Data transmission method and device, intelligent learning device, storage medium and program product

Abstract

This disclosure relates to the field of intelligent learning device technology, providing a data transmission method, apparatus, intelligent learning device, storage medium, and program product, including: acquiring learning process data to be transmitted; modulating the learning process data onto a carrier wave in the 16kHz-20kHz frequency band through an audio processing interface supported by a browser environment to generate a first acoustic data signal; and transmitting the first acoustic data signal through the speaker of the intelligent learning device for reception and decoding by an external device. The use of a browser-native supported audio processing interface eliminates the need for additional Bluetooth or WiFi modules, reducing hardware costs. Furthermore, leveraging the cross-platform nature of browsers enables seamless connectivity between devices of different brands and systems without complex pairing processes, significantly improving compatibility. Moreover, utilizing the physical near-field transmission characteristics of sound waves, the signal transmission distance is short and directional, effectively preventing the risk of long-distance eavesdropping on wireless signals.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent learning device technology, specifically to a data transmission method, apparatus, intelligent learning device, storage medium, and program product. Background Technology

[0002] With the rapid development of smart education hardware, devices such as smart learning machines, reading pens, and early education tablets are widely used in home education and classroom teaching. Smart learning devices typically need to interact with parents' mobile phones, teachers' terminals, or students' devices to achieve functions such as homework assignment, oral assessment, answer feedback, and learning progress synchronization.

[0003] In related technologies, Bluetooth or WiFi modules are usually required to achieve wireless communication. However, setting up dedicated modules not only increases hardware costs, but also leads to poor compatibility due to the complexity of cross-platform pairing. Summary of the Invention

[0004] The purpose of this disclosure is to provide a data transmission method, apparatus, intelligent learning device, storage medium, and program product to solve the above-mentioned problems.

[0005] To achieve the above objectives, this disclosure provides a data transmission method applied to an intelligent learning device, comprising: Acquire the learning process data to be transmitted; The learning process data is modulated onto a carrier wave in the 16kHz-20kHz frequency band using an audio processing interface supported by the browser environment to generate a first acoustic data signal. The first acoustic data signal is emitted through the speaker of the intelligent learning device for external devices to receive and decode.

[0006] Optionally, the step of modulating the learning process data onto a carrier wave in the 16kHz-20kHz frequency band through an audio processing interface supported by the browser environment to generate a first acoustic data signal includes: The learning process data is encoded and modulated onto multiple parallel subcarriers using the ScriptProcessorNode or AudioWorklet node of the Web Audio API, thereby generating the first acoustic data signal.

[0007] Optionally, the data transmission method further includes: Detect the spectral distribution of environmental noise to determine the interference frequency band; The subcarrier allocation strategy is dynamically adjusted according to the interference frequency band to avoid the interference frequency band.

[0008] Optionally, the data transmission method further includes: Detect the signal-to-noise ratio of the currently used subcarrier frequency band; If the signal-to-noise ratio is lower than a preset threshold, the system switches to a backup subcarrier frequency band for data transmission.

[0009] Optionally, the step of encoding and modulating the learning process data onto multiple parallel subcarriers to generate the first acoustic data signal includes: The learning process data is divided into multiple millisecond-level microdata packets; The multiple microdata packets are encoded in parallel to generate multiple acoustic data signal sub-streams corresponding to each subcarrier, and the first acoustic data signal is formed by superimposing the multiple acoustic data signal sub-streams.

[0010] Optionally, the data transmission method further includes: While transmitting the acoustic data signal substream generated by the Nth microdata packet, the N+1th microdata packet is encoded; And / or, while transmitting the acoustic data signal substream generated by the (N+1)th microdata packet, receive and process the reverse data returned by the external device for the Nth microdata packet.

[0011] Optionally, the data transmission method further includes: The intelligent learning device acquires a second acoustic data signal emitted by an external device through its microphone. The second acoustic data signal is subjected to spectral analysis using the audio processing interface supported by the browser environment, and the inverse data is demodulated from it.

[0012] Optionally, the first acoustic data signal and the second acoustic data signal are in the same frequency band and are implemented through phase inversion encoding; The demodulated reverse data includes: Extract the phase value of the second acoustic data signal; The phase value is compared with the reference phase value of the first acoustic data signal, and the reverse data is decoded based on the phase difference between the two.

[0013] Optionally, decoding the inverse data based on the phase difference between the two includes: When the phase difference is within the range of 0° ± preset threshold, it is decoded into the first binary value; When the phase difference is within the range of 180° ± a preset threshold, it is decoded into a second binary value; The decoded binary sequence is reassembled into reverse data.

[0014] Optionally, the data transmission method further includes: During the encoding process, checksums and forward error correction codes are added to the data blocks; Receive retransmission request information sent by an external device, the retransmission request information being generated when the checksum verification fails and the forward error correction encoding cannot be repaired; When a retransmission request for a specific data block is received, the data of that specific data block is retransmitted.

[0015] Optionally, the data transmission method further includes: Different transmission priorities are set for the learning process data according to the business type; Among them, oral assessment or real-time pronunciation correction services are set as the first priority, answer result feedback services are set as the second priority, and courseware download or system update services are set as the third priority. When channel resources are scarce, high-priority data is encoded and transmitted first, with the first priority taking precedence over the second priority, and the second priority taking precedence over the third priority.

[0016] This disclosure also provides a data transmission device for use in intelligent learning devices, comprising: The first processing module is configured to acquire the learning process data to be transmitted; The second processing module is configured to modulate the learning process data onto a carrier wave in the 16kHz-20kHz frequency band through an audio processing interface supported by the browser environment to generate a first acoustic data signal. The third processing module is configured to transmit the first acoustic data signal through the speaker of the intelligent learning device for external devices to receive and decode.

[0017] This disclosure also provides an intelligent learning device, including: Memory, which stores computer-executable instructions; A processor configured to execute the computer-executable instructions to implement the above-described data transmission method; A loudspeaker configured to transmit the first acoustic data signal; The microphone is configured to acquire second acoustic data signals emitted by external devices.

[0018] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described data transmission method.

[0019] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described data transmission method.

[0020] The above technical solution utilizes the browser's natively supported audio processing interface, eliminating the need for additional Bluetooth or WiFi modules and fundamentally reducing hardware costs. Furthermore, leveraging the browser's cross-platform capabilities, it enables seamless connectivity between devices of different brands and operating systems, eliminating complex pairing processes and significantly improving compatibility. Moreover, by modulating data into the 16kHz-20kHz frequency band, which is less sensitive to human hearing, and utilizing the near-field transmission characteristics of sound waves, the signal transmission distance is short and directional, effectively preventing the risk of long-distance wireless eavesdropping and ensuring the security of students' privacy data.

[0021] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a data transmission method according to an exemplary embodiment.

[0023] Figure 2 This is a block diagram illustrating a data transmission apparatus according to an exemplary embodiment.

[0024] Figure 3 This is a block diagram illustrating an intelligent learning device according to an exemplary embodiment. Detailed Implementation

[0025] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0026] In the following description, the words "first" and "second" are used only to distinguish the purpose of the description and should not be interpreted as indicating or implying relative importance or order.

[0027] With the rapid development of smart education hardware, devices such as smart learning machines, reading pens, and early education tablets are widely used in home education and classroom teaching. Smart learning devices typically need to interact with parents' mobile phones, teachers' terminals, or students' devices to achieve functions such as homework assignment, oral assessment, answer feedback, and learning progress synchronization.

[0028] In related technologies, Bluetooth or WiFi modules are usually required to achieve wireless communication. However, setting up dedicated modules not only increases hardware costs, but also leads to poor compatibility due to the complexity of cross-platform pairing.

[0029] The inventors also discovered that traditional wireless communication may face the risk of man-in-the-middle attacks or data eavesdropping when it involves students' sensitive information (such as answer records and voice data). Furthermore, traditional Bluetooth has a high transmission latency (about 200ms), which severely restricts the interactive teaching experience in scenarios such as oral training and real-time answering.

[0030] To address the aforementioned issues, the system utilizes a natively supported audio processing interface from the browser, eliminating the need for additional Bluetooth or Wi-Fi modules and fundamentally reducing hardware costs. Furthermore, leveraging the browser's cross-platform capabilities, it enables seamless connectivity across devices of different brands and operating systems, eliminating complex pairing processes and significantly improving compatibility. Additionally, by modulating the data into the 16kHz-20kHz frequency band, which is less sensitive to human hearing, and utilizing the near-field transmission characteristics of sound waves, the signal transmission distance is short and directional, effectively preventing long-distance eavesdropping risks and ensuring the security of students' privacy data.

[0031] Figure 1 This is a flowchart illustrating a data transmission method according to an exemplary embodiment, which is applied to an intelligent learning device. Please refer to [link / reference]. Figure 1 The data transmission method may include steps S1 to S3.

[0032] Step S1: Obtain the learning process data to be transmitted.

[0033] Learning process data includes, but is not limited to, at least one of the following: user voice data, answer records, and teaching control commands. For example, in an oral assessment scenario, the intelligent learning device collects the user's voice data through a microphone; in a question-and-answer scenario, the intelligent learning device acquires the user's selected answer options or handwritten input; in a teaching interaction scenario, the intelligent learning device acquires questions or control commands issued by the teacher. Learning process data can be collected in real time or pre-stored in the device's local storage unit.

[0034] Step S2: Through the audio processing interface supported by the browser environment, the learning process data is modulated onto a carrier wave in the 16kHz-20kHz frequency band to generate the first acoustic data signal.

[0035] The browser environment supports audio processing interfaces including, but not limited to, the Web Audio API. The browser engine built into the smart learning device provides native audio processing capabilities without the need to install any plugins or third-party applications.

[0036] The learning process data can be encoded and modulated using the ScriptProcessorNode or AudioWorklet node of the Web Audio API. The encoding and modulation methods can be Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), or Phase Shift Keying (PSK). Quadrature Phase Shift Keying (QPSK) modulation can also be used to map binary data to the phase changes of the carrier wave.

[0037] The 16kHz-20kHz frequency band is a high-frequency band in which the human ear is not sensitive; the human ear can usually hardly perceive sounds in this band. Therefore, it will not produce harsh noise during data transmission that would interfere with the user's normal learning experience. At the same time, the speakers and microphones of ordinary consumer electronic devices can support the sound production and pickup of this frequency band well, without the need for special hardware.

[0038] By adopting an audio processing interface natively supported by the browser, there is no need to set up additional Bluetooth or WiFi modules, which fundamentally reduces hardware costs. At the same time, by leveraging the cross-platform nature of the browser, seamless connection is achieved between devices of different brands and systems without complicated pairing processes, significantly improving compatibility. Furthermore, the data is modulated in the 16kHz-20kHz frequency band, which is not sensitive to the human ear. Utilizing the physical near-field transmission characteristics of sound waves, the signal transmission distance is short and directional, effectively preventing the risk of long-distance eavesdropping of wireless signals and ensuring the security of students' privacy data.

[0039] Step S3: The first acoustic data signal is emitted through the speaker of the intelligent learning device for external devices to receive and decode.

[0040] The built-in speaker of the intelligent learning device converts the first acoustic data signal generated in step S2 into sound waves and emits them into the surrounding space.

[0041] External devices can be parents' mobile phones, teachers' tablets, students' terminals, etc. These devices collect sound wave signals through their microphones and perform spectrum analysis and demodulation through audio processing interfaces supported by the browser environment to restore the original learning process data.

[0042] Because sound wave transmission has near-field characteristics, the effective transmission distance is typically within 1 meter, and it is directional. Therefore, only legitimate devices within this short range can receive the complete signal, effectively preventing the risk of long-distance eavesdropping and ensuring the security of students' privacy data. Furthermore, the physical layer transmission of audio signals does not require the complex encapsulation and decapsulation of Bluetooth or WiFi protocol stacks, and the end-to-end latency can be controlled within 50ms, meeting the low-latency, real-time interactive needs of scenarios such as oral assessment and real-time pronunciation correction.

[0043] In one possible implementation, step S2 may include: Using the ScriptProcessorNode or AudioWorklet node of the Web Audio API, multi-carrier modulation technology is employed to encode and modulate the learning process data onto multiple parallel subcarriers, generating the first acoustic data signal.

[0044] The ScriptProcessorNode or AudioWorklet node is an audio processing node provided by the Web Audio API, allowing developers to directly manipulate audio sample data within the browser environment. This node converts binary learning process data into corresponding modulation symbols and maps them to various subcarriers. Multicarrier modulation technology can be Orthogonal Frequency Division Multiplexing (OFDM), which divides the 16kHz-20kHz frequency band into multiple mutually orthogonal subcarriers, each carrying a portion of the data. Through parallel transmission, this effectively improves data transmission rate and interference resistance.

[0045] For example, the 16kHz-20kHz frequency band is divided into 16 parallel subcarriers, each with a bandwidth of 250Hz. QPSK modulation maps binary data to the phase changes of the subcarriers, enabling multi-channel parallel transmission. The ScriptProcessorNode processes a batch of audio data every 10ms through the onaudioprocess callback function, completing encoding and modulation in real time.

[0046] In one embodiment, the complete process of loading binary data onto an 18kHz high-frequency carrier via QPSK modulation can be as follows: First, an audio processing environment is created using AudioContext, and a sine wave with a frequency of 18000Hz is generated using OscillatorNode as the carrier signal; then, through the onaudioprocess callback function of ScriptProcessorNode, when each audio processing block (4096 samples) is triggered, the binary data to be transmitted is mapped to the corresponding phase value (45°, 135°, 225° or 315°) in groups of 2 bits using phaseMap, and multiplied with the input audio sample to achieve phase modulation; finally, the modulated signal is output to the speaker to generate an 18kHz high-frequency sound wave that is imperceptible to the human ear, thereby transmitting digital information in the form of sound waves.

[0047] The key technical parameters during the modulation process can be set as shown in Table 1:

[0048] Table 1 In one possible implementation, the data transmission method may further include: Detect the spectral distribution of environmental noise to determine the interference frequency band; The subcarrier allocation strategy is dynamically adjusted based on the interference frequency band to avoid the interference frequency band.

[0049] By utilizing the spectrum analysis function in the audio processing interface supported by the browser environment, the spectrum distribution of ambient noise can be detected in real time to identify the interference frequency band. The spectrum analysis function can be implemented through the AnalyserNode of the Web Audio API. The AnalyserNode can perform a fast Fourier transform on the ambient audio signal collected by the microphone and output the energy distribution of each frequency point in the 0-20kHz frequency band, thereby identifying the interference frequency band where the noise energy is concentrated.

[0050] Based on the identified interference frequency bands, the subcarrier allocation strategy is dynamically adjusted to avoid these interference bands. For example, when strong WiFi interference or electrical noise is detected in the 17kHz-18kHz frequency band, the subcarriers originally allocated in this band are migrated to the clean 19kHz-20kHz frequency band; or, the active set of OFDM subcarriers is reconfigured, and only subcarriers with a signal-to-noise ratio that meets the requirements are used for data transmission.

[0051] Dynamic adjustments can be made in real time during data transmission, with a response time controllable within 10ms, imperceptible to the user. By avoiding interference frequency bands, the error rate of data transmission is effectively reduced, and the communication reliability in noisy environments (such as classrooms) is improved.

[0052] In one possible implementation, the data transmission method may further include: Detect the signal-to-noise ratio of the currently used subcarrier frequency band; If the signal-to-noise ratio is lower than a preset threshold, switch to the backup subcarrier frequency band for data transmission.

[0053] The signal-to-noise ratio (SNR) of the currently used subcarrier frequency band is detected in real time. This SNR detection can be achieved through the AnalyserNode of the Web Audio API: the AnalyserNode performs spectral analysis on the received signal, extracting the signal energy and ambient noise energy within the current subcarrier frequency band, and calculating the ratio of these two values ​​as the SNR. The detection period can be set to once every 10ms-100ms to balance real-time performance and computational overhead.

[0054] The preset threshold can be set according to the actual application scenario and bit error rate requirements, for example, set to 15dB-25dB. When the signal-to-noise ratio of the currently used subcarrier frequency band is detected to be lower than the preset threshold, it indicates that the frequency band is subject to strong interference or severe signal attenuation, and continued use may lead to an increase in the data bit error rate. At this time, the system automatically switches to the backup subcarrier frequency band for data transmission.

[0055] The spare subcarrier band can be a pre-allocated independent band (e.g., 19kHz-20kHz) or a dynamically detected idle band with a high current signal-to-noise ratio. The handover process includes: stopping data transmission in the current band, reconfiguring coding and modulation parameters, rebuilding the subcarrier on the spare band, and transmitting data. The handover response time can be controlled within 10ms, imperceptible to the user. The signal-to-noise ratio detection accuracy can be controlled within ±2dB.

[0056] In one possible implementation, the data transmission method may further include: after switching to the backup subcarrier frequency band, continuing to monitor the signal-to-noise ratio of the original frequency band; when the signal-to-noise ratio of the original frequency band recovers to above a preset threshold, switching back to the original frequency band may be selected to achieve dynamic optimization of channel resources.

[0057] In one possible implementation, encoding and modulating the learning process data onto multiple parallel subcarriers to generate a first acoustic data signal may include: The learning process data is segmented into multiple millisecond-level microdata packets; Multiple microdata packets are encoded in parallel to generate multiple acoustic data signal sub-streams corresponding to each subcarrier. The first acoustic data signal is formed by superimposing multiple acoustic data signal sub-streams.

[0058] The continuous learning process data to be transmitted is divided into multiple millisecond-level microdata packets. The duration of each microdata packet can be set according to the application scenario and latency requirements, such as 10ms. For example, in a spoken language assessment scenario, the user's continuous speech stream can be segmented into 10ms segments, with each microdata packet containing a 10ms segment of speech data. This fine-grained segmentation method lays the foundation for subsequent parallel processing and low-latency transmission.

[0059] Parallel encoding of multiple microdata packets generates multiple acoustic data signal sub-streams corresponding to each subcarrier. This can be understood as simultaneously distributing the data from multiple microdata packets to different subcarriers for encoding and modulation, with each subcarrier corresponding to an independent acoustic data signal sub-stream. Parallel encoding can be achieved through the multi-channel processing capabilities of the Web Audio API, such as simultaneously calling multiple AudioWorklet nodes or processing in parallel through multiple output channels of a single node.

[0060] Multiple generated acoustic data signal sub-streams are superimposed to synthesize a first acoustic data signal. This superposition is a linear superposition in the time domain, meaning the output signal value at each moment is the sum of the instantaneous values ​​of all subcarrier signals at that moment. Superposition allows multiple parallel sub-streams to be merged into a single composite signal, which can then be transmitted through a single loudspeaker.

[0061] In this way, continuous data that originally needed to be transmitted serially is divided into micro-packets and transmitted in parallel on multiple subcarriers, effectively improving the data transmission rate. At the same time, since the duration of each micro-packet is very short (on the order of 10ms), the receiving end can start decoding immediately after receiving the first micro-packet, without waiting for all the data to arrive, thus significantly reducing end-to-end latency.

[0062] In one possible implementation, the data transmission method may further include: While transmitting the acoustic data signal substream generated by the Nth microdata packet, the N+1th microdata packet is encoded; And / or, while transmitting the acoustic data signal substream generated by the (N+1)th microdata packet, receive and process the reverse data returned by the external device for the Nth microdata packet.

[0063] Based on the aforementioned micro-data packet fragmentation and parallel encoding mechanism, this implementation method overlaps the encoding, transmission, and reception stages in time through pipelined processing to form a continuous data processing stream.

[0064] While transmitting the acoustic data signal substream generated by the Nth microdata packet, the (N+1)th microdata packet is encoded. Specifically, when the Nth microdata packet has been encoded and generates multiple corresponding acoustic data signal substreams, and is being transmitted through the speaker, the processor initiates the encoding and modulation process of the (N+1)th microdata packet in parallel. In this way, the encoding and transmission stages overlap in time, avoiding the idle waiting time caused by serial processing where each packet is encoded and then transmitted one at a time.

[0065] While transmitting the acoustic data signal substream generated by the (N+1)th microdata packet, the intelligent learning device simultaneously receives and processes the reverse data returned by the external device for the Nth microdata packet. This can be understood as follows: while the acoustic data signal substream of the (N+1)th microdata packet is being transmitted, the microphone of the intelligent learning device simultaneously acquires the second acoustic data signal returned by the external device, demodulating the reverse data (such as confirmation information, scoring results, etc.) for the Nth microdata packet. In this way, the data transmission and reverse data reception stages are performed in parallel, realizing pipelined processing for bidirectional communication. Bidirectional communication can include forward communication from the intelligent learning device to the external device, and reverse communication from the external device to the intelligent learning device.

[0066] In one embodiment of a spoken language assessment scenario, the specific processing procedure may be: Time T0-T10ms: Acquire the first 10ms micro data packet (user voice segment 1); Time interval T10ms-T20ms: Encode the first micro data packet and simultaneously acquire the second micro data packet; Time T20ms-T30ms: Transmit the substream generated by the first microdata packet, encode the second microdata packet, and acquire the third microdata packet simultaneously; Time T30ms-T40ms: Transmit the substream generated by the second microdata packet, encode the third microdata packet, receive the reverse data (such as the scoring result) for the first microdata packet, and collect the fourth microdata packet. This process is repeated to form a continuous data stream that is processed in parallel across four pipelines: acquisition, encoding, transmission, and reception.

[0067] Here, "and / or" indicates that the two pipeline methods can be implemented individually or in combination. In scenarios requiring only unidirectional transmission, only the encoding-transmission pipeline can be implemented; in scenarios requiring bidirectional interaction, both pipelines can be implemented simultaneously to achieve full-duplex real-time communication.

[0068] In one possible implementation, the data transmission method may further include: The second acoustic data signal emitted by an external device is acquired through the microphone of the intelligent learning device; The second acoustic data signal is subjected to spectral analysis using an audio processing interface supported by the browser environment, from which the inverse data is demodulated.

[0069] External devices can be parents' mobile phones, teachers' tablets, or students' terminals. After receiving the first acoustic data signal emitted by the smart learning device, the external device generates reverse data (such as data reception confirmation, control commands, cloud processing results, etc.) according to application needs, and encodes and modulates the reverse data onto high-frequency sound waves, which are then emitted through its speaker. The smart learning device's microphone collects sound wave signals from the environment in real time, which include the second acoustic data signal.

[0070] The acquired second acoustic data signal can be subjected to spectral analysis using an audio processing interface supported by the browser environment. For example, the AnalyserNode provided by the Web Audio API can be used to perform a Fast Fourier Transform on the audio signal input from the microphone to obtain the spectral distribution of the signal in the 16kHz-20kHz frequency band. Through spectral analysis, the subcarrier frequency band, signal strength, phase, and other characteristic information of the second acoustic data signal can be identified.

[0071] Based on the results of the spectrum analysis, the inverse data is demodulated from the second acoustic data signal. The demodulation process corresponds to the encoding process, using the same modulation scheme and parameter settings. For example, if the external device uses QPSK modulation, the phase value is mapped back to binary data by detecting the phase changes of each subcarrier during demodulation; if multi-carrier modulation is used, the data on each subcarrier needs to be demodulated separately and then combined and reassembled.

[0072] In one embodiment, the intelligent learning device, acting as the transmitter, encodes the input data using QPSK, then performs OFDM subcarrier allocation, and subsequently renders it into an audio signal via the Web Audio API. Simultaneously, user operation data undergoes phase-inversion encoding, and both types of encoded data are multiplexed and transmitted through the same acoustic channel. The external device, acting as the receiver, collects the audio signal through its microphone, sequentially performs FFT spectrum analysis, subcarrier extraction, and QPSK demodulation, ultimately reconstructing the data and restoring the original information.

[0073] In one embodiment, the reception of reverse data can be performed in parallel with the transmission of forward data, enabling full-duplex communication. For example, while the intelligent learning device transmits the first acoustic data signal generated by the Nth microdata packet, its microphone can simultaneously acquire and demodulate the reverse data returned by the external device in response to the (N-1)th microdata packet. This parallel processing enables real-time bidirectional interaction.

[0074] In one possible implementation, the first acoustic data signal and the second acoustic data signal are in the same frequency band and are implemented through phase inversion coding; Demodulating the reverse data can include: Extract the phase value of the second acoustic data signal; The phase value is compared with the reference phase value of the first acoustic data signal, and the reverse data is decoded based on the phase difference between the two.

[0075] Phase inversion coding is a modulation method that uses carrier phase changes to carry information. Its core principle is to represent different binary data by changing the phase of the signal on the same carrier frequency. Specifically, a reference phase (e.g., 0°) is set. When transmitting the binary value "0", the signal phase is kept consistent with the reference phase; when transmitting the binary value "1", the signal phase is inverted by 180°. The receiving end can reconstruct the carried binary data by detecting the phase offset of the received signal relative to the reference phase.

[0076] In one implementation, the first acoustic data signal transmitted by the intelligent learning device is encoded using a reference phase (e.g., 0°) and continuously carries forward learning process data. When an external device needs to transmit reverse data, it uses phase inversion encoding to transmit a second acoustic data signal on the same frequency band. Since the forward and reverse signals use the same carrier frequency band and are transmitted superimposed in the air, logical channel multiplexing is achieved through the phase dimension—the forward signal is carried in the reference phase channel, and the reverse signal is carried in the inverted phase channel.

[0077] After the intelligent learning device acquires the mixed signal through the microphone, it first separates the subcarrier frequency band where the second acoustic data signal is located through spectrum analysis. Then, using the AnalyserNode of the Web Audio API or through a custom digital signal processing algorithm, it extracts the instantaneous phase value of the second acoustic data signal at the current moment. Phase extraction can be achieved by performing a Hilbert transform on the signal or calculating the IQ quadrature components.

[0078] The reference phase value of the first acoustic data signal, cached locally, is used as a reference. Since the intelligent learning device itself encodes and transmits the first acoustic data signal, its reference phase (e.g., 0°) is known. This reference phase value can be pre-stored or recorded in real time during transmission.

[0079] The phase value of the extracted second acoustic data signal is compared with the reference phase value to calculate the phase difference Δθ. Based on the magnitude of the phase difference, the reverse data is decoded. The continuously decoded binary sequence is then reassembled according to a predetermined frame format to recover the complete reverse data. The reverse data may include confirmation information, control commands, cloud processing results, etc.

[0080] In one possible implementation, decoding the reverse data based on the phase difference between the two data points may include: When the phase difference is within the range of 0° ± preset threshold, it is decoded into the first binary value; When the phase difference is within the range of 180° ± a preset threshold, it is decoded into a second binary value; The decoded binary sequence is reassembled into reverse data.

[0081] For example, if Δθ is within the range of 0° ± a preset threshold (e.g., 0° ± 30°), the binary value of the current bearer is determined to be "0"; if Δθ is within the range of 180° ± a preset threshold (e.g., 180° ± 30°), the binary value of the current bearer is determined to be "1". The preset threshold can be set according to the system's anti-interference requirements and actual test results, and is usually between 15° and 45°. Setting a reasonable threshold range can tolerate a certain amount of noise and phase jitter while ensuring demodulation accuracy.

[0082] In one possible implementation, the data transmission method may further include: During the encoding process, checksums and forward error correction (FEC) encoding are added to the data blocks; Receive retransmission request information sent by external devices. The retransmission request information is generated when the checksum verification fails and the forward error correction encoding cannot be repaired. When a retransmission request for a specific data block is received, the data for that specific data block is retransmitted.

[0083] During the encoding process, forward error correction (FEC) codes are added to the data blocks to be transmitted. FEC codes can be Reed-Solomon (RS) encoding, such as RS(10,6) encoding, which can correct 2-byte burst errors. This means that every 6 bytes of original data generates 4 bytes of check data, forming a 10-byte encoded block. Through FEC encoding, even if a small number of bit errors occur during transmission, the receiving end can recover the original data using the check data through mathematical operations, without needing to request retransmission.

[0084] During the encoding process, a checksum is appended to each data block to verify the integrity of the data at the receiving end. The checksum can be a Cyclic Redundancy Check (CRC) code, such as using the CRC-16 or CRC-32 algorithm. CRC codes have the advantages of simple calculation and strong error detection capability, effectively detecting errors that occur during data transmission.

[0085] After receiving the first acoustic data signal and demodulating the data block, the external device first performs a cyclic redundancy check (CR). If the CR passes, it indicates that the data is complete and error-free, and an acknowledgment is returned to the intelligent learning device. If the CR fails, it attempts to repair the data using forward error correction coding. If the forward error correction coding can successfully repair the data (i.e., the error is within the error correction capability range), the repaired data is considered valid, and an ACK is returned. If the forward error correction coding cannot repair the data (i.e., the error exceeds the error correction capability range), the data block transmission is determined to have failed, and a retransmission request is generated.

[0086] When cyclic redundancy check fails and forward error correction coding cannot repair the issue, the external device generates a retransmission request message for that specific data block and sends it to the intelligent learning device via a second acoustic data signal. The retransmission request message may include the identifier of the data block to be retransmitted (such as the data block sequence number) so that the intelligent learning device can accurately locate the data that needs to be retransmitted.

[0087] The intelligent learning device receives retransmission request information sent by an external device through the aforementioned reverse data reception steps. When a retransmission request for a specific data block is identified, the device immediately reads the original data of that data block from the local cache, re-encodes and modulates it, and transmits the retransmitted version of the data block through a speaker.

[0088] The retransmitted data blocks can use the same coding parameters as the first transmission, or they can use a more robust coding method (such as increasing FEC redundancy, reducing modulation order, etc.) depending on the channel conditions to increase the probability of successful retransmission.

[0089] In scenarios where assignments are distributed in noisy classrooms, the 17-19kHz frequency band can be dynamically selected to avoid WiFi interference, allowing multiple student terminals to receive the data simultaneously with a success rate of nearly 100%. In oral exam scenarios in home appliance environments, the system can detect refrigerator compressor noise (18kHz) and automatically switch to a 16.5kHz carrier. In mobile scenarios, it resists multipath interference, eliminating intersymbol interference caused by sound wave reflection due to student movement through OFDM frequency domain equalization.

[0090] Because oral assessments require latency of <80ms (the human perception threshold), fairness must be ensured during multi-device competition for channels in classroom quizzes, and a stable interactive experience needs to be maintained in weak network environments, current Bluetooth systems have an average latency of 200ms+, causing stuttering in voice assessments. High conflict rates among multiple devices on Wi-Fi can lead to asynchronous classroom interactions, and the lack of a service priority mechanism can cause courseware downloads to block oral interaction. Therefore, it is necessary to prioritize data to ensure orderly data transmission.

[0091] In one possible implementation, the data transmission method may further include: Different transmission priorities are set for learning process data based on the type of business.

[0092] When acquiring learning process data, intelligent learning devices first identify the business type of the data. Based on the characteristics of the education scenario, the business type can be divided into three priority levels: first priority, second priority, and third priority. The first priority takes precedence over the second priority, and the second priority takes precedence over the third priority.

[0093] The first priority can include services that are extremely sensitive to latency, such as spoken language assessment and real-time pronunciation correction. These are characterized by the need for users to receive immediate feedback, and the end-to-end latency must be kept below the threshold perceptible to the human ear (typically less than 50ms); otherwise, it will severely impact user experience and teaching effectiveness. For example, in spoken language practice, users need to hear AI correction and suggestions immediately after pronouncing words; any noticeable delay will disrupt the learning rhythm.

[0094] The second priority can include functions such as providing feedback on answer results, answering questions in class, and submitting multiple-choice questions. These functions have certain requirements regarding latency, but can tolerate short wait times (usually less than 100ms). For example, after a student submits their answer to a multiple-choice question, they can see the correct / incorrect feedback within a few hundred milliseconds, and the short wait will not significantly affect the user experience.

[0095] The third priority can include background services such as courseware downloads, system updates, and learning report synchronization. These services are not sensitive to latency and can be performed when the channel is idle, without having to compete for the channel resources required for real-time interaction.

[0096] In one embodiment, oral assessment or real-time pronunciation correction services are set as the first priority, answer result feedback services are set as the second priority, and courseware download or system update services are set as the third priority. When channel resources are scarce, high-priority data is encoded and transmitted first.

[0097] To implement priority scheduling, multiple data queues can be set up in front of the encoding module, each corresponding to a different priority. The scheduler retrieves data from the queues for processing according to priority order: as long as the first priority queue is not empty, the first priority data is processed first; the second priority queue is processed only when the first priority queue is empty; and the third priority queue is processed only when both the second and first priority queues are empty. This ensures that high-priority services receive the most timely processing.

[0098] When channel resources are scarce (e.g., severe environmental interference reduces available subcarriers, or multiple devices compete for the channel simultaneously), the scheduler can execute the following strategies: suspend or delay the encoding and transmission of low-priority data to release channel resources; ensure that high-priority data is transmitted first to maintain its low latency; allocate more subcarriers or adopt more robust coding methods for high-priority data to improve transmission success rate; and allow low-priority data to wait in a buffer until the channel becomes available again before continuing transmission.

[0099] In another implementation, a certain percentage of bandwidth can be reserved for data of different priorities to prevent low-priority data from being blocked indefinitely. First priority service: Reserve 20% of bandwidth to ensure real-time transmission of critical data; Second priority service: Reserve 60% of the bandwidth to carry the main teaching interaction data; Third priority service: Reserve 20% of the bandwidth for background transmission, provided that the requirements of the first two are met.

[0100] When the actual load of high-priority services is lower than the reserved bandwidth, the remaining bandwidth can be dynamically borrowed by low-priority services to improve channel utilization.

[0101] The impact of the priority strategy is shown in Table 2:

[0102] Table 2 By setting a priority scheduling implementation method, it is possible to ensure that critical services such as oral assessment can still receive priority transmission when channel resources are scarce, maintaining a low-latency real-time interactive experience; it also differentiates the allocation of channel resources according to the importance and real-time requirements of the services, avoiding low-priority services from preempting critical resources; and it makes full use of idle resources to transmit background data while ensuring high-priority services, thereby improving channel utilization; moreover, the priority division and scheduling strategy can be flexibly adjusted according to different application scenarios and user needs, adapting to diverse educational service requirements.

[0103] Based on the same inventive concept, this disclosure also provides a data transmission device that can be applied to intelligent learning devices. Please refer to [link to relevant documentation]. Figure 2 The data transmission device 600 may include: The first processing module 601 is configured to acquire learning process data to be transmitted; The second processing module 602 is configured to modulate the learning process data onto a carrier wave in the 16kHz-20kHz frequency band through an audio processing interface supported by the browser environment to generate a first acoustic data signal. The third processing module 603 is configured to transmit a first acoustic data signal through the speaker of the intelligent learning device for external devices to receive and decode.

[0104] Optionally, the second processing module 602 may include: The first sub-processing module is configured to use a ScriptProcessorNode or AudioWorklet node of the Web Audio API to encode and modulate the learning process data onto multiple parallel subcarriers to generate the first acoustic data signal.

[0105] Optionally, the data transmission device 600 may further include: The fourth processing module is configured to detect the spectral distribution of ambient noise and determine the interference frequency band; The fifth processing module is configured to dynamically adjust the subcarrier allocation strategy according to the interference frequency band in order to avoid the interference frequency band.

[0106] Optionally, the data transmission device 600 may further include: The sixth processing module is configured to detect the signal-to-noise ratio of the currently used subcarrier frequency band; The seventh processing module is configured to switch to a backup subcarrier frequency band for data transmission when the signal-to-noise ratio is lower than a preset threshold.

[0107] Optionally, the first sub-processing module may include: The first processing unit is configured to divide the learning process data into multiple millisecond-level microdata packets; The second processing unit is configured to encode multiple micro data packets in parallel to generate multiple acoustic data signal sub-streams corresponding to each subcarrier, wherein the first acoustic data signal is formed by superimposing multiple acoustic data signal sub-streams.

[0108] Optionally, the data transmission device 600 may further include: The eighth processing module is configured to encode the (N+1)th microdata packet while transmitting the acoustic data signal substream generated by the Nth microdata packet; And / or, The ninth processing module is configured to receive and process the reverse data returned by the external device for the Nth microdata packet while transmitting the acoustic data signal substream generated by the N+1th microdata packet.

[0109] Optionally, the data transmission device 600 may further include: The tenth processing module is configured to acquire second acoustic data signals emitted by external devices via the microphone of the intelligent learning device; The eleventh processing module is configured to perform spectral analysis on the second acoustic data signal through an audio processing interface supported by the browser environment, and demodulate the inverse data from it.

[0110] Optionally, the first acoustic data signal and the second acoustic data signal are in the same frequency band and are implemented through phase inversion coding; The eleventh processing module may include: The second sub-processing module is configured to extract the phase value of the second acoustic data signal; The third sub-processing module is configured to compare the phase value with the reference phase value of the first acoustic data signal and decode the reverse data based on the phase difference between the two.

[0111] Optionally, the third sub-processing module is specifically configured as follows: When the phase difference is within the range of 0° ± preset threshold, it is decoded into the first binary value; When the phase difference is within the range of 180° ± a preset threshold, it is decoded into a second binary value; The decoded binary sequence is reassembled into reverse data.

[0112] Optionally, the data transmission device 600 may further include: The twelfth processing module is configured to add checksums and forward error correction codes to data blocks during the encoding process; The thirteenth processing module is configured to receive retransmission request information sent by external devices. The retransmission request information is generated when the checksum verification fails and the forward error correction encoding cannot be repaired. The fourteenth processing module is configured to retransmit the data of a specific data block when a retransmission request is received for that specific data block.

[0113] Optionally, the data transmission device 600 may further include: The fifteenth processing module is configured to set different transmission priorities for learning process data based on the business type. Among them, oral assessment or real-time pronunciation correction services are set as the first priority, answer result feedback services are set as the second priority, and courseware download or system update services are set as the third priority. The first priority takes precedence over the second priority, and the second priority takes precedence over the third priority. When channel resources are scarce, high-priority data is encoded and transmitted first.

[0114] Regarding the data transmission device in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the data transmission method, and will not be elaborated upon here.

[0115] Figure 3This is a block diagram illustrating an intelligent learning device 700 according to an exemplary embodiment. (See diagram below.) Figure 3 As shown, the intelligent learning device 700 may include: a processor 701, a memory 702, and a multimedia component 703, which may be one or more of a speaker and microphone, an input / output (I / O) interface 704, and a communication component 705.

[0116] The processor 701 controls the overall operation of the intelligent learning device 700 to complete all or part of the steps in the aforementioned data transmission method. The memory 702 stores various types of data to support the operation of the intelligent learning device 700. This data may include, for example, instructions for any application or method operating on the intelligent learning device 700, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0117] Multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 702 or transmitted via communication component 705. The audio component also includes at least one speaker for outputting audio signals. The speaker may be configured to emit a first acoustic data signal; the microphone may be configured to acquire a second acoustic data signal emitted by an external device. I / O interface 704 provides an interface between processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 705 is used for wired or wireless communication between the intelligent learning device 700 and other devices. Wireless communication may include Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof; therefore, the corresponding communication component 705 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0118] In an exemplary embodiment, the intelligent learning device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the data transmission method described above.

[0119] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the data transmission method described above. For example, the computer-readable storage medium may be the memory 702 including program instructions, which may be executed by the processor 701 of the intelligent learning device 700 to complete the data transmission method described above.

[0120] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a processor, which, when executed by the processor, implements the steps of the data transmission method described above.

[0121] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0122] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0123] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A data transmission method, characterized in that, Applied to intelligent learning devices, including: Acquire the learning process data to be transmitted; The learning process data is modulated onto a carrier wave in the 16kHz-20kHz frequency band using an audio processing interface supported by the browser environment to generate a first acoustic data signal. The first acoustic data signal is emitted through the speaker of the intelligent learning device for external devices to receive and decode.

2. The data transmission method according to claim 1, characterized in that, The process of modulating the learning data onto a carrier wave in the 16kHz-20kHz frequency band via an audio processing interface supported by the browser environment to generate a first acoustic data signal includes: The learning process data is encoded and modulated onto multiple parallel subcarriers using the ScriptProcessorNode or AudioWorklet node of the Web Audio API, thereby generating the first acoustic data signal.

3. The data transmission method according to claim 2, characterized in that, The data transmission method further includes: Detect the spectral distribution of environmental noise to determine the interference frequency band; The subcarrier allocation strategy is dynamically adjusted according to the interference frequency band to avoid the interference frequency band.

4. The data transmission method according to claim 2, characterized in that, The data transmission method further includes: Detect the signal-to-noise ratio of the currently used subcarrier frequency band; If the signal-to-noise ratio is lower than a preset threshold, the system switches to a backup subcarrier frequency band for data transmission.

5. The data transmission method according to claim 2, characterized in that, The step of encoding and modulating the learning process data onto multiple parallel subcarriers to generate the first acoustic data signal includes: The learning process data is divided into multiple millisecond-level microdata packets; The multiple microdata packets are encoded in parallel to generate multiple acoustic data signal sub-streams corresponding to each subcarrier, and the first acoustic data signal is formed by superimposing the multiple acoustic data signal sub-streams.

6. The data transmission method according to claim 5, characterized in that, The data transmission method further includes: While transmitting the acoustic data signal substream generated by the Nth microdata packet, the N+1th microdata packet is encoded; And / or, while transmitting the acoustic data signal substream generated by the (N+1)th microdata packet, receive and process the reverse data returned by the external device for the Nth microdata packet.

7. The data transmission method according to claim 1, characterized in that, The data transmission method further includes: The intelligent learning device acquires a second acoustic data signal emitted by an external device through its microphone. The second acoustic data signal is subjected to spectral analysis using the audio processing interface supported by the browser environment, and the inverse data is demodulated from it.

8. The data transmission method according to claim 7, characterized in that, The first acoustic data signal and the second acoustic data signal are in the same frequency band and are implemented through phase inversion encoding; The demodulated reverse data includes: Extract the phase value of the second acoustic data signal; The phase value is compared with the reference phase value of the first acoustic data signal, and the reverse data is decoded based on the phase difference between the two.

9. The data transmission method according to claim 8, characterized in that, Decoding the reverse data based on the phase difference between the two includes: When the phase difference is within the range of 0° ± preset threshold, it is decoded into the first binary value; When the phase difference is within the range of 180° ± a preset threshold, it is decoded into a second binary value; The decoded binary sequence is reassembled into reverse data.

10. The data transmission method according to claim 7, characterized in that, The data transmission method further includes: During the encoding process, checksums and forward error correction codes are added to the data blocks; Receive retransmission request information sent by an external device, the retransmission request information being generated when the checksum verification fails and the forward error correction encoding cannot be repaired; When a retransmission request for a specific data block is received, the data of that specific data block is retransmitted.

11. The data transmission method according to claim 1, characterized in that, The data transmission method further includes: Different transmission priorities are set for the learning process data according to the business type; Among them, oral assessment or real-time pronunciation correction services are set as the first priority, answer result feedback services are set as the second priority, and courseware download or system update services are set as the third priority. When channel resources are scarce, high-priority data is encoded and transmitted first, with the first priority taking precedence over the second priority, and the second priority taking precedence over the third priority.

12. A data transmission device, characterized in that, Applied to intelligent learning devices, including: The first processing module is configured to acquire the learning process data to be transmitted; The second processing module is configured to modulate the learning process data onto a carrier wave in the 16kHz-20kHz frequency band through an audio processing interface supported by the browser environment to generate a first acoustic data signal. The third processing module is configured to transmit the first acoustic data signal through the speaker of the intelligent learning device for external devices to receive and decode.

13. An intelligent learning device, characterized in that, include: Memory, which stores computer-executable instructions; A processor configured to execute the computer-executable instructions to implement the data transmission method according to any one of claims 1-11; A loudspeaker configured to transmit the first acoustic data signal; The microphone is configured to acquire second acoustic data signals emitted by external devices.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the data transmission method according to any one of claims 1-11.

15. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the data transmission method according to any one of claims 1-11.

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