Cloud computing-based smart adjustment system for vinyl record recording

Abstract

This invention relates to the field of intelligent manufacturing and precision machining technology, specifically a cloud-based intelligent adjustment system for vinyl record recording. The system includes: a data acquisition module that acquires audio data streams, cutting tool status, environmental parameters, and material properties of the master lacquer plate during the recording process; a model building module that constructs a thermo-coupling finite element model of the cutting tool and master lacquer plate; a stress processing module that determines the instantaneous physical stress at the forming points of the recording texture; a risk assessment module that calculates a standardized instantaneous distortion risk index; a risk prediction module that determines the predicted risk index for future moments; a compensation decision module that generates a compensation intensity factor; and a compensation execution module that executes a multi-dimensional adaptive compensation strategy based on the compensation intensity factor. This invention solves the quality uncertainty problem caused by the inability to predict in real time in traditional technologies, significantly improving the yield and sound fidelity of finished products.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and precision machining technology, specifically to a cloud computing-based intelligent adjustment system for vinyl record recording. Background Technology

[0002] In the vinyl record manufacturing industry, recording is a core process. Its goal is to accurately and physically convert audio signals into grooves on the master lacquer plate. This process is highly sensitive to the dynamic characteristics of the audio signal, the state of the cutting tool, and the physical properties of the lacquer plate material. Any slight fluctuation may affect the final sound quality.

[0003] Traditional recording technology relies heavily on the personal experience of operators and fixed process parameter settings. This approach lacks real-time insight into the microscopic physical effects during the recording process and cannot anticipate or avoid the risk of exceeding the molding stress limit caused by transient changes in audio signals or fluctuations in material properties. When faced with complex audio with a high dynamic range, in order to avoid physical damage, a conservative strategy of sacrificing audio dynamics is often adopted, resulting in uncertainty in recording quality and making it difficult to balance high fidelity and yield.

[0004] Therefore, how to provide an intelligent adjustment method for vinyl record recording that can predict physical molding risks in real time and perform feedforward closed-loop control is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention provides a cloud computing-based intelligent adjustment system for vinyl record burning. Specifically, the technical solution of the present invention is as follows:

[0006] The cloud-based intelligent adjustment system for vinyl record burning includes:

[0007] The data acquisition module is used to acquire audio data streams, engraving knife status, environmental parameters, and material properties of the master lacquer plate during the burning process.

[0008] The model building module is used to construct a thermo-mechanically coupled finite element model of the engraving knife and lacquer tray based on the parameters obtained by the data acquisition module.

[0009] The stress processing module is used to determine the instantaneous physical stress at the etching pattern forming point based on the thermo-coupling finite element model of the engraving knife and paint plate.

[0010] The risk assessment module is used to calculate a standardized instantaneous distortion risk index by combining instantaneous physical stress with the preset maximum allowable forming stress.

[0011] The risk prediction module processes audio data streams to determine the predicted risk index for future moments.

[0012] The compensation decision module is used to generate a compensation intensity factor based on the predicted risk index and the preset compensation activation threshold.

[0013] The compensation execution module is used to execute a multi-dimensional adaptive compensation strategy based on the compensation intensity factor.

[0014] Preferably, the stress processing module determines the instantaneous physical stress, including:

[0015] The effective strain factor is calculated based on the normalized amplitude of the audio data stream and the cutting speed of the engraving tool.

[0016] Retrieve the temperature-dependent elastic modulus corresponding to the instantaneous temperature at the contact point of the engraving tool from the material properties parameters;

[0017] Instantaneous physical stress is determined by combining the effective strain factor and the temperature-dependent elastic modulus.

[0018] Preferably, the risk assessment module calculates the instantaneous distortion risk index, including:

[0019] A standardized instantaneous distortion risk index is obtained by calculating the ratio of instantaneous physical stress to maximum allowable forming stress.

[0020] Based on the value of the instantaneous distortion risk index, the current risk status is divided into preset risk levels.

[0021] Preferably, the risk prediction module determines the predicted risk index, including:

[0022] Aggressive feature vectors are extracted from the audio data stream. These aggressive feature vectors include crest factor, spectral roll-off point, and short-term signal energy growth rate.

[0023] The input sequence, consisting of aggressive feature vectors from the current and past time steps, is submitted to a pre-defined long short-term memory network model for processing to output a predicted risk index.

[0024] Preferably, the compensation decision module generates a compensation intensity factor, including:

[0025] When the predicted risk index is greater than the compensation activation threshold, the compensation intensity factor is determined based on the difference between the predicted risk index and the compensation activation threshold, as well as the preset compensation gain coefficient.

[0026] When the predicted risk index is less than or equal to the compensation activation threshold, the compensation intensity factor is set to zero.

[0027] Preferably, the multidimensional adaptive compensation strategy includes dynamic fine-tuning of the engraving tool heating power.

[0028] Preferably, the compensation execution module performs dynamic engraving tool heating power fine-tuning, including:

[0029] The real-time power adjustment amount is determined based on the compensation intensity factor and the preset thermal compensation coefficient.

[0030] By combining the real-time power adjustment with the reference power, a real-time target power is generated, and the power of the engraving knife heating wire is adjusted accordingly.

[0031] Preferably, the multidimensional adaptive compensation strategy includes instantaneous feedforward dynamic equalization.

[0032] Preferably, the compensation execution module performs instantaneous feedforward dynamic equalization, including:

[0033] The real-time gain attenuation is determined based on the compensation intensity factor and the preset equalization compensation coefficient.

[0034] By combining the real-time gain attenuation with the original gain, a real-time target gain is generated, and the gain of a specific frequency band of the audio signal is adjusted accordingly.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. This invention upgrades vinyl recording from a traditional process that relies on manual experience to a data-driven intelligent precision manufacturing process. Its core advantage lies in building a complete closed loop from data perception, physical modeling, risk prediction to feedforward compensation, which can proactively avoid physical forming risks and solve the quality uncertainty problem caused by the inability to predict in real time in traditional technology, thus significantly improving the yield of finished products and the fidelity of sound quality.

[0037] 2. The theoretical basis of this system is its accurate physical modeling capability. By creating a thermo-mechanically coupled finite element model of the engraving tool and the paint plate, the system can dynamically reflect the nonlinear instantaneous relationship between the audio signal, the state of the engraving tool and the material response. It integrates the audio amplitude, cutting speed and the temperature-dependent elastic modulus of the material to calculate the highly accurate instantaneous physical stress, providing a reliable basis for subsequent accurate risk assessment and compensation control.

[0038] 3. This invention represents a technological leap from passive response to proactive avoidance. Its key lies in its innovative risk prediction mechanism. The system utilizes a Long Short-Term Memory (LSTM) network to process the aggressive feature vectors of audio signals, creatively constructing a computational shortcut directly from audio features to future physical risks. This avoids complex and time-consuming real-time physical simulations, advancing the intervention time by a critical few milliseconds, achieving true foresight.

[0039] 4. The system adopts a multi-dimensional adaptive compensation strategy that combines fast and slow approaches and software and hardware collaboration. By combining slow physical intervention with fast signal processing, it can suppress the forming stress within a safe threshold under extreme transient signal impact, achieving an ideal balance between risk avoidance and maintaining the original dynamics of the signal. Attached Figure Description

[0040] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0041] Figure 1 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0042] 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.

[0043] Example 1:

[0044] Please see Figure 1 A cloud-based intelligent adjustment system for vinyl record burning includes:

[0045] The data acquisition module is used to acquire audio data streams, engraving knife status, environmental parameters, and material properties of the master lacquer plate during the burning process.

[0046] The model building module is used to construct a thermo-mechanically coupled finite element model of the engraving knife and lacquer tray based on the parameters obtained by the data acquisition module.

[0047] The stress processing module is used to determine the instantaneous physical stress at the etching pattern forming point based on the thermo-coupling finite element model of the engraving knife and paint plate.

[0048] The risk assessment module is used to calculate a standardized instantaneous distortion risk index by combining instantaneous physical stress with the preset maximum allowable forming stress.

[0049] The risk prediction module processes audio data streams to determine the predicted risk index for future moments.

[0050] The compensation decision module is used to generate a compensation intensity factor based on the predicted risk index and the preset compensation activation threshold.

[0051] The compensation execution module is used to execute a multi-dimensional adaptive compensation strategy based on the compensation intensity factor.

[0052] This embodiment provides a cloud computing-based intelligent adjustment system for vinyl record burning. The system aims to solve the problem of burning quality uncertainty caused by the inability to predict and avoid physical forming risks in real time in the existing technology. By constructing a complete closed loop from physical modeling and risk prediction to feedforward compensation, it realizes intelligent and refined adjustment of the vinyl record burning process.

[0053] The system's overall workflow ranges from comprehensive data acquisition to physical-level modeling and stress analysis, followed by quantitative assessment of instantaneous risks. Deep learning models then proactively predict these risks, and based on the predictions, the hardware execution unit performs adaptive compensation before the risks actually occur. In this system, the cloud computing platform primarily handles high-performance computing tasks during the offline phase. Specifically, the large-scale finite element simulations required for the model building module, and the training of the long short-term memory network model in the risk prediction module, are all completed on the cloud server. The trained model is deployed to the local edge computing unit of the CD burner to perform real-time risk prediction and compensation decisions, thus achieving a collaborative working mode where complex model training is performed in the cloud and lightweight real-time inference is conducted at the edge.

[0054] In this embodiment, the system specifically includes the following modules:

[0055] The data acquisition module aims to provide comprehensive and real-time data input for subsequent physical modeling, risk prediction, and compensation control. In this embodiment, the module is implemented through a high-precision, multi-source heterogeneous sensor network and database interface. Specifically, it is responsible for acquiring the audio data stream during the recording process. The audio data stream refers to the original digital audio signal to be recorded onto the record, serving as the initial excitation source for physical modeling and the analysis object for risk prediction. Simultaneously, the module monitors the status of the cutting tool in real time through embedded sensors, such as the real-time temperature of the cutting tool and the micro-vibration data of the system.

[0056] In addition, environmental parameters affecting the recording environment are collected; material property parameters of the current batch of lacquer master discs are obtained by accessing the material database; the lacquer master disc refers to the disc coated with special lacquer used to make the metal mold of vinyl records, which is the direct object of the recording process; the material property parameters refer to the dataset describing the physical properties of the lacquer disc material, which serves to provide accurate material constitutive relations for thermo-mechanical coupling modeling, and its source is the mechanical and thermal test reports provided by the material supplier, such as its viscoelastic modulus and thermal softening coefficient;

[0057] The model building module aims to establish a mathematical model that can accurately describe the microscopic physical process of the engraving, serving as the physical basis for subsequent stress analysis and risk quantification. In this embodiment, the module constructs a thermo-mechanical coupling finite element model of the engraving knife and the paint plate based on the parameters obtained by the data acquisition module. The core innovation of this model is that it is not a static model, but a complex physical simulation model that can dynamically reflect the nonlinear and instantaneous interaction between the audio signal, the state of the engraving knife, and the material response.

[0058] The stress processing module aims to transform the physical model from the model building module into a quantifiable key physical indicator directly related to the engraving quality. In this embodiment, the module is based on the thermo-coupling finite element model of the engraving knife and the paint plate. Through a series of calculations, it determines the instantaneous physical stress at the engraving texture forming point. [Instantaneous physical stress] refers to the equivalent physical stress borne by the material at the precise instant when the engraving knife cuts the paint plate to form the texture. Its function is to directly quantify the degree of deformation and failure risk of the material at that moment. Its source is the calculation output of this module.

[0059] The risk assessment module aims to transform the stress value of the physical dimension into a standardized risk metric directly related to the engraving defects. In this embodiment, the module combines the instantaneous physical stress calculated by the stress processing module with a preset maximum allowable forming stress to calculate a standardized instantaneous distortion risk index. The [maximum allowable forming stress] refers to the upper limit of stress at which a specific paint pad material will not suffer macroscopic damage such as tearing or fracture under ideal cutting conditions. Its function is to provide a benchmark for risk assessment, and its source is the material's mechanical property test report.

[0060] The risk prediction module aims to predict recording risks in advance, thereby transforming the intervention mode of the entire system from reactive response to proactive avoidance. In this embodiment, the module uses deep learning technology to process the audio data stream to determine the predicted risk index within a very short time, such as milliseconds. The core value of this module lies in its ability to bypass the computationally expensive real-time physical simulation by learning the complex mapping relationship between audio features and physical risks, thus achieving low-latency and high-precision risk warning.

[0061] The purpose of the compensation decision module is to transform the predicted risk output by the upstream module into precise and quantifiable control commands that can be executed by the downstream hardware. In this embodiment, the module generates a compensation intensity factor based on the predicted risk index output by the risk prediction module and a preset compensation activation threshold. The compensation intensity factor refers to a standardized, dimensionless control signal intensity value, which is used to coordinate the adjustment range of all compensation execution mechanisms and ensure the synchronization and matching of multi-dimensional compensation strategies. Its source is the calculation output of this module.

[0062] The purpose of the compensation execution module is to transform the abstract control signals generated by the compensation decision module into specific, real-time fine-tuning operations on the hardware parameters of the burner. In this embodiment, the module executes a multi-dimensional adaptive compensation strategy based on the compensation intensity factor, such as simultaneously adjusting the burner heating power, audio dynamic equalization and cutting depth, thereby directly intervening at the physical level and suppressing the formation of potential risks.

[0063] This invention constructs a complete intelligent adjustment system from data perception, physical modeling, risk quantification, forward prediction to closed-loop control; it transforms the experience-dependent process of vinyl record burning into a data-driven, model-controllable precision manufacturing process; compared with existing technologies, this system can predict and avoid burning distortion caused by drastic changes in audio signals and fluctuations in material state in real time and with foresight, thereby significantly improving the yield and sound fidelity of the burned products without sacrificing dynamic range, and achieving a breakthrough in the stability and ultimate performance of burning quality;

[0064] To ensure system robustness, the data acquisition module incorporates sensor data validity verification logic, capable of identifying and handling sensor failures or data anomalies. In such cases, the system can switch to a preset conservative safety mode. Furthermore, the risk prediction module undergoes rigorous stress testing before model deployment, verifying its output stability under extreme signal inputs such as silence and DC bias. For unknown inputs exceeding the distribution range of the model training data, the system will simultaneously increase the uncertainty index of risk assessment and tend to trigger a more conservative compensation strategy to ensure absolute safety during the burning process.

[0065] Example 2:

[0066] The stress processing module determines instantaneous physical stress, including:

[0067] The effective strain factor is calculated based on the normalized amplitude of the audio data stream and the cutting speed of the engraving tool.

[0068] Retrieve the temperature-dependent elastic modulus corresponding to the instantaneous temperature at the contact point of the engraving tool from the material properties parameters;

[0069] Instantaneous physical stress is determined by combining the effective strain factor and the temperature-dependent elastic modulus.

[0070] Based on Example 1, this embodiment provides detailed specifications for the specific technical solution of the stress processing module in determining instantaneous physical stress, aiming to ensure the physical accuracy of stress calculation and comprehensive consideration of the influence of multiple factors.

[0071] The inherent logic of the stress processing module in determining instantaneous physical stress includes:

[0072] To comprehensively evaluate the combined effects of audio signal dynamics and recording physical motion on material deformation, this embodiment introduces and calculates an effective strain factor ∈ t ;

[0073] Effective strain factor ∈ t This refers to a dimensionless parameter whose purpose is to unify the contributions of different physical properties, namely audio amplitude and engraving speed, to material strain into a standardized metric. Its calculation formula is as follows:

[0074] ∈ t =κ a ·A t +κ v ·V t

[0075] Among them, A t The normalized amplitude of the audio data stream is a dimensionless value that reflects the instantaneous intensity of the audio signal at time t. It is derived from the audio signal acquired in real time by the data acquisition module and obtained through standardization processing.

[0076] V t The cutting speed of the engraving tool, measured in m / s, reflects the physical speed of the engraving process; it is calculated from the spindle speed of the engraving machine and the current engraving radius.

[0077] κ a Amplitude-strain weight, dimensionless, used to calibrate the contribution of amplitude to total strain;

[0078] κ v Velocity-strain weight, in units of s / m, is used to calibrate the contribution of velocity to the total strain and to ensure κ. v ·V t The term is dimensionless;

[0079] κ a and κ v The calibration method is as follows: In the finite element simulation environment of the model building module, a series of working points consisting of different constant amplitudes and constant velocities are set, the simulation is run to obtain the corresponding steady-state strain values, and the two weight coefficients are fitted by regression analysis methods such as the least squares method to ensure the accuracy of their physical meaning.

[0080] To take the material's temperature sensitivity into account, this embodiment retrieves the instantaneous temperature θ at the contact point with the engraving tool from the material's physical properties. t The corresponding temperature-dependent elastic modulus E(θ) t );

[0081] Temperature-dependent elastic modulus E(θ) t This refers to the lacquer material at a specific temperature θ. tThe stiffness measure is expressed in Pa; its function is to quantify the thermal softening effect of the material caused by the heating of the engraving tool, which is a key nonlinear factor affecting the final stress.

[0082] θ t The data is obtained by real-time measurement via a miniature thermocouple sensor on the cutting tool from the data acquisition module; the functional relationship E(θ) t The data is stored in the material database, and its data comes from tensile tests of paint tray materials at different temperatures, and is pre-calibrated.

[0083] Based on the above parameters, this embodiment combines the effective strain factor ∈ t Temperature-dependent elastic modulus E(θ) t Determine the instantaneous physical stress σ t Its calculation formula follows the generalized Hooke's law:

[0084] σ t =E(θ) t )·∈ t

[0085] Where, σ t Instantaneous physical stress, measured in Pa, is the core output of this module;

[0086] The technical logic behind this formula is that the stress within a material depends not only on the applied strain ∈ t It depends more on its current temperature θ t The degree of softness and hardness E(θ) t Through this calculation, the model can accurately capture the physical reality that the material softens due to the heating of the engraving tool, resulting in a decrease in stress response.

[0087] This invention not only establishes a stress calculation model, but more importantly, it deeply integrates three core physical processes: audio signal dynamics, recording mechanical dynamics, and material thermodynamics. It can reflect the real physical state at the moment of recording more accurately than existing technologies, providing high-fidelity input for subsequent risk assessment and greatly improving the accuracy and reliability of the entire system's decision-making.

[0088] Example 3:

[0089] The risk assessment module calculates the transient distortion risk index, including:

[0090] A standardized instantaneous distortion risk index is obtained by calculating the ratio of instantaneous physical stress to maximum allowable forming stress.

[0091] Based on the value of the instantaneous distortion risk index, the current risk status is divided into preset risk levels.

[0092] Based on Example 1, this embodiment provides a detailed definition of the specific technical solution for calculating the instantaneous distortion risk index in the risk assessment module, aiming to transform complex physical stress values ​​into an intuitive, standardized, and directly operational risk indicator.

[0093] The specific steps for calculating the transient distortion risk index in the risk assessment module include:

[0094] To eliminate the differences in physical dimensions and numerical ranges of different materials and achieve a standardized measurement of risk, this embodiment calculates the instantaneous physical stress σ. t With the maximum allowable forming stress σ max The ratio of these values ​​is used to obtain the standardized instantaneous distortion risk index R. t ;

[0095] Transient distortion risk index R t It refers to a dimensionless, standardized risk metric whose purpose is to quantify in real time how close the current working stress is to the material failure limit;

[0096] This method originates from the stress-intensity interference theory in reliability engineering; it involves dynamically adjusting the working stress σ... t With the inherent ultimate strength σ of the material max By making comparisons, the risk of failure can be scientifically assessed. The calculation formula is as follows:

[0097]

[0098] Where, σ t Its source is the instantaneous physical stress calculated by the stress processing module, with the unit being Pa;

[0099] σ max Maximum allowable forming stress, in Pa, represents the ultimate stress of a material under smooth cutting conditions without tearing; this value is an inherent property of the material, and its source is the material mechanics test reports of different paint trays in the material parameter library, preset for each paint tray;

[0100] R t The physical meaning of is the percentage of the current working stress relative to the ultimate stress that the material can withstand; this normalization step is crucial, as it provides a unified and standardized input for subsequent machine learning models and control systems, enabling the system to handle different types and batches of lacquerware materials without redesigning the core algorithm.

[0101] To transform a continuous risk index into a discrete, decision-making risk level, this embodiment is based on the transient distortion risk index R. t The numerical value is used to classify the current risk status into a preset risk level;

[0102] Safety status (R) t ≤0.9): This indicates that the current stress is far below the material limit, the etching process is stable, and the texture forming quality is high;

[0103] Warning status (0.9) <R t ≤1.2): This indicates that the stress has entered the critical region, and there is a risk of microscopic defects. The system should be on high alert.

[0104] Dangerous conditions (R) t >1.2): This indicates that the stress has significantly exceeded the elastic or plastic deformation limit of the material, and there is a high probability that macroscopic marking defects such as material tearing may occur.

[0105] The grading thresholds of 0.9 and 1.2 were not set arbitrarily; they were determined through extensive recording experiments, establishing a system containing different R values. t The numerical sequence and the corresponding calibration dataset of the audible defect level of the finished product are used to determine the cut-off point that can achieve the highest accuracy in the defect classification task using statistical analysis methods, thereby obtaining the two thresholds;

[0106] By introducing a standardized risk index R t With its clear risk classification, this invention successfully bridges the gap between complex physical processes and macroscopic control decisions. It transforms a continuously changing physical quantity into a risk signal that is both accurately quantifiable and intuitively understandable. This not only provides a clear triggering basis for subsequent automated compensation control, such as activating compensation when entering a warning state, but also provides concise and clear decision support information for manual monitoring.

[0107] Example 4:

[0108] The risk prediction module determines the predicted risk index, including:

[0109] Aggressive feature vectors are extracted from the audio data stream. These aggressive feature vectors include crest factor, spectral roll-off point, and short-term signal energy growth rate.

[0110] The input sequence, consisting of aggressive feature vectors from the current and past time steps, is submitted to a pre-defined long short-term memory network model for processing to output a predicted risk index.

[0111] Based on Example 1, this embodiment provides a detailed definition of the specific technical solution for the risk prediction module to determine the predicted risk index. The aim is to achieve rapid and forward-looking prediction of high-cost physical simulation results through low-cost signal processing and deep learning models.

[0112] The risk prediction module determines the predicted risk index R. t+Δt The process includes:

[0113] To enable the model to anticipate future risks, the system needs to extract features from the audio data stream that can predict drastic future energy changes; therefore, this embodiment extracts an aggressive feature vector F from the audio data stream. t ;

[0114] Aggressive feature vector F t It refers to a multi-dimensional vector whose purpose is to quantify the aggressiveness or suddenness of an audio signal at the current moment from the perspective of signal processing; this aggressiveness is highly correlated with the huge physical stress that may occur in the near future.

[0115] Vector composition: In this embodiment, F t At least including:

[0116] Crest factor: The ratio of the peak value to the effective value of a signal, used to measure the extreme peak level of a signal;

[0117] Spectral roll-off point: describes the shape of the energy distribution of the signal spectrum. A high roll-off point usually means richer high-frequency components, corresponding to faster engraving vibration;

[0118] Short-term growth rate of signal energy: measures the rate of change of signal energy within a millisecond-level time window, used to capture transient impact signals;

[0119] These features are derived by applying standard digital signal processing algorithms to the real-time audio data stream acquired by the data acquisition module. For example, spectrum analysis is performed using the Short Time Fourier Transform (STFT) to calculate the spectral roll-off point, and time-domain analysis is combined to calculate the peak factor and the short-term growth rate of signal energy.

[0120] To learn and simulate the complex nonlinear relationship between audio features and physical risk, this embodiment submits an input sequence consisting of aggressive feature vectors from the current and multiple past time steps to a pre-defined Long Short-Term Memory (LSTM) network model for processing, outputting a predicted risk index R. t+Δt ;

[0121] Long Short-Term Memory (LSTM) network models refer to a special type of recurrent neural network designed to effectively process and predict long-term dependencies in time-series data. In this invention, it is used to learn the temporal evolution of audio features and thereby predict future risk indices. The mathematical representation of the model is as follows:

[0122] R t+Δt =M LSTM (F t ,F t-1 ,...,F t-n ;W)

[0123] Among them, R t+ΔtThe predicted risk index output by the model at a future time Δt;

[0124] M LSTM : Represents the nonlinear mapping function of the LSTM model;

[0125] (F t ,F t-1 ,...,F t-n ): The input sequence consists of the aggressive feature vectors of the current and the past n time steps; the use of sequential input is to enable the model to capture the dynamic trend of signal changes, rather than just the instantaneous state;

[0126] W: Internal weight parameters of the LSTM model;

[0127] Model training and logical relationship: The parameter W is not preset, but obtained through supervised learning; the training dataset consists of massive audio feature sequences (F t-n...t ) and the real risk index series (R t-n...t The pairs are composed of; the true risk index sequence here is generated by the method in the second step through offline, high-precision physical simulation of historical audio, i.e., using the model and normalization calculation from the first step; the training objective is to minimize the predicted risk R. t+Δt With real risk R t+Δt The mean squared error between them; through this training, the LSTM model learns the complex mapping relationship between audio features that are easy to extract in real time and physical risks that are difficult to compute in real time;

[0128] This solution creatively applies a deep learning model to construct a computational shortcut from audio signal characteristics directly to future physical risks. It avoids the huge obstacle of performing complex and time-consuming finite element physical simulations in real-time applications, making it possible to predict risks in advance. This predictive capability advances the intervention time of the entire system by a critical few milliseconds, realizing a qualitative change from passive response to active avoidance. It is the core technical guarantee for ensuring that extreme dynamic audio signals are successfully recorded without distortion.

[0129] Example 5:

[0130] The compensation decision module generates compensation intensity factors, including:

[0131] When the predicted risk index is greater than the compensation activation threshold, the compensation intensity factor is determined based on the difference between the predicted risk index and the compensation activation threshold, as well as the preset compensation gain coefficient.

[0132] When the predicted risk index is less than or equal to the compensation activation threshold, the compensation intensity factor is set to zero.

[0133] This embodiment, based on Embodiment 1, generates a compensation intensity factor C in the compensation decision module. t The specific technical solutions were defined in detail, aiming to accurately and reasonably translate the predicted risk level into a standardized control execution intensity.

[0134] The compensation decision module generates the compensation intensity factor C. t The logic is based on a proportional control concept with a threshold, and its implementation is as follows:

[0135] The system judges and predicts the risk index R. t+Δt Has the compensation activation threshold R been exceeded? th ;

[0136] Compensation activation threshold R th This refers to a preset risk level, the purpose of which is to define when the system needs to initiate compensatory intervention; to prevent the system from overreacting to harmless, minor risk fluctuations, thereby ensuring the normal stability of the burning process;

[0137] The source of this threshold is the risk level defined in the second step; a typical setting is to set R... th The value is set to 0.9, meaning that the compensation mechanism is activated in advance when the predicted risk is about to enter the warning state.

[0138] Branch calculations are performed based on the judgment results:

[0139] When predicting the risk index R t+Δt Greater than the compensation activation threshold R th When the system deems future risks warranting intervention, it will then base its decision on the difference between the predicted risk index and the compensation activation threshold, along with a preset compensation gain coefficient k. c To determine the compensation intensity factor C t ;

[0140] C t =k c ·(R t+Δt -R th )

[0141] Among them, R t+Δt -R th This difference precisely quantifies the extent to which the predicted risk exceeds the safety threshold;

[0142] k c The compensation gain coefficient is an adjustable dimensionless parameter used to adjust the overall sensitivity of the compensation response. Its calibration method is as follows: through system identification experiments, a known disturbance is injected into the system under open-loop conditions, and the change in the risk index is measured to determine the system's transfer function. Then, based on the desired closed-loop response characteristics, the optimal gain coefficient value is calculated.

[0143] The logic of this formula is to proportionally convert the predicted risk excess into the strength of a control instruction; the greater the risk excess, the larger the compensation factor generated, and the more significant the downstream compensation action; this achieves a smooth and quantitative transformation from risk prediction to control execution.

[0144] When predicting the risk index R t+Δt Less than or equal to the compensated activation threshold R th When the system considers the future to be in a safe state and no intervention is needed, the compensation intensity factor C will be applied. t The value is determined to be zero;

[0145] C t =0

[0146] This will result in all downstream compensation execution agencies not making any adjustments, maintaining the baseline recording parameters, and ensuring the highest fidelity of the original audio signal under safe operating conditions;

[0147] By introducing a dead zone, i.e., determined by the threshold R th By employing proportional control logic, this invention achieves an intelligent and efficient compensation decision-making mechanism. It can respond quickly and proportionally to predicted risks that exceed safety boundaries, while remaining silent on normal fluctuations within the safety range, avoiding unnecessary intervention that may have a minor impact on sound quality. This decision-making method achieves an ideal balance between ensuring safety and improving sound fidelity.

[0148] Example 6:

[0149] The multidimensional adaptive compensation strategy includes dynamic fine-tuning of the engraving tool heating power.

[0150] The compensation execution module performs dynamic fine-tuning of the engraving tool heating power, including:

[0151] The real-time power adjustment amount is determined based on the compensation intensity factor and the preset thermal compensation coefficient.

[0152] By combining the real-time power adjustment with the reference power, a real-time target power is generated, and the power of the engraving knife heating wire is adjusted accordingly.

[0153] Based on Example 1, this embodiment provides a detailed definition of a specific strategy in the multidimensional adaptive compensation strategy, namely, dynamic engraving tool heating power fine-tuning, and its implementation method in the compensation execution module.

[0154] The purpose of dynamic engraving blade heating power fine-tuning is to actively and precisely adjust the engraving blade temperature to change the instantaneous physical properties of the enamel material at the contact point, thereby directly combating the engraving risks caused by high stress. The technical motivation is that moderately increasing the engraving blade temperature can locally soften the enamel material, reducing its elastic modulus, according to the formula σ... t =E(θ) t )·∈ t It can be seen that this can be achieved in strain ∈ t Effectively reduce the actual physical stress σ without changing the actual stress. t This will increase the risk index R. t Keep it within a safe range;

[0155] The process of fine-tuning the dynamic engraving tool heating power by the compensation execution module includes:

[0156] Based on the upstream input compensation intensity factor C t and a preset thermal compensation coefficient k h Determine a real-time power adjustment amount;

[0157] Thermal compensation coefficient k h This refers to a calibrated physical parameter, measured in watts (W), which serves to compensate for the dimensionless intensity factor C. t Mapped to specific heating power adjustment values;

[0158] Real-time power adjustment amount is determined by k h ·C t Given this, the real-time power adjustment is combined with a reference power P0 to generate the real-time target power P. h (t), and adjust the power of the engraving tool heating wire accordingly. The calculation formula is as follows:

[0159] P h (t)=P0+k h ·C t

[0160] Among them, P h (t): Real-time target power, in W, is the command signal sent to the cutting tool power controller;

[0161] P0: Reference power, in watts (W), under safe operating conditions, i.e., C. t When = 0, the standard heating power maintained;

[0162] k h The calibration method for the thermal compensation coefficient is as follows: A calibration dataset is established consisting of different constant power increments and their corresponding steady-state stress decreases. These data points are obtained through experimental measurements, and k is determined through linear regression analysis. hThe value is used to ensure that it accurately reflects the physical relationship between power input and risk suppression;

[0163] Logic and Execution: When the predicted risk increases, C t When increasing from 0, the real-time target power P h (t) will increase linearly based on the base power P0; the edge controller deployed locally on the burner receives P h After the (t) command, the heating power can be quickly and accurately controlled by adjusting the voltage or current applied to the heating wire inside the engraving tool;

[0164] This solution provides a direct and effective risk intervention method based on physical principles. Instead of compromising at the signal level, such as compressing dynamics, it proactively optimizes process conditions at the physical forming level. By linking the predicted risk with the engraving temperature in a feedforward manner, the system can pre-increase the engraving temperature within milliseconds before the arrival of a high-dynamic audio signal, reducing the burden on the material and thus achieving smooth engraving of high-risk segments. This is an extremely sophisticated control strategy that can greatly expand the dynamic range and complexity that the system can handle without compromising audio information.

[0165] Example 7:

[0166] Multidimensional adaptive compensation strategies include instantaneous feedforward dynamic equilibrium.

[0167] The compensation execution module performs instantaneous feedforward dynamic equalization, including:

[0168] The real-time gain attenuation is determined based on the compensation intensity factor and the preset equalization compensation coefficient.

[0169] By combining the real-time gain attenuation with the original gain, a real-time target gain is generated, and the gain of a specific frequency band of the audio signal is adjusted accordingly.

[0170] Based on Example 1, this embodiment provides a detailed definition of another specific strategy in the multidimensional adaptive compensation strategy, namely instantaneous feedforward dynamic equilibrium, and its implementation method in the compensation execution module; this strategy usually works in conjunction with other compensation methods such as dynamic engraving tool heating.

[0171] Instantaneous feedforward dynamic equalization aims to provide an auxiliary or rapid-response compensation method by instantaneously and slightly attenuating the gain of specific high-risk frequency bands in an audio signal. The technical motivation is that the risk of recording distortion is usually closely related to the instantaneous energy of specific frequency bands in the audio signal, especially high frequencies. When extreme risks are predicted and physical compensation, such as heating, may not be fast enough to completely offset the risks, making extremely brief and precise adjustments to the audio signal itself is an effective supplementary measure.

[0172] The process of the compensation execution module performing instantaneous feedforward dynamic equalization includes:

[0173] Based on the upstream input compensation intensity factor C t and a preset equilibrium compensation coefficient k e Determine a real-time gain attenuation amount;

[0174] Equilibrium compensation coefficient k e This refers to a calibrated parameter, measured in decibels (dB), whose function is to adjust the dimensionless compensation intensity factor C. t Mapped to a specific attenuation value of the audio gain;

[0175] The real-time gain attenuation is determined by k e ·C t Provided.

[0176] The real-time gain attenuation is combined with the original gain G0 to generate the real-time target gain G(t). Based on this, the gain of a specific frequency band in the audio signal is adjusted by a digital signal processor. The calculation formula is as follows:

[0177] G(t) = G0 - k e ·C t

[0178] Wherein, G(t): real-time target gain, in dB, is the control command sent to the audio processing unit;

[0179] G0: Raw gain, in dB, usually 0dB, indicating that no adjustment is made to the signal;

[0180] k e The calibration method for the equalization compensation coefficient is as follows: Select a series of representative high-risk audio segments, establish a calibration dataset consisting of different constant gain attenuation amounts and corresponding stress drop values, obtain data through offline simulation or experiments, and determine k through linear regression analysis. e The value;

[0181] When the predicted risk increases, C t When the gain is increased, the real-time target gain G(t) will decrease, thus attenuating the signal. This adjustment is usually applied to high-risk frequency bands identified through analysis, such as the high-frequency range above 8kHz. This operation is performed by a feedforward dynamic equalizer with an extremely fast response time, reaching the microsecond level, which can provide instantaneous protection before physical inertia, such as thermal inertia, or larger compensation methods take effect.

[0182] This solution provides the system with an auxiliary compensation method with extremely fast response speed. By introducing instantaneous feedforward dynamic equalization, the system forms a three-dimensional compensation system that combines fast and slow response, electrical signal processing and physical process intervention. When dealing with the risks caused by extreme transient signals, this fine-tuning can instantly reduce peaks in a manner that is almost imperceptible to the human ear. This buys valuable time for slower but more fundamental physical compensation measures, such as heating, ensuring that the actual molding stress can be suppressed below the safety threshold under any circumstances, further improving the robustness and recording quality of the system.

[0183] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention; any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0184] 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 cloud computing-based intelligent adjustment system for vinyl record recording, characterized in that: include: The data acquisition module is used to acquire audio data streams, engraving knife status, environmental parameters, and material properties of the master lacquer plate during the burning process. The model building module is used to construct a thermo-mechanically coupled finite element model of the engraving knife and lacquer tray based on the parameters obtained by the data acquisition module. The stress processing module is used to determine the instantaneous physical stress at the etching pattern forming point based on the thermo-coupling finite element model of the engraving knife and paint plate. The risk assessment module is used to calculate a standardized instantaneous distortion risk index by combining instantaneous physical stress with the preset maximum allowable forming stress. The risk prediction module processes audio data streams to determine the predicted risk index for future moments. The compensation decision module is used to generate a compensation intensity factor based on the predicted risk index and the preset compensation activation threshold. The compensation execution module is used to execute a multi-dimensional adaptive compensation strategy based on the compensation intensity factor. The stress processing module determines instantaneous physical stress, including: The effective strain factor is calculated based on the normalized amplitude of the audio data stream and the cutting speed of the engraving tool. Retrieve the temperature-dependent elastic modulus corresponding to the instantaneous temperature at the contact point of the engraving tool from the material properties parameters; Instantaneous physical stress is determined by combining the effective strain factor and the temperature-dependent elastic modulus. The risk prediction module determines the predicted risk index, including: Aggressive feature vectors are extracted from the audio data stream. These aggressive feature vectors include crest factor, spectral roll-off point, and short-term signal energy growth rate. The input sequence, consisting of aggressive feature vectors from the current and past time steps, is submitted to a pre-defined long short-term memory network model for processing to output a predicted risk index.

2. The cloud computing-based intelligent adjustment system for vinyl record recording according to claim 1, characterized in that, The risk assessment module calculates the instantaneous distortion risk index, including: A standardized instantaneous distortion risk index is obtained by calculating the ratio of instantaneous physical stress to maximum allowable forming stress. Based on the value of the instantaneous distortion risk index, the current risk status is divided into preset risk levels.

3. The cloud computing-based intelligent adjustment system for vinyl record recording according to claim 1, characterized in that, The compensation decision module generates a compensation intensity factor, including: When the predicted risk index is greater than the compensation activation threshold, the compensation intensity factor is determined based on the difference between the predicted risk index and the compensation activation threshold, as well as the preset compensation gain coefficient. When the predicted risk index is less than or equal to the compensation activation threshold, the compensation intensity factor is set to zero.

4. The cloud computing-based intelligent adjustment system for vinyl record recording according to claim 1, characterized in that, The multidimensional adaptive compensation strategy includes dynamic fine-tuning of the engraving tool heating power.

5. The cloud computing-based intelligent adjustment system for vinyl record recording according to claim 4, characterized in that, The compensation execution module performs dynamic engraving tool heating power fine-tuning, including: The real-time power adjustment amount is determined based on the compensation intensity factor and the preset thermal compensation coefficient. By combining the real-time power adjustment with the reference power, a real-time target power is generated, and the power of the engraving knife heating wire is adjusted accordingly.

6. The cloud computing-based intelligent adjustment system for vinyl record recording according to claim 1, characterized in that, The multidimensional adaptive compensation strategy includes instantaneous feedforward dynamic equilibrium.

7. The cloud computing-based intelligent adjustment system for vinyl record recording according to claim 6, characterized in that, The compensation execution module performs instantaneous feedforward dynamic equalization, including: The real-time gain attenuation is determined based on the compensation intensity factor and the preset equalization compensation coefficient. By combining the real-time gain attenuation with the original gain, a real-time target gain is generated, and the gain of a specific frequency band of the audio signal is adjusted accordingly.

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