208 results about "Relevance analysis" patented technology

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 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 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 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 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 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 discloses a water chilling unit small sample fault detection method and system based on transfer learning, and the method carries out the fault detection through an expansion causal convolution module, a dense neural network module and a classification layer which are connected in sequence. A sparse connection strategy is introduced into dense blocks of the fault detection model, and representative connection between far and near layers is only reserved in each dense block, so that the network complexity is reduced, the feature redundancy is reduced, and the feature multiplexing advantage is kept; meanwhile, the method utilizes a transfer learning strategy to transfer labeled data knowledge of a source domain to a target domain, and introduces a meta-learning thought in a fine tuning stage to carry out gradient updating. In addition, feature subsets with high discrimination ability are screened out from an original high-dimensional feature space in combination with importance scores and correlation analysis, so that the detection precision of model training is improved.

The invention discloses a depth image classification model evaluation method and system based on background pseudo-correlation measurement, and the method comprises the steps: obtaining a foreground target mask through a pre-training semantic segmentation model, and separating a foreground image; and generating various background images by using a text-to-image generation model in combination with random noise and semantic guidance weight. Then, background controllability constraint is adopted to adjust background change, meanwhile, the foreground is kept unchanged, and foreground and background images are fused to construct a composite image set; and then, inputting the synthesized images into a to-be-evaluated model, calculating category prediction probability difference, semantic representation of a feature extraction layer and an uncertainty metric value, and finally obtaining a correlation analysis error value to evaluate the dependency degree of the deep learning classification model on the pseudo-correlation features. Through combination of semantic segmentation, text-to-image generation and background controllability constraint technologies, quantification of the background pseudo-correlation dependency degree of the deep learning classification model is realized.

The invention provides a method, a system and a kit for constructing a model for evaluating the possibility that a subject suffers from gastric cancer based on a Swis-prot database, electronic equipment applying the construction method and a computer readable medium storing computer program codes. The construction method comprises the steps of obtaining a plurality of independent variables, carrying out screening and data processing on the independent variables, further screening effective independent variables in a regression model training process, reducing the number of the independent variables, obtaining a relationship among the independent variables through correlation analysis, carrying out replacement and combination by adopting the independent variables with a replacement relationship, and obtaining a regression model. And obtaining a plurality of prediction models. The prediction models can obtain highly accurate prediction results only by using fewer effective independent variables, so that the prediction efficiency is high, the application range is wide, and the cost is low. The invention provides an efficient, reliable and economical solution for the field of gastric cancer risk assessment.

The invention discloses a power load probability prediction method and system based on a neural network quantile regression model and multiple linear regression, and belongs to the technical field of power system load prediction. The method comprises the following steps: obtaining standardized data by using a longitudinal data analysis method; identifying key influence factors of the power load in the standardized data based on a Pearson correlation analysis method, and constructing a factor analysis model to quantify influence weights of the key influence factors on the power load; constructing a neural network quantile regression model based on seasonal trend decomposition to fit the key influence factors with different influence weights to obtain a quantile prediction result; based on the quantile prediction result, estimating a continuous probability distribution curve of a load common factor by adopting a non-parametric kernel density technology to obtain an interval prediction result; and constructing a multiple linear regression model to predict the load scale change, and adjusting the interval prediction result. According to the invention, the precision and calculation efficiency of load prediction are effectively improved.

A Hypothesis Generation and Testing System (HGTS) includes a framework for enabling artificial intelligence (AI) systems to autonomously formulate, test, refine, and store hypotheses using structured experimental workflows. HGTS may integrate with, but operates independently from, traditional databases, model repositories, context-aware AI databases and the like to provide a persistent, traceable, and interpretable record of hypothesis lifecycles. The system incorporates a probationary hypothesis database for unverified ideas, a validation engine for controlled experimentation, a confidence scoring and lifecycle management agentic subsystem for hypothesis evaluation, and recursive learning agents that iteratively refine models and experimental methods. Modular agents autonomously propose, test, and document hypotheses using statistical, symbolic, and deep learning techniques. The system ranks outcomes and retains full contextual metadata, enabling reproducible discovery. HGTS thereby provides a foundational mechanism for reproducible, interpretable, and self-directed intelligence, forming a cornerstone technology for artificial general intelligence (AGI).

The invention provides a mechanism model and data driven air compressor energy consumption hybrid estimation method and system, and relates to the technical field of industrial energy conservation and intelligent control. The method comprises the steps that firstly, a large number of obviously irrelevant characteristic quantities are rapidly removed through Pearson correlation analysis, and a set is obtained; capturing a nonlinear relationship by using a random forest feature analysis method, and comprehensively mining potential features to obtain a set; a correlation analysis result is taken, and an optimal feature subset is prepared for subsequent modeling; on the basis that a core mechanism model framework is reserved, a Lasso intelligent algorithm is used for learning and compensating for residual errors which cannot be covered by a mechanism model, and air compressor energy consumption prediction modeling is achieved. The hybrid model has high precision and strong generalization ability; meanwhile, the model is lightweight, calculation is efficient, and a real-time control system can be embedded conveniently.

The invention discloses a method for screening water quality driving factors of a long-distance water transfer project based on a machine learning method. The screening method comprises the following steps: acquiring a water body sample of the long-distance water transfer project; obtaining influence factor data as much as possible according to historical water quality characteristics and hydrogeological conditions of the water transfer project; constructing different factor input data sets according to the detailed data; a clustering analysis method and a correlation analysis method are applied to assist a Boruta algorithm to screen internal causes and external causes of water quality driving factors of different sections in a long-distance water transfer project, and the driving factors are obtained for importance sorting. According to the method, two statistical methods are coupled, meanwhile, the water quality driving factors of the long-distance water transfer project are screened in combination with a machine learning method, water quality driving internal causes and external causes of different sections can be rapidly screened out, importance ranking can be conducted on the driving factors, operability is high, and the method has certain popularization value and is worthy of popularization. Reliable technical guarantee is provided for a long-distance water transfer management department to treat sudden water pollution accidents, control water quality risks and improve water quality.

The application provides a large-diameter slurry shield safety stability multi-objective online optimization method and system based on GAT-LSTM-MOMPA, and belongs to the technical field of shield tunnel construction parameter multi-objective online optimization. The method comprises the following steps: obtaining monitoring data of shield construction parameters and preprocessing, and establishing an original sample set; adopting a method combining knowledge driving and correlation analysis, input parameters and output targets of a prediction model are determined; a space-time deep learning network framework GAT-LSTM is built, the mapping relationship between the input and the output is determined, and accurate prediction of the future time step optimization value is realized; through connection with MOMPA and TOPSIS, the historical value of the model is constantly updated, and accurate and effective real-time online optimization is achieved. The GAT-LSTM-MOMPA multi-objective online optimization model established by the application effectively improves the safety and stability of the shield machine operation.

The invention discloses a comprehensive energy multi-element load prediction method based on a full-stage multi-task learning architecture. The method comprises the following steps: collecting a multi-element load data set; based on the correlation analysis indexes, screening out a feature sequence strongly correlated to the electric load, the cold load and the thermal load, and forming a screened data set; processing the screened data set by adopting a multi-task feature extraction expert module, based on a processing result of the multi-task feature extraction expert module, performing dynamic screening and fusion by adopting an MOE router based on load balancing loss training; and a feature fusion result of the MOE router is predicted by using an HS-xLSTM framework, and a final prediction result is obtained. According to the invention, the performance of multi-element load prediction can be improved.

The invention discloses a power grid safety production risk early warning method based on big data mining, and relates to the technical field of power grid management. Through a big data mining technology, data is obtained from multiple core dimensions, an evaluation index set containing 36 items of data is constructed, the index coverage degree is high, correlation analysis is performed based on index data, and the early warning efficiency is high. The method combines subjective and objective combination weighting, combines subjective weight and objective weight and performs fusion analysis, avoids subjective randomness of single dependence on expert judgment and limitation that pure data driving may deviate from business logic, obtains a more accurate power grid safety production risk assessment index, and performs dynamic early warning on power grid safety production. And the efficiency and reliability of power grid safety management are improved.

The invention discloses a nuclear chemical engineering facility safety analysis method and system and electronic equipment, and belongs to the technical field of nuclear chemical plant safety analysis. The analysis method comprises the steps of obtaining an originating event list of the nuclear chemical engineering facility; according to the originating event list and the event sequence logic, respectively constructing an event tree model corresponding to each originating event; a fault tree model of a key fault branch in each event tree model is constructed, and the fault tree model comprises basic events connected with logic gates; calculating a basic event probability based on correlation analysis, human factor reliability analysis and an equipment failure database; and according to each event tree model, the fault tree model and the basic event probability, calculating a safety analysis result corresponding to each originating event. According to the method, the problems that a security analysis method based on a deterministic theory in the prior art is too conservative and low in accuracy can be solved.

This invention belongs to the field of ecological and environmental protection technology. It discloses a regional ecological protection intelligent decision support and dynamic management system based on big data. The system acquires multi-temporal ecological monitoring data, constructs an ecological factor correlation network through time-delay mutual information analysis, uses virtual intervention experiments and counterfactual comparisons to perform causal inference, and identifies real ecological action paths. It calculates lag propagation time and signal attenuation rate to establish a multi-scale time anchor sequence; identifies feedback loops and calculates loop gain and stability domain boundary parameters; constructs a dynamic evolution model containing a system of time-delay differential equations, and simulates and generates a hierarchical set of decision schemes; and performs residual analysis through real-time monitoring of ecological responses to trigger adaptive correction of model parameters and dynamic adjustment of schemes. This invention achieves a leap from correlation analysis to causal identification, and from static planning to dynamic management, improving the scientific rigor and accuracy of ecological protection decision-making.

The invention relates to the technical field of load prediction, and discloses a medium and long term power load prediction method and system based on ensemble learning. The method comprises the following steps: determining a plurality of power load prediction models as candidate base learners of integrated learning; based on Spearman correlation analysis, a power load prediction model meeting the integration requirement is screened out from the candidate base learners to serve as a base learner of integrated learning, and a pre-trained full-connection neural network serves as a meta learner of integrated learning; each base learner carries out prediction to obtain an initial load prediction result of the corresponding base learner corresponding to a future medium and long term prediction period; and performing fusion processing on each initial load prediction result through a meta-learner to obtain a target load prediction result of the target region corresponding to the future medium and long term prediction period. The prediction strategy based on Stacking ensemble learning is superior to a single model or a traditional integration scheme in prediction precision, stability and complex scene adaptability.

The invention discloses a reservoir scheduling rule extraction method based on multi-stage feature selection and a Stacking model, and relates to the technical field of reservoir scheduling. The method comprises the steps of collecting reservoir group scheduling historical data and performing normalization processing; a multi-stage feature selection strategy is adopted, firstly, a mutual information method and a random forest model are used for screening features and taking a union set, then redundancy is removed through Pearson correlation analysis, and a key factor set is obtained; dividing the key factor set data into a training set and a test set, and training a Stacking model; and inputting to-be-measured reservoir dispatching data into the trained model to obtain a reservoir water level sequence, and verifying a model effect. According to the method, the limitation of traditional feature selection and a single model is broken through, the feature quality and the model generalization ability are improved, the prediction precision is high, a high-precision decision basis can be provided for flood control benefit-making collaborative scheduling of the reservoir group, and the problems that an existing method is high in calculation complexity and poor in engineering applicability are solved.

The invention discloses an offline advertisement intelligent putting system based on big data and AI, and relates to the technical field of directional advertisement optimization, and the system comprises a causal decision module which constructs a dynamic causal map based on a panoramic situation data flow, carries out the intervention effect analysis, and generates a quantum decision vector; the quantum coding module is used for executing quantum state probability amplitude mapping and entanglement particle binding on the quantum decision vector to generate a quantum decision proposal packet; the collaborative optimization module is used for constructing a global quantum entangled state model, carrying out conflict resolution optimization on the quantum decision proposal package and generating a quantum collaborative execution instruction; and the report generation module is used for executing quantum state collapse operation on the quantum cooperative execution instruction and generating an intelligent putting report. According to the invention, by constructing the dynamic causal atlas and quantum collaborative architecture, normal form upgrading of advertisement decision from correlation analysis to causal intervention is realized, so that the system has the capability of accurately quantifying the real effect of advertisement action and millisecond-level dynamic response.

The invention discloses a mechanical drilling speed prediction method based on transfer learning under mechanism constraint, which comprises the following steps of: 1, acquiring drilling data of an adjacent well and a target well based on a well site sensor and a database, and preprocessing the acquired data; step 2, performing correlation analysis on the preprocessed data based on a Pearson's correlation coefficient method, and combining equation screening features with Pearson's coefficient screening features; 3, performing normalization processing on the screened drilling data, establishing a model based on a BP algorithm, transforming a loss function of the model, and integrating mechanism constraints to a neural network loss function in a penalty function form; and step 4, integrating the established BP model based on a migration algorithm, establishing a migration learning drilling rate prediction model under mechanism constraint, and predicting the mechanical drilling rate of the target well by using the trained model based on the established drilling rate prediction model.

The invention discloses a power system oscillation traceability analysis method and system based on multi-method probability fusion, which can effectively solve the problem of oscillation source positioning error caused by sparse measurement data and compressed sensing. Firstly, the oscillation source probability of each node is calculated through an oscillation energy method, a frequency spectrum characteristic method, a correlation analysis method, a voltage fluctuation method and a phase angle gradient method; secondly, calculating the adaptive weight of each method based on an information entropy theory, and completing multi-method probability fusion; the fusion probability is optimized in combination with a power grid topological structure, and an oscillation source candidate sequence is determined; and finally, a comprehensive probability distribution diagram and a detailed analysis report are output through a visual module, and reliable technical support is provided for safe and stable operation of a power system.

The application discloses a regional thermal power generation capacity medium and long term prediction method and system, the method comprises the following steps: based on the preset correlation analysis algorithm, the correlation analysis of regional power historical data and related factor historical data is carried out, and a preset number of key influence factors are determined; sample data is generated according to the regional power historical data and the historical data of the key influence factors, and the sample data is divided into a training set, a test set and a verification set; a prediction model based on space-time attention mechanism is constructed according to a space attention module, a time attention module and a prediction module; the prediction model based on space-time attention mechanism is trained based on the sample data, and the target prediction model is obtained after the training is completed; the thermal power generation capacity prediction value of the future preset time length is predicted based on the target prediction model, so that the medium and long term prediction of the regional thermal power generation capacity is realized reliably, and the operation risk of the thermal power enterprise is reduced.

An object of the system according to the embodiment is to analyze the ranking of companies and the correlation of disclosed information and create a new benchmark.SOLUTION: A system according to an embodiment includes a data collection unit, a correlation analysis unit, a benchmark creation unit, a report generation unit, a chatbot providing unit, and a report selling unit. The processing circuitry is configured to collect AI using the generated dataset. The correlation analysis unit analyzes the correlation of the data collected by the data collection unit. The benchmark creation unit creates a new benchmark based on the data analyzed by the correlation analysis unit. The report generation unit automatically generates a report by comparing the benchmark created by the benchmark creation unit with the company's own data. The chatbot providing unit provides information on the report generated by the report generation unit to the chatbot. The report sales unit sells the report generated by the report generation unit to another company.SELECTED DRAWING: Figure 1

We provide the system. [Solution] Meteorological data collection methods, An interface means for inputting user health status data, A database means for storing the aforementioned weather data and the aforementioned health status data, A generative artificial intelligence means for analyzing the correlation between weather changes and health status based on the aforementioned database, A prediction means for predicting the occurrence of poor health based on the aforementioned correlation analysis, An alert sending means that notifies the user based on the prediction results, A system that includes this.

The invention discloses a transformer area topology identification method and system, and the method comprises the steps: recognizing a topological relation between an electric energy meter and a distribution box through combining a principal component analysis method, a correlation coefficient and a Kmeans + + clustering algorithm; and the corresponding relation between the transformer area concentrator and each distribution box is identified by combining an electric quantity balance analysis method and a line impedance analysis method of an integrated learning algorithm based on a gradient lifting decision tree, so that compared with the traditional correlation-based analysis, the accuracy of the algorithm in processing complex data is remarkably improved, the defects in the aspect of abnormal value processing are overcome, and the accuracy of the algorithm is improved. Compared with the prior art, the method provided by the invention has the advantages that the method can better capture the complex nonlinear relation, and the electric quantity balance analysis method based on the ensemble learning algorithm of the gradient lifting decision tree can still keep high stability even under the condition of large data noise or incomplete features, thereby improving the accuracy and robustness of topology recognition.

The invention relates to a vegetation leaf dry matter content remote sensing high-precision inversion method. The method comprises the following steps: S1, constructing a vegetation leaf sample data set; s11, constructing a blade actual measurement data set; s12, generating an analog data set and / or an analog data set added with noise; s13, dividing the actual measurement data set into an actual measurement training set and an actual measurement verification set; s2, dry matter weak information features are extracted through continuous wavelet transform; s21, carrying out multi-scale analysis calculation on dry matter weak information by using a continuous wavelet transform method; s22, performing wavelet basis function transformation on the original reflection spectrum of each leaf sample to obtain wavelet coefficient characteristics; s23, carrying out correlation analysis calculation on the wavelet coefficient characteristics and the LMA; s24, a threshold value is set, and wavelet coefficient characteristics with sensitivity to LMA spectrum weak information are screened out; and S3, constructing an LMA inversion model based on wavelet coefficient coupling machine learning. The method is high in inversion precision and strong in noise robustness.