1142results about "Chemical data visualisation" patented technology

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The invention discloses a machine learning-based soil pollution risk assessment method and a machine learning-based soil pollution risk assessment device, and belongs to the technical field of soil pollution risk assessment. The split biological effect and chemical indexes in traditional evaluation are converted into unified risk driving force measurement. A nonlinear interaction entropy calculation mechanism can adaptively capture an antagonism-collaborative balance relationship between microbial communities and pollutants in a soil environment, so that the degradation potential of functional flora on specific pollutants is reflected, and the dynamic regulation effect of environmental factor fluctuation on bioavailability is quantified. According to the cross-scale feature fusion technology, the evaluation model not only can identify the current pollution space distribution, but also can pre-judge the morphological transformation trend of pollutants in biogeochemical circulation, and provides a quantitative basis with ecological interpretation for risk early warning.

The invention relates to the technical field of water quality monitoring, in particular to an automatic water quality monitoring method and system.The method comprises the steps that multiple pieces of collected water quality monitoring data are combined pairwise, dynamic coupling strength is calculated, a topological network atlas is generated, and automatic extraction and structural characterization of the dynamic coupling relation among complex water quality parameters are achieved; the limitation of dependence on manual feature recognition traditionally is overcome; secondly, matching the topological network atlas with a preset pollution mode feature library, dynamically determining a newly added abnormal mode, and outputting an abnormal feature code set, thereby solving the key defect that a static model cannot recognize an unknown pollution mode; and finally, a water quality monitoring and early warning signal is output by fusing the pollution diffusion prediction result and the abnormal feature code set, so that bidirectional verification of data driving and a mechanism model is realized, and the early warning accuracy of a water quality abnormal phenomenon is remarkably improved.

The invention relates to the technical field of meteorological data processing, provides an artificial influence weather operation optimization method and system based on multi-scale analysis, and is used for improving the operation efficiency of artificial catalysis operation. The method comprises the steps that multi-source meteorological observation data of a target area are acquired, and the multi-source meteorological observation data comprise cloud layer dynamic distribution data, atmosphere vertical motion data and water vapor flux data; performing multi-scale space-time fusion processing on the multi-source meteorological observation data to generate space-time distribution data corresponding to different meteorological scales; based on the spatial and temporal distribution data, multi-scale meteorological characteristic parameters associated with the artificial catalysis potential tag are extracted, and the multi-scale meteorological characteristic parameters comprise a cloud phase state distribution parameter, a water vapor transmission intensity parameter and a vertical movement rate parameter; and inputting the multi-scale meteorological characteristic parameters into the operation catalysis effect prediction model, and outputting an artificial catalysis operation optimization scheme of the target area.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

The invention belongs to the technical field of environmental water quality pollution monitoring, and particularly relates to a drainage basin water environment monitoring and emergency pollution rapid tracing method and system based on water quality pollution detection, and the method comprises the following steps: constructing a multi-parameter cooperative monitoring model, and carrying out the training and deployment; through distributed sampling points and sampling stations, real-time data with verification marks and time-sharing data of watershed water environment water quality pollution detection are collected; mLP and LSTM / GRU models are adopted, EEM spectrum data and environment characteristics are fused, and multi-source heterogeneous data are fused and analyzed; whether an emergency pollution event occurs or not is automatically identified according to a preset condition, the pollution types and traceability results of water environment monitoring and emergency pollution are automatically output, manual further checking is carried out, and environmental law enforcement checking is carried out. According to the invention, drainage basin water environment pollution condition monitoring and emergency pollution rapid tracking and tracing can be completed in a large-range, low-cost and high-efficiency manner so as to support environment law enforcement.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention discloses a rapid sorting method for retired lithium ion batteries based on an electrochemical impedance spectroscopy and a semi-parameter clustering algorithm, which comprises the following steps of: firstly, performing electrochemical impedance spectroscopy test and relaxation time distribution analysis on the retired lithium ion batteries to obtain electrochemical impedance spectroscopy data and relaxation time distribution peak value data; and then, extracting features from the electrochemical impedance spectroscopy data by using a convolutional auto-encoder, extracting features from relaxation time distribution peak data by using a full-connection neural network, carrying out feature splicing on the two features, and inputting the spliced features into a deep neural network to quickly obtain the estimated capacity of the retired battery. And finally, taking the ohmic internal resistance, the impedance characteristic information and the estimated capacity of the retired lithium ion battery extracted from the electrochemical impedance spectrum data and the relaxation time distribution peak value data as the input of a semi-parameter clustering algorithm, and carrying out rapid sorting on the retired battery. According to the method, the capacity of the decommissioned lithium ion battery can be quickly predicted, outliers of the decommissioned battery are eliminated, and the decommissioned battery is quickly sorted.

The invention discloses a drainage basin nitrogen and phosphorus pollution prediction method based on rainfall runoff migration, and relates to the technical field of water quality pollution prediction, and the method comprises the steps: accurately obtaining land utilization and water body distribution through a remote sensing technology, carrying out the space division of a pollution source through combining a nitrogen and phosphorus load coefficient, and constructing a rainfall runoff model. The runoff volume and time and space distribution under different rainfall events are reflected, and the spatial dynamic change of pollutants is captured through a hydrodynamic model, in combination with a convection diffusion equation and by introducing a conversion model of nitrogen and phosphorus in various forms. By integrating remote sensing data, rainfall runoff simulation, hydrodynamic force and nitrogen and phosphorus form transformation, the space-time migration and transformation process of nitrogen and phosphorus pollution in a drainage basin is described, so that the coupling relation between pollution source distribution and runoff power is revealed, the conveying and diffusion rule of pollutants in a river network is dynamically reflected, and the drainage effect is improved. And refining to transformation evolution of different nitrogen and phosphorus forms, and generating intuitive concentration distribution and thermodynamic diagrams through spatial interpolation.

The invention provides an earth surface net flux calculation method and system based on a frequency domain soil moisture equation. The method comprises the following steps: establishing a frequency domain soil moisture motion model of soil water changing along with depth; performing frequency domain decomposition on the time sequence observation data and the upper flux boundary of the site soil water; calibrating the soil parameters according to the obtained upper flux and the frequency component of the soil water; carrying out discrete Fourier decomposition on the soil moisture time sequence at the next time period and any depth, and extracting soil moisture fluctuation signals at different frequencies; according to the obtained parameters and models, carrying out back calculation on soil moisture at different frequencies to obtain surface net flux signals at different frequencies, and superposing the surface net flux signals in a time domain; and in combination with remote sensing data, obtaining the surface net flux of the regional range according to long-time soil moisture data. According to the method, the problem of the time domain is converted to the frequency domain, so that the long-time-sequence soil water sequence can be processed, and the increase of the calculation amount caused by step-by-step calculation by dividing the long-time sequence is reduced.

The invention discloses an electronic device parasitic parameter network analysis method based on point cloud deep learning and a related device, and the method comprises the steps: obtaining physical characteristic data in a laser-induced quartz material process, and the physical characteristic data comprises laser wavelength, pulse width, photon energy and material characteristics; and inputting the physical characteristic data into the trained neural network model, and predicting the damage threshold of the laser-induced quartz material. According to the machine learning method fusing physical information, unification of data efficiency and physical consistency is achieved, prediction precision and model expandability are remarkably improved, and the method is used for accurately predicting the damage threshold value of the quartz material under the specific laser condition, so that the laser processing technology is optimized, and the processing precision and efficiency of the quartz material are improved.

The invention provides a constructional engineering concrete compressive strength detection method and system.The method comprises the steps that multi-modal information of a to-be-detected concrete member for constructional engineering is collected, and the multi-modal information comprises rebound data, ultrasonic data, resistivity data and temperature data; constructing a hybrid prediction model; and outputting compressive strength data according to the mixed prediction model, and visually displaying the compressive strength data. According to the method and the system for detecting the compressive strength of the constructional engineering concrete, disclosed by the invention, multi-modal information such as ultrasonic, rebound, resistivity and temperature of the to-be-detected concrete member is input into the mixed prediction model for compressive strength prediction, and the model can be used for more accurately processing nonlinear and high-dimensional characteristics in data; a transfer learning mechanism is introduced, so that the model adapts to changes of different regions and materials under limited training data, and the generalization ability and accuracy of prediction are improved. And the compressive strength data is visually displayed, so that the analysis efficiency of engineers is improved.

The invention discloses a mixed surfactant system surface tension prediction method and system based on machine learning. Measuring system surface tension data of the mixed surfactant at different temperatures through experiments; secondly, carrying out theoretical correction and data enhancement on the original data by utilizing a Szyszkowski equation; then constructing a multi-dimensional feature space; a nested K-fold cross validation strategy is adopted; the finally established XGB prediction model is excellent in performance on a test set of an enhanced data set, the determination coefficient R2 reaches 0.9994, and the mean square error MSE is 0.0713. The method is particularly suitable for a binary mixed system containing a nonionic polyether surfactant, and not only can accurately predict the surface tension value, but also can determine key parameters such as critical micelle concentration (CMC) and the like. An efficient and reliable technical means is provided for rapid screening and optimization of a surfactant formula, and the method has wide application prospects in the fields of daily chemicals, petroleum, pharmacy and the like.

The invention provides an energy storage battery state simulation and prediction system and method based on digital twinning, and aims to solve the problems that an existing energy storage system is inaccurate in state modeling, insufficient in sensing granularity, unexplained in prediction, lagged in response and the like. According to the method, an equivalent thermal model, an electrochemical model and an aging mechanism model are adopted for modeling a battery, and a self-adaptive gating parameter fusion mechanism and data characteristics are introduced, so that cross-scale state mapping and characteristic reconstruction from a battery cell to a system level are realized; generating a baseline trajectory by using an electric-thermal-aging coupling relationship, realizing multi-time-domain closed-loop prediction by combining a data-driven residual correction mechanism, and identifying potential faults and risks in advance through a multi-time-domain anomaly judgment system; and constructing a three-dimensional topology model to dynamically display the state parameters, generating a graded alarm signal and providing operation suggestions. The system constructed by the invention has high precision, interpretability and prediction and visualization capabilities, and can significantly improve the intelligent operation and maintenance level of the energy storage battery system.

The invention relates to the technical field of crop monitoring, in particular to a crop phenotypic parameter automatic calculation and extraction method based on a multi-source remote sensing image. Comprising the steps that all images are determined to be in the same coordinate system through geographical registration, point cloud file conversion and ground specific point matching, and image registration is achieved; performing automatic or semi-automatic segmentation on the images determined in the same coordinate system to obtain a multi-source crop remote sensing image cell segmentation map; extracting plant phenotypic parameters, physical parameters and chemical parameters from the multisource crop remote sensing image cell segmentation map by using an algorithm; the plant phenotype parameters comprise a vegetation index, a plant height, a surface area, a volume, a canopy coverage degree and a vegetation projection area; the physical parameters comprise a canopy average temperature value, a canopy temperature standard deviation and a canopy temperature variation coefficient; the chemical parameters comprise chemical elements such as nitrogen, phosphorus, potassium, calcium and magnesium in soil and vegetation. The method has the advantages that large-scale data processing and high-precision area prediction are realized, and the crop growth monitoring capability is enhanced.

The invention relates to the technical field of thermal hydraulic power, in particular to a rapid prediction method for flow heat transfer characteristics of supercritical carbon dioxide of a micro-channel heat exchanger, and the method comprises the steps: dividing a training set and a test set according to a preset working condition parameter space, and generating a full-order flow field database under multiple working conditions through numerical simulation; performing eigenorthogonal decomposition on the full-order flow field database, and extracting a key mode of a dominant flow heat transfer feature to construct a reduced-order solution space; training an optimized neural network model by using the training set, and establishing a mapping relationship between the working condition parameters and the reduced-order solution space; and reconstructing flow field and temperature field information based on the mapping relation, and calculating and outputting a flow heat transfer characteristic prediction result. The method aims at solving the technical problem that the heat transfer characteristic prediction calculation efficiency of the supercritical carbon dioxide heat transfer flow of the micro-channel heat exchanger is low.

The invention relates to the technical field of road engineering, in particular to a DSR-based method for rapidly predicting low-temperature performance of asphalt, which comprises the following steps: preparing an asphalt cylinder sample; performing a strain amplitude scanning test to obtain a linear viscoelasticity interval; performing frequency scanning test to obtain asphalt dynamic modulus and phase angle data at different frequencies at low temperature; according to the prediction model, converting the dynamic modulus and the phase angle into a creep stiffness modulus and a creep stiffness modulus change rate; and finally, according to a low-temperature performance grade evaluation standard, obtaining the low-temperature grade of the asphalt sample. According to the method, the low-temperature performance of the asphalt can be rapidly predicted, a tedious bending beam rheometer test is not needed, and the test efficiency is greatly improved. The method also has the characteristics of low sample demand, simple operation, accurate result and the like, can be used for rapidly predicting the low-temperature performance of the laboratory asphalt, and also can be applied to asphalt extracted from a pavement core sample, so that the purpose of rapidly obtaining the low-temperature performance of the asphalt is achieved.

The embodiment of the invention relates to the technical field of sludge treatment, and discloses an AGI-based sludge deep dehydration conditioning method, which comprises the following steps: acquiring time sequence sensor parameters detected by a plurality of sensors at a dehydration conditioning system; acquiring an image, acquired by image acquisition equipment, of sludge at the corresponding capillary water absorption detection system; extracting an image feature vector from the at least one image according to the convolutional neural network, and encoding a corresponding time sequence sensor parameter by adopting a Transform module to generate a time sequence feature vector; fusing the image feature vector and the time sequence feature vector into an input state representation; the input state representation is input into an AGI multi-mode model for recognition processing, and output state information is generated through a multi-task decoder; and transmitting the optimal control parameters to a corresponding programmable logic controller so as to adjust the operation state of the dehydration conditioning system. By means of the method, real-time sensing, intelligent decision making and dynamic optimization of the sludge deep dehydration conditioning process are achieved.

The invention discloses an ecological environment big data processing method based on mining area pollutant migration behavior modeling, and relates to the technical field of big data processing. Comprising the steps that protection points in a target range and geodetic coordinate values of a mining area are obtained, the protection points comprise a mass residential area, an ecological protection area and a water source protection area, and the geodetic coordinate values are used for representing latitude and longitude coordinates and the altitude; and according to the first group of geodetic coordinate values and the geodetic coordinate values of the mining area, the maximum diffusion range interval of the mining area pollutants is determined, and the first group of geodetic coordinate values specifically refer to the geodetic coordinate values taken by any protection point type. According to the method, the three-dimensional geodetic coordinates of the protection point and the mining area are obtained, the pollutant diffusion range is scientifically defined, it is ensured that the space coverage of pollution monitoring is comprehensive and effective, potential threats of mining area pollution to surrounding people residential areas, ecological protection areas and water source protection areas can be accurately recognized, and the risk of mining area pollution to the surrounding people residential areas, ecological protection areas and water source protection areas is reduced. And the accuracy and pertinence of environmental risk assessment are improved.

The invention relates to an ecological environment inspection and detection information management method and system based on artificial intelligence, and the method comprises the following steps: carrying out the inspection and detection of water quality, air and soil samples collected by an ecological environment monitoring station, and obtaining environment detection data; performing environmental element association calculation based on the data to obtain element association data, and generating an environmental element distribution diagram through spatial-temporal distribution mapping; tracking a pollutant migration rule by using the distribution map, extracting pollutant migration characteristic data, and carrying out environmental impact assessment according to the pollutant migration characteristic data to form an environmental impact assessment report; according to the method, the monitoring area is subjected to risk distribution division according to the report, area environment risk distribution data is obtained, a targeted ecological environment management scheme is generated based on the data, and the technical problems that in the prior art, an effective correlation analysis means is lacked, and migration and transformation rules of pollutants among different media are difficult to reveal are solved.

The invention discloses a cooperative supervision method and system for a water area ecological environment, and belongs to the technical field of ecological environment supervision, and the method comprises the steps: carrying out the global monitoring of a target water area, obtaining a monitoring image sequence of the target water area, calculating the pollution confidence, recognizing a pollution region, quantifying the area, and generating a diffusion drive signal, obtaining a category label of the target pollutant; obtaining the motion trail of the target pollutant, reversely deducing the local flow velocity and flow direction of the water surface, identifying an abnormal target in the target water area, and generating an abnormal pollution association table in combination with the pollution area; according to the method, local data of a water area are collected in real time, a water area situation base map is generated, a water quality diffusion model is constructed, a multi-time-dimension pollution diffusion simulation result is generated, and plan deduction and risk assessment are performed, so that a supervision decision is upgraded from passive disposal to active prevention and control, and decision requirements of risk pre-judgment and active prevention and control are fully met.

Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak identification and alignment across different chromatograms is critical prior to any subsequent analysis. This work introduces a peak identification method called retention time trajectory (RTT) matching, which uses chromatographic retention times as the only input and identifies peaks associated with any subset of a predefined set of target compounds. RTT matching is also capable of reporting interferents. An RTT is a 2-dimensional (2D) curve formed uniquely by the retention times of the chromatographic peaks. The RTTs obtained from the chromatogram of a test sample and of pre-characterized library are matched and statistically compared. The best matched pair implies identification. Unlike most existing peak alignment methods, no mathematical warping or transformations are involved.

The invention relates to the technical field of soil pollution analysis, and discloses a soil heavy metal intelligent evaluation method, system and terminal, and the method comprises a multi-source heterogeneous data fusion and preprocessing step, a dynamic spatial interpolation modeling step and an intelligent visualization and decision support step. The system corresponds to the method, and the terminal equipment corresponds to the system. According to the method, comprehensive data support for soil heavy metal pollution is realized through multi-source heterogeneous data fusion and preprocessing; through dynamic spatial interpolation modeling and a dynamic parameter adjustment mechanism, high-precision grid surface layer generation is realized, and the fitting precision of pollution spatial distribution is improved; intelligent pollution grading and multi-factor dynamic calculation of pollution indexes are realized through self-adaptive grading and pollution index calculation, and the accuracy of an analysis result is improved; through intelligent visualization and decision support, a dynamic and visual decision basis is provided for multi-department collaborative governance and risk early warning.

The invention discloses a mixed gas absorption spectrum analysis method and system based on variational mode decomposition, specific laser is injected into an optical resonant cavity unit, and a detector unit continuously monitors the light intensity change and records a light intensity attenuation signal; pre-processing the recorded light intensity attenuation signal; carrying out VMD decomposition on the preprocessed ring-down signal to obtain a plurality of IMF components, respectively introducing CO2 and CO gases with known concentrations into the optical resonance unit, and recording spectral data of each single gas component; calculating the similarity and contribution degree of each IMF component, and setting a weight combination to form a joint score; the gas with the highest joint score is selected, the score is compared with an adaptive threshold value, and when the score is higher than the adaptive threshold value, the IMF component is marked as the characteristic component of the corresponding gas; and finally, gathering and outputting the marked IMF components according to gas types to obtain characteristic signals of the gases, thereby realizing multi-component gas separation in the mixed gas absorption spectrum.

The invention relates to the field of petrochemical material physical property calculation, in particular to a physical information constrained petrochemical material hybrid modeling physical property calculation system and method, and the system comprises a data processing module which achieves the standardized cleaning and fusion of multi-source heterogeneous data; the multi-scale characteristic module realizes material characteristic characterization through quantum-mesoscopic-macroscopic cross-scale modeling; the physical constraint module is embedded into a thermodynamic constitutive equation and a phase equilibrium criterion to ensure the self-consistency of the model; the component interaction module constructs a non-ideal mixing effect prediction model based on a deep potential energy field theory, and establishes a component interaction knowledge graph; the uncertainty quantification module adopts a Bayesian deep learning method to assess and predict a confidence interval, a system integrates a physical mechanism and a data driving method, physical property prediction errors are reduced by 15%-20%, meanwhile, a molecular structure-physical property associated visual analysis tool is provided, process optimization and new product development decision are supported, and the reliability of the system is improved. And the core requirements of the petrochemical industry on high-precision and interpretable physical property prediction are met.

The invention discloses a circular RNA drug sensitivity correlation identification method based on integrated multi-instance learning. The circular RNA drug sensitivity correlation identification method comprises the following steps: collecting experimental verification circular RNA and drug sensitivity correlation data; establishing a feature representation model based on a heterogeneous network and vertexes; embedding a heterogeneous graph node into the model, and extracting deep feature representation of the node; designing a meta-path instance embedding projector, and generating a plurality of meta-path instances; a circular RNA and drug sensitivity association predictor is constructed and completed; constructing an integrated heterogeneous graph network deep learning model; and outputting the meta-path instance of the circular RNA and drug pair and the attention coefficient, and carrying out interpretable analysis. According to the method, integrated learning and a deep learning model are combined, so that the reliability of the model is improved; according to the invention, interpretable analysis is carried out by utilizing the meta-path and the attention coefficient, the potential action mechanism of the circular RNA associated with the drug sensitivity can be explained, and the guidance of medical research is facilitated.

The invention discloses a customized generation method of a non-corrosive ionic liquid lubricant, and aims to solve the problem of negative correlation between corrosion and lubricating performance of ionic liquid in application of a metal friction pair. The ionic liquid has the characteristics of low volatility, high thermal stability and the like, but the electrochemical activity of zwitterions of the ionic liquid easily causes metal surface corrosion, and the corrosion and the lubricating property are in a negative correlation relationship. According to the method, through constructing a corrosion-lubrication multi-modal database, combining with the steps of feature engineering, collaborative prediction model training, non-corrosion formula directional generation, molecular dynamics Monte Carlo coupling verification, multi-objective optimization and the like, the whole-process intelligence from molecular structure and performance prediction to formula optimization is realized; and an ionic liquid formula with good lubricating performance and no corrosion is efficiently screened. According to the method, the research and development efficiency of the ionic liquid lubricant is remarkably improved, the research and development cost is reduced, and engineering application of the ionic liquid lubricant in key fields such as spaceflight, military industry and extreme manufacturing is promoted.

The invention discloses a method for depicting and analyzing a heavy non-aqueous phase pollution site based on microbial structure information, which comprises the following steps of: based on a historical geological survey report and a historical leakage event, defining a pollution analysis area, and constructing a site pollution conceptual model by combining high-density resistivity with a stable isotope tracer method; performing sample collection and detection on the target area to obtain area sample detection information; performing microflora analysis according to the regional sample detection information, and judging a potential pollution retention region of the target region to obtain pollution region analysis information; carrying out multi-source data coupling by combining pollution area analysis information and area sample detection information, and carrying out pollution field three-dimensional description on a target area to obtain an area DNAPLs pollution condition diagram; a degradation function gene interaction network is constructed, restoration potential grading is performed on a target area, and area restoration suggestion is performed, so that the limitation of a traditional investigation method is broken through, and accurate analysis and restoration assistance of pollution distribution are realized.

The invention discloses a random forest algorithm-based water-soil-gas VOCs concentration correlation prediction model concentration detection method, which is characterized in that a random forest prediction model is constructed, and a particle swarm optimization algorithm is innovatively introduced to automatically adjust and optimize key parameters of the model, so that the prediction precision and the model performance are remarkably improved; according to the method, the optimal parameter combination of 28 particle numbers, 0.9 inertia weight, 11 iterations and the like is set, so that the problems that the RF model is easy to over-fit and the training speed is low are effectively solved, a high-precision and high-efficiency intelligent prediction solution is provided for VOCs gas concentration monitoring, and the method is suitable for popularization and application. The method can be widely applied to the fields of environmental monitoring, industrial process control and the like.

The invention discloses an administration agent in-vivo release and absorption visualization method based on a CFD-PBM coupling model. The method comprises the following steps: constructing a CFD model of a physiological environment of an injection site; establishing a population balance model (PBM) of the drug particles; the PBM is embedded into a CFD model solver, multi-scale coupling simulation is carried out, and a CFD-PBM coupling model is obtained; the three-dimensional visualization engine dynamically displays drug concentration distribution and particle behaviors; reversely adjusting preparation prescription parameters by using a multi-parameter optimization algorithm; and calibrating model parameters, and generating a key data report. According to the method, the prediction precision and the research and development efficiency can be remarkably improved, and the method is suitable for development of long-acting preparations such as microspheres and implants.