1289results about "Cheminformatics data warehousing" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

The invention provides a river pollutant traceability method and a river pollutant traceability system. The method comprises the following steps: constructing a potential pollution source feature fingerprint database fused with an LSTM time sequence feature extraction model, carrying out abnormal water quality fingerprint identification on a monitored river reach, if identification is abnormal, collecting an upstream water sample, detecting to obtain a water quality fingerprint, comparing the water quality fingerprint with the abnormal water quality fingerprint, determining a target river reach according to a comparison result, and determining the target river reach according to the comparison result. The method comprises the following steps: sampling all enterprises of a target river reach in real time, comparing detection result data with detection result data of an abnormal water sample, determining potential pollution sources according to a comparison result, determining high-matching candidate sources from the potential pollution sources by utilizing an LSTM (Long Short Term Memory) time sequence feature extraction model, carrying out space transition verification on the high-matching candidate sources, and carrying out space transition verification on the high-matching candidate sources. And determining the pollution source according to the verification result. According to the method, the accuracy, efficiency and result reliability of tracing the river pollutants can be effectively improved, and the scenes of multi-source pollution, intermittent emission and the like of complex rivers can be effectively handled.

The invention belongs to the technical field of steel and iron material design, and discloses a stainless steel strength and toughness collaborative optimization method and system based on heterogeneous integration, and the method comprises the steps: constructing a database of stainless steel components, process parameters and mechanical properties; according to the database, establishing a stacked heterogeneous integration model; quantizing contribution weights of input variables of the stacked heterogeneous integrated model to mechanical properties based on an SHAP method, identifying key factors, and setting a multi-target strength and toughness collaborative optimization index to guide an optimization direction; optimal components and processes are screened according to SHAP analysis, a sample is prepared through vacuum melting, hot rolling and heat treatment, the performance is verified according to the ASTM standard, and a final optimization scheme is determined; and a cross-scale digital twinborn verification system is constructed, and collaborative optimization of alloy components, process and macroscopic performance is realized. According to the method, the problems of low efficiency and insufficient generalization ability of a single model of a traditional trial and error method are solved, and an efficient and explainable intelligent optimization scheme is provided for development of high-performance stainless steel.

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 a multi-agent-based material performance prediction and synthesis system, and the system comprises a multi-agent data enhancement module which is configured to be used for firstly disassembling a complex problem into a plurality of subtasks, and then constructing a fine tuning data set comprising Sub-CoQ question and answer pairs by starting multi-source parallel retrieval; the multi-expert debate module is configured to be used for simulating decision conflicts of different roles in material engineering and generating a direct preference optimization DPO data set through debate; the training and verification module is configured to be used for training and verifying a large model MatMind in the field of materials by utilizing supervised fine tuning SFT and reinforcement learning RLHF based on the fine tuning data set and the DPO data set; and the material performance prediction and synthesis module is configured to be used for realizing intelligent recommendation of a material performance prediction and synthesis process by importing input parameters into the large model MatMind.

The invention belongs to the technical field of iron phosphate preparation, and discloses an energy-saving control system for iron phosphate preparation based on an energy consumption scheduling model. The system is composed of a data acquisition module, an energy consumption sensing module, a preparation process modeling module, an energy consumption prediction module, an energy-saving scheduling module, an intelligent execution module, a feedback correction module, a man-machine interaction module and a remote operation and maintenance module. The energy consumption sensing module intelligently senses an energy consumption state, the preparation process modeling and energy consumption prediction module accurately predicts energy consumption, the energy-saving scheduling module generates an optimal scheduling strategy, the intelligent execution module accurately executes an instruction, and the feedback correction module realizes closed-loop adaptive regulation and control; all the modules cooperatively operate, process parameters are adjusted in real time according to actual working conditions of iron phosphate preparation, energy consumption in the preparation process is remarkably reduced, the energy utilization rate is increased, and energy-saving optimization of iron phosphate preparation is achieved.

The invention discloses an intelligent industrial wastewater treatment method based on edge calculation and multi-modal data fusion, which comprises the following steps: synchronously acquiring various data of an industrial wastewater treatment site through edge nodes, automatically adjusting and aligning the generation time of each data, and ensuring that the time error of different data is less than a set threshold value; organically fusing the image features, the chemical parameters and the component analysis report to generate pollutant description features; constructing a simplified simulation model on the edge equipment, and predicting the diffusion trend of the wastewater pollutants by using the simulation model based on the pollutant description characteristics; and when the network is interrupted, the edge equipment is automatically switched to a local mode, and the operation is continued by using the stored model and the wastewater data acquired in real time. According to the method, the simulation model is deployed through the edge equipment, so that the calculation amount can be greatly reduced, the diffusion trend of wastewater pollutants can be accurately predicted, effective treatment measures can be taken in time, and the stability and reliability of an industrial wastewater treatment system are improved.

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 relates to the technical field of ozone precursor traceability, in particular to an ozone precursor collaborative traceability method and system based on a knowledge graph, and the method comprises the following steps: obtaining precursor concentration change, recognizing an abnormal path, extracting path characteristics, carrying out standardized scoring, adjusting graph connection strength, and analyzing sequence offset to obtain collaborative nodes. And screening origin nodes in combination with time sequence meteorology to generate an origin node list. According to the method, pollution response channels are identified through precursor node concentration changes and path connection relations, focusing of key paths is enhanced, paths are scored based on multi-dimensional indexes, connection attributes are dynamically adjusted in combination with monitoring period ozone response intensity, map structure updating is achieved, and concentration response sequence offset is analyzed; according to the method, nodes with co-evolution characteristics are screened, the stable relation identification capability is improved, the path reasonability is evaluated by combining a release time sequence and meteorological conditions, the initial source positioning accuracy is improved, and the response speed and the identification precision of precursor tracing are integrally enhanced.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention provides a tunnel surrounding rock dynamic grading and blasting parameter optimization method and system, and relates to the technical field of data processing.The method comprises the steps that geological parameters of a current tunnel face are collected in real time through a multi-source sensing system deployed on the tunnel face; based on the acquired geological parameters, identifying a plurality of characteristic sampling points with geological representativeness in a tunnel face spatial domain; four non-coplanar feature sampling points are selected to construct an initial tetrahedron, the point, farthest from the surface of the current convex hull, in the remaining points is included in sequence through an iterative extension method, the surface of the convex hull is recalculated till all the feature sampling points are enveloped, and a convex polyhedron evaluation area boundary is formed. According to the invention, data full-process connection and function collaboration can be realized.

A method accessing a pre-trained specific material database associating each of a plurality of materials with a corresponding material profile, each material profile including one or more parameters including at least one of a transmit frequency and a response frequency; receiving a selection of a target material from a user; identifying first material profile associated with the target material using the pre-trained specific material database; transmitting, via an RF detection device, an RF signal into the target material using the one or more parameters for the target material associated with the first material profile; receiving, via the RF detection device, a response signal from the target material; analyzing the response signal using an AI algorithm to determine whether resonance characteristics of the response signal indicate a presence of the target material; and notifying the user if the presence of the target material is indicated by the resonance characteristics.

The invention discloses a building composite phase change material intelligent matching and optimizing method based on a large language model, and belongs to the technical field of building energy saving and intelligent material design. Basic thermophysical property data, building environment parameter data and user demand data are collected; a deep reinforcement learning technology is utilized to construct an intelligent model based on a deep Q network strategy, and generated data samples are integrated into a thermophysical property database; outputting a candidate material combination recommendation scheme by adopting a large language model; evaluating the candidate material combination recommendation scheme by using the semantic tag vector and a sorting engine to obtain a performance evaluation result; generating a performance evaluation report according to the simulation model; a multi-agent negotiation algorithm is adopted, cross-regional thermal control performance is optimized, a multi-dimensional performance comparison diagram and tuning suggestions are generated, correction information of designers is recorded, optimization is carried out, and feedback is provided in a self-adaptive mode. According to the method, the building composite phase change material combination is screened, cross-regional thermal control is optimized, adaptive schemes and suggestions are output, and the self-adaptive optimization capability of the system is improved.

The invention belongs to the technical field of composite material design, and discloses a polyimide-based composite material design method and system based on experiment-machine learning collaborative optimization. The method comprises the following steps: S1, experimental database construction and machine learning performance modeling; S2, machine learning model construction and training; S3, model evaluation and performance index output; and S4, intelligent design of the polyimide-based composite material. The multi-objective performance collaborative optimization design and preparation of the polyimide-based ternary carbon heterostructure composite material are carried out by taking experimental data as a main material and machine learning as an auxiliary material. According to the method, an experiment-model-optimization closed-loop iterative design system is constructed based on an experiment-machine learning collaborative optimization method, the method has the advantages of high prediction precision, high optimization efficiency, good multi-target adaptability and the like, the development efficiency of the polyimide-based composite material is remarkably improved, and the development cost of the polyimide-based composite material is reduced. The method is suitable for intelligent design and large-scale application and popularization of the high-performance heat-conducting electromagnetic shielding material.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

The invention discloses a xanthophyll eyesight-improving eye-protecting cream formula optimization method based on random forest hierarchical screening. The method comprises the following steps: S1, collecting historical sample information of xanthophyll eyesight-improving eye-protecting cream, preprocessing the historical sample information and storing the preprocessed historical sample information in a formula optimization database; s2, establishing a raw material-proportion multi-level feature structure tree based on raw material types and raw material proportions in the formula optimization database, generating a hierarchical feature set, and writing the hierarchical feature set into the formula optimization database; s3, forming a layered random forest initial model; s4, obtaining an optimal layered random forest optimization model and an optimal hyper-parameter combination; s5, writing the recommended formula combination interval into a formula optimization database; and S6, preparing an eye cream experimental sample through the recommended formula combination interval, testing, updating the layered random forest optimization model and the formula optimization database, and forming a formula optimization closed-loop iteration mechanism. According to the invention, the research and development efficiency of a new formula and the reliability of industrialization landing are greatly improved.

The invention belongs to the technical field of metallurgical industry process intelligent control and prediction, and discloses a self-adaptive mixed intelligent prediction method and system for smelting endpoint parameters of an electric arc furnace. Acquiring smelting process data of the electric arc furnace; constructing a dual-drive prediction system comprising a mechanism model and a data drive model; calculating a decision coefficient of a prediction value and an actual measurement value of the theoretical model, and counting an effective historical data volume; constructing a machine learning prediction model, and selecting a modeling algorithm according to the effective historical data volume; selecting a hybrid prediction strategy based on the decision coefficient and the effective historical data volume; predicting and outputting an end point carbon content predicted value and an end point temperature predicted value according to a hybrid prediction strategy; predictive deviation threshold value judgment and execution control are conducted, and the electric arc power, the oxygen blowing flow, the feeding speed or the cooling water flow are adjusted. Accurate prediction and dynamic optimization control of the electric arc furnace end point parameters are achieved, and the smelting quality, the energy utilization rate and the production stability are remarkably improved.

The invention discloses a method for designing a new pollutant perfluorinated compound adsorption material based on a large language model. The method comprises the following steps: firstly, constructing a mixed retrieval enhancement generation framework fusing a structured knowledge graph and vectorized semantic retrieval; then constructing a field knowledge graph and semantic representation big language model oriented to the PFAS adsorption material by utilizing a big language model framework; generating an initial material design framework through a large language model, performing iterative adjustment on a material structure in combination with a machine learning model, and predicting and generating a performance-optimized PFAS adsorption material; and finally, based on knowledge graph reasoning and vector storage information, combining molecular structure information, and recommending an experiment path for PFAS adsorption material synthesis and verification. According to the method, the knowledge graph and the semantic model are constructed to realize automatic extraction and systematization of domain knowledge, an interpretable design rule is generated through data reasoning, an optimization code is automatically generated through combination of LLM and ML, an experiment path is recommended to accelerate material design iteration, the experiment period is shortened, and a reliable screening scheme is provided.

The invention discloses a soil acidification improvement method, and relates to the technical field of soil improvement, according to the soil acidification improvement method, evaluation of soil attributes is completed through multi-level diagnosis and calculation of the acidification coefficient, the physical obstacle coefficient and the buffer capacity coefficient of soil, and comprehensiveness of soil acidification analysis is ensured. The problem of limitation existing in the current soil acidification analysis process is solved, meanwhile, a threshold value is used for driving decision control flow branches, an improvement scheme is flexibly upgraded based on real-time monitoring data, the soil improvement effect is improved, closed-loop learning double feedback and S6, data are fed back to S1, self-optimization of the system is achieved, the system adapts to soil changes, and the soil acidification analysis efficiency is improved. An abnormal state after improvement is warned in time, so that the soil safety is guaranteed; the method is suitable for the fields of land restoration and the like, the soil improvement efficiency is improved through data-driven intelligent decision, resource waste is reduced, soil acidification is improved and upgraded, and a technical engine is provided for agricultural sustainable development and cultivated land protection.

Embodiments relate to system and methods involving use of a technique for managing a database for producing a material composition having a thermodynamic phase. The technique can include: receiving a binary phase diagram for each material to be used as a component of a high-entropy alloy (HEA); using one or more active learning machine learning techniques for generating a feature, the feature including: a primary feature that is representative of a probability that an HEA will exhibit a solid solution phase and / or an intermetallic phase, and a physics-based feature that is representative of a factor related to formation of a desired intermetallic HEA phase; encoding the primary feature and the physics-based feature; generating an output representation of a HEA alloy composition and phase of a predicted materials composition; and selecting a HEA composition and phase that will meet a material design criterion.

The invention relates to the technical field of component analysis, in particular to an intermediate infrared spectrometer sensor verification system which comprises the following steps: acquiring spatial distribution information of a target sample through an automatic sampling module, and generating a corresponding first feature group based on the spatial distribution information; adjusting an emission wave band and a modulation strategy corresponding to a mid-infrared light source, converting mid-infrared photons into visible light signals, and generating a corresponding second feature group; performing down-sampling processing on the original spectral data to generate a corresponding third feature group; inputting the sparse spectral coefficient into a pre-trained deep learning reconstruction model, and dynamically correcting the reconstruction process in combination with an environment temperature compensation parameter to generate reconstructed spectral data corresponding to high resolution; and analyzing the reconstructed spectrum data, matching a preset substance spectrum database, extracting a characteristic absorption peak position and an intensity ratio of the target substance, and generating a corresponding final analysis result. According to the invention, the intelligence of the sensor verification system can be improved.

A method accessing a pre-trained specific material database associating each of a plurality of materials with a corresponding material profile, each material profile including one or more parameters including at least one of a transmit frequency and a response frequency; receiving a selection of a target material from a user; identifying first material profile associated with the target material using the pre-trained specific material database; transmitting, via an RF detection device, an RF signal into the target material using the one or more parameters for the target material associated with the first material profile; receiving, via the RF detection device, a response signal from the target material; analyzing the response signal using an AI algorithm to determine whether resonance characteristics of the response signal indicate a presence of the target material; and notifying the user if the presence of the target material is indicated by the resonance characteristics.

The invention discloses a molecular property prediction method and device based on a graph neural network and a large language model, and the method comprises the steps: obtaining a molecular structure graph according to the SMILES of a molecule, and the molecular structure graph comprises the structural information of atoms and functional groups; generating a description text of each functional group by using the fine-tuned large language model, and encoding the description text by using a text encoder to obtain text information of each functional group; adding text nodes and feature information in the molecular structure diagram, and adding edges between the text nodes and corresponding functional groups to obtain a final edition molecular structure diagram; and inputting the final edition molecular structure diagram into the trained molecular property predictor to obtain a prediction result of the molecular property. According to the method, structural information and text information of atoms and functional groups in molecules are fully considered, and then the information is learned by using the message passing neural network, so that molecular property prediction is more accurate, and the accuracy of molecular property prediction is improved.

The invention relates to a digital intelligent regulation and control preparation method and system for a high-solid-waste low-carbon high-performance grouting material, and solves the problems that a traditional preparation technology lacks an autonomous and controllable digital intelligent regulation and control system and is weak in adaptability to fluctuation of raw materials, and the method comprises the following steps: obtaining XRF chemical components and XRD mineral composition data of industrial solid waste raw materials; constructing a material gene database; based on the database, screening a proportioning scheme by using a performance prediction model, and predicting workability, strength development and shrinkage performance; inputting a prediction result as a fitness function into a multi-objective optimization algorithm, and outputting an optimal material gene combination; a batching scheme is generated based on the optimal combination, and a stirring process is started after technological parameters are preset; collecting data through a real-time monitoring system, and comparing the data with the digital twin model; and based on a comparison result, automatically adjusting material proportioning parameters. The high-solid-waste, low-carbon and high-performance grouting material has the advantages that accurate design and regulation of the high-solid-waste, low-carbon and high-performance grouting material are achieved, material performance is improved, and carbon emission and cost are reduced.

A drug design method based on an autoregressive model is provided, which relates to the field of drug design technologies. The method includes: applying a sub-word tokenization algorithm to biological text processing, training protein and ligand information in data sets to obtain a protein tokenizer and a ligand tokenizer, and constructing a tokenizer of the autoregressive model; processing and transforming original data in the data sets into a text form, and encoding by the tokenizer to construct a training data set for the autoregressive model; training the autoregressive model by the training data set, so that the autoregressive model can understand SMILES representations of ligands and learn an interaction mode between proteins and ligands; generating predicted ligands by using the trained autoregressive model, and post-processing through a chemical information tool to acquire candidate ligands with specific chemical structures; and evaluating and optimizing the candidate ligands to determine target candidate molecules.

The invention discloses a traditional Chinese medicine adverse reaction risk prediction method based on a heterogeneous graph neural network, and the method comprises the following steps: obtaining data, obtaining a traditional Chinese medicine-target relation file from an ETCM database, and obtaining an adverse reaction-target relation file from an ADReCS database; data preprocessing: performing data cleaning on the obtained relation file to obtain a preprocessed file; constructing an isomeric graph, taking the traditional Chinese medicine herb, the target spot and the adverse reaction adverse as three types of nodes, and constructing the isomeric graph by utilizing the pre-processing file; node features are initialized, initial feature vectors are constructed for each type of nodes herb, target and adverse, and initial embedding of all the nodes is mapped to the same dimension space; carrying out HAPM feature fusion, inputting the node features into an HAPM heterogeneous graph attention network to carry out message passing and aggregation, and outputting a representation vector of a node level; predicting and outputting; and performing verification and feedback iteration. The invention further provides a system adopting the method. The method and the system can accurately predict the adverse reaction risk of the traditional Chinese medicine.

The invention provides a mineralogical substance composition testing method and system based on multispectral data fusion. The method comprises the following steps: separating out a first characteristic interference signal caused by the oxidation state of an iron element and a second characteristic interference signal caused by the oxidation state of a manganese element by adopting wavelet transform in a superposition region of a Raman spectrum signal and a visible light absorption spectrum; performing spatial weighting on the intensity of the first feature interference signal and the intensity of the second feature interference signal to construct a multi-dimensional feature vector, inputting the multi-dimensional feature vector to a pre-trained residual neural network, and combining a radioactivity attenuation parameter measured by a gamma spectrometer to determine the radioactivity attenuation of the first feature interference signal and the second feature interference signal. And generating a mineralogical substance composition test result containing a mineral phase, an isomorphic substitution ratio, color cause analysis data and a radiation safety level. According to the method, the collaborative interpretation of the multispectral data and the radiation parameters can be realized, the mineral component analysis precision is improved, the color formation mechanism is synchronously analyzed, and the radioactive risk level is quantitatively evaluated.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

The invention discloses a microemulsion interfacial tension efficient prediction method and system based on active learning and molecular dynamics. According to the method, 217 molecular descriptors corresponding to each molecular structure are calculated by adopting an RDKit software package, and the descriptors are used for representing molecular structure characteristics and serve as input variables of a machine learning model, so that key structure information including molecular branching degree, polarity and the like is transmitted. For an oil-water-surfactant ternary interface system, the oil-water interfacial tension in the presence of a surfactant is simulated and calculated through molecular dynamics, and an IFT value is set as a model prediction target. An active learning mechanism is introduced, and iterative sample labeling in the molecular dynamics simulation process is guided; and integrating the obtained IFT data with the molecular descriptor features, constructing a machine learning data set, and training a random forest model. According to the method, the problem of screening a high-performance surfactant layer by a middle-phase microemulsion system can be solved, and the ultra-low oil-water interfacial tension can be rapidly and efficiently screened.