281 results about "Discriminant model" patented technology

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention relates to an underground equipment fault early warning and diagnosis method based on big data analysis. The method is suitable for equipment operation state monitoring and intelligent diagnosis in underground operation scenes such as mines. The method comprises the following steps: collecting multi-source data such as an equipment running state, environment parameters and operation behaviors and preprocessing the multi-source data; multiple signal features are extracted and fused to construct a unified feature vector; performing health modeling by using the residual self-encoder model to generate a health index; an early warning threshold value is dynamically set through clustering analysis and Bayesian reasoning, and anomaly recognition is achieved; after early warning is triggered, fault type identification is carried out by adopting the fusion discrimination model; performing causal reasoning and maintenance suggestion generation based on the equipment fault knowledge graph; and continuously optimizing the model in combination with operation and maintenance feedback information, and constructing a closed-loop diagnosis mechanism. The method has the characteristics of high recognition precision, high response speed, explainable result and sustainable optimization of the model.

The invention relates to the technical field of artificial intelligence and natural language processing, and particularly discloses a text content generation method based on artificial intelligence, and the method comprises the steps: obtaining natural language text input, and extracting a semantic recognition feature vector; acquiring context state data and encoding the context state data into a state recognition feature vector; generating a fusion feature vector containing a semantic and state association relationship through fusion analysis; constructing a causal discrimination model based on the fusion features, and outputting the matching confidence of semantics and states; dynamically adjusting a generation strategy according to the confidence coefficient, if the matching degree is high, generating a standard text, otherwise, triggering an error correction mechanism to output a corrected text; and finally, performing logic consistency verification on the generated text to ensure that physical constraints, technological procedures and safety standards in the industrial field are met. According to the method, by introducing multi-level feature fusion, causal reasoning, intelligent error correction and rule verification mechanisms, context perception and safety controllability in the text generation process are achieved.

The invention relates to the field of tunnel engineering geology and support design, and discloses a weak surrounding rock tunnel deformation risk discrimination method based on shear expansion-shear constitutive, which comprises the following steps: constructing a nonlinear coupling model between a shear expansion angle and shear stress, normal stress and joint parameters, and obtaining the shear expansion angle; establishing a volumetric strain rate discrimination formula; inverting an initial crustal stress tensor field; reconstructing an irregular tunnel boundary; constructing a risk level discrimination model, and outputting a risk level; supporting schemes such as supporting rigidity, anchor rod parameters and spraying layer thickness are matched according to the risk grades; establishing a model to predict a risk trend; a support adjustment suggestion is generated; collecting monitoring data to dynamically correct model parameters; and all the modules are integrated in a deployment system. According to the method, the coupling relation between the shear expansion angle and the shear strength is introduced, the coupling type constitutive discrimination model is established, the risk grading system and the support correction strategy associated with the support response are constructed, and active early warning of the high-risk section and dynamic adjustment of the support rigidity are achieved.

The invention discloses a tank equipment sealing micro-leakage intelligent early warning and positioning method, which comprises the following steps of: deploying acoustic sensors, vibration sensors, gas concentration sensors, temperature and humidity sensors and the like aiming at different positions of a tank to realize multi-mode original signal acquisition; various signals are standardized and corrected through denoising, normalization and structural difference mapping, and the influence of the environment and the material structure on the signals is compensated; extracting multi-dimensional feature parameters, and carrying out space-time weighted fusion by adopting an attention mechanism to generate feature vectors marked by confidence; according to the method, a self-learning anomaly recognition algorithm is combined, a judgment model is continuously optimized through transfer learning and online sample accumulation, micro-leakage anomaly judgment is achieved, accurate positioning of a tank leakage source point is executed based on a physical propagation model, and the sensitivity and the self-adaptive capacity of micro-leakage detection are improved; and leakage identification and positioning requirements in a complex environment and a dynamic working condition can be effectively met.

The invention provides an HCG and LH test paper identification method and system based on optical characteristics and a storage medium. The method comprises the following steps: firstly, collecting and generating a reflectivity curve of a test paper detection area and a quality control area; secondly, extracting a reflection characteristic value of a curve in a detection area for test paper validity verification, and entering a core identification stage if the test paper is qualified; preliminary judgment is carried out by calculating a similarity deviation value of a quality control area curve and an HCG / LH standard curve, when the deviation is significant, a preliminary recognition result is generated and enters a cross validation link, and a result is confirmed by comparing a reflection characteristic value with a dynamic threshold range; when the similarity deviation is not obvious or the cross validation is not passed, starting a pre-trained discrimination model to perform final arbitration; by constructing a quadruple judgment mechanism of validity verification, preliminary recognition, cross validation and model arbitration, the operation complexity and misjudgment risk of manual test paper type selection are overcome, and the automation degree of the detection process and the accuracy and reliability of the recognition result are improved.

The invention relates to the cross technical field of signal processing and artificial intelligence, in particular to a multi-source noise removal method and system based on DAE, and the method comprises the steps: 1, carrying out the data collection and feature extraction of multi-source noise and pure signals; step 2, constructing a de-noising recognition knowledge base based on feature analysis; step 3, constructing a deep denoising auto-encoder model based on a knowledge base; step 4, hierarchical training and optimization guided by a mixed loss function of the deep denoising model; step 5, denoising processing of a target signal and output evaluation based on a discrimination model; according to the invention, noise data in multiple fields such as electromagnetism, remote sensing and biological signals are integrated, a dynamic mixing strategy and a data enhancement technology are adopted, a training set which highly simulates a real environment is constructed, and a unique cross-scene adaptation module can perform adaptive adjustment according to signal characteristics of different application scenes; the problem that a traditional method is poor in scene adaptability is solved.

The invention provides a cutting tool wear state identification and residual life prediction method based on a digital twinning technology, and relates to the technical field of intelligent tool health. The method comprises the following steps: constructing a cutting process physical model consistent with a machine tool-tool-workpiece system and process parameters, generating a virtual monitoring signal and a virtual degradation track, and comparing the virtual monitoring signal and the virtual degradation track with actually measured data of a multi-source sensor to form virtual-real synchronous monitoring residual features; constructing a likelihood function based on the residual error, and jointly calibrating cutter individual and material parameters through Bayesian updating to obtain an updated digital twinborn model accurately matched with the current working condition; on this basis, a corrected state discrimination model is constructed, stable recognition of the tool wear state is achieved, a random degradation process model is adopted for quantitative prediction of wear evolution and residual life, and the accuracy and robustness of tool health monitoring and life prediction are effectively improved.

The invention discloses a cheating detection method and system applied to online interview, relates to the technical field of computer vision, and solves the problems that in the prior art, multiple behavior characteristics in interview personnel are often adopted for monitoring in the whole process, multi-data analysis in the presence of non-cheating behaviors causes waste of computing resources, and the monitoring efficiency is high. And the cheating detection method is low in efficiency. A gaze anomaly coefficient is generated according to gaze data; when the fixation abnormal coefficient is greater than the abnormal threshold value, generating a multi-modal feature according to the monitoring video; generating a multi-modal decision weight according to the interview stage label; fusing the multi-modal decision-making weight and the multi-modal features to obtain multi-modal decision-making data; and inputting the multi-modal decision data into the cheating judgment model to obtain a monitoring result, carrying out tendency evaluation on cheating monitoring, and carrying out multi-modal dynamic fusion when the cheating tendency exists so as to accurately judge the cheating condition, so that the cheating monitoring efficiency and the cheating monitoring accuracy are improved.

The invention discloses a disturbance of consciousness electroencephalogram discrimination method and system adaptive to channel deletion. According to the method, on the basis of an electroencephalogram classification model of a deep network, automatic discrimination of disturbance of consciousness (MCS and UWS) is carried out with high accuracy according to resting-state electroencephalogram energy of a patient. The model utilizes a time domain double-branch CNN module and a space-time Transform module to learn features in electroencephalogram data hierarchically; and a model architecture with variable channel dimensions is introduced, an inter-channel association learning method fusing electrode position information and a training strategy of channel random discarding are introduced, so that the model can adapt to a scene in which part of channels are missing, and the clinical applicability of the method is improved.

The invention provides an ewe oestrus detection method and device based on multi-modal feature fusion, and relates to the technical field of livestock behavior monitoring, the method comprises the following steps: acquiring behavior data and audio data of an ewe to obtain a first behavior data set and a first audio data set; inputting the first behavior data set into an ewe behavior recognition model to obtain a second behavior data set, and inputting the first audio data set into an ewe sound detection model to obtain a second audio data set; performing feature extraction on the second behavior data set and the second audio data set to obtain a multi-modal feature fusion data set; and inputting the multi-modal feature fusion data set into an ewe oestrus discrimination model to obtain an ewe oestrus detection result. According to the ewe oestrus detection method based on multi-modal feature fusion, on the basis of fully learning the multi-modal features of the ewe, whether the ewe is oestrus or not is intelligently judged through the ewe oestrus judgment model, and the accuracy and efficiency of ewe oestrus detection are improved.

The invention belongs to the technical field of software engineering, and particularly relates to a unit test method for generating a discrimination model based on retrieval. Searching a to-be-tested function, namely a test case pair, and establishing a code library; constructing an efficient code retriever based on code embedding and mixed feature extraction; constructing a multi-input matching discriminator network, preparing an own data set, and training the network; integrating the code library, the retriever and the discriminator into a local knowledge base, and performing fine adjustment on the large model based on the local knowledge base to obtain a retrieval generation discrimination model; and generating a test case based on the retrieval generation discrimination model, and testing. According to the method, the discrimination model is generated by using retrieval, corresponding change is automatically carried out according to the previous test case, and the test with higher coverage rate and quicker speed can be realized.

The invention relates to the technical field of fire safety monitoring, and discloses a linear fire detection method and system based on infrared spectrum analysis and an infrared spectrum detector, and the method comprises the steps: arranging a plurality of infrared spectrum detector nodes; the collected infrared spectrum signals are preprocessed; mapping the preprocessed spectral signal to a pre-constructed low-dimensional feature space; calculating the conditional probability density of the current infrared spectrum signal in various states; judging whether the posterior probability exceeds a set fire judgment threshold value or not; the system comprises an infrared spectrum detection module, a preprocessing module, an embedded mapping module, a discrimination module and a linkage control module. The infrared spectrum detector comprises a spectrum sensing unit, a signal processing unit, a data communication unit and a data uploading interface. According to the method, the technical effects of keeping high sensitivity and low false alarm rate under complex and changeable working conditions are achieved through a combined discrimination model of embedding mapping, kernel density estimation and Bayesian posteriori.

The invention discloses a liquid qualification detection system and method, and belongs to the technical field of intelligent detection and data analysis, multi-dimensional response signals of to-be-detected liquid under excitation of different frequencies are obtained, and a multi-dimensional response data set containing optical, acoustic, electrical and thermal physical channels is constructed; extracting a liquid component behavior characteristic spectrogram through a multi-modal characteristic fusion module; a liquid qualification judgment model is constructed based on a clustering enhanced convolutional neural network and a multi-scale attention mechanism, and classification prediction of the liquid qualification state is realized; further combining confidence coefficient calculation with historical sample database comparison, performing adaptive calibration on a prediction result, and outputting a detection result and a risk level; if the detection result is unqualified or the risk level exceeds the threshold value, an alarm mechanism is automatically triggered, and abnormal feature parameters are marked; the method has the advantages of high detection precision, high response speed, high anomaly recognition capability, excellent traceability and the like, and is suitable for scenes of liquid quality control, environment detection, biological sample analysis and the like.

The invention provides a cross-omics sparse feature selection system and method based on hierarchical causal modeling, and the system comprises a data input and preprocessing module which is used for receiving multi-omics original data of a multivariate sample; the hierarchical causal structure learning module is connected with the data input and adaptive preprocessing module and is used for constructing a cross-omics hierarchical causal topology; the causal-oriented sparse feature selection module is connected with the hierarchical causal structure learning module; and the model retraining and integration module is used for constructing a three-layer weighted integration discrimination model based on the screened markers, optimizing the fusion weight of each layer through a gradient descent algorithm, and outputting a final prediction result. According to the method, the protein-metabolism biological hierarchy relationship and serum-urine complementary information are fully utilized, and the method has good generalization ability and can be widely applied to marker mining and prediction modeling of cancers, metabolic diseases and the like, so that the accuracy and reliability of precise medical treatment are improved.

The invention provides a low-voltage flexible DC system fault detection method based on complex domain analysis, and the method comprises the steps: obtaining and sampling a transient current signal in a low-voltage flexible DC system, fitting the transient current signal into a linear combination of a group of exponential functions, and obtaining a fitting exponential function; solving the fitting exponential function in a Z domain based on a Pade approximation method to obtain a complex index of the fitting exponential function; determining a fault threshold frequency according to the operation parameters of the low-voltage flexible DC system; the complex index is used as an analysis object, and the natural oscillation frequency of the low-voltage flexible direct-current system is obtained through formula calculation; and taking the fault threshold frequency as a state circle radius, constructing a fault discrimination model based on a complex plane, comparing the natural oscillation frequency with the fault threshold frequency, and outputting a fault detection result of the low-voltage flexible DC system in combination with a real part of a complex index. According to the method, the sensitivity to noise can be reduced, and meanwhile, the requirement of fault detection for speed is met.

The invention discloses a smart city public facility supervision method based on the Internet of Things, and the method comprises the steps: carrying out the multispectral collection and space marking of a surface block of a to-be-detected circuit board, and achieving the standardized preprocessing and physical feature extraction of multi-label high-dimensional pixel data; according to the method, material spectral features and surface texture data are combined, a clustering analysis and supervision discrimination model is used for accurately discriminating the type of a block material, material attributes and multi-spectral features are fused, and pixel-level thickness prediction and dynamic adaptive correction are realized through a machine learning model. The system can automatically monitor drifting of the material and the mapping relation, and performs real-time optimization on the mapping model based on periodic calibration data.

The invention discloses a complex nonlinear MIMU (Micro Inertial Measurement Unit) signal noise reduction method based on improved modal decomposition. Dynamic adaptive optimization of noise parameters is realized by introducing Hilbert spectrum analysis into a CEEMDAN (Complex Empirical Empirical Mode Decomposition Number) algorithm; an IMF component discrimination model is constructed based on multi-dimensional feature fusion, and accurate classification of IMF components is realized; and aiming at a discrimination result, adopting a hierarchical processing strategy of combining variational mode decomposition and empirical wavelet transform for different types of mode components to realize high-quality signal reconstruction. Compared with the prior art, the method has the advantages that the signal decomposition quality is remarkably improved, the multi-feature fusion discrimination model is excellent in performance when the boundary fuzzy region is processed, the problem of discontinuity of a traditional hard threshold method at the feature boundary is solved, the signal-to-noise ratio is remarkably improved, the root-mean-square error is greatly reduced, and the noise reduction effect is remarkably improved.

The invention discloses a fault positioning optimization method and system for relay protection of a new energy power grid, and belongs to the technical field of fault positioning optimization. The system comprises a data acquisition module, an initial model generation module, a feature set optimization module, a hyper-parameter optimization module, a model optimization module and a protection control module. The particle swarm optimization algorithm is utilized to perform hyper-parameter global search and optimize the penalty coefficient and the kernel parameter, then local optimization is performed through the genetic algorithm, the penalty coefficient and the kernel parameter of the SVM fault judgment model are cooperatively optimized, the particle swarm global search and the local optimization capability of the genetic algorithm are fused, so that the hyper-parameter of the support vector machine is efficiently adapted, and the fault judgment accuracy is improved. The nonlinear fault classification robustness is remarkably improved, and the problems that in the prior art, the model parameter optimization efficiency is low, and local optimum is prone to occurring are solved.

The invention provides a generation and discrimination collaborative optimization industrial visual detection system and method, and relates to the field of computer vision and artificial intelligence. The decoder comprises a unified encoder, a reconstruction decoder, a condition generation decoder and a discrimination head; the unified encoder is used for extracting general visual features based on the to-be-detected image; the reconstruction decoder is used for obtaining a reconstruction image without defects; the condition generation decoder is used for generating a defect image containing a specified defect; the discrimination head is used for identifying the reconstructed image and outputting confidence based on an identification result; training is carried out based on a defect image generated by a condition generation decoder, and a defect category and a category probability are generated; and the collaborative optimization module is used for constructing a collaborative loss function, a task loss function and a total loss function, reversely updating a discrimination head parameter based on a gradient generated by the total loss function, and generating a decoder parameter and a unified encoder parameter. The joint evolution of the generative model and the discrimination model is realized, and the performance of industrial visual inspection can be effectively improved.

The invention belongs to the technical field of analytical chemistry and pesticide residue detection, and relates to a phosphate hydrolysis nano-enzyme sensing array-based p-nitrophenol pesticide identification and detection method, which comprises the following steps of: performing five groups of parallel data matrixes on each type of p-nitrophenol pesticide to distinguish single / five types of p-nitrophenol pesticides with different concentrations; establishing a discrimination model of different concentrations / types of pesticides by using the data matrix, and obtaining an HCA map and an LDA score map of the p-nitrophenol pesticides through hierarchical clustering analysis and linear discriminant analysis; after the absorbance value of the sample to be detected is measured, the sample to be detected can be identified and detected through comparison. According to the present invention, by using the inherent dynamic difference existing during the catalytic hydrolysis of different p-nitrophenol pesticides by CeO2, a plurality of reaction time points are selected to measure the absorbance change, the time-resolved sensing array fingerprint is constructed, and the type identification and the concentration quantification of a variety of p-nitrophenol pesticides and the mixture thereof are achieved by combining the pattern identification algorithm, such that the sensitivity is high, and the detection result is accurate. The measurable concentration range is 1-50 mu g / mL.

The invention provides an attack technology and tactical identification method oriented to honey array linkage. The method comprises the following steps: coding according to malicious sample data and associated TTP labels, and constructing a training data set; constructing an interpretable discrimination model for binary classification modeling to obtain a behavior triggering weight vector and an offset item of the malicious sample; extracting dominant trigger rules to construct a mapping inference rule base; obtaining alarm data, extracting a behavior clue based on a preset standard behavior dimension set, converting the behavior clue to obtain a trigger identifier, and mapping the alarm data into an alarm behavior vector; constructing a prediction TTP label set of the alarm behavior vector and recording a trigger path; and encapsulating the behavior semantic data, constructing an execution strategy response rule set, performing mapping in the execution strategy response rule set according to the behavior semantic data to obtain an execution strategy, and adjusting honey array deployment according to the execution strategy. By applying the method, accurate identification and transparent reasoning of the potential TTP in the attack activity can be constructed and realized, and linkage scheduling of a defense system is supported.

The invention provides a data reasoning method and a data reasoning device, and the method comprises the steps: obtaining to-be-processed data, inputting the to-be-processed data into a pre-training encoder, and obtaining a first implicit feature outputted by the pre-training encoder; wherein the pre-training encoder is obtained by jointly training a data distribution transformation model and a discrimination model, and the first implicit feature obeys first continuous probability distribution; and inputting the first implicit feature into a decoder so as to restore the first implicit feature through the decoder to obtain first generated data. According to the method and the device, the traditional Mel spectrum is no longer used as an intermediate representation form, and the implicit features are used, so that the feature information of the original data can be better reserved, the generation capability of the model is further improved, the higher-precision and more diversified generation effects are realized, and the problem of information loss in the traditional generation model is solved.

The invention provides an intelligent prediction method and device for the nitrogen content of grape canopy leaves, equipment and a storage medium. Relates to the field of agricultural information technology and computer vision crossing technology. The method comprises the following steps: acquiring a grape canopy RGB image and a corresponding total nitrogen content, and constructing an image discrimination data set and a nitrogen content prediction data set after preprocessing; constructing and training an image discrimination model, and discriminating whether the input image is a target image containing a canopy; constructing and training a nitrogen content prediction model, and taking the canopy RGB image as an input and output nitrogen content feature map; during actual prediction, an input image is screened by the discrimination model, if the input image is a target image, the target image is sent to the prediction model, and a nitrogen content prediction result is output. According to the method, efficient and accurate intelligent prediction of the nitrogen content is realized through double-model cooperation.

The invention provides an intrusion detection method and system of a security intelligent access control, and relates to the technical field of artificial intelligence security. The method comprises the following steps: acquiring multi-mode state signals of an access control card, biological recognition verification, a door magnet, a door lock and the like, and judging whether an abnormal state exists or not; if the state is an abnormal state, combining a historical verification record and a state vector, and based on a time window feature extraction and integration discrimination model, identifying an unauthorized intrusion event; meanwhile, performing target detection and tracking on video data of the access control area, generating a target trajectory, analyzing behavior characteristics, and counting the number of effective targets and an abnormal time period; and fusing the access control abnormal time period and the video abnormal time period, and realizing intelligent identification and automatic alarm of suspicious intrusion behaviors through preset rule weight and time consistency judgment. Compared with the prior art, the method has the advantages that the judgment precision and the multi-target recognition capability of abnormal events in a complex intrusion scene are improved, and the intelligence and the safety protection level of the access control system are effectively enhanced.

The invention relates to the field of vulnerability evaluation of extreme rainfall induced mass landslide, in particular to an extreme rainfall mass landslide vulnerability evaluation method fusing diffusion generation and interpretable learning, which comprises the following steps: firstly, constructing a multi-dimensional factor data set, and realizing rasterization preprocessing; secondly, establishing a feature response analysis module, identifying main control factors of landslide occurrence and eliminating redundant variables; then, a denoising diffusion probability model is introduced, and an enhanced balance sample library is constructed; then, carrying out landslide probability prediction based on a multi-algorithm fusion integrated discrimination model, and outputting a grid-level landslide occurrence probability; and finally, minimizing intra-class variance and maximizing inter-class variance by using a natural breakpoint method, determining an optimal grading threshold set, and realizing landslide susceptibility grade division and spatial mapping output. Under the extreme rainfall condition, the method can effectively characterize the susceptibility zoning influence of the mass-occurring landslide flow slip catastrophe process, and achieves high-precision landslide susceptibility evaluation.

The invention discloses a self-assembly antibacterial polypeptide generation method based on a pepGPT model, a storage medium and equipment, and the method employs an MCTS model to carry out the following cycle processes: selecting a target node, expanding child nodes of the target node based on the pepGPT model, simulating and generating a random sequence, and evaluating and obtaining a reward value of the random sequence based on a discrimination model. Performing back propagation to update statistical information of each node on the search path; and ending until a preset time or a preset number of iterations is reached, and outputting the self-assembled antibacterial polypeptide meeting a preset reward value by the MCTS model. According to the method, intelligent prediction of the multifunctional polypeptide is realized, and the search and generation efficiency and classification accuracy of the self-assembled antibacterial polypeptide are improved.

The invention provides an urban built-up area air quality space-time evolution and exposure risk prediction method, and relates to the technical field of electrical digital data processing.The space-time evolution method comprises the steps that a to-be-monitored target area is discretized into a cubic sampling domain, an execution flight path of an unmanned aerial vehicle is planned, and a sparse data set containing five-dimensional features is constructed; executing iterative up-sampling operation, and converting the sparse data set into a dense feature point set covering a sampling domain; calculating a pollutant transmission flux vector of each node in the dense feature point set, constructing a weighted reverse tracking vector pointing to a suspected source, and constructing a three-dimensional traceability probability cone in combination with turbulence intensity; extracting a centroid offset coefficient and an axial dispersion, and identifying an emission working condition label of the pollution source through an emission working condition ternary discrimination model; executing multi-source probability field superposition fusion, and determining an estimation coordinate of a pollution source; and executing forward space-time diffusion simulation based on the estimated coordinates, and outputting a three-dimensional risk early warning graph.

The invention discloses a large yellow croaker origin traceability discrimination method based on an element and FTIR fusion fingerprint spectrum. The method comprises the following steps: S1, sample treatment; s2, data acquisition: S2.1, mineral element data acquisition, and S2.2, FTIR spectrum acquisition; and S3, data analysis. Precise classification of production areas can be realized through principal component analysis (PCA), clustering analysis (CA) and linear discriminant analysis (LDA), the overall discriminant accuracy of back substitution inspection of the discriminant model reaches 100%, and the overall discriminant accuracy of cross inspection reaches 97.3%. The element and infrared spectrum fusion fingerprint technology is applied to the source tracing of the large yellow croaker producing area, and the application prospect is wide.

The invention discloses a screening method of mango pulp processing mode markers and an identification method of mango pulp processing modes, and relates to the technical field of analytical chemistry. According to the method, non-targeted metabonomics is combined with quasi-targeted metabonomics and targeted quantitative analysis, so that the characteristic markers capable of accurately distinguishing mango pulp in different processing modes are systematically screened and verified for the first time. The feature marker screened by the method can be used for constructing a discrimination model of the mango pulp processing mode, high-precision and high-reliability processing mode discrimination is realized based on the established multi-model fusion discrimination method, the discrimination efficiency and accuracy of the mango pulp processing mode are greatly improved, and the method is suitable for popularization and application. And a new technical means and theoretical basis are provided for product quality control and market supervision.