68 results about "Chemical data" patented technology

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 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.

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 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.

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 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 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 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.

A computer-implemented method may collect a set of known drug compounds from a database and create an enhanced dataset using labeled and unlabeled chemical and biological data. The computer-implemented method may train a neural network using the extended training set to generate a predicted score of the biological activity of the candidate drug compound to the biological target, where the contribution of a second plurality of unlabeled samples in training the neural network is scaled by a parameter. The computer-implemented method may output one or more fine tuning neural network models capable of generating candidate drug compounds with predicted activity. A computer-implemented method may generate a candidate drug compound by inputting the candidate drug compound having chemical data into a trained neural network model.

A method to provide information based on a health analysis to a user interface device including detecting a chemical from a human or an an animal by a radio frequency tag having a sensor portion, the radio frequency tag sending a signal to a radio frequency reader in communication with a computing device, detecting at least one vital sign of the user by a wearable device on the user, the wearable device sending a second signal containing vital sign data to the computing device, analyzing the chemical data and the vital sign data using artificial intelligence by the computing device and generating information about the chemical data and / or the vital sign data, and sending the information to a user interface device

The invention belongs to the technical field of water inrush source identification, and particularly relates to a CNN-LSTM-Attention-based multi-source data fusion water inrush source identification system, which comprises a CNN module, an LSTM module, an attention mechanism module, a CNN-LSTM-Attention model construction module and a model evaluation index module, and is characterized in that a CNN-LSTM-Attention model can effectively fuse static characteristics and dynamic evolution laws of hydrochemical data, and the model evaluation index module is used for evaluating the dynamic evolution laws of the hydrochemical data. The precision and robustness of water source identification are obviously improved; the introduction of the attention mechanism enhances the interpretability of the model, so that the model can focus on key discrimination indexes like domain experts, and the transparency and reliability of decision making are improved; the model provides a new technical path for intelligent identification of a water inrush source of a coal mine, and promotes the normal form transformation of water disaster prevention and control from'experience-driven 'to'data-driven'.

The invention discloses a feature selection method and system for screening chemical accident influence factors, and the method comprises the steps: determining all features causing a chemical accident according to the historical chemical data of a target chemical device region, and screening a plurality of features meeting a preset importance requirement for the chemical accident from the features as the pre-selected features of the chemical accident in the current time period; inputting the pre-selected features into a preset feature subset optimization model to obtain an optimal feature subset; for each feature in the pre-selected features, obtaining evaluation results which are made based on expert knowledge and respectively represent the feature importance of the current time period and the feature importance of the historical time period, evaluating the reliability degree of each evaluation result according to the source of each evaluation result, and further combining with the optimal feature subset to obtain the feature importance of the current time period and the feature importance of the historical time period. And generating a comprehensive score value representing the actual importance of each feature to the chemical accident in the current time period, thereby obtaining the optimal feature adaptive to the chemical accident in the current time period. The characteristics causing chemical accidents can be scientifically selected.

A computer implemented method for use in conjunction with a computing device, system, network, and cloud with touch screen two dimension display or augmented / mixed reality three dimension display comprising: obtaining, analyzing and detecting user blood, saliva, hair, urine, stool, fingernail, height, weight and skin sampling analysis chemistry data, mapping the blood, saliva, hair, urine, stool, fingernail, height, weight and skin data into a database associated with a specific user, applying the data with optimization equations, mapping equations to food and beverage chemistry, scoring or ranking a plurality of optimized results such that a user may order food and beverage from a food / beverage distribution point or have food / beverage delivered to the user which has been specifically optimized for their specific biochemistry characteristic target ranges. The method is particularly useful in enhancing online internet search engine results.

This invention discloses a molecular optimization method and system based on rationale rules and reinforcement learning. Specifically, this invention provides a Rationale-Opt model. This model extracts rationale rules with the potential to influence the target properties of molecules from large-scale chemical data and transforms them into executable operators, guiding a reinforcement learning-based algorithm to perform chemically intuitive structure editing, thereby optimizing the molecule. The model of this invention exhibits good performance in single-objective and multi-objective optimization tasks, as well as zero-shot and few-shot scenarios, and can still achieve good multi-objective optimization in scenarios lacking multi-objective labeled data.

A method for curation of diverse biological and / or chemical data comprising identifying a plurality of data sets derived from one or more data sources each comprising raw data points related to properties of compounds of interest; generating a configuration for a data transformation process; generating a first curated data set comprising a plurality of harmonized data points by executing the data transformation process on the plurality of data sets in accordance with the first configuration; implementing an error reduction evaluation of the plurality of harmonized data points; analyzing the output of the error reduction evaluation to detect that a data issue exists; and upon detection that the data issue exists, applying at least one adjustment to the one or more data points and / or the first configuration to reduce or eliminate the data issue to enhance a downstream process that inputs the enhanced first curated data set as a data input.

The invention relates to the technical field of soil pollution remediation, in particular to an intelligent monitoring and feedback regulation and control system for soil heavy metal pollution remediation, which comprises a cloud control center and a field execution array, the cloud control center divides a monitoring area into a plurality of homogeneity logic clusters, and establishes a logic affiliation relationship between a master control anchor point and a following node in each homogeneity logic cluster; the cloud control center compares the in-situ physicochemical data state of the main control anchor point with the vegetation remote sensing data trend corresponding to the position in real time; and when the comparison result shows that the state of the in-situ physical and chemical data meets the preset restoration standard and the trend of the vegetation remote sensing data is still within the preset stress range, the system judges that the system is in a biological lag state and triggers a state replication mechanism. The response time difference contradiction between the physicochemical truth value and the biological representation can be solved, and the lag state is accurately identified to prevent excessive repair.

The invention belongs to the field of edible fungus cultivation, and particularly discloses a volatile substance fingerprint spectrum-based lentinus edodes stick maturity rapid identification method, which comprises the following steps: S1, capturing volatile substances released by a lentinus edodes stick to be detected in a non-destructive manner; s2, obtaining volatile substance detection data of the mushroom stick to be detected; s3, generating an actual fingerprint spectrum of the to-be-detected bacteria stick based on the volatile substance detection data; and S4, inputting the characteristic parameters of the actual fingerprint spectrum into a pre-trained maturity discrimination model, and outputting a maturity identification result of the to-be-detected mushroom stick by the model, according to the invention, by detecting and analyzing the volatile substances released by the bacteria stick, qualitative judgment originally depending on subjective experience is converted into quantitative judgment according to specific chemical data and an intelligent model. According to the method, human factor interference is eliminated, the judgment accuracy is high, a unified and reliable identification standard is established, and fruiting management errors and yield loss caused by erroneous judgment are effectively avoided.

This application belongs to the field of power system grounding grid safety technology, specifically relating to a method, device, medium, and equipment for detecting the health status of a grounding grid. The method includes: collecting multi-dimensional heterogeneous data of the grounding grid to be detected, wherein the multi-dimensional heterogeneous data includes electrical data, chemical data, physical data, and excitation response data; preprocessing the multi-dimensional heterogeneous data; constructing a grounding grid health status detection model and training the model; and inputting the preprocessed multi-dimensional heterogeneous data into the trained grounding grid health status detection model to detect the health status of the grounding grid to be detected. This application, through multi-dimensional data fusion and intelligent model analysis, can achieve early and accurate diagnosis of the grounding grid health status and precise fault location.

The application provides a mineralization domain spatial distribution prediction method and system based on geological prior knowledge, and relates to the technical field of mineral exploration, wherein the method comprises the following steps: obtaining drilling core assay analysis data and magnetic survey data of a target mining area; then processing geochemical data to obtain an element concentration data set, and performing a potential field conversion on the magnetic survey data to generate a magnetization direction data set; then using a fractal algorithm to identify geochemical anomaly information from the concentration data set and determine an anomaly range, fusing the anomaly range and the magnetization direction data set, and then performing three-dimensional visualization modeling to construct a three-dimensional geological model; finally, extracting feature data in the three-dimensional geological model and inputting the feature data into a machine learning prediction model for processing to obtain a distribution map representing the existence probability of the mineralization domain in the space of the mining area. The application improves the accuracy of predicting the spatial position of a concealed mineralization domain.