1826results 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 relates to the technical field of water quality monitoring, in particular to a mining area groundwater environment monitoring system and method, and the system comprises a mining area groundwater partition recognition module, a groundwater pollution risk assessment module, a groundwater monitoring point optimization module, a groundwater pollution path analysis module and a groundwater dynamic monitoring planning module. According to the invention, through data-driven zoning and risk assessment processes, fine and dynamic management is realized in underground water environment monitoring, functional zone zoning boundaries are accurately calibrated, understanding of an underground water flowing mode is enhanced, pollution source control is more targeted, diffusion risk intensity is assessed in combination with flow velocity and pollutant release intensity, and the risk assessment accuracy is improved. The risk management strategy is optimized, the pollution diffusion range is predicted and identified more efficiently, the emergency response accuracy is improved, monitoring point arrangement is optimized through risk data, reasonable monitoring resource allocation is ensured, the sampling task weight and the time interval are dynamically adjusted, and resource optimization and monitoring efficiency improvement are achieved.

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 discloses a data-mechanism fused airport runway surface water and ice condition prediction method. The method comprises the following steps: establishing a theoretical model of a runway surface temperature field and a water and ice condition; thermal parameter inversion is carried out on the theoretical model based on actually measured data, and a dynamic theoretical model adapted to an actual application scene is obtained through multi-time iteration optimization of model parameters; and utilizing the fused time sequence data and the dynamic theoretical model to predict the water and ice condition on the surface of the airport runway, and providing dynamic evolution information of the icing process. According to the method, the problems of insufficient real-time performance and accuracy of runway icing prediction in the prior art are solved, the icing time, the ice layer thickness and the ice layer range of the runway surface can be accurately predicted, real-time deicing decision support is provided, and the runway operation safety and the airport operation efficiency are effectively improved.

The invention discloses a semiconductor catalyst intelligent design and reaction mechanism optimization method based on graph grammar, which comprises the following steps: step 1, a graph grammar model generation and optimization module: automatically generating a molecular structure model of a semiconductor catalyst, and screening out a structure combination with optimal catalytic performance; 2, a reaction path automatic derivation module which simulates the interaction between molecules and generates a reaction path diagram; 3, an intelligent catalyst design module: analyzing the relationship between the structure and the performance of the semiconductor catalyst, and dynamically adjusting the molecular structure of the catalyst; 4, a reaction mechanism visualization and interpretation module which provides graphical display of reaction paths and structure changes and supports interaction analysis of multi-dimensional data; 5, a real-time monitoring and feedback optimization module is used for monitoring key parameters in the catalytic reaction process in real time and dynamically adjusting a graph grammar model and a design scheme.

The invention relates to the technical field of information, in particular to a method for detecting and monitoring the cleaning effect of a semiconductor wafer in real time in the technical field of semiconductor wafer manufacturing, which comprises the following steps: introducing a rapid chemical analysis technology, combining an electrochemical sensor and spectral analysis, monitoring metal ion residues on the surface of the wafer in real time, and shortening the detection period; instant feedback on a production line is realized; a real-time feedback control system is constructed, according to the output of the comprehensive evaluation model, the working parameters of the cleaning equipment are automatically adjusted, the cleaning process is optimized, and the usage amount of cleaning liquid and the environmental influence are reduced; an edge computing technology is integrated, data processing and analysis tasks are sunk to a cleaning equipment end, data transmission delay is reduced, and the response speed of real-time monitoring and feedback control is increased; a visual monitoring platform is developed, key parameters, detection results and prediction trends in the cleaning process are displayed in a graphical mode, and operators can conveniently master the cleaning state in real time and quickly respond to abnormal conditions.

The invention discloses a Frechet distance-based abnormal inflow traceability method and system for a sewage treatment plant, and the method comprises the steps: carrying out the unit division of the inflow range of the sewage treatment plant through combining land parcel information, pipe network information, convergence conditions, monitoring stations and the like, and analyzing the correlation between total phosphorus and conductivity; arranging a pipe network water quality monitoring micro station, and carding a factory-network-land parcel-source pointer relation library; a sewage treatment plant big data warehouse is constructed, related data are cleaned and treated, and the data are accessed to a pollutant traceability visualization platform; a sewage plant water pollution traceability model is constructed, a water inlet total phosphorus standard exceeding event is selected to carry out traceability research, the traceability model is embedded into a visual platform, and sewage plant total phosphorus concentration standard exceeding traceability visualization is achieved. Compared with the abnormal inflow tracing method of the sewage treatment plant, the large data resource utilization rate can be greatly improved while a large amount of manpower and material resources are saved, and a basis is provided for locking secretly-discharged enterprises, predicting pollution diffusion and environment emergency.

The invention discloses a molecular property prediction large language model construction method based on a knowledge graph. The method comprises a plurality of steps: firstly, acquiring pre-training corpora by using a high-throughput data acquisition technology, including related data such as molecular attributes and metabolic pathways; then, a knowledge graph about fourteen end points is constructed based on the data, and the knowledge graph represents the relation between the molecules and the attributes and the end points of the molecules in a triple form; thirdly, pre-training the large language model by using a LoRA technology, optimizing the model by adopting an incremental learning strategy, and forming an expert large model; then, a triple related to the target endpoint is retrieved from the knowledge graph through the RAG technology, and retrieval results are sorted; and filtering the retrieved triple through cue word design and an expert large model, and extracting key information. And finally, converting the filtered triple list into a natural language, inputting the natural language into an expert large model, generating a prediction result of molecular properties, and providing a reasoning basis. According to the method, the accuracy and efficiency of molecule and endpoint chemical reaction prediction are remarkably 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 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 VOC gas leakage intelligent inspection system and method based on gas cloud imaging, and belongs to the technical field of VOC gas leakage detection. The inspection system comprises a moving track and an inspection point which are arranged along a monitored object, an intelligent sliding rail type robot which moves along the moving track, and a data processing system. The intelligent sliding rail type robot is provided with a gas cloud imaging host, and the gas cloud imaging host is provided with a medium wave infrared machine core, a visible light machine core and a wireless communication module. The intelligent slide rail type robot moves to each inspection point along the moving track, and the gas cloud imaging host shoots infrared images and visible light images of each inspection point and transmits the infrared images and the visible light images to the data processing system through the wireless communication module. According to the invention, a specific leakage point can be timely and accurately locked, the VOC leakage concentration can be determined, and full-process intelligent inspection from inspection, leakage detection processing, leakage accident archiving, leakage early warning, leakage accident processing to processing result feedback can be realized.

The invention discloses a method for determining metal fuel multi-phase structure irradiation microstructure evolution and performance change. Related physical property parameters are calculated through molecular dynamics and a first principle, and a diffusion coefficient is corrected by utilizing an irradiation acceleration theory to obtain a diffusion coefficient under a metal fuel irradiation condition; the method comprises the following steps: constructing a metal fuel polycrystalline multiphase organization structure evolution phase field model, defining phase field variables required in the model, and determining an evolution equation of the phase field variables; calculating to obtain a phase-field variable initial value, then solving the phase-field variable evolution equation, and obtaining a phase-field variable numerical solution; the phase field variable numerical solution is subjected to visualization processing, and a metal fuel polycrystalline multiphase structure distribution diagram is obtained; calculating the size and distribution of an irradiation structure in the metal fuel to obtain an evolution rule of the irradiation structure along with fuel consumption; and calculating the thermal conductivity, swelling and stress in the metal fuel by using the size and distribution of the irradiation structure in the metal fuel, and obtaining the change rule of the performance of the metal fuel along with fuel consumption.

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 a combustion chemical reaction kinetic data processing method and system for an ADN-based liquid propellant, and belongs to the technical field of data processing. According to the method, firstly, key information in the combustion process can be comprehensively captured by obtaining a combustion chemical reaction kinetic vector and an element state linkage vector; structured semantic mapping is carried out on the primitive state linkage vector to obtain a reaction state structured vector and a strategy association demand vector, which is helpful for accurate classification and positioning of combustion reaction related information. And the integration confidence weights of the first data and the second data are determined, so that the association degree of the element reaction data and the visual association labels under different integration strategies can be quantified, and the analysis of the combustion process is more scientific and meticulous. The finally determined visual monitoring result can visually present the characteristics of the element reaction data under different strategies, and clear and accurate data support is provided for deeply understanding the combustion mechanism of the ADN-based liquid propellant, optimizing the performance of the propellant and ensuring safe use.

Disclosed is an apparatus for generating a product passport, the apparatus comprising: one or more computing node(s) and one or more computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the one or more computing node(s), cause the apparatus to perform the following steps:receiving a request to provide a first decentral identifier associated with at least one first physical entity of a product supply chain and / or a recycling product chain;in response to the request, providing the first decentral identifier associated with the at least one first physical entity of the product supply chain and / or the recycling product chain;generating a product passport including the first decentral identifier and data related to the at least one first physical entity of the product supply chain and / or the recycling product chain;providing the product passport for access by a data consuming service under control of a data providing service associated with a data owner.

The invention discloses a fiber bragg grating strain measurement method and system based on deep learning, designs a double-grating structure and a deep learning analysis module, and separates temperature and strain components in grating wavelengths in real time. The system comprises a fiber grating sensor module, a data acquisition module, a deep learning analysis module and a display module, high-precision separation and real-time compensation of strain and temperature are realized through high-precision data acquisition and deep learning model processing, and the system is widely applied to the field of health monitoring of engineering structures such as bridges and tunnels.

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 relates to a method for predicting river nitrogen metabolism gene abundance and key influence factors thereof based on automatic machine learning. Comprising the following steps: collecting a river water sample, detecting water quality physicochemical indexes, and performing metagenome sequencing to obtain nitrogen metabolism gene abundance; taking conventional water quality physicochemical indexes as explanatory variables, taking nitrogen metabolism gene abundance as a target variable, adopting automatic machine learning to construct a model, evaluating the performance of the model through a mean square error (MSE) and a fitting coefficient (R), and selecting an optimal algorithm for prediction; the model analyzes the relative contribution of each water quality index through an SHAP value graph, and reveals the relationship between each water quality index and gene abundance by using a partial dependency graph (PDP). The method shows high precision and good generalization performance in the aspect of predicting the abundance of the nitrogen metabolism gene, and is suitable for describing the nitrogen dynamic process of a river water body. The model can explain, analyze and quantify the influence of different water quality indexes on the nitrogen metabolism gene afterwards, and a scientific basis is provided for river water quality management and nitrogen metabolism optimization regulation and control.

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 provides a C / C composite material ablation morphology prediction method considering a complex microstructure, and solves the technical problem that the prediction result is not accurate enough due to the fact that the influence of the material microstructure on the ablation morphology is not fully considered in the existing prediction method. A random sequence adsorption algorithm is adopted to create C / C composite material microscopic representative volume cell elements based on a three-dimensional voxel grid, and under a finite element transient heat transfer analysis framework, ablation criteria of all components of the C / C composite material are respectively established according to temperature information and chemical reaction kinetic parameter information; and finally, C / C composite material microscopic ablation morphology prediction comprehensively considering complex microstructure characteristics is realized.

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 provides a high-performance material visual screening system based on machine learning and an application method thereof. The system comprises a feature extraction module, a machine learning model training module, a prediction module and a feedback optimization module. The data preprocessing module is used for performing primary processing on the original data, wherein the primary processing comprises removal of invalid data and abnormal values; the feature extraction module is used for converting a time domain signal into a frequency domain signal by adopting Fourier transform, extracting a frequency domain feature, and performing data dimension reduction by adopting principal component analysis (PCA); the machine learning model training module is used for ensuring the generalization ability of the model by adopting a cross validation technology in the training process, and accelerating the convergence process of the model through a gradient descent method; the prediction module is used for predicting the material performance of the high-performance material to obtain a prediction result; and the feedback optimization module is used for automatically optimizing model parameters by adopting Bayesian optimization, analyzing a prediction result of the model and dynamically adjusting the model parameters so as to improve the prediction precision. Through the scheme of the embodiment of the invention, the law can be learned from a large amount of experimental data by a machine learning technology, and the material performance can be quickly and accurately predicted.

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.