22313results about "Component separation" patented technology

The invention discloses a traditional Chinese medicine identification and analysis system based on clustering analysis, and relates to the technical field of medicine analysis and identification, and the system comprises the following modules: a data acquisition module, which is used for obtaining traditional Chinese medicine multi-modal data and processing asynchronous stream data, the multi-modal data comprises microscopic images, spectral data, metabonomics data and electronic nose / tongue sensing data, and the step of processing the asynchronous stream data comprises timestamp marking, multi-device data calibration and feature caching. According to the traditional Chinese medicine identification and analysis system based on clustering analysis, through collaborative design of a multi-modal hypergraph space-time encoder and a drug property causal intervention mechanism, multi-dimensional and multi-level leap-type improvement of a traditional Chinese medicine identification technology is realized, and the core contradiction of disjunction of a traditional clustering algorithm and domain knowledge is solved; and a universal framework of cross-modal dynamic learning is provided for the field of medical informatics.

The invention belongs to the technical field of atomized liquid component analysis, and discloses an intelligent analysis system and method based on atomized liquid components, and the method comprises the following steps: carrying out multi-parameter sampling on atomized liquid to obtain pyrolysis data; performing temperature gradient analysis to generate a pyrolysis spectrogram; identifying trace markers to generate a feature library; analyzing the component evolution track to generate a dynamic evolution spectrum; carrying out partition demarcation processing to generate an atomization characteristic spectrum; performing thermodynamic analysis to generate a reaction map; analyzing component diffusion to generate quantized data; constructing a sensory response prediction engine; performing intelligent analysis to generate response feature data; evaluating and generating a quality report; and performing optimization processing to generate formula suggestions and executing accurate blending. According to the method, accurate analysis and evaluation of atomized liquid components are achieved through multi-dimensional analysis, a scientific basis is provided for product optimization, and safety and taste experience are effectively balanced.

The invention relates to the technical field of soil pollution component analysis, and discloses an agricultural soil pollution prediction method. The method comprises the following steps: carrying out soil physicochemical property detection through layered gridding sampling, determining the heavy metal content, the organic matter content and the enzyme activity, and obtaining a pollution characteristic data set; monitoring diffusion characteristics of pollutants by adopting an electrochemical sensing detection array, and generating a migration rule model; a multi-parameter monitoring system is used for detecting the pH value, the oxidation-reduction potential and the ion activity, and a dynamic change sequence is formed; establishing a colloid interface action response matrix, and constructing a pollutant accumulation model; determining the occurrence form and the ion concentration of the pollutants, and generating an interaction map; and finally, constructing an early warning system, and outputting a prevention and control scheme. According to the invention, through cooperative application of multiple analysis and test means, and in combination with interface action mechanism research of pollutants in soil, whole-process intelligent monitoring of soil pollution prediction is realized.

The invention aims to provide a method and a device for determining the peak area of chloride ions based on ion chromatography. The method for determining the peak area of chloride ions based on ion chromatography comprises the following steps: step 1, identifying a chromatographic peak: peak separation, peak qualitation, baseline correction, peak trailing treatment and peak overlapping treatment; and step 2, calculating the chromatographic peak area: peak definition, integration algorithm, calibration curve application, repeatability verification, impurity peak deduction and main peak area calculation. According to the method, a chromatographic peak identification and chromatographic peak area calculation mode is designed, so that the technical problem that other impurity ions may be introduced to influence chloride ion chromatographic peak identification and accurate area calculation in an existing mode of determining organic chlorine through ion chromatography is solved.

The invention discloses a method for comprehensively identifying PFAS in an environment by combining targeted analysis, suspicious screening and non-targeted identification. The method comprises the following steps: S1, carrying out liquid chromatography-high resolution mass spectrometry fullscan / DDA / DIA analysis on an environmental sample; s2, carrying out liquid chromatography-high resolution mass spectrometry analysis on the plurality of PFAS standard substances; s3, according to a public chemical database and literature, collecting names and molecular formulas of a plurality of PFASs, calculating an accurate mass number, and jointly establishing a suspicious screening database in combination with an open-source MS2 spectrogram library; s4, establishing a non-targeted identification work flow by combining the mass number characteristics of the PFAS, the characteristic MS2 fragments, the characteristic neutral loss and the homolog characteristics; and S5, integrating the targeted analysis compound, the candidate compound subjected to suspicious screening and the candidate compound subjected to non-targeted identification, and comprehensively identifying the PFAS in the environmental sample. According to the method disclosed by the invention, the known and unknown perfluoroalkyl / polyfluoroalkyl compounds are fully identified through three complementary analysis technologies.

The present invention discloses a method for detecting and applying C1-C2-alkyl-substituted phenol isomers, which relates to the technical field of environmental detection and includes: selecting a corresponding sample pretreatment process to prepare a test sample according to the petroleum content range in the sample; under a mobile phase of methanol-5.0-6.5 mmol / L ammonia water, using LC-MS to detect the test sample to obtain the concentration of the first component and the standardized concentrations of co-eluted 3,5-dimethylphenol and 2,6-dimethylphenol; under a mobile phase of acetonitrile-4.0-10.0 mmol / L ammonia water, using LC-MS to detect the test sample to obtain the concentration of 3,5-dimethylphenol, or the standardized concentrations of co-eluted 3,5-methylphenol and 3-ethylphenol, or the standardized concentrations of co-eluted 2,6-dimethylphenol and 2,4-dimethylphenol; under a mobile phase of methanol-5.0-6.5 mmol / L ammonia water or acetonitrile-4.0-10.0 mmol / L ammonia water, using LC-MS to separately detect the calibration samples of the co-eluted components, and calculate the conversion factor of the co-eluted components and the concentration of the other component. The present invention realizes the accurate quantification of phenol and 12 C1-C2-alkyl-substituted phenol isomers.

The invention discloses an online multi-channel rapid gas chromatographic analysis system and method, particularly relates to the technical field of gas chromatographic analysis, and is used for solving the problems of baseline noise rise and trace component detection precision reduction caused by physical coupling and signal interference when multiple detectors work in parallel in the prior art. Interference signal features of adjacent detection channels are extracted, a chaotic or linear compensation signal is dynamically generated in combination with harmonic primary and secondary sorting and recursive quantization factor analysis, and common-mode interference of a target channel is suppressed. Electromagnetic isolation parameters of a shielding structure are adjusted in real time based on the baseline signal after noise suppression, and accurate matching of impedance and interference spectrum is achieved; further separating time domain overlapped signals and analyzing asymmetric distortion characteristics, and eliminating system errors through normalization calibration; dynamic interference suppression, electromagnetic shielding optimization and signal distortion traceability are combined, the anti-interference capability and analysis precision of multi-channel detection are remarkably improved, and the method is suitable for high-throughput rapid detection scenes of complex samples.

The invention relates to the technical field of chromatographic analysis, in particular to a glycolide content detection method based on liquid chromatography, which comprises the following steps: preparing a to-be-detected sample solution and diluting the to-be-detected sample solution into standard stock solutions with different concentrations, and respectively obtaining a to-be-detected chromatogram and standard chromatograms with different concentrations through a liquid chromatograph; determining the baseline deflection of the to-be-detected chromatogram; obtaining a target chromatographic peak in the to-be-detected chromatogram and a corresponding chromatographic peak curve; calculating a disturbed evaluation value, an asymmetry coefficient, a residual interference degree and a discrimination coefficient of the to-be-detected chromatogram, and evaluating the to-be-detected chromatogram; obtaining a fitting function according to different concentrations and the change relation between the peak areas corresponding to the chromatographic peaks in the corresponding standard chromatograms, and determining the glycolide content by combining the peak area corresponding to the target chromatographic peak. According to the method, the detection precision of the glycolide content can be improved, and the glycolide content can be measured more accurately.

The present invention discloses a characterization method and application of C0-C2-alkyl-substituted phenols, relating to the technical field of environmental detection. The method includes: selecting a corresponding sample pretreatment process to prepare a test sample according to the petroleum content range in the sample; detecting the test sample by LC-MS under a mobile phase system of methanol-5.0-6.5 mmol / L ammonia water to obtain the concentration of the first component and the normalized concentrations of co-eluted 3,5-dimethylphenol and 2,6-dimethylphenol; detecting the test sample by LC-MS under a mobile phase system of methanol-7.0-11.5 mmol / L ammonia water to obtain the concentration of 3,5-dimethylphenol; detecting calibration samples of 3,5-dimethylphenol and 2,6-dimethylphenol respectively by LC-MS under a mobile phase system of methanol-5.0-6.5 mmol / L ammonia water, calculating a conversion factor, and obtaining the concentration of 2,6-dimethylphenol; and obtaining the contents of 13 single components of C0-C2-alkyl-substituted phenols based on the concentrations of the first component, 3,5-dimethylphenol and 2,6-dimethylphenol. The present invention realizes the quantitative characterization of 13 single components of C0-C2-alkyl-substituted phenols.

The invention discloses a liquid chromatogram flow velocity real-time detection and optimal control method based on multi-point sensing. The method comprises the following steps: S1, constructing a distributed acquisition network; s2, constructing a fluid transmission topological graph and generating a multi-point flow velocity time sequence; s3, inputting the fluid transmission topological graph and the multi-point flow velocity time sequence into a FlowFormer model to generate a flow velocity evolution prediction map; s4, executing local gradient scanning, and generating an optimization control objective function; s5, calling a self-adaptive fox swarm algorithm controller, inputting a flow velocity evolution prediction map and an optimization control objective function, and generating an optimal adjustment strategy; s6, the control parameters of the liquid chromatography system are adjusted in real time and fed back to the FlowFormer model; and S7, when abnormal flow velocity change is detected, triggering an adaptive fox swarm algorithm controller to execute a local escape strategy. According to the invention, multi-point sensing and an intelligent optimization algorithm are fused, and real-time detection and optimal control of the liquid chromatogram flow velocity are realized.

The invention provides a chromatography-mass spectrometry automatic integration method, system, equipment and medium for multiple sample items, and the automatic integration method comprises the following steps: converting an original spectrogram of a chromatography-mass spectrometry to generate a standardized spectrogram picture; performing peak recognition on the standardized spectrogram picture by using a trained deep learning peak recognition algorithm to obtain a candidate peak classification result; screening the candidate peak classification results based on the spectrogram classification result to obtain a preliminary integration result; performing peak clustering on the multi-sample items; carrying out peak recall on samples without peak reservation; and carrying out consistency correction on all reserved peaks. According to the method, deep learning model prediction is carried out on the chromatography-mass spectrometry image, peak clustering, peak recall and peak boundary inconsistency correction are carried out in combination with multi-sample data, manual judgment on the peak position and the peak shape of the chromatography-mass spectrometry can be replaced, and the method is high in accuracy, high in specificity and high in accuracy. And the false positive of the conventional automatic integration method is effectively reduced.

The invention discloses an organic sample analysis method based on micro gas chromatography and a chromatographic analyzer, particularly relates to the technical field of micro gas chromatography, and is used for solving the problems of unstable separation performance and low complex sample detection precision caused by insufficient uniformity of a stationary phase coating of an existing micro chromatographic column. The method comprises the following steps: constructing a stationary phase distribution initial profile by continuously pre-charging carrier gas, alternately applying segmented temperature gradient, micro-negative pressure and directional pulse flow to remodel a uniform distribution layer, generating a correction factor set of a temperature-resistance coupling relationship by combining a correction mixture and a tracer gas pulse, dynamically adjusting a carrier gas flow velocity and a temperature program to generate a matched operation curve, and finally, carrying out dynamic calibration. High-precision separation detection and quantitative analysis are achieved, stable separation performance is maintained in a miniaturized structure by cooperatively controlling stationary phase distribution optimization and real-time parameter feedback correction, and the detection precision and reproducibility of a wide-boiling-range organic mixture are improved.

The invention discloses a greenhouse gas monitoring and direct discharge flux accounting method for a sewage treatment plant. The method mainly comprises the following steps: distributing points along a sewage treatment path according to a point distribution principle; installing a floating gas collecting device, and connecting the floating gas collecting device with an online detector or an air sampler; on-line or off-line measurement modes are selected according to different working conditions of the sewage treatment plant; monitoring temperature, on-site wind speed, air pressure, gas flow and pipeline pressure; the direct emission flux of the greenhouse gas is corrected and calculated on the basis of measured data and a formula provided by the invention. According to the method, the direct emission flux of the greenhouse gas of the sewage treatment plant can be flexibly and accurately calculated, the greenhouse gas emission unit is identified, and the targeted carbon reduction work of the sewage treatment plant is effectively guided.

The invention provides a method and a system for screening and constructing a small molecule polypeptide simulant based on interaction between proteins, relates to the technical field of biology, and comprises the following steps: predicting an interaction model of RECK protein and ADAM10 protein through a molecular docking platform, and analyzing interaction sites of the RECK protein and the ADAM10 protein. And then, determining a KAZAL region of the RECK protein according to the interaction site, and predicting a sequence structure of the KAZAL region. And then carrying out molecular docking on the KAZAL region and ADAM10 protein, designing a plurality of peptide fragments with the length of 30 amino acids, and screening the peptide fragment sequence with the highest score as candidate polypeptide. And finally, performing species homology comparison and quality control detection on the candidate polypeptide, and verifying the binding capacity and biological function of the candidate polypeptide to ADAM10 protein through cell experiments. The invention provides a method for screening to obtain a polypeptide simulant specifically bound with ADAM10 protein. The polypeptide simulant can be used for treating ADAM10 related diseases.

The invention provides a method for determining hydrocarbon components in mixed aromatic hydrocarbons by using comprehensive two-dimensional gas chromatography-mass spectrometry. The method comprises the following steps: optimizing a target function to make a chromatographic peak signal obtained by a separation model accord with a chromatographic separation process mechanism, fully utilizing complementary information of chromatography and mass spectrometry, improving separation precision, and guiding analysis of complex aliasing peaks; analyzing overlapped chromatographic peaks determined by principal component analysis by adopting the established separation model to obtain a chromatographic peak signal representing a single component after separation, and matching the chromatographic peak signal with a corresponding mass spectrum; in combination with a single chromatographic peak signal and a mass spectrum obtained through separation, according to characteristic fragments and chromatographic retention time in the mass spectrum, component types corresponding to chromatographic peaks are judged, and characterization of the composition of the mixed aromatic hydrocarbon is achieved; a separated single chromatographic peak is subjected to quantitative analysis, the content of each component is calculated by adopting an external standard method in contrast with a standard curve, a quantitative result of the mixed aromatic hydrocarbon sample is obtained, and the measurement precision of the mass fraction of each hydrocarbon component is improved.

The invention discloses a method for constructing a true and adulterated wine discrimination model, and particularly relates to the technical field of wine detection. The method comprises the following steps: extracting component data, a spectrum curve, smell fingerprints and texture information of wine liquid through nondestructive testing, carrying out standardization processing and feature fusion on multi-modal data, extracting distribution similarity features and domain offset robustness features, constructing a comprehensive feature vector, and evaluating a generalization ability value of a true and adulterated wine discrimination model by utilizing a machine learning model; aiming at a low generalization ability model, adopting a transfer learning method to carry out multi-stage training and optimization, and evaluating the detection accuracy of the model on unseen brands and batches in combination with cross validation; and finally, inputting the multi-modal characteristics of the to-be-detected wine on the optimized model, and outputting a true and false wine judgment result and a confidence score. According to the invention, the stability and adaptability of the true and adulterated wine discrimination model can be significantly improved, and it is ensured that the method has high accuracy, high generalization ability and wide market applicability in detection of wine of different brands and batches.

The invention relates to the technical field of biomarkers, in particular to a blood biomarker combination for prediction and auxiliary diagnosis of ovarian reserve hypofunction (DOR), a corresponding detection kit and application of the blood biomarker combination. According to the invention, the content change of the nicotinamide mononucleotide (NMN) and the metabolite combination thereof in serum or plasma is screened for the first time and can be used as a detection index for predicting the ovarian reserve function decline. Based on the biomarker combination, the invention further provides an in-vitro detection kit containing a related standard substance, an internal standard solution and an extracting solution, and a data analysis method combined with a random forest model, so that an individual with a normal ovarian reserve function and an individual with hypofunction can be accurately distinguished. The method is noninvasive, simple, convenient, high in sensitivity and suitable for early recognition and layered screening of clinical ovarian reserve hypofunction.

The invention relates to the field of methane change dynamic traceability analysis, and discloses a methane change dynamic traceability analysis and early warning system based on artificial intelligence, and the system comprises a data collection module which collects methane concentration data, constructs a two-state three-dimensional monitoring network, and calculates a relative standard deviation; the multi-source analysis module is used for merging calibration environment data, generating a three-dimensional matrix, iteratively updating a class cluster mean value and judging whether a burst leakage traceability process is triggered or not; capturing a time sequence dependency relationship, adding abnormal data optimization, analyzing and triggering residual drive and retraining; the anomaly recognition module is used for constructing a four-dimensional source feature matrix and dynamically adjusting the source feature weight; constructing an observation data matrix, and solving the contribution proportion of each source to the methane concentration; iteratively optimizing the source intensity parameter until convergence; and the early warning response module is used for dynamically updating a historical residual mean value and a standard deviation, judging a response level, reversely tracking an air mass track, positioning a potential emission source coordinate, and triggering a corresponding early warning response if an abnormal degree condition is met.

The invention discloses a propolis liquid quality detection data processing method and system, and relates to the technical field of data processing.The method comprises the steps that spectral data of a batch of propolis liquid to be detected is obtained, the content of symbolic components is analyzed, and production stage parameters are obtained; the technical key points are as follows: dynamic correction of a threshold value is realized through simulation feedback, a balance point is found between detection accuracy and efficiency, on one hand, a correction coefficient and feedback rate comparison mechanism is introduced, and whether threshold value correction needs to be performed or not is intelligently judged according to a simulation feedback result; the problem that the detection efficiency is reduced due to redundant correction actions is effectively avoided, and simplification and high efficiency of the detection process are ensured; and on the other hand, when the correction feedback displays a better result, secondary correction can be accurately performed on the adaptive threshold value, and the adaptive adjustment mechanism enables the threshold value setting to be closer to the detection requirement, so that the influence of parameter adjustment on the adaptive threshold value is reflected more comprehensively, and the detection accuracy is further improved.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention relates to the technical field of natural gas energy management and safety monitoring, and discloses a safety detection system and method suitable for natural gas hydrogen doping, and the system comprises a data collection module which is arranged at a key node of a natural gas pipeline network, and is used for collecting the spectral data, temperature, humidity and pressure parameters of a natural gas hydrogen-doped mixed gas; the edge calculation module is arranged in the nodes of the data acquisition module and is used for carrying out real-time preprocessing, compression and aggregation on the acquired data; the central data analysis module communicates with the edge calculation module and is used for receiving and analyzing the processed data and carrying out gas component calculation and anomaly detection; and the calibration module is used for adjusting sampling parameters of the sensor based on the real-time environment parameters to ensure the detection precision. According to the invention, multi-mode real-time monitoring, dynamic calibration, efficient anomaly detection and intelligent safety feedback are realized, and the detection precision, response speed and system safety under complex working conditions are improved.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

Provided herein are methods and systems for diagnosing pathological conditions using machine learning and artificial intelligence models. An exemplary method can include loading an abundance matrix that represents mass spectrometry reads of a sample of a plurality of volatile organic compounds (VOCs) extracted from a biological sample. The abundance matrix can represent each of the plurality of VOCs in a mass-to-charge ratio dimension, an abundance dimension, and a retention time dimension. The method can include providing the abundance matrix to a machine-learning model. The machine- learning model can be trained on abundance matrixes of biomarkers of VOCs collected from healthy and diseased subjects. The method can include receiving, from the machine-learning model, a prediction of a state of a disease state.

The invention discloses an unconventional feed quality evaluation grading method and system, and particularly relates to the technical field of feed quality evaluation. Conventional nutrients, functional nutrients and harmful substance components are comprehensively analyzed through a high-precision detection technology; constructing a multi-factor synergistic toxicity evaluation model, quantifying the synergistic effect of the various harmful substances based on the toxicity equivalent factors and a matrix interaction analysis method, and outputting a synergistic toxicity index; the overall quality of feed samples is evaluated by combining detection results and toxicity indexes, the feed samples are classified into high-quality, qualified and unqualified grades, targeted application suggestions and risk control measures are provided, a cloud database is constructed to record evaluation data, and an Internet of Things sensor is used for monitoring supply chain quality fluctuation in real time, automatically identifying abnormities and triggering early warning. Dynamic management of feed quality is achieved, scientificity and accuracy of quality evaluation of unconventional feed are effectively improved, potential safety hazards are reduced, and safe popularization and efficient application of the system in the breeding industry are promoted.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention discloses a liquid chromatography-tandem mass spectrometry detection method and system, and the method comprises the steps: designing a multi-dimensional gradient elution program according to the physicochemical properties of a target compound, and obtaining a chromatographic separation spectrogram; performing efficient ionization on the target compound according to the chromatographic separation spectrogram to obtain an ionization signal; according to the ionization signal, fragmenting the target ions, and obtaining a fragment ion spectrogram through fragment ion screening; according to the fragment ion spectrogram, a multivariable data analysis algorithm based on machine learning is adopted, a standard curve of a target compound and an interferent database are combined, quantitative analysis is carried out, and a quantitative result is generated through a dynamic interference correction technology in combination with principal component analysis and a partial least square method. By utilizing the embodiment of the invention, the sensitivity, the accuracy and the real-time performance of detection can be improved through multi-dimensional gradient elution, dynamic ionization and fragmentation parameter optimization in combination with an analysis means of machine learning.

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention discloses an application of an electronic waste pollution human body exposure fingerprint spectrum in exposure risk prediction, and the human body exposure fingerprint spectrum is composed of more than three of exposure markers of 2, 4, 6-tribromophenol, 2, 4, 5-trichlorophenol, 4-bromophenol, 1-hydroxypyrene and bisphenol A. The invention further discloses an application of the electronic waste pollution human body exposure fingerprint spectrum in exposure risk prediction. According to the method, a systematic method of urine pollutant exposure omics is adopted for the first time, comprehensive screening and characterization of pollutant exposure markers related to specific components and by-products are achieved in electronic waste exposure worker groups, and the method is the most systematic organic pollutant exposure research known in the field at present.

The present invention discloses a cloud-based intelligent management method for industrial supervision of biotechnology products, which relates to the field of information technology. The method includes S1, obtaining characteristic parameter data of biotechnology products, collecting multidimensional data in real time from the production process, S2, using a distributed computing architecture, parallel processing of characteristic parameter data, dividing computing tasks in the cloud system, and distributing them to multiple computing nodes, S3, according to a preset molecular level analysis algorithm, deep mining of characteristic parameter data, analyzing the changing trend of active ingredients and dynamic structural characteristics, and evaluating molecular structure stability indicators; the cloud-based intelligent management method for industrial supervision of biotechnology products dynamically updates characteristic maps, optimizes fluctuation trend prediction, and maintains the accuracy and stability of the monitoring model. The method effectively realizes real-time monitoring and analysis of biotechnology products, and provides an intelligent solution for industrial supervision.

The invention relates to the technical field of liquid chromatography-mass spectrometry series connection, and particularly discloses a method for detecting folic acid metabolites of red blood cells through the liquid chromatography-mass spectrometry series connection technology. And an image analysis algorithm based on artificial intelligence and deep learning is utilized to extract a chromatographic separation state, so that gradient parameters are dynamically adjusted, the requirement of manual intervention is reduced, and the separation process of a target object in a complex matrix is favorably optimized. Particularly, key features in a chromatogram can be automatically identified and quantified based on a deep learning method, and gradient elution parameters are intelligently and dynamically adjusted according to the information, so that more optimized substance separation and detection can be realized. For example, if separation of a certain component is not ideal, the separation effect of the component can be improved by adjusting the proportion of the mobile phase B, so that the gradient elution process is optimized.