mobile phone, using various interfaces and lines to connect various parts of the entire mobile phone, by running or executing the software programs and/or modules stored in the memory 920, and calling the data stored in the memory 920. Various functions of the mobile phone and processing data, so as to monitor the mobile phone as a whole. Optionally, the processor 980 may include one or more processing units; preferably, the processor 980 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc. , the modem processor mainly deals with wireless communication. It can be understood that, the above-mentioned modulation and demodulation processor may not be integrated into the processor 980.

The mobile phone also includes a power supply 990 (such as a battery) for supplying power to various components. Preferably, the power supply can be logically connected to the processor 980 through a power management system, so as to manage charging, discharging, and power consumption management functions through the power management system.

Although not shown, the mobile phone may also include a camera, a Bluetooth module, and the like, which will not be repeated here.

In this embodiment of the present application, the processor 980 included in the terminal device may perform the functions in the foregoing embodiments shown in FIG. 3 to FIG. 6 , and details are not described herein again.

Please refer to FIG. 10 , which is a possible schematic diagram of the communication device 1000 involved in the above-mentioned embodiment provided for the embodiment of the present application. The communication device may specifically be the first device or the second device in the foregoing embodiment. The communication device 1000 may include, but is not limited to, a processor 1001 , a communication port 1002 , a memory 1003 , and a bus 1004 . In this embodiment of the present application, the processor 1001 is used to control the actions of the communication device 1000 .

Furthermore, the processor 1001 may be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logical blocks, modules and circuits described in connection with this disclosure. The processor may also be a combination that implements computing functions, such as a combination comprising one or more microprocessors, a combination of a digital signal processor and a microprocessor, and the like. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above may refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

Optionally, the memory 1003 may store the first mapping table.

It should be noted that the communication device shown in FIG. 10 can be specifically used to implement the functions of the steps performed by the first device or the second device in the method embodiments corresponding to FIG. 3 to FIG. For the specific implementation manner of the communication device shown in FIG. 10 , reference may be made to the descriptions in the respective method embodiments corresponding to FIG. 3 to FIG. 6 , which will not be repeated here.

Embodiments of the present application further provide a computer-readable storage medium that stores one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the possible implementations of the communication device described in the foregoing embodiments. method, wherein the communication device may specifically be the first device or the second device in the method embodiments corresponding to FIG. 3 to FIG. 6 .

Embodiments of the present application further provide a computer program product that stores one or more computers. When the computer program product is executed by the processor, the processor executes the method for possible implementations of the above communication device, wherein the communication device may specifically It is the first device or the second device in the method embodiments corresponding to FIG. 3 to FIG. 6 .

An embodiment of the present application further provides a chip system, where the chip system includes a processor, which is configured to support the communication device to implement the functions involved in the possible implementation manners of the communication device. In a possible design, the chip system may further include a memory for storing necessary program instructions and data of the communication device. The chip system may be composed of chips, or may include chips and other discrete devices, wherein the communication device may specifically be the first device or the second device in the method embodiments corresponding to FIG. 3 to FIG. 6 .

An embodiment of the present application further provides a network system architecture, where the network system architecture includes the foregoing communication device, and the communication device may specifically be the first device and/or the second device in the method embodiments corresponding to FIG. 3 to FIG. 6 .

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

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, may be located in one place; or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

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 alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, and the computer software products are stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes .

Claims

A data processing method, comprising:

The first device obtains the first numerical value used by the first device to play the target signal and the device type of the second device;

the first device determines the weight coefficient of the second device;

The first device determines, according to the weight coefficient and the first value, a second value used for playing the target signal on the second device;

The first device sends the second value to the second device, so that the second device uses the second value to play the target signal.
The method according to claim 1, wherein the first device determines the weight coefficient of the second device, comprising:

The first device determines the weight coefficient of the second device according to a first mapping table, where the first mapping table is used to represent the association relationship between the device type of the second device and the weight coefficient.
The method according to claim 1 or 2, wherein the method further comprises:

The first device receives a third value sent by the second device, where the third value is a value obtained after the second value is adjusted;

The first device updates the first mapping table according to the third value to obtain a second mapping table, where the weight coefficient of the second device in the second mapping table is the same as that in the first mapping table. The weight coefficients of the second devices are not the same.
The method according to claim 3, wherein after the first device receives the third value sent by the second device, the method further comprises:

The first device acquires a target feature, and the target feature includes at least one of the first numerical value, the playback location of the second device, the system time of the second device, and the third numerical value;

The first device trains the model to be trained according to the target feature to obtain a prediction weight model.
The method according to claim 4, wherein the method further comprises:

The first device obtains a fourth numerical value according to the first numerical value and the prediction weight model;

The first device sends the fourth numerical value to the second device.
The method according to any one of claims 1 to 5, wherein,

The target signal includes an audio signal, the first value includes a volume value used by the audio signal to be played on the first device, and the second value includes the audio signal to be played on the second device the volume value used; or

The target signal includes a video signal, the first value includes a volume value or a brightness value used by the video signal to be played on the first device, and the second value includes the video signal in the second value. The volume or brightness value used for playback on the device; or

The target signal includes an image, the first value includes a brightness value or a scaling ratio of the image used to play the image on the first device, and the second value includes the image in the first device. The brightness value or the scaling of the image used for playback on the device; or

The target signal includes text, the first value includes a playback speed value used by the text to be played on the first device, and the second value includes a value used by the text to be played on the second device the playback speed value.
A data processing method, comprising:

the second device receives the target signal;

The second device receives a second value sent by the first device, where the second value is obtained by the first device according to the first value, the device type of the second device, and the first mapping table. The numerical value is the numerical value used by the first device to play the target signal, and the first mapping table is used to represent the relationship between the device type of the second device and the weighting coefficient, and the weighting coefficient is used according to the first numerical value obtains the second numerical value;

The second device plays the target signal using the second value.
The method according to claim 7, wherein the method further comprises:

The second device obtains a third numerical value, and the third numerical value is a numerical value obtained after the second numerical value is adjusted;

The second device sends the third value to the first device, so that the first device updates the first mapping table to obtain a second mapping table, and the second mapping table in the second mapping table The weight coefficient of the device is different from the weight coefficient of the second device in the first mapping table.
The method according to claim 7 or 8, wherein,

The target signal includes an audio signal, the first value includes a volume value used by the audio signal to be played on the first device, and the second value includes the audio signal to be played on the second device the volume value used; or

The target signal includes a video signal, the first value includes a volume value or a brightness value used by the video signal to be played on the first device, and the second value includes the video signal in the second value. The volume or brightness value used for playback on the device; or

The target signal includes an image, the first value includes a brightness value or a scaling ratio of the image used to play the image on the first device, and the second value includes the image in the first device. The brightness value or the scaling of the image used for playback on the device; or

The target signal includes text, the first value includes a playback speed value used by the text to be played on the first device, and the second value includes a value used by the text to be played on the second device the playback speed value.
A data processing method, comprising:

The first device obtains the first value and the device type of the second device used for playing the target signal in the first device;

the first device determines the weight coefficient of the second device;

The first device sends the first value and the weight coefficient to the second device, so that the second device determines the second value of the second device according to the first value and the weight coefficient numerical value.
The method according to claim 10, wherein the first device determines the weight coefficient of the second device, comprising:

The first device determines the weight coefficient of the second device according to a first mapping table, where the first mapping table is used to represent the association relationship between the device type of the second device and the weight coefficient.
The method according to claim 10 or 11, wherein the method further comprises:

The first device receives a third value sent by the second device, where the third value is a value obtained after the second value is adjusted;

The first device updates the first mapping table according to the third value to obtain a second mapping table, where the weight coefficient of the second device in the second mapping table is the same as that in the first mapping table. The weight coefficients of the second devices are not the same.
The method according to claim 12, wherein after the first device receives the third value sent by the second device, the method further comprises:

The first device acquires a target feature, and the target feature includes at least one of the first numerical value, the playback location of the second device, the system time of the second device, and the third numerical value;

The first device trains the model to be trained according to the target feature to obtain a prediction weight model.
The method of claim 13, wherein the method further comprises:

The first device obtains a fourth numerical value according to the first numerical value and the prediction weight model;

The first device sends the fourth numerical value to the second device.
The method according to any one of claims 10 to 14, wherein,

The target signal includes an audio signal, the first value includes a volume value used by the audio signal to be played on the first device, and the second value includes the audio signal to be played on the second device the volume value used; or

The target signal includes a video signal, the first value includes a volume value or a brightness value used by the video signal to be played on the first device, and the second value includes the video signal in the second value. The volume or brightness value used for playback on the device; or

The target signal includes an image, the first value includes a brightness value or a scaling ratio of the image used to play the image on the first device, and the second value includes the image in the first device. The brightness value or scaling of the image used for playback on the device; or

The target signal includes text, the first value includes a playback speed value used by the text to be played on the first device, and the second value includes a value used by the text to be played on the second device the playback speed value.
A data processing method, comprising:

the second device receives the target signal;

The second device receives the first numerical value and the weight coefficient sent by the first device, where the first numerical value is the numerical value used when the first device plays the target signal;

The second device determines a second value according to the first value and the weighting coefficient;

The second device plays the target signal using the second value.
The method according to claim 16, wherein the second device determines the second value according to the first value and the weighting coefficient, comprising:

The second device calculates the product of the first numerical value and the weighting coefficient to obtain the second numerical value.
The method according to claim 16 or 17, wherein the method further comprises:

The second device obtains a third numerical value, and the third numerical value is a numerical value obtained after the second numerical value is adjusted;

The second device uses the third value to play the target signal;

The second device sends the third value to the first device, so that the first device uses the third value to update the weight coefficient of the second device in the first mapping table.
The method according to claim 18, wherein after the second device acquires the third value, the method further comprises:

The second device acquires a target feature, and the target feature includes at least one of the playback location of the second device, the system time of the second device, and the third value;

The second device trains the model to be trained according to the target feature to obtain a prediction weight model.
The method of claim 19, wherein the method further comprises:

The second device obtains a fourth value according to the first value and the prediction weight model;

The second device plays the target signal using the fourth value.
The method according to any one of claims 16 to 20, wherein,

The target signal includes an audio signal, the first value includes a volume value used by the audio signal to be played on the first device, and the second value includes the audio signal to be played on the second device the volume value used; or

The target signal includes a video signal, the first value includes a volume value or a brightness value used by the video signal to be played on the first device, and the second value includes the video signal in the second value. The volume or brightness value used for playback on the device; or

The target signal includes an image, the first value includes a brightness value or a scaling ratio of the image used to play the image on the first device, and the second value includes the image in the first device. The brightness value or scaling of the image used for playback on the device; or

The target signal includes text, the first value includes a playback speed value used by the text to be played on the first device, and the second value includes a value used by the text to be played on the second device the playback speed value.
A data processing method, comprising:

the second device receives the target signal;

The second device receives a first value from the first device, where the first value is a value used by the first device to play the target signal;

the second device determines a weight coefficient;

The second device determines a second value according to the first value and the weighting coefficient;

The second device plays the target signal using the second value.
The method according to claim 22, wherein the second device determines the weight coefficient, comprising:

The second device determines the weight coefficient according to a first mapping table, where the first mapping table is used to represent the association relationship between the device type of the second device and the weight coefficient.
The method according to claim 22 or 23, wherein the method further comprises:

The second device obtains a third numerical value, and the third numerical value is a numerical value obtained after the second numerical value is adjusted;

The second device plays the target signal using the third value.
The method of claim 24, wherein the method further comprises:

The second device uses the third value to update the first mapping table to obtain a second mapping table, where the weight coefficient of the second device in the second mapping table is the same as the first mapping table in the first mapping table. The weight coefficients of the two devices are different.
The method according to claim 24 or 25, wherein after the second device acquires the third value, the method further comprises:

The second device acquires a target feature, and the target feature includes at least one of the playback location of the second device, the system time of the second device, and the third value;

The second device trains the model to be trained according to the target feature to obtain a prediction weight model.
The method of claim 26, wherein the method further comprises:

The second device obtains a fourth numerical value according to the first numerical value and the prediction weight model;

The second device plays the target signal using the fourth value.
The method according to any one of claims 22 to 27, characterized in that,

The target signal includes an audio signal, the first value includes a volume value used by the audio signal to be played on the first device, and the second value includes the audio signal to be played on the second device the volume value used; or

The target signal includes a video signal, the first value includes a volume value or a brightness value used by the video signal to be played on the first device, and the second value includes the video signal in the second value. The volume or brightness value used for playback on the device; or

The target signal includes an image, the first value includes a brightness value or a scaling ratio of the image used to play the image on the first device, and the second value includes the image in the first device. The brightness value or scaling of the image used for playback on the device; or

The target signal includes text, the first value includes a playback speed value used by the text to be played on the first device, and the second value includes a value used by the text to be played on the second device the playback speed value.
A first device, comprising: a processor coupled to a memory, the memory being used to store a program or an instruction, when the program or instruction is executed by the processor, the The first device performs the method of any one of claims 1 to 6, or causes the first device to perform the method of any one of claims 10 to 15.
A second device, characterized in that it comprises: a processor, the processor is coupled to a memory, the memory is used to store a program or an instruction, and when the program or instruction is executed by the processor, the The second device performs the method of any one of claims 7 to 9, or causes the first device to perform the method of any one of claims 16 to 21, or causes the first device to perform A method as claimed in any one of claims 22 to 28.
A communication system, characterized by comprising: the first device as claimed in claim 29 , and/or the second device as claimed in claim 30 .
A computer-readable medium, characterized in that a computer program or instruction is stored thereon, and when the computer program or instruction is run on a computer, the computer is made to execute the method according to any one of claims 1 to 6 , or perform a method as claimed in any one of claims 7 to 9, or perform a method as claimed in any one of claims 10 to 15, or perform a method as claimed in any one of claims 16 to 21 method, or performing a method as claimed in any one of claims 22 to 28.