38 results about "Statistical significance" patented technology

The invention provides a heat supply system thermal load prediction method based on time sequence compensation and hybrid learning, and the method comprises the steps: constructing a thermal load prediction input variable screening mechanism based on two-dimensional analysis, and achieving the scientific screening of input variables through the dual verification of a Pearson's correlation coefficient matrix and a significance test; a thermal load-temperature time sequence synchronization system based on dynamic time shift compensation is innovatively designed, and the problem of time sequence dislocation caused by instability of manual experience adjustment in a central heating system is solved; an ES-LSTM collaborative prediction architecture is provided, and a three-level prediction model of'trend decomposition-random learning-dynamic weighting 'is established. According to the method, statistics significance test and thermodynamic mechanism analysis are combined in a breakthrough manner, a dynamic compensation system with a time sequence self-correction capability is innovatively researched and developed, a hybrid model collaborative prediction mechanism is constructed, and a complete technical chain from data preprocessing to prediction model architecture is formed.

The invention relates to a training method of a respiratory tract infection disease progress and prognosis prediction model. The training method comprises the following steps: extracting mRNA from peripheral blood of a target patient, and carrying out transcriptome sequencing to obtain a sequencing result; based on the ferroptosis related gene set, comparing ferroptosis score differences of two groups of patients with community-acquired pneumonia and sepsis, and screening corresponding ferroptosis related genes with statistical significance from a sequencing result; screening out genes meeting preset conditions from the ferroptosis related genes based on LASSO regression; and establishing an RTI clinical outcome prediction model through logistic regression by taking whether the patient is sepsis or not as an outcome dichotomy variable and taking the screened gene expression quantity as a prediction variable. According to the invention, after the prediction model is subjected to machine learning screening such as LASSO and the like, the core feature with the highest prediction value is reserved, so that the risk of over-fitting of the model on training data is reduced.

The invention provides a closed watershed lake water level evolution prediction method based on multi-source data, and relates to the technical field of hydrological resources. The method comprises the following steps: firstly, collecting natural environment and social economic multi-source data of a closed drainage basin, and constructing a normalized multi-source feature input matrix; carrying out total element analysis on the features and extracting a nonlinear response threshold value; and meanwhile, Mantel statistical test is executed on the feature matrix to obtain a statistical correlation coefficient. And judging the characteristics which simultaneously meet the statistical significance and mechanism threshold conditions as real causal driven factors, and eliminating false related factors which are statistical significance but have no physical threshold. And finally, reconstructing a prediction model based on the screened causal-driven feature set, and outputting a water level evolution prediction value. Through'statistics-mechanism 'dual verification, interference of false correlation is effectively eliminated, the problem that a traditional black box model cannot distinguish causal and coincident trends is solved, and physical interpretability and robustness of water level prediction are remarkably improved.

The invention discloses a multi-technology fusion prediction method based on hypergraph modeling, and relates to the technical field of computers. According to the method, hyperedge weights are corrected by introducing a probability-based zero model so as to eliminate deviations caused by patent number differences among different technical fields, and then a multi-technology fusion mode with statistical significance is identified. Further converting a multi-technology fusion prediction problem into a hyperedge prediction problem in a hypergraph, calculating an average similarity among technologies in a technology combination by adopting a random walk algorithm with restart, and establishing a mapping relation between a feature and a hyperedge formation probability through a logistic regression model by taking the average similarity as the feature; therefore, prediction of the technology fusion probability is realized. According to the method, the fusion trend of three or more technical fields can be effectively identified and predicted, the method is more suitable for the analysis requirement of current multi-technology cross-field cross fusion, and data support and decision basis are provided for technical innovation layout and industrial policy planning.

The embodiment of the invention discloses an ecological system simplification processing method and device based on breakpoint regression, and the method comprises the steps: setting a function component simplification sequence for a target ecological system; according to the function component simplification sequence, gradually removing corresponding function components from the target ecological system, and monitoring and collecting system performance indexes of the target ecological system; using a breakpoint regression method to identify a critical simplified point where the system performance index is suddenly changed; and according to the statistical significance index, the effect quantity and the system structure difference of the target ecological system before and after the critical simplification point, checking whether the target ecological system reaches an unsimplification degree or not. According to the method, the breakpoint regression method is introduced into the non-simplification research of the ecological system, the non-simplification critical state of the ecological system can be objectively and accurately recognized, and a scientific decision basis is provided for the fields of ecological system protection, ecological system design optimization and the like.

The invention discloses an adaptive enhanced dynamic graph contrast learning multivariate time sequence classification method, which is oriented to a multivariate time sequence classification task under the condition of few labels, and comprises the following steps: 1) an adaptive enhanced strategy; 2) dynamic graph comparison learning; and 3) carrying out two-stage combined training. According to the method, in a preprocessing stage, trend-periodic decomposition and statistical significance test based on random permutation are combined to judge the strength of time sequence dependence, and an enhanced recommendation strategy matched with data characteristics is generated; and dynamic graph comparison characterization learning and a two-stage training mechanism are introduced, so that the classification performance and the model stability are improved while unlabeled data are fully utilized.

The application discloses a kind of high-rise building group health monitoring method and system based on timing InSAR, belong to building health monitoring technical field.The method includes: obtaining multi-temporal SAR image, extracting scattering point deformation time series;Identify high-rise building group and group according to spatial position;To single building vertically divided height layer;Calculate the average deformation sequence of each height layer, the seasonal deformation parameters of the same height layer of the same building are counted, and a group deformation reference model is constructed;Abnormal height layer is identified based on dynamic threshold and statistical significance test;Map the anomaly to the corresponding building, and if there are multiple abnormal height layers in the same building, the structure abnormal risk is determined.The method does not require external temperature data, can effectively distinguish seasonal deformation and structural anomaly, and improve monitoring accuracy and automation level.

To provide an estimation and control method for frequency errors of extreme events.SOLUTION: The present invention proposes a frequency shift functional and a frequency shift functional estimation formula based on probability estimation, and constructs a method for reversely solving a confidence frequency shift value epsilon according to the frequency shift functional to improve a frequency distribution function. The present invention is suitable for estimating and controlling errors of occurrence frequency prediction of extreme events in various frequency statistical models by using hydrological observation data under probability statistical significance, so that the prediction accuracy and reliability of hydrological extreme events such as storm surge, rainstorm flood, tsunami, and the like are improved, risks caused by extreme natural disasters are reduced, and loss of life and property that may be caused is reduced.SELECTED DRAWING: Figure 1

The invention relates to the technical field of data processing, and discloses a report generation method based on statistical significance weighting and causal constraint, which comprises the following steps: receiving structured data; performing cleaning, normalization, feature engineering and basic statistical analysis on the data to generate an insight data set; executing a statistical significance test, and performing preliminary screening on the insight data according to a preset significance threshold value; based on the screened insight data; performing dynamic weighting and priority ranking on all contents to be reported to generate a report core abstract; and taking the generated report core abstract and the causal constraint mark as input, driving a natural language generation model, and finally outputting a report. According to the method, the risk of excessively interpreting random fluctuation is eliminated from the source by screening insight data; through the causal relationship graph, the accuracy and business guidance of the graph can be improved; the causal constraint marks are used for declaring to ensure that all conclusions are verified by causal reasoning, and the logic accuracy of the report is guaranteed.

The application relates to the technical field of data fusion, in particular to a multi-dimensional data feature fusion method for enterprises combined with AI technology. The method takes the attribute data of any dimension as the attribute to be analyzed for the enterprises in the same industry, determines the statistical significance of the attribute to be analyzed and the risk value through a K-S test method, compares the attribute to be analyzed and the risk value to determine the risk deviation consistency of the attribute to be analyzed, combines the statistical significance and the risk deviation consistency to determine the importance of risk assessment, adds the attribute data to a candidate set according to the size order of the importance, determines the recognition degree of the attribute data added to the candidate set, selects the candidate set corresponding to the maximum recognition degree as the final candidate set, and takes the attribute data in the final candidate set as the feasibility attribute data to perform weighted fusion on the feasibility attribute data according to the recognition degree. The application only performs feature fusion on the selected attribute, avoids data redundancy, and improves the data fusion effect.

The invention discloses a medical term analysis method based on a knowledge graph technology, and the method comprises the following steps: obtaining medical standard literatures of different time versions, and constructing a medical term knowledge graph sequence containing time dimension information; based on the medical term knowledge graph sequence, calculating semantic representation changes of the target medical term at different time points to obtain a semantic evolution path of the target medical term; acquiring clinical disease course data related to the target medical term; based on a statistical significance test method, calculating the support degree of the clinical disease course data to emerging semantics in the semantic evolution path, and generating a quantitative verification result; and dynamically updating and optimizing the medical term knowledge graph sequence according to the quantitative verification result. According to the method, prospective active perception and accurate verification of emerging semantics are realized, and the maintenance mode of the knowledge graph can be converted into active optimization from post passive updating, so that potential medical risks caused by indefinite term evolution are effectively reduced.

The invention discloses a method for predicting heart failure based on left ventricular ejection fraction, which develops and trains a primitive model to automatically provide help and evaluate the level of clinical decision based on LVEF heart failure. The training model is prevented from using any or biased clinical variables, and the following two steps are ensured: investigating the statistical significance of each variable to distinguish the three categories; secondly, a novel dimension reduction technology is adopted to visually observe the characterization of the optimal variable in the radial direction, and each LVEF-based HF category is separated; and on the basis, the performance of the developed model is trained, the importance of the most important clinical variables is discussed, and the significance of the clinical variables is explained in detail based on the application of LVEF-based deep learning in HF analysis, so that patient data of three heart failure types are distinguished.

This invention discloses a method for determining the root density threshold under optimal erosion reduction effect, relating to the fields of soil and water conservation and vegetation engineering technology. The method includes: obtaining sediment yield and root density sequences; constructing a fitting model of sediment yield variation with root density using a nonlinear fitting algorithm; detecting abrupt change points using various hydrological abrupt change detection algorithms; constructing an evaluation model including consistency, statistical significance, effect size, and repeatability indicators; calculating a comprehensive score for each candidate abrupt change point; selecting the candidate abrupt change point with the highest comprehensive score as the optimal root density threshold; and verifying the optimal root density threshold using a multiple verification algorithm, outputting the threshold result that passes verification. This method overcomes the shortcomings of relying on experience and lacking unified standards in determining the optimal erosion reduction root density threshold through algorithmic processes and systematic evaluation and verification, achieving automated and objective determination of the root density threshold.

The invention discloses a method for predicting the material demand of a deep learning model based on swarm intelligence, and aims to solve the problems that a conventional hyper-parameter optimization method is low in efficiency and is liable to fall into local optimum. The method comprises the following steps: firstly, constructing a deep learning model containing the number of layers, the number of hidden nodes, a learning rate and regularization parameters, and defining an optimization interval of each hyper-parameter; secondly, designing an improved sparrow search algorithm (ISSA), enhancing global search capability through Cauchy variation disturbance, balancing exploration and development of a refraction reverse learning strategy, and adjusting a self-adaptive early warning value to improve convergence stability so as to optimize a hyper-parameter combination; further taking a verification set RMSE as a fitness target, combining with parallelization calculation to accelerate model performance evaluation, and screening optimal hyper-parameter configuration; and finally, predicting material demand data by using the optimized model, performing transverse comparison with LSTM, ARIMA, GRU, GCN and other models, and verifying the effectiveness of the model through multiple indexes such as RMSE, MAPE and R. Experiments show that the ISSA optimization method provided by the invention significantly reduces prediction errors, the optimized model has higher accuracy and robustness in a complex material demand scene, and meanwhile, the reliability of performance improvement is confirmed through statistical significance test.

The invention relates to the technical field of data fusion, in particular to an enterprise multi-dimensional data feature fusion method combined with an AI technology. According to the method, for enterprises in the same industry, attribute data of any dimension serves as attributes to be analyzed, and statistical significance of the attributes to be analyzed and risk values is determined through a K-S test method; comparing the to-be-analyzed attribute with the risk value, and determining the risk deviation consistency of the to-be-analyzed attribute; the importance of risk assessment is determined in combination with statistical significance and risk deviation consistency; according to the importance sequence, adding the attribute data into the alternative set, and determining the identification degree of adding the attribute data into the alternative set; selecting the alternative set corresponding to the maximum identification degree as a final alternative set; and taking the attribute data in the final alternative set as feasibility attribute data, and performing weighted fusion on the feasibility attribute data according to the identification degree. According to the method, only the selected attributes are subjected to feature fusion, data redundancy is avoided, and the data fusion effect is improved.

The invention discloses a bridge influence line identification method and a damage verification method based on a physical information neural network, and relates to the field of bridge health monitoring, and the method comprises the steps: 1, extracting an initial influence line through employing bridge physical parameters and actual measurement dynamic response, and constructing a multi-dimensional feature project; 2, constructing a PINN model based on intelligent boundary learning, and adjusting natural learning bridge endpoint physical constraints through dynamic weights to realize high-precision prediction of influence lines; and 3, establishing a self-adaptive threshold value based on health sample statistical characteristics, and realizing intelligent judgment of bridge damage in combination with multiple verification logics (simple threshold value, statistical significance and regional continuity). According to the method, the noise immunity and the boundary accuracy of influence line identification are effectively improved, and the bridge damage can be quickly and automatically positioned and the severity can be evaluated.

The invention provides a cardiac postoperative infection risk prediction method and system, and the method comprises the following steps: S1, data collection: collecting clinical data and image data of a patient, the image data comprising spleen artery hemodynamic parameters obtained through stomach and abdomen ultrasound; s2, feature preliminary screening: performing Logistic single-factor regression analysis on the clinical data and the image data, and screening out candidate features according to statistical significance; and S3, analysis and calculation: carrying out Lasso regression analysis on the candidate features, determining key features and weight coefficients thereof, and calculating to obtain an infection risk probability value after the cardiac surgery. The infection risk prediction method and prediction system provided by the invention can specifically predict and identify infected high-risk patients so as to guide clinical intervention measures to be taken as soon as possible.

The invention discloses a unimolecule conductivity measurement data consistency evaluation method, electronic equipment and a storage medium. The method comprises the following steps: firstly, carrying out histogram statistics on two groups of single-molecule conductance data to extract a peak position characteristic sample; then executing Welch's t test and double-single-side test (TOST), and simultaneously judging the statistical significance and the physical equivalence of the data under a preset physical equivalence limit; for the non-normal distribution sample, further carrying out robust judgment by adopting Mann-Whitney U test and Hodges-Lehmann estimation, and carrying out robust judgment on the sample according to the robust judgment; and finally, constructing a Bayesian probability model based on Student-t distribution, and calculating a posterior probability of a difference parameter falling into an actual equivalent interval (ROPE) through MCMC sampling. According to the method, the frequency acknowledgement and the Bayesian inference are fused, the problem that the physical consistency cannot be confirmed only by depending on value test in the prior art is solved, and a quantitative repeatability evaluation standard is provided for single molecule sensing and junction cracking experiments.

This invention belongs to the field of runoff prediction technology, and specifically discloses a runoff prediction method and system based on statistical significance constraints. The method includes: extracting features from hydrological and meteorological time-series data of the target prediction section and at least one upstream hydrological station to obtain candidate features; performing statistical significance testing and multiple hypothesis testing on the candidate features, combined with information theory correlation screening and feature redundancy screening, to obtain a feature subset; constructing a runoff prediction model including a variable selection subnetwork, a time lag gating subnetwork, and a main prediction network; training the runoff prediction model using the feature subset and historical runoff increments of the target prediction section; and realizing runoff prediction based on the trained runoff prediction model. This invention can improve the reliability of feature inputs and the stability of runoff prediction output.

The invention discloses a spatial aggregation detection method for large-scale disease data, and the method comprises the following specific steps: A, dividing a to-be-detected region into a plurality of geographic units, carrying out the modeling of the to-be-detected region into an undirected graph structure based on the geographic units, and creating an adjacent matrix; b, constructing a candidate scanning window set of each geographic unit based on an adjacent constraint control mechanism and a low-risk region elimination mechanism; c, searching a maximum likelihood cluster in the scanning window set based on a spatial search mechanism of the scanning window and taking the maximum likelihood cluster as a candidate result; and D, carrying out Monte Carlo simulation test and statistical significance analysis on the maximum likelihood cluster in the candidate results obtained in the step C, and selecting the aggregation region conforming to a set significance threshold as the aggregation region. According to the method, the accuracy and the detection efficiency of disease space aggregation detection under large-scale geographic data and fine scale can be effectively improved.

The invention relates to the technical field of bed separation grouting, in particular to an evaluation method for determining a bed separation grouting range based on intelligent drilling parameter inversion lithologic change. The core of the method is to introduce a hidden structure dip angle parameter, establish an information magnitude correlation model of a strong transformation ratio coefficient, a deformation ratio coefficient and a stratum structure, distinguish lithologic anomaly caused by the structure and characteristics of a rock stratum, and solve the problem of inversion deviation of a traditional method. Dynamic data comparison is formed through two times of drilling and calculation before and after grouting, the statistical significance of interval estimation quantification difference is combined, evaluation is converted into a quantifiable confidence level index, and the limitation of experience judgment is broken through. And directly outputting a filling effect conclusion and an adjustment suggestion based on the comparison of the confidence level and the discrimination threshold, optimizing the decision-making efficiency, and reducing the cost and the construction period. And meanwhile, drilling multi-dimensional parameter values are deeply excavated, and upgrading of data from collection and storage to decision support is achieved.

The invention provides a TTF early warning method and system aiming at EGFR TKIs single drug intervention NSCLC, and is applied to the technical field of medical data processing. The method comprises the following steps: processing patient data to generate treatment failure time data; constructing a time-dependent Cox model, describing the influence of each variable on a risk function through the model, calculating the risk ratio among the variables based on the risk function model, and generating risk ratio data; analyzing each covariable based on a univariate time-dependent Cox model, screening variables with statistical significance or clinical correlation with patient survival in the univariate time-dependent Cox model, and adding the screened variables into a multivariate time-dependent Cox model; processing the multivariable time dependence Cox model based on parameter accuracy, proportional risk hypothesis test, akaike information criterion, consistency index and clinical correlation to generate a model evaluation result; and processing the target patient information to generate target scoring information.

The invention provides a regional policy dynamic comparison system and method based on a large language model, and the method comprises the steps: monitoring a government official website and a white list site in real time, and automatically triggering collection when detecting that the policy text change frequency exceeds a preset threshold value; using a large language model to extract policy structured features according to the dynamic cue words; accessing a government open database, and quantifying the actual effect of the policy through a Bayesian structure time sequence model; calculating the statistical significance of the policy term difference through a Wilcoxon symbol rank test, and carrying out statistical significance labeling; the cue word is dynamically optimized through a deep deterministic strategy gradient reinforcement learning agent; and generating a visual comparison report containing statistical saliency labels and a deviation degree comparison matrix. According to the invention, automatic analysis and dynamic comparison of policies in each region are realized, and the problem that cross-region policy analysis is difficult to quantify, verify and make decisions is solved.

The invention discloses a high-rise building group health monitoring method and system based on a time sequence InSAR, and belongs to the technical field of building health monitoring. The method comprises the following steps: acquiring a multi-temporal SAR image, and extracting a scattering point deformation time sequence; identifying high-rise building groups and grouping the high-rise building groups according to spatial positions; vertically dividing a single building into height layers; calculating an average deformation sequence of each height layer, counting seasonal deformation parameters of the same height layer of the same kind of buildings, and constructing a group deformation reference model; identifying an abnormal height layer based on a dynamic threshold and a statistical significance test; and mapping the abnormity to a corresponding building, and if the same building has a plurality of abnormal height layers, determining a structure abnormity risk. The method does not need external temperature data, can effectively distinguish seasonal deformation and structural abnormality, and improves the monitoring accuracy and automation level.

The application discloses a process industry abnormal root cause diagnosis method fusing a symbolic directed graph and statistical testing. Firstly, an initial symbolic directed graph SDG model is constructed based on a process mechanism; then, process data is preprocessed and statistical correlation between variables is mined by using an association rule; substitution testing and a Benjamini-Hochberg process are introduced to verify statistical significance of each candidate edge in the initial SDG, and a false discovery rate is controlled; finally, a Bootstrap resampling technique is used to evaluate a recurrence probability of the edge, and unstable connections are removed, so that an optimized sparse SDG model is obtained. When an abnormality occurs, reverse backtracking reasoning is performed based on the optimized model, and an abnormal propagation path and a root cause are determined. The application effectively removes false correlations through statistical inference, improves the accuracy and robustness of root cause diagnosis, and the model has strong interpretability, thereby providing an effective technical scheme for intelligent operation and maintenance of process industries.

A computer-implemented monitoring method for analyzing vibrations to detect reliably a machine error during a process automation. The method includes a statistical analysis of at least one series of measured sensor data or at least one section thereof to ascertain a respective deviation of the measured sensor data according to at least three criteria. The statistical analysis provides a null hypothesis and an expected value and calculates the statistical significance with respect to a deviation from a specified null hypothesis in order to output a warning if the statistical significance deviates from the null hypothesis, and / or for a specified significance level α, the probability of a deviation from the null hypothesis is greater than the significance level α.

Provided is an analysis device and the like that achieves improved prediction accuracy even when training data exhibiting large variability among data points are used.　The analysis device comprises: a first acquisition unit that acquires first data relating to characteristics of samples; a second acquisition unit that acquires second data relating to characteristics of the samples; a learning unit that generates a trained model on the basis of sample data groups including the first data acquired by the first acquisition unit and the second data acquired by the second acquisition unit for the known samples; and an analysis unit that inputs the first data acquired by the first acquisition unit for an unknown sample to the trained model generated by the learning unit and outputs the result of analysis on the unknown sample. The learning unit tests for significant differences between the sample data groups by a statistical hypothesis test method, selects combinations of the subject data groups determined to have a significant difference, and generates training data.

The invention discloses a reservoir bottom oxygen deficit judgment method based on a layering stability threshold value, and the method comprises the steps: obtaining the matching data of a reservoir stability layering index SSI and dissolved oxygen DO, and dividing the preprocessed matching data into a training set and a verification set; based on the SSI in the training set, generating a plurality of candidate thresholds, calculating a classification accuracy rate and an F1 score corresponding to each candidate threshold, and screening out an optimal SSI threshold and a threshold stable interval thereof; based on the optimal SSI threshold value, dividing the training set into an anoxic group and a non-anoxic group, and verifying whether the difference of SSI and DO between the two groups has statistical significance or not by using a statistical test method; and when the difference has statistical significance, checking the judgment accuracy of the optimal SSI threshold value and the threshold value stable interval by using the verification set, and when the judgment accuracy is not lower than a preset threshold value, finishing the final checking of the threshold value. According to the method, through a standardized process and multi-dimensional verification, DO state rapid judgment based on SSI is realized, and the reliability, universality and flexibility of a threshold value are improved.

This invention relates to the field of computational biology, specifically to a method and system for inferring kinship relationships in stratified populations across the entire genome. The method involves performing principal component analysis on the genotype data of the entire genome to determine c principal components for analysis, performing regression analysis to obtain the significance p-value of each molecular marker on each principal component, identifying molecular markers with significance p-values ​​exceeding a given threshold as ancestry information markers, and removing ancestry information markers to obtain non-ancestry information markers. Based on the non-ancestry information markers and the standardized genotype codes of all individuals on the non-ancestry information markers, the method calculates estimated kinship coefficients between all pairs of individuals. Statistical significance tests are performed on the estimated kinship coefficients based on theoretical expectation and asymptotic variance to determine whether a significant kinship exists between each pair of individuals. This invention is applicable to kinship inference in large-scale stratified populations and effectively controls the calculation bias caused by population stratification.

The present application belongs to the technical field of biomedical signal processing, and specifically relates to a depression prediction method based on gender and season-specific neural activity, aiming to improve the accuracy of the prediction model. The method includes obtaining electroencephalogram signals of multiple depression patients, and recording the gender attribute, data collection season, and depression severity scale score of each depression patient. The electroencephalogram signals are preprocessed, and based on the preprocessed electroencephalogram signals, a multi-dimensional feature set of multiple brain regions in multiple sub-frequency bands is extracted; the multi-dimensional feature set includes frequency domain features, nonlinear dynamic features, and inter-brain region information flow features. With gender attribute and data collection season as influencing factors, statistical analysis is performed on the multi-dimensional feature set, and features with statistical significance are selected to form a target feature subset. The target feature subset is used to train a machine learning model, and the trained prediction model is used to output the depression severity quantification index prediction result of the depression patient.