CN205230404U - Learning machine - Google Patents

Abstract

The utility model is applicable to the field of learning machines and provides a learning machine, the learning machine includes: a voice signal acquisition circuit for collecting voice signals, the voice signal acquisition circuit is connected with the processor; a radio frequency identification for collecting radio frequency signals A circuit, the radio frequency identification circuit is connected to the processor; a processor that receives and recognizes the voice signal and the radio frequency signal, and extracts information corresponding to the recognition result from the memory, the processor is respectively connected to the The voice signal acquisition circuit, the radio frequency identification circuit, the memory and the audio response circuit are connected; the audio response circuit receives and broadcasts the information corresponding to the recognition result, and the audio response circuit is connected with the processor. Since the utility model can interact with the user through the voice signal and the radio frequency signal, the interactivity is increased and the user's good experience is provided.

Description

a learning machine

technical field

The embodiment of the utility model belongs to the field of learning machines, in particular to a learning machine.

Background technique

With the continuous development of science and technology, electronic products are gradually integrated into people's learning and life, and a variety of learning machine products have also appeared for education. Learning machine is a popular portable learning device among students. Compared with other mobile terminals, learning machine pays more attention to the application of learning resources and teaching strategies. Functions such as multi-language learning, standard professional dictionaries, and free memory expansion have begun to become the mainstream means of competition for learning machines.

Early learning machines were connected to TVs, and used the TV screen for output and display, which was not portable. Later, they gradually turned to various electronic dictionaries with smaller sizes and independent display screens, providing personal information management, games, Chinese-English dictionaries, Chinese-English translation and other functions. However, in the existing learning machines, since all programs are solidified on the memory of the learning machine, their interactivity is relatively poor.

Utility model content

The embodiment of the utility model provides a learning machine, aiming at solving the problem of poor interaction between the learning machine and users.

The utility model embodiment is achieved like this, a kind of learning machine, described learning machine comprises:

A voice signal acquisition circuit for collecting voice signals, the voice signal acquisition circuit being connected to the processor;

A radio frequency identification circuit for collecting radio frequency signals, the radio frequency identification circuit is connected to the processor;

A processor that receives and recognizes the voice signal and the radio frequency signal, and extracts information corresponding to the recognition result from the memory, the processor is respectively connected with the voice signal acquisition circuit, the radio frequency identification circuit, the memory and the audio response circuit connect;

An audio response circuit that receives and broadcasts information corresponding to the recognition result, the audio response circuit is connected to the processor.

In the embodiment of the utility model, since the learning machine provided by the utility model can interact with the user through the voice signal and the radio frequency signal, the interactivity is increased and the user's good experience is provided.

Description of drawings

Fig. 1 is the structural diagram of the first kind of learning machine that the utility model first embodiment provides;

Fig. 2 is the structural diagram of the second learning machine provided by the second embodiment of the utility model;

Fig. 3 is the structural diagram of the third learning machine provided by the third embodiment of the present invention;

Fig. 4 is a structural diagram of a fourth learning machine provided by the fourth embodiment of the present invention.

detailed description

In order to make the purpose, technical solution and advantages of the utility model clearer, the utility model will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the utility model, and are not intended to limit the utility model.

In the embodiment of the utility model, the voice signal acquisition circuit collects the voice signal, the radio frequency identification circuit collects the radio frequency signal, and sends the collected voice signal and radio frequency information to the processor, and the processor receives and recognizes the voice signal and the radio frequency signal signal, and then extract information corresponding to the recognition result from the memory, and send the extracted information corresponding to the recognition result to the audio response circuit for broadcast.

In order to illustrate the technical solution described in the utility model, the following specific examples will be used for illustration.

Embodiment one:

Fig. 1 shows the structural diagram of the first learning machine provided by the first embodiment of the present invention. For the convenience of description, only the parts related to the embodiment of the present invention are shown.

The learning machine includes: a voice signal acquisition circuit 11 , a radio frequency identification circuit 12 , a processor 13 , a memory 14 and an audio response circuit 15 . in:

A voice signal acquisition circuit 11 for collecting voice signals, the voice signal acquisition circuit 11 is connected to the processor 13 .

Specifically, the voice signal collection circuit 11 may be a microphone, a pickup, and other devices.

A radio frequency identification circuit 12 for collecting radio frequency signals, the radio frequency identification circuit 12 is connected with the processor 13 .

The radio frequency identification circuit 12 includes a radio frequency antenna and a radio frequency transmission interface, the radio frequency transmission interface includes but not limited to the following interfaces: near field communication (NearFieldCommunication, NFC) interface, Bluetooth interface, wireless fidelity (WirelessFidelity, WiFi) interface, 2.4G wireless communication interface, etc.

A processor 13 that receives and recognizes the voice signal and the radio frequency signal, and extracts information corresponding to the recognition result from the memory 14, the processor 13 is respectively connected to the voice signal acquisition circuit 11, the radio frequency identification circuit 12, The memory 14 and the audio response circuit 15 are connected.

It should be noted that the memory 14 stores the correspondence between the recognition result of recognizing the voice signal and the text data, the correspondence between the recognition result of the radio frequency signal recognition and the text data, and the corresponding text data. Wherein, when the processor 13 extracts the information corresponding to the recognition result from the memory 14, the recognition result obtained by the processor 13 needs to be compared with the recognition result stored in the memory 14 through a comparator.

An audio response circuit 15 that receives and broadcasts information corresponding to the recognition result, the audio response circuit 15 is connected to the processor 13 .

Wherein, the audio response circuit 15 includes an audio processing circuit, an audio power amplification circuit, and a speaker. After receiving the information corresponding to the recognition result, the audio processing circuit is first used for sound effect processing, then the audio power amplifier circuit is passed, and finally the sound broadcast is realized through the loudspeaker.

In the first embodiment of the present utility model, the voice signal collection circuit 11 collects voice signals, the radio frequency identification circuit 12 collects radio frequency signals, and sends the collected voice signals and radio frequency information to the processor 13, and the processor 13 receives and recognizes the The voice signal and the radio frequency signal extract information corresponding to the recognition result from the memory 14, and send the extracted information corresponding to the recognition result to the audio response circuit 15 for broadcast. Since the learning machine provided by the utility model can interact with the user through the voice signal and the radio frequency signal, the interactivity is increased and the user's good experience is provided.

Embodiment two:

Fig. 2 shows the structural diagram of the second learning machine provided by the second embodiment of the present invention. In Fig. 2, the learning machine also includes a camera 21, a button circuit 22, a touch sensor 23, but in actual conditions , the learning machine may only include one or more of the above devices, which will not be repeated here. Specifically, the learning machine includes:

A camera 21 for collecting image data, the camera 21 is connected to the processor 13 . After the image data is collected by the camera 21, it is sent to the processor 13 for identification, and the processor 13 extracts information corresponding to the identification result from the memory 14, and finally sends the extracted information corresponding to the identification result to the audio response circuit 15 for response , or send to the motor driver response described later.

and / or,

A key circuit 22 for collecting key signals, the key circuit 22 is connected to the processor 13 . After the button circuit 22 collects the button signal, it is sent to the processor 13 for identification, and the processor 13 extracts information corresponding to the identification result from the memory 14, and finally sends the extracted information corresponding to the identification result to the audio response circuit 15 response.

and / or,

A touch sensor 23 for collecting change data of the touch capacitance of the learning machine, the touch sensor 23 is connected to the processor 13 . The touch sensor 23 obtains the change data of the touch capacitance through the touch sheet, and then sends the obtained change data of the touch capacitance to the processor 13 for identification, and the processor 13 extracts the information corresponding to the identification result from the memory 14, and finally extracts the information corresponding to the identification result. The information corresponding to the recognition result is sent to the audio response circuit 15 for response, or sent to the motor driver described later for response.

In the second embodiment of the present invention, since the learning machine also includes one or more devices in the camera 21, the button circuit 22, and the touch sensor 23, it can receive various interactive information sent by the user in various ways, thereby extremely It greatly improves the interaction between the learning machine and the user, and improves the user's good experience.

Embodiment three:

Fig. 3 shows the structural diagram of the third learning machine provided by the third embodiment of the present invention. In Fig. 3, the learning machine also includes a temperature sensor 31, a photosensitive sensor 32, an acceleration sensor 33, a magnetic sensor 34, Gyro sensor 35, but in actual situations, the learning machine can only include one or more of the above-mentioned devices, or, on the basis of FIG. 3, also include one or There are multiple devices, which will not be described in detail here. Specifically, the learning machine includes:

A temperature sensor 31 for collecting the temperature of the learning machine, the temperature sensor 31 is connected to the processor 13 . After the temperature sensor 31 collects the temperature data, it sends it to the processor 13, and the processor 13 sends the temperature data to the audio response circuit 15 for response.

and / or,

A photosensitive sensor 32 that converts light signals around the learning machine into electrical signals, and the photosensitive sensor 32 is connected with the processor 13 . The photosensitive sensor 32 includes but not limited to a phototransistor, a photodiode, a photoresistor and the like. The light change data around the learning machine is obtained through the photosensitive sensor 32, and then the light change data is converted into an electrical signal and sent to the processor 13, and the processor 13 sends the temperature data to the audio response circuit 15 for response.

and / or,

An acceleration sensor 33 that collects the acceleration of the learning machine, the acceleration sensor 33 is connected to the processor 13 . After the acceleration sensor 33 collects the acceleration of the learning machine, it is sent to the processor 13, and the processor 13 sends the acceleration of the learning machine to the audio response circuit 15 to respond, or the processor 13 identifies the acceleration of the learning machine received, and then from Information corresponding to the recognition result is extracted from the memory 14, and finally the extracted information corresponding to the recognition result is sent to the motor driver response described later.

and / or,

A magnetic sensor 34 for collecting environmental magnetic field data of the learning machine, the magnetic sensor 34 is connected to the processor 13 . Obtain the environmental magnetic field data of learning machine x, y, z three axes by magnetic sensor 34, the unit of this numerical value is micro-Tesla (micro-Tesla), represent with uT, magnetic sensor 34 sends the environmental magnetic field data after processing to To the processor 13, the processor 13 sends the acceleration of the learning machine to the audio response circuit 15 to respond, or the processor 13 extracts the information corresponding to the recognition result from the memory 14, and finally sends the extracted information corresponding to the recognition result to the motor driver response described later.

and / or,

A gyro sensor 35 that collects the angular acceleration of the learning machine, and the gyro sensor 35 is connected to the processor 13 . The angular acceleration data of the x, y, and z axes of the learning machine is obtained through the gyro sensor 35, and the unit of the angular acceleration is radians/second. The gyro sensor 35 sends the processed angular acceleration data to the processor 13, and the processor 13 sends the angular acceleration data of the learning machine to the audio response circuit 15 for response, or the processor 13 extracts and recognizes the result from the memory 14 Corresponding information, and finally the extracted information corresponding to the recognition result is sent to the motor driver response described later.

In the third embodiment of the present invention, since the learning machine also includes one or more devices in a temperature sensor 31, a photosensitive sensor 32, an acceleration sensor 33, a magnetic sensor 34, and a gyroscope sensor 35, it can obtain more information from the learning machine. status information and respond to the user, which increases the frequency of interaction with the user, and the status information obtained by the learning machine can also be combined with the received interaction information, so that the learning machine can add new interaction methods, thus greatly improving the learning machine. Interactivity with users improves user experience.

Embodiment four:

Fig. 4 shows the structural diagram of the fourth learning machine provided by the fourth embodiment of the present invention. In Fig. 4, the learning machine also includes a light display circuit 41 and a motor driver 42, but in actual situations, the The learning machine described above may only include one or more of the above-mentioned devices, or, on the basis of FIG. 4 , also include one or more of the devices in FIG. 3 , which will not be repeated here. Specifically, the learning machine includes:

Receive the information corresponding to the recognition result of recognizing the voice signal or the information corresponding to the recognition result of recognizing the key signal or the information corresponding to the recognition result of recognizing the change data of the touch capacitance sent by the processor 13, and execute the corresponding The light display circuit 41 is indicated by multi-channel and multi-color lights, and the light display circuit 41 is connected with the processor 13 . The light display circuit 41 realizes multi-channel and multi-color light indications through the LED drive circuit. Light display circuit 41 can realize light display in normal mode, including various single-group display, multi-group display, multi-group dynamic display, combined light display, etc. In addition, the light display circuit 41 can also receive interactive Information (such as the image data sent by the camera 21, the button signal sent by the button circuit 22, the change data of the touch capacitance sent by the touch sensor 23) or state information (the temperature data sent by the temperature sensor 31, the electrical signal sent by the photosensitive sensor 32, the acceleration The acceleration sent by the sensor 33, the environmental magnetic field data sent by the magnetic sensor 34, the angular acceleration sent by the gyro sensor 35) to perform specific light display.

and / or,

The motor driver 42 that receives the information corresponding to the recognition result of recognizing the voice signal sent by the processor 13 or the recognition result of recognizing the change data of the touch capacitance, and executes the corresponding action, the motor driver 42 and The processor 13 is connected. The motor driver 42 is mainly to drive a certain part of the learning machine to perform an action. For example, when the learning machine is a humanoid robot, if the information corresponding to the recognition result of the voice signal is to raise the arm, the motor driver 42 drives the learning machine to raise the arm. .

Optionally, the learning machine includes:

Power switching circuit for switching between USB powered and battery powered. Specifically, when it is detected that the USB interface is inserted, the learning machine is powered by using USB power supply.

and / or,

A voltage conversion circuit that converts the input voltage into the voltage required by the internal devices of the learning machine. Specifically, the voltage conversion circuit includes: a low dropout regulator (Low Dropout Regulator, LDO) conversion circuit, a DC-DC DC-DC conversion circuit, and a power management unit (Power Management Unit, PMU).

Optionally, the learning machine includes:

displaying the information corresponding to the recognition result sent by the processor 13 for recognizing the voice signal, or displaying the information corresponding to the recognition result sent by the processor 13 for recognizing the radio frequency signal, the display is related to the processing Device 13 is connected. In addition, the display can also display the status information of the learning machine (such as the temperature data sent by the temperature sensor 31, the electrical signal sent by the photosensitive sensor 32, the acceleration sent by the acceleration sensor 33, the environmental magnetic field data sent by the magnetic sensor 34, the gyroscope sensor 35 angular acceleration sent).

Optionally, the processor 13 includes at least one of the following interfaces:

USB interface, Bluetooth interface, I2C interface, Universal Asynchronous Receiver/Transmitter (UART) interface. Through these interfaces, the learning machine can be connected with other terminals and servers to realize data transmission and sharing.

Optionally, the processor 13 can be a single-chip microcomputer chip or an ARM chip, and when it is an ARM chip, its model can be AllwinnerA33IC, of course, it can also be other models, which are not limited here.

Optionally, the memory 14 includes at least one of the following:

Double rate synchronous dynamic random access memory 14 (DoubleDataRate, DDR), FLASH, TF card, secure digital card (SecureDigitalMemoryCard, SD) card. Among them, TF card is T-Flash, also known as MicroSD, which is a kind of memory card.

In the fourth embodiment of the present invention, since the learning machine also includes a light display circuit 41 and/or a motor driver 42, it can respond to user operations in various ways, thereby greatly improving the interaction between the learning machine and the user. Improve user experience.

The above is only a specific embodiment of the present utility model, but the scope of protection of the present utility model is not limited thereto. Anyone familiar with the technical field can easily think of changes or changes within the technical scope disclosed by the utility model Replacement should be covered within the protection scope of the present utility model. Therefore, the protection scope of the present utility model should be based on the protection scope of the claims.

Claims (10)

1. A learning machine, characterized in that the learning machine comprises: Collecting the speech signal acquisition circuit of speech signal, described speech signal acquisition circuit is connected with processor; A radio frequency identification circuit for collecting radio frequency signals, the radio frequency identification circuit is connected to the processor; A processor that receives and recognizes the voice signal and the radio frequency signal, and extracts information corresponding to the recognition result from the memory, the processor is respectively connected with the voice signal acquisition circuit, the radio frequency identification circuit, the memory and the audio response circuit connect; An audio response circuit that receives and broadcasts information corresponding to the recognition result, the audio response circuit is connected to the processor. 2. learning machine according to claim 1, is characterized in that, described learning machine comprises: A camera for collecting image data, the camera is connected to the processor; and / or, A key circuit for collecting key signals, the key circuit is connected to the processor; and / or, A touch sensor for collecting change data of the touch capacitance of the learning machine, the touch sensor is connected to the processor. 3. learning machine according to claim 1, is characterized in that, described learning machine comprises: Collect the temperature sensor of the temperature of learning machine, described temperature sensor is connected with described processor; and / or, A photosensitive sensor that converts the light signal around the learning machine into an electrical signal, and the photosensitive sensor is connected to the processor; and / or, Gather the acceleration sensor of the acceleration of learning machine, described acceleration sensor is connected with described processor; and / or, Collect the magnetic sensor of the environmental magnetic field data of learning machine, described magnetic sensor is connected with described processor; and / or, A gyro sensor that collects the angular acceleration of the learning machine, and the gyro sensor is connected to the processor. 4. learning machine according to claim 2, is characterized in that, described learning machine comprises: Receive the information corresponding to the recognition result of recognizing the voice signal or the information corresponding to the recognition result of recognizing the button signal or the information corresponding to the recognition result of recognizing the change data of the touch capacitance sent by the processor, and execute the corresponding A light display circuit with multi-channel and multi-color light indications, the light display circuit is connected to the processor; and / or, A motor driver that receives the information corresponding to the recognition result of recognizing the voice signal sent by the processor or the recognition result of recognizing the change data of the touch capacitance, and executes corresponding actions. The motor driver is connected with the processing device connection. 5. learning machine according to claim 1, is characterized in that, described learning machine comprises: A power switch circuit for switching between USB power supply and battery power supply; and / or, A voltage conversion circuit that converts the input voltage into the voltage required by the internal devices of the learning machine. 6. learning machine according to claim 5, is characterized in that, described voltage conversion circuit comprises: Low dropout linear regulator LDO conversion circuit, DC-DC DC-DC conversion circuit, power management unit PMU. 7. The learning machine according to any one of claims 1 to 6, wherein the learning machine comprises: displaying the information corresponding to the recognition result sent by the processor to recognize the voice signal, or displaying the information corresponding to the recognition result sent by the processor to recognize the radio frequency signal, the display is connected to the processor . 8. The learning machine according to any one of claims 1 to 6, wherein the processor comprises at least one of the following interfaces: USB interface, Bluetooth interface, I2C interface, asynchronous transceiver UART interface. 9. The learning machine according to any one of claims 1 to 6, wherein the model of the processor is AllwinnerA33IC. 10. The learning machine according to any one of claims 1 to 6, wherein the memory comprises at least one of the following: Double rate synchronous dynamic random access memory DDR, FLASH, TF card, secure digital card SD card.