99 results about "Multivariate statistical" patented technology

The invention discloses a method and a system for analyzing a pollution source of a rainwater pipe network. The method comprises the following steps of: 1, obtaining a first key fluorescence characteristic parameter combination according to basic physicochemical indexes by adopting a fluorescence characteristic parameter prediction model; 2, three-dimensional fluorescence characteristic parameters of the pollution source are obtained, and a pollution source three-dimensional fluorescence characteristic data set is obtained; screening by adopting a multivariate statistical method to obtain a second key fluorescence characteristic parameter combination; 3, determining a fluorescence characteristic parameter combination according to the first key fluorescence characteristic parameter combination and the second key fluorescence characteristic parameter combination; 4, inputting the fluorescence characteristic parameter combination into the Bayesian mixture model to obtain the contribution rate of the pollution source to the receptor water quality, and completing the analysis of the pollution source of the rainwater pipe network; according to the method, the DOM fluorescent fingerprint information is predicted by adopting basic data; and based on the three-dimensional fluorescence characteristic parameters and in combination with a Bayesian mixture model, precise quantitative analysis of the pipe network pollution source is realized.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention relates to a multi-mode-based pit mud quality detection method, device, equipment and medium, and the method comprises the following steps: acquiring near infrared spectrum data NIR and mid-infrared spectrum data MIR of a pit mud sample to obtain original spectrum data of the pit mud sample, and preprocessing the original spectrum data to obtain standard spectrum data; dynamically adjusting a data fusion mode of NIR and MIR by adopting a multivariate statistical analysis algorithm based on the standard spectral data, and further constructing an NIR-MIR fusion model; carrying out spectral analysis on the pit mud sample based on an NIR-MIR fusion model so as to extract key component information in the pit mud sample; and based on the key component information, according to a preset multi-dimensional evaluation strategy, carrying out comprehensive evaluation on the pit mud sample, and generating a quality evaluation result. The pit mud detection device has the effect of improving pit mud detection efficiency.

The invention provides a comprehensive evaluation method for dissemination characteristics of minerals in coal of different particle sizes, and relates to the field of coal quality analysis. The method comprises the following steps: collecting coal analysis samples of different size fractions, and preparing coal polished sections of all the size fractions; the method comprises the following steps: collecting microscopic images, and constructing a representative microscopic image sample database of each size fraction; carrying out size fraction judgment, generating a component segmentation image on a single particle and a component segmentation image on a microscopic image, and extracting dissemination characteristic parameters of mineral substances in coal of each size fraction; key parameters are screened out, and comprehensive evaluation is carried out on the dissemination characteristics of the minerals in the coal of different size fractions. According to the method, an image processing technology and multivariate statistical analysis are combined, and qualitative and quantitative analysis of the dissemination characteristics of the minerals in the coal is achieved. Compared with an existing microscopic image analysis method, the method has the advantages that the human subjective influence and the labor intensity are remarkably reduced, and the analysis result has higher objectivity and reproducibility.

The invention relates to the technical field of electrochemical detection and food quality safety, and particularly discloses an edible oil rancidity degree rapid detection system based on an electrochemical sensor, comprising: a data acquisition module for receiving an original electrochemical response signal and performing format standardization, abnormal data rejection and integrity verification; the data preprocessing module adopts a statistical optimization algorithm to reduce noise, correct baseline drift, generate a unified dimension data set and complete stability verification; the feature analysis module is used for extracting rancidity related feature parameters through statistical correlation analysis, and carrying out fusion calculation after significance verification to obtain feature values representing rancidity; and the evaluation and judgment module inputs the characteristic value into a pre-training model, outputs a quantitative index through multivariate statistical calculation, dynamically sets a safety threshold in combination with the edible oil type, and outputs a rancidity judgment conclusion after comparison. The method depends on statistical data processing and analysis, the detection reliability and accuracy are improved, and the method is suitable for edible oil quality safety rapid screening and quality monitoring.

The invention relates to a wind power plant state monitoring technology, in particular to a multi-unit joint monitoring model based on spatial-temporal correlation, which comprises the following steps: S1, performing cross reference on multiple units to eliminate spatial correlation; s2, vector autoregression prediction is carried out, and time correlation is eliminated; and S3, monitoring a multivariable statistical process based on a residual vector. Aiming at the problem that modeling is interfered by spatial-temporal correlation of wind power plant-level multi-unit state data, the invention provides a multi-unit joint monitoring model, fault feature extraction is realized through multi-unit cross reference, and the spatial non-stationary trend among wind power plant units is eliminated; based on a vector autoregression (VAR) model, carrying out joint modeling on the data of the multiple units, eliminating time autocorrelation among the multiple units, and obtaining a stable and independent residual vector; and a multivariable residual control chart is established, the state of the wind power plant is jointly monitored, and a fault unit is accurately identified. The model guarantees the operation reliability of the wind turbine generator, and effectively reduces the operation and maintenance cost of a wind power plant.

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

The invention discloses an electrolysis system fault intelligent detection system and method based on an explosion-proof robot dog, and relates to the technical field of electrolysis system intelligent detection, and the method comprises the steps: collecting electrolytic cell environment data and electrolytic cell operation state data in real time through the explosion-proof robot dog, and employing a Kalman filtering and timestamp synchronization method, carrying out filtering and de-noising processing and converting into a uniform timestamp; fusing the processed electrolytic cell environment data and the electrolytic cell operation state data through a multivariable statistical method to obtain electrolytic cell multi-source fusion state data, and performing real-time compensation by using a linear regression model; a random forest training method is adopted, and the compensated historical data of the multi-source fusion state of the electrolytic cell are utilized to train the isolated forest model to obtain a short circuit detection model; data are collected through the explosion-proof robot dog, and Kalman filtering and timestamp synchronous processing are applied, so that the safety of data collection is ensured, and the accuracy of data and the consistency of time dimensions are also ensured.

The invention relates to a cherry tomato storage period evaluation method and application, and belongs to the technical field of analysis and detection. The method for evaluating the storage period of the cherry tomatoes comprises the following steps: acquiring a cherry tomato sample; loading into a headspace bottle and incubating to obtain a headspace gas; carrying out headspace-gas chromatography-ion mobility spectrometry detection; vOCal analysis software is adopted for data collection; carrying out qualitative analysis on the volatile components by adopting a database; a cherry tomato classification model is constructed by adopting a multivariate statistics method, so that cherry tomato samples in different storage periods are classified. The evaluation method disclosed by the invention can be used for monitoring, classifying and judging the change of the volatile components of the cherry tomato samples under different storage conditions, and has the characteristics of high throughput, fast response, good repeatability, high accuracy and strong analysis capability.

The invention discloses a packaging machinery health state evaluation method and system based on multi-source data fusion, and relates to the technical field of multi-source data fusion, and the method comprises the steps: carrying out the fault root cause positioning according to a feature vector obtained through the decomposition of a main feature value, and obtaining a main evaluation dimension which causes the reduction of a comprehensive health degree index; according to the method, the evaluation dimension mapped by the component with the maximum absolute value in the feature vector corresponding to the main feature value is analyzed, accurate positioning of the fault root cause is achieved, the process effectively strips coupling correlation among dimension data on the basis of the multivariate statistical principle, the problems of misjudgment and missed judgment caused by judgment depending on experience of operation and maintenance personnel in the traditional technology are solved, and the accuracy of the fault root cause is improved. When the comprehensive health degree index of the equipment is reduced, the problem can be quickly locked from electrical, mechanical, process or environment dimensions, the troubleshooting time is greatly shortened, the maintenance response efficiency is improved, and a clear direction is provided for targeted equipment maintenance.

The invention discloses a method and system for automatically detecting unsafe behaviors of workers based on machine vision, and relates to the field of industrial safety, and the method comprises the steps: collecting and preprocessing the behavior data of the workers, the behavior data of the workers comprising position information, equipment state, certificate state and motion trail; performing unsafe behavior detection on the preprocessed operator behavior data to generate a behavior target detection frame; inputting the behavior target detection frame into a target tracking algorithm to perform multi-target tracking, and generating a personnel movement track data set; performing multivariate statistical analysis on the behavior feature vector by using a quantitative theory to generate an unsafe behavior reaction matrix; and evaluating a risk level in the unsafe behavior reaction matrix through a fuzzy analytic hierarchy process, and performing safety early warning notification according to the risk level. According to the method, multivariate statistical analysis is carried out on the behavior feature vectors through a quantification theory, and deep quantification and comprehensive evaluation of the unsafe behavior features of the workers are realized.

The present invention discloses a method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function, which relates to the technical fields of surface engineering and performance evaluation. The method includes collecting the electronic work function data and material characteristic data on the surface of ductile iron; extracting the characteristics of the electronic work function data to obtain the electronic work function characteristics; establishing a mathematical model based on the electronic work function characteristics to analyze the correlation between the electronic work function data and the material characteristic data; analyzing the relationship between the electronic work function characteristics and the wear resistance, and establishing a comprehensive evaluation model to evaluate the wear resistance of ductile iron. The present invention combines characterization techniques, complex data analysis methods and multivariate statistical models at the same time. It can not only deeply understand the connection between the electronic structure and macroscopic properties of materials, but also establish a comprehensive evaluation model to predict the wear resistance of ductile iron; this method guides the design of materials and the optimization of the production process.

The invention discloses a method and system for evaluating breast milk nutritional ingredient composition by using a multivariate statistical method and application, and belongs to the field of nutritional ingredient evaluation.The method comprises the following steps that the content of nutritional ingredients in to-be-evaluated breast milk or formula food is obtained to serve as an original data variable; carrying out normalization processing on original data variables to eliminate dimensional differences, and carrying out centralization processing; inputting the centralized data into a pre-constructed evaluation model to obtain a score output by the evaluation model, comparing the score with a preset median, and if the difference value is smaller, indicating that the result is closer to breast milk; wherein the evaluation model comprises four common factors obtained through principal component analysis and a scoring model obtained through loads and influence degrees in factor analysis. According to the method, macro nutrients and micronutrients of the breast milk are simplified by adopting a principal component multivariate statistical method, and main indexes are selected from numerous quality evaluation indexes to evaluate the nutritional ingredients of the breast milk or the formula food.

The invention discloses a method, a device and equipment for defining an ecological underground water level in a desertification region, and relates to the field of underground water level localization, and the method comprises the following steps: carrying out underground water dynamic simulation according to meteorological data, hydrological data, geological data and social economic data; correlation analysis is carried out according to underground water level change conditions and ecological data in different media in the desertification region, and key ecological variables are obtained; determining an ecological health threshold value according to underground water level change conditions and key ecological variables in different media in the desertification region; according to the meteorological data, the hydrological data and the geological data, determining key environment variables influencing ecological system response characteristics under different underground water level conditions; and determining the ecological underground water level by using a multivariate statistical method according to the underground water level change condition, the key ecological variable, the ecological health threshold value and the key environment variable in different media in the desertification region. According to the invention, the underground water range required for maintaining the health of the ecological system in the research area can be defined.

The invention relates to a characteristic marker combination for identifying honeysuckle tea varieties and an identification method thereof, and belongs to the technical field of variety tea identification. The characteristic marker combination comprises chlorogenic acid, swertioside, secoxyloganin, galuteolin, isochlorogenic acid A and isochlorogenic acid C. The invention further discloses a kit for detecting the secoxyloganin. The contents of the characteristic markers are simultaneously detected through a high performance liquid chromatography (HPLC) or liquid chromatography-mass spectrometry (LC-MS) technology, and different honeysuckle tea varieties can be rapidly and accurately identified by combining multivariate statistical analysis (such as principal component analysis or clustering analysis). The identification method provided by the invention has the characteristics of strong specificity, high stability and high sensitivity, is suitable for large-scale sample analysis, and can be widely applied to quality control, authenticity identification and market supervision of honeysuckle tea varieties.

The invention relates to the technical field of medical biology, and discloses a traditional Chinese medicine quality marker screening method and an angelica dahurica variety quality marker group, the traditional Chinese medicine quality marker screening method and the angelica dahurica variety quality marker group comprise the following steps: respectively crushing, extracting and detecting a newly bred angelica dahurica variety and a farm variety; carrying out pretreatment on mass spectrum data by adopting MZmine 2.5 software; and the coumarin components are screened by adopting a multi-stage mass loss window. According to the traditional Chinese medicine quality marker screening method and the angelica dahurica variety quality marker group, through integration of ultra-high performance liquid chromatography-tandem mass spectrometry and polygon quality defect filtering, extended virtual molecular formula library screening, characteristic molecular network, network annotation transmission and other calculation strategies, a plurality of coumarin components are successfully annotated; and multivariate statistical analysis is adopted, so that the quality marker of the newly cultivated angelica dahurica variety is obtained.

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 present invention relates to the field of oil and gas exploration and development, and discloses a coal seam thickness prediction method, device and equipment based on well-seismic combination, which can obtain the real acoustic wave curve corresponding to the target layer segment and the constructed coal seam comprehensive identification factor curve. The real acoustic wave curve is subjected to wavelet transformation to extract the low-frequency component curve; the coal seam comprehensive identification factor curve is subjected to multivariate statistics to extract the high-frequency component curve. The low-frequency component curve and the high-frequency component curve are modulated to obtain a pseudo-acoustic wave curve. Based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method, the thickness distribution of the coal seam to be measured is determined. The present invention combines the coal seam comprehensive identification factor curve and the seismic wave impedance inversion method to predict the coal seam thickness, effectively solving the problem of the difficulty in predicting the coal seam thickness in the complex structural area of ​​the oil field with no wells or few wells, improving the efficiency and accuracy of coal seam thickness prediction in the complex structural area of ​​the oil field, and diversifying the coal seam thickness prediction method in the complex structural area of ​​the oil field.

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 invention discloses a strong wine authenticity identification method fusing multivariate statistics and metabolic feature extraction, relates to the technical field of strong wine authenticity identification, and overcomes the limitation that a traditional method only depends on peak area or mass-to-charge ratio for distinguishing by incorporating non-dominant structure difference in a metabolic map into an analysis model. A traditional method is difficult to distinguish metabolic differences caused by different processes or environmental changes, but according to the method, through multi-dimensional non-targeted metabolic characteristic analysis, the influence of the processing processes on the metabolic structures of the wine products can be accurately captured, and the distinguishing precision of the wine products of the same variety, different processing batches, the same raw material and different processes is remarkably improved. According to the method disclosed by the invention, the comprehensive isomerism index FCY is constructed by combining the structural isomerism variance and the map complexity factor, so that the difficulty in judgment caused by complex structures and hidden changes of wine samples is effectively solved.

The invention discloses a pyrazine-flavor-based multi-mode intelligent identification method for a Maotai-flavor liquor process. The method comprises the following steps: pretreatment: pretreating Maotai-flavor liquor samples of different brewing processes; collecting sensory data of electronic noses and electronic tongues of Maotai-flavor baijiu of different brewing processes; establishing a liquid phase detection method for the characteristic components of the alkylpyrazine in the Maotai-flavor liquor; verifying a component analysis method through precision, stability and repeatability experiments; component quantitative analysis based on liquid chromatography fluorescence detection; performing clustering and difference evaluation on Maotai-flavor liquor samples of different brewing processes by adopting multivariate statistical analysis; establishing a Maotai-flavor liquor process identification model: utilizing VIPgt in multivariate statistics; establishing different machine learning multi-classification models for different brewing processes of Maotai-flavor liquor according to the common characteristics of 1, and performing multi-model comparison; and based on the optimal performance classification model, establishing an SHAP model of the Maotai-flavor liquor brewing process identification model, and globally explaining the identification model by adopting an SHAP algorithm.

The invention discloses a credit card user marketing business distribution method and system, and the method comprises the steps: carrying out the analysis of the basic data of a credit card user through employing a multivariate statistical analysis method, and obtaining a first target user; classifying the first target users according to the regional information to form a plurality of second target users, and distributing the second target users to corresponding regional banks; determining a marketing mode according to the credit card use information of the second target user; determining a marketing scheme of the second target user according to the credit card consumption information of the second target user; and pushing the corresponding marketing scheme to the second target user according to the marketing mode of the second target user, evaluating the marketing effect, and optimizing the influence strategy according to the evaluation result, so that the method effectively improves the efficiency and accuracy of credit card customer management. Effective marketing can be implemented for stock customers, and the consumption permeability is improved. Banks are helped to realize business development, market competitiveness is enhanced, customer experience is improved, and customer satisfaction and loyalty are improved.

The present invention proposes a multi-source domain EEG signal analysis method with multi-modal representation. The present invention first uses multi-manifold mapping to extract the common invariant representation of the multi-source domain and the target domain, while taking into account the low-dimensional structure and multivariate statistical characteristics of the EEG signal. At this stage, the CORAL loss is calculated to guide the model to obtain high-quality common invariant representation. Secondly, the multi-source domain is decomposed and one-to-one feature extraction is performed. At this time, the MMD loss is used to guide the model to obtain high-quality private invariant representation. Finally, a softmax classifier is used for classification. The effectiveness of the method was evaluated on the public MI1 and MI2 datasets, as well as the EEG signal dataset collected by the team. Experimental results show that the present invention performs superiorly in multi-subject scenarios.

The application designs a satellite communication earth station monitoring protocol design method based on atlas clustering, which is suitable for the satellite communication earth station operation and maintenance control field, the classification processing and monitoring protocol planning and design of the controlled equipment and its parameters. The relationship and mode between the earth station monitoring data are revealed by mainly designing a multivariate statistical analysis and atlas clustering processing scheme of monitoring data, so as to better understand and identify different characteristics and behaviors, and realize the inductive design of the earth station monitoring protocol. The application has the characteristics of clear and easy to understand, good continuity, strong scalability and the like, and can especially solve the problems of non-standard satellite communication earth station monitoring protocol design process, disorder protocol content and poor continuity and the like.

The invention relates to the field of oil-gas exploration and development, and discloses a coal seam thickness prediction method, device and equipment based on well-to-seismic combination, which can obtain a real acoustic curve corresponding to a target layer section and a constructed coal seam comprehensive identification factor curve. Performing wavelet transform on the real acoustic curve to extract a low-frequency component curve; and performing multivariate statistics on the coal seam comprehensive identification factor curve to extract a high-frequency component curve. And modulating the low-frequency component curve and the high-frequency component curve to obtain a pseudo-acoustic curve. And determining the thickness distribution of the to-be-measured coal seam based on the pseudo-acoustic curve and the seismic wave impedance inversion mode. The method combines the coal seam comprehensive identification factor curve and the seismic wave impedance inversion mode to predict the coal seam thickness, effectively solves the problem of high difficulty in predicting the coal seam thickness in a well-free and less-well area in an oil field complex structure area, and improves the prediction efficiency and prediction accuracy of the coal seam thickness in the oil field complex structure area. The coal seam thickness prediction mode of the oil field complex structure area is diversified.

The present disclosure discloses a method for evaluating the nutritional status in plants, using visible-to-shortwave (VIS-SWIR) infrared reflectance spectroscopy of carbohydrates, particularly in almond trees. The method comprises obtaining a dry plant sample (i.e. leaves, branches and roots), digesting and grinding it to a powder, capturing predetermined spectral data of the ground plant sample, correlating said spectral data to predetermined materials and evaluating said nitrogen status of said plants according to predetermined multivariate statistical models. The data obtained from this method facilitates crop management and fertilization by providing the nitrogen status of the plants based on non-structural carbohydrates.

The invention discloses a questionnaire system and method, and relates to the technical field of information processing, and the questionnaire system comprises a questionnaire design module, a questionnaire publishing module, a data collection module and a data analysis module. Wherein the data analysis module adopts a mode of combining descriptive statistical analysis with deep analysis such as correlation analysis, difference test, multivariate statistical analysis and text analysis to realize deep and comprehensive data analysis; the questionnaire method comprises the steps of questionnaire design, questionnaire release, data collection and data analysis, the questionnaire system and method can improve the efficiency and flexibility of questionnaire design, improve the accuracy and efficiency of data collection, guarantee the safety of system use and data privacy, remarkably improve the efficiency and quality of questionnaire work, and reduce the labor intensity of workers. And powerful support is provided for data collection and analysis in various fields.

The invention discloses a method and a system for constructing a multi-source data comprehensive safety evaluation model of an energy storage battery system, and the method comprises the steps: calculating the component score accumulated contribution degree and comprehensive component score of each index parameter by adopting a multivariate statistical analysis method; obtaining the risk score of each index parameter, calculating the cloud probability feature value of each risk level based on a cloud model, and forming a risk cloud probability data set; according to a Transform architecture based on a multi-head attention mechanism, an evaluation model is constructed; adjusting the number of heads in a multi-head attention mechanism according to the accumulated contribution degree of the component scores, and training the model by taking the comprehensive component scores and the risk cloud probability data set as an input data set and an output data set of the evaluation model; optimizing hyper-parameters of the trained evaluation model by adopting a subtraction optimizer algorithm improved based on golden sine by taking F1 score optimization as an optimization target; and the evaluation model after hyper-parameter optimization is used as a multi-source data comprehensive safety evaluation model of the energy storage battery system, so that the accuracy and the reliability of an evaluation result are improved.

The invention belongs to the field of pancreatic duct adenocarcinoma prediction, and provides a pancreatic duct adenocarcinoma prediction method and system based on oral flora. The pancreatic ductal adenocarcinoma prediction method based on oral flora comprises the following steps: acquiring relative abundance information of each biomarker in a potential biomarker set for diagnosing pancreatic ductal adenocarcinoma of a to-be-detected person; using the relative abundance information of each biomarker in the biomarker set and a pre-trained multivariate statistical model to obtain a probability value of suffering from pancreatic ductal adenocarcinoma; the method comprises the following steps: performing microbiome difference analysis on saliva, duodenal juice and pancreatic tissue samples of a pancreatic ductal adenocarcinoma patient group and a pancreatic benign disease patient group, and taking an intersection of differential bacteria in the samples to obtain a potential biomarker set for diagnosing the pancreatic ductal adenocarcinoma.

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.