100 results about "Chemical data" patented technology

The invention relates to the technical field of drug release control. By providing a multi-modal data fusion nano-drug release kinetics prediction method, device and equipment and a medium, the method comprises the following steps: acquiring permeation path image data of a nano-drug in a target tissue, and acquiring drug release rate chemical data; performing time dimension synchronization processing on the permeation path image data and the drug release rate chemical data to generate a multi-modal space-time correlation data set; performing feature extraction processing on the multi-modal time-space correlation data set to obtain nano-drug permeation behavior features and release dynamics fluctuation features; constructing a space-time diagram neural network prediction model; training the space-time diagram neural network prediction model to obtain a trained prediction model; and generating a personalized drug release scheme through a reverse optimization algorithm on the basis of the drug release kinetics prediction curve and the quantitative index of the degree of variability, so as to achieve the technical effects of improving the prediction precision, enhancing the model adaptability and reducing the curative effect volatility.

The invention discloses a rare metal mineralization potential comprehensive evaluation method based on rock geochemistry, and aims to solve the problems of single evaluation index and disjunction between field observation and indoor analysis in the prior art. The method comprises the following steps: obtaining geological data of an analysis area, and screening a target area based on a favorable structure background and an exposed feature criterion; identifying mineralogical and lithofacies markers and collecting sampling data; the method comprises the following steps: acquiring rock geochemical data of a sample sampled by a previous person, including multi-dimensional data including major elements, trace elements, isotope composition and the like, and calculating a quantitative evaluation index according to the data; and finally, establishing a comprehensive evaluation model by adopting a weighted scoring method to realize mineralization potential grade division and target region accurate delineation in a three-dimensional space. According to the method, organic combination of macroscopic geologic features and microcosmic geochemical data is realized, and a complete technical system from field observation to indoor analysis to engineering decision is formed.

The invention belongs to the technical field of gold mineralization data processing, and particularly relates to a gold mineralization data processing method based on a metric learning enhanced variational auto-encoder, and the method comprises the steps: carrying out the standardization processing of geochemical multi-element original data, and screening out feature vectors of which the contribution degrees to anomaly recognition exceed a set threshold value, and forming a training set; training a metric learning enhanced variational auto-encoder model comprising an encoder, a decoder and a metric constraint module by using the training set; a trained metric learning enhanced variational auto-encoder model is applied to actual geochemical data, geochemical sample points with obvious abnormal features are identified by calculating reconstruction errors of samples in the actual geochemical data and category distances of potential spaces, complex geochemical element distribution can be described more accurately, and the accuracy of geochemical element distribution is improved. And the sensitivity and the recognition capability of the subtle anomaly are improved.

The invention relates to the technical field of remote sensing prospecting, and discloses a uranium-copper multi-metal remote sensing prospecting method which comprises the following steps: S1, data acquisition and preprocessing; s2, alteration information is extracted; s3, multi-source information comprehensive analysis and prospecting prediction; s4, alteration abnormity verification is carried out; and S5, delineating the prospecting target area. According to the method, vegetation spectrum interference is stripped by carrying out principal component analysis on visible light-short wave infrared of a remote sensing image, weak iron staining and hydroxyl alteration signals are extracted, a geological and remote sensing coupling technology is carried out, alteration anomaly is superposed with a geological map, geochemical data and a construction interpretation result, a comprehensive remote sensing anomaly map is obtained, and the comprehensive remote sensing anomaly map is obtained. According to the method, the multi-parameter grading model GGS is established, and the target region is graded based on the multi-parameter grading model GGS, so that the probability of missed judgment of the target region is reduced.

The invention provides a gold mine prospecting target area intelligent delineation method and system based on multi-source geological data, and relates to the technical field of mineral exploration. Remote sensing images, geophysical exploration and earth surface rock chemical data associated with gold mine mineralization are collected, and according to the gold mine mineralization characteristics, the gold mine prospecting target area intelligent delineation method and system based on the multi-source geological data are obtained; correcting gold element space distribution difference, decomposing mineral spectrum mixing information, and strengthening mineralization related characteristic information. Then mining a data feature association relationship based on the feature information, the physical exploration data and the geochemical data, constructing a metallogenic association network, and inputting the feature information to obtain metallogenic association parameters; calculating mineralization probability values point by point in combination with spatial distribution characteristics of the multi-source geological data, generating a continuous mineralization probability curved surface, finally screening areas with probability values higher than a preset threshold value, locking a conforming closed area in combination with a typical gold ore deposit model, and recording coordinate boundary information to generate a gold ore prospecting target area map. The intelligent and accurate delineation of the gold ore prospecting target area can be realized.

The invention provides a mineralization domain spatial distribution prediction method and system based on geological priori knowledge, and relates to the technical field of mineral exploration, and the method comprises the steps: obtaining drilling core test analysis data and magnetic measurement data of a target mining area; processing the geochemical data to obtain an element concentration data set, and performing potential field conversion on the magnetic measurement data to generate a magnetization direction data set; identifying geochemical anomaly information from the concentration data set by using a fractal algorithm, determining an anomaly range, fusing the anomaly range and the magnetization direction data set, performing three-dimensional visual modeling, and constructing a three-dimensional geologic model; and finally, extracting feature data in the three-dimensional geologic model, and inputting the feature data into a machine learning prediction model for processing to obtain a distribution diagram representing the existence probability of the mineralization domain in the mining area space. According to the invention, the accuracy of blind mineralization domain spatial position prediction is improved.

The invention belongs to a uranium mine multivariate information fusion method, and particularly relates to a sandstone type uranium mine geochemical data multivariate information fusion method. The method comprises the following steps: (1) carrying out standardization processing on original geochemical analysis element data of the sandstone type uranium mine; (2) calculating a correlation coefficient matrix of the original data, and screening elements with correlation coefficients greater than 0.4 as sample data of information fusion; step (3), calculating a covariance matrix of the sample data; step (4), calculating; (5) calculating an accumulated variance contribution rate; (6) calculating the score coefficient of each principal component, and calculating the score of each principal component; and step (7), according to the comprehensive principal component data, making a comprehensive principal component map, and carrying out geological anomalous body identification. The method has the remarkable effects that multi-information fusion is carried out, the influence of external interference factors can be reduced, the purpose of improving the geochemical data abnormal contrast and the data processing reliability is achieved, and a reliable basis is provided for delineation of the prospective area of the sandstone type uranium mine.

The present invention discloses a method and system for fusing remote sensing hyperspectral data with a geochemical element map. The method comprises: obtaining to-be-processed geochemical data and its corresponding remote sensing hyperspectral image, and performing preprocessing to obtain a geochemical element map and a hyperspectral surface reflectance image; enhancing and extracting feature information from the geochemical element map using a local singularity analysis method to obtain a local singularity index map; extracting diagnostic absorption features associated with preset target chemical elements from the hyperspectral surface reflectance image to obtain a hyperspectral diagnostic absorption feature map; then constructing a regression equation model using a random forest model; and inverting the singularity index based on the regression equation model and the diagnostic absorption features to obtain a high-resolution geochemical element local singularity index map for the target area. The present invention can fuse the geochemical element map with the spectral information of ground objects in the hyperspectral image, enriching the spatial details of the geochemical data and improving its accuracy.

The invention discloses a medicine extraction system and method based on a molecular biological technology and chromatographic equipment, and belongs to the technical field of pharmaceutical separation and purification. The medicine extraction system based on the molecular biological technology comprises a medicine extraction module, the medicine extraction module comprises a chromatographic column, a constant flow pump, a sample injector and a collector, the output end of the constant flow pump is connected with an inlet of the chromatographic column, the sample injector is connected with the chromatographic column through a valve, and the collector receives eluent output by the chromatographic column; the operation module is used for acquiring physical and chemical data of the target product, determining first operation data of the chromatographic equipment according to the physical and chemical data, and generating a first matching vector according to the physical and chemical data and the first operation data; predicting a prediction extraction result of the target product according to the first matching vector and a preset clustering recognition space; and the control module is used for controlling the constant flow pump according to the first operation data and the prediction extraction result, and the production efficiency is improved by reducing the test verification work.

A method involving collecting a first geochemical data set for a first plurality of produced water samples; collecting a second plurality of produced water samples; performing geochemical analyses on the second plurality of produced water samples to form a second geochemical data set; and combining the first and second geochemical data sets into a database. The method further includes determining, by a subject matter expert, a water type for each produced water sample in the database and training a machine-learned model with the database to predict the water type of a produced water sample given its geochemical data. The method further includes collecting a third plurality of produced water samples, performing geochemical analysis on the third plurality of produced water samples, and determining, with the trained machine-learned model, the water type for each produced water sample in the third plurality of produced water samples using the third geochemical data set.

The present invention discloses a chemical data processing and analysis method and system for chemical production, and relates to the field of data processing technology. The method comprises: real-time collection of chemical variables in chemical production; determining a first window step size at the first moment based on the overall change of the chemical variables in each data window in a first time period before the first moment; traversing the chemical variables based on the first window step size and a preset window length to obtain the next data window, and returning to loop execution based on the overall change of the chemical variables in each data window in the first time period before the first moment, determining the first window step size at the first moment until a chemical anomaly condition is met, thereby obtaining multiple data windows; and determining the consistency of each chemical variable with the abnormal change of chemical production, as well as the control degree of each chemical variable on the abnormal change of chemical production, based on the local change of each chemical variable in each data window in a second time period before the chemical anomaly moment.

The invention relates to the technical field of production management, and discloses an intelligent scheduling management method and system for liquid crystal display screen production laminating equipment, and the method comprises the steps: S1, obtaining laminating tasks of target management equipment, collecting physical and chemical data of the target management equipment in real time after any laminating task is completed, and storing the physical and chemical data in a database; the physical and chemical data is analyzed based on a convolutional neural network and a multi-layer perceptron, a residual environment vector is generated, then a glue environment management value is calculated, a single-task transition management value and a global risk accumulation management value are generated through a long-short-term memory network and the like, and a scheduling management index and a dynamic management threshold are obtained in combination with a fitting equipment utilization rate and the like. And finally, a management instruction is generated through comparison, so that the interference of different glue leveling can be avoided, the conditions of alignment slippage and poor local curing of the liquid crystal display screen are reduced, and the effect of dispatching management of the laminating equipment is achieved.

The application discloses a kind of mine target area prediction method, system, equipment and storage medium, including obtaining the element content chart of the region to be detected, and according to element content chart constructs chemical data set and is calculated to obtain element feature map, element feature map is carried out to obtain element scale feature map by inflation convolution operation, constructs a well-trained mine target area prediction source task network and multiple same structures target convolutional neural network, element scale feature map is input in corresponding target convolutional neural network with selected weight, to control target convolutional neural network in calculation by selected weight and carry out voting election to obtain final prediction result, can be combined multiple scale features to carry out mine target area prediction, while selectively migrate weight parameters in mine target area prediction source task network are used for each scale network training, solve the problem of few data, improve the accuracy and reliability of intelligent prospecting prediction.

A battery electrochemical intelligent analysis system and method based on charge-discharge curve data includes: a user interaction layer that receives charge-discharge data files and parameter configurations, and obtains natural language analysis requirements; an intelligent analysis layer, based on the LangGraph framework, adopting a two-layer architecture of coordination and execution layers, managing and coordinating supervisory agents, data processing agents, electrochemical analysis agents, data plotting agents, quality review agents, analysis report agents, and question-answering agents through state graphs, completing a complete intelligent analysis task from data processing, electrochemical parameter analysis, chart plotting, quality review, report generation to question-answering interaction; and an output layer that receives charts, reports, and question-answering results generated by the intelligent analysis layer, converts the format, and transmits them to the user interaction layer for display or download. This invention achieves automated and intelligent battery electrochemical data analysis without programming, lowers the professional threshold, and improves analysis efficiency and standardization.

The invention discloses a protein ligand binding affinity prediction method based on two-channel hierarchical interactive learning, and particularly relates to an affinity prediction method based on two-channel hierarchical interactive learning. The method comprises the following steps: S1, based on a protein-ligand compound space structure containing a three-dimensional structure and chemical data, constructing six chemical entity interaction diagram networks from an atomic scale to a substructure scale; s2, extracting covalent and non-covalent interaction information of protein-ligands from the internal and external channels at the same time by taking a dual-channel coding framework as a backbone; and S3, adopting a hierarchical interactive learning strategy. According to the affinity prediction method based on the two-channel hierarchical interactive learning, provided by the invention, understanding of protein-ligand complex interaction is enhanced through a technology of modeling richness and complexity of protein-ligand interaction information, so that the protein-ligand binding affinity is comprehensively and accurately predicted.

The invention provides a method for determining kneading first solidification of graphite negative electrode slurry and application of the method, and relates to the technical field of batteries. According to the method, multiple graphite negative electrode key physical and chemical data are extracted, and the kneading first fixation which is objective and difficult to quantify is digitalized in combination with the homogenate kneading first fixation and the fitting function equation, so that a green researcher can quickly formulate homogenate process parameters, and the problems of pole pieces and batteries caused by unstable slurry are avoided.

The present invention discloses a method and system for mineral resource classification prediction based on multi-source small sample joint learning. The method includes: pre-processing to construct a meta-training task; establishing an abnormal sample mining module for collaborative adversarial training at the latent space feature level and the sample level; training the abnormal sample mining module and generating samples to obtain highly abnormal samples; establishing a feature joint embedding module to obtain an initial class prototype, and randomly selecting highly abnormal samples for fine-tuning to obtain a final class prototype; and predicting the target task through iterative training of the network through the meta-training task. The present invention can effectively improve the quality of generated samples and the performance of the sample generator, so that the model can fully mine the deep features of multivariate geophysical and chemical data and enhance the ability to fit the true distribution of the data; the introduction of geological constraints can learn the metallogenic laws within the multi-source samples to a greater extent, and fully improve the robustness and accuracy of the mineral resource classification prediction model.

This invention relates to the field of computational chemistry data processing technology, and in particular to a DMol3 electron density difference data processing system and file generation method. The system includes: an input module for acquiring electron density files in specific formats for the overall system and each component generated by DMol3 calculations; a processing module for automatically reading the files and performing numerical calculations on the electron density differences; and an output module for simultaneously generating the calculation results into a first format file and a second format file. This invention, through a multi-format parallel output architecture, solves the technical problems of cumbersome post-processing workflows, single result file formats, and incompatibility with visualization software in existing DMol3 electron density difference data, achieving automation of the data processing workflow and cross-platform compatibility for result display.

The present invention belongs to the field of oil and gas development technology and discloses a method for identifying shale paleoenvironments based on elemental geochemical analysis: the method comprises the following steps: preparing the necessary elemental geochemical data and calculating the required ratios and indices; restoring the paleohumidity and paleotemperature of the target layer based on the elemental geochemical data; restoring paleoclimate characteristics based on the restored paleohumidity and paleotemperature of a single well, and drawing a vertical paleoclimate evolution characteristic map; analyzing paleosalinity changes through a combination of qualitative and quantitative paleosalinity analysis; performing redox restoration based on redox conditions and hypoxia-oxygen enrichment properties; and drawing a vertical paleowater environment evolution characteristic map based on the restored paleosalinity and redox properties of a single well. The method of the present invention improves the precision and accuracy of paleoenvironmental identification.

The application discloses a kind of safety evaluation method, system, equipment and procedure of chemical storage place, it is related to dangerous chemical management technical field, the method includes: to the space-time alignment of multi-source data;Wherein, the multi-source data includes storage chemical data, environmental data, equipment state data, management data and historical data;Extract the spatial feature of the multi-source data after space-time alignment, generate spatial risk weight chart;Pre-training risk evaluation model;The weight in the spatial risk weight chart is mapped to the risk evaluation model, and risk grade is obtained.Through the processing scheme of the present disclosure, potential risks of storage place can be found in time, early warning and emergency response are started, the possibility of accident occurrence is effectively reduced, and the safety of chemical storage place is improved.

The invention discloses a chemical data missing process fault detection method and system, and the method comprises the steps: converting two-dimensional data into a three-dimensional tensor through employing MDT, and carrying out the linear prediction and nonlinear prediction of an incomplete value through employing linear smooth CP and CP-SAE, and carrying out the reconstruction of normal data. And extracting a residual error and a feature space of the data in the data missing process through CP-SAE. And combining features extracted by smooth CP-decomposition and CP-SAE, and establishing three statistical magnitudes to realize fault detection. According to the method, the MDT and smooth CP decomposition method is used for solving the problems of data missing and time delay of data sample sampling, and the timeliness is high. The CP-SAE feature extraction robustness is high, and compared with a conventional linear method, the precision is high, and the response speed is high. The method is simple to operate, does not need repeated operation, and can extract the key features of the data while reconstructing the complete data set only by operating on the incomplete data set. The missing data can be effectively supplemented, the process monitoring requirement can be met, and the precision is high.

The invention belongs to the technical field of white spirit brewing, and particularly relates to a white spirit yield prediction method based on a machine learning algorithm, which comprises the following steps: (1) obtaining wine making process data and / or pit-entering fermented grain physical and chemical data; (2) processing the data obtained in the step (1); (3) screening out characteristic variables which have great influence on the model as modeling characteristics; and (4) constructing a Baijiu yield prediction model, the model being selected from a random forest or XGBoost, and hyper-parameters of the model being adjusted and optimized by a Bayesian optimization algorithm or a genetic algorithm. The baijiu yield prediction method provided by the invention is high in accuracy and has important significance for developing intelligent brewing.

The invention discloses a laboratory dangerous behavior alarm method based on multi-dimensional data analysis, and the method comprises the steps: analyzing historical data, constructing a dangerous behavior feature library stored in a state transition chain form, and representing a key path from an operation combination to an accident through each chain; operation, equipment and chemical data are collected in real time through a multi-source sensor, and a current association action mode is extracted through time sequence segmentation and dynamic association analysis; matching the associated action mode with a state transition chain in a feature library, and positioning a current risk node; according to the backward transfer relation of the state transfer chain from the current node, predicting the most likely follow-up risk action or state; and if the predicted action belongs to the key node for directly triggering the accident, early warning is given out before implementation of the predicted action. According to the method, advanced identification and accurate early warning of risk evolution in a complex behavior sequence are realized through structured characterization of the risk path and real-time chain matching prediction, and the timeliness, accuracy and prevention capability of alarm are remarkably improved.

The invention belongs to the field of engine oil state monitoring, and discloses an intelligent engine oil state monitoring method and system based on multi-parameter sensing and deep learning. Preprocessing the collected engine oil physical and chemical data; inputting the engine oil physical and chemical data into the bidirectional LSTM engine oil life prediction model, and dynamically generating an engine oil remaining use cycle evaluation value through the bidirectional LSTM engine oil life prediction model; and judging the oil level of the residual use cycle evaluation check-in machine of the engine oil. According to the intelligent engine oil state monitoring method and system integrating multi-parameter real-time sensing, dynamic data compensation, edge calculation optimization, cloud intelligent analysis and graded early warning linkage, the limitation that a traditional engine oil detection means is single in function, lags behind time and depends on experience judgment is broken through; a conventional engine oil monitoring device is upgraded into an intelligent operation and maintenance platform integrating state sensing, service life prediction, fault early warning and maintenance decision.

The present application relates to the technical field of meat detection, and particularly relates to a data-driven meat quality rapid detection method and system, which collects the physical and chemical data such as spectrum, image, pH value, temperature and humidity, volatile gas and the like of a meat sample through a multi-modal sensor, constructs a meat quality characteristic map, analyzes the map by using a machine learning algorithm, identifies key influencing factors and extracts quality characteristic data such as spectrum, texture and chemistry, analyzes the influence law of different factors on quality based on the characteristic data, obtains meat quality influence data, determines the judgment parameters of each quality grade in combination with the influence data, constructs and trains a deep learning detection model, inputs the real-time data of the meat to be detected into the model, realizes rapid identification and evaluation of quality, makes grade judgment according to the evaluation result, and automatically executes classification processing, improves detection accuracy and stability, and realizes non-destructive, intelligent and efficient meat quality detection.

The embodiment of the invention provides a dangerous chemical database design method and system, and belongs to the technical field of data processing. The system comprises a system management module, a database management module, a hazardous chemical data module, a scene adaptation module and a dynamic analysis and evaluation module. The system management module comprises a user management sub-module and a role management sub-module; the database management module comprises a hazardous chemical substance data management sub-module and a report data management sub-module; the hazardous chemical substance data management sub-module is used for newly adding, editing and deleting hazardous chemical substance data; the report data management sub-module is used for newly adding, editing and deleting report data; the hazardous chemical data module is used for storing data information of chemicals; the scene adaptation module is used for adapting to different management scenes; the dynamic analysis and evaluation module is used for generating a management scheme. According to the invention, customer groups can be analyzed according to basic data, customer resources can be developed in a targeted manner, and statistical data of chemical risk assessment can be summarized and processed.

The invention provides a tea processing strategy determination method based on chemical and sensory characteristics and a related device, and relates to the technical field of tea processing. Obtaining a tea sample; carrying out sensory evaluation on the tea samples, wherein the sensory evaluation comprises subjective sensory scoring and objective electronic sensory analysis; carrying out chemical component analysis on the tea leaf sample, wherein the chemical component analysis comprises detection of non-volatile compounds and volatile compounds; on the basis of sensory evaluation and chemical analysis results, correlation chemical data and sensory attributes are analyzed through multivariate statistics to generate analysis results; optimized tea leaf processing parameters including time, temperature or intensity parameters of a processing technology are determined according to an analysis result, and the processing parameters are scientifically optimized by integrating sensory evaluation and chemical analysis and applying multivariate statistics to analyze associated data, so that the method has the advantages.

The present application relates to the technical field of chemical detection, in particular to a dangerous chemical data acquisition and detection method based on Raman spectrum. The method comprises the following steps: obtaining target noise degree values of each spectrum data point on a peak signal segment and relative deviation degree values of each spectrum data point on a baseline signal segment, distributing initial weights of the spectrum data points on the original Raman spectrum signal according to the target noise degree values and the relative deviation degree values to obtain initial weights of each spectrum data point on the original Raman spectrum signal, processing the original Raman spectrum signal according to the initial weights and the asymmetric least squares method to obtain a target Raman spectrum signal, and detecting the chemical to be detected according to the target Raman spectrum signal. The present application can improve or ensure the detection effect of the dangerous chemicals.

The invention relates to a method, a system and a medium for monitoring and evaluating natural attenuation of a polluted site, and belongs to the technical field of pollution monitoring. Microbial populations are enriched by utilizing a porous structure to obtain microbial information of the polluted site, and then the microbial information of the polluted site is tracked and evaluated; the composition and activity of the microbial community, the migration and transformation of the pollutants and the biodegradation potential of different pollutant concentrations are obtained, so that whether the pollutants can be subjected to microbial degradation in an actual underground water environment or not is evaluated based on the composition and activity of the microbial community, the migration and transformation of the pollutants and the biodegradation potential of different pollutant concentrations; and a site natural attenuation monitoring system is established according to pollutant migration and transformation characteristics, geochemical data and microbial data. According to the method, the change rule of pollutants in a polluted site is analyzed by taking biodegradation change as a main element, the main attenuation process of natural attenuation is determined, a pollutant migration and transformation analytical model is established, and the remediation effect of the pollutants is predicted.

The application discloses a grounding grid corrosion rate prediction method, medium and system, and comprises the following steps: training a neural network model by using a first grounding grid corrosion rate and first grounding grid soil physical and chemical data to obtain a grounding grid corrosion rate prediction inversion model; inputting a second grounding grid corrosion rate into the grounding grid corrosion rate prediction inversion model to output second grounding grid soil physical and chemical data; merging the first grounding grid soil physical and chemical data and the second grounding grid soil physical and chemical data to obtain third grounding grid soil physical and chemical data, and merging the first grounding grid corrosion rate and the second grounding grid corrosion rate to obtain a third grounding grid corrosion rate; training the neural network model by using the third grounding grid soil physical and chemical data and the third grounding grid corrosion rate, optimizing the weight value and the threshold value through a simulated annealing algorithm to obtain a grounding grid corrosion rate prediction model; and inputting actually collected soil physical and chemical data into the grounding grid corrosion rate prediction model to output a grounding grid corrosion rate. The application has high prediction accuracy.