Detection method of rubber pyrolysis components based on infrared spectroscopy

Abstract

The present invention discloses a method for detecting rubber pyrolysis components based on infrared spectroscopy technology, specifically relating to the technical field of rubber pyrolysis component detection. The method is used to solve the problems of high-concentration signal saturation, missed detection of low-concentration components, and data faults in the pyrolysis stage caused by fixed parameters in existing infrared spectroscopy detection methods. By dynamically dividing the boundaries of the pyrolysis stages and adjusting the spectrometer scanning frequency and integration time in real time, adaptive matching of detection parameters and product release rates is achieved. At the same time, cross-stage spectral data fusion and pyrolysis kinetic continuity closed-loop verification technology are combined to eliminate time domain offset errors and reconstruct a complete spectral data chain. Finally, through a collaborative correction mechanism of multi-stage weight allocation and integration time compensation, high-confidence component types and concentration ratios are output, providing accurate data support for rubber pyrolysis process optimization and pollutant monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of rubber pyrolysis component detection, and more particularly to a rubber pyrolysis component detection method based on infrared spectroscopy technology. Background Art

[0002] Rubber pyrolysis component detection optimizes the process and monitors pollutant emissions by analyzing the chemical composition of pyrolysis products. Infrared spectroscopy technology has become the core detection method in this field due to its advantages of rapid response, non-contact and simultaneous multi-component analysis. In existing technologies, infrared spectroscopy equipment realizes component identification and quantitative analysis by collecting characteristic absorption spectra and combining them with database comparison, and is widely used in laboratories and industrial scenarios.

[0003] However, rubber pyrolysis exhibits significant dynamic nonlinear characteristics, and its product release rate fluctuates dramatically with the pyrolysis stage, making it difficult for the fixed detection mode of traditional infrared spectroscopy to adapt to the instantaneous differences in product release rates at different stages. High-concentration gas signals are prone to saturation and distortion, and low-concentration components are missed due to insufficient acquisition frequency, ultimately restricting the accuracy of component analysis and the reliability of process control. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method for detecting rubber pyrolysis components based on infrared spectroscopy technology to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The method for detecting the pyrolysis components of rubber based on infrared spectroscopy technology includes the following steps:

[0007] S1. Monitoring the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main and residual decomposition stages;

[0008] S2. Extract the intensity variation trend of the preset characteristic absorption peaks in each decomposition stage. If the intensity variation trend deviates from the preset trend model, redivide the decomposition stage boundaries based on the spectral intensity gradient difference between adjacent decomposition stages.

[0009] S3, adjusting the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundaries;

[0010] S4, truncating the saturated spectrum data of the main decomposition stage, and fusing it with the spectra of the initial decomposition stage and the residual decomposition stage according to the time axis to generate the full-stage fused spectrum data;

[0011] S5. Verify the pyrolysis kinetic continuity of the full-stage fused spectral data. If the time domain offset between the temperature change rate inflection point in the initial decomposition stage and the spectral intensity gradient extreme value in the main decomposition stage exceeds the preset phase change matching interval, perform dynamic collaborative parameter correction and re-collect spectral data.

[0012] S6. Matching the full-stage fused spectral data after the re-collected spectral data fusion with the preset characteristic absorption peak database, and outputting the component type and concentration ratio of the rubber pyrolysis product.

[0013] In a preferred embodiment, monitoring the temperature change rate of the rubber pyrolysis to divide the pyrolysis process into initial, main and residual decomposition stages includes:

[0014] The temperature change rate curve during the rubber pyrolysis process is collected in real time. Based on the mutation point where the temperature change rate curve first reaches the preset mutation critical value, the starting boundary of the initial decomposition stage is divided;

[0015] When the temperature change rate continues to rise from the starting boundary of the initial decomposition stage and reaches the preset peak threshold for the first time, the initial decomposition stage is determined to be over and the main decomposition stage begins;

[0016] When the temperature change rate drops from the peak value to the preset rate attenuation threshold, the termination boundary of the main decomposition stage is determined;

[0017] According to the release stability of the residual decomposition products, the period during which the temperature change rate is continuously lower than the preset residual decomposition threshold is marked as the termination boundary of the residual decomposition stage.

[0018] In a preferred embodiment, the intensity variation trend of the preset characteristic absorption peak of each decomposition stage is extracted. If it deviates from the preset trend model, the decomposition stage boundary is re-divided based on the spectral intensity gradient difference of adjacent decomposition stages, including:

[0019] Extracting real-time intensity data of preset characteristic absorption peaks in the initial decomposition stage, main decomposition stage, and residual decomposition stage, and generating intensity change trend curves for each decomposition stage;

[0020] The intensity change trend curve of the current decomposition stage is compared point by point with the preset trend model. If the absolute value of the intensity deviation of several consecutive sampling points exceeds the preset tolerance range, it is determined to be a deviation from the preset trend model;

[0021] When deviating from the preset trend model, the spectral intensity gradient difference between the current decomposition stage and the adjacent decomposition stage at the decomposition stage boundary is calculated;

[0022] If the spectral intensity gradient difference is greater than the preset gradient threshold, the boundary of the current decomposition stage is moved toward the adjacent decomposition direction with a smaller spectral intensity gradient until the spectral intensity gradient difference is less than or equal to the preset gradient threshold;

[0023] After updating the decomposition stage boundary, the characteristic absorption peak intensity change trend curve of the decomposition stage on both sides of the moving boundary is re-extracted and a secondary deviation judgment is performed. If there is still deviation, the spectral intensity gradient difference calculation and boundary movement operation are iteratively performed.

[0024] In a preferred embodiment, adjusting the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundaries includes:

[0025] According to the re-divided termination boundaries of the initial decomposition stage, the main decomposition stage and the residual decomposition stage, the scanning frequency of each decomposition stage is dynamically adjusted according to the preset scanning frequency proportional coefficient corresponding to the decomposition stage type;

[0026] Based on the real-time intensity peak value of the characteristic absorption peak in each decomposition stage, the integration time is adjusted according to the preset integration time mapping relationship;

[0027] When the movement of the decomposition stage boundary causes time overlap or gap between adjacent decomposition stages, a scanning frequency gradient rule of the transition interval is generated based on the scanning frequency difference between adjacent decomposition stages, so that the scanning frequency changes continuously according to the time gradient in the transition interval.

[0028] In a preferred embodiment, the scanning frequency of the main decomposition stage is higher than that of the initial decomposition stage and the residual decomposition stage, and the integration time of the initial decomposition stage is shorter than that of the main decomposition stage and the residual decomposition stage;

[0029] The mapping relationship between the scanning frequency proportional coefficient and the integration time is determined by calibrating the pyrolysis process of different rubber types and is associated with the spectral intensity gradient difference and the preset gradient threshold.

[0030] In a preferred embodiment, the saturated spectrum data of the main decomposition stage is truncated and fused with the spectra of the initial decomposition stage and the residual decomposition stage along the time axis to generate the full-stage fused spectrum data, including:

[0031] Before truncating the saturated spectrum data of the main decomposition stage, the intensity saturation threshold of the characteristic absorption peak of the main decomposition stage is monitored in real time based on the adjusted integration time and scanning frequency. When the intensity of several consecutive sampling points exceeds the preset saturation threshold, truncation is performed;

[0032] The truncation operation retains the spectral data of the last unsaturated sampling point before saturation occurs, and according to the adjacent relationship between the initial decomposition stage and the residual decomposition stage on the time axis, the spectral data in the set time window before and after the truncation point are aligned with the spectra of the adjacent stages in time order;

[0033] If there is a time gap between the cutoff point and the adjacent stage during alignment, the compensated spectral data within the time gap interval is generated by linear interpolation based on the spectral intensity gradient change trend of the decomposition stages on both sides of the time gap;

[0034] The truncated main decomposition stage data, compensation spectrum data, and the spectra of the initial decomposition stage and the residual decomposition stage are fused along the time axis to generate full-stage fused spectrum data.

[0035] In a preferred embodiment, the pyrolysis kinetic continuity of the full-stage fused spectral data is verified. If the time domain offset between the temperature change rate inflection point of the initial decomposition stage and the spectral intensity gradient extreme value of the main decomposition stage exceeds the preset phase change matching interval, dynamic collaborative parameter correction is performed and spectral data is re-collected, including:

[0036] The inflection point time of the temperature change rate in the initial decomposition stage and the extreme value time of the spectral intensity gradient in the main decomposition stage were extracted to calculate the time domain offset;

[0037] When the time domain offset exceeds the preset phase change matching range, dynamic collaborative parameter correction is performed and spectral data is re-collected.

[0038] In a preferred embodiment, dynamically coordinating parameter correction and recollecting spectra includes:

[0039] According to the deviation direction of the time domain offset, the judgment threshold and the truncation threshold are synchronously corrected based on the inverse proportional square relationship between the temperature change rate judgment threshold in the initial decomposition stage and the spectrum saturation truncation threshold in the main decomposition stage;

[0040] Generate scanning frequency switching rules and integration time compensation coefficient based on real-time pyrolysis reaction activation energy;

[0041] The spectral data are re-collected using the revised judgment threshold, cutoff threshold, scanning frequency switching rule and integration time compensation coefficient until the time domain offset falls into the preset phase change matching interval and the difference in spectral intensity distribution between adjacent decomposition stages is lower than the preset difference threshold.

[0042] In a preferred embodiment, the full-stage fused spectral data after the re-collected spectral data fusion is matched with a preset characteristic absorption peak database to output the component types and concentration ratios of the rubber pyrolysis products, including:

[0043] Extract the characteristic absorption peak positions and relative intensities of each decomposition stage in the full-stage fusion spectrum data, and perform peak-by-peak matching with the characteristic peaks of standard substances stored in the preset characteristic absorption peak database;

[0044] The weight coefficients are assigned to the successfully matched characteristic peaks according to the decomposition stage type, wherein the weight coefficient of the main decomposition stage is higher than that of the initial decomposition stage and the residual decomposition stage, and the weight coefficient of the initial decomposition stage is higher than that of the residual decomposition stage;

[0045] The weighted concentration ratio of each component is calculated based on the sum of the products of the relative intensity of the matching peak and the weight coefficient of the corresponding decomposition stage;

[0046] The weighted concentration ratio is corrected in combination with the integral time compensation coefficient, and the correction coefficient used is inversely proportional to the integral time compensation coefficient;

[0047] When the difference in the corrected concentration ratios of the same component at different decomposition stages exceeds a preset ratio difference threshold, the weighted concentration ratio at the main decomposition stage is preferentially used as the final output value.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. By dynamically tracking the nonlinear characteristics of the pyrolysis process, a coordinated response mechanism between detection parameters and product release rate is established. By real-time monitoring the coupled relationship between the temperature change rate and the spectral intensity gradient difference, a two-variable criterion for the pyrolysis stage boundary is formed, enabling dynamic iteration of the division between the initial, main, and residual decomposition stages. By introducing a real-time feedback correction mechanism for stage division errors, when the characteristic absorption peak intensity trend deviates from the preset model, the boundary conditions are reconstructed by the spectral intensity gradient difference between adjacent stages. This achieves automatic matching of the spectrometer scanning frequency, integration time, and nonlinear characteristics of the product release rate, enabling the spectral acquisition system to self-regulate with the violent fluctuations of the pyrolysis reaction.

[0050] 2. By constructing a time-domain fusion mechanism for multi-stage spectral data and a pyrolysis kinetics continuity verification system, high-fidelity analysis of the pyrolysis process is achieved. By truncating the saturated spectral segment of the main decomposition stage and splicing it with the adjacent stage spectra on the time axis, fused spectral data is constructed that can avoid signal distortion while retaining the independence of the characteristics of each stage. Further, based on the time-domain offset analysis of the temperature change rate inflection point and the spectral gradient extreme value, a phase change matching verification algorithm is established, and dynamic collaborative correction parameters are used to perform closed-loop verification of the pyrolysis kinetic process. This dual guarantee mechanism ensures the continuity of spectral data in the time dimension and the strict correspondence with the pyrolysis reaction process, so that the component identification and concentration quantification results of the products of all stages are more in line with the actual pyrolysis reaction process, providing high-precision data support for process control. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention is a flow chart of the method for detecting rubber pyrolysis components based on infrared spectroscopy technology. DETAILED DESCRIPTION

[0052] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] Example: Figure 1 The present invention provides a method for detecting rubber pyrolysis components based on infrared spectroscopy technology, which comprises the following steps:

[0054] S1. Monitoring the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main and residual decomposition stages;

[0055] S2. Extract the intensity variation trend of the preset characteristic absorption peaks in each decomposition stage. If the intensity variation trend deviates from the preset trend model, redivide the decomposition stage boundaries based on the spectral intensity gradient difference between adjacent decomposition stages.

[0056] S3, adjusting the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundaries;

[0057] S4, truncating the saturated spectrum data of the main decomposition stage, and fusing it with the spectra of the initial decomposition stage and the residual decomposition stage according to the time axis to generate the full-stage fused spectrum data;

[0058] S5. Verify the pyrolysis kinetic continuity of the full-stage fused spectral data. If the time domain offset between the temperature change rate inflection point in the initial decomposition stage and the spectral intensity gradient extreme value in the main decomposition stage exceeds the preset phase change matching interval, perform dynamic collaborative parameter correction and re-collect spectral data.

[0059] S6. Matching the full-stage fused spectral data after the re-collected spectral data fusion with the preset characteristic absorption peak database, and outputting the component type and concentration ratio of the rubber pyrolysis product.

[0060] S1. Monitor the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main and residual decomposition stages, including:

[0061] The temperature change rate curve during the rubber pyrolysis process is collected in real time. The temperature change rate curve is collected by thermocouples or infrared temperature measuring devices. The measurement accuracy of thermocouples is ±0.5°C, and the spectral response range of infrared temperature measuring devices is 8-14μm. The acquisition frequency is set to at least 10 samples per second, and the temperature data is input into the embedded processor to generate a numerical sequence of temperature change rate. The embedded processor uses the difference method to calculate the temperature change of adjacent sampling points, and then divides it by the sampling time interval to obtain the temperature change rate in units of ℃ / s. If there is noise interference in the collected data, the moving average filter method is used for smoothing. The filter window width is set to 5-10 sampling points. For example, a window width of 7 sampling points is selected, that is, the arithmetic mean of the temperature change rate of the current sampling point and the previous 6 sampling points is taken as the filter output value.

[0062] Among them, the preset mutation critical value, rate attenuation threshold and residual decomposition threshold are obtained by calibrating the pyrolysis process of different rubber types.

[0063] The starting boundary of the initial decomposition stage is determined by a preset mutation critical value. The preset mutation critical value is set by monitoring the pyrolysis process of at least three types of rubber through calibration experiments, and recording the temperature change rate value corresponding to the starting point of the initial decomposition stage of each rubber. For example, the starting point rates of natural rubber measured in three experiments were 3.1℃ / s, 3.3℃ / s, and 3.2℃ / s. The average of 3.2℃ / s was taken and a 15% margin was added to obtain the preset mutation critical value of 3.7℃ / s. When making the actual judgment, the interval where the rates of the first three consecutive sampling points exceeded the critical value is searched in the temperature change rate curve, and the time corresponding to the first sampling point is marked as the starting boundary of the initial decomposition stage. For example, when the natural rubber is heated to the 12th minute, the rates of the three consecutive sampling points are 3.8℃ / s, 3.9℃ / s, and 4.0℃ / s respectively, and the 12th minute is marked as the starting boundary.

[0064] The boundary judgment process between the initial decomposition stage and the main decomposition stage is as follows: the temperature data in the reactor is collected in real time through a high-precision thermocouple array (sampling frequency ≥ 10Hz), and the embedded processor calculates the temperature change rate (ΔT / Δt, unit ℃ / s) and generates a continuous curve. When the system detects that the temperature change rate exceeds the preset mutation critical value for the first time (such as ΔT / Δt ≥ 0.8℃ / s) to trigger the start of the initial stage, it continues to track the rate increase trend and dynamically calibrates the peak threshold (default value range: ΔT / Δt = 2.5-4.0℃ / s, the specific value is pre-calibrated by thermogravimetric analysis according to the type of rubber, for example, natural rubber NR is set to 3.2℃ / s). When the rate curve reaches or exceeds the threshold for the first time, two judgments are performed simultaneously: ① Verify whether the current point is a local maximum leading point (i.e., d 2 (ΔT / Δt) / dt 2≤0), eliminating noise interference. ② If confirmed to be effective, the initial decomposition phase end timestamp (t1) is immediately marked and the main decomposition phase control protocol is activated (for example, increasing the carrier gas flow rate by 20% to match the demand for intense decomposition). This process takes less than 50ms and ensures that the phase switching is synchronized with the reaction kinetics. A typical example is the natural rubber pyrolysis rate reaching 3.2°C / s at t1 = 218s, and the system immediately switches to the main phase control mode. The error rate is verified by FTIR product spectra to be less than 1.5%.

[0065] The termination boundary of the main decomposition phase is determined based on a preset rate attenuation threshold. Taking styrene-butadiene rubber (SBR) as an example, the calibration experiment measured a peak rate of 6.0°C / s for the main decomposition phase. At the end of five experiments, the rate-to-peak ratios were 38%, 42%, 40%, 39%, and 41%, respectively. The middle value of 40% is taken as the attenuation ratio, corresponding to a rate attenuation threshold of 2.4°C / s. In actual determination, the termination boundary of the main decomposition phase is marked when the rate of temperature change drops from the peak and the rate at five consecutive sampling points is below the threshold. For example, after the SBR peaks at 6.0°C / s, the rate at five consecutive sampling points is 2.3°C / s, 2.2°C / s, 2.3°C / s, 2.4°C / s, and 2.3°C / s, respectively. This indicates the termination of the main decomposition phase.

[0066] The termination boundary of the residual decomposition phase is determined when the temperature change rate remains below the preset residual decomposition threshold for more than 60 seconds. Using nitrile rubber as an example, the calibration experiment monitors the fluctuation amplitude of the temperature change rate during the residual decomposition phase, with a standard deviation of less than ±5%. If the residual phase rate fluctuates within the range of 0.5-0.7°C / s with a standard deviation of 0.03°C / s, the residual decomposition threshold is set at 0.7°C / s. If the residual phase rate fluctuates near the threshold and the standard deviation exceeds the standard, the monitoring time is extended to 90 seconds and reassessed until the fluctuation amplitude meets the standard or the maximum monitoring time of 120 seconds is reached.

[0067] In the calibration experiment, rubber samples with a particle size of 1-3 mm and a mass of 50-100 g were placed in a pyrolysis reactor with a temperature control accuracy of ±1°C and heated at a rate of 10°C / min to 600°C. The experiment was repeated three times for each rubber type, and the rate distribution of each stage was calculated after removing outliers. For example, the mean rate of the initial decomposition stage of natural rubber is 3.2°C / s with a standard deviation of 0.3°C. The reference range is 3.2±0.6°C / s, and the actual mutation threshold is set at 3.7°C / s to ensure coverage of individual differences.

[0068] S2. Extract the intensity change trend of the preset characteristic absorption peaks in each decomposition stage. If it deviates from the preset trend model, re-divide the decomposition stage boundaries based on the spectral intensity gradient difference of adjacent decomposition stages, including:

[0069] Extract the real-time intensity data of the preset characteristic absorption peaks in the initial decomposition stage, the main decomposition stage, and the residual decomposition stage. The preset characteristic absorption peaks are determined according to the chemical composition of the rubber pyrolysis products. For example, when styrene-butadiene rubber is pyrolyzed, the characteristic absorption peak corresponding to the stretching vibration of the CH bond of the benzene ring is selected, and its wave number range is 3050-3100cm -1 Real-time intensity data is acquired by an infrared spectrometer at a sampling frequency of at least 5 times per second, generating intensity trend curves for each decomposition stage. The horizontal axis of the intensity trend curve is the time axis, and the vertical axis is the relative intensity value of the characteristic absorption peak. The relative intensity value is the ratio of the peak intensity of the absorption peak to the baseline intensity. The baseline intensity is determined by the minimum intensity value of the adjacent wavenumber interval.

[0070] The intensity change trend curve of the current decomposition stage is compared point by point with the preset trend model. The preset trend model is established through calibration experiments. The specific method is: conduct at least three pyrolysis experiments on the same type of rubber, record the average value of the characteristic absorption peak intensity of each decomposition stage over time, and use it as the preset trend model. For example, the preset trend model of the main decomposition stage of styrene-butadiene rubber is that the intensity increases linearly from an initial value of 0.1 to 0.8, and decays after maintaining the peak value for 5 seconds. When comparing point by point, if the absolute value of the intensity deviation of five consecutive sampling points exceeds the preset tolerance range, it is judged to deviate from the preset trend model. The preset tolerance range is determined based on the calibration experiment. For example, the tolerance range of styrene-butadiene rubber is ±15%, that is, when the difference between the actual intensity and the model intensity exceeds 15%, the deviation judgment is triggered.

[0071] When a deviation from the preset trend model is determined, the spectral intensity gradient difference between the current decomposition stage and the adjacent decomposition stage at the decomposition stage boundary is calculated. The spectral intensity gradient difference is calculated as the absolute value of the difference between the average intensity change rates of three sampling points on either side of the boundary between adjacent decomposition stages: the intensity data of three sampling points on each side of the current decomposition stage boundary are selected, and the average intensity change rate of the adjacent decomposition stage is calculated for each.

[0072] For example, the current decomposition stage is the main decomposition stage, and the intensities of the three sampling points at the boundary between it and the initial decomposition stage are 0.75, 0.78, and 0.80. The intensities of the three sampling points in the adjacent initial decomposition stage are 0.70, 0.72, and 0.74. Then the average intensity change rate of the main decomposition stage is (0.78-0.75) / Δt+(0.80-0.78) / Δt, and the average intensity change rate of the initial decomposition stage is (0.72-0.70) / Δt+(0.74-0.72) / Δt. The absolute value of the difference between the two is the spectral intensity gradient difference, where Δt is the sampling time interval.

[0073] If the spectral intensity gradient difference is greater than a preset gradient threshold, the boundary of the current decomposition stage is moved toward the adjacent decomposition direction with a smaller spectral intensity gradient. The preset gradient threshold is determined through calibration experiments. For example, in five experiments on styrene-butadiene rubber, the average gradient difference during normal pyrolysis was calculated to be 0.05 / s. The preset gradient threshold was set to 0.08 / s (the average plus a 60% margin).

[0074] The specific steps of the boundary movement operation are: move the boundary of the current decomposition stage by one sampling point toward the adjacent decomposition stage, re-extract the intensity data on both sides of the moved boundary, and calculate the gradient difference. For example, if the main decomposition stage boundary is originally located at 120 seconds and is moved to 119 seconds, the gradient difference is recalculated. If it still exceeds the threshold, it is moved to 118 seconds until the difference is less than or equal to 0.08 / s.

[0075] After updating the decomposition stage boundary, the characteristic absorption peak intensity change trend curve of the decomposition stage on both sides of the moving boundary is re-extracted and a secondary deviation judgment is performed. The conditions for the secondary deviation judgment are the same as the first judgment, that is, the intensity deviation of five consecutive sampling points exceeds the tolerance range. If there is still deviation, the gradient difference calculation and boundary movement operation are iterated, and a maximum of three iterations are performed to avoid an infinite loop. For example, nitrile rubber still deviates after the first boundary movement. The secondary judgment found that the gradient difference was 0.09 / s. After continuing to move the boundary to 117 seconds, the gradient difference dropped to 0.07 / s, and the iteration was stopped and the final boundary position was locked.

[0076] In the calibration experiment, the preset trend model, tolerance range and gradient threshold settings must cover at least three rubber types (such as natural rubber, styrene-butadiene rubber, and nitrile rubber). For example, the tolerance range of natural rubber is ±10%, and the gradient threshold is 0.06 / s; the tolerance range of nitrile rubber is ±12%, and the gradient threshold is 0.07 / s. Each parameter is obtained through pyrolysis experiment statistics. The experimental conditions are consistent with step S1, including sample morphology (particles with a particle size of 1-3mm), heating rate (10℃ / min) and pyrolysis termination temperature (600℃). The sampling point movement step size of the boundary movement operation matches the sampling frequency of the infrared spectrometer. For example, when sampling 5 times per second, each step size corresponds to a time offset of 0.2 seconds.

[0077] The absolute value of the intensity deviation is calculated by subtracting the intensity at the current sampling point from the intensity at the corresponding time point in the preset trend model. The absolute value is then divided by the model intensity value to convert it into a percentage deviation. For example, if the current intensity is 0.82 and the model intensity is 0.70, the absolute deviation is (0.82 - 0.70) / 0.70 × 100% = 17.1%. Exceeding the 15% tolerance range triggers a judgment.

[0078] The time axis of the preset trend model is aligned with the time axis of the actual pyrolysis process through the stage starting boundary. For example, if the starting boundary of the initial decomposition stage is the 10th second, the 0th second of the model corresponds to the actual 10th second.

[0079] S3. Adjusting the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundaries, including:

[0080] Based on the newly defined endpoints of the initial, main, and residual decomposition stages, the scanning frequency of each decomposition stage is dynamically adjusted according to the preset scanning frequency scaling factor corresponding to each decomposition stage. This preset scanning frequency scaling factor is determined through calibration experiments. The calibration method involves performing at least three pyrolysis experiments on the same rubber type, calculating the minimum scanning frequency required to achieve saturation of the characteristic absorption peak intensity within each decomposition stage, and then calculating the ratio of the scanning frequency of the main decomposition stage to the scanning frequency of the initial decomposition stage as the scaling factor.

[0081] For example, in three experiments with styrene-butadiene rubber, the minimum scanning frequency for the main decomposition phase was 50 Hz, the initial decomposition phase was 30 Hz, and the residual decomposition phase was 25 Hz. Therefore, the scanning frequency ratio for the main decomposition phase was 50 / 30 = 1.67, and for the residual decomposition phase was 50 / 25 = 2.0. In practice, a ratio range of 1.5-2.0 was used to account for individual differences. During adjustment, the scanning frequency for the main decomposition phase was set to 1.67 times that of the initial decomposition phase, and the residual decomposition phase was set to 2.0 times that of the initial decomposition phase.

[0082] Based on the real-time peak intensity of the characteristic absorption peaks in each decomposition stage, the integration time is adjusted according to a preset integration time mapping relationship. The preset integration time mapping relationship is established through calibration experiments. The specific method is as follows: in the calibration experiment, the ratio of the peak intensity of the characteristic absorption peaks in each decomposition stage to the noise level is monitored. When the ratio exceeds the preset signal-to-noise ratio threshold, the corresponding minimum integration time is recorded as the baseline value. For example, when the peak intensity of the main decomposition stage of natural rubber is 0.8, the minimum integration time corresponding to a signal-to-noise ratio of 20:1 is 100ms. When the peak intensity of the initial decomposition stage is 0.3, the integration time corresponding to a signal-to-noise ratio of 20:1 is 50ms. During actual adjustment, the integration time of the initial decomposition stage is set to 0.6 times the baseline value (50×0.6=30ms), and the integration time of the main decomposition stage and the residual decomposition stage is set to 1.1 times the baseline value (100×1.1=110ms), ensuring that the high-concentration signal is not saturated and the signal-to-noise ratio of the low-concentration signal meets the standard.

[0083] When the shifting decomposition phase boundaries causes temporal overlap or gaps between adjacent decomposition phases, a frequency gradient rule for the transition interval is generated based on the frequency differences between the adjacent decomposition phases. This rule is generated by applying a temporal linear interpolation method to the frequency differences between the adjacent decomposition phases, creating a smooth transition within the overlapping or gap intervals.

[0084] For example, if the main decomposition phase has a scan frequency of 50 Hz and the residual decomposition phase has a scan frequency of 25 Hz, and if boundary shifting results in a 2-second overlap between the two phases, the scan frequency starts at 50 Hz and decreases by 6.25 Hz (50-25) / 2 / 4) every 0.5 seconds until it reaches 25 Hz. The linear interpolation step size is synchronized with the sampling interval of the infrared spectrometer. For example, if the sample rate is 10 times per second, the frequency is adjusted every 0.1 second.

[0085] The calibration experiment of the preset scanning frequency proportional coefficient and integration time mapping relationship needs to cover at least three rubber types (such as natural rubber, styrene-butadiene rubber, and nitrile rubber). The calibration experimental conditions are consistent with steps S1 and S2, including sample morphology (1-3mm particles), heating rate (10℃ / min), pyrolysis termination temperature (600℃) and spectral acquisition parameters (wavenumber range, baseline interval). For example, in the integration time mapping relationship of nitrile rubber, the baseline integration time of the initial decomposition stage is 40ms, the main decomposition stage is 90ms, and the residual decomposition stage is 85ms. The integration time adjustment coefficient of each stage is associated with the spectral intensity gradient difference in step S2. When the gradient difference exceeds 0.1 / s, the integration time coefficient is increased by 0.1 to improve the signal-to-noise ratio.

[0086] The frequency gradient rule for the transition interval works in conjunction with the time-domain offset determination in step S5 to ensure data acquisition continuity. For example, if step S5 determines that the time-domain offset exceeds the phase change matching interval, the frequency gradient rule automatically extends the transition interval to gradually converge the time-domain offset to within the threshold. The maximum duration of the transition interval is limited to 5 seconds to prevent excessive extensions from affecting detection efficiency. This duration is determined by statistically analyzing the maximum boundary movement during normal pyrolysis in a calibration experiment.

[0087] All parameters in the calibration experiment (scanning frequency scaling factor, integration time factor, and transition interval duration) are set based on the spectral intensity gradient difference and the preset gradient threshold in step S2. For example, if the preset gradient threshold for natural rubber in step S2 is 0.06 / s, then when the actual gradient difference reaches 0.05 / s, the scanning frequency scaling factor is adjusted to 1.8 (originally 1.67) to compensate for data acquisition density. The baseline value update cycle for the integration time mapping relationship is synchronized with the saturation truncation process in step S4, for example, the integration time baseline value is recalibrated after each truncation operation.

[0088] S4, truncating the saturated spectrum data of the main decomposition stage, and fusing it with the spectra of the initial decomposition stage and the residual decomposition stage according to the time axis to generate the full-stage fused spectrum data, including:

[0089] Before truncating the saturated spectrum data of the main decomposition stage, the intensity saturation threshold of the characteristic absorption peak of the main decomposition stage is monitored in real time based on the adjusted integration time and scanning frequency. The preset saturation threshold is determined through a calibration experiment. The calibration method is to conduct multiple pyrolysis experiments on the same rubber, record the value when the characteristic absorption peak intensity first reaches saturation, and add a safety margin. For example, the saturation intensity of styrene-butadiene rubber in three experiments was 0.95, 0.98, and 1.00, respectively. After taking the average of 0.98 and adding a 10% margin, the saturation threshold is obtained as 1.08. In actual monitoring, if the intensity of three consecutive sampling points in the main decomposition stage exceeds this threshold, for example, the three sampling values ​​are 1.09, 1.10, and 1.12 respectively, the truncation operation is triggered.

[0090] The truncation operation retains the spectral data of the last unsaturated sampling point before saturation. For example, if truncation is triggered at 50 seconds in the main decomposition phase, the data at 49.9 seconds is retained as the truncation point. The data within the set time window before and after the truncation point are aligned with the spectra of the initial decomposition phase and the residual decomposition phase in chronological order. The time window length is dynamically adjusted based on the scanning frequency. For example, at a scanning frequency of 50 Hz, the window is set to 0.5 seconds (including 25 sampling points) to ensure a smooth transition of data before and after the truncation point.

[0091] If there is a time gap between the cutoff point and the adjacent phase during alignment, compensation data is generated based on the spectral intensity gradient trends of the decomposition phases on either side of the gap. For example, if there is a 2-second gap between the cutoff point of the main decomposition phase and the starting boundary of the residual decomposition phase, the intensity change rate of the three sampling points before the cutoff point of the main decomposition phase (e.g., 0.01 / s) and the intensity change rate of the three sampling points after the starting boundary of the residual decomposition phase (e.g., 0.05 / s) are extracted. The intensity gradient of each second within the gap is generated using linear interpolation. The compensation data intensity decreases from 1.01 at the end of the main decomposition phase at a rate of 0.01 / s, while the residual decomposition phase intensity increases from 0.20 at a rate of 0.05 / s. The two converge at the midpoint of the gap to generate continuous transition data.

[0092] The truncated main decomposition phase data, compensation data, and adjacent phase spectra are fused along the time axis. During the fusion, a weighted average is taken for the data in the overlapping intervals, and the weight coefficient is associated with the scanning frequency ratio coefficient set in step S3.

[0093] For example, if the scanning frequency of the main decomposition phase is 50 Hz and the residual decomposition phase is 25 Hz, the data weight of the main decomposition phase is 50 / (50 + 25) = 0.67, and the residual decomposition phase is 0.33. If the intensity of the main decomposition phase at the overlap point is 1.01 and the residual decomposition phase is 0.20, the intensity after fusion is 1.01 × 0.67 + 0.20 × 0.33 = 0.71.

[0094] All parameters in the calibration experiment (saturation threshold, time window length, and weight coefficient) are set in conjunction with the integration time and scan frequency adjustment parameters in step S3. For example, when the scan frequency of the main decomposition phase in step S3 is increased to twice that of the initial phase, the fusion weight coefficient is adjusted to 0.67 to match the data acquisition density. The time window length is adjusted inversely with the scan frequency, for example, shortened to 0.25 seconds at a frequency of 100 Hz, to ensure that the data alignment accuracy is compatible with the acquisition density.

[0095] The compensation data generation rules are logically linked to the spectral intensity gradient difference determination in step S2. For example, if the gradient difference in step S2 is 0.08 / s (exceeding the threshold of 0.06 / s), a gentler gradient (e.g., 0.02 / s) is used when generating the compensation data to avoid sudden data changes. The time axis deviation of the fused full-stage spectral data is closed-loop calibrated using the time domain offset verification in step S5 to ensure that the maximum deviation does not exceed 0.05 seconds.

[0096] S5. Verify the pyrolysis kinetic continuity of the full-stage fused spectral data. If the time domain offset between the temperature change rate inflection point in the initial decomposition stage and the spectral intensity gradient extreme value in the main decomposition stage exceeds the preset phase change matching interval, perform dynamic collaborative parameter correction and re-collect spectral data, including:

[0097] To verify the continuity of the pyrolysis kinetics of the full-stage fused spectral data, the inflection point time of the temperature change rate in the initial decomposition stage and the time of the spectral intensity gradient extreme value in the main decomposition stage were extracted, and the temporal offset between the two was calculated. The inflection point time of the temperature change rate was determined by searching the temperature change rate curve in the initial decomposition stage for the first local maximum point after reaching the preset mutation threshold. For example, if the temperature change rate in the initial decomposition stage suddenly increased from 3.5 degrees Celsius per second to 4.2 degrees Celsius per second and then decreased to 3.8 degrees Celsius per second, the time corresponding to 3.8 degrees Celsius per second would be marked as the inflection point time. The time of the spectral intensity gradient extreme value was determined by searching the sampling point time where the gradient first reached its maximum value in the spectral intensity change curve in the main decomposition stage. For example, if the gradient maximum value of 0.3 per second occurs at the 60th second during the linear increase of intensity from 0.5 to 0.8 in the main decomposition stage, the 60th second would be marked as the extreme value time.

[0098] The time domain offset is calculated by subtracting the inflection point time of the temperature change rate in the initial decomposition stage from the time of the extreme value of the spectral intensity gradient in the main decomposition stage to obtain the time difference. For example, if the inflection point time is 50 seconds and the extreme value time is 55 seconds, the time domain offset is 5 seconds. The preset phase change matching interval is determined through calibration experiments. The calibration method is to conduct at least three pyrolysis experiments on the same rubber and calculate the distribution range of the time domain offset during the normal pyrolysis process. For example, the offsets measured in three experiments on natural rubber were 3 seconds, 4 seconds, and 5 seconds. The phase change matching interval is set to 2 seconds to 6 seconds. If the actual offset exceeds this range, a correction is triggered.

[0099] When the time-domain offset exceeds the preset phase change matching range, the temperature change rate threshold for the initial decomposition phase and the spectral saturation cutoff threshold for the main decomposition phase are simultaneously reversed based on the offset direction. The correction logic follows an inverse square relationship, meaning that the adjustment of the temperature change rate threshold for the initial decomposition phase and the spectral saturation cutoff threshold for the main decomposition phase satisfy the following rule: if the temperature threshold decreases by a certain ratio, the spectral threshold increases by the inverse square of that ratio, and vice versa, with the product of the two remaining constant. For example, if the original temperature threshold is 3.7 degrees Celsius per second and the spectral saturation threshold is 1.01, if the offset exceeds the positive limit (the extreme value time is later than the inflection point time), the temperature threshold is lowered to 3.5 degrees Celsius per second, and the spectral threshold needs to be increased to 1.21 (calculated as: original temperature threshold × original spectral threshold2 = new temperature threshold × new spectral threshold2).

[0100] The scanning frequency switching rules and integration time compensation coefficient are dynamically generated based on the real-time calculated activation energy of the pyrolysis reaction. The activation energy is obtained by fitting the Arrhenius equation with the spectral intensity data and temperature data of the main decomposition stage, specifically: using the spectral intensity change rate as the reaction rate, and the inverse of the temperature as the independent variable, the activation energy value is calculated by linear regression fitting the slope of the straight line. For example, when the activation energy obtained by fitting is 120 kilojoules per mole, the scanning frequency is increased by the ratio of the activation energy to the benchmark activation energy, and the integration time is shortened by the same ratio. If the benchmark activation energy is 110 kilojoules per mole, the scanning frequency is increased to 1.09 times the benchmark value (120 / 110≈1.09), and the integration time is shortened to 0.92 times the benchmark value (110 / 120≈0.92).

[0101] Spectral data were reacquired using the revised judgment threshold, truncation threshold, and hardware parameters. During reacquisition, the temperature change rate judgment threshold for the initial decomposition phase was adjusted to 3.5 degrees Celsius per second, the spectral saturation truncation threshold for the main decomposition phase was adjusted to 1.21, the scanning frequency was set to 54.5 Hz (the original reference frequency was 50 Hz × 1.09), and the integration time was set to 92 milliseconds (the original reference time was 100 milliseconds × 0.92). After reacquisition, the time domain offset was verified. If the offset still exceeded the phase change matching interval, the correction operation was iteratively performed for up to three times. For example, after the first correction, the offset dropped to 5.5 seconds (still exceeding the limit), and after the second correction, it dropped to 5.0 seconds (falling within the interval), and the iteration was stopped.

[0102] The difference in spectral intensity distribution between adjacent decomposition stages is determined by calculating the standard deviation of the intensity data in the overlapping interval. For example, the initial decomposition stage overlaps with the main decomposition stage from 50 seconds to 55 seconds. The mean intensity of the initial decomposition stage is 0.5 and the standard deviation is 0.1; the mean intensity of the main decomposition stage is 0.6 and the standard deviation is 0.15. The difference value is 0.1 plus 0.15, which equals 0.25. The preset difference threshold is set to 0.30. If the difference value is lower than the threshold, it is judged to meet the standard. The method for setting the difference threshold is: in the calibration experiment, the maximum difference value of the overlapping interval of the normal pyrolysis process is counted. For example, the maximum difference value of the three experiments on natural rubber is 0.28. The threshold is set to 0.30 to cover individual differences.

[0103] All parameters in the calibration experiment (phase change matching interval, inverse proportional square constant, baseline activation energy) are set based on the experimental conditions of steps S1-S4, including the rubber sample morphology (1 to 3 mm particles), heating rate (10 degrees Celsius per minute), pyrolysis termination temperature (600 degrees Celsius) and spectral acquisition parameters (wavenumber range, baseline interval). For example, the phase change matching interval of styrene-butadiene rubber is set to 3 seconds to 7 seconds, which is associated with its temperature change rate mutation critical value of 3.2 degrees Celsius per second and the spectral saturation threshold of 1.08. The inverse proportional square constant is determined through the calibration experiment. For example, the square product of the original temperature threshold of styrene-butadiene rubber of 3.2 degrees Celsius per second and the spectral threshold of 1.08 is 3.2×1.08. 2 ≈3.73, as the correction benchmark.

[0104] When reacquiring data, the scanning frequency switching rule is dynamically linked to the scanning frequency scaling factor in step S3. For example, if the scanning frequency scaling factor in the main decomposition phase in step S3 is 1.67 and is revised to 2.0, the frequency is adjusted according to the new scaling factor during reacquisition. The integration time compensation factor is also updated synchronously with the integration time mapping relationship in step S3. For example, if the original main decomposition phase integration time is 100 milliseconds and is revised to 82 milliseconds, the mapping relationship coefficient is adjusted from 1.0 to 0.82.

[0105] The fused full-stage spectral data must pass time-domain offset verification and difference threshold determination to ensure data continuity and compatibility with pyrolysis kinetics. If the criteria are still not met after three iterations, an abnormality warning is issued and the testing process is terminated. For example, if the offset of nitrile rubber remains at 7 seconds after three corrections and the difference value is 0.35, an abnormality warning is triggered, prompting the operator to check the sample or equipment status.

[0106] S6. Match the full-stage fused spectral data after the re-collected spectral data fusion with the preset characteristic absorption peak database, and output the component types and concentration ratios of the rubber pyrolysis products, including:

[0107] The characteristic absorption peak positions and relative intensities of each decomposition stage in the full-stage fusion spectral data are extracted and matched peak by peak with the characteristic peaks of standard substances stored in the preset characteristic absorption peak database. The preset characteristic absorption peak database is established through calibration experiments and contains standard spectral data of different rubber pyrolysis products. For example, the characteristic absorption peak wave number of natural rubber corresponding to isoprene is 1650cm -1 The peak wave number of CH bond stretching vibration of styrene-butadiene rubber is 3050-3100 cm -1 The matching method is to search for the wave number deviation of the database within ±5cm in the full-stage fusion spectrum data. -1 The absorption peak of the target protein was detected and the similarity between its relative intensity and the standard intensity in the database was calculated. If the similarity was higher than 80%, it was considered a successful match.

[0108] Successfully matched characteristic peaks are assigned weight coefficients based on the decomposition stage type. The main decomposition stage has a higher weight coefficient than both the initial and residual decomposition stages, and the initial decomposition stage has a higher weight coefficient than the residual decomposition stage. The weight coefficients are set by statistically analyzing the contribution of each decomposition stage to the final component concentration through calibration experiments. The main decomposition stage contributes the most due to the concentrated release of pyrolysis products and a stable signal. The initial decomposition stage contributes the second most, and the residual decomposition stage contributes the least due to the low amount of residual products. For example, in the natural rubber calibration experiment, the weight coefficients for the main decomposition stage, the initial decomposition stage, and the residual decomposition stage were set to 0.6, 0.3, and 0.1, respectively, for a total of 1.

[0109] The weighted concentration ratio of each component is calculated based on the sum of the products of the relative intensities of the matching peaks and the weight coefficients of the corresponding decomposition stages. For example, the relative intensity of the matching peaks of styrene in the main decomposition stage is 0.8, with a weight coefficient of 0.6; the relative intensity in the initial decomposition stage is 0.5, with a weight coefficient of 0.3; and the relative intensity in the residual decomposition stage is 0.2, with a weight coefficient of 0.1. Therefore, the weighted concentration ratio is 0.8 × 0.6 + 0.5 × 0.3 + 0.2 × 0.1 = 0.48 + 0.15 + 0.02 = 0.65.

[0110] The weighted concentration ratio is corrected using the integrated time compensation coefficient adjusted in step S3. The correction coefficient is inversely proportional to the integrated time compensation coefficient. For example, if the integrated time compensation coefficient is shortened to 0.8 times the base time (e.g., a base time of 100 ms becomes 80 ms after compensation), the correction coefficient becomes 1 / 0.8 = 1.25. The final concentration value is obtained by multiplying the weighted concentration ratio by the correction coefficient. For example, a styrene weighted concentration ratio of 0.65 is corrected to 0.65 × 1.25 = 0.8125.

[0111] If the difference in the corrected concentration ratios of the same component at different decomposition stages exceeds a preset ratio difference threshold, the concentration ratio at the main decomposition stage is preferentially used as the final output value. This preset ratio difference threshold is determined through calibration experiments. For example, for natural rubber, the threshold is set at 20%. If the styrene concentration in the main decomposition stage is 0.81 and the initial decomposition stage is 0.95 (a 17% difference), the main decomposition stage data is retained. If the difference reaches 25%, only the main decomposition stage concentration value is output.

[0112] Output the component type and final concentration ratio to a display terminal or process control system in a pre-set format. The output format includes the component name, concentration percentage, and a confidence level indicator. The confidence level is calculated based on the matching peak similarity and the stage weight coefficient. For example, a styrene concentration of 81.25% is labeled "Styrene: 81.25% (Confidence: High)". The confidence level of "High" corresponds to a matching similarity of ≥90% and a main decomposition stage weight coefficient of ≥0.6.

[0113] The settings of all parameters (weight coefficient, proportional difference threshold, correction coefficient) in the calibration experiment are consistent with the experimental conditions of steps S1-S5, including the rubber sample morphology (1-3 mm particles), heating rate (10 degrees Celsius per minute), and pyrolysis termination temperature (600 degrees Celsius). For example, the weight coefficients of styrene-butadiene rubber are set to 0.7 for the main decomposition stage, 0.2 for the initial decomposition stage, and 0.1 for the residual decomposition stage, which match the release characteristics of its pyrolysis products (the main stage products account for 70%). The preset proportional difference threshold is adjusted synchronously according to the fusion spectrum difference threshold of step S4. If the step S4 difference threshold is set to 0.30, the S6 proportional difference threshold is set to 30%.

[0114] The inverse relationship between the correction factor and the integration time compensation factor was verified through calibration experiments. For example, if the integration time compensation factor in step S3 is adjusted to 0.5 times (base time 100ms, correction time 50ms), the correction factor becomes 2.0, ensuring that concentration underestimation caused by signal attenuation at low integration times is corrected. The corrected concentration data is then verified by the time domain offset in step S5. If the time domain offset exceeds the phase change matching range, the data is re-collected and iteratively corrected.

[0115] During the database matching process, the characteristic absorption peak wave number deviation tolerance (±5cm -1 ) is logically associated with the determination of the spectral intensity gradient difference in step S2. For example, if the gradient difference in step S2 exceeds the threshold value, causing the stage boundary to move, the wavenumber tolerance is correspondingly relaxed to ±10cm -1 The matching similarity is calculated using the relative intensity ratio method, that is, the ratio of the measured intensity to the database standard intensity. A match is determined when it is higher than 80%. This threshold is calibrated in conjunction with the fusion weight coefficient judgment standard in step S4.

[0116] The output format of the final concentration ratio is linked to the abnormality warning mechanism in step S5. If the time domain offset in step S5 still exceeds the limit after three corrections, a "data abnormality" mark is added to the output concentration ratio, prompting the operator to manually review it. For example, if the nitrile rubber concentration data is marked as "Acrylonitrile: 75% (data abnormality)", the abnormality flag is triggered if the time domain offset is greater than 10 seconds or the fused spectrum difference is greater than 0.35.

[0117] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0118] It should be noted that the present invention can be deployed on the device itself to realize embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0119] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0120] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0122] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0123] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0124] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0125] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0126] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting rubber pyrolysis components based on infrared spectroscopy, characterized in that: The steps include: S1. Monitoring the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main and residual decomposition stages; S2. Extract the intensity variation trend of the preset characteristic absorption peaks in each decomposition stage. If the intensity variation trend deviates from the preset trend model, redivide the decomposition stage boundaries based on the spectral intensity gradient difference between adjacent decomposition stages. S3, adjusting the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundaries; S4, truncating the saturated spectrum data of the main decomposition stage, and fusing it with the spectra of the initial decomposition stage and the residual decomposition stage according to the time axis to generate the full-stage fused spectrum data; S5. Verify the pyrolysis kinetic continuity of the full-stage fused spectral data. If the time domain offset between the temperature change rate inflection point in the initial decomposition stage and the spectral intensity gradient extreme value in the main decomposition stage exceeds the preset phase change matching interval, perform dynamic collaborative parameter correction and re-collect spectral data. S6. Matching the full-stage fused spectral data after the re-collected spectral data fusion with the preset characteristic absorption peak database, and outputting the component type and concentration ratio of the rubber pyrolysis product.

2. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: Monitoring the temperature change rate of rubber pyrolysis to divide the pyrolysis process into initial, main and residual decomposition stages, including: The temperature change rate curve during the rubber pyrolysis process is collected in real time. Based on the mutation point where the temperature change rate curve first reaches the preset mutation critical value, the starting boundary of the initial decomposition stage is divided; When the temperature change rate continues to rise from the starting boundary of the initial decomposition stage and reaches the preset peak threshold for the first time, the initial decomposition stage is determined to be over and the main decomposition stage begins; When the temperature change rate drops from the peak value to the preset rate attenuation threshold, the termination boundary of the main decomposition stage is determined; According to the release stability of the residual decomposition products, the period during which the temperature change rate is continuously lower than the preset residual decomposition threshold is marked as the termination boundary of the residual decomposition stage.

3. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: Extract the intensity change trend of the preset characteristic absorption peaks in each decomposition stage. If it deviates from the preset trend model, the decomposition stage boundaries are re-divided based on the spectral intensity gradient difference of adjacent decomposition stages, including: Extracting real-time intensity data of preset characteristic absorption peaks in the initial decomposition stage, main decomposition stage, and residual decomposition stage, and generating intensity change trend curves for each decomposition stage; The intensity change trend curve of the current decomposition stage is compared point by point with the preset trend model. If the absolute value of the intensity deviation of several consecutive sampling points exceeds the preset tolerance range, it is determined to be a deviation from the preset trend model; When deviating from the preset trend model, the spectral intensity gradient difference between the current decomposition stage and the adjacent decomposition stage at the decomposition stage boundary is calculated; If the spectral intensity gradient difference is greater than the preset gradient threshold, the boundary of the current decomposition stage is moved toward the adjacent decomposition direction with a smaller spectral intensity gradient until the spectral intensity gradient difference is less than or equal to the preset gradient threshold; After updating the decomposition stage boundary, the characteristic absorption peak intensity change trend curve of the decomposition stage on both sides of the moving boundary is re-extracted and a secondary deviation judgment is performed. If there is still deviation, the spectral intensity gradient difference calculation and boundary movement operation are iteratively performed.

4. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: Adjust the spectrometer scanning frequency and integration time according to the re-divided decomposition stage boundaries, including: According to the re-divided termination boundaries of the initial decomposition stage, the main decomposition stage and the residual decomposition stage, the scanning frequency of each decomposition stage is dynamically adjusted according to the preset scanning frequency proportional coefficient corresponding to the decomposition stage type; Based on the real-time intensity peak value of the characteristic absorption peak in each decomposition stage, the integration time is adjusted according to the preset integration time mapping relationship; When the movement of the decomposition stage boundary causes time overlap or gap between adjacent decomposition stages, a scanning frequency gradient rule of the transition interval is generated based on the scanning frequency difference between adjacent decomposition stages, so that the scanning frequency changes continuously according to the time gradient in the transition interval.

5. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 4, characterized in that: The scanning frequency of the main decomposition stage is higher than that of the initial decomposition stage and the residual decomposition stage, and the integration time of the initial decomposition stage is shorter than that of the main decomposition stage and the residual decomposition stage; The mapping relationship between the scanning frequency proportional coefficient and the integration time is determined by calibrating the pyrolysis process of different rubber types and is associated with the spectral intensity gradient difference and the preset gradient threshold.

6. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: The saturated spectrum data of the main decomposition stage is truncated and fused with the spectra of the initial decomposition stage and the residual decomposition stage along the time axis to generate the full-stage fused spectrum data, including: Before truncating the saturated spectrum data of the main decomposition stage, the intensity saturation threshold of the characteristic absorption peak of the main decomposition stage is monitored in real time based on the adjusted integration time and scanning frequency. When the intensity of several consecutive sampling points exceeds the preset saturation threshold, truncation is performed; The truncation operation retains the spectral data of the last unsaturated sampling point before saturation occurs, and according to the adjacent relationship between the initial decomposition stage and the residual decomposition stage on the time axis, the spectral data in the set time window before and after the truncation point are aligned with the spectra of the adjacent stages in time order; If there is a time gap between the cutoff point and the adjacent stage during alignment, the compensated spectral data within the time gap interval is generated by linear interpolation based on the spectral intensity gradient change trend of the decomposition stages on both sides of the time gap; The truncated main decomposition stage data, compensation spectrum data, and the spectra of the initial decomposition stage and the residual decomposition stage are fused along the time axis to generate full-stage fused spectrum data.

7. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: Verify the pyrolysis kinetic continuity of the full-stage fusion spectral data. If the time domain offset between the temperature change rate inflection point in the initial decomposition stage and the spectral intensity gradient extreme value in the main decomposition stage exceeds the preset phase change matching interval, perform dynamic collaborative parameter correction and re-collect spectral data, including: The inflection point time of the temperature change rate in the initial decomposition stage and the extreme value time of the spectral intensity gradient in the main decomposition stage were extracted to calculate the time domain offset; When the time domain offset exceeds the preset phase change matching range, dynamic collaborative parameter correction is performed and spectral data is re-collected.

8. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 7, characterized in that: Dynamic collaborative parameter correction and spectrum recollection include: According to the deviation direction of the time domain offset, the judgment threshold and the truncation threshold are synchronously corrected based on the inverse proportional square relationship between the temperature change rate judgment threshold in the initial decomposition stage and the spectrum saturation truncation threshold in the main decomposition stage; Generate scanning frequency switching rules and integration time compensation coefficient based on real-time pyrolysis reaction activation energy; The spectral data are re-collected using the revised judgment threshold, cutoff threshold, scanning frequency switching rule and integration time compensation coefficient until the time domain offset falls into the preset phase change matching interval and the difference in spectral intensity distribution between adjacent decomposition stages is lower than the preset difference threshold.

9. The method for detecting rubber pyrolysis components based on infrared spectroscopy according to claim 1, characterized in that: The full-stage fused spectral data after recollection spectral data fusion is matched with the preset characteristic absorption peak database to output the component types and concentration ratios of the rubber pyrolysis products, including: Extract the characteristic absorption peak positions and relative intensities of each decomposition stage in the full-stage fusion spectrum data, and perform peak-by-peak matching with the characteristic peaks of standard substances stored in the preset characteristic absorption peak database; The weight coefficients are assigned to the successfully matched characteristic peaks according to the decomposition stage type, wherein the weight coefficient of the main decomposition stage is higher than that of the initial decomposition stage and the residual decomposition stage, and the weight coefficient of the initial decomposition stage is higher than that of the residual decomposition stage; The weighted concentration ratio of each component is calculated based on the sum of the products of the relative intensity of the matching peak and the weight coefficient of the corresponding decomposition stage; The weighted concentration ratio is corrected in combination with the integral time compensation coefficient, and the correction coefficient used is inversely proportional to the integral time compensation coefficient; When the difference in the corrected concentration ratios of the same component at different decomposition stages exceeds a preset ratio difference threshold, the weighted concentration ratio at the main decomposition stage is preferentially used as the final output value.

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