A method and system for cross-terminal voice input and metadata repair based on H5

Abstract

This invention relates to the field of Internet audio data processing technology, specifically to a cross-terminal voice input and metadata repair method and system based on H5. The method includes: a data receiving step: receiving real-time segmented data streams and terminal environment fingerprints; a model building step: injecting the environment fingerprint to build an ideal model and generating a theoretical drift benchmark; a feature mapping comparison step: extracting real-world features and comparing them with the benchmark to determine the metadata deviation type; a repair execution step: adaptively verifying and repairing the data stream based on the deviation type; and a reconstruction generation step: rewriting the metadata header based on the repaired data payload to generate a standardized audio file. This invention, through reverse engineering using an ideal model, accurately distinguishes between hardware anomalies and network packet loss, improving the usability of Web audio acquisition in complex network environments.

Description

Technical Field

[0001] This invention relates to the field of Internet audio data processing technology, specifically to a cross-terminal voice input and metadata repair method and system based on H5. Background Technology

[0002] Existing H5 cross-terminal voice input systems typically include a front-end acquisition terminal and a server-side processing center. H5 refers to HyperText Markup Language 5, which defines a standard interface for multimedia acquisition on web pages. The front-end acquisition terminal uses the web audio interface to capture raw sound data and segments it into data streams, uploading them to the server-side processing center. The server-side processing center receives the data streams and usually trusts the sampling rate and duration parameters reported by the front-end acquisition terminal. It directly concatenates the received segmented data and constructs a file header based on the metadata information provided by the front-end acquisition terminal to generate a playable audio file, achieving persistent storage of the web-side recording data. However, inherent deviations in the front-end device's crystal oscillator or non-linear suspension of the browser process running in the background can cause inconsistencies between the actual physical characteristics of the uploaded data and the reported metadata. This results in audio files generated by the server having abnormal pitch or corrupted file structures, leading to problems where the recording files cannot be recognized or played. Summary of the Invention

[0003] The purpose of this invention is to provide a cross-terminal voice recording and metadata repair method and system based on H5, which avoids abnormal pitch or structural damage of recording files caused by inherent deviations in the crystal oscillator of the front-end device or non-linear suspension of the browser process, and can accurately distinguish between hardware anomalies and network packet loss by constructing an ideal model to generate standardized audio files; specifically, the technical solution of this invention is as follows: Receive real-time segmented data stream and terminal environment fingerprint uploaded by H5 terminal, wherein the real-time segmented data stream includes audio payload and timing marker; Construct an ideal standard container model, and deduce a theoretical drift benchmark containing the expected data form based on the terminal environment fingerprint in the ideal standard container model; Extract the real-time sharded data stream's real-time data features, and compare these real-time data features with the theoretical drift benchmark using feature mapping to determine the type of metadata deviation. Based on the metadata deviation type, the real-time fragmented data stream is adaptively verified and repaired, and the metadata header information is rewritten according to the repaired data payload to generate a standardized audio file.

[0004] Preferably, the construction of the ideal standard container model, in which a theoretical drift benchmark containing the expected data format is derived based on the terminal environment fingerprint, includes: establishing a theoretical lossless recording duration calculation model based on a virtual clock, wherein the theoretical lossless recording duration calculation model does not depend on the duration parameter reported by the H5 terminal, but is based on the physical byte volume to construct a linear ideal benchmark; querying a preset H5 terminal difference entropy library to convert the terminal environment fingerprint into a corresponding environmental interference factor; and injecting the environmental interference factor into the linear ideal benchmark to simulate and generate the theoretical drift benchmark that should be generated under the terminal environment fingerprint.

[0005] Preferably, the environmental interference factor includes a resampling factor, a nonlinear truncation probability model, and a temporal hole operator; wherein, the resampling factor is used to simulate the deviation between the hardware sampling rate and the standard sampling rate, the nonlinear truncation probability model is used to simulate the expected probability of data being nonlinearly suspended and truncated due to abnormal browser process scheduling, and serves as a global data loss coefficient, and the temporal hole operator is used to simulate out-of-order data caused by network jitter.

[0006] Preferably, the step of extracting the actual data features of the real-time segmented data stream and comparing the actual data features with the theoretical drift benchmark to determine the metadata deviation type includes: calculating the actual physical attributes of the real-time segmented data stream, the actual physical attributes including the actual number of bytes and the actual physical frequency derived from the actual waveform zero-crossing rate; comparing the initial metadata reported by the H5 terminal with the physical features derived from the theoretical drift benchmark; when there is a linear frequency offset between the sampling rate claimed by the initial metadata and the actual physical frequency derived from the actual waveform zero-crossing rate, the metadata deviation type is determined to be sampling rate drift; when the sequence number of the real-time segmented data stream does not match the timing model of the theoretical drift benchmark, the metadata deviation type is determined to be data truncation or frame loss.

[0007] Preferably, the adaptive verification and repair of the real-time fragmented data stream based on the metadata deviation type includes: in response to the metadata deviation type being sampling rate drift, calculating the target resampling rate based on the theoretical drift benchmark, and performing resampling processing on the real-time fragmented data stream; in response to the metadata deviation type being data truncation or frame loss, determining the missing position using the ideal standard container model, and inserting a silent frame or comfort noise at the missing position to align the time axis.

[0008] Preferably, the step of rewriting the metadata header information based on the repaired data payload to generate a standardized audio file includes: calculating the total payload size of the repaired real-time segmented data stream; reverse-calculating the file header information block parameters; and rewriting the RIFF block information using the file header information block parameters to ensure that the player can recognize the standardized audio file.

[0009] Preferably, the terminal environment fingerprint includes user agent information, context sampling rate, and browser buffer size.

[0010] A cross-terminal voice input and metadata repair system based on H5 includes: a data acquisition module for receiving real-time segmented data streams and terminal environment fingerprints uploaded by H5 terminals, wherein the real-time segmented data streams include audio payloads and timing markers; The simulation modeling module is used to construct an ideal standard container model, and in the ideal standard container model, a theoretical drift benchmark containing the expected data form is deduced based on the terminal environment fingerprint. The differential analysis module is used to extract the real-time data features of the real-time sharded data stream, and compare the real-time data features with the theoretical drift benchmark through feature mapping to determine the type of metadata deviation. The repair execution module is used to adaptively verify and repair the real-time segmented data stream based on the metadata deviation type, and rewrite the metadata header information according to the repaired data payload to generate a standardized audio file.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs an ideal standard container model and injects terminal environment fingerprints, discards unreliable front-end time parameters, and instead establishes a theoretical lossless recording duration calculation model based on a virtual clock. It also constructs a linear ideal benchmark based on physical byte volume. By querying a preset H5 terminal difference entropy library, the terminal environment fingerprint is transformed into a corresponding environmental interference factor, enabling the system to simulate and generate a theoretical drift benchmark containing the expected data form. This provides a rigorous mathematical basis for accurately distinguishing hardware characteristics from network faults. 2. This invention extracts the real-time fragmented data stream's actual data characteristics and compares them with the theoretical drift benchmark through feature mapping. It calculates the actual number of bytes and the actual waveform zero-crossing rate to derive the actual physical frequency, and compares the initial metadata reported by the H5 terminal with the physical characteristics derived from the theoretical drift benchmark. During the detection phase, it determines whether the sequence number of the real-time fragmented data stream matches the timing model of the theoretical drift benchmark, thereby effectively distinguishing between the hardware's inherent mute protection mechanism and data truncation or frame loss caused by network jitter. This avoids misjudgment caused by signal characteristics and ensures the accuracy of metadata deviation type identification. 3. This invention performs adaptive verification and repair based on metadata deviation type. For sampling rate drift, it calculates the target resampling rate based on theoretical drift benchmark and performs resampling processing on real-time segmented data streams. For data truncation or frame loss, it uses an ideal standard container model to determine the missing position and inserts silent frames or comfort noise at the missing position to align the time axis. This achieves absolute linear restoration of audio on the physical time axis, ensuring the continuity of the sound and the consistency of energy in the restored audio. 4. This invention reverse-engineers the metadata header information based on the repaired data payload, calculates the total payload size of the repaired real-time segmented data stream, and reverse-engineers the file header information block parameters. It then uses these parameters to rewrite the RIFF block information, ensuring that the generated audio file conforms to the standard format specifications. This solves the problem of missing or damaged file headers caused by browser process crashes or abnormal interruptions, greatly improving the usability and playback compatibility of cross-terminal voice input in complex mobile network environments. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0014] Example 1: Please see Figure 1 A method for cross-terminal voice input and metadata repair based on H5 includes: receiving a real-time segmented data stream and a terminal environment fingerprint uploaded by an H5 terminal, wherein the real-time segmented data stream contains an audio payload and a timing marker; constructing an ideal standard container model, and deriving a theoretical drift benchmark containing the expected data format based on the terminal environment fingerprint in the ideal standard container model; extracting the actual data features of the real-time segmented data stream, comparing the actual data features with the theoretical drift benchmark through feature mapping to determine the metadata deviation type; performing adaptive verification and repair on the real-time segmented data stream based on the metadata deviation type, and rewriting the metadata header information according to the repaired data payload to generate a standardized audio file.

[0015] This embodiment details the overall execution logic of the H5-based cross-terminal voice recording and metadata repair method. This method aims to solve the problems of sampling rate drift and metadata corruption caused by browser process abnormalities in existing H5 web page recording on heterogeneous hardware terminals.

[0016] The system performs data reception steps through WebSocket or HTTP long connection channels to obtain real-time segmented data streams collected and transcoded by the H5 frontend through the AudioWorklet or ScriptProcessorNode interface. This data stream is divided into binary data packet sequences with fixed time intervals or byte sizes. The audio payload carries the actual sound data, and the timing marker includes the SequenceID and timestamp generated by the frontend. At the same time, the system captures the terminal environment fingerprint, which serves as a set of parameters identifying the hardware and software characteristics of the frontend's operating environment. This fingerprint is derived from the HTTP request header and JavaScript environment probe.

[0017] The system introduces the concept of synthetic analysis, constructing an ideal standard container model on the server side. This is a virtual audio processing sandbox based on standard parameters such as a 16kHz sampling rate and 16-bit bit depth. The system does not directly trust the metadata uploaded from the front end, but instead infers the audio data format that should be generated on a specific terminal based on the terminal environment fingerprint, i.e., the theoretical drift benchmark. On this basis, the system extracts the physical attributes of the actual received data and performs feature mapping comparison to identify metadata deviation types, including sampling rate drift, data truncation, or frame loss. Then, based on the determined deviation type, the system performs adaptive verification and repair operations such as resampling or frame interpolation on the original data stream. The system reverse reconstructs the file header of WAV or MP3 according to the actual physical payload size, ensuring that the generated audio file can be recognized and played in any player.

[0018] This embodiment constructs an ideal model and injects environmental fingerprints for reverse engineering. In the H5 recording scenario, it can accurately distinguish between pitch abnormalities caused by hardware and data loss caused by the network. Even if the mobile browser crashes due to screen lock or insufficient memory, resulting in the loss of metadata, it can still generate playable standardized audio files through physical reconstruction, which greatly improves the usability of Web audio acquisition in complex mobile network environments.

[0019] Example 2: Constructing an ideal standard container model, and in the ideal standard container model, deriving a theoretical drift benchmark containing the expected data form based on the terminal environment fingerprint, including: establishing a theoretical lossless recording duration calculation model based on a virtual clock, the theoretical lossless recording duration calculation model does not depend on the duration parameters reported by the H5 terminal, but is based on the physical byte volume to construct a linear ideal benchmark; querying a preset H5 terminal difference entropy library to convert the terminal environment fingerprint into a corresponding environmental interference factor; injecting the environmental interference factor into the linear ideal benchmark to simulate and generate the theoretical drift benchmark that should be generated under the terminal environment fingerprint.

[0020] Environmental interference factors include resampling factor, nonlinear truncation probability model and time-series hole operator; among them, resampling factor is used to simulate the deviation between hardware sampling rate and standard sampling rate, nonlinear truncation probability model is used to simulate the expected probability of data nonlinear suspension and truncation caused by abnormal browser process scheduling, and serves as global data loss coefficient, and time-series hole operator is used to simulate out-of-order data caused by network jitter.

[0021] This embodiment further refines the specific process of model construction and injection. The core lies in abandoning unreliable front-end time parameters and instead constructing a benchmark based on physical determinism. The system establishes a theoretical lossless recording duration calculation model based on a virtual clock. This model is completely independent of the duration parameters reported by the front-end API, relying on the counter in the server-side receiving buffer to establish a linear ideal timing benchmark based on the physical byte volume. The specific structure of the ideal standard container model is a virtual audio data processing space allocated in the server-side memory, with preset standard sampling rate and standard bit depth as parameter boundaries. The specific structure of the theoretical lossless recording duration calculation model based on the virtual clock is a linear algebraic function with the total amount of physical bytes actually received by the server as the independent variable, and its formula is as follows: The theoretical lossless recording duration is mapped and output through this operational logic structure; in, Linear ideal timing reference, derived from model calculations, physically represents the theoretical lossless recording duration, in seconds; The total number of physical bytes received, sourced from the server's receive buffer count, is measured in bytes. The preset standard sampling rate, such as 16000Hz, is derived from the system configuration. Standard bit depth, such as 16 bits, originates from system configuration; : Number of channels, sourced from system configuration; physical unit: individual; constants in the formula. The conversion factor between bytes and bits represents... ; For example, when the server has received a total of physical bytes... 32,000 bytes, standard sampling rate configured in the system 16000Hz, bit depth 16-bit, number of channels When the value is 1, the theoretical lossless recording duration can be calculated using the linear ideal timing reference formula. It takes 1.0 second; The system queries a pre-defined H5 terminal difference entropy database. This database uses a key-value pair structure for storage. The key is the hash value of the user agent information, and the value is the set of hardware characteristic parameters calibrated through offline black-box testing. The specific method for converting this into the corresponding environmental interference factor is as follows: The system extracts the user agent information string from the terminal's environmental fingerprint and calculates its corresponding hash value. Then, this hash value is used as the primary key to input into the pre-defined difference entropy database for precise key-value pair matching. If a match is successful, the corresponding value, i.e., the set of hardware characteristic parameters, is directly extracted from the database. The set of hardware characteristic parameters includes the crystal oscillator offset rate. Maximum cutoff rate Sensitivity coefficient and temporal hole feature boundary parameters Specifically, the crystal oscillator offset rate The reciprocal of the factor is assigned to the resampling factor, which will result in the maximum cutoff rate. and sensitivity coefficient Assign a nonlinear truncation probability model and define the boundary parameters of the time series hole feature. By assigning a time-series void operator, the instantiation and transformation of environmental interference factors can be completed; To ensure that those skilled in the art can obtain these key parameters, this embodiment clarifies the offline calibration method for the library: an automated test cluster containing the target device model is constructed; a standard 1kHz sine wave and a mute alternating reference signal are input; and the server calculates the crystal oscillator offset rate by analyzing the frequency offset of the received signal. By simulating a high-load environment using scripts, the packet loss rate under different buffer sizes was statistically analyzed, and the maximum truncation rate was obtained through fitting. and sensitivity coefficient The specific fitting process employs the nonlinear least squares method, constructing the following objective loss function. : in, To determine the number of test sample points, For a buffer size of The actual packet loss rate is as follows. The range of values ​​is Dimensionless; The safety buffer threshold is preset to 4096 bytes in this embodiment; the system uses the Gauss-Newton iteration method or the Levinberg-Marquardt algorithm to solve for the safety buffer threshold. Minimize the parameter pair Set empirical initial values ​​during iterative calculations. This allows for precise parameter calibration. To ensure the consistency of dimensions in exponentiation, Defined as a sensitivity coefficient, its physical unit is 1000 kJ / m². This is used to make the difference in buffer sizes dimensionless; in the above formula and subsequent calculations, The physical unit is a byte. , The crystal oscillator offset rate measured in the preceding steps is dimensionless. In particular, addressing the technical challenge of obtaining parameters from the entropy library for unknown terminal devices, such as newly released models, which is of concern to those skilled in the art, this embodiment designs an adaptive cold start deduction logic: When the entropy library returns an empty value, the system does not interrupt the process, but instead starts the parameter estimation subroutine in real time; specifically, for the crystal oscillator offset rate... The system utilizes the connection to establish the initial pre- The data stream is calculated per second, and the ratio of the server's physical received byte rate to the front-end's claimed rate is used to obtain a real-time estimate through linear regression fitting. ; For truncation parameters and The system calls a preset general security baseline, whose parameters are taken from the statistical median of all Android or iOS devices of the same operating system in the entropy library, for example... Similarly, for the temporal void operator... Feature boundary parameters This parameter establishes the time... Temporal voiding operator with independent variable The effective scope, that is, when physical time... When the timing hole mask is triggered, it is activated; otherwise, the mask is invalid. The system has a default value. The time interval is 0.2 seconds to handle situations where entropy library parameters cannot be obtained; this mechanism ensures that the system can still generate effective environmental interference factors even in the absence of offline calibration. The system injects environmental interference factors, whether obtained from database lookup or real-time estimation, into the linear model to generate a theoretical drift baseline. The specific injection process involves using the resampling factor from the environmental interference factors as a scaling parameter for the base sampling rate on the time axis, transforming the time-series hole operator into a start / stop mask parameter for data generation on the time axis, and strictly adhering to the aforementioned procedures during computation. and The boundary constraints are determined by the multiplication logic between the two factors, which together limit the actual data generation rate at a specific physical moment. The nonlinear truncation probability model is used as the global data loss coefficient, and both are substituted into and applied to the theoretically expected number of bytes. The calculation formula makes the originally ideal fixed byte growth rate subject to dynamic correction by interference factors; This benchmark is specifically embodied in a time evolution model that includes deterministic trajectories and probabilistic features. ,in, This represents the probability of nonlinear truncation, which physically means the expected probability that data will be nonlinearly suspended and truncated due to browser process scheduling anomalies under the current terminal environment fingerprint; it is used to accurately describe the expected shape of the data stream on the time axis; based on this model, the theoretical expected byte size is... The calculation formula is as follows: Finally, the expected damage data sequence derived over physical time from this formula is used as the theoretical drift benchmark. in, The variable is the integral follower variable, which is defined in the physical coordinate system as the absolute elapsed time relative to the physical start time of the recording session, in seconds. ); This is the offset of the start time of the current unprocessed fragment on the global physical timeline; The end time offset of this slice, and the integration interval. This represents the theoretical physical duration that the segment should cover, ensuring that the integration interval strictly corresponds to the physical timeline. To resolve the logical contradiction that the server cannot know the actual physical start time of the front-end, this embodiment initially defines the session start time as the physical time when the server receives the first data packet of the session. However, in order to overcome network transmission delays The resulting fixed time axis offset leads to phase misalignment in subsequent feature map comparisons. To address this, the system incorporates adaptive phase calibration logic during model construction. By comparing the sequence number of the first arriving fragment with the temporal void operator in the theoretical model... The characteristic boundary, in the cold start phase of connection establishment, i.e. In order to eliminate network transmission delay The resulting fixed time axis offset triggers the system to execute phase alignment logic and calculate the delay compensation value in reverse. ,in, The preset initialization threshold is used to define the duration of the cold start observation window, typically set to 1 to 2 seconds. The specific reverse calculation formula is as follows: in, Temporal void operator The characteristic physical moment of the jump from 1 to 0, for example ; The sequence number of the first valid fragment received by the server; The theoretical duration of a single fragment; If the calculation result of the above formula is This indicates that the first fragment has exceeded the hardware mute protection period, at which point the system will forcibly set... This assumes the front-end is generated. It follows a monotonically increasing logic and has not been reset; if it detects... If a non-monotonic mutation occurs, the system will reset the logical time. for And re-execute the phase alignment logic; the system uses this formula to force the logical start point of the first slice to be aligned to the physical moment when the hardware mute protection ends; And the integral variable was modified to This correction ensures that the constructed theoretical drift benchmark is strictly aligned in phase with the actual data generation time, thus providing an effective logical basis for subsequent feature mapping comparison and eliminating unknown network latency interference. This represents the start time offset of the current unprocessed fragment on the global timeline. This is the end time offset of the current fragment; : Resampling factor, the value of which is equal to the crystal oscillator offset rate of this model recorded in the entropy database. The reciprocal of, that is Or, in unknown terminal scenarios, the results may originate from the aforementioned real-time estimates. The physical meaning is the inherent proportional deviation between the actual sampling rate and the nominal value of the hardware ADC. : Temporal void operator, corresponding to the embodiment used to simulate out-of-order or timing anomalies caused by network jitter; In the specific physical model of this embodiment, the operator is instantiated to describe the inherent scheduling hole characteristics of browser kernels, such as Webkit or Blink, under a specific OS. These characteristics are macroscopically manifested as time-series discontinuities similar to network jitter, but have deterministic patterns. For example, some Android devices have a mute protection mechanism at the beginning of the recording. Defined as A step function that is 1 when the condition is met and 0 otherwise; this operator is used to reserve valid missing data positions in the reference. : A nonlinear truncation probability model used to simulate the expected probability of data nonlinear suspension and truncation due to abnormal browser process scheduling, and used as a global data loss coefficient; system definition Browser buffer size in the terminal environment fingerprint Nonlinear mapping function: in, The safety buffer threshold is set to 4096 bytes; The maximum cutoff rate, such as 0.1 or the aforementioned common baseline value; This is the sensitivity coefficient, such as 0.002 or the aforementioned common baseline value. and All parameters are specific to this model or adaptively derived parameters retrieved from the entropy database; the formula clarifies that the smaller the buffer, the higher the probability of a process being suspended, leading to legitimate frame drops; This embodiment reconstructs a flawed ideal model on the server side using the above formula; this model not only outputs the expected byte size in scalar form. More importantly, it established and The time-varying reference coordinate system constituted provides a mathematical basis for the feature mapping comparison in subsequent embodiments; the innovation of this model lies in: It predicts the errors that the hardware should produce; in subsequent steps, any errors that conform to the prediction of the model, such as specific sampling rate deviations or startup silence, are considered to be normal metadata, that is, in line with hardware characteristics; while anomalies that deviate from the model are judged as metadata deviations that need to be repaired. To ensure the feasibility of the above theoretical model in a digital computer system, the system employs a discretized numerical computation method to handle the integral formula in its engineering implementation; specifically, the system divides the time axis into tiny discrete time slices. For example, for 10ms, using the Riemann sum approximation to replace the continuous integral, the discretized calculation logic is as follows: in, Indicates the first The absolute time position of each discrete time step is determined by the following method: That is, from the current shard start time Start accumulating step size; The sum of the distance steps is calculated using the following formula: ,in, This indicates a floor operation; it ensures that discrete sampling points are strictly aligned with the continuous physical time axis. Meanwhile, regarding the construction of the H5 terminal difference entropy library, this embodiment uses a standardized JSON data structure for storage to ensure the readability of parameters; a typical entropy library entry structure is shown below: {"hash": "ua_hash_value", "params": {"k": 1.00025,"beta_type": "step", "beta_threshold_ms": 200,"eta": 0.1,"theta": 0.002}}; Through this explicit data structure definition, discretization algorithm, and the aforementioned adaptive cold start deduction mechanism, those skilled in the art can accurately reproduce the generation process of the theoretical drift benchmark, regardless of whether the terminal device is known or unknown.

[0022] Example 3: Extract the actual data features of the real-time segmented data stream and compare them with the theoretical drift benchmark through feature mapping to determine the metadata deviation type. This includes: calculating the actual physical attributes of the real-time segmented data stream, including the actual number of bytes and the actual physical frequency derived from the actual waveform zero-crossing rate; comparing the initial metadata reported by the H5 terminal with the physical features derived from the theoretical drift benchmark; when there is a linear frequency offset between the sampling rate claimed by the initial metadata and the actual physical frequency derived from the actual waveform zero-crossing rate, the metadata deviation type is determined to be sampling rate drift; when the sequence number of the real-time segmented data stream does not match the timing model of the theoretical drift benchmark, the metadata deviation type is determined to be data truncation or frame loss.

[0023] This embodiment details the specific logic of differential analysis and deviation determination. The core lies in performing feature mapping comparison, i.e., comparing the structural consistency between the actual data stream and the theoretical benchmark in the time-frequency domain. This process strictly relies on the theoretical drift benchmark constructed in Embodiment 2. The hardware parameters are as follows; the specific steps are as follows: Step 1: Physical Attribute Extraction and Zero-Crossing Rate Calculation. The system performs windowing analysis on the real-time segmented data stream and calculates the actual physical frequency derived from the zero-crossing rate of the actual waveform. To prevent background noise interference, a threshold with a dead zone is used. The calculation formula is as follows: in, The context sampling rate corresponding to the current data fragment, in units of This parameter is introduced to convert the dimensionless zero-crossing density ratio into a physically meaningful frequency value, so as to facilitate comparison with the standard expected value. Make a direct comparison; The sign function with dead-zone threshold is explicitly defined mathematically as follows: This definition ensures that only when the signal amplitude substantially crosses... Zero-crossing events are only counted when the noise tolerance range is met; among them, This refers to the audio sampling interval, measured in seconds. This represents the total number of sampling points within the current calculation window, expressed in units of points. For the first The amplitude of each discrete audio sample point, the normalized value, and the range of values. ; Step 2: The topology determination system for sampling rate drift performs a comparison operation, which does not directly rely on statistical values, but instead introduces the hardware resampling factor from the theoretical drift benchmark. That is, crystal oscillator offset rate The reciprocal of the value, see Example 2; derive the theoretical physical characteristics, specifically including the theoretical zero-crossing rate that should be exhibited under the influence of the terminal hardware offset. And the theoretical flow rate benchmark derived from physical bytes; based on the current context sampling rate. Corresponding standard zero-crossing rate expectation For example, the preset frequency in voice mode is 1450Hz, combined with Calculate the theoretical zero-crossing rate that the terminal should exhibit physically. : Calculate the actual frequency offset: calculate the ratio of the actual measured value to the standard value. To overcome measurement noise, a sliding window statistical method is used. mode Deviation type determination: Perform topological matching with the theoretical factor; if any of the following conditions are met, the metadata bias type is determined to be sampling rate drift: Condition A, i.e., crystal oscillator drift: Implement dual orthogonal verification in the frequency domain and transmission domain to address the potential logic loop defects of a single zero-crossing rate determination, namely, the difficulty in distinguishing low signal characteristics caused by the low frequency drift. The low sampling rate caused by the hardware clock has been corrected to: in, , The threshold Defined as the allowable error limit for time-domain flow velocity measurement. This represents the actual physical byte flow rate ratio. This represents a logical AND operation, which requires that both the frequency domain offset and the time domain velocity simultaneously meet their respective threshold conditions. The calculation formula is as follows: in, Define the current sliding window Total number of physical bytes received internally. To eliminate the equivalent physical duration after network jitter, the calculation method is as follows: the system maintains a length of... A sliding window that records the contents of the window. The physical reception time of each fragment's server With cumulative bytes Linear regression fitting is performed using the least squares method: Using regression slope Calculate the smoothed physical duration of the current window: This process filters out single-transmission network latency through regression analysis. Random fluctuations; Only when frequency domain shift With time domain transfer offset They all point to the same hardware drift factor. At that time, and satisfying ,in This threshold, defined as the allowable error limit for time-domain flow rate measurement, confirms that the deviation originates from hardware crystal oscillator drift. ; The core logic of this dual verification mechanism lies in: Frequency domain features characterizing signal content, while The physical time-domain characteristics characterizing data transmission are orthogonal at the physical level; they only become apparent when both simultaneously deviate from and point to the same drift factor. Only then can we rule out simple signal content or simple network congestion interference, thus constituting the necessary and sufficient condition for determining hardware crystal oscillator drift. This dual verification mechanism ensures that the system will not misinterpret measurements due to signal content, such as low-frequency components. The underestimation of the error is interpreted as a drift consistent with the hardware model, thus ensuring the accuracy of subsequent resampling parameters. The correctness of the threshold; The selection criteria are based on the typical frequency offset range and measurement tolerance of consumer-grade crystal oscillators, typically within... to Between these, plus the measurement error margin introduced by network jitter, it is set to Right now This is sufficient to cover the vast majority of non-faulty hardware drift; Condition B, i.e., configuration error: in, , This indicates a coarse-grained sampling rate configuration error, such as 48k data being incorrectly labeled as 16k; this threshold... The selection is based on the minimum ratio difference between adjacent standard sampling rates, such as... The difference is about 8%, and setting it to 5% can effectively distinguish between hardware drift and configuration errors; Step 3: The topology determination system for data truncation or frame loss compares the sequence number of the real-time data stream. Compared to the theoretical drift benchmark; when the sequence number difference between adjacent fragments is detected. At that time, the system triggers differential mask interval verification: mapping the sequence number gap to a missing interval on the time axis. The mismatch mentioned above, "when the sequence number of the real-time fragmented data stream does not match the timing model of the theoretical drift reference," specifically refers to the mapping of the missing sequence number intervals in the real-time data stream on the time axis, which cannot be verified by the timing gap operator in the theoretical drift reference. Provide a reasonable explanation, namely that the missing information exceeds the hardware's inherent silent protection period; calculate the interval and its relationship with the hardware's inherent hole operator. overlap rate : Decision logic: If If it is determined to be hardware silent protection, it will not be marked as metadata deviation; The metadata deviation type is determined to be data truncation or frame loss. This loss is caused by network jitter and needs to be repaired by triggering frame interpolation. By introducing and In participating in the determination, this embodiment strictly adheres to the embodiment limitation of comparing real-world characteristics with theoretical drift benchmarks.

[0024] Example 4: Adaptive verification and repair of real-time fragmented data streams based on metadata deviation types include: in response to the metadata deviation type being sampling rate drift, calculating the target resampling rate based on the theoretical drift benchmark and resampling the real-time fragmented data stream; in response to the metadata deviation type being data truncation or frame loss, determining the missing position using an ideal standard container model and inserting silent frames or comfort noise at the missing position to align the time axis.

[0025] This embodiment details the specific repair process for different types of deviations, especially how to use theoretical benchmarks to ensure the accuracy of repair parameters; The repair system responding to sampling rate drift determines the target resampling rate based on a theoretical drift benchmark. Unlike conventional methods that rely solely on real-time calculated values, this system prioritizes using baseline parameters to ensure stability: if condition A is determined in step 4, the precise value from the entropy database is directly retrieved. This avoids resampling rate jitter caused by short-term signal fluctuations; if condition B is determined, the calculated value is taken: ; The system initiates an M-phase multiphase filter bank to perform variable sampling processing on the real-time segmented data stream; to ensure the reproducibility of the algorithm, the system sets the number of phases. Prototype low-pass filter length The filter coefficients are generated using a Kaiser window-weighted Sinc function, and the cutoff frequency is set to: in, The input signal sampling rate, Target resampling rate; both are measured in Hertz (Hz); parameters It is based on the Caesar window design formula. The calculation shows that, dB is a preset stopband attenuation index, which ensures that aliasing noise during resampling is below the threshold perceptible to the human ear; here, the unit of A is explicitly dB. and The functional relationship originates from the classic empirical design formula of the Kaiser window; phase separation coefficient The extraction formula is: in, During resampling calculations, the system calculates the phase index in real time based on the input-output time ratio. in, For the current output sampling point at the target resampling rate The normalized value of the time axis position is calculated using the following formula: Here This is the sequence index of the current output sampling point; and only the corresponding... The branch is convolved with the input signal; through this deterministic phase selection mechanism, aliasing noise is effectively suppressed, and the data volume is reduced accordingly. The scale is precisely corrected; the repair system responds to data truncation or frame loss by using an ideal standard container model to determine the location and duration of the missing data. Comfortable noise is inserted at the breakpoints to align the time axis. The noise generation formula is as follows: in, For the generated first One noise sample point; It is the root mean square energy value of the most recent complete segment in the preceding sequence, used to maintain the energy continuity of the background noise. The formula for calculating the energy calibration and attenuation composite coefficient is as follows: in, The crest factor correction term for uniformly distributed noise is set to a value of [value missing] in this embodiment. , This is a comfort attenuation factor, with a value ranging from 0.6 to 0.8; this attenuation factor The value range is an empirical interval derived from a large number of subjective listening tests, i.e., Mean Opinion Score (MOS): This empirical interval [0.6, 0.8] is based on statistical analysis of no less than 500 double-blind listening tests, ensuring that the MOS score decay does not exceed 0.5 points; when At times, energy collapse at the frame interpolation point can cause a noticeable auditory break; when If the background noise itself fluctuates greatly, it can easily introduce abrupt noise pulses; a value of 0.6 to 0.8 can achieve the best balance between maintaining auditory continuity and avoiding abrupt noise. This embodiment directly calls the theoretical benchmark. By controlling resampling parameters and using a model to calculate the precise interpolation frame length, absolute linear restoration of audio on the physical timeline is achieved.

[0026] Example 5: Based on the repaired data payload, the metadata header information is rewritten to generate a standardized audio file. This includes: calculating the total size of the payload of the repaired real-time segmented data stream, where payload refers to the pure audio binary data part of the audio file excluding the header metadata; and reversing the calculation of the file header information block parameters, and rewriting the RIFF block information using the file header information block parameters to ensure that the player can recognize the standardized audio file.

[0027] This embodiment details the process of rewriting the metadata header information after all fragment processing is complete. After receiving, repairing, and splicing all fragments, the system generates the final standardized audio file and initiates a reverse calculation process to determine the file header information block parameters. Taking WAV format as an example, the system needs to rewrite the RIFF block information and define the total file length parameter. The calculation formula is as follows: in, Total file length, derived from cumulative calculation, physically refers to the physical size of the final generated audio file, in bytes; The standard WAV header has a fixed length, derived from the format specification; in this embodiment, the standard value is used. The physical meaning is the space occupied by metadata, and the unit is Byte; The total number of fragments after repair is derived from counter statistics and physically represents the number of segments that make up the file; it is dimensionless. : No. The data payload size of each repaired segment is derived from actual measurement and its physical meaning is the number of bytes of effective audio data, in bytes. The system overwrites the WAV header information using the calculated total load; specifically, the system will sum the load... Write the Subchunk2Size region with an offset of 40 to 43 bytes, and then calculate... ,Right now Write the ChunkSize area at offsets of 4 to 7 bytes to ensure that the size declared in the file header is strictly consistent with the physical size; at the same time, force the sampling rate field at offsets of 24 to 27 bytes to be rewritten to the system standard sampling rate; Furthermore, to strictly comply with the RIFFWAVE format specification and ensure that the repaired file does not exhibit playback issues such as speed increase or pitch shifting in different players, the system, in addition to rewriting the size parameters, must also recalculate and overwrite the data transfer rate parameters in the format blocks; the specific calculation and writing steps are as follows: Calculate the block alignment unit. : in, This is the floor operator, which means rounding up the value within the parentheses to the smallest integer not less than that value; The standard bit depth is 16. The number of channels is 1; the system writes the calculation result to the file header offset position between bytes 32 and 33; the average byte transfer rate is calculated. : in, The target standard sampling rate is 16000; the system writes the calculation results to the file header offset position between 28 and 31 bytes. This embodiment solves the dead file problem by post-event statistical analysis of the actual payload size and reverse rewriting the header, including ChunkSize, SampleRate, ByteRate, and BlockAlign. Even if the recording is abnormally interrupted due to a browser crash, the uploaded data segments can still form a legal audio file that can be recognized by any player. This is of decisive significance for scenarios with high reliability requirements, such as evidence recording or medical consultation recording.

[0028] Example 6: Terminal environment fingerprints include user agent information, context sampling rate, and browser buffer size.

[0029] This embodiment clarifies the specific composition and technical application of terminal environment fingerprint; the terminal environment fingerprint consists of three core dimensions: user agent information, which is used to identify browser kernels such as Webkit and Blink and operating system version, which is the primary key for querying the difference entropy library; The context sampling rate, obtained through the front-end AudioContext.sampleRate interface, reflects the browser's perception of the hardware sampling rate and is used to compare with the actual physical characteristics to identify logical inconsistencies; the browser buffer size reflects the latency characteristics of the front-end's audio processing. The smaller the buffer, the higher the probability of timing holes (frame drops), and vice versa; this parameter is used to calibrate the nonlinear truncation probability model. By collecting these three types of fingerprints, the system can construct a high-fidelity terminal profile, thereby accurately reproducing the front-end recording environment on the server side. This profile mechanism enables the server to provide targeted compensation for the unusual behavior of different brands of mobile phones and browsers, significantly improving the device compatibility coverage of the solution.

[0030] Example 7: Please see Figure 2 A cross-terminal voice input and metadata repair system based on H5 includes: a data acquisition module for receiving real-time segmented data streams and terminal environment fingerprints uploaded by H5 terminals, wherein the real-time segmented data streams contain audio payloads and timing markers; a simulation modeling module for constructing an ideal standard container model, and deriving a theoretical drift benchmark containing expected data forms based on the terminal environment fingerprints in the ideal standard container model; a differential analysis module for extracting real-world data features from the real-time segmented data streams, comparing the real-world data features with the theoretical drift benchmarks through feature mapping to determine the metadata deviation type; and a repair execution module for adaptively verifying and repairing the real-time segmented data streams based on the metadata deviation type, and rewriting the metadata header information based on the repaired data payload to generate standardized audio files.

[0031] This embodiment constructs an H5-based cross-terminal voice input and metadata repair system running on a cloud server or edge computing node. The system includes four core functional modules in its architecture: a data acquisition module for establishing WebSocket or HTTP long connections to receive real-time fragmented data streams and terminal environment fingerprints uploaded by H5 terminals. This module integrates a circular buffer to temporarily store out-of-order data packets to cope with network jitter; and a simulation modeling module for loading a pre-set H5 terminal difference entropy library and, based on the received environment fingerprint, instantiating an ideal standard container model, calculating and outputting the theoretical drift benchmark in real time. The differential analysis module, as the core decision unit of the system, executes the feature mapping comparison logic. This module includes an FFT (Fast Fourier Transform) subunit, which is used to extract audio spectral features and calculate the zero-crossing rate. The repair execution module includes a resampling engine and a waveform synthesizer. According to the instructions of the differential analysis module, it performs frequency conversion or hole patching operations on the data stream, and finally calls the file system interface to rewrite the file header and output a standardized audio file. This embodiment decouples the complex signal processing flow through modular design, realizing the automated conversion from an unreliable H5 front-end to a highly reliable standardized audio file; the collaborative work of each module is particularly suitable for large-scale concurrent voice acquisition scenarios, such as online customer service systems or remote medical consultation platforms, and can effectively reduce the operation and maintenance costs and data cleaning costs caused by differences in client environments.

[0032] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for cross-terminal voice input and metadata repair based on H5, characterized in that, include: Receive real-time segmented data stream and terminal environment fingerprint uploaded by H5 terminal, wherein the real-time segmented data stream includes audio payload and timing marker; Construct an ideal standard container model, and in the ideal standard container model, deduce a theoretical drift benchmark containing the expected data form based on the terminal environment fingerprint; Extract the real-time sharded data stream's real-time data features, and compare the real-time data features with the theoretical drift benchmark using feature mapping to determine the metadata deviation type; Based on the metadata deviation type, the real-time fragmented data stream is adaptively verified and repaired, and the metadata header information is rewritten according to the repaired data payload to generate a standardized audio file.

2. The method for cross-terminal voice input and metadata repair based on H5 according to claim 1, characterized in that, The construction of the ideal standard container model, in which a theoretical drift benchmark containing the expected data format is derived based on the terminal environment fingerprint, includes: A theoretical lossless recording duration calculation model based on a virtual clock is established. This theoretical lossless recording duration calculation model does not depend on the duration parameters reported by the H5 terminal, but is based on a linear ideal benchmark constructed from physical bytes. Query the preset H5 terminal difference entropy library and convert the terminal environment fingerprint into the corresponding environmental interference factor; The environmental interference factor is injected into the linear ideal benchmark to simulate and generate the theoretical drift benchmark that should be generated under the terminal environmental fingerprint.

3. The method for cross-terminal voice input and metadata repair based on H5 according to claim 2, characterized in that, The environmental interference factors include a resampling factor, a nonlinear truncation probability model, and a temporal hole operator; wherein, the resampling factor is used to simulate the deviation between the hardware sampling rate and the standard sampling rate, the nonlinear truncation probability model is used to simulate the expected probability of data being nonlinearly suspended and truncated due to abnormal browser process scheduling, and serves as a global data loss coefficient, and the temporal hole operator is used to simulate out-of-order data caused by network jitter.

4. The method for cross-terminal voice input and metadata repair based on H5 according to claim 2, characterized in that, The step of extracting real-world data features from the real-time segmented data stream and comparing these features with the theoretical drift benchmark through feature mapping to determine the type of metadata deviation includes: Calculate the actual physical attributes of the real-time fragmented data stream, including the actual number of bytes and the actual physical frequency derived based on frequency domain feature parameters; Compare the initial metadata reported by the H5 terminal with the physical characteristics derived from the theoretical drift benchmark; When there is a linear frequency offset between the sampling rate claimed by the initial metadata and the actual physical frequency derived from the zero-crossing rate of the actual waveform, the metadata deviation type is determined to be sampling rate drift. When the sequence number of the real-time fragmented data stream does not match the timing model of the theoretical drift benchmark, the metadata deviation type is determined to be data truncation or frame loss.

5. The method for cross-terminal voice input and metadata repair based on H5 according to claim 1, characterized in that, The adaptive verification and repair of the real-time sharded data stream based on the metadata deviation type includes: In response to the metadata deviation type being sampling rate drift, the target resampling rate is calculated based on the theoretical drift benchmark, and the real-time fragmented data stream is resampled. In response to the metadata deviation type being data truncation or frame loss, the missing location is determined using the ideal standard container model, and a silent frame or comfort noise is inserted at the missing location to align the timeline.

6. The method for cross-terminal voice input and metadata repair based on H5 according to claim 1, characterized in that, The step of rewriting the metadata header information based on the repaired data payload to generate a standardized audio file includes: The total payload size of the real-time fragmented data stream after statistical repair; The file header information block parameters are calculated in reverse, and the RIFF block information is rewritten using the file header information block parameters to ensure that the player can recognize the standardized audio file.

7. The method for cross-terminal voice input and metadata repair based on H5 according to claim 1, characterized in that, The terminal environment fingerprint includes user agent information, context sampling rate, and browser buffer size.

8. A cross-terminal voice input and metadata repair system based on H5, used to implement the method of any one of claims 1-7, characterized in that, include: The data acquisition module is used to receive real-time segmented data streams and terminal environment fingerprints uploaded by H5 terminals. The real-time segmented data streams include audio payloads and timing markers. The simulation modeling module is used to construct an ideal standard container model, and in the ideal standard container model, a theoretical drift benchmark containing the expected data form is deduced based on the terminal environment fingerprint. The differential analysis module is used to extract the real-time data features of the real-time sharded data stream, and compare the real-time data features with the theoretical drift benchmark through feature mapping to determine the type of metadata deviation. The repair execution module is used to adaptively verify and repair the real-time segmented data stream based on the metadata deviation type, and rewrite the metadata header information according to the repaired data payload to generate a standardized audio file.

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