356 results about "Relevance analysis" patented technology

The invention discloses a light industry supply chain multi-modal data fusion analysis method based on deep learning, and the method comprises the following steps: carrying out the cleaning and standardization processing of text, image, audio and video data collected in a supply chain environment, and constructing a standardized multi-modal data set; then, a special feature extraction network is adopted to generate each modal feature vector, and a feature incidence matrix is constructed through cross-modal correlation analysis; feature weights are dynamically adjusted in combination with a domain knowledge rule base, multi-modal feature interaction is achieved through a cross-modal attention fusion network, and unified fusion features are generated through a self-attention mechanism; and finally, constructing a supply chain decision model, and mapping the fusion feature into a supply chain state evaluation result and an optimization parameter. According to the method, knowledge rule constraint and a deep attention mechanism are fused, supply chain situation awareness precision and decision reliability can be effectively improved, and technical support is provided for intelligent management of the light industry supply chain.

The invention belongs to the field of data analysis, and discloses a data analysis method for engineering consultation digital intelligent management, which comprises the following steps: mapping multi-project heterogeneous data to a unified three-dimensional space-time grid through space-time grid division to form a grid mapping data set; detecting data format and attribute conflicts based on neighborhood correlation analysis, and distributing calibration data through dynamic weight; missing values and abnormal fluctuations are processed by using a time sequence autoregression model and a smoothing technology, and a complete data set with continuous time is constructed by combining linear interpolation and multi-dimensional data fusion; key indexes are extracted to construct a digital mirror image containing spatial positions, time sequences and attribute association, real-time data are synchronized, and outlier grid points are corrected; and multi-scene operation conditions are constructed based on the mirror image data, and a dynamic adjustment scheme is realized. According to the method, heterogeneous data are integrated through space-time grids, digital twinning and multi-scene simulation technologies are combined, and full-period management and risk intelligent decision-making of engineering data are achieved.

The invention relates to a cotton field carbon sink dynamic evaluation method and system based on multi-source data fusion. The method comprises the following steps: firstly, comprehensively collecting multi-source data of a cotton field area environment, and processing the multi-source data by using a time window alignment technology to generate a multi-source heterogeneous data set; based on the data set, key features are extracted through a data mining means, and a key influence factor weight table is generated; and then, inputting the key influence factor weight table and the multi-source heterogeneous data set into the trained optimization evaluation model together to obtain cotton field carbon sink space-time distribution characteristics. And finally, carrying out correlation analysis on the spatial-temporal distribution characteristics and meteorological continuity data, and generating a curve capable of visually displaying the dynamic change of the cotton field carbon sink along with time. The method overcomes the limitation of a single data source, greatly improves the accuracy and comprehensiveness of cotton field carbon sink evaluation, can accurately reflect the real state of cotton field carbon sink under different time-space conditions, and provides a key basis for agricultural managers to formulate scientific and reasonable cotton field planting management strategies.

The invention relates to the technical field of deep learning, in particular to an AI chip test parameter adaptive optimization method based on deep learning, which comprises the following steps: acquiring historical test data of an AI chip, and calculating correlation strength among different failure modes based on the historical test data; identifying a failure coupling matrix according to the edge weight, and converting a preset static detection parameter constraint boundary into a dynamic constraint space changing along with a failure detection state; a multi-level optimization framework is constructed, the upper layer executes failure type correlation analysis and generates constraint propagation information, the middle layer optimizes a parameter cluster based on the constraint propagation information, and the lower layer adjusts a single detection parameter and outputs a parameter optimization result; establishing a neural network mapping model, and obtaining a nonlinear mapping relationship between the detection parameters and the failure types; based on the physical state parameters, the nonlinear mapping relation is adjusted, the dynamic constraint space is updated, parameter optimization is executed again, a parameter optimization result is output, and an optimal test parameter combination is output.

The invention relates to the technical field of power system measurement, and discloses a current transformer error dynamic monitoring method and system.The current transformer error dynamic monitoring method comprises the steps that current transformer time sequence data and a system event log are obtained; constructing a time sequence causal graph to represent the time correlation between the event and the error change; identifying potential causal links by applying a counter causal model; designing a multi-world simulation engine to generate an anti-fact scene; quantifying a causal effect by comparing actual observation with an anti-fact simulation result; establishing a monitoring mechanism to track key trigger events in real time; generating a dynamic causal interpretation report and adjusting a compensation strategy; according to the method, the limitation of traditional correlation analysis is broken through, the causal relationship and the correlation can be accurately distinguished, the real triggering factor of the error change of the current transformer can be accurately identified, the false alarm rate and the missing report rate are reduced, and the accurate dynamic monitoring of the error of the current transformer is realized.

Provided in the present invention are a method and system for real-time update of a medical knowledge base, and a medium and a device. The method comprises: acquiring medical literature data in real time; analyzing the medical literature data, so as to obtain first structured data; performing correlation analysis on the first structured data and second structured data in a medical knowledge base, so as to obtain an analysis result; on the basis of the analysis result, determining whether to update the medical knowledge base; identifying the differences of the medical knowledge base before and after the update, so as to obtain a difference identification result; assigning a unique version number to each difference in the difference identification result, so as to obtain version update records; and on the basis of the version update records, using incremental hash table technology to perform data storage for each difference. The present application has a higher update efficiency without the risk of omission; and by means of difference analysis and by using incremental hash table technology to store difference data, the present application significantly reduces data storage space requirements, and also improves data access speed.

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

The invention relates to the technical field of data processing, and particularly provides an address text correlation analysis method, system and device and a storage medium, and the method comprises the steps: receiving a target address text; based on the semantics of the target address text, retrieving a plurality of candidate standard address texts from a pre-constructed address knowledge base; on the basis of the target address text and the retrieved candidate standard address text, a cue word is constructed, and the cue word comprises an instruction used for guiding a large language model to conduct address relevance judgment and context information; and inputting the cue word into a large language model trained in an address field, and obtaining and analyzing the output of the large language model to determine a standard address text associated with the target address text and the association degree thereof. According to the method, the domain knowledge base and the large language model are fused, so that the accuracy and practicability of address correlation analysis are remarkably improved.

The invention discloses a multi-source data resource key information mining method and system, and the method comprises the steps: obtaining multi-source data of various forms and types, and carrying out the preprocessing; performing division according to data types; word frequency statistics and word correlation analysis are carried out on the character data, and numerical value meanings of the digital data are extracted through context semantic correlation; constructing a cross-modal key information data set based on the key information data set of each data type; designing a multi-branch long-short-term memory neural network model, inputting a cross-modal key information data set into the model for training, and generating key information mining models for different fields by dynamically adjusting network parameters; and selecting to carry out key information real-time extraction and updating on the optimal model, generating a knowledge graph, and carrying out visual operation. The method has the advantages that through multi-source data fusion and processing, the long-short term memory neural network model is combined, the knowledge graph is generated, and domain-specific key information mining and visualization operation are efficiently carried out.

The invention belongs to the technical field of traffic pollution prediction, and particularly relates to a LightGBM-based multi-source data fusion pollutant concentration prediction method, which comprises the following steps of: 1, integrating and processing multi-source data, and constructing a plurality of feature sets; and 2, carrying out feature analysis on meteorological, pollutant and traffic index variables through correlation analysis and time sequence analysis, and providing support for modeling and model interpretation. And step 3, dividing a training test set according to a time sequence, and training and optimizing the LightGBM pollution prediction model through parameter tuning by taking minimization of RMSE as a target. And 4, verifying the performance of the model from prediction precision, spatial distribution and wind direction influence. And 5, analyzing and displaying the key driving factor and quantifying the contribution of the key driving factor. According to the invention, through feature engineering processing and model optimization of heterogeneous data such as weather and traffic, high-precision prediction and influence factor analysis of lane-level pollutant concentration are realized.

The invention provides a crop population three-dimensional reconstruction and organ phenotypic character analysis method, and relates to the field of three-dimensional reconstruction, and the method comprises the steps: collecting cotton envelope image data, carrying out the data preprocessing, and carrying out the motion recovery processing and Gaussian sputtering of the cotton envelope image data, the method comprises the following steps of: preprocessing a group cotton point cloud, removing a ground point cloud, obtaining a complete cotton group point cloud model, segmenting a cotton individual point cloud model and a corresponding organ point cloud model from the complete cotton group point cloud model, automatically analyzing cotton key phenotypic characters, and performing correlation analysis. According to the method, a cotton original habitat three-dimensional point cloud model is constructed based on mobile visual equipment, and key characters such as cotyledon node height, plant height and leaf area can be automatically analyzed, so that the problems that phenotypic character extraction in the prior art is high in cost, low in flux, easy to make mistakes, inconsistent in standard, generally destructive, labor-consuming, time-consuming and the like are solved.

The invention discloses an indoor personnel trajectory tracking and anti-interference method based on a millimeter wave radar. The method comprises the following steps: firstly, dividing echo data into dynamic and static point cloud branches for respective processing; performing static clutter suppression on the dynamic point cloud branch, then performing speed dimension fast Fourier transform, and performing direct processing on the static point cloud branch to obtain a distance-Doppler spectrum; determining a target signal and a background clutter; carrying out point cloud screening by taking the point with the strongest energy as a spectrum peak center, and constructing compact representation of a target; and finally, predicting the target state of the current frame, and carrying out target track association. And carrying out track intersection judgment and recording an intersection state, and outputting a human body target position and a track. The method provided by the invention can dynamically adapt to human body sensing requirements in different scenes by combining various correlation analysis technologies, has relatively low calculation complexity and relatively high robustness, and is particularly suitable for human body sensing detection tasks in low-power-consumption application scenes. The system has the advantages of low cost, small size and low power consumption.

The invention discloses a summer rainfall sub-season prediction method and system fused with multi-scale deep learning, and the method comprises the steps: collecting multi-source weather forecast data and observation data, and carrying out the empirical orthogonal decomposition of the observation data, and obtaining a rainfall main mode and a mode sequence; performing multi-scale signal extraction on the observation data, and performing correlation analysis on the observation data and the modal sequence to obtain respective weight fields; constructing and training a deep learning model fusing a multi-pole attention mechanism and time sequence decomposition; inputting the forecast data into the trained model to carry out transfer learning, and optimizing the model; and substituting forecast data of preset time into the trained model to generate a high-quality summer rainfall sub-season forecast product. According to the method, the synergistic effect of sea, land and gas and the interaction of multi-scale signals are fully considered, the model is constructed based on an artificial intelligence method and a numerical model forecasting product, the sub-season forecasting skill of summer rainfall is effectively improved, and the method plays an important role in disaster prevention and reduction.

The invention discloses a lithofacies intelligent identification method, device and equipment, a medium and a program product, and the method comprises the steps: building a sample data set through logging parameters, drilling parameters and historical drilling data corresponding to lithofacies labels, screening out a feature subset for modeling based on correlation analysis, and building a lithofacies identification model based on a Transform neural network; and training the constructed lithofacies identification model by using the sample data set to obtain an optimized lithofacies identification model, inputting the feature subset of the well to be identified into the optimized lithofacies identification model, and outputting a lithofacies prediction result of a corresponding depth. The intelligent lithofacies identification method relies on the Transform model, has high identification precision, high automation degree and strong generalization ability, can effectively integrate two types of information of logging data and drilling engineering data, provides a more comprehensive basis for lithofacies judgment, and can meet the requirement of real-time lithofacies identification while drilling.

The invention discloses a power demand data analysis method and system based on artificial intelligence, and belongs to the technical field of intelligent power grid and power system automation, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the preprocessing of the data; analyzing a causal relationship among the multi-source data by using a causal discovery algorithm, eliminating irrelevant factors, selecting a final variable, and outputting a causal reasoning result; constructing a deep learning model, and outputting a prediction result; and optimizing a power dispatching strategy according to a prediction result. According to the method, through the time alignment and anomaly detection mechanism of the multi-source heterogeneous data, the problem of time sequence dislocation during multi-protocol data fusion is effectively solved, and the data quality is improved; in combination with a causal discovery algorithm and statistical test, a real causal relationship is accurately identified, and false correlation interference in traditional correlation analysis is eliminated; the time sequence neural network and the causal reasoning model are fused, the interpretability of the model is enhanced while historical data time sequence features are reserved, and the prediction robustness under the extreme working condition is remarkably improved.

The embodiment of the invention provides a reservoir capacity dynamic monitoring method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining a reservoir surface feature data set through the unmanned aerial vehicle, determining the boundary of a water area, scanning the bottom of the reservoir, obtaining the underwater terrain elevation and sediment distribution data, and constructing a three-dimensional terrain model; and determining terrain and deposit change data based on the three-dimensional terrain model dynamic change data of the continuous time sequence. Sedimentation space distribution characteristics are obtained through change rate analysis, and the sediment transportation amount and the sedimentation and deposition space rule are determined in combination with the water flow speed. Key driving factors are screened through correlation analysis, a sedimentation high-risk area is determined according to the key driving factors, and the change of sediments in the area is monitored, so that the change of the reservoir capacity is mastered. The reservoir siltation can be accurately and dynamically monitored, the high-risk area and the change mode are defined, the dynamic database is constructed to update the capacity change in real time, and important technical support is provided for reservoir management and flood control and disaster reduction.

The invention discloses a flora prediction and petroleum pollution remediation method based on machine learning. The method comprises the following steps: collecting multiple groups of experimental data of a diesel oil pollution sample treated by a microbial agent, extracting environmental factors, microbial community characteristics and target response variables to construct a training data set after missing value processing, abnormal value detection and standardized pretreatment, and importing the data set into a preset machine learning model to obtain a training result; carrying out feature learning, classification training and hyper-parameter optimization by adopting a GridSearchCV method in combination with 10-fold cross validation; evaluating the correlation between a target response variable classification result and the features through a multivariable Pearson's correlation matrix, and constructing an optimal test set; and finally, selecting an optimal prediction model according to a preset index. According to the method, the model training quality is improved through data preprocessing and correlation analysis, efficient flora prediction and algorithm application evaluation are achieved by means of multiple machine learning algorithms, scientific support is provided for petroleum pollution remediation, and remediation accuracy and efficiency are improved.

The invention discloses a lithium battery health state prediction method based on CNN-Transform-LSTM, and belongs to the technical field of lithium battery state monitoring and health management. Meanwhile, constant-current charging time and average charging voltage and average charging current in the whole process are extracted from a charging curve to serve as health features, and high-relevance features are screened through relevance analysis. Firstly, local features in data are extracted by using a convolutional neural network, and redundant information is reduced; then, using Transform to capture global dependence, and identifying a long-term health trend; and finally, capturing long-term dependence and trend in time through a long-short-term memory network, and ensuring smooth and stable prediction. According to the method, the later aging process can be predicted according to the first 70% of data of the whole life cycle of the lithium battery, and compared with an existing advanced model, the prediction precision and generalization ability are improved.

The invention provides an SAR (Synthetic Aperture Radar) anti-interference evaluation method and device based on maximum variance combined with layered entropy weight, and relates to the technical field of radar anti-interference. Comprising the following steps: constructing an evaluation index matrix according to a plurality of target SAR echoes; dividing the plurality of evaluation indexes into two layers of indexes by using a Pearson correlation analysis algorithm according to the evaluation index matrix and a preset threshold value; according to the plurality of evaluation indexes, the random number and the normalized evaluation index matrix, constructing a maximum variance evaluation optimization model which takes a maximized weighted evaluation matrix and a utility value matrix as objective functions and takes a difference degree weight row vector as a parameter to be solved; according to the maximum variance evaluation optimization model and the evaluation index matrix, determining a maximum difference evaluation matrix by using an interior point method; and according to the maximum difference evaluation matrix, the evaluation index matrix and the two layers of indexes, determining a plurality of evaluation values corresponding to the plurality of anti-interference methods by using a layered entropy weight method. In this way, the accuracy of anti-interference evaluation of various anti-interference methods is high.

The invention relates to a TL-AEAT-BIGRU post-compression yield prediction method based on data joint driving under knowledge constraint, which adopts a CWGAN-GP model based on conditional constraint to perform data enhancement on a small amount of multi-source data, adopts a Pearson + mRmR correlation analysis algorithm to determine main control factors influencing post-compression yield, and performs prediction on the post-compression yield by using a TL-AEAT-BIGRU model. And classifying reservoir categories through a main component analysis method based on the reservoir classification standard established by the former. Correlation experience knowledge between the post-compression yield and the input main control factors is combined with the TL-AEAT-BiGRU post-compression yield prediction model, and the screened main control factors are used as input for post-compression yield prediction. The result shows that compared with other yield prediction models, the model has the best performance in the post-pressure yield prediction of the research area. An ablation experiment shows that each module of the model contributes to improvement of the yield prediction effect, so that the yield prediction accuracy of the model is effectively improved, the problem that the yield prediction precision of the reservoir after pressure is not high under the condition that the sample size is small is effectively solved, and the method has guiding significance on yield analysis of an oil and gas well.

The invention relates to a cut tobacco dryer predictive control method based on multi-model fusion. The method comprises the following steps: acquiring preprocessing data; combining correlation analysis and random forest to screen features; a prediction model is constructed by using three algorithms of random forest, XGBoost and AdaBoost respectively; generating a training set and a verification set by using the historical production data, training the three prediction models by using the training set, and verifying the three prediction models by using the verification set; determining the weight according to the mean square error of the three prediction models, and carrying out weighted average on the prediction result of the fusion model to obtain a cut tobacco dryer outlet moisture prediction model based on multi-model fusion; and according to the predicted outlet moisture change trend, optimizing through a genetic algorithm, and adjusting the cut tobacco dryer control parameters. The advantages of the three models are fully utilized for fusion prediction, dynamic parameter optimization is carried out according to the predicted trend in combination with the genetic algorithm, and accurate prediction and self-adaptive control of outlet moisture are achieved.

The invention discloses a power demand prediction method, system and equipment based on market behavior influence deduction and a conditional diffusion model in the technical field of power market operation and demand prediction. The method comprises the following steps: constructing a market subject profit function model according to obtained power demand influence factors, carding a causal relationship between power demands and the power demand influence factors, and performing system dynamics simulation verification on the power demands to obtain a power demand simulation evaluation result; screening the power demand influence factors according to an evaluation result, performing data preprocessing on a data set of the power demand key influence factors, and expanding the preprocessed data set by using a diffusion model to obtain an expanded data set; performing correlation analysis and frequency domain transformation on the expanded data set to obtain a correlation coding matrix and a frequency domain data set; and inputting the expanded data set, the correlation coding matrix and the frequency domain data set into a Transform model to obtain a power demand prediction result.

The invention relates to the field of data analysis, in particular to a multi-source monitoring data-oriented dynamic trend assessment method, which comprises the following steps of: acquiring and preprocessing multi-source monitoring data to obtain a historical data sliding window and a basic smoothing coefficient for trend assessment; performing weighted correlation analysis on the disturbance variable and the measured variable change sequence to obtain a working condition response decoupling factor; obtaining a trend stability factor by evaluating the geometric morphology of the smooth trajectory of the measured variable; performing working condition response and trend stability combined correction on the basic smoothing coefficient to obtain a dynamic smoothing coefficient; a smooth trend value and a heat exchanger sub-health early warning signal are obtained by performing exponential weighted moving average and change rate judgment on a measured variable, and the problem that a fixed parameter EWMA cannot distinguish a working condition adjustment response and a tiny fault trend under a multivariable complex working condition is solved.

The invention relates to the technical field of water quality monitoring, in particular to a water quality monitoring prediction method and system based on multi-factor correlation analysis, and the method comprises the following steps: determining a water quality evaluation index, and collecting water quality evaluation index data of a water body in a monitored drainage basin; evaluating the water quality grade and acquiring a water quality monitoring data set of the water body in the monitored drainage basin; based on the water quality monitoring data set, the relevance between the water quality evaluation indexes is judged through correlation analysis, and then a data support set of the water quality evaluation indexes is constructed; according to the data support set of the water quality evaluation indexes, using an index prediction model to obtain predicted values of the water quality evaluation indexes; and predicting the future water quality grade of the detected water body according to the predicted value of each water quality evaluation index, and triggering early warning when an early warning condition is met. According to the method, the accuracy of water quality monitoring and prediction is improved through multi-factor correlation analysis and classification modeling.

The invention belongs to the field of cardiac surgery, and particularly relates to a cardiac surgery preoperative pulmonary complication prediction method which comprises the following steps: data preprocessing including abnormal value processing, missing value processing, feature correlation analysis, data discretization and data standardization, abnormal values and irrelevant data in the case data set are filtered and removed, and interference of the abnormal values on follow-up model learning is avoided; according to the method, data quality and feature validity are guaranteed from the source through a hierarchical progressive data preprocessing process; noise interference to model learning is avoided by filtering abnormal values and irrelevant data; for missing values, differentiation processing is carried out according to the missing rate (features with the missing rate larger than 30% are removed, and features with the missing rate smaller than 30% are filled with median, regression prediction or mode according to variable types), compared with an existing model simple filling mode, data deviation is greatly reduced, and data integrity and reliability are improved.

The invention provides a coal mine rock burst dynamic early warning method based on digital twinborn and edge calculation, and relates to the technical field of coal mine rock burst prevention and control, and the method comprises the steps: collecting working face multi-source data through a distributed Internet of Things sensing network, and constructing a three-dimensional geological digital twinborn body; calculating a ground pressure dynamic potential energy index and an edge calculation efficiency factor based on the real-time monitoring data; deploying a dynamic risk assessment model at an edge node, and performing risk dynamic grading in combination with a digital twinborn simulation result; optimizing an edge computing task allocation strategy and constructing a multi-algorithm collaborative early warning mechanism; multi-node early warning cross validation is realized through spatio-temporal correlation analysis; and triggering a grading early warning signal according to the dynamic threshold value and synchronously updating the digital twinborn parameters. According to the invention, through deep fusion of digital twinning and edge calculation, real-time processing and dynamic modeling of multi-source data are realized, and the problems of high delay and data islands of a traditional system are solved; a hybrid algorithm architecture and dynamic parameter optimization are utilized to improve the generalization ability of the model, and the false alarm rate is reduced.

The invention relates to an intelligent power document generation method based on multi-modal memory fusion, and the method comprises the following steps: S1, obtaining power business original data, and carrying out the preprocessing of the original data, and obtaining the preprocessed power multi-modal data; s2, according to the preprocessed power multi-modal data, performing multi-modal representation and alignment to obtain a power multi-modal vector; s3, constructing a power document multi-modal memory library according to the power multi-modal vector, the original document pointer and the metadata; s4, performing intention analysis and slot filling according to the intention or instruction of the user, and obtaining a candidate evidence set strongly related to the task; and S5, performing deep context correlation analysis according to the candidate evidence set, and obtaining a final power document through a collaborative screening mechanism. According to the method, the comprehensiveness and the accuracy of the document content are remarkably improved.

The invention discloses a server hardware link diagnosis method and a server hardware link diagnosis system, relates to the technical field of computer system fault diagnosis, and discloses the server hardware link diagnosis method and the server hardware link diagnosis system. Through the steps of obtaining a diagnosis rule configuration file, collecting software and hardware state data, carrying out matching analysis to generate a diagnosis path, executing diagnosis processing, carrying out correlation analysis, generating a fault report and the like, the problems of software and hardware diagnosis splitting and path stiffness in the prior art are solved, and collaborative diagnosis and dynamic path generation of software and hardware faults are realized. And the fault positioning accuracy and the system recovery timeliness are improved.

The invention relates to a talent data analysis method and device based on multi-dimensional data fusion and electronic equipment, and the method comprises the steps: carrying out the clustering of multi-dimensional talent data, obtaining a data set, extracting target data from the data set, and obtaining the performance result of the target data; generating an internal driving force result of the target data, performing first-order motivation correlation analysis based on the performance result and the internal driving force result to obtain a first-order motivation analysis result, and generating a second-order motivation analysis result corresponding to the target data according to the performance result and the performance level average score; and generating an evaluation result of the multi-dimensional talent data based on the first-order motivation analysis result and the second-order motivation analysis result. Quantitative analysis is carried out through value output withdrawn from a first-order motivation analysis result and value change withdrawn from a second-order motivation analysis result, a complete quantitative evaluation model is provided, multi-dimensional and multi-level analysis can be carried out on talent data on the whole, and the limitation of talent data analysis in the prior art is avoided.

The determination of chemical mixture components is vital to a multitude of scientific fields. Oftentimes various spectroscopic methods are employed to decipher the molecular composition of these complex mixtures. The sheer density of spectral features of different molecules present in such observations may make unambiguous assignment to individual species using these methods challenging. Yet, components of a mixture are commonly chemically related due to environmental processes or shared precursor molecules. Therefore, along with investigating the spectroscopic signals, analysis of the structural and chemical relevance of a molecule is an important consideration when determining which species are present in a mixture. Machine-learning molecular embedding methods are used with a relevance module to determine the likelihood of a molecule being present in a mixture based on the other known species, chemical priors, and spectroscopic information. By incorporating this metric, the mixture components can be identified with extremely high accuracy (∼ 97%).