204 results about "Fluorescence spectroscopy" patented technology

The invention provides an online quality detection method of a nerve-soothing and brain-nourishing liquid based on multispectral fusion, and aims at solving the bottleneck problems of long offline time consumption, weak multi-index detection capability, poor dynamic adaptability and the like in the traditional detection technology, and realizing intelligent quality control of the whole process of the production technology. The device integrates an ultraviolet-visible spectrum module, a near-infrared spectrum module and a fluorescence spectrum module, constructs a multi-scale residual convolutional network to extract near-infrared spectrum characteristics, analyzes a fluorescence excitation-emission matrix through bidirectional LSTM, and cooperates with dynamic baseline correction of an ultraviolet spectrum, so that the icariin content detection precision is improved by 40% compared with that of a traditional HPLC method, the heavy metal residue detection lower limit reaches 0.05 ppm, and the detection accuracy is greatly improved. And an innovative solution is provided for intelligent production of traditional Chinese medicine preparations.

The invention provides a lake water chemical oxygen demand internal and external source pollution quantitative tracing method and system, and relates to the technical field of pollution quantitative tracing. The method comprises the following steps: performing three-dimensional fluorescence spectrum analysis on water samples at different point positions of a lake to obtain fluorescence excitation / emission matrix spectrum EEMS data; performing parallel factor analysis on the EEMS data, identifying and classifying the fluorescent components of the dissolved organic matter DOM, and determining the type characteristics of each component; based on the maximum fluorescence intensity and the corresponding COD value of the fluorescence component, establishing a standardized multiple linear regression equation by adopting ridge regression analysis, and calculating the contribution percentage of each fluorescence component to the COD of the lake water body; analyzing influence paths of potential variables on DOM migration and transformation, and quantifying direct contributions and indirect contributions of different pollution sources to each fluorescent component through path coefficients; and calculating the total contribution of lake-entering rivers, bottom mud release and phytoplankton to the COD of the lake water body.

The invention relates to the technical field of spectral data processing, and provides a soil heavy metal content detection and correction method based on an X-ray fluorescence spectrum, which comprises the following steps: acquiring a plurality of XRF spectral signals of a to-be-detected soil sample as an original spectral curve; obtaining the overall similarity of each original spectrum curve and the initial interference weight of each peak point in the original spectrum curves; decomposing to obtain a plurality of component signals; constructing a plurality of local windows of each component signal; obtaining a noise interference factor of each local window in each original spectrum curve; obtaining an adjustment reference coefficient of each local window in each original spectrum curve; obtaining a filtering adjustment coefficient of each wavelength in each original spectrum curve; dynamically filtering and smoothing the original spectrum curve to obtain a to-be-measured spectrum signal; and detecting the soil heavy metal content based on the to-be-detected spectral signal. The invention aims to solve the problem of heavy metal content detection deviation caused by noise in an X-ray fluorescence spectrum.

The invention relates to the technical field of municipal engineering, and provides a sewage pipeline low-pollution water inflow and infiltration diagnosis method based on a fluorescence spectrum image, and the method comprises the steps: collecting different types of sewage and different types of low-pollution water in a sewage pipeline troubleshooting region, and according to the mixing condition of the sewage and the low-pollution water in the sewage pipeline troubleshooting region, determining whether the sewage and the low-pollution water are mixed or not; the collected different types of sewage and different types of low-pollution water are mixed according to different proportions, a three-dimensional fluorescence spectrophotometer is used for detecting a mixed water sample, a fluorescence spectrum image corresponding to the mixed water sample is obtained, and according to the fluorescence spectrum image corresponding to the mixed water sample, the sewage type and proportion and the low-pollution water type and proportion, the low-pollution water is detected. And training the water sample type diagnosis model and the water sample type proportion diagnosis model, and performing low-pollution water inflow and infiltration diagnosis on the sewage pipeline to be diagnosed based on the models. In this way, low-pollution water inflow and infiltration diagnosis can be achieved when multiple types of sewage coexist in a sewage pipeline.

The invention discloses a laser-induced Raman and fluorescence spectrum testing system, which relates to the technical field of spectrum testing and comprises a laser module, a harmonic separation mirror, a frequency doubling crystal, a first cavity mirror, a sample pool, a second cavity mirror and a spectrum detection unit, laser output by the laser module sequentially passes through the harmonic separation mirror, the frequency doubling crystal and the first cavity mirror; a high-transmittance film and a high-reflection film are respectively plated on two surfaces of the harmonic separation mirror, and the harmonic separation mirror reflects the laser with the required wavelength to the sample cell; the second cavity mirror is used for turning back the laser from the sample cell; a harmonic separation mirror, a first cavity mirror, a second focusing lens and a second cavity mirror form a double-beam-waist laser resonant cavity. The first cavity mirror and the second cavity mirror are spherical reflectors and are respectively plated with high-reflection films. The first cavity mirror and the first focusing lens are confocal, and the second cavity mirror and the focusing lens are confocal. And two beam waist focuses of the laser resonant cavity are respectively arranged at the positions of the frequency doubling crystal and the sample pool, so that the optimal frequency doubling efficiency and sample excitation efficiency are realized.

The invention relates to the technical field of component analysis, and discloses an X-ray fluorescence spectrum-based rock and ore silicate component analysis method, which comprises the following steps: S1, sample collection and pretreatment: collecting, crushing and drying an initial sample, S2, sample tabletting preparation, S3, standard sample preparation, S4, X-ray fluorescence spectrum analysis, and S5, data processing and result analysis. According to the method, a complex interference characteristic mode between elements can be automatically captured through an established deep learning model and learning of a large number of known components and fluorescence spectrum data, and in an example of correcting interference between multiple elements in rocks and mines, the model can utilize a network structure of the model and comprises an established convolutional layer, a pooling layer and a full-connection layer. Effective information is extracted from multiple groups of sample data, the interference relation between elements is accurately learned, and compared with a traditional method, the precision of analyzing the content of silicate components in rock ore is remarkably improved, so that a measurement result is closer to a real value.

The invention discloses a thermal detection system and a chiral substance identification method, and relates to the technical field of micro-nano optics, the thermal detection system and the chiral substance identification method, based on the structure of the thermal detection system and the steps of the chiral substance identification method, a chiral substance to be detected is configured into a corresponding solution to be detected; a solution to be detected is sampled and placed in the absorber, the surface plasmon effect generated when the metal resonance layer receives chiral light irradiation is utilized, a corresponding thermal signal is output, then the thermal signal is captured and analyzed through the thermal detection system, and high-precision identification of chiral substances is achieved. Compared with a traditional circular dichroism and circular polarization fluorescence spectrophotometer device, the technical scheme provided by the invention is simple in structure, convenient to operate and relatively low in requirements on the concentration and the volume of a detected sample.

The invention belongs to the technical field of chemical element detection, and provides a method for measuring arsenic content in wires, cables and materials by X-ray fluorescence spectrometry. The method comprises the following steps: S1, performing pretreatment on a wire and cable sheath or an insulating layer to obtain a to-be-tested sample; s2, respectively obtaining a plurality of standard samples and corresponding arsenic element contents; s3, respectively carrying out X-ray fluorescence spectrum testing on the plurality of standard samples under set testing conditions to obtain fluorescence intensity of the plurality of standard samples; s4, obtaining a calibration curve according to the fluorescence intensity and the arsenic element content of the plurality of standard samples; s5, performing X-ray fluorescence spectrum testing on the to-be-tested sample under set testing conditions to obtain the fluorescence intensity of the to-be-tested sample; and S6, obtaining the arsenic element content in the to-be-tested sample according to the fluorescence intensity and the calibration curve of the to-be-tested sample. Therefore, the arsenic element content in wires, cables and materials can be accurately and quickly measured.

The invention discloses a blue-green algae type discrimination method based on a three-dimensional fluorescence spectrum. The method comprises the following steps: S0, obtaining a to-be-detected sample; the method comprises the following steps: S1, acquiring original three-dimensional fluorescence spectrum data; s2, preprocessing the original three-dimensional fluorescence spectrum data; rayleigh scattering and Raman scattering in the original three-dimensional fluorescence spectrum are removed, and smooth denoising is carried out on the original three-dimensional fluorescence spectrum; s3, spectral features are extracted based on a parallel factor-peak ratio method, and spectral feature vectors are obtained; and S4, carrying out missing value supplement and data standardization processing on the obtained spectral feature vectors, inputting the processed feature vectors into a trained LightGBM multi-classifier, outputting probabilities by a classification model, and selecting a category with the maximum probability. According to the method, the distribution mechanism of the blue-green algae fluorescent pigment is combined, and the fluorescent component characteristics and the relative content characteristics of the fluorescent components of the blue-green algae are comprehensively considered, so that the three-dimensional fluorescence spectrum information of the blue-green algae sample can be more comprehensively, completely and meticulously represented, and the variety discrimination of different blue-green algae with smaller difference is realized.

The invention provides a precious metal analyzer accurate spot measurement method and system based on a micro-area x fluorescence spectrum, and the method comprises the steps: applying an X-ray light beam to a preset to-be-measured point, adjusting the focusing position of the X-ray light beam through a micro-light beam focusing technology, and generating a micro-area light spot which is aligned with the spot measurement position of the preset to-be-measured point in real time; collecting an X fluorescence signal generated by excitation of the micro-area light spot; performing dynamic noise suppression processing on the X fluorescence signal by using a preset adaptive digital filter to generate a denoised X fluorescence signal; inputting the de-noised X fluorescence signal into a preset spectrum peak value automatic identification algorithm to obtain characteristic peak value data; and matching the characteristic peak value data with a preset precious metal element type database to generate an element spot measurement result. According to the method, the composite problems of displacement drift, signal distortion and element misjudgment in microcell point measurement are solved, and the precision and efficiency of precious metal analysis are remarkably improved.

Provided are a coal testing system and method, and a storage medium. The system comprises: a detection unit, which is used for collecting spectral data of a coal sample under test; a training unit, which is used for constructing, by means of the spectral data, neural networks across neural network dimensions on the basis of a greedy search, and obtaining a coal sample test model by means of performing training on the basis of simulated samples which have been subjected to sample augmentation; and an analysis unit, which is used for executing fusion processing on the spectral data to obtain target spectral data, and on the basis of the coal sample test model, executing target spectral data inference to obtain a coal test result, wherein the detection unit comprises a near-infrared spectroscopy collection module and an X-ray fluorescence spectroscopy collection module.

The invention provides a sea surface oil film thickness measuring method based on optimized laser-induced fluorescence spectrum wave bands, and belongs to the technical field of spectral analysis. The method comprises the following steps: emitting an excitation light beam to a to-be-measured sea surface oil film by using a laser, and exciting the oil film to generate a fluorescence signal; receiving the fluorescence signal based on a spectrum acquisition device, and obtaining fluorescence spectrum data of the oil film; selecting a characteristic wave band from the fluorescence spectrum data through a sparse partial least square model, and predicting the thickness of the sea surface oil film based on the characteristic wave band in combination with Lasso regression; meanwhile, the selection of characteristic wave bands is optimized by using a hedera helix algorithm. According to the method, the characteristic wave band of oil film detection is selected through the sparse partial least square model, the oil film measurement characteristic wave band is efficiently and accurately obtained, full-wave band collection is avoided, and the detection efficiency is improved; and in combination with the hedera helix optimized sparse partial least square model, the accuracy of characteristic wave band selection and the generalization of the model are further improved, and the precision and efficiency of oil film thickness measurement are remarkably improved.

A digital-physical twin system and method for environmental process modeling and forecasting are disclosed. The system includes a digital twin server configured to receive environmental data from a real-world environment, including hyperspectral and spectroscopic imaging, simulate environmental process transitions using a predictive model based on the environmental data, and generate parameters for a physical experiment designed to validate or refine the predictive model. A physical twin device, comprising a scaled and instrumented representation of the real-world environment, is configured to execute the physical experiment under controlled conditions. Experimental data is returned to the digital twin to iteratively refine the predictive model in a closed-loop learning cycle using self-supervised and reinforcement learning. The system supports spatially-spectrally selective experimentation, including fluorescence spectroscopy, to enhance environmental sensing. This architecture enables scalable, sample-efficient modeling of processes such as post-wildfire hydrology, vegetation regrowth, and soil change.

The invention relates to a three-dimensional fluorescence spectrum image super-resolution reconstruction method for a water quality detection instrument, which is suitable for reconstructing a low-resolution and sparse three-dimensional fluorescence spectrum image collected by a portable water quality detection instrument into a high-resolution full spectrum. According to the method, an efficient super-resolution reconstruction network architecture composed of a feature extraction module, an up-sampling module and a trainable multi-Gaussian mixture point spread function is constructed. A joint loss function combining a reconstruction error and a degradation consistency error is designed, and a joint training strategy is adopted to synchronously optimize a network and PSF parameters, so that accurate reduction of a fluorescence peak shape structure and enhancement of spectrum authenticity are realized. The applicability and robustness of the portable water quality detection instrument based on the three-dimensional fluorescence spectrum in pollutant identification and analysis tasks are improved, and the portable water quality detection instrument is suitable for actual scenes such as intelligent water quality monitoring.

A water quality analysis system is calibrated without using any organic fluorescence material such as phenanthrene. The water quality analysis system analyzes a component to be measured contained in a liquid sample by a fluorescence spectroscopy, and that comprises a light irradiation section that irradiates the liquid sample with a light having an excitation wavelength of the component to be measured, a light detection section that detects a fluorescence of the component to be measured emitted from the liquid sample, a calculation unit that calculates a concentration of the component to be measured by using a fluorescence intensity obtained by the light detection section, and a solid fluorescence reference member that is arranged on an optical path between the light irradiation section and the light detection section and that emits the fluorescence by the light from the light irradiation section at a time of calibration.

The invention provides a copper concentrate sample traceability method based on X-ray fluorescence spectrum and isotope detection, and relates to the technical field of copper concentrate traceability. Data sampling is carried out on subjective factors and objective factors in the copper concentrate sample sampling process, data storage is carried out based on a block chain to improve the safety and accuracy of data, discretization, standardization, abnormal value correction and principal component analysis processing are carried out on the data analysis result of the copper concentrate sample, and therefore data standardization is guaranteed, and the accuracy of the data analysis result is improved. Based on a first mining area feature fingerprint database set obtained through historical data mining, determining a target mining area to which the copper concentrate sample belongs and a corresponding first similarity; by means of the technical scheme, standardization processing of the copper concentrate analysis result can be achieved, then the consistency rule is extracted, the confidence probability is given out for the traceability result in combination with the sampling process data, and therefore the obtained traceability result is more accurate.

The invention provides an urban rainwater pipe network mixed connection tracing method based on a pollution source fluorescence spectrum library in combination with machine learning. The method relates to the fields of numerous pollution sources around a pipe network, complex mixing and connecting conditions and the like. The method comprises the following steps: sampling through an inspection well to obtain a water sample; extracting an organic matter multi-dimensional characteristic matrix from the water sample by adopting technologies such as three-dimensional fluorescence spectroscopy (EEM) and the like; and carrying out fluorescence data analysis on the multi-dimensional characteristic matrix by using an NPLSDA (N-Way Partial Least Squares Discriminant Analysis) model, carrying out comparative analysis and identification, and determining the source of the pollution source. According to the technology, the specific source of mixed connection sewage of the rainwater pipe network can be quickly diagnosed through mixed connection rainwater well sampling analysis, and efficient and convenient decision support is provided for mixed connection transformation and traceability investigation of the urban rainwater pipe network.

The invention discloses an iron ore composition detection method and system based on machine learning, and relates to the technical field of data processing.The method comprises the steps that iron ore feature information of target iron ore to be detected is collected, and a plurality of iron ore samples are collected; performing drying and grinding parameter optimization according to the iron ore characteristic information, drying and grinding the plurality of iron ore samples, and collecting the particle size and mass in each drying and grinding process to obtain a plurality of particle size sequence sets and a plurality of mass sequences; preparing a plurality of detection samples, and performing sample quality analysis according to the optimized drying and grinding parameters, the plurality of particle size sequence sets and the plurality of quality sequences to obtain a plurality of sample quality coefficients; and carrying out X-ray fluorescence spectrum detection on the plurality of detection samples to obtain a plurality of spectrum detection results, and carrying out component identification on the plurality of spectrum detection results according to the plurality of sample mass coefficients to obtain an iron ore component detection result. The technical problem of poor iron ore component detection accuracy in the prior art is solved.

The invention belongs to the field of soil dissolved organic matter analysis, and provides a multispectral-based DOM detection and reclamation influence evaluation method, which comprises the following steps: step 1, collecting dry farmland soil samples of different reclamation years, and obtaining soil column samples of a plough layer, a plow pan and subsoil; 2, extracting, centrifuging and filtering the soil sample to obtain a DOM solution; 3, detecting the DOM solution by adopting an ultraviolet-visible absorption spectrum; step 4, adopting a three-dimensional fluorescence spectrum and combining parallel factor analysis to obtain fluorescence components and source characteristics of the DOM solution; step 5, analyzing molecular composition and characteristic parameters of the DOM solution by adopting solid-phase extraction in combination with Fourier transform ion cyclotron resonance mass spectrometry; and step 6, based on the detection data in the steps 3-5, evaluating the influence of reclamation years on the composition, structure and degradation characteristics of the DOM. According to the method, multi-dimensional detection of soil DOM is realized through a multi-spectrum and mass spectrum combined technology, and DOM is comprehensively analyzed from a macrostructure to molecular composition.

The invention belongs to the technical field of environmental monitoring, and particularly relates to a water pollution source identification method based on adaptive three-dimensional fluorescence spectrum (3D-EEM) analysis and a multi-scale convolutional neural network (ACNN-MS). Specifically, the invention provides a method combining a self-adaptive three-dimensional fluorescence spectrum analysis technology and a multi-scale convolutional neural network (MS-CNN), 3D-EEM data are obtained through an improved high-sensitivity three-dimensional fluorescence spectrometer, residual connection is introduced in model construction to avoid the gradient disappearance problem, and the method has the advantages that the method is simple and easy to implement, and the real-time performance of the 3D-EEM data is improved. And a new adaptive wavelength selection algorithm (AWSSA) is utilized to screen the most representative wavelength interval for high-precision calculation, so that the defects of high detection limit, low precision, need of opening a black box and difficulty in traceability positioning of a traditional spectrum and algorithm are overcome. According to the method, the pollution source can be efficiently positioned, the early warning information can be provided, and the quick response capability of the water body pollution event is remarkably improved.

The present invention relates to the field of the monitoring of photochemical cycloaddition reactions, and relates to a method for in-situ monitoring of the progress of a photochemical cycloaddition reaction by means of fluorescence spectroscopy. The method for in-situ monitoring of the progress of a photochemical cycloaddition reaction by means of fluorescence spectroscopy of the present invention comprises: preparing a fluorescent material that can be subjected to a photochemical cycloaddition reaction; causing the fluorescent material to be subjected to a photochemical cycloaddition reaction under the irradiation of ultraviolet visible light; monitoring a fluorescence change of the material by means of fluorescence spectroscopy; and performing in-situ monitoring on the photochemical cycloaddition reaction to monitor the reaction progress of the occurred photochemical reaction. Compared with conventional in-situ monitoring technology, the method for in-situ monitoring of the progress of a photochemical cycloaddition reaction by means of fluorescence spectroscopy has the advantages of being simple to operate, rapid and efficient, and easy to capture signals, having a wide range of application, and reducing the experimental costs and the operation difficulty.

In general, in one aspect, the invention features a method of fluorescence spectroscopy including providing a high-performance sensor that combines imaging with high intrinsic time resolution and high

This invention relates to a method, system, medium, and product for X-ray fluorescence spectrometry detection of bauxite, belonging to the field of bauxite detection technology. The method first acquires fluorescence spectral data and mining area environmental fingerprint data of the sample to be tested, and determines the initial concentration by background noise stripping. Then, a mineral phase interference network is constructed, and the interfacial radiation attenuation rate and secondary fluorescence excitation probability are calculated based on mineral phase characteristics to generate an interphase coupling interference matrix. Next, a stress field model is constructed based on environmental parameters, and the interference matrix is ​​corrected using the lattice distortion coefficient. The coexisting mineral phase combination is input into the environmental adaptive interference model to determine the nonlinear matrix effect deviation value. Finally, iterative compensation correction is performed until the difference between adjacent correction values ​​is less than a threshold, outputting accurate grade data of the target metal element. Implementing the technical solution provided in this application can improve the accuracy of X-ray fluorescence spectrometry detection of bauxite.

The present invention is a water quality analysis system (100) which performs calibration of the water quality analysis system without using organic fluorescent substances such as phenanthrene, and analyzes the measurement target components contained in a liquid sample using fluorescence spectroscopy. The water quality analysis system (100) comprises: a light irradiation unit (2) which irradiates the liquid sample with light having an excitation wavelength of the measurement target components; a light detection unit (3) which detects the fluorescence of the measurement target components from the liquid sample; a computing device (4) which uses the fluorescence intensity obtained by the light detection unit (3) to calculate the concentration of the measurement target components; and a solid fluorescent reference component (5) which is arranged in the light path between the light irradiation unit (2) and the light detection unit (3) during calibration and emits fluorescence due to the light of the light irradiation unit (2).

This application belongs to the field of agricultural technology, and discloses a method and system for dynamically evaluating the efficacy of herbicides. The efficacy is evaluated by two methods, namely, the colorimetric method and the plant fluorescence spectroscopy method. The weights of the two evaluation results are dynamically adjusted according to environmental parameters, and the final efficacy evaluation result is obtained by comprehensively combining the two evaluation results according to the weights, which can effectively overcome environmental interference and improve the accuracy and reliability of herbicide efficacy evaluation.

Multivariate machine learning (ML) techniques can be applied to an XRF spectra and mitigate matrix effects and enable simultaneous quantification of composition, even when markers elements or ions of interest are imperceptible in the XRF spectra. Physical (e.g., density) and chemical (e.g., total dissolved solids and hardness) properties of the material can be also quantified using ML techniques.

The present invention provides a method for measuring the major and minor components of porcelain insulators. The measuring method comprises the following steps: determining the measuring range of the main elements according to the chemical composition of each type of porcelain insulator. Selecting a plurality of standard samples according to the measuring range of the main elements. Performing X-ray fluorescence spectrum detection on the plurality of standard samples respectively to obtain a plurality of corresponding fluorescence spectrum data of the standard samples. Establishing a mapping relationship between the fluorescence spectrum value corresponding to each major element and the element content according to the plurality of corresponding fluorescence spectrum data of the standard samples. Determining the element content of each major element in the porcelain insulator to be tested based on the mapping relationship between the fluorescence spectrum value corresponding to each major element and the element content according to the fluorescence spectrum data of the object to be tested. The above-mentioned method for measuring the major and minor components of porcelain insulators is based on the method of X-ray fluorescence spectrum detection for determination, has short steps, short determination cycle, low intensity, and high accuracy and precision of the determination results.

This invention discloses a method for quantitative analysis of multiple components in oxidized slag based on X-ray fluorescence spectroscopy, belonging to the field of industrial process detection technology. The method includes: acquiring the raw energy spectrum data of a standard oxidized slag sample and the oxidized slag sample to be tested, and performing background subtraction and smoothing noise reduction; extracting the integral intensity of the characteristic peaks of target elements, establishing spectral interference correction relationships and matrix effect compensation relationships; establishing regression prediction models and residual compensation models for each target oxide based on the compensated characteristic intensities; inputting the characteristics of the sample to be tested into the models, and outputting the final predicted values ​​of each target oxide; improving the accuracy of feature information extraction in the multi-component system of oxidized slag, reducing errors caused by spectral overlap, excitation effects, and matrix effects, and improving the accuracy, result stability, and prediction consistency of quantitative analysis of multiple target oxides under different sample conditions, making it suitable for rapid detection and analysis of complex oxidized slag samples.

The invention discloses a photonic crystal fluorescence enhanced printing synthesis DNA chip cleaning effect verification method, which comprises: S1, preparing a photonic crystal microchip, and forming a hydrophobic substrate and a hydrophilic detection area; s2, building a chip test platform, collecting a reaction cleaning liquid to-be-tested sample within a specific time, and dropwise adding the to-be-tested sample to the surface of the photonic crystal microchip; s3, heating the photonic crystal microchip, and enriching the to-be-detected substance in the hydrophilic detection area after the sample solution is dried; s4, detecting the fluorescence spectrum signal emitted by the to-be-detected object through fluorescence spectrometry so as to realize detection of the to-be-detected object; and S5, judging the cleaning effect of the chip according to the change of the fluorescence intensity. According to the verification method, the chip cleaning effect can be judged according to the fluorescence intensity change, the method is also suitable for trace detection under the extremely low concentration condition, the sensitivity higher than that of a common substrate such as glass is achieved, the detection limit is reduced, and more accurate analysis under the low concentration condition is achieved.

The invention discloses a sulfur-nitrogen co-doped carbon quantum dot modified polyaspartic acid derivative as well as a preparation method and application thereof. The derivative is prepared by taking citric acid and L-cysteine as precursors, synthesizing sulfur-nitrogen co-doped carbon quantum dots (SN-CDs) with rich functional groups on the surface by adopting a hydrothermal method, and introducing the sulfur-nitrogen co-doped carbon quantum dots into a side chain of polyaspartic acid (PASP) through graft copolymerization. The derivative not only retains the complexing ability of polyaspartic acid with metal ions such as calcium and magnesium, but also has excellent scale and corrosion inhibition performance; the optical characteristics of the carbon quantum dots are inherited, and stable fluorescence can be emitted under the excitation of specific wavelength, so that in-situ and real-time concentration monitoring in an industrial circulating cooling water system by utilizing a fluorescence spectrum technology is supported. The product does not contain phosphorus, is environment-friendly, conforms to the development trend of a green water treatment technology, and provides an innovative material solution for intelligent monitoring and fine management of a circulating water system while improving the comprehensive performance of scale and corrosion inhibition.