127 results about "Chemical data" patented technology

The invention relates to the technical field of material determination, and particularly discloses an intelligent screening method and system for traditional Chinese medicine decoction pieces, and the method comprises the steps: S1, obtaining decoction piece standard data of to-be-detected traditional Chinese medicine decoction pieces, collecting decoction piece physical data and decoction piece chemical data of the to-be-detected traditional Chinese medicine decoction pieces, and obtaining historical detection data of the to-be-detected traditional Chinese medicine decoction pieces; s2, calculating a feature deviation value of each feature in the physical feature vector of the to-be-detected traditional Chinese medicine decoction piece and a physical deviation value of the to-be-detected traditional Chinese medicine decoction piece; s3, constructing a chemical detection model, and determining chemical deviation data of the to-be-detected traditional Chinese medicine decoction pieces; and S4, determining the traditional Chinese medicine decoction piece grade of the to-be-detected traditional Chinese medicine decoction pieces and performing intelligent screening. According to the method, a comprehensive and accurate decoction piece standard system can be constructed, multi-dimensional and quantitative evaluation of the quality of the decoction pieces is realized, the grade of the decoction pieces is comprehensively and accurately judged, subjective errors and quality misjudgment are avoided, the screening efficiency and accuracy are improved, and a scientific solution is provided for quality control of traditional Chinese medicines.

The invention discloses a microwave and radio frequency integrated treatment system which comprises a multi-source information acquisition module, a feature extraction module, a feature modeling module and an instruction generation module. The multi-source information acquisition module acquires internal temperature distribution data and contact impedance spectroscopy data of a focus through a sensor array, and forms focus materialization data in combination with focus space information; the feature extraction module performs space-time registration on the focus materialization data by using a space-time registration algorithm to obtain focus comprehensive thermal impedance features; the feature modeling module extracts space-time evolution features of the comprehensive thermal impedance features of the focus by using a lightweight convolutional neural network, and constructs a thermal impedance dynamic expansion model; the instruction generation module generates an initial control instruction sequence used for controlling the microwave and radio frequency integrated treatment equipment based on the thermal impedance dynamic expansion model in combination with the treatment scheme.

The invention discloses a method and a system for estimating the content of key metal elements of a crusting based on a hyperspectrum, and belongs to the technical field of deep sea mineral detection. The method comprises the following steps: selecting a crusting sample, obtaining hyperspectral data and chemical data of the crusting sample, establishing a point selection data set at a corresponding position, deleting abnormal sample points, and calculating the content of the key metal elements of the crusting sample; the method comprises the following steps: selecting a characteristic wave band of a crusting sample through preprocessing and CARS to obtain a characteristic wave band data set; and based on an MLP neural network model, performing model training through the data set, and constructing an estimation model for estimating the grade of the crusting sample. Compared with the prior art, the method has high accuracy and high predictive capacity, by inverting chemical data of the whole crusting section and applying the scheme for estimating the content of the key metal elements of the crusting, nondestructive grade and abundance analysis of the crusting sample can be achieved, and a reliable scientific basis is provided for seabed detection and multi-metal mineral exploitation.

The invention discloses a safety evaluation method, system, equipment and program for a chemical storage place, and relates to the technical field of dangerous chemical management, and the method comprises the steps: carrying out the time-space alignment of multi-source data; wherein the multi-source data comprises stored chemical data, environment data, equipment state data, management data and historical data; extracting spatial features of the multi-source data after space-time alignment, and generating a spatial risk weight map; pre-training a risk evaluation model; and mapping the weight in the spatial risk weight map to the risk evaluation model to obtain a risk level. Through the treatment scheme disclosed by the invention, potential risks of the storage place can be found in time, early warning can be performed in advance, emergency response can be started, the possibility of accidents can be effectively reduced, and the safety of the chemical storage place can be improved.

The invention relates to the technical field of chemical data processing, and provides a retrieval enhancement generation method and system oriented to chemical corpus data. The method comprises the steps that a special lexicon and a general lexicon for chemical knowledge are built, comprehensive and accurate chemical term resources are provided for a large model, and the large model and the RAG technology can better extract and understand the terminologies of the chemical industry; by designing a chemical engineering knowledge weighted graph, all entity words and text blocks in the graph are endowed with different weights, and each knowledge element can be paid attention to different degrees according to the weights in the retrieval process; a dynamic self-adaptive mechanism is introduced, the content of the chemical knowledge special lexicon can be dynamically enriched according to the updating and changing conditions of the knowledge in the chemical field, new chemical terms are contained in the chemical knowledge special lexicon in time, it is ensured that the large model can rapidly learn and master the latest knowledge, and the application performance and effect of the large model in the chemical field are remarkably improved.

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 mining area heavy metal monitoring method and system based on spatio-temporal evolution, relates to the technical field of environmental pollution monitoring, and solves the technical problem of inaccurate monitoring result caused by incomplete data and single analysis method in the prior art. According to the method, time sequence hyperspectral data and time sequence geochemical data of a target area are collected, space-time alignment is carried out, and space-time consistency of the data is ensured; the hyperspectral data and the geochemical data are processed through a first model and a second model which are constructed based on an artificial intelligence model, a heavy metal inversion result and a prediction result are obtained, the weight of the heavy metal inversion result and the weight of the prediction result are dynamically adjusted through a weight adjustment function set in a third model, and a heavy metal monitoring result is comprehensively obtained; according to the method, the reliability and correlation of different data sources are fully considered, the actual concentration and distribution condition of the heavy metals in the mining area can be reflected more accurately, and a reliable basis is provided for environmental governance and risk assessment of the mining area.

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 discloses a chemical data processing analysis method and system for chemical production, and relates to the technical field of data processing. The method comprises the following steps: collecting chemical variables in chemical production in real time; determining a first window step length at the first moment based on the overall change condition of the chemical variables in each data window within a first time period before the first moment; and traversing the chemical variables based on the first window step length and a preset window length to obtain a next data window, and returning to circularly execute the process of determining the first window step length at the first moment based on the overall change condition of the chemical variables in each data window within a first time period before the first moment until the chemical abnormal condition is met, obtaining a plurality of data windows; and on the basis of the local change condition of each chemical variable in each data window in a second time period before the chemical abnormal moment, the abnormal change consistency of each chemical variable and chemical production and the abnormal change control degree of each chemical variable on the chemical production are respectively determined.

The invention relates to the technical field of textile engineering, and discloses an automatic high-precision collaborative degumming system for mulberry silk fabrics, which comprises the following modules: an automatic control module for monitoring and precisely controlling the degumming process in real time; the collaborative operation module is used for coordinating work between each device and the station and is responsible for collaborative operation of multiple devices; the data acquisition and sensing module is used for acquiring and transmitting various physical and chemical data in the mulberry silk fabric degumming process; the data analysis and optimization module is used for optimizing a degumming process in real time through a big data technology and a machine learning algorithm; the intelligent feedback and early warning module is used for monitoring the running state in real time and giving out early warning and automatically adjusting when the running state is abnormal; and the user management and interface module is used for interaction of operators. According to the method, big data analysis and a machine learning algorithm are combined, the degumming parameters can be automatically adjusted according to different characteristics of the mulberry silk fabric, the temperature and humidity, the pH value and the degumming time are optimized, and the degumming process is more accurate.

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 relates to the technical field of soil detection, in particular to a soil salinity and alkalinity degree detection method and a soil salinity and alkalinity degree detection system applied to anti-corrosion operation of a grounding grid. Acquiring at least two groups of soil physicochemical data information and at least two groups of environment state data information of the area where the grounding grid is located; performing multi-channel data feature processing on the at least two groups of soil physical and chemical data information to obtain a first soil corrosion influence feature set; performing fluctuation analysis on the at least two groups of environment state data information to obtain a correction factor set; performing feature correction on the first soil corrosion influence feature set based on the correction factor set to obtain a second soil corrosion influence feature set; inputting the second soil corrosion influence feature set into a preset soil corrosion prediction model to obtain a soil corrosion degree index; various factors are comprehensively considered, and the accuracy of corrosion prediction and the effectiveness of anti-corrosion measures are improved.

The invention belongs to the technical field of soil ecological risk monitoring, and discloses a soil ecological risk assessment method and system based on multi-source data analysis. The system comprises a data acquisition module, a data preprocessing module, a comprehensive data analysis module, a database module, a risk dynamic early warning module and a decision report output module, the data preprocessing module is used for preprocessing a chemical data set to obtain a second chemical data set; the comprehensive data analysis module is used for processing the second chemical data set to obtain a risk prediction value; the risk dynamic early warning module is used for analyzing the risk prediction value and outputting a risk decision report; the decision report output module is used for processing and outputting the risk decision report; in general, the method has the remarkable advantages of being high in data prediction accuracy, good in auxiliary evaluation capability and high in risk decision-making practicability.

This application is applicable to the field of resource exploration technology, and provides a method and device for preferentially selecting gold ore target areas based on geochemical anomalies of elements. The method includes: obtaining geochemical sample data of multiple sampling points in the research area; training a converter model using the geochemical sample data to obtain a trained converter model; initializing the model parameters of the trained converter model to obtain a student model; performing self-distillation training on the student model using the geochemical sample data to obtain a geochemical data reconstruction model; inputting the geochemical sample data of the sampling points into the geochemical data reconstruction model for processing to obtain the reconstructed geochemical data of the sampling points; calculating the geochemical anomaly scores of each sampling point based on the reconstructed geochemical data of each sampling point, and determining the gold ore target areas in the research area based on the geochemical anomaly scores of multiple sampling points. This application can meet the need for accurately preferentially selecting target areas in gold ore exploration.

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 present application discloses an Internet of Things intelligent monitoring and alarm method and system for water quality detection, which relates to the technical field of environmental protection monitoring. The method includes: respectively sampling water quality electrochemical data for corresponding monitored water area blocks based on each Internet of Things node; extracting electrochemical time-series sensing features respectively corresponding to each water quality electrochemical data; extracting pattern time-series dependence features corresponding to the electrochemical time-series sensing features based on a water quality anomaly recognition model to predict water quality anomaly risks; identifying corresponding water quality anomaly risk event types based on an anomaly event recognition model; and performing water area water quality pollution alarm operations according to the block identifiers of each risk monitoring water area block and the corresponding water quality anomaly risk event types. Thus, based on the distributed water quality monitoring of Internet of Things nodes and intelligent risk identification and water quality pollution early warning operations, a significant improvement in the real-time performance, comprehensive coverage, and intelligent management ability of water quality monitoring is achieved.

The invention discloses an environmental data real-time monitoring and analysis method and system for a planting field, relates to the technical field of agricultural planting, and aims to solve the technical problems that hidden changes of environmental data cannot be monitored, multi-source data is difficult to comprehensively analyze, and problem diagnosis and processing are inaccurate in the prior art. Comprising the following steps: S1, monitoring planting environment data of a planting field in real time to obtain water quality data, soil physical and chemical data, soil microorganism data and crop image data corresponding to different positions; s2, arranging the four types of data according to a time sequence, and generating a water quality data change curve, a soil physical and chemical data change curve, a soil microorganism data change curve and a crop image data change curve; and S3, scoring the four types of data change curves according to corresponding parameters, the multi-source planting field environment data is collected in real time, the environment deterioration condition and position can be accurately judged through analysis, and the monitoring problem in the prior art is solved.

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 present application discloses a pathogen recognition and classification method and a processing device based on deep learning. The processing device includes an air disinfection processing device and related subsystems. The pathogen recognition and classification method includes: receiving multi-modal sensor data regarding a target space; wherein the multi-modal sensor data includes at least one of environmental data, chemical data, biological data, and optical data; analyzing the multi-modal sensor data to obtain the pathogen type and concentration; and adjusting the working parameters of the air disinfection processing device and related subsystems according to the pathogen type and concentration to perform targeted disinfection on the target space. Through the above method, rapid recognition and response to different pathogens and chemical pollutants are achieved, and the energy usage efficiency of the system is improved.

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.