32 results about "Multivariate statistical" patented technology

The invention relates to the technical field of industrial process soft measurement and quality control, and discloses a paper quality prediction method based on multivariate statistical latent variable fusion and space-time transformation, which comprises the following steps: acquiring space-time sequence data of a multi-source sensor in a papermaking process, constructing a space-time diagram structure reflecting a topological relation of equipment, and preprocessing. Then, multi-view latent variables are extracted through non-negative matrix factorization, independent component analysis and robust principal component analysis, attention fusion is conducted on the latent variables through an LV fusion module, and fusion latent variables are obtained; and inputting the fusion latent variable and original node data into a multi-scale convolution auto-encoder to obtain spatial feature embedding, and inputting the spatial feature embedding and the fusion latent variable into a space-time Transform module together to realize joint modeling of space correlation and time dependence. And finally, outputting a paper quality predicted value through a linear regression module. The method can achieve the accurate prediction of the paper quality under a high-dimensional and multi-noise working condition, and is suitable for online monitoring and modeling updating.

The application discloses a method for identifying original tea tree varieties by using characteristic metabolites, and particularly relates to the field of identifying tea tree varieties, which comprises the steps of sample preparation, metabolite extraction, liquid chromatography-mass spectrometry detection, data correction processing, characteristic metabolite construction and multivariate statistical clustering determination, etc.; in view of the internal metabolic characteristics formed in the long-term natural domestication process of the floating Liang chestnut leaf population, a high-dimensional characteristic metabolite vector is constructed by using the stable numerical distribution mode of the fresh leaves of the floating Liang chestnut on the sixteen characteristic metabolites; after the peak intensity data of each sample is corrected, filtered and structured, the obtained characteristic vector is input into the Euclidean distance hierarchical clustering model, so that the metabolic composition difference between the samples to be identified and the standard floating Liang chestnut leaf population is unsupervised statistically distinguished.

The embodiment of the invention discloses a multi-time-scale prediction method and system for cyanobacterial bloom, and the method comprises the steps: obtaining a cyanobacterial bloom spatial-temporal distribution prediction result through a pre-constructed hydrodynamic water quality bloom model, and obtaining a cyanobacterial bloom spatial-temporal distribution prediction result through a pre-constructed prediction model based on data driving based on a current chlorophyll a concentration sequence. Obtaining a chlorophyll a concentration prediction value sequence, and finally generating cyanobacterial bloom prediction information of the first time scale. And based on weather forecast data, obtaining a chlorophyll a concentration predicted value of a second time scale through a hydrodynamic water quality and water bloom model. Based on the historical environment monitoring data, through a pre-constructed multivariate statistical regression model, obtaining a cyanobacterial bloom intensity index; based on a historical chlorophyll a concentration monitoring sequence, a cyanobacterial bloom prediction result of a third time scale is finally obtained by analyzing a chlorophyll a concentration periodic change rule; the predicted periods of the first time scale, the second time scale and the third time scale are increased in sequence.

The application discloses a process quality detection method, system, equipment and medium based on wavelet packet decomposition and T2 control chart, which comprises the following steps: collecting original signals of multiple production characteristics of a chemical process in real time to obtain stable section signals; performing wavelet packet decomposition on the preprocessed stable section signals to obtain multiple sub-band signals; calculating multivariate statistical values of the sub-band signal data one by one and drawing control charts; calculating control limits of T2 statistics of the sub-band signals under normal working conditions; comparing the multivariate statistical values of the real-time sub-band signals with the control limits to determine whether a fault occurs and output a fault point; performing periodic adaptive updating, collecting the chemical process signals confirmed as normal working conditions in a period as a new training sample set at intervals of a preset time period, and repeatedly calculating the control limits of the new training sample set. The application improves the sensitivity and reliability of abnormal detection and the real-time monitoring capability of an industrial site.

The invention discloses a multi-agent cooperative behavior and result quality two-dimensional evaluation method and platform. The invention relates to the field of engineering education evaluation and civil engineering informatization, and provides a multi-subject collaborative behavior and engineering achievement quality two-dimensional evaluation method and platform. According to the method, for the multi-specialty joint graduation design of building-structure-construction and the like, firstly, cross-specialty collaboration process data of students are collected in a BIM collaboration platform, and a collaboration behavior time sequence model of student-role-operation-time-object is constructed; secondly, objective engineering quality indexes such as a structure safety reserve coefficient, a material use amount and a standardization degree are automatically extracted from a final building scheme, a structure scheme and a construction organization design; and then establishing a correlation model between the collaborative behavior characteristics and the result quality indexes by using a multivariate statistics or machine learning method, and calculating a collaborative contribution degree score for each student to realize quantitative evaluation of teams and individuals. The platform can also give an early warning of potential collaboration risks to teachers in real time, and provides an objective basis for joint completion teaching intervention and multi-dimensional score evaluation.

The invention discloses a neonatal early-onset septicemia risk prediction method and system based on maternal factors, and the method comprises the steps: firstly collecting maternal data, including maternal clinical and laboratory data; carrying out variable processing and screening on the maternal data to obtain significant variables related to the risk of neonatal early-onset septicemia; establishing a multivariable statistical prediction model based on the significant variables; and outputting a risk score of the neonatal early-onset septicemia by using the prediction model, wherein the risk score is used for individualized risk assessment. The method has the advantages that multivariate analysis can be integrated to assist clinical decision making, and the EOS risk can be predicted individually.

The invention discloses a semiconductor valve analysis method and system based on multi-agent cooperation and an attention mechanism, and relates to the technical field of semiconductor equipment test and intelligent analysis, and the method comprises the steps: carrying out the denoising, missing value filling and test condition label matching operation of all-condition test data, and outputting a standardized test data matrix; fusing a multivariate statistical method and a space attention mechanism, and focusing a test data dimension which has obvious influence on the performance of the gas path system; performing dynamic weight distribution on historical data and real-time data of the multiple test items; and other intelligent agents are linked according to user operation feedback to dynamically adjust the attention focusing direction. According to the method, the problem of association fuzziness in traditional test data analysis is solved, and the precision of performance evaluation and prediction of the gas path system is improved.

The invention relates to the technical field of water environment treatment and cyanobacterial bloom early warning, in particular to a cyanobacterial bloom induction factor identification and toxin risk grading evaluation system, which comprises a multi-source data acquisition module used for acquiring environment big data, hydro-meteorological data and historical water bloom monitoring data; and the data preprocessing and fusing module is used for cleaning the multi-source data and aligning the time sequence. According to the invention, environment big data, hydro meteorological data and historical water bloom monitoring data are acquired through the multi-source data acquisition module, and the data are integrated by combining a credibility weighted fusion algorithm of the data preprocessing and fusion module, so that the problem of inaccurate multi-source data integration is solved; and the key induction factor identification and dynamic weight determination module mines and screens potential factors through multivariate statistical analysis and association rules, and then adjusts weights according to real-time and historical data deviation degrees by a sliding window dynamic weight mechanism, so that the problem of disjunction between static weights and an actual water bloom driving mechanism in the prior art is solved.

This invention discloses a method for reverse analysis and source tracing of undesirable sensory components in cigarette smoke based on sensory omics, belonging to the field of tobacco processing technology. It includes: using waste tobacco dust as raw material, preparing extracts of different sensory intensities through gradient concentration ethanol extraction and adding them to blank cigarettes to construct a full-gradient sample library; using a dual sensory evaluation method to obtain dynamic characteristic data of undesirable sensory components in the smoke; coupling three chromatographic-mass spectrometry techniques to detect three types of components in the smoke in all dimensions, constructing a dynamic sensory and chemical component dual database; screening undesirable sensory association markers through a multivariate statistical and random forest coupled model; reverse tracing the source of the markers, combining single / compound component verification, quantifying the contribution and synergistic effect of each component, and identifying core key components. This invention achieves precise correlation between sensory and chemical components, solving the problems of weak targeting, inability to trace sources, and inability to quantify contributions, while also realizing the high-value utilization of waste tobacco dust.

The application discloses a soil pollutant tracing analysis method based on multivariate statistical analysis and RAG, and relates to the technical field of environmental data analysis.The method comprises the following steps: collecting soil samples according to an industry standard and performing standardization pretreatment on pollutant concentration data; sequentially performing Spearman correlation analysis and principal component analysis (PCA) to extract pollutant correlation and contribution; constructing a similarity matrix and a contribution matrix and splicing to generate a pollutant feature matrix; based on the constructed literature vector knowledge base, using search enhancement to generate a RAG framework, combining statistical characteristics and text similarity to calculate a tracing score; generating a ternary group of 'pollutant-tracing score-literature' by the system, collating soil point sample metadata, and automatically generating an analysis result report according to a predetermined report template and providing the report to a user.

This invention relates to the field of traditional Chinese medicine identification, specifically to a method and system for identifying medicinal snake bile based on metabolomics and machine learning. The method includes: obtaining metabolic extracts from samples and detecting them using liquid chromatography-mass spectrometry (LC-MS) to obtain mass spectrometry data for all samples; preprocessing the mass spectrometry data to extract characteristic peak intensity information of metabolites and constructing a metabolite characteristic matrix for all samples; using multivariate statistical analysis to screen and distinguish differential metabolites between medicinal and non-medicinal snake bile; constructing and training a machine learning classification model; and inputting the preprocessed mass spectrometry data of the snake bile sample to be tested into the machine learning classification model to determine whether its source is medicinal or non-medicinal snake bile. This invention overcomes the limitations of traditional identification methods, which are highly subjective and difficult to quantify, and achieves a shift from experience-based judgment to objective data-driven identification.

This invention provides a method for screening and grading quality indicators for Rhododendron seedlings, thereby determining grading standards and classifying seedling quality. Through systematic testing of various quality indicators of one-year-old Rhododendron seedlings, and by comprehensively applying multivariate statistical analysis, principal component analysis was used to extract the three key indicators with the highest contribution, serving as the main basis for seedling grading. Combined with K-means clustering algorithm, seedlings were scientifically divided into four quality grades based on these core indicators. Verification has shown that these grading indicators can effectively reflect the growth of Rhododendron seedlings after transplanting, providing a scientific basis for quality control of Rhododendron seedlings in production.

This invention provides a model and method for identifying different varieties of celery based on the characteristic odor differences of an electronic nose, belonging to the field of plant identification technology. The method of this invention includes the following steps: (1) using an electronic nose to collect odor sensing information of celery samples, including sensors sensitive to aromatic components, nitrogen oxides, sulfides, hydrides, alkanes, alcohols, and aldehydes; (2) using the stable response value of the sensor as a variable, performing cluster analysis and principal component analysis in sequence to obtain the preliminary classification and grouping of celery samples; (3) using orthogonal partial least squares discriminant analysis to verify the correctness of the grouping, constructing an OPLS-DA qualitative identification model, and the score map showing the formation of three independent clustering regions, with complete separation between groups and no overlap; (4) projecting the response value into the model and determining its variety category based on the projection position. This invention achieves rapid, accurate, and non-destructive identification of celery, Chinese celery, and hybrid offspring by combining electronic nose technology with multivariate statistical analysis methods.

This invention discloses a rapid detection method for capsanthin content based on multispectral data, belonging to the field of non-destructive rapid detection and spectral analysis technology of crop components. It solves the problems of traditional chemical detection methods, such as sample destruction, time-consuming and labor-intensive methods, and inability to achieve high-throughput rapid analysis. This invention acquires spectral reflectance data of chili samples through multispectral imaging. After standardization, a two-step method based on multivariate statistical indicators and model residual analysis is used to accurately remove outlier samples to construct a high-quality modeling set. Then, cross-validation is used to train various machine learning regression models to select the optimal prediction model. The spectral data of the sample to be tested is then input into this model after the same preprocessing, and the reliability of the prediction results is evaluated using a weighted neighborhood consistency index. Ultimately, this achieves rapid, accurate, and non-destructive quantitative detection of capsanthin content, providing efficient and reliable technical support for high-throughput intelligent sorting of chili quality.

The invention belongs to the technical field of areca-nut quality analysis and identification, and provides an areca-nut quality classification and identification method based on a gas-phase electronic nose and multivariate statistical analysis, and the method comprises the following steps: preparing an areca-nut sample, collecting an odor fingerprint spectrum through a gas-phase electronic nose system, constructing a basic data matrix according to the odor fingerprint spectrum, and carrying out principal component analysis. The method comprises the following steps: constructing an OPLS-DA analysis model, obtaining a key differential compound list, constructing a key data matrix according to the key differential compound list, performing hierarchical clustering analysis on key differential compounds and an areca nut sample, generating a corresponding clustering heat map, constructing an SIMCA classification model, and classifying the areca nut sample according to the clustering heat map. And inputting the key data matrix into an SIMCA classification model to obtain a category judgment result of the areca nut sample. The areca nut quality classification and identification method based on the gas phase electronic nose and multivariate statistical analysis provided by the invention can replace the existing artificial sensory evaluation, and meets the rapid, objective, accurate and quantitative online detection quality control requirements in industrial production.

The invention discloses an identification method and application of a traditional Chinese medicine composition taste quality marker, and belongs to the technical field of traditional Chinese medicine taste evaluation. The identification method comprises the following steps: S1, establishing a sensory prediction model: collecting electronic tongue data and human body sensory evaluation data of a traditional Chinese medicine composition sample, and establishing a correction model according to the electronic tongue data and the human body sensory evaluation data; s2, collecting chemical group data and sensory group data: identifying chemical components of the traditional Chinese medicine composition by adopting a UPLC-Q-Exactive / MS (Ultra Performance Liquid Chromatography-Quantitative Exactive / Mass Spectrometry) analysis method to obtain semi-quantitative data of the chemical components; and S3, screening and verifying the key sensory markers: performing multivariate statistical modeling, locking a key marker group, and performing molecular docking verification. According to the method, a high-resolution mass spectrometry analysis technology, an electronic tongue and a human body sensory score are combined, chemical data and sensory data are deeply correlated by utilizing a multivariate statistical method, and candidate markers with high variable importance are screened out.

The application discloses a satellite radiation product space downscaling method based on a cloud image, and relates to the technical field of remote sensing data processing, and comprises the following steps: acquiring low-resolution satellite radiation data and high-resolution cloud image data; upscaling the high-resolution cloud image data to the same resolution as the low-resolution satellite radiation data, and establishing a multivariate statistical mapping relationship between the two at the same resolution; applying the mapping relationship to the high-resolution cloud image data to generate an initial radiation prediction field; determining the spatial distribution weight of each high-resolution pixel in the low-resolution pixel to which the high-resolution pixel belongs according to the initial radiation prediction field; and performing spatial reconstruction on the low-resolution satellite radiation data according to the spatial distribution weight to obtain high-resolution satellite radiation products. The application effectively overcomes the problem of insufficient accuracy caused by the neglect of cloud influence and direct resampling in the prior art, and improves the spatial detail accuracy and physical rationality of the downscaling product under complex cloud conditions.

The invention discloses an underground water pollution traceability method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the displacement and rainfall of a to-be-analyzed region, determining the key point or direction of water quality detection according to the property of the to-be-analyzed region, and obtaining detection data through water quality detection; analyzing the detection data by utilizing multivariate statistical analysis, and determining characteristic pollutants; and determining the source and composition of the underground water according to the specific pollutants. According to the technical scheme, the composition and the main pollution source of the underground water can be accurately and effectively analyzed, and a basis is provided for subsequent underground water treatment.

The invention discloses a method for rapidly identifying organic or conventionally planted wheat products based on multi-element analysis and a multivariate statistical model, which comprises the following steps: respectively preparing samples of wheat bran, coarse flour and flour (optional whole grains), obtaining element fingerprints by utilizing ICP-OES, and combining log10 + Z-score pretreatment and VIPgt; a PLS-DA model corresponding to a sub-product is constructed according to a variable screening strategy of 1 and 2, and organic and conventional wheat products are rapidly distinguished. The single sample detection period is less than 2 hours, and batch screening of customs / quality inspection institutions is supported. The technology compatibility is high, the method is suitable for standard laboratory ICP-OES equipment, and special instruments are not needed; the flour sub-product only uses three elements of B / K / Se, and the latent variable is less than or equal to 2, so that the high accuracy required by engineering can be achieved; the multi-element combination of the coarse flour and the bran can further improve the precision and the robustness. The method is low in detection cost, high in flux and suitable for supervision and enterprise sampling inspection.

This invention discloses a method and system for assessing the health status of packaging machinery based on multi-source data fusion, belonging to the field of multi-source data fusion technology. It includes locating the root cause of faults based on the feature vectors obtained from the decomposition of the principal eigenvalues, thus identifying the main assessment dimensions leading to a decline in the overall health index. This invention achieves precise location of the root cause of faults by analyzing the assessment dimensions mapped to the component with the largest absolute value in the feature vector corresponding to the principal eigenvalue. This process, based on multivariate statistical principles, effectively isolates the coupling correlation between data from different dimensions, avoiding the misjudgments and omissions caused by the reliance on the experience of maintenance personnel in traditional technologies. When the overall health index of the equipment declines, it can quickly pinpoint whether the problem originates from electrical, mechanical, process, or environmental dimensions, significantly shortening fault diagnosis time, improving maintenance response efficiency, and providing a clear direction for targeted equipment repair.

The invention discloses a method for identifying adulteration of fish oil by combining Raman spectrum with multivariate statistical analysis. The method comprises the following steps: collecting the Raman spectrum of fish oil to be detected by using a Raman spectrometer; the collected Raman spectrum is subjected to preprocessing of denoising, baseline removal and normalization; dividing the spectral data into a training set and a test set according to a ratio of 7: 3; on the basis of traditional PCA, an intra-class scattering matrix and an inter-class scattering matrix are calculated, generalized Rayleigh quotients are calculated according to the intra-class scattering matrix and the inter-class scattering matrix, and sorting is carried out according to the generalized Rayleigh quotients; and training the linear discriminant analysis model by using data of the training set, and taking a result of the test set as a final classification result. Raman spectrum and machine learning are combined, solution consumption is low, sample preparation steps are simple, complex sample pretreatment is not needed, analysis time is short, and rapid detection and classification can be achieved. The analysis time is short, the classification accuracy is high, and the classification accuracy of the fish oil with the adulteration proportion of 5% reaches 95% or above.

The invention discloses a forest carbon sink evaluation and sink increase regulation and control method based on multifunctional collaborative analysis, and belongs to the technical field of forest carbon sink evaluation management. A forest quadrat is selected in a research area, a vegetation community in the quadrat is subjected to layered investigation, and litters and soil samples are collected at the same time; calculating a plurality of carbon sink function indexes based on investigation and sampling data, and obtaining scores of the indexes after standardization processing; the standardized scores of the multiple carbon sink function indexes are integrated, and a comprehensive forest multifunctional carbon sink capacity value is obtained through calculation by adopting an average value method; the tree diversity, the shrub diversity, the herbal diversity and the number of arbor seedlings serve as regulation factors, and the cooperation and tradeoff relation between the regulation factors and the forest multifunctional carbon sink capacity is analyzed and evaluated through multivariate statistics; and according to a quantitative evaluation result, identifying key regulatory factors, and matching corresponding sink increasing management strategies for regulatory factors of different action types to realize directional regulation.

The application discloses a method for identifying varieties of smooth-skin kumquat and crisp honey kumquat based on metabolomics and application, for the first time, metabolomics technology is applied to the identification of the new variety of crisp honey kumquat and the traditional variety of smooth-skin kumquat, the endogenous metabolites of kumquat are detected to the maximum extent, the one-sidedness of the traditional analysis method is avoided, and the accuracy of the result is improved; a large amount of original data detected based on LC-MS non-target scanning is combined with a multivariate statistical analysis method, six characteristic metabolites for identifying the crisp honey kumquat and the smooth-skin kumquat are obtained, and the relative content threshold of the characteristic metabolites of the two kumquat varieties is determined, so that the two kumquat variety samples and unknown variety kumquat samples can be rapidly distinguished, and a scientific basis is provided for the quality control of the kumquat.

The application discloses an industrial process monitoring method based on a lightweight deep principal component analysis-autoencoder model, and comprises the following steps: collecting related process variable data of an industrial process online, pre-processing the collected data, inputting the pre-processed data into a trained lightweight deep principal component analysis-autoencoder model, obtaining T 2 statistical quantity and an SPE statistical quantity, comparing the statistical quantity and the SPE statistical quantity with corresponding control limits, and outputting a monitoring result; the lightweight deep principal component analysis-autoencoder model utilizes a PCA dimension reduction module to reduce the dimension of the data score, inputs the score into an autoencoder, and outputs the reconstruction data. The application improves the calculation efficiency of the model, the calculation speed is obviously improved compared with a traditional deep network model, and compared with a single multivariate statistical model, the deep hierarchical cascading model structure has strong feature extraction capability which cannot be compared with the single multivariate statistical model, and effectively solves the complex industrial process monitoring problem.

The application discloses a kind of circuit breaker mechanical state non-interventional multidimensional perception optimal arrangement method, it is related to circuit breaker optimal arrangement technical field, to solve the problem that when arranging circuit breaker, there is no targeted arrangement according to the actual situation of circuit breaker.The confirmation of the arrangement position of circuit breaker is automatically carried out by the constructed bayesian network and performance influence relationship, the subjectivity and uncertainty of artificial decision are reduced, the multi-dimensional characteristic data of circuit breaker and performance evaluation data are combined, the influence relationship between data is analyzed using multivariate statistical method, the objectivity and accuracy of decision are improved, different types of data are extracted using special feature extraction method, to ensure that the key information of each data type can be effectively captured.This targeted feature extraction method helps to improve the accuracy and efficiency of subsequent analysis, through in-depth analysis of multi-dimensional characteristic data, early diagnosis of circuit breaker mechanical state and potential fault prediction are realized.

The invention discloses a structure damage alarm method based on piezoelectric guided wave-adaptive network probability association, and belongs to the technical field of aerospace structure health monitoring, and the method comprises the steps: employing probability statistical modeling combined with a dynamic window slippage method to obtain a reference alarm feature and a damage alarm feature of each channel; fusing the reference alarm features of the channels by adopting a truncated mean network fusion method to obtain a fused reference alarm feature, and fusing the damage alarm features of the channels to obtain a fused damage alarm feature; based on the fused reference alarm features, adaptively constructing a multivariate statistical feature fusion threshold by using statistical parameters; and when the fused damage alarm features are greater than a multivariate statistical feature fusion threshold, carrying out structural damage alarm. The implementation process is simple and efficient, the damage alarm sensitivity under the influence of the time-varying environment can be improved, and the false alarm probability can be effectively reduced while the damage of the monitored structure is comprehensively monitored.

The present disclosure relates to a method and a system for determining whether a product is genuine by using XRF, and provides a method and a system for determining whether a product is genuine, in which: XRF data for the product is collected; multivariate statistical analysis is performed on the collected XRF data to generate a determination model enabling determination of whether a product is genuine; XRF data for a first product for which whether the product is genuine is to be determined is received; and whether the first product is genuine is determined on the basis of the XRF data for the first product.

The present application relates to the element analysis-based multivariate statistical analysis method, system and medium, belong to technical field, the present application is through according to the target marine product sample's δ 18 O value, historical temperature and salinity data of each target water area Construct inversion prediction model, so as to utilize the inversion prediction model to invert the water temperature change curve and water salinity change trend data experienced by the current marine product during the whole growth period, finally according to the water temperature change curve and water salinity change trend data experienced by the current marine product during the whole growth period Dynamic matching, locking the most possible growth area, and correcting the source in combination with the event log occurred in the target area.The present application upgrades stable isotope analysis from simple origin fingerprint comparison to the application of paleoenvironment reconstruction technology in marine product tracing, utilizes the characteristics of shell body recording environmental information, traces through physical model inversion and time series data matching, the method is scientific and rigorous, and the result is more persuasive.

The invention discloses a quantitative evaluation method for enterprise food safety risks. Belongs to the technical field of food safety management and risk assessment, and discloses a food safety risk assessment method which scientifically selects main risk factors in a food production process by establishing an industry general risk assessment mathematical model, and formulates a quantitative scoring standard and a grading system based on three dimensions of harmfulness, occurrence possibility and social sensitivity. An analytic hierarchy process and an entropy weight method are used to determine index weights, and a literature metering tool and a multivariate statistical method are combined to optimize an evaluation model. High-risk factors are accurately monitored and dynamically graded by combining first-round qualitative evaluation and second-round quantitative evaluation. According to the method, an enterprise is supported to establish a personalized risk prediction model according to internal and external data, and dynamic management and control, intelligent early warning and systematic management of risks are realized based on a dynamic grading threshold value and continuous data training, so that the food safety risk identification and control capability of the enterprise is effectively improved.

The application relates to the technical field of data analysis, in particular to a shale oil sweet spot evaluation method based on well logging curves and influenced by magmatic hydrothermal fluid, which comprises the following steps: collecting well logging and well logging data of shale oil wells, judging favorable reservoir sections through gas logging data; using a PSO particle swarm algorithm to optimize a BP neural network model, predicting TOC, and determining oil layer development sections; determining geology "sweet spot" development sections based on the change characteristics of sensitive well logging curves of each shale oil core well to be measured in a research area; using a multivariate statistical method to predict the content of each mineral based on whole rock diffraction data, using a brittle mineral content method and a rock mechanics parameter method to calculate a brittleness index, and determining engineering "sweet spots"; and combining the geology "sweet spot" and the engineering "sweet spot" to establish a shale oil "sweet spot" identification model influenced by magmatic hydrothermal fluid. The application aims to accurately evaluate shale oil "sweet spots" influenced by magmatic hydrothermal fluid.