3701results about "Raman scattering" patented technology

The invention discloses a water quality heavy metal pollution detection method and system based on Raman spectrum.The water quality heavy metal pollution detection method comprises the steps that water body Raman scattering light is collected in situ through a miniature optical fiber probe, and a continuous time sequence spectrum signal flow is generated; wavelet transform is combined with self-adaptive threshold setting, and high-frequency noise and effective spectral signals are separated; dynamically strengthening the characteristic peak of the target heavy metal through a frequency domain characteristic screening module, and inhibiting a water molecule interference peak at the same time; generating a cross-domain fusion feature vector; processing the fusion feature vector through a pre-trained heavy metal concentration prediction model, and outputting concentration prediction values of various heavy metals; a confidence score is dynamically calculated based on a deviation between a current predicted value and historical data distribution, and model parameter update and system calibration are automatically triggered. The method has the advantages that through acousto-optic signal cross-domain fusion and closed-loop self-calibration, the target peak is dynamically strengthened while water molecule interference is inhibited, and the real-time performance, the anti-interference performance and the prediction precision of heavy metal detection are improved.

The invention relates to the field of Raman spectrums, and discloses a Raman spectrum nonlinear generation method driven by mixed machine learning, which is used for meeting the requirements of a modern Raman spectrum analysis technology on mixture spectrum data with wide coverage, complete types and reliable quality. Comprising the following steps: performing weighted linear combination on pure component spectrums based on a concentration ratio to generate a primary mixture spectrum; the data diversity is enhanced; generating a prediction spectrum; generator parameters are optimized through multi-level feature comparison and adversarial training of the discriminator; matching and deleting false peaks, and reconstructing physical reasonable characteristic peaks; and dynamically adjusting the intensity of the characteristic peak, and outputting high-fidelity spectrum data. According to the method, by fusing physical constraint and deep learning, the peak position precision, intensity consistency and noise robustness of the generated spectrum are remarkably improved, quantitative analysis of a complex mixture in a wide concentration range is supported, and an efficient solution is provided for Raman spectrum database construction and chemical detection.

In one embodiment, a system includes a pump light source configured to produce a pump beam of light at a pump frequency and a Stokes light source configured to produce a Stokes beam of light at a Stokes frequency, where the pump and Stokes frequencies are offset by a frequency offset Ω. The system further includes a photonic integrated circuit (PIC) that includes multiple optical waveguides configured to: direct the pump and Stokes beams of light to a sample; receive a Raman signal produced by coherent Raman scattering of the pump and Stokes beams of light at the sample; and direct the Raman signal to an optical receiver. The optical receiver is configured to detect the Raman signal. The optical receiver includes a probe light source configured to produce a probe beam of light at a probe frequency, an optical detector, and an electronic circuit.

The invention discloses an SERS (Surface Enhanced Raman Scattering) sensing chip with stable reusability as well as a preparation method and application of the SERS sensing chip, and belongs to the technical field of photoelectric sensing devices. Comprising a substrate; the semiconductor photocatalyst is positioned on the surface of the substrate; the co-oxidant and the co-reducing agent are positioned on the surface of the semiconductor photocatalyst; the occupying molecule is covalently connected with an exposure site of the auxiliary reducing agent in space; the template molecule is in covalent linkage or coordination linkage with an exposure site of the pro-oxidant in space; wherein the template molecule can be subjected to photocatalytic degradation, so that the pro-oxidant obtains the exposure site again. The template molecules in the sensing chip can be quickly removed through space confinement photocatalytic degradation, and the accuracy of quantitative analysis is improved by using the inorganic imprinted polymer as internal standard molecules.

The invention provides a Bi-BDO fermentation pH dissolved oxygen dynamic optimization method based on an online Raman spectrum, belongs to the technical field of biological fermentation process control, and aims to solve the problems of unstable process and low efficiency caused by fermentation control lag and incapability of sensing the real metabolic state of cells in the prior art. The method comprises the following steps: acquiring the concentrations of a target product BDO and key byproducts such as acetic acid and ethanol in the fermentation liquor in real time through an online Raman spectrum; according to the method, a metabolic stress index is originally proposed and constructed, the index is obtained by performing weighted operation on the instantaneous generation rate of the by-product and the target product, and the index is used for quantitatively characterizing the intrinsic metabolic stress level of the cells in real time. The control strategy of maintaining the metabolic stress index in the preset optimal stable interval is taken as a core control strategy, the conversion from passive response to active prediction in the fermentation process is realized, and the yield, the stability and the batch repeatability of Bi-BDO production are remarkably improved.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention belongs to the technical field of nondestructive testing, and discloses a gold ore and iron ore metal content detection method. The method comprises the following steps: acquiring an XRF energy spectrum by using an X-ray tube, acquiring an LIBS spectrum by using double-pulse Nd: YAG laser, and acquiring a Raman spectrum by using a semiconductor laser; performing baseline correction on the LIBS spectrum, performing Savitzky-Golay smoothing pretreatment on the Raman spectrum, and respectively extracting three types of spectrum characteristics; constructing a physical constraint network model, inputting XRF, LIBS and Raman features, and then outputting the correction content, fusion weight and LIBS dominant factors of Fe / Au elements; and finally, through a dynamic weight fusion strategy, combining XRF, LIBS fusion weights and network output parameters, calculating to obtain the content of the gold ore and the content of the iron ore. The technical breakthrough of multi-element cooperative detection and complex matrix interference suppression is realized, and the detection precision of Fe and Au is effectively improved.

The invention relates to the field of ulcerative colitis, and discloses a multi-parameter monitoring system for evaluating the intestinal barrier function of ulcerative colitis, which is used for realizing the goal of automatically adjusting a treatment scheme according to a monitoring result by constructing a closed-loop feedback treatment system. According to the invention, by designing the nanoprobe of which the surface is modified with a specific binding element and combining with a swallowing capsule endoscopy, real-time imaging and molecular level dynamic tracking of the intestinal barrier are realized, genetically engineered bacteria are constructed to quantify intestinal permeability abnormality and local inflammation level, and an intestinal barrier function damage score and a disease progress prediction result are output; individual monitoring is achieved through transfer learning, ultrasonic elastography, optical coherence tomography, wearable equipment for measuring intestinal electric field impedance spectroscopy and other technologies are adopted, an intestinal wall mechanics-electrophysiology composite parameter set is obtained, and the evaluation accuracy is improved.

The invention discloses a heparin impurity detection method and system based on a Raman spectrum. Impurities in heparin production are efficiently detected through a Raman spectrum technology, and a production process is optimized. The method comprises the following steps: firstly, obtaining original Raman spectrum data of a heparin production sample, performing quality analysis, and optimizing data to obtain parameters; extracting spectral data features, and generating a three-dimensional feature matrix; detecting abnormal spectral characteristics by using a density clustering algorithm, identifying impurities, and obtaining impurity detection data; and the impurity change trend is analyzed in combination with production timestamp data, and production process parameters are optimized. According to the method, the impurities can be accurately detected in real time, the production process is dynamically optimized, and the quality and safety of a heparin product are ensured. The system realizes efficient quality control, and has a wide application prospect, especially in the field of biological pharmacy.

The invention relates to a terahertz-Raman integrated detection system and method. The detection system comprises a fusion mainframe box and an integrated probe arranged at a sample detection end on the fusion mainframe box, a Raman spectrum module, a terahertz spectrum module, a terahertz optical module and a data analysis module are arranged in the fusion mainframe box; the Raman spectrum module is composed of a Raman laser, a Raman light filter, a Raman grating and a Raman detector which are sequentially connected through a light path; the terahertz spectrum module is composed of a terahertz femtosecond laser, a transmitting photoconductive antenna, a receiving photoconductive antenna and a terahertz detector which are sequentially connected through a light path; the data analysis module is in communication connection with the Raman detector and the terahertz detector and is used for synchronously processing data transmitted by the Raman detector and the terahertz detector. Compared with the prior art, the integrated probe synchronously collects two kinds of spectral data and carries out fusion processing on the two kinds of spectral data through the data analysis module, and the device has the remarkable effects that the device integration degree is high, the detection efficiency and accuracy are greatly improved, and the application range is wide.

A platform for characterizing biomolecules, biological tissue sections, viruses, or other particles of interest in a non-invasive, high-throughput, cost-effective, and label-free manner. The platform includes a merged set of imaging modalities including and capable of interferometric microscopy and wide-field or confocal microscopy (such as, for example, a wide-field super-resolution surface enhanced Raman spectroscopy, SERS), although some aspects may be employed with data sets only received from wide-field or confocal microscopy. Related devices, systems, methods, techniques, and variations to the same are also provided.

The invention discloses a method and system for identifying perfluorooctanoic acid pollutants in tap water, and relates to the field of water environment new pollutant detection, and the method comprises the steps of SERS spectral signal collection, signal preprocessing, bimodal learning device construction and training verification, and concentration prediction result generation based on numerical value and image bimodal. The to-be-predicted data is put into the trained bimodal learning device for prediction and identification, and the concentration of the detection sample is output, so that batch rapid detection based on surface enhanced Raman spectroscopy and bimodal deep learning is realized, and high-precision identification and prediction based on small sample data are realized; the accurate detection result of the perfluorooctanoic acid pollutants in the tap water is obtained.

The invention relates to the technical field of purity monitoring and anomaly detection in the production process of ultra-pure zinc oxide, and discloses a purity anomaly detection method for production of ultra-pure zinc oxide. A disturbance direction coding vector is generated through time sequence segmentation and disturbance sensitivity mapping, and a collaborative structure embedding matrix is constructed based on channel weighting and time window fusion and used for representing a collaborative disturbance relation between monitoring channels; by taking the collaborative structure embedding matrix as input, carrying out structure frequency spectrum transformation and attributive suppression operation to obtain a diffusion disturbance structure response value; the diffusion disturbance structure response value serves as input, the disturbance direction gradient is calculated, a disturbance total variation mapping value is formed, and a disturbance anomaly probability value is generated in combination with local neighborhood average and deviation degree measurement; finally, a power logarithmic equilibrium loss function is adopted for optimization, and stable convergence and high-precision detection under multi-channel disturbance are achieved. The anomaly detection model provided by the invention can effectively perform purity anomaly detection on the ultra-pure zinc oxide production process.

The invention relates to a data analysis technology, and discloses a spectral feature and artificial intelligence fused grain heavy metal detection method and system.The method comprises the steps that a near-infrared diffuse reflection spectrum, a laser-induced breakdown spectrum and a surface enhanced Raman spectrum of a grain sample are obtained, a matrix accumulation area thermodynamic diagram is constructed according to the near-infrared diffuse reflection spectrum, and the matrix accumulation area thermodynamic diagram is obtained; the method comprises the following steps: optimizing a laser-induced breakdown spectrum based on a thermodynamic diagram of a matrix accumulation area, carrying out dual-channel spatial-temporal feature extraction on a surface enhanced Raman spectrum and the optimized laser-induced breakdown spectrum to obtain a fusion feature vector, carrying out weighted fusion optimization on the fusion feature vector based on a signal-to-noise ratio of multispectral data to obtain an optimized fusion feature vector, and finally obtaining a fusion feature vector. And carrying out migration adaptation on a pre-trained grain detection model by using a pre-marked new grain sample, and carrying out heavy metal content identification on the grain sample by using the adaptive grain detection model according to the optimized fusion feature vector to obtain a heavy metal detection result. The precision of grain heavy metal detection can be improved.

The invention relates to a recycled plastic material purity monitoring method based on artificial intelligence, and the method comprises the steps: collecting surface visual image data and spectral signal data of a recycled plastic material, environment parameter data and corresponding recycled plastic material purity data, and carrying out the data processing; constructing a double-branch deep neural network model, and respectively extracting a surface visual image feature vector and a spectral signal feature vector from the surface visual image data and the spectral signal data and fusing the surface visual image feature vector and the spectral signal feature vector; in combination with the plastic recycled material fusion feature vector, a plastic feature data set is adopted to train and optimize a double-branch deep neural network model; and collecting real-time surface visual image data, real-time spectral signal data and real-time environmental parameter data of the to-be-monitored plastic recycled material, and inputting the data into the plastic recycled material purity monitoring model to obtain a real-time purity value of the plastic recycled material. The accuracy and efficiency of monitoring the purity of the recycled plastic material can be improved, and the problems that manual detection is low in efficiency and large in error are solved.

The invention relates to the technical field of quality detection, and provides an aluminum product quality multi-scale detection method and system, and the method achieves the comprehensive detection and analysis of the multi-scale features of an aluminum product from the surface to the interior through the construction of a closed-loop detection system from data acquisition, feature association to feedback adjustment. According to the method, the defect mode of the aluminum product can be accurately identified, the defect reason can be accurately positioned, and the process parameters are effectively adjusted based on the defect mode, so that the quality of the aluminum product is remarkably improved, the production efficiency is improved, the production cost is reduced, the market competitiveness of the product is enhanced, and an innovative and efficient solution is provided for quality control in the aluminum product production process.

The invention relates to the technical field of production monitoring control, and discloses a method and a system for monitoring production of eggs capable of reducing blood sugar based on fermented feed. The method comprises the following steps: detecting the hypoglycemic active components of the fermented feed through a multispectral sensor array to obtain spectral characteristic data; establishing a laying hen blood glucose metabolism correlation model by using a partial least squares regression algorithm; dynamically predicting the egg hypoglycemic index based on the incidence matrix and performing function grading; regulating and controlling formula parameters of the fermented feed in real time by adopting a particle swarm optimization algorithm; and carrying out production process quality tracing monitoring and abnormity early warning according to the optimized formula instruction. According to the method, the monitoring precision, the prediction accuracy and the quality stability of the production of the blood-sugar-reducing eggs are improved.

The invention belongs to the technical field of biology, and particularly relates to a group of paired monoclonal antibodies for resisting H9N2 subtype avian influenza viruses and a surface enhanced Raman spectroscopy rapid detection test strip established based on the antibodies. The paired monoclonal antibody comprises a labeled antibody 1E9 and a capture antibody 3C10 which are prepared by taking hemagglutinin protein of the H9N2 subtype avian influenza virus as antigens and can be specifically combined with the H9N2 subtype avian influenza virus. According to the invention, an SERS (Surface Enhanced Raman Scattering) probe Au-coated Pt-4ATP is used as a marker and is coupled with a labeled antibody 1E9 to prepare a labeled probe, and the labeled probe is paired with a capture antibody 3C10 to establish an SERS immunochromatography test strip. The test strip has a colorimetric and Raman dual interpretation mode, can be used for qualitative and quantitative detection, has the advantages of strong specificity, high sensitivity, good repeatability and stability and the like, is simple and rapid in clinical detection operation, and provides reliable technical support for on-site rapid screening of subtype viruses.

The invention relates to the technical field of spectral imaging detection, in particular to a spontaneous-stimulated Raman co-localization single-cell substructure atlas analysis system and a spontaneous-stimulated Raman co-localization single-cell substructure atlas analysis method. The system comprises a femtosecond pulse excitation module used for outputting two beams of broadband femtosecond pulse laser with different frequencies and continuously adjustable optical power; the Stokes light modulation module is used for modulating the Stokes light and carrying out space beam combination on the Stokes light and the pump light; the pump light modulation module is used for modulating pump light; the spontaneous Raman laser continuous adjusting module is used for outputting spontaneous Raman laser; the stimulated Raman exciting light co-positioning module is used for combining the combined laser and the spontaneous Raman laser again, focusing the combined laser and the spontaneous Raman laser on a sample, exciting a stimulated Raman scattering signal, converting the stimulated Raman scattering signal into a digital signal and carrying out data acquisition; and the spectrum detection module is used for collecting the spontaneous Raman scattering light which is reflected and returned along the original light path, and collecting a spectrum signal to form a Raman spectrum. The method has the advantage of improving the accuracy of single-cell heterogeneity structure and cancer cell type discrimination.

The invention discloses a method and a system for identifying a spectrogram of a quaternary ammonium salt extracting solution in a textile, and relates to the technical field of material detection. Comprising the following steps: collecting different textile samples without quaternary ammonium salts and samples containing quaternary ammonium salts with different concentrations, determining extraction conditions, and extracting the quaternary ammonium salts to prepare a sample extracting solution; raman spectrums of all sample extracting solutions are collected, the spectrum containing the quaternary ammonium salt sample and the spectrum not containing the sample are divided into a plurality of sub-waveband areas, feature data are extracted, and the similarity is calculated. Based on the similarity change of the samples with different concentrations, defining the weight coefficient of the sub-band region; and performing similarity calculation on the spectrum of the to-be-detected textile extracting solution, and judging whether the to-be-detected textile extracting solution contains quaternary ammonium salt. And if not, further detecting by using Fourier transform infrared spectroscopy, and determining the quaternary ammonium salt concentration of the to-be-detected textile through the quaternary ammonium salt concentration prediction model. The calculation resources are reduced, and the detection precision and efficiency of the quaternary ammonium salt are improved.

The invention discloses an air pressure control method for microwave plasma chemical vapor deposition diamond, and particularly relates to the technical field of microwave plasma chemical vapor deposition, and the method comprises the steps of S1, spectrum real-time monitoring, S2, machine learning prediction, S3, multi-stage air pressure regulation and control, S4, multi-parameter cooperative control, and S5, air pressure closed-loop control. According to the method, high-precision prediction of the optimal air pressure value is achieved through combination of spectrum real-time monitoring and a machine learning model, the diamond deposition quality is effectively improved by means of a multi-stage air pressure regulation and control and multi-parameter cooperative control strategy, the surface flatness and crystallinity are remarkably improved, meanwhile, precise air pressure closed-loop control and efficient gas flow are coordinated, and the precision of the diamond deposition is improved. In addition, all the modules of the system are in close fit, the stability of the deposition environment is guaranteed, the process fluctuation is reduced, the stability and reliability of the whole deposition process are enhanced, and powerful guarantee is provided for high-quality and high-efficiency production of diamonds.

In one embodiment, a system includes a pump light source configured to produce a pump beam of light at a pump frequency and a Stokes light source configured to produce a Stokes beam of light at a Stokes frequency, where the pump and Stokes frequencies are offset by a frequency offset Ω. The system also includes one or more optical elements configured to: direct the pump and Stokes beams of light to a sample, and collect a Raman signal produced by coherent Raman scattering of the pump and Stokes beams of light at the sample. The system further includes an optical receiver configured to detect the Raman signal. The optical receiver includes a probe light source configured to produce a probe beam of light at a probe frequency, where the probe light source includes a wavelength-tunable laser.

The invention discloses a seawater corrosion environment biaxial test system and method based on Raman spectrum. The seawater corrosion environment biaxial test system comprises a biaxial in-situ loading device, a seawater corrosion environment simulation box and a Raman spectrum measuring instrument. According to the biaxial in-situ loading system coupled with the seawater corrosion environment and used for Raman spectrum measurement, simulation of a complex stress state and the seawater corrosion environment is achieved by coupling the biaxial in-situ loading equipment and the seawater corrosion environment simulation box, and meanwhile, based on the Raman spectrum measurement instrument, the measurement accuracy is further improved through data analysis. Key information such as chemical product evolution laws in the stress corrosion process is obtained in situ, and a technical foundation is laid for clarification of a corrosion failure mechanism of a material under a real deep-sea service condition.

In one embodiment, a system includes a first light source configured to produce a first beam of light at a first frequency and a second light source configured to produce a second beam of light at a second frequency, where the first and second frequencies are offset by a frequency offset Ω. The system also includes one or more optical elements configured to: direct the first and second beams of light to a sample, and collect a Raman signal produced by coherent Raman scattering of the first and second beams of light within the sample. The system further includes an optical receiver configured to detect the Raman signal. The optical receiver includes a third light source configured to produce a third beam of light at a third frequency, and an optical detector configured to coherently mix a portion of the Raman signal with the third beam of light.

The invention discloses a bacteria Raman spectrum chemical component cross-domain analysis method based on deep transfer learning, and belongs to the field of Raman spectrum intelligent analysis, the method comprises the following steps: collecting multi-source Raman spectrum data, and pre-processing the multi-source Raman spectrum data to obtain pre-processed spectrum data; constructing a deep transfer learning model, extracting spectral features of the preprocessed spectral data by the deep transfer learning model through a deep full-connection neural network and a multi-head self-attention mechanism, and eliminating distribution difference between devices by using adversarial transfer learning to obtain a Raman spectrum feature vector; performing nonlinear dimension reduction on the Raman spectrum feature vector to obtain a dimension-reduced low-dimensional feature matrix; based on the dimensionality-reduced low-dimensional feature matrix, a dynamic field adaptation and meta-learning fine tuning strategy is adopted to realize small sample fine tuning, and an optimized model is obtained; and outputting chemical component category judgment and concentration prediction results based on the optimized model.

In one embodiment, a system includes (i) a pump light source configured to produce a pump beam of light at a pump frequency and (ii) a Stokes light source configured to produce a Stokes beam of light at a Stokes frequency, where the pump and Stokes frequencies are offset by a frequency offset Ω. The system also includes one or more optical elements configured to: (i) direct the pump and Stokes beams of light to a sample along counterpropagating beam paths and (ii) collect a Raman signal produced by coherent Raman scattering of the pump and Stokes beams of light at the sample. The system further includes an optical receiver configured to detect the Raman signal. The optical receiver includes a probe light source configured to produce a probe beam of light at a probe frequency, an optical detector, and an electronic circuit.

In one embodiment, a Raman spectroscopy system includes a pump light source configured to produce a pump beam of light at a pump frequency and a Stokes light source configured to produce a Stokes beam of light at a Stokes frequency, where the pump and Stokes frequencies are offset by a frequency offset Ω. The system further includes one or more optical fibers configured to (i) direct the pump and Stokes beams to a sample located external to the system and (ii) direct a Raman signal to the system, where the Raman signal is produced by coherent Raman scattering of the pump and Stokes beams of light at the sample. The system also includes an optical receiver configured to detect the Raman signal. The optical receiver includes a probe light source and an optical detector configured to coherently mix the Raman signal with a probe beam of light.

The invention discloses a correction method for saturation spectral line intensity in a laser spectrum, and relates to the technical field of spectral data analysis, the method comprises the following steps: identifying saturation characteristics in the spectrum, determining a saturation point based on an intensity threshold, and recording the original intensity of the saturation point; determining a target spectrum peak wavelength range containing a saturation point; eliminating saturation points from the wavelength range of the target spectrum peak, and retaining unsaturation points; fitting the unsaturated point through a nonlinear function to obtain the maximum fitting strength; if the maximum fitting strength is lower than the original strength, performing strength correction by adopting a linear extension mode by taking an unsaturated point as a reference, and otherwise, performing strength correction directly based on a fitting model; and outputting the corrected spectral intensity data, and calculating the maximum intensity of the spectral peak and the integral area of the spectral peak. According to the method, the accuracy of saturation judgment and the reliability of a correction result are improved, and a direct intensity data basis and core derivative parameters are provided for quantitative analysis.

The invention provides a mineralogical substance composition testing method and system based on multispectral data fusion. The method comprises the following steps: separating out a first characteristic interference signal caused by the oxidation state of an iron element and a second characteristic interference signal caused by the oxidation state of a manganese element by adopting wavelet transform in a superposition region of a Raman spectrum signal and a visible light absorption spectrum; performing spatial weighting on the intensity of the first feature interference signal and the intensity of the second feature interference signal to construct a multi-dimensional feature vector, inputting the multi-dimensional feature vector to a pre-trained residual neural network, and combining a radioactivity attenuation parameter measured by a gamma spectrometer to determine the radioactivity attenuation of the first feature interference signal and the second feature interference signal. And generating a mineralogical substance composition test result containing a mineral phase, an isomorphic substitution ratio, color cause analysis data and a radiation safety level. According to the method, the collaborative interpretation of the multispectral data and the radiation parameters can be realized, the mineral component analysis precision is improved, the color formation mechanism is synchronously analyzed, and the radioactive risk level is quantitatively evaluated.

The invention discloses an ultrasonic phased array driven graphene oxide flow stripping and closed-loop regulation and control system and method, and the system comprises an ultrasonic phased array module, and a piezoelectric transducer array element in the ultrasonic phased array module controls sound beam focusing and energy distribution through a phase delay controller, and carries out the stripping of graphite oxide flowing in a flow channel; a micro-Raman spectrometer in the micro-fluidic-Raman detection module collects characteristic peak data of a graphene oxide sheet layer in a micro-fluidic chip in real time and sends the characteristic peak data to a signal analysis unit; and the feedback control module is used for receiving the layer number distribution data output by the signal analysis unit, dynamically adjusting ultrasonic parameters of the phase delay controller, and controlling the diverter valve to guide substandard products into a secondary treatment channel and finally return the substandard products to the flow channel for stripping again. The system and the method for stripping the graphene oxide based on coupling of the ultrasonic phased array and the micro-fluidic chip have the advantages of efficient stripping, accurate detection, closed-loop optimization and the like.