CN116450026B

CN116450026B - Method and system for identifying touch operation

Info

Publication number: CN116450026B
Application number: CN202310716688.2A
Authority: CN
Inventors: 付博
Original assignee: Honor Device Co Ltd
Current assignee: Honor Device Co Ltd
Priority date: 2023-06-16
Filing date: 2023-06-16
Publication date: 2023-10-20
Anticipated expiration: 2043-06-16
Also published as: CN116450026A

Abstract

The application provides a method and a system for identifying touch operation, comprising the following steps: the method comprises the steps that a plurality of first terminal devices respectively send first data and preset touch type data to a first server, the first server trains a preset neural network model by adopting the plurality of first data and the plurality of preset touch type data to obtain an updated neural network model, and the updated neural network model is configured to a second terminal device. The pre-configured neural network model is trained through the first data of the users with different ages, sexes, finger thickness degrees and the like and the corresponding preset touch types, and compared with the pre-configured neural network model, the finally obtained updated neural network model learns richer and more various touch data, has higher recognition capability, and is beneficial to improving the success rate of the users for realizing auxiliary functions through touch operation after being configured to the second terminal equipment, so that the user experience is improved.

Description

Method and system for identifying touch operation

Technical Field

The present application relates to the field of terminal technologies, and in particular, to a method and a system for identifying touch operations.

Background

The screen capturing, regional screen capturing or screen recording is an auxiliary function commonly used by a user in the process of using the terminal equipment, and the user can quickly save the content displayed on the screen of the terminal equipment through corresponding touch operation for sharing, collection or teaching display and the like.

At present, the terminal device can realize auxiliary functions such as screen capturing, regional screen capturing or screen recording by responding to the target touch operation (for example, joint touch operation) of the user, and the identification of the target touch operation depends on the identification accuracy of the neural network model in the terminal device to the target touch operation input by the user. In training a neural network model, most of the data used to train the neural network model is collected from in-house developers due to the difficulty of the data collection personnel to recall and the limited number of prototypes available for data collection.

The recognition rate of the neural network model configured to the terminal equipment to the target touch operation is low, so that the success rate of auxiliary functions such as screen capturing, regional screen capturing or screen recording by using the target touch operation by a user is low, and the user experience is seriously influenced.

Disclosure of Invention

The application provides a method and a system for identifying touch operation, which improve the success rate of auxiliary functions such as screen capturing, regional screen capturing or screen recording by a user by using target touch operation by improving the identification success rate of a neural network model configured to terminal equipment, thereby improving the user experience.

In a first aspect, the present application provides a method for identifying a touch operation, where the method is applied to a system including a plurality of first terminal devices, a second terminal device, and a first server, where the plurality of first terminal devices are each installed with an internal release operating system, and the second terminal device is installed with a user release operating system, and the method includes: the plurality of first terminal devices respectively respond to first touch operation of a user, collect first data corresponding to the first touch operation through the internal test version operating system, and send the first data and preset touch type data to the first server, wherein the first data comprises capacitance data and/or acceleration sensor data corresponding to the first touch operation; the first server receives a plurality of first data and a plurality of preset touch type data from the plurality of first terminal devices, trains a preset neural network model by using the plurality of first data and the plurality of preset touch type data to obtain an updated neural network model, and configures the updated neural network model to the second terminal device.

It should be appreciated that the plurality of first terminal devices are installed with an internal metering operating system that includes a first dynamic library that includes code that is operable to enable collection of user touch data and code that is operable to identify a type of touch operation.

It should also be understood that the plurality of first terminal devices respectively belong to a plurality of users, the plurality of users are all users willing to participate in the internal measurement, and the plurality of users are differentiated users, for example, the data of age, sex, finger thickness degree and the like of the plurality of users are different.

In the embodiment of the application, a plurality of first terminal devices respectively send first data and preset touch type data to a first server, the first server trains a preset neural network model by adopting the plurality of first data and the plurality of preset touch type data to obtain an updated neural network model, and the updated neural network model is configured to a second terminal device. The pre-configured neural network model is trained through the first data of the users with different ages, sexes, finger thickness degrees and the like and the corresponding preset touch types, and compared with the pre-configured neural network model, the finally obtained updated neural network model learns richer and more various touch data, has higher recognition capability, then the updated neural network model is configured to the second terminal equipment, the recognition capability of the second terminal equipment to the touch operation of the users is improved, the success rate of the users for realizing auxiliary functions through the touch operation is improved, and the user experience is improved.

With reference to the first aspect, in certain implementation manners of the first aspect, the updated neural network model meets a preset condition, where the preset condition is that a recognition success rate of the updated neural network model is greater than or equal to a first threshold.

Alternatively, the first threshold may be any value greater than 95%, which is not particularly limited by the present application.

With reference to the first aspect, in certain implementation manners of the first aspect, the training the preconfigured neural network model using the plurality of first data and the plurality of preset touch type data to obtain an updated neural network model includes: the first server trains a preset neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a first neural network model, wherein the first neural network model is a neural network model in the training process; the first server inputs standard touch operation sample data into the first neural network model to obtain a first identification result of the first neural network model on the standard touch operation sample data; the first server compares the first identification result with the touch type label of the standard touch operation sample data to obtain the identification success rate of the first neural network model; and if the identification success rate of the first neural network model is greater than or equal to the first threshold value, the first server determines the first neural network model as the updated neural network model.

Optionally, the first neural network model may be a neural network model after the preset number of iterations of the preconfigured neural network model, or may be a neural network model trained for a preset duration.

In one possible implementation manner, the standard touch sample data may be an average value of capacitance values and/or acceleration values generated when a plurality of users with the same gender, the same age range and the same finger thickness perform a touch operation of a preset touch type, and the average value is used to represent gesture standardization degree and strength of the touch operation of the users with the same type, and may be used as an identification reference of the neural network model.

In the embodiment of the application, the identification success rate of the first neural network model is verified by adopting standard touch sample data so as to determine the first neural network model with the identification success rate greater than or equal to a first threshold value as an updated neural network model. By adopting the standard touch sample for verification, misjudgment on the capability of the neural network model due to the fact that data for verification are not representative can be avoided, and training efficiency of the neural network model is effectively improved.

With reference to the first aspect, in certain implementation manners of the first aspect, the training the preconfigured neural network model using the plurality of first data and the plurality of preset touch type data to obtain an updated neural network model includes: the first server trains a preset neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a second neural network model, wherein the second neural network model is a neural network model in the training process; the first server respectively sends the second neural network model to the plurality of first terminal devices; the plurality of first terminal devices receive the second neural network model, respond to second touch operation of a user respectively, acquire second data corresponding to the second touch operation, and acquire a first touch type true value of the second touch operation, wherein the second data comprises capacitance value data and/or acceleration sensor data corresponding to the second touch operation; the plurality of first terminal devices respectively input the second data to the second neural network model, obtain a second identification result of the second neural network model on the second data, and send the second identification result and the first touch type true value to the first server; the first server receives a plurality of second recognition results and a plurality of first touch type reality values, compares the plurality of second recognition results with the plurality of first touch type reality values respectively to obtain a recognition success rate of the second neural network model, and if the recognition success rate of the second neural network model is greater than or equal to a first threshold value, the first server determines the second neural network model as the updated neural network model.

It should be understood that the second neural network model may be the same as or different from the first neural network model, which is not particularly limited in the present application.

In one possible implementation, the first server may send the second neural network model to the plurality of first terminal devices in a system upgrade manner through Over The Air (OTA) technology.

It should be understood that after the plurality of first terminal devices receive the second neural network model, the preconfigured neural network model is replaced by the second neural network model, and the touch operation input by the user is identified through the second neural network model.

In the embodiment of the application, the second neural network model in the training process is sent to the plurality of first terminal devices, and the recognition success rate of the second neural network model is verified through the recognition of the second neural network model to the real touch operation of the plurality of users corresponding to the plurality of first terminal devices, so that the obtained recognition success rate corresponding value is more real and reliable, and the more reliable updated neural network model is facilitated to be obtained.

With reference to the first aspect, in certain implementation manners of the first aspect, the system further includes a second server, training a preconfigured neural network model using the plurality of first data and the plurality of preset touch type data, and obtaining an updated neural network model includes: the first server trains a preset neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a third neural network model, wherein the third neural network model is a neural network model in the training process; the first server sends the third neural network model to the plurality of first terminal devices respectively; the plurality of first terminal devices receive the third neural network model, respond to third touch operation of a user respectively, acquire third data, and acquire a second touch type true value of the third touch operation, wherein the third data comprises capacitance data and/or acceleration sensor data corresponding to the third touch operation; the plurality of first terminal devices respectively input the third data to the third neural network model, obtain a third identification result of the third neural network model on the third data, and send the third identification result and the second touch type real value to the second server; the second server receives a plurality of third recognition results and a plurality of second touch type true values, compares the third recognition results with the second touch type true values respectively to obtain the recognition success rate of the third neural network model, and sends the recognition success rate of the third neural network model to the first server; and if the identification success rate of the third neural network model is greater than or equal to the first threshold value, the first server determines the third neural network model as the updated neural network model.

It should be understood that the third neural network model may be the same as or different from the first neural network model and the second neural network model, which is not particularly limited in the present application.

In the embodiment of the application, the second server is used for comparing the plurality of third recognition results with the plurality of second touch type true values respectively to obtain the recognition success rate of the third neural network model, and then the recognition success rate is transmitted to the first server, so that the operation pressure of the first server can be reduced, the stable operation of the first server is facilitated, and the calculation force guarantee is provided for training the neural network model.

With reference to the first aspect, in certain implementation manners of the first aspect, the configuring the updated neural network model to the second terminal device includes: the first server sends system upgrade data to the second terminal equipment, wherein the system upgrade data comprises the updated neural network model; and the second terminal equipment receives the system upgrading data and configures the updated neural network model.

It should be understood that the second terminal device is provided with a user version operating system, and the system upgrade data sent by the first server to the second terminal device is the system upgrade data corresponding to the user version operating system.

With reference to the first aspect, in certain implementation manners of the first aspect, before the first server sends system upgrade data to the second terminal device, the method includes: the second terminal equipment sends a system upgrade data request to the first server; the first server sending system upgrade data to the second terminal device, including: and the first server sends system upgrade data to the second terminal equipment based on the system upgrade data request.

It should be understood that the first data sent by the plurality of first terminal devices to the first server includes, but is not limited to, capacitance data and/or acceleration sensor data corresponding to the first touch operation, and may further include an identifier of the plurality of first terminal devices, alternatively, the identifier may be a respective mobile device identifier (mobile equipment identifier, MEID), an international mobile device identifier (international mobile equipment identity, IMEI) of the plurality of first terminal devices, or a unique identifier of other such devices, which is not limited in this application.

In one possible implementation manner, the first server may match, from the database, the model of the acceleration sensor and the model of the capacitance sensor to each first terminal device according to the identifiers of the plurality of first terminal devices, use first data of the same model of the acceleration sensor and the same model of the capacitance sensor and preset touch type data corresponding to the first data as a training set, train a preconfigured neural network model, and obtain a plurality of updated neural network models, where the plurality of updated neural network models respectively correspond to the model of the acceleration sensor and the model of the capacitance sensor.

Illustratively, the model number of the acceleration sensor of the first terminal device a is (1) and the model number of the capacitance sensor is (1), the model number of the acceleration sensor of the first terminal device B is (2) and the model number of the capacitance sensor is (1), the model number of the acceleration sensor of the first terminal device C is (1) and the model number of the capacitance sensor is (1), the model number of the acceleration sensor of the first terminal device D is (2) and the model number of the capacitance sensor is (1), then the first data and the preset touch type data corresponding to the first terminal device a and the first terminal device C are included in the same training set, and the first data and the preset touch type data corresponding to the first terminal device B and the first terminal device D are included in another training set. It should be understood that the number of the first terminal devices that are the same as the model number of the acceleration sensor and the model number of the capacitance sensor of the first terminal device A, B, C, D is multiple, so that different training sets may be used to train the preconfigured neural network model to obtain multiple different updated neural network models, and the multiple updated neural network models may be identified according to the model number of the acceleration sensor and the model number of the capacitance sensor.

In one possible implementation, before the first server sends the system upgrade data to the second terminal device, the second terminal device sends a system upgrade data request to the first server, where the system upgrade data request includes an identifier of the second terminal device, and the first server may match, based on the system upgrade data request, a model of the acceleration sensor and a model of the capacitance sensor corresponding to the identifier of the second terminal device from a database, and include in the system upgrade data an updated neural network model that is consistent with the model of the acceleration sensor and the model of the capacitance sensor of the second terminal device, and send the updated neural network model to the corresponding second terminal device.

In the embodiment of the application, the updated neural network model which is consistent with the model of the acceleration sensor and the model of the capacitance sensor of the second terminal equipment is sent to the corresponding second terminal equipment, so that the data error caused by inconsistent model of the acceleration sensor and model of the capacitance sensor when training the pre-configured neural network model is reduced, the reliability of the updated neural network model is improved, the adaptation degree of the updated neural network model sent to the second terminal equipment and the second terminal equipment is improved, the success rate of realizing auxiliary functions by a user through touch operation is improved, and the user experience is improved.

With reference to the first aspect, in some implementation manners of the first aspect, the second terminal device responds to a fourth touch operation of a user, and obtains fourth data through the user version operating system, where the fourth data includes capacitance data and/or acceleration sensor data corresponding to the fourth touch operation; the second terminal device inputs the fourth data to the updated neural network model to obtain a fourth identification result of the updated neural network model on a fourth touch operation; and realizing an auxiliary function according to the touch type corresponding to the fourth identification result.

It should be understood that the fourth touch operation is a touch operation corresponding to the preset touch type.

With reference to the first aspect, in some implementation manners of the first aspect, the touch type corresponding to the fourth identification result includes: finger tip click, finger tip sliding, finger tip tap, finger belly click, finger belly sliding, finger belly tap, side nail click, side nail sliding, double finger tap, double finger scaling, finger joint tap and finger joint sliding.

With reference to the first aspect, in certain implementations of the first aspect, the auxiliary functions include screen capturing, screen recording, and area screen capturing.

In a second aspect, the present application provides a system for identifying touch operations, where the system includes a plurality of first terminal devices, a second terminal device, and a first server, where the plurality of first terminal devices are each installed with an internal version detection operating system, and the second terminal device is installed with a user version operating system; the first terminal device is configured to: responding to a first touch operation of a user, collecting first data corresponding to the first touch operation through the internal test version operating system, and sending the first data and preset touch type data to the first server, wherein the first data comprises capacitance data and/or acceleration sensor data corresponding to the first touch operation; the first server is configured to: receiving a plurality of first data and a plurality of preset touch type data from the plurality of first terminal devices, training a preset neural network model by adopting the plurality of first data and the plurality of preset touch type data to obtain an updated neural network model, and configuring the updated neural network model to the second terminal device.

With reference to the second aspect, in certain implementations of the second aspect, the updated neural network model meets a preset condition, where the preset condition is that a recognition success rate of the updated neural network model is greater than or equal to a first threshold.

With reference to the second aspect, in certain implementations of the second aspect, the first server is configured to: training a pre-configured neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a first neural network model, wherein the first neural network model is a neural network model in the training process; the first server is further configured to: inputting standard touch operation sample data into the first neural network model to obtain a first identification result of the first neural network model on the standard touch operation sample data; the first server is further configured to: comparing the first identification result with the touch type label of the standard touch operation sample data to obtain the identification success rate of the first neural network model; the first server is further configured to: and if the identification success rate of the first neural network model is greater than or equal to the first threshold value, determining the first neural network model as the updated neural network model.

With reference to the second aspect, in certain implementations of the second aspect, the first server is configured to: training a preconfigured neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a second neural network model, wherein the second neural network model is a neural network model in the training process; the first server is further configured to: respectively transmitting the second neural network model to the plurality of first terminal devices; the first terminal device is further configured to: receiving the second neural network model, responding to a second touch operation of a user, obtaining second data corresponding to the second touch operation, and obtaining a first touch type true value of the second touch operation, wherein the second data comprises capacitance data and/or acceleration sensor data corresponding to the second touch operation; the first terminal device is further configured to: inputting the second data into the second neural network model to obtain a second identification result of the second neural network model on the second data, and sending the second identification result and the first touch type true value to the first server; the first server is further configured to: and receiving a plurality of second recognition results and a plurality of first touch type true values, respectively comparing the plurality of second recognition results with the plurality of first touch type true values to obtain the recognition success rate of the second neural network model, and determining the second neural network model as the updated neural network model if the recognition success rate of the second neural network model is greater than or equal to a first threshold value.

With reference to the second aspect, in certain implementations of the second aspect, the system further includes a second server; the first server is configured to: training a preconfigured neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a third neural network model, wherein the third neural network model is a neural network model in the training process; the first server is further configured to: respectively transmitting the third neural network model to the plurality of first terminal devices; the first terminal device is further configured to: receiving the third neural network model, responding to third touch operation of a user, obtaining third data, and obtaining a second touch type true value of the third touch operation, wherein the third data comprises capacitance value data and/or acceleration sensor data corresponding to the third touch operation; the first terminal device is further configured to: inputting the third data into the third neural network model, obtaining a third identification result of the third neural network model on the third data, and sending the third identification result and the second touch type true value to the second server; the second server is used for: receiving a plurality of third recognition results and a plurality of second touch type true values, comparing the third recognition results with the second touch type true values respectively to obtain the recognition success rate of the third neural network model, and sending the recognition success rate of the third neural network model to the first server; the first server is further configured to: and if the identification success rate of the third neural network model is greater than or equal to the first threshold value, determining the third neural network model as the updated neural network model.

With reference to the second aspect, in certain implementations of the second aspect, the first server is further configured to: transmitting system upgrade data to the second terminal equipment, wherein the system upgrade data comprises the updated neural network model; the second terminal device is configured to: and receiving the system upgrade data and configuring the updated neural network model.

With reference to the second aspect, in certain implementations of the second aspect, the second terminal device is further configured to: sending a system upgrade data request to the first server; the first server is further configured to: and sending system upgrade data to the second terminal equipment based on the system upgrade data request.

With reference to the second aspect, in certain implementations of the second aspect, the second terminal device is further configured to: responding to a fourth touch operation of a user, and acquiring fourth data through the user version operating system, wherein the fourth data comprises capacitance data and/or acceleration sensor data corresponding to the fourth touch operation; the second terminal device is further configured to: inputting the fourth data into the updated neural network model to obtain a fourth identification result of the updated neural network model on a fourth touch operation; and realizing an auxiliary function according to the touch type corresponding to the fourth identification result.

With reference to the second aspect, in some implementations of the second aspect, the touch type corresponding to the fourth recognition result includes: finger tip click, finger tip sliding, finger tip tap, finger belly click, finger belly sliding, finger belly tap, side nail click, side nail sliding, double finger tap, double finger scaling, finger joint tap and finger joint sliding.

With reference to the second aspect, in certain implementations of the second aspect, the auxiliary functions include screen capturing, screen recording, and area screen capturing.

In a third aspect, an embodiment of the present application provides an apparatus for identifying a touch operation, where the apparatus includes a transceiver module and a processing module, where the processing module is configured to: responding to a first touch operation of a user, and collecting first data corresponding to the first touch operation through an internal plate detection operating system, wherein the first data comprises capacitance data and/or acceleration sensor data corresponding to the first touch operation; the transceiver module is used for: and sending the first data and the preset touch type data to a first server.

Optionally, the processing module is further configured to: responding to a second touch operation of a user, acquiring second data corresponding to the second touch operation, and acquiring a first touch type true value of the second touch operation, wherein the second data comprises capacitance value data and/or acceleration sensor data corresponding to the second touch operation, and the second data is used for inputting the second data into a second neural network model to obtain a second recognition result of the second neural network model on the second data; the transceiver module is further configured to: and sending the second identification result and the first touch type true value to the first server.

Optionally, the transceiver module is further configured to: receiving a third neural network model; the processing module is further configured to respond to a third touch operation of a user, obtain third data, and obtain a second touch type real value of the third touch operation, where the third data includes capacitance value data and/or acceleration sensor data corresponding to the third touch operation, and input the third data to the third neural network model to obtain a third recognition result of the third neural network model on the third data; the transceiver module is further configured to: and sending the third identification result and the second touch type true value to the second server.

In a fourth aspect, an embodiment of the present application further provides an apparatus for identifying a touch operation, including a processor and a memory, where the memory is configured to store code instructions, and the processor is configured to execute the code instructions to perform steps performed by a first terminal device in a method described in any one of possible implementations of the first aspect.

In a fifth aspect, the present application provides a computer program product comprising: a computer program (which may also be referred to as code, or instructions) which, when executed, causes a computer to perform the steps performed by a first terminal device in a method described by any one of the possible implementations of the first aspect.

In a sixth aspect, the present application provides a computer readable storage medium storing a computer program (which may also be referred to as code, or instructions) which, when run on a computer, causes the computer to perform the steps performed by a first terminal device in a method described in any one of the possible implementations of the first aspect.

Drawings

FIG. 1 is a schematic diagram of a communication system suitable for use in embodiments of the present application;

fig. 2 is a schematic structural diagram of a terminal device according to an embodiment of the present application;

fig. 3 is a software structure block diagram of a terminal device according to an embodiment of the present application;

FIG. 4 is a schematic flow chart of a method for recognizing touch operation according to an embodiment of the present application;

fig. 5 is a schematic diagram of a touch operation data collection flow provided in an embodiment of the present application;

fig. 6 is a schematic diagram of another touch operation data collection flow provided in an embodiment of the present application;

fig. 7 is a schematic diagram of another touch operation data collection flow provided in an embodiment of the present application;

FIG. 8 is a schematic diagram of an operation interface according to an embodiment of the present application;

FIG. 9 is a schematic block diagram of an apparatus for recognizing touch operation according to an embodiment of the present application;

Fig. 10 is a schematic block diagram of another apparatus for recognizing a touch operation according to an embodiment of the present application.

Detailed Description

The technical scheme of the application will be described below with reference to the accompanying drawings.

In order to clearly describe the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first", "second", etc. are used to distinguish the same item or similar items having substantially the same function and effect. It will be appreciated by those of skill in the art that the words "first," "second," and the like do not limit the amount and order of execution, and that the words "first," "second," and the like do not necessarily differ.

It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.

In the process of using the terminal equipment, the user can quickly save the content displayed on the screen of the terminal equipment through auxiliary functions such as screen capturing, regional screen capturing or screen recording and the like, so as to share, collect or teach and show activities and the like.

At present, the terminal device can respond to the pressing operation of a user on a combined key (such as a start (Home) key, a shutdown key, a volume key, a shutdown key and the like) to realize auxiliary functions such as screen capturing, regional screen capturing or screen recording, but because the mode of realizing the auxiliary functions through the combined key generally requires the user to use two hands to perform the key, the operation is not fast, and error touch is easy to occur in the operation process, and the user experience is not good, so that the terminal device can respond to the target touch operation (such as touch operation of double-finger joint knocking) of the user on any display interface at present, and can realize the auxiliary functions such as screen capturing, regional screen capturing or screen recording more quickly.

The recognition of the target touch operation by the terminal equipment depends on the recognition accuracy of the target touch operation input by the user by the neural network model in the terminal equipment. When the neural network model is trained, because the data collection personnel are difficult to collect, the number of prototypes available for data collection is limited, most of data used for training the neural network model is collected from internal research personnel, and the collected sample data is not rich enough, so that the recognition rate of the neural network model configured for terminal equipment at present to target touch operation is low, the success rate of auxiliary functions such as screen capturing, regional screen capturing or screen recording by using the target touch operation by a user is low, and the user experience is seriously influenced.

In view of this, the present application provides a method for identifying a touch operation, which improves the success rate of identifying a neural network model configured to a terminal device by enriching sample data of the neural network model, so that the success rate of performing auxiliary functions such as screen capturing, regional screen capturing or screen recording by a user using a target touch operation is improved, and further user experience is improved.

Fig. 1 is a communication system 100 suitable for use in an embodiment of the present application, the communication system 100 comprising a plurality of first terminal devices, such as the first terminal device 101, the first terminal device 102, the first terminal device 103 shown in fig. 1, the communication system 100 further comprising at least one second terminal device, such as the second terminal device 104 shown in fig. 1, the communication system 100 further comprising at least one first server, such as the first server 105 shown in fig. 1, the first server 105 being capable of communicating with the first terminal device 101, the first terminal device 102, the first terminal device 103, and the second terminal device 104.

Optionally, the first terminal device and the second terminal device may be a mobile phone, a tablet computer, a smart bracelet, or the like, which is not limited in the embodiment of the present application.

It should be appreciated that the communication system 100 described above may also include a greater number of first terminal devices, as the application is not limited in this regard.

Fig. 2 is a schematic structural diagram of a terminal device 200 according to an embodiment of the present application. The structure shown in fig. 2 may be applied to either the first terminal device or the second terminal device, and the present application is not limited thereto.

The terminal device 200 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (universal serial bus, USB) interface 130, a charge management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, keys 190, a motor 191, an indicator 192, a camera 193, a display 194, and a subscriber identity module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyro sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, and the like.

It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the terminal device 200. In other embodiments of the application, terminal device 200 may include more or less components than illustrated, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units, such as: the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and/or a neural network processor (neural-network processing unit, NPU), etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors.

The controller can generate operation control signals according to the instruction operation codes and the time sequence signals to finish the control of instruction fetching and instruction execution.

A memory may also be provided in the processor 110 for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory may hold instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to reuse the instruction or data, it can be called directly from the memory. Repeated accesses are avoided and the latency of the processor 110 is reduced, thereby improving the efficiency of the system.

In some embodiments, the processor 110 may include one or more interfaces. The interfaces may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous receiver transmitter (universal asynchronous receiver/transmitter, UART) interface, a mobile industry processor interface (mobile industry processor interface, MIPI), a general-purpose input/output (GPIO) interface, a subscriber identity module (subscriber identity module, SIM) interface, and/or a universal serial bus (universal serial bus, USB) interface, among others.

The I2C interface is a bi-directional synchronous serial bus comprising a serial data line (SDA) and a serial clock line (derail clock line, SCL). In some embodiments, the processor 110 may contain multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, charger, flash, camera 193, etc., respectively, through different I2C bus interfaces. For example: the processor 110 may be coupled to the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through an I2C bus interface to implement a touch function of the terminal device 200.

The I2S interface may be used for audio communication. In some embodiments, the processor 110 may contain multiple sets of I2S buses. The processor 110 may be coupled to the audio module 170 via an I2S bus to enable communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may transmit an audio signal to the wireless communication module 160 through the I2S interface, to implement a function of answering a call through the bluetooth headset.

PCM interfaces may also be used for audio communication to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 may also transmit audio signals to the wireless communication module 160 through the PCM interface to implement a function of answering a call through the bluetooth headset. Both the I2S interface and the PCM interface may be used for audio communication.

The UART interface is a universal serial data bus for asynchronous communications. The bus may be a bi-directional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is typically used to connect the processor 110 with the wireless communication module 160. For example: the processor 110 communicates with a bluetooth module in the wireless communication module 160 through a UART interface to implement a bluetooth function. In some embodiments, the audio module 170 may transmit an audio signal to the wireless communication module 160 through a UART interface, to implement a function of playing music through a bluetooth headset.

The MIPI interface may be used to connect the processor 110 to peripheral devices such as a display 194, a camera 193, and the like. The MIPI interfaces include camera serial interfaces (camera serial interface, CSI), display serial interfaces (display serial interface, DSI), and the like. In some embodiments, processor 110 and camera 193 communicate through a CSI interface to implement the photographing function of terminal device 200. The processor 110 and the display 194 communicate via a DSI interface to implement the display function of the terminal device 200.

The GPIO interface may be configured by software. The GPIO interface may be configured as a control signal or as a data signal. In some embodiments, a GPIO interface may be used to connect the processor 110 with the camera 193, the display 194, the wireless communication module 160, the audio module 170, the sensor module 180, and the like. The GPIO interface may also be configured as an I2C interface, an I2S interface, a UART interface, an MIPI interface, etc.

The USB interface 130 is an interface conforming to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface 130 may be used to connect a charger to charge the terminal device 200, or may be used to transfer data between the terminal device 200 and a peripheral device. And can also be used for connecting with a headset, and playing audio through the headset. The interface may also be used to connect other terminal devices, such as AR devices, etc.

It should be understood that the interfacing relationship between the modules illustrated in the embodiment of the present application is only illustrative, and does not constitute a structural limitation of the terminal device 200. In other embodiments of the present application, the terminal device 200 may also use different interfacing manners, or a combination of multiple interfacing manners in the foregoing embodiments.

The charge management module 140 is configured to receive a charge input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charge management module 140 may receive a charging input of a wired charger through the USB interface 130. In some wireless charging embodiments, the charge management module 140 may receive wireless charging input through a wireless charging coil of the terminal device 200. The charging management module 140 may also supply power to the terminal device through the power management module 141 while charging the battery 142.

The power management module 141 is used for connecting the battery 142, and the charge management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140 to power the processor 110, the internal memory 121, the display 194, the camera 193, the wireless communication module 160, and the like. The power management module 141 may also be configured to monitor battery capacity, battery cycle number, battery health (leakage, impedance) and other parameters. In other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charge management module 140 may be disposed in the same device.

The wireless communication function of the terminal device 200 can be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, a modem processor, a baseband processor, and the like.

The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the terminal device 200 may be used to cover a single or multiple communication bands. Different antennas may also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed into a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 may provide a solution including 2G/3G/4G/5G wireless communication applied to the terminal device 200. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA), etc. The mobile communication module 150 may receive electromagnetic waves from the antenna 1, perform processes such as filtering, amplifying, and the like on the received electromagnetic waves, and transmit the processed electromagnetic waves to the modem processor for demodulation. The mobile communication module 150 can amplify the signal modulated by the modem processor, and convert the signal into electromagnetic waves through the antenna 1 to radiate. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be provided in the same device as at least some of the modules of the processor 110.

The modem processor may include a modulator and a demodulator. The modulator is used for modulating the low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used for demodulating the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low frequency baseband signal to the baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and then transferred to the application processor. The application processor outputs sound signals through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays images or video through the display screen 194. In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be provided in the same device as the mobile communication module 150 or other functional module, independent of the processor 110.

The wireless communication module 160 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (wireless fidelity, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field wireless communication technology (near field communication, NFC), infrared technology (IR), etc., applied to the terminal device 200. The wireless communication module 160 may be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the electromagnetic wave signals, filters the electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be transmitted from the processor 110, frequency modulate it, amplify it, and convert it to electromagnetic waves for radiation via the antenna 2.

In some embodiments, antenna 1 of terminal device 200 is coupled to mobile communication module 150 and antenna 2 is coupled to wireless communication module 160 such that terminal device 200 may communicate with a network and other devices via wireless communication techniques. The wireless communication techniques may include the Global System for Mobile communications (global system for mobile communications, 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), time division code division multiple access (time-division code division multiple access, TD-SCDMA), long term evolution (long term evolution, LTE), BT, GNSS, WLAN, NFC, FM, and/or IR techniques, among others. The GNSS may include a global satellite positioning system (global positioning system, GPS), a global navigation satellite system (global navigation satellite system, GLONASS), a beidou satellite navigation system (beidou navigation satellite system, BDS), a quasi zenith satellite system (quasi-zenith satellite system, QZSS) and/or a satellite based augmentation system (satellite based augmentation systems, SBAS).

The terminal device 200 implements display functions through a GPU, a display screen 194, an application processor, and the like. The GPU is a microprocessor for image processing, and is connected to the display 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 110 may include one or more GPUs that execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos, and the like. The display 194 includes a display panel. The display panel may employ a liquid crystal display (liquid crystal display, LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED) or an active-matrix organic light-emitting diode (matrix organic light emitting diode), a flexible light-emitting diode (flex), a mini, a Micro led, a Micro-OLED, a quantum dot light-emitting diode (quantum dot light emitting diodes, QLED), or the like. In some embodiments, the terminal device 200 may include 1 or N display screens 194, N being a positive integer greater than 1.

The terminal apparatus 200 can realize a photographing function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.

The ISP is used to process data fed back by the camera 193. For example, when photographing, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electric signal, and the camera photosensitive element transmits the electric signal to the ISP for processing and is converted into an image visible to naked eyes. ISP can also optimize the noise, brightness and skin color of the image. The ISP can also optimize parameters such as exposure, color temperature and the like of a shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects the optical image onto the photosensitive element. The photosensitive element may be a charge coupled device (charge coupled device, CCD) or a Complementary Metal Oxide Semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, which is then transferred to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV, or the like format. In some embodiments, the terminal device 200 may include 1 or N cameras 193, N being a positive integer greater than 1.

The digital signal processor is used for processing digital signals, and can process other digital signals besides digital image signals. For example, when the terminal device 200 selects a frequency bin, the digital signal processor is used to fourier transform the frequency bin energy, or the like.

Video codecs are used to compress or decompress digital video. The terminal device 200 may support one or more video codecs. In this way, the terminal device 200 can play or record video in various encoding formats, for example: dynamic picture experts group (moving picture experts group, MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

The NPU is a neural-network (NN) computing processor, and can rapidly process input information by referencing a biological neural network structure, for example, referencing a transmission mode between human brain neurons, and can also continuously perform self-learning. Applications such as intelligent cognition of the terminal device 200 can be implemented through the NPU, for example: image recognition, face recognition, speech recognition, text understanding, etc.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to realize expansion of the memory capability of the terminal device 200. The external memory card communicates with the processor 110 through an external memory interface 120 to implement data storage functions. For example, files such as music, video, etc. are stored in an external memory card.

The internal memory 121 may be used to store computer executable program code including instructions. The internal memory 121 may include a storage program area and a storage data area. The storage program area may store an application program (such as a sound playing function, an image playing function, etc.) required for at least one function of the operating system, etc. The storage data area may store data (such as audio data, phonebook, etc.) created during use of the terminal device 200, and the like. In addition, the internal memory 121 may include a high-speed random access memory, and may further include a nonvolatile memory such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (universal flash storage, UFS), and the like. The processor 110 performs various functional applications of the terminal device 200 and data processing by executing instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.

The terminal device 200 may implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor, etc. Such as music playing, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal output and also to convert an analog audio input into a digital audio signal. The audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be disposed in the processor 110, or a portion of the functional modules of the audio module 170 may be disposed in the processor 110.

The speaker 170A, also referred to as a "horn," is used to convert audio electrical signals into sound signals. The terminal device 200 can listen to music or to handsfree talk through the speaker 170A.

A receiver 170B, also referred to as a "earpiece", is used to convert the audio electrical signal into a sound signal. When the terminal device 200 receives a telephone call or voice information, it is possible to receive voice by bringing the receiver 170B close to the human ear.

Microphone 170C, also referred to as a "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or transmitting voice information, the user can sound near the microphone 170C through the mouth, inputting a sound signal to the microphone 170C. The terminal device 200 may be provided with at least one microphone 170C. In other embodiments, the terminal device 200 may be provided with two microphones 170C, and may implement a noise reduction function in addition to collecting sound signals. In other embodiments, the terminal device 200 may further be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify the source of sound, implement directional recording functions, etc.

The earphone interface 170D is used to connect a wired earphone. The earphone interface 170D may be a USB interface 130 or a 3.5mm open mobile terminal platform (open mobile terminal platform, OMTP) standard interface, a american cellular telecommunications industry association (cellular telecommunications industry association of the USA, CTIA) standard interface.

The pressure sensor 180A is used to sense a pressure signal, and may convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be disposed on the display screen 194. The pressure sensor 180A is of various types, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a capacitive pressure sensor comprising at least two parallel plates with conductive material. The capacitance between the electrodes changes when a force is applied to the pressure sensor 180A. The terminal device 200 determines the intensity of the pressure according to the change of the capacitance. When a touch operation is applied to the display 194, the terminal device 200 detects the intensity of the touch operation according to the pressure sensor 180A. The terminal device 200 may also calculate the position of the touch from the detection signal of the pressure sensor 180A. In some embodiments, touch operations that act on the same touch location, but at different touch operation strengths, may correspond to different operation instructions. For example: and executing an instruction for checking the short message when the touch operation with the touch operation intensity smaller than the first pressure threshold acts on the short message application icon. And executing an instruction for newly creating the short message when the touch operation with the touch operation intensity being greater than or equal to the first pressure threshold acts on the short message application icon.

The gyro sensor 180B may be used to determine a motion gesture of the terminal device 200. In some embodiments, the angular velocity of the terminal device 200 about three axes (i.e., x, y, and z axes) may be determined by the gyro sensor 180B. The gyro sensor 180B may be used for photographing anti-shake. Illustratively, when the shutter is pressed, the gyro sensor 180B detects the angle of the shake of the terminal device 200, calculates the distance to be compensated by the lens module according to the angle, and allows the lens to counteract the shake of the terminal device 200 by the reverse movement, thereby realizing anti-shake. The gyro sensor 180B may also be used for navigating, somatosensory game scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, the terminal device 200 calculates altitude from barometric pressure values measured by the barometric pressure sensor 180C, aiding in positioning and navigation.

The magnetic sensor 180D includes a hall sensor. The terminal device 200 can detect the opening and closing of the flip cover using the magnetic sensor 180D. In some embodiments, when the terminal device 200 is a folder type machine, the terminal device 200 may detect opening and closing of the folder according to the magnetic sensor 180D. And then according to the detected opening and closing state of the leather sheath or the opening and closing state of the flip, the characteristics of automatic unlocking of the flip and the like are set.

The acceleration sensor 180E can detect the magnitude of acceleration of the terminal device 200 in various directions (typically three axes). The magnitude and direction of gravity can be detected when the terminal device 200 is stationary. The method can also be used for identifying the gesture of the terminal equipment, and is applied to the applications such as horizontal and vertical screen switching, pedometers and the like. In the embodiment of the application, the acceleration sensor can be used for collecting the magnitude of the mechanical force applied to the screen when the user performs the touch operation, so as to measure the acting force of different users or different touch operations as the screen.

A distance sensor 180F for measuring a distance. The terminal device 200 may measure the distance by infrared or laser. In some embodiments, the terminal device 200 may range using the distance sensor 180F to achieve fast focusing.

The proximity light sensor 180G may include, for example, a Light Emitting Diode (LED) and a light detector, such as a photodiode. The light emitting diode may be an infrared light emitting diode. The terminal device 200 emits infrared light outward through the light emitting diode. The terminal device 200 detects infrared reflected light from a nearby object using a photodiode. When sufficient reflected light is detected, it can be determined that there is an object near the terminal device 200. When insufficient reflected light is detected, the terminal device 200 may determine that there is no object in the vicinity of the terminal device 200. The terminal device 200 can detect that the user holds the terminal device 200 close to the ear to talk by using the proximity light sensor 180G, so as to automatically extinguish the screen to achieve the purpose of saving electricity. The proximity light sensor 180G may also be used in holster mode, pocket mode to automatically unlock and lock the screen.

The ambient light sensor 180L is used to sense ambient light level. The terminal device 200 may adaptively adjust the brightness of the display 194 based on the perceived ambient light level. The ambient light sensor 180L may also be used to automatically adjust white balance when taking a photograph. The ambient light sensor 180L may also cooperate with the proximity light sensor 180G to detect whether the terminal device 200 is in a pocket to prevent false touches.

The fingerprint sensor 180H is used to collect a fingerprint. The terminal device 200 can utilize the collected fingerprint characteristics to realize fingerprint unlocking, access an application lock, fingerprint photographing, fingerprint incoming call answering and the like.

The temperature sensor 180J is for detecting temperature. In some embodiments, the terminal device 200 performs a temperature processing strategy using the temperature detected by the temperature sensor 180J. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the terminal device 200 performs a reduction in the performance of a processor located near the temperature sensor 180J in order to reduce power consumption to implement thermal protection. In other embodiments, when the temperature is below another threshold, the terminal device 200 heats the battery 142 to avoid the low temperature causing the terminal device 200 to shut down abnormally. In other embodiments, when the temperature is below a further threshold, the terminal device 200 performs boosting of the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperatures.

The touch sensor 180K, also referred to as a "touch device". The touch sensor 180K may be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is for detecting a touch operation acting thereon or thereabout. The touch sensor may communicate the detected touch operation to the application processor to determine the touch event type. Visual output related to touch operations may be provided through the display 194. In other embodiments, the touch sensor 180K may also be disposed on the surface of the terminal device 200 at a different location than the display 194.

The bone conduction sensor 180M may acquire a vibration signal. In some embodiments, bone conduction sensor 180M may acquire a vibration signal of a human vocal tract vibrating bone pieces. The bone conduction sensor 180M may also contact the pulse of the human body to receive the blood pressure pulsation signal. In some embodiments, bone conduction sensor 180M may also be provided in a headset, in combination with an osteoinductive headset. The audio module 170 may analyze the voice signal based on the vibration signal of the sound portion vibration bone block obtained by the bone conduction sensor 180M, so as to implement a voice function. The application processor may analyze the heart rate information based on the blood pressure beat signal acquired by the bone conduction sensor 180M, so as to implement a heart rate detection function.

The capacitance sensor 108N may acquire a capacitance value of the screen of the terminal device 200. In some embodiments, when a user performs a touch operation on a screen of the terminal device, a capacitance value of each capacitive node included in the screen may change, and the capacitive sensor may obtain the capacitance value of the screen to indicate the capacitance change when the screen is touched.

The keys 190 include a power-on key, a volume key, etc. The keys 190 may be mechanical keys. Or may be a touch key. The terminal device 200 may receive key inputs, generating key signal inputs related to user settings and function controls of the terminal device 200.

The motor 191 may generate a vibration cue. The motor 191 may be used for incoming call vibration alerting as well as for touch vibration feedback. For example, touch operations acting on different applications (e.g., photographing, audio playing, etc.) may correspond to different vibration feedback effects. The motor 191 may also correspond to different vibration feedback effects by touching different areas of the display screen 194. Different application scenarios (such as time reminding, receiving information, alarm clock, game, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect may also support customization.

The indicator 192 may be an indicator light, may be used to indicate a state of charge, a change in charge, a message indicating a missed call, a notification, etc.

The SIM card interface 195 is used to connect a SIM card. The SIM card may be contacted and separated from the terminal apparatus 200 by being inserted into the SIM card interface 195 or by being withdrawn from the SIM card interface 195. The terminal device 200 may support 1 or N SIM card interfaces, N being a positive integer greater than 1. The SIM card interface 195 may support Nano SIM cards, micro SIM cards, and the like. The same SIM card interface 195 may be used to insert multiple cards simultaneously. The types of the plurality of cards may be the same or different. The SIM card interface 195 may also be compatible with different types of SIM cards. The SIM card interface 195 may also be compatible with external memory cards. The terminal device 200 interacts with the network through the SIM card to realize functions such as communication and data communication. In some embodiments, the terminal device 200 employs esims, namely: an embedded SIM card. The eSIM card can be embedded in the terminal device 200 and cannot be separated from the terminal device 200.

The software system of the terminal device 200 shown in fig. 2 may use a layered architecture, an event driven architecture, a micro-core architecture, a micro-service architecture, or a cloud architecture. The software structure of the terminal device 200 is exemplified below by a layered Android system.

Fig. 3 is a software architecture block diagram of a terminal device 200 according to an embodiment of the present application.

The layered architecture divides the software into several layers, each with distinct roles and branches. The layers communicate with each other through a software interface. In some embodiments, the Android system is divided into four layers, namely an application layer, an application program framework layer, a system runtime layer and a kernel layer from top to bottom.

The application layer may include a series of application packages. As shown in fig. 3, the application package may include software updates, cameras, gallery, calendar, talk, map, navigation, WLAN, bluetooth, music, video, short message, etc. applications.

In the embodiment of the application, the application layer of the first terminal device further comprises a data collection application program, which is used for guiding the user to make a first touch operation corresponding to the preset touch type and storing the corresponding first data when the user opens the program.

The application framework layer provides an application programming interface (application programming interface, API) and programming framework for application programs of the application layer. The application framework layer includes a number of predefined functions.

As shown in fig. 3, the application framework layer may include a touch operation manager, a window manager, a content provider, a view system, a phone manager, a resource manager, a notification manager, and the like.

The touch operation manager is used for responding to the touch operation of the user to realize corresponding auxiliary functions, for example, the touch operation can be tapped by the double-finger joints of the user to realize screen capturing and the like.

The window manager is used for managing window programs. The window manager can acquire the size of the display screen, judge whether a status bar exists, lock the screen, intercept the screen and the like.

The content provider is used to store and retrieve data and make such data accessible to applications. The data may include video, images, audio, calls made and received, browsing history and bookmarks, phonebooks, etc.

The view system includes visual controls, such as controls to display text, controls to display pictures, and the like. The view system may be used to build applications. The display interface may be composed of one or more views. For example, a display interface including a text message notification icon may include a view displaying text and a view displaying a picture.

The telephony manager is used to provide the communication functions of the terminal device 200. Such as the management of call status (including on, hung-up, etc.).

The resource manager provides various resources for the application program, such as localization strings, icons, pictures, layout files, video files, and the like.

The notification manager allows the application to display notification information in a status bar, can be used to communicate notification type messages, can automatically disappear after a short dwell, and does not require user interaction. Such as notification manager is used to inform that the download is complete, message alerts, etc. The notification manager may also be a notification in the form of a chart or scroll bar text that appears on the system top status bar, such as a notification of a background running application, or a notification that appears on the screen in the form of a dialog window. For example, a text message is prompted in a status bar, a prompt tone is emitted, the terminal equipment vibrates, and an indicator light blinks.

The system operation library layer is divided into two parts, namely a C/C++ program library and an Android operation time library.

The Android runtime library comprises a core library and a virtual machine. The Android runtime library is responsible for scheduling and management of the Android system.

The core library consists of two parts: one part is a function which needs to be called by java language, and the other part is a core library of android.

The application layer and the application framework layer run in virtual machines. The virtual machine executes java files of the application layer and the application framework layer as binary files. The virtual machine is used for executing the functions of object life cycle management, stack management, thread management, security and exception management, garbage collection and the like.

The C/C++ library may include a plurality of functional modules. For example: surface manager (surface manager), media library (media library), three-dimensional graphics processing library (e.g., openGL ES), two-dimensional graphics engine (e.g., SGL), dynamic library (so library), etc.

The surface manager is used to manage the display subsystem and provides a fusion of 2D and 3D layers for multiple applications.

Media libraries support a variety of commonly used audio, video format playback and recording, still image files, and the like. The media library may support a variety of audio and video encoding formats, such as MPEG4, h.264, MP3, AAC, AMR, JPG, PNG, etc.

The three-dimensional graphic processing library is used for realizing three-dimensional graphic drawing, image rendering, synthesis, layer processing and the like.

The two-dimensional graphics engine is a drawing engine for 2D drawing.

The dynamic library is a code that a program is only de-linked when running, and multiple programs can share the code of the dynamic library.

In the embodiment of the application, the first dynamic library configured by the first terminal device can be used for collecting the first data when the user opens the data collection application program, and is used for identifying the touch operation of the user and realizing the corresponding auxiliary function based on the identified touch type when the user does not open the application program.

In the embodiment of the application, the second dynamic library configured by the second terminal device can be used for responding to the touch operation of the user on any display interface, and realizing the corresponding auxiliary function when the touch operation is identified to belong to a preset touch type.

The kernel layer is a layer between hardware and software. The inner core layer at least comprises display drive, camera drive, audio drive, acceleration sensor drive and capacitance sensor drive.

The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be implemented independently or combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

Fig. 4 is a schematic flowchart of a method 400 for identifying touch operations according to an embodiment of the present application, where the method 400 may be applied to the communication system 100 shown in fig. 1, a hardware structure of a first terminal device and a second terminal device may be shown in fig. 2, a software structure may be shown in fig. 3, the first terminal device is installed with an internal test version operating system, and the second terminal device is installed with a user version operating system, but embodiments of the present application are not limited thereto. Referring to fig. 4, the method 400 may include the steps of:

S401, respectively responding to first touch operation of a user by a plurality of first terminal devices, and collecting first data corresponding to the first touch operation through an internal plate detection operating system, wherein the first data comprises capacitance data and/or acceleration sensor data corresponding to the first touch operation.

S402, the plurality of first terminal devices respectively send the first data and the preset touch type data to the first server. Correspondingly, the first server receives a plurality of first data and a plurality of preset touch type data from a plurality of first terminal devices.

S403, training the pre-configured neural network model by the first server through a plurality of first data and a plurality of preset touch type data to obtain an updated neural network model.

S404, the first server configures the updated neural network model to the second terminal equipment. Correspondingly, the second terminal device configures the updated neural network model.

It should be appreciated that the plurality of first terminal devices are installed with an internal metering operating system that includes a first dynamic library that includes code operable to enable collection of user touch operation data and code operable to identify a type of touch operation.

Alternatively, the preconfigured neural network model may refer to a joint model, or any other model corresponding to a touch operation, which is not limited by the present application.

In one possible implementation manner, the first terminal device installs a data collection application, when a user opens the data collection application, the first terminal device detects that the data collection application is in an operating state, and then the first dynamic library starts a flow of touch operation data collection. For example, taking the first terminal device as a mobile phone, the data collection flow on the first terminal device may be as shown in fig. 5, where the user clicks and opens the data collection application 501 shown in the a diagram in fig. 5, and displays a touch operation type selection interface shown in the b diagram in fig. 5, where the user may select a touch operation type to be input at the interface, for example, after selecting "finger joint touch" in the b diagram in fig. 5, the first terminal device displays a guide interface shown in the c diagram in fig. 5, and the user may perform a corresponding first touch operation (finger joint touch operation) according to the preset touch type and the designated position displayed by the guide interface.

In one possible implementation, the designated positions shown in the c-chart in fig. 5 flash sequentially, so as to instruct the user to perform corresponding touch operations sequentially using the preset touch types (for example, may be double-finger joint tapping) according to the sequence of flashing of the designated positions.

In a possible implementation manner, the guiding interface of the c diagram in fig. 5 may further include a strength prompt (such as a tap, etc.) of the double-knuckle tap, a progress prompt of data collection, etc., and the guiding interface of fig. 5 may also further include other guiding interfaces, which is not limited in particular by the present application.

In one possible implementation manner, after the user completes the corresponding first touch operation according to the instruction of the guiding interface shown in the c diagram in fig. 5, the first terminal device responds to the click operation of the user on the "completed" shown in the c diagram in fig. 5, and stores the first data corresponding to the first touch operation and the corresponding preset touch type data under the preset directory.

In one possible implementation manner, after the collection of the first data is completed, the plurality of first terminal devices participating in the collection of the data respectively send the first data and the preset touch type data to the first server. Taking the example that the first terminal device is a mobile phone, the plurality of first terminal devices may respond to the operations performed by the user according to the flows of fig. 6 and fig. 7, to implement the transmission of the first data and the preset touch type data, for example. As shown in a diagram in fig. 6, the first terminal device displays a setting submenu shown in b diagram in fig. 6 in response to a clicking operation of a user on the setting, the first terminal device displays a BetaClub first page shown in a diagram in fig. 7 in response to a clicking operation of a user on the b diagram in fig. 6, the user can click a "bill of lading" at the bottom of the first page, enter a device type selection interface shown in b diagram in fig. 7, the user can select a device type of the user at the interface, for example, can select a "mobile phone", the first terminal device displays an interface shown in c diagram in fig. 7 in response to the selection, the user can select an application (for example, a data collection application 501) needing to submit data by clicking the interface shown in d diagram in fig. 7, the first terminal device finds first data and corresponding preset touch type data from below the preset catalog in response to the user's selection operation of d diagram in fig. 7, and sends the data to the first server.

In one possible implementation manner, the first dynamic library includes a code of a preset node, where the preset node is used to identify a flow of collecting touch operation data and a flow of identifying a type of touch operation, and if the data collection application is in an operation state, the preset node is identified as 1 and is used to instruct the first dynamic library to start the flow of touch operation data collection; if the data collection application program on the first terminal device is not in the running state, the preset node identifier is 0, and the preset node identifier is used for indicating the first dynamic library to start a process for identifying the touch operation of the user. The first dynamic library further includes a preconfigured neural network model. Taking the touch operation of the user as the finger joint operation and the auxiliary function corresponding to the operation as the screen capturing as an example, when the preset node mark is 0, the user uses the double finger joints to strike the screen of the first terminal device, the first terminal device responds to the touch operation of the user, the data of the touch operation is input into a preconfigured neural network model, and if the touch operation is identified as the double finger joint strike, the auxiliary function of the screen capturing is correspondingly realized.

As an optional embodiment, the updated neural network model satisfies a preset condition, where the success rate of identifying the updated neural network model is greater than or equal to the first threshold.

One possible implementation manner of S403 includes: the method comprises the steps that a first server trains a pre-configured neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a first neural network model, wherein the first neural network model is a neural network model in the training process; the method comprises the steps that a first server inputs standard touch operation sample data to a first neural network model to obtain a first identification result of the first neural network model on the standard touch operation sample data; the first server compares the first identification result with a touch type label of standard touch operation sample data to obtain the identification success rate of the first neural network model; if the recognition success rate of the first neural network model is greater than or equal to a first threshold value, the first server determines the first neural network model as an updated neural network model.

Another possible implementation manner of S403 includes: the method comprises the steps that a first server trains a preconfigured neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a second neural network model, wherein the second neural network model is a neural network model in the training process; the first server respectively sends the second neural network model to a plurality of first terminal devices; the method comprises the steps that a plurality of first terminal devices receive a second neural network model, respond to second touch operation of a user respectively, acquire second data corresponding to the second touch operation, and acquire a first touch type true value of the second touch operation, wherein the second data comprise capacitance value data and/or acceleration sensor data corresponding to the second touch operation; the plurality of first terminal devices respectively input second data into a second neural network model to obtain a second identification result of the second neural network model on the second data, and send the second identification result and the first touch type true value to a first server; the first server receives a plurality of second recognition results and a plurality of first touch type true values, compares the plurality of second recognition results with the plurality of first touch type true values respectively to obtain a recognition success rate of the second neural network model, and if the recognition success rate of the second neural network model is greater than or equal to a first threshold value, the first server determines the second neural network model as an updated neural network model.

In one possible implementation, the first server may send the second neural network model to the plurality of first terminal devices by Over The Air (OTA) technology in a manner that upgrades the instrumented operating system.

In a possible implementation manner, the auxiliary function that can be realized by the touch operation of the user double-finger joint knocking is screen capturing, the user can recognize that the second touch operation of the user is double-finger joint knocking through the second neural network model in the first terminal device, and realize the corresponding screen capturing auxiliary function, and the user can enter an uploading interface of the touch type real value by clicking a feedback button popped up by the display interface, and select to upload the first touch type real value corresponding to the second touch operation.

For example, taking the first terminal device as a mobile phone, the second touch operation as a double-finger joint knocking and the corresponding auxiliary function as a screen capturing as an example, the identification of the second touch operation on the first terminal device and the uploading flow of the first touch type true value of the second touch operation are described. Fig. 8 is a schematic diagram of an operation interface of a first terminal device according to an embodiment of the present application, as shown in a diagram a in fig. 8, where a user may perform a second touch operation on any interface of the first terminal device, that is, a tapping operation of a double-finger joint, and the first terminal device may obtain second data corresponding to the second touch operation in response to the second touch operation of the user, input the second data to a second neural network model, and when the second neural network model recognizes that the second touch operation is the double-finger joint tapping operation, perform a screen capturing on a current screen by the first terminal device, and display a screen capturing result in a lower right corner as shown in a diagram b in fig. 8. In one possible implementation, the interface may pop up an icon 801 shown in a c diagram in fig. 8, and the first terminal device may display a touch type upload interface shown in a d diagram in fig. 8 in response to a clicking operation of the icon 801 by a user, and select a double-knuckle tap to send a second recognition result (recognition of the second touch operation as the double-knuckle tap) and a first touch type true value (double-knuckle tap) to the first server.

In one possible implementation manner, the plurality of first terminal devices may further compare the second recognition result with the actual value of the first touch type, and send only the comparison result to the first server, so as to save channel resources used for communication between the first terminal device and the first server.

In the embodiment of the application, the second neural network model in the training process is sent to the plurality of first terminal devices, and the recognition success rate of the second neural network model is verified through the recognition of the second neural network model to the real second touch operation of the plurality of users corresponding to the plurality of first terminal devices, so that the obtained recognition success rate is more real and reliable, and the more reliable updated neural network model is facilitated to be obtained.

Yet another possible implementation manner of the step S403 includes: the first server trains a preconfigured neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a third neural network model, wherein the third neural network model is a neural network model in the training process; the first server sends a third neural network model to a plurality of first terminal devices respectively; the method comprises the steps that a plurality of first terminal devices receive a third neural network model, respond to third touch operation of a user respectively, acquire third data, acquire a second touch type true value of the third touch operation, and the third data comprise capacitance value data and/or acceleration sensor data corresponding to the third touch operation; the plurality of first terminal devices respectively input third data into a third neural network model to obtain a third identification result of the third neural network model on the third data, and send the third identification result and a second touch type true value to a second server; the second server receives a plurality of third recognition results and a plurality of second touch type true values, compares the third recognition results with the second touch type true values respectively to obtain a recognition success rate of the third neural network model, and sends the recognition success rate of the third neural network model to the first server; if the recognition success rate of the third neural network model is greater than or equal to the first threshold value, the first server determines the third neural network model as an updated neural network model.

Alternatively, the second server may be a server corresponding to the big data platform, which is not particularly limited in the present application.

One possible implementation manner of S404 includes: the first server sends system upgrade data to the second terminal equipment, wherein the system upgrade data comprises an updated neural network model; the second terminal device receives the system upgrade data and configures an updated neural network model.

Another possible implementation manner of S404 includes: before a first server sends system upgrade data to a second terminal device, the second terminal device sends a system upgrade data request to the first server; the first server transmits system upgrade data to the second terminal device based on the system upgrade data request.

It should be understood that the first data sent by the plurality of first terminal devices to the first server includes, but is not limited to, capacitance data and/or acceleration sensor data corresponding to the first touch operation, and may also include identifiers of the plurality of first terminal devices.

Alternatively, the identification of the first terminal device may be a respective mobile equipment identification code (mobile equipment identifier, MEID) of the plurality of first terminal devices, an international mobile equipment identification code (international mobile equipment identity, IMEI), or other unique identification of the device, which is not limited in this regard.

In one possible implementation manner, the first server may match, from the database, the model of the acceleration sensor and the model of the capacitance sensor to each first terminal device according to the identifiers of the plurality of first terminal devices, and use first data, the model of the acceleration sensor and the model of the capacitance sensor of which are the same, and preset touch type data corresponding to the first data as a training set, to train a preset neural network model, so as to obtain a plurality of updated neural network models, where the plurality of updated neural network models respectively correspond to a set of models of the acceleration sensor and the capacitance sensor.

The database may be a database maintained by a terminal equipment manufacturer, where the database includes an identifier of a terminal equipment, a model of an acceleration sensor and a model of a capacitance sensor configured by the terminal equipment corresponding to the identifier of the terminal equipment, and a mapping relationship of the three. The database may be provided in the first server or in another server, which is not limited in the present application.

Illustratively, the model number of the acceleration sensor of the first terminal device a is (1) and the model number of the capacitance sensor is (1), the model number of the acceleration sensor of the first terminal device B is (2) and the model number of the capacitance sensor is (1), the model number of the acceleration sensor of the first terminal device C is (1) and the model number of the capacitance sensor is (1), the model number of the acceleration sensor of the first terminal device D is (2) and the model number of the capacitance sensor is (1), then the first data and the preset touch type data corresponding to the first terminal device a and the first terminal device C are included in the same training set, and the first data and the preset touch type data corresponding to the first terminal device B and the first terminal device D are included in another training set. It should be understood that the number of first terminal devices that are the same as the type of the acceleration sensor and the type of the capacitance sensor of the first terminal device A, B, C, D is enough, so that different training sets may be used to train the preconfigured neural network model to obtain a plurality of different updated neural network models, and the plurality of updated neural network models may be identified according to the type of the acceleration sensor and the type of the capacitance sensor.

In one possible implementation, before the first server sends the system upgrade data to the second terminal device, the second terminal device sends a system upgrade data request to the first server, where the system upgrade data request may include an identifier of the second terminal device, and the first server may match, based on the system upgrade data request, a model of the acceleration sensor and a model of the capacitance sensor corresponding to the identifier of the second terminal device from a database, and include in the system upgrade data an updated neural network model that is consistent with the model of the acceleration sensor and the model of the capacitance sensor of the second terminal device, and send the updated neural network model to the corresponding second terminal device.

In the embodiment of the application, the updated neural network model which is consistent with the model of the acceleration sensor and the model of the capacitance sensor of the second terminal equipment is sent to the corresponding second terminal equipment, so that the data error caused by inconsistent model of the acceleration sensor and the model of the capacitance sensor of the second terminal equipment and model of the capacitance sensor during training of the training pre-configured neural network model is reduced, the reliability of the updated neural network model is improved, the adaptation degree of the updated neural network model sent to the second terminal equipment and the second terminal equipment is also improved, the success rate of realizing auxiliary functions by a touch operation of a user is improved, and the user experience is improved.

As an optional embodiment, the second terminal device responds to a fourth touch operation of the user, and obtains fourth data through the user version operating system, wherein the fourth data comprises capacitance data and/or acceleration sensor data corresponding to the fourth touch operation; the second terminal equipment inputs fourth data into the updated neural network model to obtain a fourth identification result of the updated neural network model on fourth touch operation; and realizing an auxiliary function according to the touch type corresponding to the fourth identification result.

In one possible implementation manner, the touch type corresponding to the fourth recognition result includes: finger tip click, finger tip sliding, finger tip tap, finger belly click, finger belly sliding, finger belly tap, side nail click, side nail sliding, double finger tap, double finger scaling, finger joint tap and finger joint sliding.

In one possible implementation, the auxiliary functions include screen shots, recordings, and regional screen shots.

The method of the embodiment of the present application is described in detail above with reference to fig. 1 to 8, and the apparatus of the embodiment of the present application will be described in detail below with reference to fig. 9 and 10.

Fig. 9 is a schematic block diagram of an apparatus 900 for recognizing a touch operation according to an embodiment of the present application. As shown in fig. 9, the apparatus 900 includes a transceiver module 901 and a processing module 902. The processing module 902 is configured to: responding to a first touch operation of a user, and collecting first data corresponding to the first touch operation through an internal plate detection operating system, wherein the first data comprises capacitance data and/or acceleration sensor data corresponding to the first touch operation; the transceiver module 901 is configured to: and sending the first data and the preset touch type data to a first server.

Optionally, the processing module 902 is further configured to: responding to a second touch operation of the user, acquiring second data corresponding to the second touch operation, and acquiring a true value of a first touch type of the second touch operation, wherein the second data comprises capacitance data and/or acceleration sensor data corresponding to the second touch operation, and the processing module 902 is further configured to: inputting the second data into a second neural network model to obtain a second recognition result of the second neural network model on the second data; the transceiver module 901 is further configured to: and sending the second identification result and the first touch type true value to the first server.

Optionally, the transceiver module 901 is further configured to: receiving a third neural network model; the processing module 902 is further configured to: in response to a third touch operation of the user, obtaining third data, and obtaining a true value of a second touch type of the third touch operation, where the third data includes capacitance value data and/or acceleration sensor data corresponding to the third touch operation, and the processing module 902 is further configured to: inputting the third data into a third neural network model to obtain a third recognition result of the third neural network model on the third data; the transceiver module 901 is further configured to: and sending the third identification result and the second touch type true value to the second server.

It should be understood that the apparatus 900 may be specifically configured as the first terminal device in the foregoing embodiment, and may be configured to perform the steps and/or flows corresponding to the first terminal device in the foregoing method embodiment.

Fig. 10 is a schematic block diagram of an apparatus 1000 for recognizing a touch operation according to an embodiment of the present application, and as shown in fig. 10, the apparatus 1000 includes a processor 1001, a transceiver 1002, and a memory 1003. Wherein the processor 1001, the transceiver 1002 and the memory 1003 communicate with each other through an internal connection path, the memory 1003 is used for storing instructions, and the processor 1001 is used for executing the instructions stored in the memory 1003 to control the transceiver 1002 to transmit signals and/or receive signals.

It should be understood that the apparatus 1000 may be specifically configured as the first terminal device in the foregoing embodiment, and may be configured to perform the steps and/or flows corresponding to the first terminal device in the foregoing method embodiment. The memory 1003 may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type. The processor 1001 may be configured to execute instructions stored in a memory, and when the processor 1001 executes instructions stored in a memory, the processor 1001 is configured to perform the steps and/or flows of the method embodiments corresponding to the first terminal device described above. The transceiver 1002 may include a transmitter that may be used to implement various steps and/or processes for performing transmit actions corresponding to the transceiver described above, and a receiver that may be used to implement various steps and/or processes for performing receive actions corresponding to the transceiver described above.

It should be appreciated that in embodiments of the present application, the processor of the apparatus described above may be a central processing module (central processing unit, CPU), which may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor executes instructions in the memory to perform the steps of the method described above in conjunction with its hardware. To avoid repetition, a detailed description is not provided herein.

The application also provides a computer readable storage medium for storing a computer program for implementing each step and/or flow corresponding to the first terminal device in the above method embodiment.

The present application also provides a computer program product comprising a computer program (which may also be referred to as code, or instructions) which, when run on a computer, is adapted to perform the steps and/or flows of the above-described method embodiments corresponding to the first terminal device.

Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a specific implementation of the present application, but the scope of the embodiments of the present application is not limited thereto, and any person skilled in the art may easily think about changes or substitutions within the technical scope of the embodiments of the present application, and all changes and substitutions are included in the scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for recognizing touch operations, the method being applied to a system including a plurality of first terminal devices, a second terminal device and a first server, the plurality of first terminal devices each being installed with an internal test version operating system, the second terminal device being installed with a user version operating system, the plurality of first terminal devices respectively belonging to a plurality of users and the plurality of users being differentiated users, the internal test version operating system including a first dynamic library including codes for realizing collection of user touch operation data and codes for recognizing types of touch operations, the method comprising:

the plurality of first terminal devices respectively respond to first touch operation of a user, collect first data corresponding to the first touch operation through the internal test version operating system, and send the first data and preset touch type data to the first server, wherein the first data comprises capacitance data and/or acceleration sensor data corresponding to the first touch operation;

the first server receives a plurality of first data and a plurality of preset touch type data from the plurality of first terminal devices, trains a preset neural network model by using the plurality of first data and the plurality of preset touch type data to obtain an updated neural network model, and configures the updated neural network model to the second terminal device;

The system further includes a second server, the training the pre-configured neural network model using the plurality of first data and the plurality of pre-set touch type data, the obtaining the updated neural network model includes:

the first server trains a preset neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a third neural network model, wherein the third neural network model is a neural network model in the training process;

the first server sends the third neural network model to the plurality of first terminal devices respectively;

the plurality of first terminal devices receive the third neural network model, respond to third touch operation of a user respectively, acquire third data, and acquire a second touch type true value of the third touch operation, wherein the third data comprises capacitance data and/or acceleration sensor data corresponding to the third touch operation;

the plurality of first terminal devices respectively input the third data to the third neural network model, obtain a third identification result of the third neural network model on the third data, and send the third identification result and the second touch type real value to the second server;

The second server receives a plurality of third recognition results and a plurality of second touch type true values, compares the third recognition results with the second touch type true values respectively to obtain the recognition success rate of the third neural network model, and sends the recognition success rate of the third neural network model to the first server;

and if the identification success rate of the third neural network model is greater than or equal to a first threshold value, the first server determines the third neural network model as the updated neural network model.

2. The method of claim 1, wherein the updated neural network model meets a preset condition that an identification success rate of the updated neural network model is greater than or equal to a first threshold.

3. The method of claim 1, wherein said configuring the updated neural network model to the second terminal device comprises:

the first server sends system upgrade data to the second terminal equipment, wherein the system upgrade data comprises the updated neural network model;

And the second terminal equipment receives the system upgrading data and configures the updated neural network model.

4. A method according to claim 3, characterized in that before the first server sends system upgrade data to the second terminal device, the method comprises:

the second terminal equipment sends a system upgrade data request to the first server;

the first server sending system upgrade data to the second terminal device, including:

and the first server sends system upgrade data to the second terminal equipment based on the system upgrade data request.

5. The method according to claim 3 or 4, characterized in that the method further comprises:

the second terminal equipment responds to a fourth touch operation of a user, fourth data are obtained through the user version operation system, and the fourth data comprise capacitance data and/or acceleration sensor data corresponding to the fourth touch operation;

the second terminal device inputs the fourth data to the updated neural network model to obtain a fourth identification result of the updated neural network model on a fourth touch operation;

And the second terminal equipment realizes an auxiliary function according to the touch type corresponding to the fourth identification result.

6. The method of claim 5, wherein the touch type corresponding to the fourth recognition result comprises: finger tip click, finger tip sliding, finger tip tap, finger belly click, finger belly sliding, finger belly tap, side nail click, side nail sliding, double finger tap, double finger scaling, finger joint tap and finger joint sliding.

7. The method of claim 6, wherein the auxiliary functions include screen shots, recordings, and area shots.

8. The system for identifying touch operation is characterized by comprising a plurality of first terminal devices, a second terminal device and a first server, wherein the first terminal devices are provided with an internal test version operating system, the second terminal devices are provided with user version operating systems, the first terminal devices respectively belong to a plurality of users, the users are differentiated users, and the internal test version operating systems comprise a first dynamic library, and the first dynamic library comprises codes for realizing the collection of user touch operation data and codes for identifying the type of touch operation;

The first terminal device is configured to: responding to a first touch operation of a user, collecting first data corresponding to the first touch operation through the internal test version operating system, and sending the first data and preset touch type data to the first server, wherein the first data comprises capacitance data and/or acceleration sensor data corresponding to the first touch operation;

the first server is configured to: receiving a plurality of first data and a plurality of preset touch type data from a plurality of first terminal devices, training a preset neural network model by adopting the plurality of first data and the plurality of preset touch type data to obtain an updated neural network model, and configuring the updated neural network model to the second terminal device;

the system further comprises a second server;

the first server is configured to: training a preconfigured neural network model by adopting a plurality of first data and a plurality of preset touch type data to obtain a third neural network model, wherein the third neural network model is a neural network model in the training process;

the first server is further configured to: respectively transmitting the third neural network model to the plurality of first terminal devices;

The first terminal device is further configured to: receiving the third neural network model, responding to third touch operation of a user, obtaining third data, and obtaining a second touch type true value of the third touch operation, wherein the third data comprises capacitance value data and/or acceleration sensor data corresponding to the third touch operation;

the first terminal device is further configured to: inputting the third data into the third neural network model, obtaining a third identification result of the third neural network model on the third data, and sending the third identification result and the second touch type true value to the second server;

the second server is used for: receiving a plurality of third recognition results and a plurality of second touch type true values, comparing the third recognition results with the second touch type true values respectively to obtain the recognition success rate of the third neural network model, and sending the recognition success rate of the third neural network model to the first server;

the first server is further configured to: and if the identification success rate of the third neural network model is greater than or equal to a first threshold value, determining the third neural network model as the updated neural network model.

9. The system of claim 8, wherein the updated neural network model meets a preset condition that an identification success rate of the updated neural network model is greater than or equal to a first threshold.

10. The system of claim 8, wherein the first server is further configured to: transmitting system upgrade data to the second terminal equipment, wherein the system upgrade data comprises the updated neural network model;

the second terminal device is configured to: and receiving the system upgrade data and configuring the updated neural network model.

11. The system of claim 10, wherein the second terminal device is further configured to: sending a system upgrade data request to the first server;

the first server is further configured to: and sending system upgrade data to the second terminal equipment based on the system upgrade data request.

12. The system according to claim 10 or 11, wherein the second terminal device is further configured to: responding to a fourth touch operation of a user, and acquiring fourth data through the user version operating system, wherein the fourth data comprises capacitance data and/or acceleration sensor data corresponding to the fourth touch operation;

The second terminal device is further configured to: inputting the fourth data into the updated neural network model to obtain a fourth identification result of the updated neural network model on a fourth touch operation;

the second terminal device is further configured to: and realizing an auxiliary function according to the touch type corresponding to the fourth identification result.

13. The system of claim 12, wherein the touch type corresponding to the fourth recognition result includes: finger tip click, finger tip sliding, finger tip tap, finger belly click, finger belly sliding, finger belly tap, side nail click, side nail sliding, double finger tap, double finger scaling, finger joint tap and finger joint sliding.

14. The system of claim 13, wherein the auxiliary functions include screen shots, recordings, and area shots.