Rapid detection method for gelation time of environmental protection adhesive based on near infrared spectrum

Abstract

This invention relates to a rapid detection method for the gelation time of environmentally friendly adhesives based on near-infrared spectroscopy, belonging to the fields of analytical chemistry and instrumental analysis. Addressing the problems of existing technologies relying on empirical models and being susceptible to interference from solvent evaporation, this invention obtains the continuous spectral matrix of the reaction process; extracts the interference feature vector space matrix of the substrate solvent and constructs an orthogonal complementary space projection operator to perform spatial projection denoising on the spectral matrix; extracts moving window slice data along the time axis and calculates the spectral information entropy parameter to construct an evolution sequence curve; performs a difference operation on this curve and extracts the time corresponding to the zero-crossing point where the second derivative trajectory line changes from positive to negative as the gelation time. This invention eliminates the need to establish a chemometric prediction model and completely eliminates substrate solvent interference by utilizing orthogonal mapping and thermodynamic catastrophe monitoring, achieving universal and accurate locking of the absolute gelation time of various environmentally friendly adhesives.

Description

Technical Field

[0001] This invention belongs to the field of analytical chemistry and instrumental analysis technology, specifically relating to a rapid detection method for the gelation time of environmentally friendly adhesives based on near-infrared spectroscopy. Background Technology

[0002] Near-infrared spectroscopy, with its advantages of being non-destructive, real-time, and requiring no complex pretreatment, has been widely used in recent years for monitoring the reaction processes of polymers. In the production and application of environmentally friendly adhesives, such as waterborne polyurethane adhesives, modified soybean protein adhesives, or lignin-based adhesives, accurately determining the gelation time—the critical point at which the system transitions from a liquid state to a three-dimensional cross-linked solid state—is crucial for controlling process parameters and ensuring product quality. However, when using near-infrared spectroscopy for in-situ online monitoring of environmentally friendly adhesives containing large amounts of water or polar solvents, extremely stringent spectral interpretation challenges are often encountered.

[0003] Environmentally friendly adhesive systems typically contain a high proportion of base solvents, such as water. During the curing reaction, which involves heating or exothermic processes, the base solvent undergoes rapid volatilization. Simultaneously, the hydrogen bonding state of water molecules within the system changes non-linearly with temperature. This dramatic change in physical and chemical states directly leads to the broadening, shift, and sharp non-linear drift in intensity of the strong hydroxyl absorption peaks near 1450 nm and 1940 nm in the near-infrared spectrum. This significant spectral baseline fluctuation, dominated by the base solvent—a masking effect—completely overwhelms and covers the extremely weak overtone and combination frequency absorption characteristics of the carbon-hydrogen or nitrogen-hydrogen bonds formed by the crosslinking of polymer monomers.

[0004] To overcome the aforementioned interference, current mainstream techniques typically employ chemometric algorithms such as partial least squares. However, these empirical models, heavily reliant on prior data, have inherent limitations: they are based on the assumption that the test samples and calibration set samples are in extremely similar physicochemical environments. In practical industrial applications, minor adjustments to the solids content of environmentally friendly adhesives, slight differences between batches of raw materials, or changes in on-site stirring rates can all alter the scattering coefficient and absorption background of the spectrum. Once the reaction system deviates from the original modeling context, the prediction error of traditional chemometric models increases significantly and drastically, even becoming completely ineffective. To maintain detection accuracy, extensive manual sampling and recalibration of the model are necessary for each finely adjusted formulation. This not only consumes enormous time and manpower but also results in extremely poor generalization ability of such detection methods under varying practical conditions, making it impossible to achieve truly plug-and-play, universal, and rapid detection of environmentally friendly adhesives with unknown or confidential formulations. Summary of the Invention

[0005] The purpose of this invention is to provide a rapid detection method for the gelation time of environmentally friendly adhesives based on near-infrared spectroscopy. This method solves the problem that in the existing technology, when monitoring environmentally friendly adhesives, the traditional near-infrared spectroscopy method causes serious "spectral masking" and "baseline drift" due to the large amount of evaporation of the base solvent, resulting in extreme dependence and easy deviation from specific chemometric prediction models, thus making it impossible to universally and accurately lock the absolute gelation time automatically.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A rapid detection method for the gelation time of environmentally friendly adhesives based on near-infrared spectroscopy includes:

[0008] Obtain a continuous time-series near-infrared spectral data matrix of environmentally friendly adhesive during the curing reaction process;

[0009] Extract the interference feature vector space matrix of the substrate solvent;

[0010] Construct the orthogonal complement spatial projection operator of the interference feature vector space matrix, and multiply the continuous time series near-infrared spectral data matrix by the orthogonal complement spatial projection operator to obtain the purified spectral data matrix;

[0011] The time window slice data is extracted by applying a moving window algorithm to the purified spectral data matrix along the time axis;

[0012] Calculate the spectral information entropy parameter corresponding to each time window slice data, and construct the evolution sequence curve of time and the spectral information entropy parameter;

[0013] The evolution sequence curve is subjected to a difference operation to generate a second derivative trajectory line. The time coordinates corresponding to the zero-crossing point on the second derivative trajectory line from a positive value to a negative value are extracted as the gelation time.

[0014] Furthermore, the interference feature vector space matrix of the extracted substrate solvent includes:

[0015] The absolute value of the time derivative of the integral of the full-spectral absorbance in the initial stage of the reaction is used as the macroscopic evolution rate.

[0016] When the macroscopic evolution rate monitored in real time drops to less than or equal to the preset rate threshold, the current moment is recorded as the cutoff time.

[0017] A continuous spectral data between the reaction initiation time zero and the cutoff time is used to construct an initial matrix;

[0018] The initial matrix is ​​subjected to independent component analysis algorithm with the cost of maximizing negative entropy as the evaluation function, and the mutually independent pure source spectral feature vectors and corresponding concentration time series are extracted.

[0019] Calculate the proportion of negative elements and variance of each concentration time series, and multiply the variance by the absolute value of the Pearson correlation coefficient to obtain the cross-scoring coefficient;

[0020] The pure source spectral feature vector corresponding to the highest cross-scoring coefficient is extracted to construct the interference feature vector space matrix.

[0021] Furthermore, the interference feature vector space matrix of the extracted substrate solvent includes:

[0022] Collect a set of pure substrate solvent spectra under different temperature gradients;

[0023] Calculate the covariance matrix of the pure substrate solvent spectrum set and perform eigenvalue decomposition to extract eigenvectors and eigenvalues;

[0024] Arrange the eigenvalues ​​in descending order and calculate the quotient of the sum of the first few eigenvalues ​​and the sum of the total eigenvalues ​​as the cumulative variance contribution rate.

[0025] The first few eigenvectors whose cumulative variance contribution rate is greater than or equal to the preset variance threshold are selected to construct the interference eigenvector space matrix.

[0026] Furthermore, the steps of obtaining the purified spectral data matrix and extracting the time coordinates corresponding to the zero-crossing points on the second derivative trajectory line respectively include:

[0027] Calculate the mean absorbance and global standard deviation of a single-frame purification spectrum. Subtract the mean absorbance from the absorbance of each wavelength of the single-frame purification spectrum and divide by the global standard deviation to complete the standard normal variable transformation correction.

[0028] The evolution sequence curve is reconstructed by convolution using a polynomial smoothing filter algorithm.

[0029] Based on the fact that the slope of the first derivative is less than the preset slope limit and the second derivative crosses the zero mark to lock two adjacent discrete time nodes, the floating-point timestamp is calculated using the linear interpolation root-finding formula as the gelation time.

[0030] Furthermore, the calculation of the spectral information entropy parameter corresponding to each of the time window slices includes:

[0031] Calculate the first-order difference sequence of absorbance at corresponding wavelengths for adjacent frames within the current time window slice data;

[0032] The absorbance difference is distributed to a preset statistical interval, and the probability density distribution of each interval is accumulated.

[0033] The product of the probability density distribution of each interval and its natural logarithm is summed and the result is negative. The result is used as the spectral information entropy parameter.

[0034] Furthermore, the calculation of the spectral information entropy parameter corresponding to each of the time window slices includes:

[0035] The first-order time series of spectral absorbance within the time window slice data is combined into a low-dimensional state vector according to the set delay time parameter.

[0036] Extract the maximum value of the absolute difference between any two low-dimensional state vectors at different times across all corresponding dimensional coordinate components as the spatial distance;

[0037] Calculate the standard deviation of the first-order time series of absorbance within the current time window slice data, and multiply the standard deviation by the proportional adjustment coefficient to obtain the tolerance threshold value;

[0038] The number of vector pairs whose spatial distance is less than the tolerance threshold is counted to calculate the basic probability in the low-dimensional space. The dimension of the low-dimensional state vector is expanded by one dimension and then the progressive probability in the high-dimensional space is calculated again.

[0039] Calculate the ratio of the progressive probability in the high-dimensional space to the basic probability in the low-dimensional space, and take the negative natural logarithm of this ratio as the spectral information entropy parameter.

[0040] Furthermore, extracting the time coordinate corresponding to the zero-crossing point on the second derivative trajectory line where a positive value crosses the zero mark and enters a negative value as the gelling time includes:

[0041] Obtain the absolute value of the instantaneous fall rate and the absolute depth of the cumulative fall from the spectral entropy parameter at a specific time point;

[0042] Multiply the absolute value of the instantaneous fall rate by the first weighting coefficient, multiply the absolute depth of the cumulative fall by the second weighting coefficient, and add the products of the two to construct a multidimensional weighted evaluation scoring function.

[0043] Traverse the time nodes after the set monitoring dead zone, and extract the horizontal coordinate node that makes the value of the multidimensional weighted evaluation scoring function reach the global maximum value as the gelation time.

[0044] Furthermore, before obtaining the continuous time-series near-infrared spectral data matrix of the environmentally friendly adhesive during the curing reaction process, the method further includes:

[0045] The original digital value of weak light intensity is obtained through an analog-to-digital converter;

[0046] The mathematical formula for dynamic light intensity compensation is called inside the field programmable gate array, and the original weak light intensity digital quantity is multiplied by a specific exponent of the natural constant to obtain the absolute light intensity value.

[0047] The specific exponent power is the product of the scattering attenuation coefficient and the equivalent fixed optical path length.

[0048] The beneficial effects of this invention are:

[0049] 1. This invention extracts moving window slice data along the time axis and calculates the spectral information entropy parameter, transforming the detection dimension from the traditional "prediction based on concentration at a specific wavelength" to "monitoring the topological mutation of the microscopic thermodynamic order of the system." Since the gelation process is inevitably accompanied by the freezing of molecular networks and a sharp decrease in degrees of freedom, this fundamental physical law applies to any cross-linked system. Therefore, this solution does not require the prior establishment of complex chemometric prediction models and possesses strong adaptive generalization capabilities for environmentally friendly adhesives of different formulations or batches, achieving plug-and-play functionality without calibration.

[0050] 2. This invention extracts the spatial matrix of interference feature vectors of the substrate solvent and constructs its orthogonal complementary spatial projection operator to perform spatial forced projection calculations on the continuously acquired mixed spectral data matrix. This technique, at a multi-dimensional geometric level, forces the drastically nonlinear time-dependent water peak drift and solvent interference to zero, perfectly exposing the masked weak crosslinking signals, and fundamentally solving the technical problem of detection distortion caused by chemical masking effects in highly volatile systems.

[0051] 3. In the preferred embodiment of the present invention, an Independent Component Analysis (ICA) algorithm is introduced to dynamically extract interference features from the initial reaction spectrum, eliminating the dependence on pure solvent physical samples and fitting the closed feeding conditions of confidential formulations; at the same time, by combining polynomial convolution reconstruction and second derivative zero-crossing feature capture, high-frequency electronic and mechanical noise caused by large agitator shear and bubble rupture is effectively filtered out, ensuring the absolute accuracy and uniqueness of the machine automatically locking the gelation time. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is the overall flowchart for the detection of the entropy change spectrum of the environmentally friendly adhesive in this invention;

[0054] Figure 2 This is a schematic diagram of the blind source separation solvent interference extraction algorithm of the present invention;

[0055] Figure 3 This is a schematic diagram illustrating the principle of sample entropy calculation based on phase space reconstruction in this invention.

[0056] Figure 4 This is a schematic diagram illustrating the optimal implementation principle of orthogonal projection denoising and calculus root finding in this invention.

[0057] Figure 5 This is an architecture diagram of the automated detection system with deep integration of software and hardware according to the present invention. Detailed Implementation

[0058] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0059] A rapid detection method for the gelation time of environmentally friendly adhesives based on near-infrared spectroscopy includes:

[0060] Obtain a continuous time-series near-infrared spectral data matrix of environmentally friendly adhesive during the curing reaction process;

[0061] Extract the interference feature vector space matrix of the substrate solvent;

[0062] Construct the orthogonal complement spatial projection operator of the interference feature vector space matrix, and multiply the continuous time series near-infrared spectral data matrix by the orthogonal complement spatial projection operator to obtain the purified spectral data matrix;

[0063] The time window slice data is extracted by applying a moving window algorithm to the purified spectral data matrix along the time axis;

[0064] Calculate the spectral information entropy parameter corresponding to each time window slice data, and construct the evolution sequence curve of time and the spectral information entropy parameter;

[0065] The evolution sequence curve is subjected to a difference operation to generate a second derivative trajectory line. The time coordinates corresponding to the zero-crossing point on the second derivative trajectory line from a positive value to a negative value are extracted as the gelation time.

[0066] Regarding the appendix Figure 1-5 The following explanation is provided;

[0067] Appendix Figure 1 Overall Flowchart for the Gelation Spectral Entropy Change Detection of Environmentally Friendly Adhesives;

[0068] This paper demonstrates the entire detection process of Scheme A, the core and fundamental method of this invention, from spectral acquisition, extraction of interference space, orthogonal projection denoising, calculation of information entropy, all the way to the final second-order calculus root finding.

[0069] Appendix Figure 2Schematic diagram of the blind source separation solvent interference extraction algorithm;

[0070] The paper details how to automatically extract the solvent interference matrix from the mixed spectrum under the limited condition of "not being able to obtain the a priori spectrum of the pure solvent".

[0071] Appendix Figure 3 : Schematic diagram of sample entropy calculation based on phase space reconstruction;

[0072] It presents an algorithmic process that abandons simple Shannon entropy and instead uses "Tukens phase space reconstruction" and "Chebyshev distance" from nonlinear dynamics to quantify the topological differences of microstates and ultimately calculate the sample entropy.

[0073] Appendix Figure 4 Schematic diagram of the optimal implementation principle of orthogonal projection denoising and calculus root finding;

[0074] It combines the most robust mathematical tools in this invention: SVD matrix decomposition, orthogonal projection formula, SNV standard normal variable transformation, Savitzky-Golay polynomial smoothing, and interpolation root-finding algorithm.

[0075] Appendix Figure 5 Architecture diagram of an automated testing system with deep integration of software and hardware;

[0076] The system's physical entity and logical control architecture are demonstrated, covering the probe sensing layer, FPGA hardware compensation layer, DSP / CPU heterogeneous computing layer, and the physical execution layer that ultimately triggers the PLC alarm.

[0077] Example 1

[0078] In the curing monitoring of environmentally friendly adhesives such as waterborne polyurethane or soy protein, the practical application of near-infrared spectroscopy has been hampered by a process bottleneck: the 30%-50% water or polar solvent in the formulation causes extreme nonlinear shifts in the -OH absorption peaks around 1450nm and 1940nm when they evaporate upon heating. This masking effect of the water peaks completely obliterates the weak combination and overtone signals representing the crosslinking of polymer segments, such as the CH bond at 1680nm or the NH bond at 2200nm. The conventional solution is to establish a partial least squares (PLS) "spectrum-viscosity" prediction model. In our early workshop trials, we found that once the production line was fine-tuned by 2% of the solid content, or the crosslinking agent from a different origin was changed, the prediction error of the original PLS model would soar to over 40%, sometimes even outputting negative viscosity values. Recalibration requires offline sampling to prepare at least 50 gradient standard samples, which takes more than a week.

[0079] The factory site simply could not tolerate such poor generalization ability and maintenance costs. This solution abandons the chemometric modeling approach, and the underlying hardware consists of a spectrometer, a 24-bit analog-to-digital converter (ADC), a field-programmable gate array (FPGA), an ARM-based central processing unit (CPU), and a graphics processing unit (GPU). The spectrometer's diffuse reflection fiber optic probe (set to 2mm optical path) is directly immersed 30cm below the surface of the liquid in the reactor equipped with an anchor-type stirrer (set to 120RPM).

[0080] Near-infrared light emitted from a tungsten halogen lamp penetrates the reactants, and the scattered photons strike an indium gallium arsenide detector. The analog voltage signal is sliced ​​by an ADC and fed into the industrial computer's memory. The CPU allocates a contiguous address space in RAM and combines the discrete spectral frames into a two-dimensional matrix. .matrix The row dimension represents the time node with a sampling frequency of 1 Hz, and the column dimension corresponds to 256 discrete wavelength channels in the range of 900-2600 nm. To remove substrate solvent interference, the overlapping problem of chemical absorption must be transformed into a geometrically orthogonal problem in mathematical space. Simply relying on the first derivative of the spectrum or the standard normal variable transformation (SNV) can only eliminate the baseline shift caused by physical particle scattering, and has no effect on the broadening of chemical absorption bands caused by changes in the hydrogen bonding state of water molecules. The first step of the algorithm is to construct the interference feature vector space matrix. Before conducting actual gel testing online, inject pure water or a basic solvent without crosslinking agent into the reaction vessel.

[0081] The heating jacket is activated, and the temperature rise profile is set from 25°C to 80°C. The probe acquires a set of pure solvent spectra at different temperatures and evaporation rates. The CPU calculates the covariance matrix of this set of calibration spectra, and the GPU's tensor core performs singular value decomposition (SVD). The spectral variations of the base solvent have inherent physical degrees of freedom. A cumulative variance contribution rate formula is introduced for evaluation:

[0082] In this formula, The first in descending order 1 eigenvalue, This represents the total number of wavelength nodes. The hard-coded threshold is set to... Before selection One (usually) or When the principal component is reached, the cycle terminates. The eigenvectors span a low-dimensional subspace that encloses the physical modes of solvent interference. Real-time monitoring begins, and the GPU takes over the computation. The interference matrix is ​​then solved. Orthogonal projection operator:

[0083] It is the transpose matrix. This is the inverse of the inner product matrix. The mixed spectral matrix. Multiply by operator , obtained This refers to absorption fluctuations in the original signal caused solely by water evaporation and hydrogen bond breaking. Perform the subtraction formula:

[0084] In a multidimensional geometric coordinate system, the original data matrix It was forcibly projected into the orthogonal complementary space that forms a perfect 90-degree angle with the solvent interference space. At this point, even if the moisture content inside the vessel evaporates drastically from 50% to 10%, the water peak at 1450 nm will shift by [a certain amount]. The matrix was also forcibly zeroed out by mathematical rules. The carbon-oxygen double bond formation signal, previously hidden deep within the baseline, was now fully exposed. This achieved noise reduction. The matrix no longer uses the PLS algorithm to calculate absolute concentration because the mapping of local spectral intensity is still limited by the minute batch differences in the formulation. We shifted the detection focus from "finding specific absorption peaks" to "measuring the thermodynamic disorder of microscopic molecular motion".

[0085] The physical essence of gelation is the cross-linking of free oligomers within the system into a three-dimensional network. Macroscopically, this manifests as a surge in viscosity and loss of fluidity; microscopically, it manifests as a forced freeze of the system's degrees of freedom (molecular translational and rotational capabilities). The CPU allocates a FIFO (First-In-First-Out) sliding buffer queue in memory. The window length is set to 10 time frames (i.e., 10 seconds), and the step size is set to 1 frame. The first-order difference sequence of the spectral absorbance of adjacent nodes within the window is extracted.

[0086] The amplitude of micro-absorbance fluctuations is divided into Divide the data into several equidistant micro-intervals. Count the frequency of falling into each interval to obtain the probability density. Calling Shannon's information entropy formula:

[0087] In the initial stage of a liquid reaction, small molecules undergo Brownian motion in the solvent. The weak spectral fluctuations captured by the probe exhibit disordered white noise characteristics, with a probability distribution tending towards an average. The value oscillated in the high range of 2.8 to 3.2. At the instant the critical gelation point was reached, the macromolecular network locked the local Brownian motion. The randomness of the spectral signal disappeared, and fluctuations concentrated in a very small number of regular frequency bands induced by the mechanical stirring impeller. Probability density It is extremely concentrated in a specific range. The calculated spectral information entropy... An extremely steep vertical drop will occur (typically from 3.0 to below 1.2 within 5 to 8 seconds).

[0088] The CPU will compare the midpoints of each time point with... Value binding refreshes the time-entropy evolution sequence in memory. In industrial settings, the churning of agitators or the occasional bursting of large bubbles can create localized downward spikes in the evolution sequence. The CPU sets the time dead zone parameter. Seconds. During the initial 5 minutes of the reaction, the feeding shock causes the entropy value to fluctuate violently, and all out-of-bounds events are discarded directly in the underlying registers. After crossing the dead time, the curve is smoothed using Savitzky-Golay polynomial convolution filtering (parameters set to 15-point window, 3rd order polynomial).

[0089] The filtered curve is subjected to two consecutive differential discretization operations, outputting the first derivative curve representing the rate of change and the second derivative curve representing the acceleration. The machine's decision logic is fixed with absolute root-finding conditions: the second derivative curve must reach its first absolute zero-crossing point on the horizontal axis, where it crosses the zero mark from a positive value to a negative value, and the value of the first derivative at that moment must be less than a set slope limit. .

[0090] The root-finding probe captures this node and extracts the corresponding system RTC timestamp as the absolute curing time of the environmentally friendly adhesive. The industrial control computer sends a 24V trigger level directly to the workshop programmable logic controller (PLC) via the digital I / O board, sounding the on-site buzzer and shutting off the steam valve of the reactor heating jacket.

[0091] Implementation 2

[0092] In actual adhesive production lines, requiring customers to provide pure base solvent spectra at different temperature gradients is an idea that is extremely detached from reality. The formulations of many environmentally friendly adhesives (such as modified soybean protein adhesives or custom waterborne polyurethanes) are core secrets, and on-site material feeding is often done by directly supplying pre-mixed emulsions, where there is simply no physical valve for extracting a single pure solvent.

[0093] If the production line uses a continuously fed, fully enclosed pipeline, shutting down to specifically collect a set of baseline spectra of the solvent would incur costs in the tens of thousands of yuan per instance. Without obtaining pure solvent spectra, it is impossible to establish the interference feature vector space, rendering conventional orthogonal denoising methods ineffective. This solution abandons the path of extracting prior pure solvent samples and instead focuses on blind source separation directly from the mixed reactants. The hardware is deployed on a 2000L reactor equipped with a 120RPM anchor stirrer. A fiber optic probe penetrates 30 cm below the liquid surface through a flange, and the reflected near-infrared light signal is sliced ​​at a sampling rate of 100Hz by a 24-bit analog-to-digital converter (ADC) and written into the memory of a GPU equipped with an ARM Cortex-A53 processor and 1024 CUDA cores. To extract the solvent signal from the mixed spectrum, the first step is to automatically extract the "initial curing stage," where only physical evaporation occurs and no chemical cross-linking takes place. In the early stages of trial and error in the workshop, we attempted to hard-code a fixed time (e.g., the first 180 seconds) as the cutoff point. This is completely impractical in reality. The solvent evaporation rate differs greatly between the 5°C environment in winter and the 35°C environment in summer. Fixed-time slices would mix in a large amount of polymer crosslinking signals, contaminating subsequent models.

[0094] The system introduces dynamic determination based on the integral evolution rate of absorbance. (Set time) The absorbance integral across the entire 900-2600nm wavelength range is Calculate the macroscopic evolution rate:

[0095] In the initial stage of feeding and heating, a large amount of water or polar solvents escape from the liquid surface. It will reach a peak value of 0.08 AU / s. As oligomers form, the macroscopic viscosity of the liquid increases from 200 mPa·s, the intermolecular hydrogen bond binding force increases, and solvent evaporation is hindered. The trend is downward. The system has a preset dead zone threshold. AU / s. When detected At that time, the hardware timer stamps a timestamp. 0 to The continuous spectral data during the period were packed into an initial matrix. At this point, the baseline drift is dominated by the single kinetic of solvent evaporation. This is in contrast to the matrix of absorption peaks superimposed from moisture, initiator, and initial prepolymer. Extracting specific interference vectors is a challenging mathematical problem. The industry typically attempts multivariate curve-resolved alternating least squares (MCR-ALS), but the MCR algorithm is highly dependent on the estimated initial concentration. Given an incorrect initial matrix when the formulation is completely unknown, the ALS iteration will get stuck in a local dead end within the first 10 iterations. Principal component analysis (PCA) is also ineffective. While the principal components extracted by PCA are mathematically forced to be orthogonal, in the world of physicochemical chemistry, the spectral absorption bands of water molecules and polymers severely overlap, lacking mathematical orthogonality.

[0096] We switched the architecture to an Independent Component Analysis (ICA) model. Its core mapping model is: . It is the collected mixing matrix. It is the spectrum of an unknown pure component. These are unknown concentration kinetic evolution coefficients. The core task of the GPU is to find the unmixing matrix using unconstrained optimization algorithms. This makes the output infinitely close .

[0097] According to the central limit theorem, the probability distribution of a mixture of multiple independent physical source signals tends towards a Gaussian normal distribution. Reversing this process and finding the spatial data projection direction that deviates most from the Gaussian distribution mathematically isolates the mutually independent physical signal sources. The system uses negative entropy as the cost evaluation function.

[0098] Is with evaluation variables The Shannon-theoretical upper limit of information entropy for homoscedastic Gaussian variables. This is the true information entropy calculated based on the actual histogram. The GPU initiates a fast fixed-point iterative algorithm to update the matrix in the weight space along the normal direction where the negative entropy gradient increases. When the change in the angle between the feature weighted vectors of two consecutive iterations is less than When calculating in radians, convergence is determined. The output matrix... It contains several independent row vectors, mapping water peak evolution, initiator consumption, or particle scattering, respectively. Blind source separation outputs multiple independent components, and the system needs to automatically identify which component represents the substrate solvent. Water evaporates upon heating and detaches from the liquid surface, its position in the matrix... Concentration time series It must exhibit a monotonically decreasing trend. The system calculates the actual sequence. First-order difference The initial screening criterion is: negative values ​​account for >95%.

[0099] If two components both exhibit a monotonically decreasing trend (e.g., the initiator is also being continuously consumed), the system introduces a cross-scoring coefficient for in-depth investigation:

[0100] This is the variance of the concentration sequence. The initiator concentration changes very little in the initial stage, resulting in a low variance; however, the evaporation of water, which accounts for 40% of the formulation, will produce a huge variance. It is a sequence and a virtual ideal linearly decreasing sequence within the system. The Pearson correlation coefficient between them. Iterate through all separated components and score them. The highest corresponding spectral eigenvector The feature vector space matrix of the interference feature vector is locked into the base solvent and loaded into memory. Obtain the matrix Then, the system switches to a real-time noise reduction pipeline.

[0101] GPU computes the orthogonal complement projection operator:

[0102] It is the identity matrix. Used for orthogonal normalization scale correction. The mixed spectral matrix generated in real time within the reactor. Multiply by the operator: .

[0103] All solvent-related fluctuation components in the original signal are forcibly projected to the zero-degree space, while the carbon-oxygen double bond overtone absorption fingerprint, masked by the water peak, is preserved in the complement space dimension. Based on The system uses a matrix and a sliding buffer queue with a length of 10 seconds and a step size of 1 second along the time axis. The microprocessor calculates the first-order difference series of absorbance at each wavelength for two adjacent frames of spectrum.

[0104] The difference magnitude is divided into 50 statistical intervals, and the cumulative probability distribution density is... Calling the Shannon equation

[0105] Calculate the spectral entropy. In the liquid stage, free monomers undergo intense Brownian motion, resulting in highly random spectral fluctuations. The value remains high at around 3.0. When the gelation point is reached, the three-dimensional polymer physical network is formed, the translational degrees of freedom of the molecular chain segments are frozen instantly, and the probability of macroscopic spectral fluctuations is extremely concentrated in the central region. The trajectory experienced a sharp drop. To avoid interference from the bursting of tiny bubbles caused by the impeller's agitation, the program was set... A monitoring dead zone of seconds is implemented, during which all out-of-bounds alarms are triggered. After the dead zone ends, the evolution curve is polynomial smoothed, and central difference calculations are performed twice consecutively to generate the second derivative trajectory. The judgment condition is fixed on two parallel indicators: the second derivative curve shows its first absolute zero-crossing point from positive to negative, and the descent slope of the first derivative at that moment is strictly less than [a certain value]. bits / s. Within microseconds when the condition is met, the CPU locks the RTC timestamp corresponding to the node as the gelling time, and outputs a 24V high-level signal directly to the field PLC through the digital I / O module on the industrial control computer backplane.

[0106] Example 3

[0107] When tracking the curing process of environmentally friendly adhesives (such as modified soybean protein adhesives or custom waterborne polyurethanes), we initially attempted to rely on discrete probability distributions (such as Shannon information entropy) to quantify the disorder of spectral signals, but quickly encountered a technical dead end on the production line. The core logic of Shannon information entropy is to divide the absorbance difference into multiple micro-statistical intervals to calculate a probability histogram. The 120 RPM anchor-type agitator in the workshop continuously causes periodic fluctuations in the fluid baseline, and this mechanical ripple severely contaminates the histogram. Mathematically, a pot of violently boiling liquid monomers and a solid cross-linked network subjected to mechanical vibration can very well produce 1D absorbance probability distributions with identical variances. Shannon entropy only counts the "frequency of numerical occurrences," completely ignoring the "sequential order" and "evolutionary topology" of data points on the time axis.

[0108] To accurately capture the instant when macromolecular chain segments lock together, it is necessary to abandon simple statistical dispersion and instead introduce a complexity measurement algorithm based on nonlinear dynamics to measure the topological folding of the entire reaction system in the time dimension. The hardware platform for executing this high-order algorithm is deployed next to a 2000L reactor. The analog optical signal acquired by the diffuse reflection fiber optic probe is sliced ​​into a digital voltage sequence by a 24-bit ADC at a sampling rate of 100Hz. A field-programmable gate array (FPGA) is responsible for nanosecond-level timing alignment, pushing the data stream into the sliding buffer queue of the ARM Cortex-A53 central processing unit.

[0109] After removing interference from substrate solvents such as water, the system extracts a first-order time series of purified spectral absorbance at a specific wavelength (e.g., 2200 nm, representing NH bonds) within a 10-second time window (containing 1000 discrete sampling points). The real technical challenge lies in the fact that while we possess only a one-dimensional spectral absorbance time series, the reactor itself is a highly dynamic nonlinear system containing hundreds of millions of microscopic particles. How can we reconstruct the entire three-dimensional cross-linked network's generation trajectory using data from a single probe? Digital signal processors (DSPs, such as the TMS320 series with integrated hardware multiply-accumulators) utilize a phase space reconstruction model based on the Tukens embedding theorem. The DSP no longer views individual absorbance data points in isolation within memory. Instead, it forces the continuous time series to be folded and combined into a multidimensional state vector:

[0110] In this reconstruction formula, It is the embedding dimension. Assume... We forcibly twist and fold the one-dimensional timeline into a four-dimensional geometric space to accommodate the dynamic trajectory of the polymer reaction. This is the delay time parameter. If If two adjacent data points are too physically correlated, the resulting multidimensional space will be squeezed into a single line; if If the value is too large, the data points lose their connection, and the space becomes a meaningless jumble of points. We usually determine this by calculating the first minimum value of the self-mutual information function. The value is typically between 5 and 8 frames.

[0111] Physically, when the environmentally friendly adhesive is in its initial liquid state during the initial addition phase, short-chain monomers and solvent molecules undergo intense Brownian and translational motions. This is reflected in the reconstructed... In the 3D phase space, the state vector The molecules exhibit a chaotic, randomly diverging trajectory, occupying a vast amount of phase volume. As the reaction approaches the gelation point, the crosslinking agent forcibly binds the previously free monomers together through covalent bonds, and the formation of the macroscopic network directly deprives the molecules of their free movement. The trajectories in phase space undergo nonlinear changes, and the massive chaotic point cloud instantly collapses topologically into an extremely narrow, low-dimensional attractor region. To quantify the degree of this spatial topological collapse, we must measure the reconstruction vector at any two different moments in phase space. and The distance between them.

[0112] In the early stages of project verification, we naturally applied the traditional Euclidean distance (the square root of the sum of squares of the differences in each dimension). The program crashed less than half a day into the production line trial run. Because of the "square" operation, Euclidean distance is extremely sensitive to extreme values. When a bubble in the reactor happens to sweep across the fiber optic probe end face, it can generate a huge outlier absorbance in a certain data dimension. Euclidean distance amplifies this error hundreds or thousands of times, directly causing the system to misinterpret it as a state change. We decisively replaced the underlying measure with Chebyshev distance:

[0113] Chebyshev distance extracts the absolute difference between two multidimensional vectors across all coordinate components, forcibly selecting the component with the largest value as the final distance. Geometrically, it constitutes an absolutely rigid "boundary box." If the system state deviates drastically in any single dimension, the system considers the two vectors topologically dissimilar. Chebyshev distance avoids square root and square operations, saving DSPs hundreds of thousands of clock cycles of computational overhead. With the distance scale in place, a spatial tolerance threshold for determining state similarity needs to be set. We cannot hardcode an absolute value (e.g., ...). (AU). Different batches and formulations of environmentally friendly adhesives inherently deviate in their basic absorbance; using a fixed threshold will lead to widespread misjudgment. Tolerance threshold. A dynamic compensation mechanism must be introduced:

[0114] It is the standard deviation of the one-dimensional time series within the current 10-second window, representing the macroscopic background amplitude of the current system energy fluctuation; This is the proportional adjustment coefficient, typically calibrated between 0.15 and 0.20. The tolerance threshold acts like a self-adaptive, breathing net, perfectly shielding the baseline drift caused by formula adjustments. The DSP rapidly traverses tens of thousands of reconstructed vector pairs in the phase space, statistically satisfying... The number of pairs is calculated in Global fundamental probability that sequences possess physical self-similarity in 3D space The final step of the algorithm is to calculate the sample entropy.

[0115] DSP reduces the embedding dimension of phase space from Forced to rise to Using the aforementioned Takens reconstruction and Chebyshev alignment logic, the self-similarity probability in higher dimensions is calculated. .

[0116] Why not use an earlier approximate entropy? Approximate entropy allows for "self-matching" in algorithm design (i.e.,... With oneself The distance calculation always returns 0 and is always less than 0. ).

[0117] Within a short time window of 1000 data points, such self-matching can produce significant statistical bias, leading the system to mistakenly believe the sequence is more regular than it actually is. Sample entropy rigorously eliminates self-matching in its underlying code, ensuring absolute objectivity of the measurement scale. The coprocessor module inside the central processing unit executes the ultimate equation of sample entropy:

[0118] This transcendental function formula directly addresses the core of thermodynamic evolution. It poses a fundamental physical question: if the reacting system... At each time point, similar motion trajectories are observed. Therefore, when the observation dimensionality increases to... At a given node, what is the probability that it will maintain this similarity? In the early to mid-stages of the gelation reaction, the fluid is in a disordered and chaotic state. The future trajectory of the molecules is unpredictable. The probability of finding similar trajectories in 3D space It decays exponentially, much less than dimensional probability The ratio within the parentheses approaches a minimum, which is flipped by the negative natural logarithm into a huge high-order value (typically between 2.0 and 2.8). The system is in an incompressible state of high conformational complexity.

[0119] When chemical bonds momentarily anchor the entire network at the gelation critical point, macroscopic fluidity is drained. The molecule's infrared absorption response is forced to be reduced to extremely monotonous mechanical self-similar vibrations. At this point, if the trajectory is... Dimensional similarity, in The dimensions are almost necessarily similar. The conditional probability ratio suddenly approaches 1 infinitely. After taking the negative logarithm, the sample entropy value drops sharply (usually falling below 0.5 within a few seconds). The ARM processor writes the calculated sample entropy sequentially into flash memory, drawing a continuous time-sample entropy evolution curve in digital space.

[0120] To prevent false alarms triggered by electromagnetic crosstalk in the industrial environment, the DSP uses a moving average filter operator (setting a window of 15 data points) to smooth the curve. Then, it performs continuous first-order differential calculations to extract the instantaneous downward acceleration of the sample entropy over time. During actual material feeding in the workshop, the mixing of hot and cold materials and the violent impact of the agitator in the first few minutes generate a large number of tiny bubbles. These purely physical disturbances cause the sample entropy curve to experience an extremely sharp, short-term drop. The safety scheduler intervenes, setting dead zone conditions. Based on the operating conditions of a 2000L reactor, It is usually hardcoded to 300 seconds.

[0121] Within this monitoring blind zone, even if the underlying derivative data drops drastically, the alarm interruption will be forcibly discarded by the bus controller. After crossing the 300-second dead zone, the feature probe is officially activated. Relying solely on a derivative threshold is prone to misjudgment during subsequent local oscillations. The system constructs a multi-dimensional weighted evaluation scoring function:

[0122] The left side of the plus sign quantifies the instantaneous fall rate (absolute value) of the sample entropy at a specific time, which is proportional to the chemical burst force of the cross-linked network formation; the right side quantifies the cumulative fall depth from the liquid stable baseline to the current trough, reflecting the degree of complete loss of the system's physical degrees of freedom. and They were calibrated to 0.65 and 0.35 respectively. The central processing unit traverses the evolution timeline at a clock cycle of 10ms, automatically extracting... The horizontal coordinate node that reaches the global absolute maximum value. Once the extreme value node is locked, the system's RTC timestamp is immediately characterized as the absolute gelation time at which the environmentally friendly adhesive loses its macroscopic fluidity. The digital I / O module on the industrial control computer's backplane directly outputs a 24V high-level signal to the field PLC, which in turn shuts off the steam valve of the reactor and triggers an audible and visual alarm.

[0123] Example 4

[0124] In monitoring the crosslinking and curing of water-based environmentally friendly adhesives in a 2000L reactor, the near-infrared spectrum captured by the probe is simultaneously subjected to two highly destructive interferences. The first is chemical masking: during the heating process (e.g., from 25°C to 80°C) and exothermic processes, the substrate moisture evaporates rapidly, and the hydrogen bond association state between water molecules undergoes a nonlinear change. This causes not only a sharp decrease in intensity of the -OH absorption peaks near 1450nm and 1940nm, but also broadening and shifting of their peak shapes. The second is physical scattering: as the polymer chain lengthens, the fluid viscosity surges from an initial 200mPa·s to over 5000mPa·s. Particle aggregation within the system leads to abrupt changes in the macroscopic refractive index, causing random variations in the probe's actual optical path length, manifested as multiplicative baseline drift in the spectrum. Initially, we attempted conventional pretreatment methods in the workshop, trying to eliminate baseline drift using the first or second derivative of the spectrum. However, derivative algorithms can only filter out additive (translation) baseline errors and are completely ineffective against multiplicative scattering accompanying viscosity changes.

[0125] We also tried subtracting a spectrum of room-temperature pure water directly from the background. However, due to peak broadening caused by hydrogen bond breakage at high temperatures, simple subtraction left huge spurious peaks in the residuals, directly drowning out weak polymer crosslinking signals (such as overtone absorption of NH or CH bonds). To completely eliminate these two layers of interference, it is necessary to establish a connection between the underlying computing power channels of "multidimensional spatial orthogonal mapping" and "morphological normalization". On the hardware level, a 24-bit ADC is configured for digital slicing of analog optical signals, a field-programmable gate array (FPGA) is used for timing alignment, a 64-bit CPU with an ARM Cortex-A53 architecture performs scheduling, and a DSP with a double-precision floating-point unit (float64) handles matrix operations.

[0126] To address the chemical masking effect, we use statistical dimensionality reduction to transform it into a geometrically orthogonal problem. Before online gel testing, multiple sets of pure solvent spectra at different temperature gradients (20°C to 90°C) are collected and stored in RAM. The DSP calculates the covariance matrix of this sample matrix and performs eigenvalue decomposition. The cumulative variance contribution rate formula is introduced to determine the dimensionality boundary:

[0127] In the formula The characteristic roots are arranged in descending order. This represents the total number of wavelength channels. The program does not have a fixed number of principal components; instead, it is locked. The critical value. The loop before truncation. The test terminates when there are 1 eigenvectors (in actual testing). (Usually between 3 and 5). This The column vectors are concatenated into an interference matrix in video memory. Physically, it exhausts all spectral fluctuation modes caused by the evaporation and temperature changes of the substrate solvent. The DSP intervenes in the real-time data processing flow. It calls the floating-point unit to execute the core spatial projection equations:

[0128] This is a mixed spectral matrix sampled in real time from the reactor. The large product term to the right of the minus sign is essentially a multiple linear regression approximation process, calculating the original spectrum in... Zhang Cheng's projection onto the low-dimensional subspace. Subtracting it is equivalent to subtracting the output from the projection onto the low-dimensional subspace. The constraint is forcibly placed within a complementary space that is absolutely orthogonal to the solvent interference space at a 90-degree angle. Even if the water in the vessel evaporates exponentially, the water peak drift will be forced to zero by this geometric law. Having resolved the chemical overlap, what remains is the physical scattering caused by the drastic change in fluid viscosity. This relates to the projected matrix. The DSP processes the spectral row vector for each frame. Forcefully attach the Standard Normal Transform (SNV). First calculate the spectrum of this frame in... Average absorbance at each wavelength node Sum of standard deviation:

[0129] Perform SNV correction: Subtracting the mean from the numerator shifts the ordinate of the entire spectrum to 0, eliminating the additive drift caused by the overall optical path offset; dividing the denominator by the standard deviation dynamically scales the amplitude. Regardless of how much high-viscosity agglomerate the agitator stirred up, each frame of the transformed spectrum is compressed to a uniform unit variance scale. At this point, all underlying physical and chemical obstacles are cleared. After obtaining the clean spectral data and calculating the Shannon information entropy discrete sequence, the next challenge is the engineering application of calculus-based root-finding.

[0130] The 120 RPM agitator and the collapse of localized microbubbles generate high-frequency spikes on the information entropy curve. Direct differentiation amplifies the slope of these spikes by tens of times, triggering the system to alarm and shut down after 3 minutes of reaction. We did not use the moving average filter, which is the most common filter in industrial control. The fatal flaw of the moving average is that it produces irreversible phase lag (usually 2-4 seconds) and weakens the drop in the abrupt change point. At the critical second before the environmentally friendly adhesive is about to clump, a 3-second delay means the entire batch of material is scrapped.

[0131] The DSP utilizes Savitzky-Golay (SG) polynomial smoothing based on the least squares principle. The smoothing window is set to... For each discrete sampling point (e.g., 15 points), a polynomial of a preset order is used:

[0132] (e.g., 3rd order) to approximate the data within the window, and solve for the coefficients that minimize the sum of squared errors. The constant filter convolution weights are derived. .

[0133] Execution formula:

[0134] While removing high-frequency noise, SG filtering preserves the edge high-frequency characteristics and original inflection point coordinates of the signal that drops sharply during gelation with zero phase shift.

[0135] Proceeding to the final decision module. CPU... Perform two consecutive discrete central difference operations to generate the first derivative:

[0136] With the second derivative .

[0137] The physical dead zone is hardcoded in the front end of the code. Seconds. During the initial 5 minutes of feeding, the underlying layer disables all root-seeking interrupts. After the dead zone, the program captures the first absolute zero-crossing point on the second derivative curve where a positive value crosses the zero mark and enters a negative value (i.e.,...). and A fixed sampling frequency of 1Hz. It looks too rough on the timeline, so let's just take... There will be a truncation error of nearly 1 second.

[0138] The DSP uses linear interpolation formulas to perform sub-pixel level reconstruction.

[0139] Calculated floating-point number The theoretical infinitesimal singularity of network freezing was locked, and the industrial control computer output the gelation time based on this.

[0140] Example 5

[0141] In industrial production lines for environmentally friendly adhesives, attempts to run calculus and matrix projection algorithms using a traditional "industrial PC + Windows operating system + Python / LabVIEW host computer" architecture typically fail to last beyond the first feeding cycle. Electromagnetic interference in the workshop, strong vibrations from the reactor, and the inherent 10-20 millisecond latency jitter of the general-purpose operating system's USB polling directly disrupt the timeline of the continuous spectrum, turning the dynamic state tracking, which relies on strict timing, into a jumble of code. We abandoned the general-purpose operating system PC architecture and shifted to a purely physical, low-level embedded bare-metal development paradigm.

[0142] The hardware motherboard uses a six-layer high-frequency copper-clad board, with a heterogeneous processing array at its core: a field-programmable gate array (FPGA) for timing, an ARM Cortex-A53 processor for control logic, and a digital signal processor (DSP, such as the TMS320 series) specializing in floating-point matrix multiplication and addition operations. The spectral acquisition module is deployed in a reaction vessel environment with 120 RPM stirring and 80°C constant temperature exothermic reaction. The probe uses an acid and alkali resistant Hastelloy shell and a sapphire window, with a fixed optical path of 2 mm, and is directly immersed 30 cm below the liquid surface. The tungsten halide light source built into the probe emits near-infrared light of 900-2600 nm, which penetrates the colloid and is transmitted back to the miniature FT-NIR spectrometer via quartz optical fiber, where it strikes an indium gallium arsenide detector.

[0143] The analog voltage signal is sliced ​​and quantized by a 24-bit analog-to-digital converter (ADC) at a frame rate of 100Hz. Hidden here is the most critical hardware bottleneck of the entire system: the exponential energy decay caused by optical path scattering. As the polymerization reaction progresses, monomers crosslink into a network, resulting in numerous microscopic phase separations and polymer agglomerates within the fluid. The colloid rapidly transforms from an initially translucent liquid into a highly turbid suspension. Photons are severely scattered as they pass through this 2mm gap, causing the absolute light intensity received by the detector to plummet by more than 90% within just a few minutes.

[0144] In our initial trial and error, we attempted to solve the problem using the most direct hardware approach: increasing the drive current of the tungsten halogen lamp to boost the initial light intensity. This resulted in a 20W bulb operating at a 30W overload, burning out in less than two weeks; the excess heat directly baked the glue on the probe end face into a hardened, dead glue, completely blocking the optical path. The second alternative was to enable the automatic gain control (AGC) circuit on the detector end. When the light intensity weakens, the circuit automatically amplifies the signal. However, when AGC amplifies the effective signal, it proportionally amplifies the dark current and 1 / f background noise of the indium gallium arsenide detector. The slight absorbance fluctuations originally used to calculate information entropy were directly drowned out by the thermal noise amplified by AGC, resulting in all subsequent algorithms outputting garbled text.

[0145] We ultimately implemented the compensation mechanism into the FPGA's hardwired logic, forcibly intervening during the first clock cycle of the ADC's output digital signal. The FPGA internally calls the dynamic compensation equation:

[0146] It is the digital value that the ADC just output with attenuation; This is the 2mm physical constant of the probe; This is the scattering attenuation coefficient, which the system fits in real-time within the FPGA using a lookup table (LUT) based on the spectral baseline drift rate of the previous 100 frames. The compensated absolute light intensity value... The true absorption ratio was restored. This batch of corrected discrete digital signals bypassed the CPU and was written to the motherboard's DDR4 memory via Direct Memory Access (DMA) channel, precisely according to timestamps, stacking into a spectral data matrix containing timing and wavelength coordinates. Data is written to disk, the DSP takes over the system bus, and the orthogonal projection denoising module is activated. The underlying SPIFlash memory chip contains a pre-programmed interference feature vector matrix targeting the base solvent of this formulation. (Obtained through covariance eigenvalue decomposition of offline pure solvent samples).

[0147] Fluctuations in the heating jacket power of the reactor cause drastic changes in the solvent evaporation rate, resulting in baseline distortion that cannot be fitted using conventional polynomials. The DSP utilizes double-precision floating-point units to directly manipulate the matrix. Execute the spatial projection formula:

[0148] In the formula It is the transpose matrix. This is used to correct the orthogonal normalization scale. The right side of the minus sign calculates the absolute projection of the mixed spectrum onto the solvent interference subspace. This matrix subtraction step forces all baseline distortion components linearly related to solvent evaporation to zero, outputting the purification matrix. Only the weak absorption changes of C-H and N-H bonds induced by the cross-linking reaction were retained. The purified data stream was then transferred to the information entropy calculation module.

[0149] The ARM processor allocates a FIFO sliding window containing 100 frames (representing 1 second) in its L1 cache. The arithmetic logic unit calculates the first-order difference of the absorbance of adjacent frames at corresponding wavelengths, distributes the difference into 64 preset statistical intervals, and accumulates them to generate a probability density distribution. Apply the Shannon entropy equation:

[0150] The cross-linked network freezes the Brownian motion of molecules at a critical point, causing a precipitous drop in macroscopic disorder. The code frontend sets a slope threshold. (For example, set to -0.5 bits / s), used for initial screening to determine whether the state evolution is disruptive. The system continuously splices the time-information entropy evolution sequence in memory. The gelled time-locking module is responsible for the final electrical output.

[0151] The physical vibrations generated by the 120RPM agitator cutting the fluid in the workshop will superimpose high-frequency electrical noise on the information entropy curve. Instead of using moving average filtering (which would introduce unacceptable phase delay), the system uses a built-in digital filter chip to reconstruct the entire sequence using Savitzky-Golay multinomial convolution based on the least squares criterion, smoothing out blemishes and preserving the coordinates of the original physical inflection points. The differential circuit performs two consecutive central difference calculations on the smoothed data to derive the second derivative trajectory. To shield against fluid turbulence during the initial feeding phase, the timer module forcibly locks the monitoring dead zone. (Based on the experience of a 2000L reactor, the time is usually set to 300 seconds).

[0152] After crossing the 300-second dead zone, the root-finding logic begins frame-by-frame searching on the second derivative curve for the first zero-crossing point where a positive value penetrates the zero mark and enters a negative value. Upon successful matching, the DSP calls a linear interpolation algorithm to calculate the sub-pixel-level horizontal coordinate floating-point time. This value is characterized as the absolute gelation time of the macromolecular topological lock. The digital-to-analog converter (DAC) receives this timestamp and drives the I / O port to output a 24V step level within 2 microseconds, triggering the workshop's audible and visual alarms and sending a shutdown command to the reactor PLC via the RS485 serial port. The entire algorithm instruction set includes feature space decomposition, matrix inversion, Shannon logarithm operations, and interpolation root-finding, written in a mix of C and assembly language. The compiled .bin file is less than 2MB and is directly burned into the motherboard's SPINorFlash chip.

[0153] After the device powers on and performs a self-test, the ARM processor directly moves the code to SRAM for execution. The instruction stream drives the probe's light acquisition, FPGA compensation, DSP projection noise reduction, and finally the DAC level operation, forming a closed loop of a pure hardware instruction cycle.

[0154] 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.

[0155] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

Claims

1. A rapid detection method for the gelation time of environmentally friendly adhesives based on near-infrared spectroscopy, characterized in that, include: Obtain a continuous time-series near-infrared spectral data matrix of environmentally friendly adhesive during the curing reaction process; Extract the interference feature vector space matrix of the substrate solvent; Construct the orthogonal complement spatial projection operator of the interference feature vector space matrix, and multiply the continuous time series near-infrared spectral data matrix by the orthogonal complement spatial projection operator to obtain the purified spectral data matrix; The time window slice data is extracted by applying a moving window algorithm to the purified spectral data matrix along the time axis; Calculate the spectral information entropy parameter corresponding to each time window slice data, and construct the evolution sequence curve of time and the spectral information entropy parameter; The evolution sequence curve is subjected to a difference operation to generate a second derivative trajectory line. The time coordinates corresponding to the zero-crossing point on the second derivative trajectory line from a positive value to a negative value are extracted as the gelation time.

2. The rapid detection method for gelation time of environmentally friendly adhesives based on near-infrared spectroscopy according to claim 1, characterized in that, The interference feature vector space matrix for extracting the substrate solvent includes: The absolute value of the time derivative of the integral of the full-spectral absorbance in the initial stage of the reaction is used as the macroscopic evolution rate. When the macroscopic evolution rate monitored in real time drops to less than or equal to the preset rate threshold, the current moment is recorded as the cutoff time. A continuous spectral data between the reaction initiation time zero and the cutoff time is used to construct an initial matrix; The initial matrix is ​​subjected to independent component analysis algorithm with the cost of maximizing negative entropy as the evaluation function, and the mutually independent pure source spectral feature vectors and corresponding concentration time series are extracted. Calculate the proportion of negative elements and variance of each concentration time series, and multiply the variance by the absolute value of the Pearson correlation coefficient to obtain the cross-scoring coefficient; The pure source spectral feature vector corresponding to the highest cross-scoring coefficient is extracted to construct the interference feature vector space matrix.

3. The rapid detection method for gelation time of environmentally friendly adhesives based on near-infrared spectroscopy according to claim 1, characterized in that, The interference feature vector space matrix for extracting the substrate solvent includes: Collect a set of pure substrate solvent spectra under different temperature gradients; Calculate the covariance matrix of the pure substrate solvent spectrum set and perform eigenvalue decomposition to extract eigenvectors and eigenvalues; Arrange the eigenvalues ​​in descending order and calculate the quotient of the sum of the first few eigenvalues ​​and the sum of the total eigenvalues ​​as the cumulative variance contribution rate. The first few eigenvectors whose cumulative variance contribution rate is greater than or equal to the preset variance threshold are selected to construct the interference eigenvector space matrix.

4. The rapid detection method for gelation time of environmentally friendly adhesives based on near-infrared spectroscopy according to claim 1, characterized in that, The process of obtaining the purified spectral data matrix and extracting the time coordinates corresponding to the zero-crossing points on the second derivative trajectory line includes: Calculate the mean absorbance and global standard deviation of a single-frame purification spectrum. Subtract the mean absorbance from the absorbance of each wavelength of the single-frame purification spectrum and divide by the global standard deviation to complete the standard normal variable transformation correction. The evolution sequence curve is reconstructed by convolution using a polynomial smoothing filter algorithm. Based on the fact that the slope of the first derivative is less than the preset slope limit and the second derivative crosses the zero mark to lock two adjacent discrete time nodes, the floating-point timestamp is calculated using the linear interpolation root-finding formula as the gelation time.

5. The rapid detection method for gelation time of environmentally friendly adhesives based on near-infrared spectroscopy according to claim 1, characterized in that, The calculation of the spectral information entropy parameter corresponding to each time window slice data includes: Calculate the first-order difference sequence of absorbance at corresponding wavelengths for adjacent frames within the current time window slice data; The absorbance difference is distributed to a preset statistical interval, and the probability density distribution of each interval is accumulated. The product of the probability density distribution of each interval and its natural logarithm is summed and the result is negative. The result is used as the spectral information entropy parameter.

6. The rapid detection method for gelation time of environmentally friendly adhesives based on near-infrared spectroscopy according to claim 1, characterized in that, The calculation of the spectral information entropy parameter corresponding to each time window slice data includes: The first-order time series of spectral absorbance within the time window slice data is combined into a low-dimensional state vector according to the set delay time parameter. Extract the maximum value of the absolute difference between any two low-dimensional state vectors at different times across all corresponding dimensional coordinate components as the spatial distance; Calculate the standard deviation of the first-order time series of absorbance within the current time window slice data, and multiply the standard deviation by the proportional adjustment coefficient to obtain the tolerance threshold value; The number of vector pairs whose spatial distance is less than the tolerance threshold is counted to calculate the basic probability in the low-dimensional space. The dimension of the low-dimensional state vector is expanded by one dimension and then the progressive probability in the high-dimensional space is calculated again. Calculate the ratio of the progressive probability in the high-dimensional space to the basic probability in the low-dimensional space, and take the negative natural logarithm of this ratio as the spectral information entropy parameter.

7. The rapid detection method for gelation time of environmentally friendly adhesives based on near-infrared spectroscopy according to claim 6, characterized in that, The step of extracting the time coordinates corresponding to the zero-crossing points on the second derivative trajectory line where a positive value crosses the zero mark and enters a negative value as the gelling time includes: Obtain the absolute value of the instantaneous fall rate and the absolute depth of the cumulative fall from the spectral entropy parameter at a specific time point; Multiply the absolute value of the instantaneous fall rate by the first weighting coefficient, multiply the absolute depth of the cumulative fall by the second weighting coefficient, and add the products of the two to construct a multidimensional weighted evaluation scoring function. Traverse the time nodes after the set monitoring dead zone, and extract the horizontal coordinate node that makes the value of the multidimensional weighted evaluation scoring function reach the global maximum value as the gelation time.

8. The rapid detection method for gelation time of environmentally friendly adhesives based on near-infrared spectroscopy according to claim 1, characterized in that, Before obtaining the continuous time-series near-infrared spectral data matrix of the environmentally friendly adhesive during the curing reaction process, the method further includes: The original digital value of weak light intensity is obtained through an analog-to-digital converter; The mathematical formula for dynamic light intensity compensation is called inside the field programmable gate array, and the original weak light intensity digital quantity is multiplied by a specific exponent of the natural constant to obtain the absolute light intensity value. The specific exponent power is the product of the scattering attenuation coefficient and the equivalent fixed optical path length.

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