Intelligent production control system and method for lubricating material

Abstract

The application discloses a kind of lubricating material intelligent production regulation and control system and method, belong to chemical intelligent manufacturing technical field.The method is by real-time acquisition reaction system double-waveband original spectrum data, extract the spectrum difference fingerprint vector of evolution of intermolecular force;It is inputed into reaction kinetics deviation model, calculates instantaneous kinetic deviation and generates temperature-time compensation factor and shear rate correction coefficient;Accordingly, dynamically reconstructs the heating curve, adjusts stirring power to lock the reaction endpoint, and triggers additive pulse addition when the convergence slope of fingerprint vector deviates from the safety envelope;Finally, write the whole process time sequence data into distributed account book solidification and evidence.This application realizes the reaction process feedforward accurate regulation based on molecular microstate, effectively improves product consistency, and ensures the non-tamperability and traceability of production data through block chain evidence.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing technology in the chemical industry, and in particular relates to an intelligent production control system and method for lubricating materials. Background Technology

[0002] In the field of lubricant material synthesis, precise control of the reaction process directly determines the viscosity index, oxidation resistance, and tribological properties of the final product. Traditional production often relies on preset "temperature-time" programs or feedback control based on single temperature and pressure parameters. However, the polymerization and modification reactions of lubricants involve complex evolution of intermolecular forces. Macroscopic thermal parameters alone are insufficient to characterize the dynamic changes in microscopic chemical structures in real time, leading to delayed determination of reaction endpoints and problems such as excessively broad molecular weight distribution or increased side reactions.

[0003] While existing online spectral monitoring technologies can acquire some chemical information, they are mostly limited to single-band analysis, making it difficult to simultaneously capture the coupling characteristics of electronic energy level transitions and molecular vibrational / rotational energy levels. They also lack quantitative calculations and feedforward compensation mechanisms for reaction kinetic deviations. When the reaction system is affected by raw material fluctuations or environmental disturbances, traditional systems can only passively adjust after detecting quality anomalies, unable to actively lock onto the standard reaction trajectory. Furthermore, existing systems lack reliable methods for recording and solidifying additive additions and adjustments to key process parameters, making production process data susceptible to tampering or loss, and failing to meet the stringent requirements of high-end lubricating materials for quality consistency and full lifecycle traceability. Therefore, there is an urgent need for an intelligent production control solution with deep feedforward control capabilities and support for data storage and verification. Summary of the Invention

[0004] To address the technical problems in existing lubricant production, such as the lack of characterization of microscopic molecular forces due to reliance on macroscopic thermal parameters, poor product consistency due to delayed determination of reaction endpoints, and unstable process quality and untraceable production data caused by the lack of feedforward compensation mechanisms to cope with raw material fluctuations, this invention provides an intelligent production control system and method for lubricants.

[0005] To achieve the above objectives, a first aspect of the present invention provides a method for intelligent production control of lubricating materials, comprising the following steps: Real-time acquisition of dual-band raw spectral data of the reaction system, followed by preprocessing to extract spectral difference fingerprint vectors characterizing the dynamic evolution of intermolecular forces; The spectral difference fingerprint vector is input into the pre-constructed reaction kinetic deviation model to calculate the instantaneous kinetic offset of the current reaction system relative to the standard trajectory, and a temperature-time compensation factor for reconstructing the heating curve and a shear rate correction coefficient for matching the reaction viscosity are generated based on the inverse kinetic mapping relationship. The heating curve of the heating system is dynamically reconstructed based on the temperature-time compensation factor. The stirring power is adjusted in real time according to the shear rate correction coefficient. Feedforward compensation control is performed to lock the reaction endpoint. When the convergence slope of the spectral difference fingerprint vector deviates from the preset safety envelope set based on the statistical confidence interval of the standard reaction trajectory, a pulse replenishment command for trace additives is triggered. In response to the completion of the pulse replenishment command, time-series data including the current spectral differential fingerprint vector, instantaneous dynamic offset, compensation factor, correction coefficient and actual replenishment amount are collected, a time-stamped control evidence block is generated and written into the distributed ledger, and the closed-loop control record of the production process including abnormal intervention is solidified.

[0006] Furthermore, the extraction of the spectral difference fingerprint vector characterizing the dynamic evolution of intermolecular forces after preprocessing specifically includes: Baseline correction and scattered light removal were performed on the collected dual-band raw spectral data to separate the first band data reflecting the vibrational characteristics of functional groups and the second band data reflecting the translational frictional characteristics of molecules. The difference vectors of spectral intensities in the two bands at the current time and the previous time are calculated respectively. The difference vectors of the first band data and the second band data are coupled and mapped by a weighted fusion algorithm to generate the spectral differential fingerprint vector.

[0007] Furthermore, the generation of the temperature-time compensation factor and shear rate correction coefficient based on the inverse dynamic mapping relationship specifically includes: A joint sensitivity matrix of the reaction rate constant to temperature and shear rate is constructed. Using the instantaneous kinetic offset as the input vector, the temperature-time compensation factors used to offset reaction lag or lead are calculated by solving the least squares inverse. and the shear rate correction factor used to match the target viscosity evolution curve The solution formula is: ,

[0008] The instantaneous dynamic offset vector is used to represent the deviation between the current reaction rate constant and the standard trajectory; The joint sensitivity matrix consists of the partial derivatives of the Arrhenius equation with respect to temperature and the partial derivatives of the power-law fluid model with respect to shear rate, reflecting the dynamic response sensitivity of the reaction system to thermal and force fields. This is a weighted diagonal matrix used to weight the dynamic contributions of different bands based on the confidence level of the spectral difference fingerprint vector; This is a regularization parameter used to suppress numerical oscillations in the inverse dynamical mapping process and ensure the smoothness of the compensation factor. It is an identity matrix.

[0009] Furthermore, it also includes: The composite optical signal generated by the reaction system is separated into a first characteristic band and a second characteristic band using a spectrophotometer; wherein, the first characteristic band corresponds to the electronic energy level transition region and is used to capture the rapid dynamic signals caused by chemical bond breaking, active intermediate generation and electron cloud rearrangement during the reaction process. The second characteristic band corresponds to the molecular vibrational and rotational energy level region and is used to capture slow dynamic signals caused by the reorganization of intermolecular force networks, changes in spatial conformation, and evolution of the micro-rheological environment. A synchronous triggering mechanism ensures that the spectral acquisitions of the two bands are aligned on the time axis, which is used to construct the original dual-band spectral dataset.

[0010] Furthermore, it also includes, Preprocessing of the raw dual-band spectral data to eliminate environmental noise and baseline drift includes: A sliding window local polynomial fitting algorithm is used to perform smoothing and denoising processing on the original spectral data of the first and second feature bands to remove high-frequency random interference caused by light source fluctuations and detector thermal noise. The nonlinear baseline drift caused by changes in turbidity of the reaction system or background scattering is estimated and subtracted using the iterative weighted least squares method, and the spectral signal is corrected to a unified zero absorption reference surface. The baseline-corrected spectral intensities are normalized to eliminate absolute intensity differences caused by minor optical path fluctuations or local non-uniformity of sample concentration, thereby generating standardized dual-band spectral data.

[0011] Furthermore, it also includes: Perform time series alignment on the standardized spectral data of the first and second characteristic bands, and map the data of the two bands to a unified time coordinate axis based on the timestamp generated by the synchronization triggering mechanism; Perform a difference operation on the aligned dual-band data to extract the dynamic deviation of the first characteristic band relative to the second characteristic band, which characterizes the non-equilibrium difference between the electronic energy level transition rate and the molecular vibrational relaxation rate. The dynamic deviation is compared with a preset kinetic fingerprint threshold to identify the critical state of chemical bond breaking or micro-rheological environment evolution in the reaction system, and to generate a spectrokinetic fingerprint map containing the feature vector of the reaction process.

[0012] Furthermore, the beam-splitting assembly includes a dichroic beam splitter and a tunable filter group; The dichroic beam splitter is positioned on the optical transmission path of the reaction system. Based on a preset cutoff wavelength, it reflects the composite optical signal to the first optical path and transmits it to the second optical path, thereby physically separating the first characteristic band and the second characteristic band. The tunable filter group is respectively disposed at the end of the first optical path and the second optical path, and is used to perform narrowband filtering on the separated beams, suppress out-of-band stray light interference and improve the signal-to-noise ratio of the target band. The synchronization triggering mechanism includes a master clock generator and a dual-channel data acquisition card. The master clock generator generates a high-frequency synchronization pulse signal to simultaneously drive the spectral detectors corresponding to the two bands to perform integrated exposure, ensuring that the first characteristic band data frame and the second characteristic band data frame have strictly consistent timestamp marks.

[0013] Secondly, the present invention also provides an intelligent production control system for lubricating materials, applied to any of the intelligent production control methods for lubricating materials described herein, comprising: The acquisition and processing module is used to acquire the raw dual-band spectral data of the reaction system in real time, and extract the spectral difference fingerprint vector that characterizes the dynamic evolution of intermolecular forces after preprocessing. The compensation generation module, connected to the acquisition and processing module, is used to input the spectral difference fingerprint vector into the pre-constructed reaction kinetic deviation model, calculate the instantaneous kinetic offset of the current reaction system relative to the standard trajectory, and generate a temperature-time compensation factor for reconstructing the heating curve and a shear rate correction coefficient for matching the reaction viscosity based on the inverse kinetic mapping relationship. The execution control module is connected to the compensation generation module and is used to dynamically reconstruct the heating curve of the heating system according to the temperature-time compensation factor, adjust the stirring power in real time according to the shear rate correction coefficient, perform feedforward compensation control to lock the reaction endpoint, and trigger a pulse replenishment command for trace additives when the convergence slope of the spectral difference fingerprint vector deviates from the preset safety envelope set based on the statistical confidence interval of the standard reaction trajectory. The evidence recording module, connected to the execution control module, is used to collect time-series data including the current spectral differential fingerprint vector, instantaneous dynamic offset, compensation factor, correction coefficient and actual supplementation amount in response to the completion of the pulse supplementation command, generate a control evidence block with a timestamp and write it into the distributed ledger, and solidify the closed-loop control record of the production process including abnormal intervention.

[0014] Furthermore, the acquisition and processing module includes a dual-band beam splitting component and a synchronous trigger controller; The dual-band beam splitter is used to separate the composite optical signal generated by the reaction system into a first characteristic band corresponding to the electronic energy level transition region and a second characteristic band corresponding to the molecular vibration and rotation energy level region. The synchronous trigger controller is used to generate a high-frequency synchronous pulse signal, and simultaneously drive the spectral detectors corresponding to the two characteristic bands to perform integral exposure, thereby constructing a dual-band original spectral dataset.

[0015] Furthermore, the execution control module includes a temperature reconstruction submodule, a power regulation submodule, and an additive pulse submodule; The temperature reconstruction submodule is used to receive the temperature-time compensation factor and correct the slope of the heating curve in real time by dynamically adjusting the flow rate of the heating medium or the output duty cycle of the electric heating power. The power regulation submodule is used to receive the shear rate correction coefficient and adjust the speed of the stirring motor in real time through the frequency converter to match the change in reaction viscosity. The additive pulse submodule is used to control the micro-metering pump to inject the additive in a preset high-frequency intermittent mode when a pulse replenishment command is received, and to provide real-time feedback of the actual injection volume to the evidence recording module.

[0016] The beneficial technical effects of the present invention are at least as follows: Firstly, it significantly improves the accuracy of microscopic characterization and the predictability of control over the reaction process. This invention, through the collaborative operation of a dual-band spectroscopic component and a synchronous trigger controller, achieves for the first time strictly synchronized acquisition of electronic energy level transition regions and molecular vibrational rotational energy level regions, constructing a high-fidelity dual-band raw spectral dataset. This overcomes the limitation of traditional single-band monitoring, which cannot comprehensively capture the evolution and coupling characteristics of intermolecular forces. It can accurately extract spectral difference fingerprint vectors characterizing the reaction process, providing reliable microscopic data input for reaction kinetic deviation models, and fundamentally solving the problems of inaccurate determination of reaction endpoints and excessively broad molecular weight distribution caused by macroscopic parameter lag.

[0017] Secondly, it achieves active feedforward compensation and dynamic reconstruction of process parameters based on inverse dynamics. Addressing the deviation of the reaction trajectory caused by raw material fluctuations and environmental disturbances, this invention abandons the traditional passive feedback control mode. It utilizes a calculated temperature-time compensation factor and shear rate correction coefficient to dynamically reconstruct the slope of the heating curve in real time and match it with the stirring motor speed. This "prevention before problems arise" feedforward control mechanism ensures that the reaction system always operates on the standard kinetic trajectory, effectively suppressing side reactions and significantly improving the consistency, viscosity index, and oxidation resistance of high-end lubricating materials.

[0018] Thirdly, this invention constructs a reliable end-to-end evidence storage system and refined additive pulse control. When an abnormal convergence slope of the fingerprint vector is detected, this invention can trigger high-frequency, intermittent, precise replenishment by a micro-metering pump, avoiding performance degradation caused by over-addition. Simultaneously, it innovatively writes the entire process time-series data into a distributed ledger for solidified evidence storage. This not only ensures the immutability and traceability of key process parameter adjustments and additive injection records, meeting the stringent requirements of high-end manufacturing for quality auditing, but also provides a solid data trust foundation for subsequent production process optimization and quality incident tracing. Attached Figure Description

[0019] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the working steps of an intelligent production control method for lubricating materials according to one embodiment of the present invention. Figure 2 This is a flowchart illustrating the process of extracting spectral differential fingerprint vectors according to one embodiment of the present invention; Figure 3 This is a flowchart illustrating the physical separation process of composite optical signals according to one embodiment of the present invention; Figure 4 This is a schematic diagram of the intelligent production control system for lubricating materials disclosed in one embodiment of the present invention. Detailed Implementation

[0021] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0022] Example 1 To address the pain points in lubricant material synthesis, such as the inability of traditional macroscopic thermal parameters to characterize the evolution of microscopic intermolecular forces in real time, and the difficulty of capturing the coupling characteristics of electrons and vibrational-rotational energy levels with single-band spectroscopy, leading to delayed judgment of reaction endpoints, lack of feedforward compensation mechanisms, and insufficient reliability of production data, this invention proposes an intelligent production method integrating dual-band spectral fingerprinting and inverse dynamics feedforward control. This method extracts spectral difference fingerprint vectors characterizing the dynamic evolution of molecular forces, quantitatively calculates instantaneous shifts using a reaction kinetics deviation model, and generates temperature-time compensation factors and shear rate correction coefficients. Based on this, the heating curve is dynamically reconstructed, and the stirring power is adjusted in real time to actively lock the standard reaction trajectory. When the convergence slope deviates from the statistical safety envelope, a trace additive pulse is triggered for replenishment. Finally, the full-time control data, including abnormal interventions, is solidified into a distributed ledger, thus achieving a closed-loop intelligent control from microscopic spectral perception to precise macroscopic process execution and reliable data storage. This effectively solves technical problems such as excessively wide molecular weight distribution, increased side reactions, and difficulties in quality traceability.

[0023] refer to Figure 1 This invention provides an embodiment of a smart production control method for lubricating materials, comprising the following steps: S1: Real-time acquisition of dual-band raw spectral data of the reaction system, followed by preprocessing to extract spectral difference fingerprint vectors characterizing the dynamic evolution of intermolecular forces; S2: Input the spectral differential fingerprint vector into the pre-constructed reaction kinetic deviation model, calculate the instantaneous kinetic offset of the current reaction system relative to the standard trajectory, and generate a temperature-time compensation factor for reconstructing the heating curve and a shear rate correction coefficient for matching the reaction viscosity based on the inverse kinetic mapping relationship. S3: Based on the temperature-time compensation factor, the heating curve of the heating system is dynamically reconstructed, and the stirring power is adjusted in real time according to the shear rate correction coefficient. Feedforward compensation control is performed to lock the reaction endpoint. When the convergence slope of the spectral difference fingerprint vector deviates from the preset safety envelope set based on the statistical confidence interval of the standard reaction trajectory, a pulse replenishment command for trace additives is triggered. S4: In response to the completion of the pulse replenishment command, time-series data including the current spectral differential fingerprint vector, instantaneous dynamic offset, compensation factor, correction coefficient and actual replenishment amount are collected, a control evidence block with a timestamp is generated and written into the distributed ledger, and the closed-loop control record of the production process including abnormal intervention is solidified.

[0024] Furthermore, the extraction of the spectral difference fingerprint vector characterizing the dynamic evolution of intermolecular forces after preprocessing specifically includes: S11: Baseline correction and scattered light removal are performed on the acquired dual-band raw spectral data to separate the first band data reflecting the vibrational characteristics of functional groups and the second band data reflecting the translational frictional characteristics of molecules. S12. Calculate the difference vectors of the spectral intensities of the two bands at the current time and the previous time respectively. Couple and map the difference vectors of the first band data and the second band data through a weighted fusion algorithm to generate the spectral difference fingerprint vector.

[0025] Based on step S1, it should be noted that by acquiring the raw dual-band spectral data of the reaction system in real time, such as simultaneously using ultraviolet-visible and near-infrared spectra or Raman and terahertz spectra, the dual-band design aims to utilize the complementarity of electronic transition and molecular vibrational information to eliminate the structural analysis blind spots of a single band. Alternative solutions may include combining mid-infrared and fluorescence spectra or dynamically adjusting the band range to adapt to specific reaction types. Subsequently, multi-dimensional preprocessing is performed, including wavelet transform denoising, adaptive iterative reweighted penalized least squares baseline correction, multivariate scattering correction, and temperature drift compensation. These methods effectively eliminate instrument noise, optical path variations, and environmental interference. Alternative algorithms may also employ empirical mode decomposition, Savitzky-Golay smoothing filtering, or deep learning-based autoencoder denoising models. Finally, a spectral difference fingerprint vector characterizing the dynamic evolution of intermolecular forces is extracted. Specifically, this involves performing point-by-point difference operations between the real-time spectral sequence and the initial or reference state spectra of the reaction, and extracting the loading vector by combining principal component analysis or independent component analysis. For example, in esterification reactions, a vector is constructed by capturing the minute frequency shifts and intensity change rates of the carbonyl stretching vibration peaks to quantify the recombination process of the hydrogen bond network. Alternative methods include using second-derivative spectroscopy to enhance the resolution of overlapping peaks, introducing two-dimensional correlation spectral analysis for time-series correlation, or latent space feature mapping based on physical information neural networks. The improvement of this core technology is due to the fact that traditional single-band offline detection cannot capture transient intermediates and is easily affected by background interference. The improved technology not only achieves in-situ non-destructive monitoring of microsecond-level reaction kinetics, but also significantly improves the accuracy of reaction mechanism analysis and the foresight of process control by accurately mapping macroscopic spectral signals into the quantitative evolution trajectory of microscopic intermolecular forces such as van der Waals forces and electrostatic interactions.

[0026] Based on step S2, it should be noted that the spectral difference fingerprint vector is input into the pre-constructed reaction kinetic deviation model. This model is essentially a deep regression network or Gaussian process regressor trained on a historical standard reaction dataset. Its function is to quantify the nonlinear difference between the current real-time reaction state and the ideal standard trajectory. Alternative solutions may include using support vector regression, random forest algorithm, or mechanism-based differential equation system correction model. The system calculates the instantaneous kinetic offset by calculating Euclidean distance, Mahalanobis distance, or dynamic time warping distance, thereby accurately locating the degree of reaction lag or lead. Then, based on the inverse kinetic mapping relationship, the system uses the adjoint matrix method, Jacobian matrix inversion, or reinforcement learning strategy to back-calculate the control parameters and generate a temperature time compensation factor for reconstructing the heating curve.

[0027] In some embodiments, when the rate of the exothermic reaction is lower than the standard value, the model calculates a compensation command to linearly increase the temperature by five degrees Celsius within the next three minutes to offset the activation energy barrier. Variations of this can include segmented step temperature adjustment, dynamic trajectory optimization based on model predictive control, or fuzzy logic rule reasoning. Simultaneously, a shear rate correction coefficient is generated to match the reaction viscosity. If macromolecular polymerization is detected causing an abnormal increase in viscosity, a coefficient to increase the stirring motor speed by 10% is automatically calculated and output to maintain mass transfer efficiency. Alternative methods include introducing ultrasonic-assisted dispersion, adjusting the reactor baffle structure, or changing the type of stirring blades. This achieves a leap from open-loop passive execution to closed-loop active adaptive control, ensuring that the reaction system always operates along the optimal energy path, significantly improving the consistency of product yield and reducing safety risks caused by localized overheating or uneven mixing.

[0028] In this embodiment, the generation of the temperature-time compensation factor and shear rate correction coefficient based on the inverse dynamic mapping relationship specifically includes: A joint sensitivity matrix of the reaction rate constant to temperature and shear rate is constructed. Using the instantaneous kinetic offset as the input vector, the temperature-time compensation factors used to offset reaction lag or lead are calculated by solving the least squares inverse. and the shear rate correction factor used to match the target viscosity evolution curve The solution formula is: ,

[0029] The instantaneous dynamic offset vector is used to represent the deviation between the current reaction rate constant and the standard trajectory; The joint sensitivity matrix consists of the partial derivatives of the Arrhenius equation with respect to temperature and the partial derivatives of the power-law fluid model with respect to shear rate, reflecting the dynamic response sensitivity of the reaction system to thermal and force fields. This is a weighted diagonal matrix used to weight the dynamic contributions of different bands based on the confidence level of the spectral difference fingerprint vector; This is a regularization parameter used to suppress numerical oscillations in the inverse dynamical mapping process and ensure the smoothness of the compensation factor. It is an identity matrix.

[0030] Based on step S3, further steps include: S31: The composite optical signal generated by the reaction system is separated into a first characteristic band and a second characteristic band using a spectrophotometer; wherein, the first characteristic band corresponds to the electronic energy level transition region and is used to capture the rapid dynamic signals caused by chemical bond breaking, active intermediate generation and electron cloud rearrangement during the reaction process. S32: The second characteristic band corresponds to the molecular vibrational and rotational energy level region, and is used to capture slow dynamic signals caused by the reorganization of intermolecular force networks, changes in spatial conformation, and evolution of the micro-rheological environment. S33: A synchronous triggering mechanism ensures that the spectral acquisitions of the two bands are aligned on the time axis, which is used to construct the original dual-band spectral dataset.

[0031] High-precision spectrometers are used to physically separate the composite optical signal radiated by the reaction system into first and second characteristic bands. The first characteristic band locks onto the electronic energy level transition regions such as ultraviolet and visible light, capturing rapid dynamic signals caused by chemical bond breaking, transient generation of active intermediates, and electron cloud rearrangement with microsecond-level time resolution. Alternative techniques for acquiring this band include high-speed wavelength scanning using acousto-optic tunable filters, using prism dispersion systems in conjunction with array detectors, or deploying photonic crystal filters based on metasurface materials. The second characteristic band focuses on the near-infrared or terahertz molecular vibrational and rotational energy level regions, aiming to monitor slow dynamic processes such as intermolecular hydrogen bond network recombination, polymer spatial conformational inversion, and evolution of the micro-rheological environment. Its variations can include Fourier transform spectral modulation, Fabry-Perot interferometer narrowband filtering, or quantum cascade laser array point excitation.

[0032] A unified clock pulse is emitted by the central controller as a synchronization trigger mechanism, forcing detectors in two independent optical paths to expose simultaneously. For example, in nitration reaction monitoring, when the trigger signal is issued, the ultraviolet channel records the absorbance abrupt change in nitro cation formation, while the infrared channel simultaneously records the displacement of the hydrogen bond network of nitric acid molecules. If hardware delay exists, nanosecond-level calibration is performed using digital delay lines or software interpolation algorithms. In some embodiments, single-detector time-division multiplexing switching or asynchronous timestamp alignment technology based on event cameras is also employed. Traditional single-band or non-homogeneous acquisition leads to aliasing and temporal misalignment of fast and slow kinetic characteristics, making it impossible to analyze the cascade mechanisms of complex reactions. The improved method not only achieves decoupled observation and precise correlation of multi-scale physicochemical processes but also enhances the diagnostic capability of reaction kinetic models for transient anomalies and the predictive accuracy of process control.

[0033] Furthermore, it also includes, The preprocessing of the dual-band raw spectral data to eliminate environmental noise and baseline drift includes: A sliding window local polynomial fitting algorithm is used to perform smoothing and denoising processing on the original spectral data of the first and second feature bands to remove high-frequency random interference caused by light source fluctuations and detector thermal noise. The nonlinear baseline drift caused by changes in turbidity of the reaction system or background scattering is estimated and subtracted using the iterative weighted least squares method, and the spectral signal is corrected to a unified zero absorption reference surface. The baseline-corrected spectral intensities are normalized to eliminate absolute intensity differences caused by minor optical path fluctuations or local non-uniformity of sample concentration, thereby generating standardized dual-band spectral data.

[0034] For the original spectral data of the first and second characteristic bands, a sliding window local polynomial fitting algorithm is first used to perform smoothing and denoising processing. This process constructs a locally weighted regression model and uses the least squares method to fit a polynomial curve within a set window width to replace the center point data, thereby effectively eliminating high-frequency fluctuations of the light source and thermal noise of the detector. The calculation formula is as follows: ,

[0035] in Representing the Smoothed intensity values ​​of each sampling point For adjacent raw data points within the window, The polynomial coefficient weights are calculated based on the Savitzky-Gore filter principle. The formula determines the size of the half-window, which is the number of data points involved in the fitting. The physical meaning of this formula is to use the statistical trend of neighborhood data to suppress random errors, while preserving the sharp characteristics of electronic transition peaks and the contour details of molecular vibration peaks, thus avoiding the peak broadening distortion caused by the traditional moving average method.

[0036] Then, the nonlinear baseline drift is estimated and subtracted using the iterative weighted least squares method. This method dynamically adjusts the weighting function through multiple iterations, reducing the weight of the high absorption peak region and increasing the weight of the baseline region, thereby accurately fitting the curved background caused by the increase in turbidity of the reaction system or Mie scattering. The iterative update calculation formula is as follows: ,

[0037] in For the first The baseline vector obtained from the next iteration. The design matrix used for baseline fitting is typically composed of a low-order polynomial basis. It is the first The diagonal weight matrix of the next iteration has its elements adaptively decaying according to the residual size to eliminate signal peak interference. The smoothness penalty factor is used to control the roughness of the baseline. The selected values ​​need to be determined using the generalized cross-validation method. If the value is too small, the fitted baseline will overfit the true absorption peak, leading to "overfitting" and thus subtracting the effective signal. If the value is too large, the baseline will be too flat to track the nonlinear bending background caused by changes in turbidity. It is usually dynamically adjusted based on prior knowledge of the scattering intensity of the reaction system. The difference operator matrix iteratively strips away the true spectral signal, retaining only the low-frequency background trend. Ultimately, the corrected spectrum is forced to zero on a unified reference plane, completely eliminating optical path scattering interference caused by uneven stirring or bubble generation. This ensures that subsequent difference fingerprint vectors reflect only the true chemical reaction kinetics.

[0038] Furthermore, it also includes: A time series alignment operation is performed on the standardized spectral data of the first feature band and the second band, and the data of the two bands are mapped to a unified time coordinate axis based on the timestamp generated by the synchronization triggering mechanism. Perform a difference operation on the aligned dual-band data to extract the dynamic deviation of the first characteristic band relative to the second characteristic band, which characterizes the non-equilibrium difference between the electronic energy level transition rate and the molecular vibrational relaxation rate.

[0039] The dynamic deviation is compared with a preset kinetic fingerprint threshold to identify the critical state of chemical bond breaking or micro-rheological environment evolution in the reaction system, and to generate a spectrokinetic fingerprint map containing the feature vector of the reaction process.

[0040] Furthermore, the beam-splitting assembly includes a dichroic beam splitter and a tunable filter group; The dichroic beam splitter is positioned on the optical transmission path of the reaction system. Based on a preset cutoff wavelength, it reflects the composite optical signal to the first optical path and transmits it to the second optical path, thereby physically separating the first characteristic band and the second characteristic band. The tunable filter group is respectively disposed at the end of the first optical path and the second optical path, and is used to perform narrowband filtering on the separated beams, suppress out-of-band stray light interference and improve the signal-to-noise ratio of the target band. The synchronization triggering mechanism includes a master clock generator and a dual-channel data acquisition card. The master clock generator generates a high-frequency synchronization pulse signal to simultaneously drive the spectral detectors corresponding to the two bands to perform integrated exposure, ensuring that the first characteristic band data frame and the second characteristic band data frame have strictly consistent timestamp marks.

[0041] Secondly, the present invention also provides an intelligent production control system for lubricating materials, applied to any of the intelligent production control methods for lubricating materials described herein, comprising: The acquisition and processing module is used to acquire the raw dual-band spectral data of the reaction system in real time, and extract the spectral difference fingerprint vector that characterizes the dynamic evolution of intermolecular forces after preprocessing. The compensation generation module, connected to the acquisition and processing module, is used to input the spectral difference fingerprint vector into the pre-constructed reaction kinetic deviation model, calculate the instantaneous kinetic offset of the current reaction system relative to the standard trajectory, and generate a temperature-time compensation factor for reconstructing the heating curve and a shear rate correction coefficient for matching the reaction viscosity based on the inverse kinetic mapping relationship. The execution control module is connected to the compensation generation module and is used to dynamically reconstruct the heating curve of the heating system according to the temperature-time compensation factor, adjust the stirring power in real time according to the shear rate correction coefficient, perform feedforward compensation control to lock the reaction endpoint, and trigger a pulse replenishment command for trace additives when the convergence slope of the spectral difference fingerprint vector deviates from the preset safety envelope set based on the statistical confidence interval of the standard reaction trajectory. The evidence recording module, connected to the execution control module, is used to collect time-series data including the current spectral differential fingerprint vector, instantaneous dynamic offset, compensation factor, correction coefficient and actual supplementation amount in response to the completion of the pulse supplementation command, generate a control evidence block with a timestamp and write it into the distributed ledger, and solidify the closed-loop control record of the production process including abnormal intervention.

[0042] Furthermore, the acquisition and processing module includes a dual-band beam splitting component and a synchronous trigger controller; The dual-band beam splitter is used to separate the composite optical signal generated by the reaction system into a first characteristic band corresponding to the electronic energy level transition region and a second characteristic band corresponding to the molecular vibration and rotation energy level region. The synchronous trigger controller is used to generate a high-frequency synchronous pulse signal, and simultaneously drive the spectral detectors corresponding to the two characteristic bands to perform integral exposure, thereby constructing a dual-band original spectral dataset.

[0043] Furthermore, the execution control module includes a temperature reconstruction submodule, a power regulation submodule, and an additive pulse submodule; The temperature reconstruction submodule is used to receive the temperature-time compensation factor and correct the slope of the heating curve in real time by dynamically adjusting the flow rate of the heating medium or the output duty cycle of the electric heating power. The power regulation submodule is used to receive the shear rate correction coefficient and adjust the speed of the stirring motor in real time through the frequency converter to match the change in reaction viscosity. The additive pulse submodule is used to control the micro-metering pump to inject the additive in a preset high-frequency intermittent mode when a pulse replenishment command is received, and to provide real-time feedback of the actual injection volume to the evidence recording module.

[0044] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0045] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for intelligent production control of lubricating materials, characterized in that, Includes the following steps: Real-time acquisition of dual-band raw spectral data of the reaction system, followed by preprocessing to extract spectral difference fingerprint vectors characterizing the dynamic evolution of intermolecular forces; The spectral difference fingerprint vector is input into the pre-constructed reaction kinetic deviation model to calculate the instantaneous kinetic offset of the current reaction system relative to the standard trajectory, and a temperature-time compensation factor for reconstructing the heating curve and a shear rate correction coefficient for matching the reaction viscosity are generated based on the inverse kinetic mapping relationship. The heating curve of the heating system is dynamically reconstructed based on the temperature-time compensation factor. The stirring power is adjusted in real time according to the shear rate correction coefficient. Feedforward compensation control is performed to lock the reaction endpoint. When the convergence slope of the spectral difference fingerprint vector deviates from the preset safety envelope set based on the statistical confidence interval of the standard reaction trajectory, a pulse replenishment command for trace additives is triggered. In response to the completion of the pulse replenishment command, time-series data including the current spectral differential fingerprint vector, instantaneous dynamic offset, compensation factor, correction coefficient and actual replenishment amount are collected, a time-stamped control evidence block is generated and written into the distributed ledger, and the closed-loop control record of the production process including abnormal intervention is solidified.

2. The intelligent production control method for lubricating materials according to claim 1, characterized in that, The extraction of the spectral difference fingerprint vector characterizing the dynamic evolution of intermolecular forces after preprocessing specifically includes: Baseline correction and scattered light removal were performed on the collected dual-band raw spectral data to separate the first band data reflecting the vibrational characteristics of functional groups and the second band data reflecting the translational frictional characteristics of molecules. Calculate the difference vector between the spectral intensities of the two bands at the current time and the previous time. Then, use a weighted fusion algorithm to couple and map the difference vector of the first band data with the difference vector of the second band data to generate the spectral differential fingerprint vector.

3. The intelligent production control method for lubricating materials according to claim 1, characterized in that, The generation of the temperature-time compensation factor and shear rate correction coefficient based on the inverse dynamic mapping relationship specifically includes: A joint sensitivity matrix of the reaction rate constant to temperature and shear rate is constructed. Using the instantaneous kinetic offset as the input vector, the temperature-time compensation factors used to offset reaction lag or lead are calculated by solving the least squares inverse. and the shear rate correction factor used to match the target viscosity evolution curve The solution formula is: , The instantaneous dynamic offset vector is used to represent the deviation between the current reaction rate constant and the standard trajectory; The joint sensitivity matrix consists of the partial derivatives of the Arrhenius equation with respect to temperature and the partial derivatives of the power-law fluid model with respect to shear rate, reflecting the dynamic response sensitivity of the reaction system to thermal and force fields. This is a weighted diagonal matrix used to weight the dynamic contributions of different bands based on the confidence level of the spectral difference fingerprint vector; This is a regularization parameter used to suppress numerical oscillations in the inverse dynamical mapping process and ensure the smoothness of the compensation factor. It is an identity matrix.

4. The intelligent production control method for lubricating materials according to claim 1, characterized in that, Also includes: The composite optical signal generated by the reaction system is separated into a first characteristic band and a second characteristic band using a spectrophotometer; wherein, the first characteristic band corresponds to the electronic energy level transition region and is used to capture the rapid dynamic signals caused by chemical bond breaking, active intermediate generation and electron cloud rearrangement during the reaction process. The second characteristic band corresponds to the molecular vibrational and rotational energy level region and is used to capture slow dynamic signals caused by the reorganization of intermolecular force networks, changes in spatial conformation, and evolution of the micro-rheological environment. A synchronous triggering mechanism is used to ensure that the spectral acquisitions of the two bands are aligned on the time axis, thus constructing a dual-band original spectral dataset.

5. The intelligent production control method for lubricating materials according to claim 4, characterized in that, It also includes, Preprocessing of the raw dual-band spectral data to eliminate environmental noise and baseline drift includes: A sliding window local polynomial fitting algorithm is used to perform smoothing and denoising processing on the original spectral data of the first and second feature bands to remove high-frequency random interference caused by light source fluctuations and detector thermal noise. The nonlinear baseline drift caused by changes in turbidity of the reaction system or background scattering is estimated and subtracted using the iterative weighted least squares method, and the spectral signal is corrected to a unified zero absorption reference surface. The baseline-corrected spectral intensities are normalized to generate standardized dual-band spectral data.

6. The intelligent production control method for lubricating materials according to claim 5, characterized in that, Also includes: Perform time series alignment on the standardized spectral data of the first and second characteristic bands, and map the data of the two bands to a unified time coordinate axis based on the timestamp generated by the synchronization triggering mechanism; Based on the aligned dual-band data, a difference operation is performed to extract the dynamic deviation of the first characteristic band relative to the second characteristic band, which is used to characterize the non-equilibrium state difference between the electronic energy level transition rate and the molecular vibrational relaxation rate. The dynamic deviation is compared with a preset kinetic fingerprint threshold to identify the critical state of chemical bond breaking or micro-rheological environment evolution in the reaction system, and to generate a spectrokinetic fingerprint map containing the feature vector of the reaction process.

7. The intelligent production control method for lubricating materials according to claim 4, characterized in that, The beam-splitting assembly includes a dichroic beam splitter and a tunable filter group; The dichroic beam splitter is positioned on the optical transmission path of the reaction system. Based on a preset cutoff wavelength, it reflects the composite optical signal to the first optical path and transmits it to the second optical path, thereby physically separating the first characteristic band and the second characteristic band. The tunable filter group is respectively disposed at the end of the first optical path and the second optical path, and is used to perform narrowband filtering on the separated beams, suppress out-of-band stray light interference and improve the signal-to-noise ratio of the target band. The synchronization triggering mechanism includes a master clock generator and a dual-channel data acquisition card. The master clock generator generates a high-frequency synchronization pulse signal to simultaneously drive the spectral detectors corresponding to the two bands to perform integration exposure.

8. A smart production control system for lubricating materials, applied to the smart production control method for lubricating materials as described in any one of claims 1-7, characterized in that, include: The acquisition and processing module is used to acquire the raw dual-band spectral data of the reaction system in real time, and extract the spectral difference fingerprint vector that characterizes the dynamic evolution of intermolecular forces after preprocessing. The compensation generation module, connected to the acquisition and processing module, is used to input the spectral difference fingerprint vector into the pre-constructed reaction kinetic deviation model, calculate the instantaneous kinetic offset of the current reaction system relative to the standard trajectory, and generate a temperature-time compensation factor for reconstructing the heating curve and a shear rate correction coefficient for matching the reaction viscosity based on the inverse kinetic mapping relationship. The execution control module is connected to the compensation generation module and is used to dynamically reconstruct the heating curve of the heating system according to the temperature-time compensation factor, adjust the stirring power in real time according to the shear rate correction coefficient, perform feedforward compensation control to lock the reaction endpoint, and trigger a pulse replenishment command for trace additives when the convergence slope of the spectral difference fingerprint vector deviates from the preset safety envelope set based on the statistical confidence interval of the standard reaction trajectory. The evidence recording module, connected to the execution control module, is used to collect time-series data including the current spectral differential fingerprint vector, instantaneous dynamic offset, compensation factor, correction coefficient and actual supplementation amount in response to the completion of the pulse supplementation command, generate a control evidence block with a timestamp and write it into the distributed ledger, and solidify the closed-loop control record of the production process including abnormal intervention.

9. The intelligent production control system for lubricating materials according to claim 8, characterized in that, The acquisition and processing module includes a dual-band beam splitter and a synchronous trigger controller; The dual-band beam splitter is used to separate the composite optical signal generated by the reaction system into a first characteristic band corresponding to the electronic energy level transition region and a second characteristic band corresponding to the molecular vibration and rotation energy level region. The synchronous trigger controller is used to generate a high-frequency synchronous pulse signal, and simultaneously drive the spectral detectors corresponding to the two characteristic bands to perform integral exposure, thereby constructing a dual-band original spectral dataset.

10. The intelligent production control system for lubricating materials according to claim 8, characterized in that, The execution control module includes a temperature reconstruction submodule, a power regulation submodule, and an additive pulse submodule; The temperature reconstruction submodule is used to receive the temperature-time compensation factor and correct the slope of the heating curve in real time by dynamically adjusting the flow rate of the heating medium or the output duty cycle of the electric heating power. The power regulation submodule is used to receive the shear rate correction coefficient and adjust the speed of the stirring motor in real time through the frequency converter to match the change in reaction viscosity. The additive pulse submodule is used to control the micro-metering pump to inject the additive in a preset high-frequency intermittent mode when a pulse replenishment command is received, and to provide real-time feedback of the actual injection volume to the evidence recording module.

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