173results about "Chemical data visualisation" patented technology

This invention relates to the field of mechanical property monitoring technology for mine backfill bodies, specifically to a system and method for acquiring and analyzing tensile strength data of mine backfill bodies. The system comprises a data acquisition unit, a physical information coupling unit, a tensile strength inversion unit, and a spatiotemporal evolution mapping unit. This invention acquires the waveguide flight time and energy attenuation coefficient of the backfill body using an embedded guided wave sensor array. It uses the monotonically increasing law of hydration degree as a physical constraint to train the physical information coupling network, outputting a compliant hydration degree time series. Based on a hydration degree-tensile strength mapping relationship library, it inverts and fits to generate a strength growth curve, which is then loaded into a four-dimensional digital twin model to invert the internal tensile strength gradient field. It automatically identifies local abnormal regions where the growth rate is below the safety threshold, improving the physical rationality and accuracy of the inversion results, and realizing visualization of the spatiotemporal evolution of backfill body strength and early safety warning.

This invention discloses a non-invasive method, system, device, and storage medium for monitoring student attention based on gas sensing, belonging to the field of non-invasive sensing technology. The method includes: calculating a net physiological gas signal based on environmental baseline data and initial gas concentration data of the target student; calculating physiological characteristic indicators of the target student based on the net physiological gas signal, and determining the target student's metabolic rate level based on the physiological characteristic indicators; obtaining a state time-series signal based on the net physiological gas signal according to a preset discrimination criterion; inputting the net physiological gas signal and the state time-series signal into a pre-trained attention monitoring model to obtain the probability distribution of the target student's attention state; and determining the target student's attention state based on the target student's metabolic rate level and the probability distribution of the attention state. This invention assesses the cognitive state by capturing the gas released by the student, achieving non-invasive monitoring.

This invention relates to the field of intelligent predictive control technology, and more particularly to a digital twin predictive-driven electrochemical intelligent control system and method for circulating cooling water, comprising a physical operation device and a control device. The control device collects water quality monitoring parameters through a data acquisition module, and a state assessment module uses the entropy weight method to map and calculate a comprehensive index. A trend inference module inputs the comprehensive index into a long short-term memory network model for forward propagation, outputting a predicted water quality index, which is then iteratively optimized by a hyperparameter optimization unit using a sparrow search algorithm to optimize the network connection parameters. An instruction optimization module compares the predicted water quality index with a safety threshold to generate control instructions, driving the physical operation device to perform electric field adjustment, mechanical scaling, and coordinated water replenishment and chemical dosing. A virtual mapping module constructs a three-dimensional digital model to achieve state early warning. This invention solves the technical problem of lag in existing feedback control, achieving precise pre-control of water quality in circulating cooling water systems.

The application discloses a kind of visualization methods for microcosmic action mechanism of steam displacement heavy oil, which comprises: obtaining the porous structure information of rock sample;According to the structure information of rock sample, design pore network structure and make microfluidic chip;Build steam displacement heavy oil visualization implementation system, and adopt steam displacement heavy oil visualization implementation system to collect the phase distribution and temperature distribution of microfluidic chip in displacement process;Boundary extraction is carried out to experimental picture, and the dynamic change process of each phase in displacement process is obtained and the interaction relationship with temperature distribution change is obtained.The application designs pore network chip according to the pore structure information of rock sample, obtains the phase distribution and temperature distribution in displacement process, and is used to study the microcosmic phase distribution characteristics and temperature distribution characteristics of fluid in heavy oil reservoir reservoir, and then the microcosmic mechanism of reservoir fluid movement is explored, so as to provide a practical visualization method for the research of steam drive oil mechanism.

The present application belongs to the technical field of oil and gas field development chemical agent design, and particularly relates to a design method and system of an ultra-low interfacial tension oil displacement agent based on AI4S. A cross-modal retrieval is used to construct a molecular structure-reservoir condition mapping database, high-dimensional embedding vectors are generated by RDKit / ChemBERTa / Mol2Vec to construct a knowledge graph, historical schemes are retrieved by a RAG mechanism and input into a DeepSeek-R1 / Qwen3 large model, an interpretable molecular design and action mechanism are generated, and the feedback closed loop optimization is verified by experiments, so that the data-AI-experiment closed loop is realized, and the research and development efficiency and design accuracy are significantly improved.

This invention relates to the field of semiconductor material detection technology, specifically to a method and system for detecting gallium vacancy concentration in gallium nitride (GaN) materials. The method includes: acquiring photoluminescence spectral data sets of a GaN sample under multiple excitation wavelengths; extracting the emission spectral intensity sequence corresponding to each excitation wavelength; calculating the peak intensity ratio between the yellow band emission peak and the band-edge emission peak; and constructing an initial GaN characterization parameter sequence. An improved Kalman filter algorithm with a dynamic weight allocation mechanism for multiple observation matrices is used to suppress temporal noise in the initial parameter sequence, resulting in an optimized GaN characterization parameter sequence. The optimized parameter sequence is then imported into a pre-trained concentration inversion model for feature mapping, outputting the detected GaN vacancy concentration value of the GaN sample. Based on this detection result, material quality grading is determined. This method broadens the dimensions of spectral information acquisition, effectively suppresses temporal noise interference, and achieves quantitative analysis of GaN vacancy concentration.

This material designing device is provided with: a search unit (121) that sets a generation condition (21) for generating a three-dimensional structure (23) of a material, the generation condition (21) including information indicating at least one of the characteristic and structure of the material; a cross-sectional image generation unit (123) that generates a cross-sectional image (22) of the material on the basis of the generation condition (21) set by the search unit (121); and a three-dimensional structure generation unit (124) that generates the three-dimensional structure (23) of the material on the basis of the cross-sectional image (22) generated by the cross-sectional image generation unit (123).

The invention relates to the technical field of CO2 burying and storing amount calculation, in particular to an exhausted condensate gas reservoir CO2 structure burying and storing amount calculation method and device, electronic equipment, a storage medium and a computer product. The method comprises the following steps: constructing a depleted condensate gas reservoir CO2 buried substance balance equation; deducing the depleted condensate gas reservoir CO2 burying material balance equation, and constructing a depleted condensate gas reservoir CO2 structure burying amount calculation model; based on the depleted condensate gas reservoir CO2 structure burying stock calculation model, the depleted condensate gas reservoir CO2 structure burying stock is obtained. According to the method, the change of a mixed gas deviation factor after CO2 injection, the change of the saturation degree of residual stratum condensate oil in the CO2 injection process and the influence of deformation of rocks and bound water are considered. The calculation result of an example shows that the CO2 structure burying amount under different burying pressures in the new method is basically matched with the calculation result of a numerical simulation method, and the error is kept within 6.42%.

A compound-protein binding affinity prediction method performed by at least one processor includes receiving compound data and protein data, which interact with each other, generating an attribute vector of a compound and an attribute vector of a protein based on the input compound data and the input protein data, calculating an attention value based on the attribute vector of the compound and the attribute vector of the protein, generating a first interaction matrix based on the attention value, learning a first AI model to predict a binding affinity and a non-covalent interaction of compound-protein by using the first interaction matrix as learning data, and predicting the binding affinity and the non-covalent interaction of the compound-protein based on an output value of the first AI model.

The invention belongs to the technical field of liquid metal corrosion numerical simulation, and particularly relates to a liquid metal corrosion simulation method based on a near-field dynamic reaction diffusion equation. By adopting a near-field dynamics method, the problems of heterogeneity, damage and the like can be naturally simulated; the corrosion process is regarded as a chemical reaction process, the relation between a reaction term and the current density and the overpotential can be directly obtained through formula derivation, the influence of the concentration gradient on the corrosion process is relieved, and the reversible reaction can be simulated; a phase change mechanism of autonomous evolution of the interface is combined, an autonomous moving solid-liquid interface is obtained, a random mechanism of mechanical key formation and fracture is introduced into a phase change model, and random characteristics in the random mechanism are described. According to the method, the influence of some parameters or conditions on the liquid metal corrosion process can be researched while the liquid metal corrosion process can be visually and physically described, and favorable help is provided for research in the field of liquid metal corrosion.

The application provides a kind of energy storage battery state simulation and prediction system and method based on digital twinning, aiming at solving the problems of inaccurate state modeling, insufficient perception granularity, uninterpretable prediction and response lag of existing energy storage system.The method uses equivalent thermal model, electrochemical model and aging mechanism model to model the battery, introduces adaptive gating parameter fusion mechanism and data characteristics, realizes cross-scale state mapping and feature reconstruction from cell to system level;Using the electric-thermal-aging coupling relationship to generate baseline trajectory, combined with the data-driven residual correction mechanism to realize multi-time domain closed-loop prediction, identify potential faults and risks in advance through multi-time domain anomaly judgment system;Build a three-dimensional topological model to dynamically display state parameters, generate hierarchical alarm signals and provide operation suggestions.The system constructed by the application has high precision, interpretability, prediction and visualization capabilities, which can significantly improve the intelligent operation and maintenance level of energy storage battery system.

This invention discloses an artificial intelligence-based method for predicting drug interactions based on external entity association mapping, comprising the following components: (1) Extraction of intrinsic properties of drug molecules: The spatial geometry and atomic properties of molecules are processed using a Uni-Mol pre-trained model to obtain drug molecule characterization. (2) External entity association mapping: Projection blocks are constructed using a knowledge graph, and complex association patterns of external entities corresponding to drugs are captured through graph neural networks and global perception logic. (3) Deep fusion of multi-source heterogeneous information: Information is efficiently integrated using attention mechanisms and multilayer perceptrons to form the final fused characterization. (4) Prediction and decoding of interactions: The specific types of interactions are accurately decoded and predicted using a decoder architecture. (5) Interactive visualization and decision support: Visualization tools and AI decision support are provided to help researchers reduce trial-and-error costs and make scientific decisions.

Invention name: A plant chlorophyll content detection method based on hyperspectral and machine learning Abstract: The present application belongs to the field of hyperspectral imaging and machine learning technology, and discloses a plant chlorophyll rapid determination method based on hyperspectral and machine learning. The determination system of the present application comprises a hyperspectral imaging device, a spectral preprocessing module, a characteristic wavelength extraction module, a machine learning modeling module and a chlorophyll content prediction module, and is connected in turn through a data processing unit. The method combines hyperspectral imaging technology to obtain the original spectral data of the plant sample, filters the key wavelengths through spectral preprocessing and feature extraction algorithm, and establishes a chlorophyll content prediction model by using random forest regression, partial least squares regression and other machine learning models. The present application can realize the rapid and non-destructive detection of plant chlorophyll without chemical reagents, improve the determination accuracy and efficiency, and overcome the shortcomings of complex operation, long time consumption and strong destructiveness of the traditional detection method.

Disclosed is an apparatus for generating a chemical product passport, the apparatus comprising: one or more computing nodes; and one or more computer-readable media having thereon com puter-executable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform the following steps: receiving a request to provide a decentral identifier associated with emission data and a data owner, in response to the request, generating the chemical product passport including the decentral identifier and data related to the emission data; providing the chemical product passport for access by a data consuming service controlled by a data providing service associated with the data owner.

This invention discloses a strategy optimization method for digital mapping of soil carbon-nitrogen ratio, relating to the fields of soil fertility assessment and land health management. The invention includes the following steps: acquiring soil organic carbon and total nitrogen sample data from 0-0.3m depth; collecting data on five types of environmental variables—climate, topography, vegetation, soil properties, and human activities—and extracting them to sampling points; designing carbon-nitrogen ratio modeling strategies: direct modeling, indirect modeling, and zonal direct modeling; constructing a soil carbon-nitrogen ratio prediction model based on the quantile random forest algorithm; validating the model accuracy, and evaluating the applicability and stability of different modeling strategies through cross-validation and error analysis. This invention nests sampling and modeling design and integrates machine learning, optimizing the accuracy of soil carbon-nitrogen ratio inversion and providing a scientific basis for soil quality assessment and ecological environment management.

This invention discloses a molecular generation method and apparatus based on a hierarchical consistent diffusion model, belonging to the field of drug discovery. The method includes: acquiring the three-dimensional structural information of a target protein pocket and constructing a protein map; calculating normalized persistent entropy based on the protein map and performing feature mapping through a pre-trained topological proxy network to output pocket topological features; sampling initial noise coordinates from a standard normal distribution and initial discrete states from a uniform distribution to generate a noise atom map and a noise template map; iteratively executing a back-diffusion process using a pre-trained hierarchical consistent diffusion model based on the protein map, pocket topological features, noise atom map, and noise template map to output a ligand atom map and a ligand template map; and performing geometric consistency correction on the ligand atom map based on the ligand template map, and constructing a ligand molecule based on the corrected ligand atom map. Therefore, by implementing this invention, the accuracy of molecular generation can be improved.

Disclosed is a computer-implemented method for providing material identifier package usable for recycling of batteries, the method comprising the steps of: - providing at least one battery identifier and corresponding material configuration data associated with at least one component of the battery to be recycled; - determining at least one material identifier package by relating the at least one material configuration provided by the material configuration data to at least one material configuration processable by at least one plant; - providing the at least one material identifier package usable for recycling batteries including the at least one battery identifier.

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 relates to a chemical reaction prediction system, a control method thereof, and a learning method for a chemical reaction prediction system. More specifically, the present invention relates to a chemical reaction prediction system that performs forward reaction prediction based on electron flow, a control method thereof, and a learning method for a chemical reaction prediction system.

The application belongs to the technical field of food safety, and particularly relates to a volatile organic compound detection device based on olfactory visualization technology and a detection method thereof. The device comprises a fixed frame structure, a control module is arranged above the fixed frame structure, a detection platform is arranged below the fixed frame structure, a pipette and an image acquisition device and a sensor control device are respectively arranged above both sides of the detection platform, and the control module is connected with the pipette, the image acquisition device and the sensor control device. The control module is used for controlling the pipette, the image acquisition device and the sensor control device to operate in the detection platform area. The application can prepare a color-sensitive sensor printed with a metal porphyrin, obtain volatile gas of an organic compound, and cause a coordination reaction with the volatile gas. Image data before and after the reaction of the volatile gas with the organic compound is detected and analyzed through the image acquisition device, and corresponding chemometrics methods are combined, so that rapid quantitative analysis of the organic gas can be realized.

The application discloses a traditional Chinese medicine small molecule Gibbs free energy prediction method and system based on deep learning. The application receives a traditional Chinese medicine small molecule in the form of a SMILES string or a molecular structure file, and after structure verification and standardization processing, the traditional Chinese medicine small molecule is converted into a molecular graph representation. The molecular graph is input into a deep learning model. The model captures the molecular structure characteristics and interaction relationship through a graph representation learning mechanism, refines the representation through multi-layer updating, and finally outputs the predicted Gibbs free energy value of the traditional Chinese medicine small molecule through a pooling operation. The test set determination coefficient R2 is greater than or equal to 0.99. The application improves the calculation efficiency and prediction accuracy while maintaining accuracy. In addition, the model of the application supports general equipment deployment, and has strong deployment flexibility.

This invention provides a method for identifying polysaccharides from traditional Chinese medicine (TCM) based on multispectral fusion and machine learning, applicable to the rapid and non-destructive identification of polysaccharides from different types and origins. The method first uses infrared and Raman spectroscopy to acquire and construct a raw spectral dataset of TCM polysaccharide samples. The dataset is then preprocessed using standardization to eliminate noise, baseline drift, and other non-sample-related interferences. Effective spectral information is then filtered through feature extraction, followed by feature-level complementary fusion of the infrared and Raman spectral features. Based on the fused feature set, a machine learning classification model is constructed and trained using a cross-validation strategy to achieve accurate identification of the type and origin of TCM polysaccharides. Furthermore, the SHAP interpretability analysis tool is used to analyze the key spectral feature bands upon which the model's discrimination decisions rely, and to analyze the correlation between these key bands and specific functional groups and chemical bond vibrations in the TCM polysaccharide molecules, providing chemical mechanism support for the identification results.

This invention relates to the field of unconventional oil and gas field development technology, specifically to a method for determining dynamic capillary force in coal and rock. The method includes: selecting target core samples to prepare coal samples and obtaining the full-scale pore size distribution of the coal samples; constructing a three-dimensional random pore network model based on the full-scale pore size distribution; obtaining a topology-corrected static pore network model; establishing a dynamic model of contact angle variation with pore pressure; establishing dynamic deformation equations for pore and throat radii variation with effective stress; and simulating the gas-driven water process under different pore pressures based on an intrusion permeation algorithm, outputting capillary force-saturation curves under different pore pressure conditions, and forming a dynamic capillary force surface plot. This application has significant application value and promising prospects in the field of unconventional oil and gas field development, significantly improving the scientific nature of reservoir seepage behavior characterization and the rationality of development engineering decisions.

The present application relates to the technical field of super surface material performance prediction, aiming at solving the problems of narrow applicability, low precision and lack of dynamic feedback of existing prediction methods, and proposes a super surface material dielectric property evolution prediction method and system under space radiation environment. The method is: constructing a dynamically updated physical prior knowledge base; constructing a multi-field coupling calculation module, based on the knowledge base, carrying out radiation transport field, defect-carrier evolution field and dielectric field calculation, and at least establishing a dynamic feedback mechanism of defect-carrier evolution field and dielectric field; the output results of the module are visualized; based on experimental data, the multi-field coupling calculation results are verified and optimized, the parameters of the calculation module are calibrated, and the verified parameters and experimental data are stored in the physical prior knowledge base, realizing the dynamic updating of the physical prior knowledge base and the iterative optimization of the calculation module. The present application is suitable for the anti-radiation design and performance life evaluation of super surface devices in space environment such as aerospace and satellite.

The application relates to the technical field of soil analysis, in particular to a cane field soil organic matter influencing factor analysis method and system based on multi-source data, which comprises the following steps: acquiring the spatial distribution of all sampling points, using different colors to distinguish different years and different cane fields; calculating a spatial autocorrelation index to determine whether the sampling point distribution is aggregated, discrete or random; dividing the cane fields and analyzing the soil nutrients of each cane field for comparative analysis; constructing a random forest regression model; extracting feature data of each cane field and using SHAP values for interpretation; drawing a single feature data of each cane field and a corresponding SHAP value relationship scatter diagram to obtain a SHAP dependence diagram; using a PLS-SEM algorithm to estimate the random forest model to obtain direct effects or indirect effects; and verifying the importance ranking of SHAP by comparing the total effects of different driving factors. The application realizes closed-loop analysis from spatial pattern identification to machine interpretation.

The application relates to the technical field of sewage treatment, and discloses a sewage treatment process method based on an improved target genetic algorithm optimized LSTM, which is characterized in that the process method comprises the following steps: step S1, sewage treatment data acquisition and pretreatment; step S2, sewage treatment process modeling based on an LSTM; and step S3, multi-target genetic algorithm optimization design. The method comprehensively considers multi-dimensional performance indexes such as reaction time, energy consumption and biogas yield of a sewage treatment system, takes the emission standards of chemical oxygen demand (COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), total nitrogen (TN) and total phosphorus (TP) as constraints, and realizes intelligent and multi-target optimal configuration of sewage treatment process operation parameters.