607results about "Molecular entity identification" patented technology

The present application relates to the field of farmland nutrient analysis, and particularly relates to a carbon-nitrogen labeled coupled analysis farmland ecosystem nutrient migration quantification method, which comprises the following steps: obtaining carbon-nitrogen concentration time series by collecting and preprocessing carbon-nitrogen isotope monitoring data; obtaining trajectory evolution optimization factors by performing two-dimensional collaborative trajectory evolution analysis on the carbon-nitrogen concentration time series; obtaining jump distortion optimization factors by performing real migration flux jump distortion feature analysis on the carbon-nitrogen concentration time series; obtaining nutrient migration mode division results by jointly modifying the trajectory evolution optimization factors and the jump distortion optimization factors on the basis of time series distance; and obtaining carbon-nitrogen nutrient migration evaluation results by performing quantification analysis on the nutrient migration mode division results, so as to solve the problem of migration mode division distortion caused by the forced alignment of secondary fluctuation misalignment and the confusion of deep background noise artifacts in the carbon-nitrogen isotope time series clustering of the farmland ecosystem by the existing multi-dimensional dynamic time warping algorithm.

The application provides an amorphous alloy magnetocaloric performance prediction method and system based on interpretable machine learning, relates to the technical field of material science and engineering, and comprises the following steps: step 1, constructing an amorphous alloy magnetocaloric performance data set, wherein the data set comprises alloy composition data, physical and chemical descriptors, external conditions and structural characteristics of a plurality of amorphous alloy samples and corresponding experimental values of maximum magnetic entropy change and Curie temperature; step 2, screening and optimizing the features in the data set, eliminating the collinearity between the features and performing recursive feature elimination to determine the final feature subsets for predicting the maximum magnetic entropy change and the Curie temperature respectively; and step 3, establishing machine learning models for predicting the maximum magnetic entropy change and the Curie temperature respectively through the final feature subsets, and training and optimizing the models to obtain the trained prediction models. The application realizes a closed loop of high-precision performance prediction, physical mechanism analysis and directional composition design.

The present invention belongs to the technical field of automated steel production and discloses a method for predicting the composition of molten iron applied to an automated steel production process, wherein the method is: performing a sampling and an examination of molten iron in a process in which the molten iron is tapped from a torpedo tank into a ladle, and calculating a loss of composition of this molten iron, wherein the composition of the molten iron is calibrated before the molten iron is tapped into the ladle;Performing a sampling and analysis of the molten iron in a process where the molten iron is transferred from the ladle to a converter, and calculating any loss in the composition of this molten iron, wherein the composition of the molten iron is calibrated before the molten iron is transferred to the converter. The present invention is easy to use and implement, and can acquire more accurate and reliable real-time parameters for computational models at all levels; in its specific application, the present invention does not require the modernization of existing automated steelmaking equipment; and it can be applied to a wide range of automated steelmaking scenarios.

This invention discloses a method and system for predicting and assessing adverse drug reactions by integrating multimodal data, belonging to the field of drug safety assessment technology. It constructs a multimodal data acquisition module to obtain clinical medication data, drug molecular characteristic data, patient physiological indicator data, and environmental impact data, forming a multidimensional feature vector of drug-patient interaction. It then constructs a risk demand vector for adverse drug reactions and calculates the risk factor fit between the drug-patient combination and the corresponding drug. Finally, it calculates the intensity of the comprehensive risk effect and the risk value of adverse reactions. Finally, it aggregates all medication risks for the patient at the current time point, combines environmental factors to construct an individualized dynamic risk threshold, and achieves risk level determination and real-time early warning. This enables continuous quantitative assessment and dynamic prediction of adverse drug reactions, improving the accuracy and individualization of risk identification.

This application discloses an online assessment method and system for the biotoxicity of groundwater complex pollution, relating to the field of environmental monitoring technology. The system includes a multi-channel monitoring device, a data processing and calculation unit, and an early warning output unit. The method includes: acquiring time-series data of the movement trajectories of each fish in multiple independent monitoring channels; extracting multi-dimensional behavioral features from each monitoring channel; determining the baseline values ​​of the behavioral features of each monitoring channel at the current moment using a dynamic baseline model; comparing the multi-dimensional behavioral features with the baseline values ​​to calculate standardized feature offsets; fusing the standardized feature offsets of each monitoring channel to obtain a comprehensive feature vector, which is then input into a biotoxicity assessment model to output a comprehensive assessment result of the biotoxicity of the water body. This method intelligently learns the complex mapping relationship between complex pollution characteristics and biological responses, achieving more sensitive, accurate, and intelligent online monitoring and assessment of groundwater complex pollution.

The application discloses a white granite type high-purity quartz purity prediction method and application thereof, and belongs to the technical field of rock purity prediction. The prediction method encodes and assigns one or more prediction parameters, such as main mica types, rock structures, rock structures, feldspar types, quartz types, solid inclusions, liquid inclusions, solid inclusion quantity, aluminum impurity element content grading, titanium and lithium element content grading, calcium and phosphorus element content grading, potassium and sodium element content grading and grain factor grading, and then predicts the quartz purity in the ore based on a random forest classifier prediction model or a support vector machine prediction model. The comparison between the actual measurement and the prediction result proves that the prediction method has accurate and reliable prediction results, realizes the replacement of manual detection by artificial intelligence prediction, greatly reduces the manpower and material resources, and provides an efficient and accurate prediction method for quartz purity prediction.

The application provides a peak detection and qualitative method for full two-dimensional mass spectrum data based on a multi-modal model and related equipment, and comprises the following steps: performing supervised fine-tuning training on a pre-trained multi-modal large language model by using a mixed data set, performing reinforcement learning training on an initial peak detection and qualitative model by using a reinforcement learning fine-tuning data set, inputting preprocessed multi-modal input data into the peak detection and qualitative model to perform data extraction, and obtaining a corresponding data extraction result; by combining the multi-modal large language model and the reinforcement learning fine-tuning training, the method can accurately detect target peaks when processing full two-dimensional mass spectrum data, and can efficiently analyze compounds; the use of a low-rank synthetic data set helps to enhance the accuracy of model data processing; in the reinforcement learning fine-tuning stage, the structure generation strategy of the model in unknown compound analysis is optimized, so that the model can cope with complex situations of unknown compounds and optimize the structure generation capability.

The application discloses a hydrophobic-carbonization coupled chloride ion diffusion and binding prediction method and system, and belongs to the field of inorganic cementing material durability numerical analysis and multi-field coupled modeling. The method comprises the following steps: dividing the internal chloride ion of the material into free and bound chloride ions, and establishing a total chloride ion concentration expression relationship; under the condition of two-dimensional transmission, a chloride ion diffusion-binding coupled control equation is established based on the mass conservation and Fick diffusion law, and a hydrophobic correction factor and a carbonization influence factor are introduced; the Langmuir isothermal adsorption model and the first-order kinetic equation are used to describe the chloride ion binding behavior; the non-steady-state migration and binding process of the chloride ion in the cementing material is numerically solved, and the space-time distribution of the chloride ion concentration under different conditions is obtained. The application can quantitatively describe the coupled influence of hydrophobic modification and carbonization on the diffusion and binding behavior of the chloride ion, has high prediction precision, and is suitable for durability analysis and engineering life evaluation of alkali-activated materials and geopolymer.

A non-transitory computer-readable recording medium stores therein a training program that causes a computer to execute a process including first inputting a first image capturing a target compound to an encoder of an auto-encoder including a latent space that is isometric with respect to an input space, second inputting a latent variable output by the encoder and a typical compound model corresponding to a typical case of a three-dimensional structure of the target compound to a decoder of the auto-encoder, and updating parameters of the encoder and the decoder, based on a reconfiguration error between a second image reconfigured based on an output of the encoder and the first image.

The application discloses an alumina calcination process monitoring method based on DCS control, which comprises the following steps: obtaining alumina raw material information and calcination requirements; inputting the alumina raw material information and the calcination requirements into a preset alumina process model; the alumina process model outputs a calcination result and process parameter suggestions; calcining alumina raw materials in a calcination device according to the process parameter suggestions, and obtaining calcination information of the alumina raw materials in the calcination device in real time; when the calcination information is input into the alumina process model, the alumina process model judges whether the calcination information is normal, and if not, the operation of the calcination device is stopped. The alumina calcination process can be intelligently monitored.

The present application provides a kind of biogas slurry chemical oxygen demand online monitoring method and system, the method comprises: obtaining the current characteristic data of biogas slurry, the characteristic data includes multiple in dissolved oxygen, turbidity, oxidation-reduction potential and conductivity;The current characteristic data of the biogas slurry is input into least square regression model, and the current chemical oxygen demand predicted value of the biogas slurry output by the least square regression model is obtained;The least square regression model is obtained by training by taking the characteristic data sample of the biogas slurry as characteristic variable, and taking the measured value of the chemical oxygen demand corresponding to the biogas slurry as target variable.The present application realizes the rapid, low-cost detection of biogas slurry chemical oxygen demand by real-time monitoring of multiple characteristic data, constructing multi-parameter fusion partial least square regression prediction model.

This invention relates to the field of air pollutant detection technology and discloses a method for detecting excessive emissions of air pollutants. The method includes: acquiring pollutant concentration data from multiple monitoring points in a target area to form a raw dataset; performing outlier detection and correction, removing sensor malfunction outliers to obtain a preprocessed dataset; extracting concentration change trend characteristics within a preset time window for each monitoring point to construct a spatiotemporal feature matrix; inputting the matrix into a pollutant diffusion model to calculate the transmission paths and intensities between monitoring points, generating a regional transmission network; identifying potential source areas of abnormal pollutant concentration fluctuations based on the network and marking them as candidate areas for verification; performing multi-scale concentration gradient analysis on the candidate areas to determine key monitoring areas; deploying mobile equipment in the key monitoring areas to collect high-precision component data; comparing this data with a standard emission source feature library, matching emission source types with similarity exceeding a threshold, determining whether emissions exceed standards, and generating a detection report.

According to a molecular comparison system and a method thereof provided by the present disclosure, after a substance analysis result is obtained, on the basis of a molecular list and matching degree values as well as related questions and corresponding weights in a knowledge management system, a correlation database is used to determine and calculate by means of an AI LLM, so as to further determine the molecular species of a compound causing damage.

The application relates to the technical field of blood drug concentration detection, and discloses a blood drug concentration analysis method based on mass spectrometric detection, which comprises the following steps: adopting microfluidic technology in combination with solid-phase extraction to pretreat a blood sample to obtain a concentrated solution, so as to remove interfering components and concentrate target drug molecules; according to the physicochemical properties of the target drug molecules, the concentrated solution is selected to flow through a biosensor or is directly introduced into a chromatographic unit, and the target drug molecules in the liquid phase are converted into gas-phase ions when flowing through the biosensor; chromatography-mass spectrometry is adopted to detect the gas-phase ions or the concentrated solution, so as to obtain mass spectrum data; a machine learning model is used to analyze the spectrum data, to identify the drug types and calculate the concentration of the target drug molecules, and to generate an analysis report. The method is suitable for target drugs with different physicochemical properties, simplifies the detection process, and improves the applicability and accuracy of detection.

A method of analysing a structure of a composition of matter in a sample comprising obtaining a data set comprising a plurality of spectra from the composition, from a first method of analysis dividing each of the spectra into a plurality of bins determining a control parameter or parameters indicative of synchronised fluctuations in signal intensity across some or all channels, resulting in universal correlation between said bins determining a partial covariance of different bins across the plurality of spectra using the control parameter to correct the correlation of intensity fluctuations between said bins.

The present application relates to the technical field of data processing, in particular to a multi-source data integration management method and system for pollution source monitoring, which comprises the following steps: determining an abnormal mutation point in the electrical data sequence of a desulfurization wastewater purification device in a historical time period; obtaining the power supply abnormality significance feature of the desulfurization wastewater purification device according to the difference between the electrical data before and after the abnormal mutation point; determining the pollutant concentration growth trend after the starting point of the rising trend of the pollutant concentration in the current time period; adjusting the smoothing coefficient in the cubic exponential smoothing algorithm according to the power supply abnormality significance feature, the pollutant concentration growth trend and the change correlation degree of the electrical data and the pollutant concentration, so as to improve the accuracy of the smoothing coefficient; and predicting the pollutant concentration by using the cubic exponential smoothing algorithm according to the smoothing coefficient, so as to improve the accuracy of the pollutant concentration prediction and realize reliable and accurate management of industrial desulfurization wastewater.

The present application belongs to the technical field of sewage treatment, and discloses a method and system for accurately predicting water quality changes in a sewage treatment process, which comprises the following steps: collecting multi-source water quality data of a target sewage area in a sewage treatment process in real time through a multi-parameter water quality sensor; screening key input features from the multi-source water quality data; constructing a time series prediction model based on a long short-term memory network and a mechanism model based on a sewage treatment process; deeply fusing the time series prediction model and the calibrated mechanism model through a series connection architecture to construct a water quality prediction model, and optimizing the water quality prediction model through a multi-objective optimization algorithm; inputting the key input features into the optimized water quality prediction model for processing to generate a prediction result, learning and correcting the residual error in the prediction result, and obtaining a corrected prediction result. The present application effectively avoids problems such as instability of the treatment system or exceeding of the effluent water quality caused by sudden changes in water quality.

This invention relates to the field of coal quality testing technology, specifically to an online method and system for detecting coal quality entering the furnace; it includes: a multimodal data acquisition step: acquiring microwave scattering parameters and surface element characteristic spectra using a microwave tomography sensor array and a surface spectrometer, respectively; a physical field inversion and modeling step: reconstructing the complex permittivity distribution map based on the scattering parameters to establish a three-dimensional physical distribution model including density and moisture; a field-spectrum coupling deduction step: using the characteristic spectra as boundary constraints, and combining them with the complex permittivity distribution map to deduce the internal chemical element distribution; and a volume-weighted quantization step: performing volume-weighted integration on the three-dimensional physical model and chemical distribution to output comprehensive coal quality parameters; this invention eliminates the detection blind spots of single technologies and significantly improves the representativeness and safety of full-section detection of coal entering the furnace.

The present application relates to the technical field of sewage treatment, and particularly relates to a method for evaluating purification efficiency of urban sewage pipeline. Based on the topological structure and historical operation data of the target sewage pipeline network, the key internal nodes are selected and the monitoring points are arranged through in-situ process sensitivity sorting; the concentration data of the reference water quality indexes of all water quality monitoring nodes and the hydraulic flow velocity data between adjacent nodes are synchronously collected within a preset evaluation period; based on the above data and in combination with the geometric parameters of the pipeline, the purification efficiency of each pipe section is characterized and quantified to generate a pipe section purification characteristic data table; the dynamic simulation and spatial interpolation of the internal purification process of the pipeline network are performed to generate a dynamic quantification atlas; based on the atlas and the characteristic data table, the pipe section efficiency is evaluated, and the abnormal cause analysis is performed in association with the sensitivity characteristics of the key internal nodes. The multi-dimensional dynamic correlation of the in-situ purification process inside the pipeline network is described and visualized, and the fine and intelligent level of the pipeline network management is improved.

A general photobioreactor microbial growth rate prediction model and method thereof, the prediction model comprising a pretreatment module, a fluid dynamics calculation module, a light transmission module and a microbial growth rate mechanism module; first, a physical model of the photobioreactor is obtained; second, according to the obtained physical model and the calculation region, mesh division is carried out to construct the pretreatment module; then, the initial value of the growth parameter of the microbial particles in the photobioreactor is calculated; finally, the microbial growth rate in the photobioreactor is calculated and predicted through a coupled model of computational fluid dynamics, light intensity transmission law and microbial growth mechanism; the present application is based on physical modeling, and the computational fluid dynamics, light intensity transmission and microbial growth mechanism are coupled, thereby expanding the generality of the growth rate prediction; and the model can more accurately predict the microbial growth rate in any photobioreactor.

This invention discloses a multi-objective optimization method for the composition design of modified asphalt binders, relating to the field of road engineering material design technology. The method first obtains multiple sets of material composition and performance data based on indoor experiments to construct a snapshot matrix. Then, singular value decomposition is performed on the snapshot matrix to extract intrinsic orthogonal decomposition bases, and an AdaBoost algorithm is used to construct a reduced-order model that characterizes the nonlinear mapping relationship between material composition and performance. Based on this, a multi-objective optimization mathematical model incorporating performance indicators, cost objectives, and component constraints is established. Finally, a non-dominated sorting genetic algorithm with an elitist strategy is used to solve the model to output the Pareto front, thereby selecting the optimal formulation. This invention effectively solves the problem of balancing softening point, ductility, and cost by replacing traditional massive trial-and-error experiments with a reduced-order model, achieving global optimization of the comprehensive performance of modified asphalt binders.

The application relates to an energy efficiency optimization method, medium and system of a capacitive deionization system, which is suitable for energy-saving operation regulation and control of the capacitive deionization system. The method firstly constructs a multi-physical field simulation model coupling electric field, fluid and mass transfer, simulates an electric loss distribution curve in a deionization process, and determines preliminary adsorption time length parameters according to the electric loss distribution curve. Then, the adsorption time length parameters and preliminary desorption time length parameters that are optimal in energy efficiency are calculated and corrected by comprehensively considering factors such as auxiliary system power consumption and pumping energy consumption, so that adaptive adjustment of system cycle operation parameters under different working conditions is realized. Finally, under the premise of ensuring electrode full regeneration, the water inflow speed and desorption time length parameters in the desorption stage are adjusted based on the simulation model, the water recovery rate and energy recovery efficiency of the system are improved, and the overall energy consumption is reduced. The application realizes intelligent operation control of the electric adsorption process under the condition of multiple working conditions, and has high engineering adaptability and popularization value.

The present application belongs to the technical field of pollutant control, and discloses a tannery deodorization method and system based on reaction cluster classification and polarity matching. The deodorization method comprises the following steps: step 1: constructing a tannery pollution characteristic database, and collecting characteristic pollution factor concentration data in the environment in real time; step 2: calculating the current odor polarity index and the comprehensive odor load according to the database of step 1 and the collected data; step 3: determining the category of pollutants according to the current odor polarity index calculated in step 2, obtaining the combination ratio of deodorants according to the category of pollutants, determining the odor load state according to the size of the comprehensive odor load and the preset threshold, and obtaining the deodorant spraying parameters according to the odor load state; and spraying the deodorants according to the combination ratio of the deodorants and the spraying parameters. The present application converts parameters into a single polarity control variable, determines the specific deodorant ratio and spraying parameters according to the current pollutant state.

This invention relates to the field of hydrogen content detection technology in copper melts, and particularly to a method for detecting hydrogen content in copper melts based on ultrasonic dehydrogenation. The method includes: simultaneously acquiring acoustic response signals and spatial temperature distribution signals of the copper melt; extracting acoustic feature parameters related to hydrogen content based on the acoustic response signals, and dividing the melt space into multiple response regions based on the spatial temperature distribution signals; inputting the acoustic feature parameters and spatial information of the response regions into a pre-constructed mapping model to obtain the spatiotemporal distribution information of hydrogen content; extracting temporal evolution features and spatial change features based on the spatiotemporal distribution information; adjusting the acquisition parameters of the acoustic response signals and the sampling weights of each response region according to the temporal evolution features and spatial change features, and performing signal acquisition and updating of the spatiotemporal distribution information of hydrogen content in the next cycle. This invention enables dynamic and accurate detection of hydrogen content in copper melts, adapting to the spatiotemporal dynamic changes of hydrogen content during the dehydrogenation process.

The application discloses a rare earth steel loss thickness prediction method based on machine learning, and belongs to the technical field of metal material corrosion and protection. The method includes two parallel technical schemes: the first scheme adopts a staged modeling strategy, a first model is first constructed to predict the laboratory accelerated corrosion loss thickness, a second model is then constructed to establish the mapping relationship between the laboratory and the real atmosphere corrosion, and finally the two models are jointly optimized; the second scheme adopts an end-to-end integrated modeling strategy, and a single model is used to directly realize the prediction from the input features to the real atmosphere loss thickness. In the two schemes, the rare earth element content is taken as the core feature, key parameters are selected through feature engineering, and real atmosphere data are used for model training and optimization. The application can accurately quantify the influence of the rare earth content on corrosion resistance, solve the accurate mapping problem between the laboratory data and the atmosphere data, and significantly improve the prediction accuracy.

This invention relates to the field of cement building materials technology, specifically disclosing a method for improving the strength of silicate clinker based on mineral and crystal composition analysis. The method involves collecting 3-day and 28-day compressive strength test data of silicate clinker and quantitatively analyzing the mineral and crystal composition data using XRD. A mineral composition and strength zoning statistical method is employed to obtain appropriate C3S or C2S content to maintain a relatively good overall level of 3-day and 28-day compressive strength of the clinker. Then, a stepwise multiple linear regression analysis is used to establish the relationship between the 3-day and 28-day compressive strength of the clinker, clinker liter weight, f-CaO, the content of other chemical components, and the mineral composition and crystal content of the clinker. Specific analytical methods are used to determine optimization measures, further optimizing and improving the clinker strength. Using the above method, the 3-day and 28-day compressive strength of silicate clinker can be improved simultaneously.

The application discloses an ecological environment monitoring method and system based on multi-target correlation. The method comprises the following steps: collecting original training data sets including independent variable data representing environmental variable characteristics and target variable data representing ecological environment; constructing a lag correlation matrix with the maximum sum of lag correlation coefficients of two target variables in the lag period as the target; analyzing the correlation between different target variables according to the correlation matrix, the partial correlation matrix and the lag correlation matrix, and generating enhanced features including lag correlation features, interaction features, graph embedding features and time window statistical features; splicing the enhanced features with the independent variable data to generate enhanced independent variable features; training a preset ecological environment monitoring model with the enhanced independent variable features as the input and the target variable data as the label, and the trained ecological environment monitoring model is used for monitoring the ecological quality of a target region. The application improves the prediction performance of ecological environment monitoring.