1755 results about "Correlation coefficient" patented technology

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

The invention provides an urban drainage pipe network monitoring data cleaning and intelligent prediction method, and the method comprises the steps: firstly obtaining pipe network monitoring data, and carrying out the classification tracking and repairing of missing values; adopting a dynamic IQR algorithm based on a sliding window to adaptively identify abnormal candidate points; secondly, introducing a pipe network topological relation, comparing upstream and downstream data change trends, eliminating non-physical anomalies caused by equipment faults, and reserving real hydraulic events; calculating the physical delay time between the nodes by using the cross correlation coefficient; and finally, constructing a random forest model, taking upstream historical data after delay alignment as feature input, and realizing accurate prediction of a future water level and quantification of a feature contribution degree. According to the method, a physical mechanism and machine learning are fused, the problems that data cleaning lacks adaptivity and a deep learning model lacks interpretability are effectively solved, and the accuracy of waterlogging early warning is improved.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention provides an exoskeleton man-machine motion intention cooperative control method and device and a storage medium, and relates to the technical field of robot motion control signal processing. According to the method, signal phase differences are acquired through energy buttons at human joints, and an original phase difference sequence is formed and preprocessed; calculating a cross correlation coefficient of the target and the associated joint based on the preprocessed sequence, and constructing a motion feature set in combination with the joint speed and acceleration; constructing a prediction model by using a long-short-term memory network, and outputting motion intention and joint position prediction information through multi-feature fusion training; and analyzing the prediction information by adopting a second-order linear impedance model, generating a driving torque instruction and sending the driving torque instruction to the servo motor for execution. According to the method, signal phase difference analysis, LSTM prediction and impedance control are fused, the problems of response lag and poor adaptability of a traditional exoskeleton are solved, the control real-time performance, accuracy and cooperative fluency are improved, and the use requirements of different users in different scenes are met.

The invention provides a new energy automobile high-voltage system dynamic risk assessment method and device based on multi-source data fusion, and is applied to the technical field of data processing. According to the method, data such as high-voltage component operation parameters, battery states, environment perception, fault history and whole vehicle control instructions are obtained. Pre-processing the first working condition label, the second working condition label and the third working condition label, analyzing a CONTROLSIGNAL field to obtain a working mode of the high-voltage system, and generating a dynamic working condition label; combining a Pearson's correlation coefficient and XGBoost feature importance, screening risk sensitive features from the two, and forming a risk related feature subset; the method comprises the following steps of: establishing an improved Bayesian network model, training a subset by using an improved Bayesian network model, establishing an exclusive risk assessment model for different working conditions, reasoning real-time data by using the model to obtain a probability value of each risk dimension, and generating a dynamic risk assessment result and an early warning signal in combination with grade standard judgment.

The invention discloses a power distribution area topology identification method and system based on an intelligent fusion terminal and correlation coefficients, and the method comprises the steps: collecting the multi-source data of a power distribution area through an edge fusion terminal, and carrying out the data preprocessing and feature extraction at a terminal side; carrying out parallel calculation on a voltage fluctuation Pearson's correlation coefficient and a load change Kendall rank correlation coefficient, and constructing a correlation coefficient matrix in different time periods; calculating an adaptive weight based on the load fluctuation entropy, and fusing a dual-mode correlation coefficient; carrying out topology generation by using a graph neural network, and outputting an edge existence probability and a node hierarchy; performing physical constraint optimization on the initial topology by adopting a genetic algorithm; the system comprises an edge fusion terminal cluster, a cloud analysis platform, a topology verification module and a terminal management platform. According to the method, topology recognition precision and dynamic adaptability are remarkably improved, bimodal correlation coefficients and graph neural network space modeling are creatively fused, and a complex topological structure is precisely restored.

The invention discloses a hydraulic engineering monitoring and early warning system and method based on artificial intelligence, and belongs to the technical field of hydraulic engineering. Gate operation data are collected through a gate sensor group to form a gate state data set; floating object data are collected through a high-definition camera and an image recognition technology, and the floating object data are marked as floating substance points containing volume, form and coordinates; dividing a flood discharge area into grid units, and recording floating object information in each grid to form a spatial distribution data set; predicting a migration path and duration based on the water flow data and the floating object parameters, marking a grid overlapped with the gate area as a core grid, and identifying a high-risk grid; dynamically associating floating object parameters and gate state parameters in the core grid, calculating an association coefficient, and positioning a potential contact risk grid; according to the floating object accumulation rate and the correlation coefficient, early warning grades are divided, and gate blockage early warning is generated; meanwhile, high-risk floating objects passing through the gate are tracked, and the path of the high-risk floating objects is predicted to generate salvage early warning reminding.

The invention relates to the technical field of fault diagnosis, in particular to a multi-modal bearing fault diagnosis method based on cross-domain transfer learning, which comprises the following steps: constructing a source domain and a target domain; calculating fault frequency characteristics and revolution frequency of the bearing; calculating a first feature vector; performing correlation coefficient and strategy feature standardization pipeline operation, collaborative feature screening strategy and dimension reduction on the first feature vector, and obtaining a second feature vector by using inherent importance and arrangement importance of a random forest classifier; training a plurality of benchmark test models by using the second feature vector of the source domain; evaluating the effectiveness of the second feature vector and determining a performance baseline; and training the target domain by using the 1D-CNN network, carrying out end-to-end cross-domain migration training on the 1D-CNN network by using the comprehensive loss function of the source domain and the target domain, and outputting a predicted fault type. The problem that the accuracy of transfer learning is affected due to lack of multi-modal data screening in an existing method is solved.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably improved.

The invention discloses a multivariate fusion coal mine disaster risk intelligent identification and early warning method and system, and belongs to the technical field of coal mine major disaster risk intelligent identification and early warning. The method comprises the following steps: collecting multi-index data of a coal mine working face as an initial data set, and preprocessing time series data by using a one-dimensional depth channel attention residual convolution-LSTM model and a chain-based multi-interpolation model; performing Pearson's correlation coefficient and Spearman level correlation analysis on the preprocessed data, and determining different major disaster risk monitoring and early warning index sets; risk precursor, interference and noise signals of different indexes are analyzed and marked, a sample enhancement model based on the de-noising diffusion probability is input to complete sample expansion, an MACN model is trained, and a multivariate index risk fusion identification method based on the MAKER and a comprehensive early warning rule are established for major disasters. The method has the advantages of dynamic adaptability, configurability, high interpretability and reliability under the condition of small samples.

The invention discloses a joint multi-modal entity relationship extraction method and system based on information representation and semantic alignment, and the method comprises the steps: obtaining sample data composed of an original image and a text, and obtaining the feature representation of visual information and text information based on the sample data; aligning the feature representation of the visual information and the feature representation of the text information by using a progressive modal semantic alignment strategy; by introducing a multi-layer correlation mapping mechanism guided by fine granularity, the correlation coefficient represented by the features of the aligned visual information and text information is judged, and features irrelevant to a task core are filtered; performing visual representation and text interaction by using a multi-modal interaction module to obtain multi-modal semantic features; carrying out weighted mapping on the multi-modal semantic features by utilizing a routing weighting function, and finally obtaining multi-modal feature representation; and sending the multi-modal feature representation into a word pair relation label extractor, and extracting an entity, an entity relation and an entity attribute quintuple.

The invention provides a drainage basin water quality abnormity tracing method and system based on time sequence fluctuation characteristics, and the method comprises the steps: taking each monitoring point in a drainage basin as a graph node, constructing a directed edge connected with the graph node according to the spatial relation between the monitoring points and the water flow direction, and forming a drainage basin graph network; acquiring historical monitoring data, extracting amplitude variation features and fluctuation features from the historical monitoring data, and calculating a time sequence fluctuation feature vector; traversing graph nodes with directed edge connection, constructing index pairs corresponding to the graph nodes, analyzing the correlation of the index pairs under different lag dimensions, and calculating response lag dimension coefficients; according to the time sequence fluctuation feature vector and the response lag dimension coefficient, calculating an abnormal correlation coefficient between graph nodes, and constructing an abnormal transmission path; and according to the abnormal transmission path, dividing a pollution troubleshooting area for a worker to carry out troubleshooting treatment. The water quality abnormal fluctuation excitation source position can be accurately positioned, and the water quality abnormal analysis efficiency and the abnormal source positioning precision are improved.

The invention discloses a photovoltaic power interval prediction method based on a GRU-LSTM combined neural network, and belongs to the technical field of photovoltaic power interval prediction. The method comprises the following steps: S1, taking historical power generation data as original wind-solar power generation power prediction data, processing the data, and screening related meteorological characteristics by adopting a Pearson correlation coefficient; s2, a GRU-LSTM combination model is constructed, and related hyper-parameters are set; s3, taking the screened related meteorological features as input for training, calculating a photovoltaic point prediction result according to a weight coefficient, and performing related error evaluation; and S4, based on the photovoltaic power point prediction result, calculating a photovoltaic power interval prediction result by using a quantile regression technology, and detecting performance evaluation through a test set. According to the method, the minimum prediction error correlation index is taken as the target, the influence of different weathers on photovoltaic power processing is considered, the Pearson's correlation coefficient analysis is utilized to select more representative meteorological characteristics, and the combined model and the quantile regression technology are utilized to finally obtain the photovoltaic power interval prediction result.

The invention provides a civil structure deformation anomaly detection method based on time series data, and relates to the field of civil structure deformation anomaly detection. A disturbance intensity response value, a disturbance curvature and a local disturbance folding feature are constructed, a range adjustment enhancement value is formed by combining a symbol jump mark and a neighborhood disturbance difference value, a latent guide feature is generated based on the range adjustment enhancement value, a disturbance reconstruction feature is constructed through superposition of a diffusion residual error and an asymmetric difference item, normalization mapping is completed to obtain a normalization feature, and the normalization feature is obtained. A disturbance spinor modulation factor is constructed in combination with a nonlinear suppression correlation coefficient, a disturbance spinor tensor is formed through a logarithmic compression channel and a square amplification channel and by applying quadrature phase coding, a tensor potential mapping map is generated under a path coupling and self-coupling mechanism, disturbance energy offset and extreme value deflection are constructed based on the tensor potential mapping map, and disturbance energy offset and extreme value deflection are obtained. And a probability potential index is formed, and civil structure deformation anomaly detection model training is completed based on the probability potential index, so that civil structure deformation anomaly detection is realized.

The invention belongs to the technical field of artificial intelligence, and provides a horizontal well fracture porosity prediction method and system, a storage medium and equipment, and the method comprises the steps: calculating and generating a plurality of corresponding fracture indication curves based on horizontal well logging data of a target layer; based on the vertical well resistivity imaging crack porosity, verifying curve correlation between any two of the first crack indication curve, the second crack indication curve and the third crack indication curve to obtain a plurality of corresponding correlation coefficients; under the condition that any one of the multiple correlation coefficients is greater than a first preset threshold value, performing iterative training on the initial fracture porosity prediction model based on the training data set, and generating an optimized fracture porosity prediction model; and performing prediction processing on the target horizontal well fracture porosity based on the optimized fracture porosity prediction model, and generating and outputting a corresponding target horizontal well fracture porosity curve.

The invention discloses a complex medium controllable source electromagnetic multi-parameter three-dimensional inversion method and system, and the method comprises the steps: carrying out the three-dimensional space discretization of an exploration region, and carrying out the multi-time iteration inversion of a discrete exploration region through initializing a multi-parameter geologic model based on a multi-parameter inversion target function, and obtaining a multi-parameter three-dimensional inversion result; wherein the multi-parameter inversion objective function comprises a data fitting constraint, a model constraint and a cross gradient constraint, and the multi-parameter model comprises resistivity, magnetic conductivity, a charging rate, a frequency correlation coefficient and a time constant; and a hexahedral mesh discrete calculation region is adopted in inversion, and a finite element linear equation set is obtained through a vector finite element method, so that multi-parameter three-dimensional forward modeling calculation is realized. According to the method, the induced polarization effect and the magnetic conductivity influence are considered at the same time, multi-parameter three-dimensional inversion is achieved, the application precision and reliability of the controllable source electromagnetic method under the complex geological condition are remarkably improved, and rich geophysical basis is provided.

The invention discloses a GCN-LSTM-based tailing dam multi-point settlement deformation prediction method. The method comprises the steps of collecting and preprocessing original settlement monitoring data of monitoring points in a research area; constructing a graph structure for settlement data between all monitoring point pairs, setting a threshold value, connecting node pairs with significant correlation coefficient relationships by using edges, constructing a weighted undirected graph, and converting the weighted undirected graph into a normalized adjacent matrix as the input of a graph convolutional network GCN; extracting the spatial topology of each monitoring point by using a GCN; capturing long-term time dependence in the settlement process by using a gating mechanism of the LSTM network; and fusing the GCN and the LSTM network, and finally outputting a predicted value through a full connection layer. According to the method, the spatial topological graph among the monitoring points of the tailing dam is constructed, and the GCN and LSTM networks are fused, so that the spatial correlation among the monitoring points and the time dynamic characteristics of the settlement data are effectively captured, and high-precision tailing dam multipoint settlement prediction is realized.

The invention relates to a slope monitoring point arrangement method based on sparse spatial correlation and displacement sensitivity. The method comprises the following steps: firstly, carrying out geological survey on a site slope to obtain variability parameters of a slope soil body; then, a slope finite element model is constructed on the basis of slope geometric parameters and the variability parameters, and the displacement states of the slope under different parameter conditions are determined on the basis of the slope finite element model; grading the slope sensitive area based on the displacement state; obtaining displacement data based on the grade of the slope sensitive area, and determining a displacement correlation coefficient and a spatial correlation coefficient based on the displacement data by adopting a Pearson's correlation coefficient and a digital image correlation technology; and finally, determining a slope high-correlation area based on the displacement correlation coefficient and the spatial correlation coefficient, and setting a monitoring point position. The scientificity and accuracy of slope monitoring point arrangement are improved, and the method has certain significance for predicting and preventing slope disasters.

The invention relates to the technical field of wireless communication, and discloses an information source number detection method and system based on a non-circular signal. The method comprises the following steps: calculating a compensation sample covariance matrix of a received signal, and obtaining a sample canonical correlation coefficient through Takagi decomposition of the compensation sample covariance matrix; constructing a marginal likelihood function of the maternal typical correlation coefficient; taking a sample canonical correlation coefficient as an estimated value of a corresponding matrix, and establishing an estimated statistic by applying a minimum description length criterion; and obtaining the number of the parent typical correlation coefficients when the estimation statistic is minimized, and taking the number as an estimation value of the actual number of the non-circular signals. According to the method, the characteristic that the non-circular signal compensation covariance is not zero is fully utilized, the marginal likelihood function with the minimum redundancy parameters is adopted to construct the statistics, high-precision estimation of the number of the non-circular signals can be achieved, and necessary guarantee is provided for practical application scenes such as direction of arrival estimation and wave velocity formation.

The invention discloses a water conservancy equipment life prediction and fault monitoring method. The method comprises the following steps: multi-modal data acquisition and dynamic preprocessing: constructing an acquisition frequency adaptive model; multi-domain fusion feature extraction and adversarial dimension reduction: multi-domain feature extraction is carried out, then a generator of a Wasserstein GAN architecture is constructed to reconstruct original features, a discriminator calculates a Wasserstein distance, the generator is forced to reconstruct interlayer features of the discriminator through feature matching loss, and high-correlation features are screened in combination with a maximum information coefficient and a Pearson's correlation coefficient; constructing and training a gated attention fusion network; and constructing a fault monitoring model based on a Copula theory: capturing a normal operation mode of equipment, then estimating feature edge distribution through an empirical distribution function corrected by KDE, constructing a D-vine Copula structure to hierarchically capture nonlinear dependency, calculating an AnmallyScore index integrating edge anomaly and correlation sudden change penalty, and triggering an alarm when the AnmallyScore index is greater than a threshold value.

The invention relates to a harmonic traceability method based on an electric energy quality index monitoring cloud platform, which is realized based on the electric energy quality monitoring cloud platform of an electric power system, and comprises the following steps: S1, selecting an attention node; s2, selecting a harmonic traceability time period; s3, selecting a correlation coefficient calculation item; s4, collecting and processing harmonic data of all feeders of each concerned node, and drawing a voltage distortion rate and harmonic current trend curve contrastive analysis chart of each feeder; and S5, the electric energy quality monitoring cloud platform uses a built-in traceability algorithm to calculate correlation coefficients, screens feeder lines whose correlation coefficients are greater than K, and outputs a harmonic traceability report. According to the invention, one-key positioning and accurate identification are carried out on the system harmonic source based on the electric energy quality monitoring cloud platform by adopting synchronous electric energy quality data of multiple monitoring points according to an advanced correlation traceability algorithm, so that the harmonic traceability accuracy and working efficiency are greatly improved, and the harmonic traceability cost is reduced.

The invention relates to the technical field of anti-seismic performance detection, in particular to an anti-seismic performance detection method and system for an assembled steel-wood composite structure, and the method comprises the steps: arranging a multidirectional acoustic emission sensor in a steel-wood structure node area, applying simulated seismic vibration, and synchronously collecting acoustic emission signals; the method comprises the following steps: preprocessing collected acoustic emission signals, dividing the acoustic emission signals into signals corresponding to microcracks and contact slippage, respectively calculating an energy attenuation rate and a phase offset, and constructing a matrix; micro-impact is applied to the steel-wood node through an electromagnetic excitation device, a response signal is collected, resonant frequency is extracted, the drift distance between the resonant frequency and a reference value is calculated, and the severity of interface damage is determined according to the value of the drift distance; the damage correlation index calculation takes the characteristic value of the interface wave propagation characteristic matrix and the resonance frequency drift distance as input, the damage correlation index is calculated through the correlation coefficient, and the damage grade is divided according to the correlation coefficient. And differential characterization of different damage types is realized through the interface wave propagation characteristic matrix.

The invention belongs to the technical field of electric power transaction, and particularly relates to an electric power spot transaction clearing and bidding optimization method based on computing power prediction, which comprises the following steps: collecting and preprocessing multi-source data, and screening a core feature set through mutual information entropy, Pearson's correlation coefficients and variance expansion factors; a CNN-LSTM attention model is built, and output load, power output and electricity price fluctuation interval prediction results are calibrated through error feedback; a dynamic bidding decision mathematical model is constructed, and elastic constraints are set in combination with prediction parameters; solving by adopting an improved particle swarm algorithm, and outputting an optimal bidding strategy; a node marginal electricity price mechanism is fused for accurate clearing; and verifying a clearing result in a layered manner, and performing deviation feedback iterative optimization. According to the method, a whole-process closed-loop mechanism is constructed, the prediction precision and the bidding and clearing collaboration are improved, the system operation cost is reduced, the transaction real-time requirement is met, the power grid safety and the market subject income are guaranteed, and the method has important practical value.

The invention relates to the technical field of mechanical equipment fault diagnosis, and discloses a fault diagnosis method and system for an automatic tensioning control valve of a scraper conveyor. The method comprises the following steps: acquiring pressure and flow data of a main flow channel and an auxiliary flow channel through a sensor array, and extracting frequency domain characteristics to obtain a standardized frequency spectrum; detecting parameter coupling abnormity based on the spectrum cross correlation coefficient and generating an abnormal index; environment interference and medium viscosity data are fused, and non-linear feature distribution and correction deviation representing the mechanical state are obtained through classification and filtering analysis; matching the correction deviation with a preset fault mode, determining a fault type, and generating a fault positioning coordinate in a three-dimensional space by using an optimization algorithm; and finally, performing fluid dynamic simulation through the high-fidelity virtual model to verify the coordinate stability, and outputting accurate early warning when conditions are met. Through cooperation of multi-source information fusion and an intelligent algorithm, the accuracy of early-stage internal fault diagnosis of the complex double-flow-channel valve is improved.

The invention discloses a purchase demand prediction method combined with PMC material management and control, and belongs to the technical field of material management and control data processing, and the method comprises the steps: obtaining a material consumption sequence and corresponding timestamp data, and employing a time sequence decomposition method to separate trend components and seasonal components, and obtaining a consumption rule feature sequence; if the fluctuation amplitude of the consumption rule feature sequence exceeds a preset threshold value, determining the consumption rule feature sequence as a basic demand predicted value sequence; obtaining a market price index sequence, and calculating a correlation coefficient between the basic demand predicted value sequence and the market price index sequence; if the absolute value exceeds a set threshold value, updating a basic demand predicted value sequence through a time sequence model; generating a purchase plan sequence according to the basic demand predicted value sequence and the inventory average value; and generating a final output report. The invention discloses a problem that a current material management and control mode cannot capture key changes in time and is difficult to balance contradictions between inventory and production.

The invention discloses a fixed source carbon emission data quality comprehensive evaluation method and system, and relates to the technical field of carbon emission monitoring, and the method comprises the steps: S1, collecting dual-source monitoring and production data, carrying out the invalid recognition, emission amount accounting aggregation, working condition classification and correlation coefficient construction, and building a score working condition basic data set through variable coefficient detection; s2, calculating a correlation coefficient of the to-be-inspected valid data, and obtaining a valid data risk comprehensive score through multi-dimensional inspection scoring and in combination with an industry weight; s3, calculating an invalid data proportion as an invalid data risk score; and S4, performing two-dimensional risk rating on the valid data and the invalid data, and outputting a four-dimensional result. The system realizes the method through four layers of modules of data input, processing, risk evaluation and result output, and solves the technical problems that the existing data quality evaluation only pays attention to valid data, the inspection method is single, and a complete dual-source collaborative quality control and full-dimensional quality control scheme is lacked.

The invention discloses a high arch dam operation modal parameter automatic identification method and system based on discharge excitation. The method comprises the following steps: 1) obtaining a vibration displacement response signal: determining a penalty factor and an optimal decomposition layer number based on an adaptive multivariate variational mode decomposition algorithm to obtain an optimal IMF component of each sensor channel signal, and performing IMF component screening and signal reconstruction through a frequency domain cross correlation coefficient to realize adaptive noise reduction of the signal; 2) establishing a Monte Carlo three-dimensional stability diagram based on a random subspace recognition algorithm driven by a covariance matrix; and 3) modal parameter automatic identification based on an intelligent clustering algorithm. According to the method, the noise is suppressed by automatically optimizing the modal component reconstruction signal of the multi-sensor vibration signal; a Monde-Carlo three-dimensional stability diagram is established in combination with a Monde-Carlo theory and a covariance driven random subspace method to determine a model order, and automatic interpretation of the stability diagram is realized by applying improved fuzzy clustering, so that operation modal parameters of the high arch dam are accurately identified.

The invention relates to an evapotranspiration component prediction method, system and device and a medium. The method comprises the following steps: acquiring four types of meteorological elements, including net radiation, air temperature, wind speed and relative humidity, influencing evapotranspiration components in a target area, and screening core meteorological elements in combination with area vegetation coverage and soil moisture retention characteristics; constructing a core meteorological element historical simulation sequence, comparing the core meteorological element historical simulation sequence with a corresponding historical observation sequence to calculate a Pearson's correlation coefficient, determining an initial fusion prediction value based on the coefficient, applying an emergence constraint, and generating a core meteorological element future prediction sequence; inputting the prediction sequence into a hydrological cycle model for simulation and correction to obtain a future soil moisture prediction result of the target area; and jointly inputting the future prediction sequence of the core meteorological elements and a future soil moisture prediction result into the double-layer evapotranspiration model, outputting and correcting a primary evapotranspiration component sequence, and finally obtaining an adjusted evapotranspiration component sequence. The method can improve prediction precision and stability.

The invention provides a full-spectrum water quality multi-parameter dynamic inversion method and a model construction method thereof, and belongs to the technical field of spectral analysis based on machine learning. By constructing a closed-loop optimization architecture of noise reduction, wavelength optimization, turbidity correction and adaptive modeling, high-precision detection of four water quality parameters of chemical oxygen demand, total organic carbon, total nitrogen and nitrate nitrogen is realized. According to the method, a variational mode decomposition and improved threshold translation invariant wavelet combined noise reduction algorithm is provided, a kurtosis-correlation coefficient dual index is adopted to screen noise components, a multi-translation average strategy is combined to suppress a pseudo-Gibbs phenomenon, and meanwhile, a global parameter collaborative tuning mechanism based on Bayesian optimization is designed; a cross-module hyper-parameter coupling space is established in the whole process of noise reduction, wavelength optimization, turbidity correction and modeling. Through a multi-dimensional feature enhancement mechanism, the model feature representation capability is improved by 35%, and an innovative solution is provided for multi-parameter online detection in a complex water quality scene.

The invention provides a heart transplantation post-operation continuous physiological monitoring and anomaly recognition method and system based on wearable equipment, and relates to the technical field of physiological monitoring. According to the method, an individualized dynamic reference interval and a multi-index correlation coefficient matrix are constructed by collecting baseline data such as electrocardio, blood pressure and blood oxygen, abnormal state detection is achieved in combination with the Mahalanobis distance of a unified feature vector and a multi-task recognition model, and then a multi-physics field digital twin model is called to simulate hemodynamic evolution. And high-confidence abnormity is judged according to a coupling rule, so that the intellectualization and response efficiency of postoperative monitoring are improved.