3160 results about "Correlation coefficient" patented technology

The invention discloses a coal and rock mass fracture instability multivariate signal fusion analysis method, and belongs to coal and rock mass analysis. The method comprises the following steps: carrying out time-frequency domain processing on an acoustic emission signal to obtain an accumulated ringing characteristic value # imgabs0 # and an accumulated b characteristic value # imgabs1 #; a main control frequency characteristic value # imgabs2 # and an amplitude characteristic value # imgabs3 # are accumulated; a spatial characteristic value # imgabs4 # of acoustic emission; fitting a fitting function # imgabs7 # from the characteristic value # imgabs5 # to the characteristic value # imgabs6 # respectively; according to the fitting function # imgabs8 #, calculating a correlation coefficient matrix R between the characteristic values # imgabs9 # to # imgabs10 #; according to the matrix R, determining a weight coefficient # imgabs11 # of each characteristic value; a first-order derivative function # imgabs13 # and a second-order derivative function # imgabs14 # of the fitting function # imgabs12 # are obtained, and according to the first-order derivative function # imgabs15 # and the second-order derivative function # imgabs16 #, a demarcation time point # imgabs17 # in the coal and rock mass damage process is determined; and the boundary time point # imgabs19 # is corrected by using the weight coefficient # imgabs18 #, and a corrected boundary time point # imgabs20 # is obtained. In order to solve the problems that an existing coal and rock mass fracture instability judgment index is single, and coal and rock mass fracture instability early warning is low in single index precision, the early warning precision is improved through multi-element signal fusion.

The invention provides an insurance customer fraud detection method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a nonlinear mapping relation between a feature variable and a target variable, calculating a partial correlation coefficient between features, recognizing a causal relation, and generating an insurance fraud field causal atlas; identifying a trigger factor based on a time sequence intervention effect and an anti-fact effect; calculating a transfer coefficient and loop strength of the causal path to obtain a link credibility score; and fusing the trigger factor and the link credibility score to generate an early warning score, and extracting a maximum cumulative transfer effect path to output an early warning result. According to the method, the insurance fraud behavior can be accurately identified, and the accuracy and interpretability of fraud detection are improved.

The invention provides an automobile magnesium alloy pre-twin crystal deformation data analysis system, and relates to the technical field of data processing, and the system comprises a twin crystal correction module which is provided with a first target monitoring point and a second target monitoring point in a pre-twin crystal area, the first target monitoring point is located at a crystal boundary intersection, and the second target monitoring point is located at the center of a twin crystal zone; acquiring orientation difference angle and strain energy density difference data of the two monitoring points in real time, calculating a covariance matrix of change characteristics of the two monitoring points, and correcting a proliferation rate parameter and an orientation rotation parameter in the dynamic evolution matrix according to a correlation coefficient of the covariance matrix to obtain a corrected dynamic evolution matrix; and the optimization module is used for adjusting a hot extrusion process parameter group according to the twin crystal density gradient distribution output by the corrected dynamic evolution matrix, the parameter group comprises a mold temperature, an extrusion speed and a strain path, and the twin crystal density is dynamically controlled in a target interval. According to the invention, the complex stress state of the automobile part under the actual working condition can be accurately simulated.

The invention discloses an ACM data analysis method and a data monitoring system, and belongs to the technical field of aircraft maintenance, and the method comprises the steps: constructing an ACM data set, setting a feature engineering processing method, carrying out coupling feature extraction through setting a dynamic window and calculating a dynamic correlation coefficient, dividing working conditions, eliminating fluctuation interference, and generating a structured time series data set; the method comprises the steps of obtaining ACM reference data of aircrafts of the same model, setting a reference fault judgment method, judging latest data in a structured time sequence data set, screening abnormal parameters, judging whether the ACM breaks down or not by combining an actual temperature adjusting effect, reducing single-parameter misjudgment, setting a coupling fault positioning method, diagnosing the faulty ACM, and distinguishing true faults and false anomalies. Meanwhile, a prediction scheduling method is set, fault prediction, health state prediction and service life evaluation are carried out on the fault-free ACM, the probability of sudden faults is reduced, and non-planned flight stopping is reduced.

The invention relates to an aviation big data intelligent analysis method based on trajectory anomaly detection, and belongs to the technical field of aviation data processing. The method comprises the following steps: acquiring operation state data and flight parameters of an aircraft, and extracting aerodynamic parameters of an aircraft type and deviation data corresponding to horizontal navigation and vertical navigation; carrying out stream batch integrated processing on the data through a real-time processing engine, and carrying out space-time reference unification to obtain standardized data; performing data cleaning, abnormal point correction and missing value interpolation on the standardized data; performing pneumatic-performance correlation analysis after data processing, and quantifying the influence of parameter deviation on the climbing performance through a dynamic response model to obtain a strong correlation coefficient; performing coupling analysis through the climbing rate attenuation prediction model to obtain an aircraft climbing performance anomaly prediction result; and generating a correction instruction according to the persistent climb rate anomaly of the performance anomaly prediction result. The efficient and accurate analysis of the aviation big data is realized, and the safety and reliability of the operation of the aircraft are effectively improved.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

Systems, methods, and software can be used to determine risks of software files. In some aspects, a method includes: obtaining an input, wherein the input comprises a binary file; determining a second set of feature vectors of the input; performing a canonical correlation analysis (CCA) on the second set of feature vectors and a first set of feature vectors to obtain a first vector and a second vector; calculating a correlation coefficient value of the first vector and the second vector; obtaining a third set of feature vectors based on the correlation coefficient value; and providing, based on the third set of feature vectors, information indicating a level of a security risk of the input and information indicating features associated with the security risk of the input.

The invention relates to the technical field of environmental parameter measurement, in particular to an indoor environment quality intelligent evaluation method based on multi-dimensional data fusion, which comprises the following steps: step 1, performing time-space synchronous acquisition on multi-source data; 2, a dynamic correlation model is constructed, a parameter coupling matrix is generated based on inter-parameter time-delay cross-correlation analysis, the time-delay cross-correlation analysis is achieved through sequence translation in a sliding time window and correlation coefficient calculation, an environment state class is constructed according to a parameter mean vector, fluctuation intensity and abnormal event marks, and a dynamic correlation model is constructed; each environment state class is bound with an independent parameter contribution degree weight set; step 3, performing double-stage environment quality evaluation; and step 4, adaptive increment optimization: when the deviation between the comprehensive evaluation index and the subjective evaluation exceeds a threshold value, adjusting a parameter contribution degree weight set of an environment state class, and periodically reconstructing a parameter coupling matrix. The prediction capability is improved by considering the time-delay coupling between the parameters; the system has self-learning and optimization capabilities, and the stability of long-term operation of the system is improved.

The invention belongs to the technical field of centrifugal fan fault prediction, and provides a centrifugal fan fault trend prediction method based on multi-source information fusion, and the method comprises the following steps: in the operation process of a centrifugal fan, capturing the change trend of data distribution through multi-source data monitoring and statistical property analysis; and whether data non-stationary change of the centrifugal machine is caused by equipment aging is identified. Based on a time and space two-dimensional comparison analysis framework, physical mechanism verification is combined, suspicious aging features are locked by calculating relative deviation and aging trend index quantitative indexes, and then an average correlation coefficient and trend consistency are utilized to construct a correlation consistency index to verify feature reliability, so that the method has the capability of positioning a specific aging part, and the reliability of the aging part is improved. The problem of misjudgment caused by sensor faults or environmental interference in traditional fault diagnosis is optimized, the accuracy of aged part recognition is improved, and a clear object is provided for targeted maintenance.

The invention discloses a cardiovascular disease risk prediction method and system based on dietary multi-modal data and ensemble learning, and the method comprises the steps: constructing a multi-modal set through integrating multi-source heterogeneous data such as demographic statistics, dietary nutrition, clinical physiological and biochemical indexes and lifestyles; data cleaning is completed based on a box plot method and missing value processing, and key features are screened through Pearson's correlation coefficients, variance expansion factors and feature importance evaluation; a plurality of heterogeneous base models are fused by adopting a Stacking integration framework, a meta-feature matrix is generated through five-fold layered cross validation, and multi-level decision fusion is realized through a logistic regression meta-model; and quantifying the contribution weight of the dietary characteristics to the risk in combination with an SHAP method, and generating a visual interpretation chart and personalized intervention suggestions. According to the method, the accuracy and the stability of a prediction result are remarkably improved, the contribution degree and the action mechanism of dietary factors and other characteristics to the prediction result can be deeply analyzed, powerful support is provided for accurate prevention and personalized treatment of cardiovascular diseases, and the method has good practical value.

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 discloses a scientific paper academic value measurement method based on semantic fusion and reference behavior analysis. The method comprises the following steps: determining a literature database according to a target research field, constructing a field keyword list, and retrieving and collecting an initial literature data set; the internal innovation value is quantified; quantizing an external approved value; the step of constructing the academic value comprehensive measurement model comprises the sub-steps of applying an entropy weight method to distribute index weights; calculating an academic value comprehensive score through linear weighting; checking and evaluating through a Spearman correlation coefficient; according to the method, a comprehensive evaluation system based on multi-dimensional semantics and behavior characteristics is constructed, internal knowledge contribution and external recognition value of the papers are systematically fused, objectivity and precision of scientific research paper academic value evaluation can be effectively improved, and therefore the problems of evaluation time lag, reference behavior deviation and subjective weighting existing in a traditional method are solved, and the scientific research paper academic value evaluation efficiency is improved. And the method has good universality and popularization value.

The embodiment of the invention discloses a water supply network abnormity identification method and device, electronic equipment and a storage medium, and relates to the technical field of water supply network abnormity identification, and the method comprises the steps: collecting pressure data from each monitoring point, carrying out the preprocessing, employing a Pearson's correlation coefficient to construct a dynamic pressure correlation matrix according to the pressure data, carrying out abnormity judgment and restoration on the monitoring equipment; the pressure difference standard deviation of each monitoring point in adjacent time periods is calculated to recognize the water hammer effect; constructing a residual error distribution reference through a machine learning algorithm based on historical monitoring data, calculating a real-time pressure residual error of each monitoring point, calculating a comprehensive anomaly index according to the residual error distribution reference, and carrying out anomaly judgment and early warning; and a Kriging interpolation method and a semi-variation function are adopted to generate pressure residual error space distribution according to the real-time pressure residual error and display the pressure residual error space distribution. The problems that in the prior art, data verification is not systematic, space-time relevance is lacked, the abnormal classification dimension is single, misjudgment is prone to occurring, and the response efficiency is low are solved.

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 relates to an extra-high voltage converter transformer OLTC fault diagnosis method fusing multi-feature information and RIME-CNN-BiLSTM-SAM, and belongs to the technical field of intelligent monitoring of power systems. The method comprises the following steps: firstly, constructing a vibration signal fault data set based on an OLTC mechanical fault simulation experiment; secondly, decomposing an OLTC vibration signal into a plurality of intrinsic mode components by adopting REMD, and preferably selecting components containing rich fault feature information based on a correlation coefficient; then, the multi-scale permutation entropy of IMFs is calculated, time / frequency statistical analysis and the multi-scale permutation entropy are combined, OLTC multi-dimensional vibration characteristic parameters are integrally and locally concerned, and principal component analysis is introduced to carry out characteristic fusion and dimension reduction of OLTC vibration information; and finally, optimizing the hyper-parameters of the CNN-BiLSTM-SAM fault diagnosis model by using the RIME, and realizing the construction of the converter transformer OLTC intelligent fault diagnosis model. The method has a remarkable effect in the aspect of extracting OLTC fault feature information, the fault type recognition rate is superior to that of an existing method, and the effectiveness of the method in converter transformer OLTC fault diagnosis is verified.

The invention belongs to the technical field of landslide susceptibility analysis, and relates to a landslide susceptibility ensemble learning evaluation method considering spatial heterogeneity partitioning and factor feature screening, which comprises the following steps: generating a landslide sample based on historical landslide catalog data, and selecting a non-landslide sample through environmental factor frequency ratio analysis; the method comprises the following steps of: extracting static and dynamic environment factor data sets, realizing factor space interpretation force transformation by utilizing a t-SNE-ISO clustering algorithm and a feature screening strategy, eliminating high-correlation factors through a Pearson correlation coefficient method, quantifying interpretation force of each factor on landslide space differentiation by combining a geographic detector, screening optimal feature combinations under global and partition frameworks respectively, and performing landslide space differentiation on the landslide space. According to the method, a Stacking integrated learning framework is combined with CNN, DNN, MLP-based learners and LR element learners, a landslide susceptibility probability prediction model is formed, the generalization ability and prediction accuracy of the model are improved, and the method is especially suitable for landslide high-incidence areas with severe topographic relief and complex geological conditions.

The invention discloses an intelligent archive content evaluation system based on deep learning, which relates to the technical field of deep learning, and comprises the steps of inputting a standardized feature vector into a deep learning model, calculating a time sequence waveform factor and a semantic kurtosis factor in combination with a rule matching reference, and generating an evaluation confidence coefficient matrix; the cosine similarity and the Spearman correlation coefficient are included, and the comparison index threshold triggers abnormal marking and grading early warning. According to the method, a multi-model fusion deep learning evaluation model is constructed and comprises a semantic understanding model, a risk evaluation model and a value classification model, split attributes are dynamically optimized in combination with an evaluation rule decision tree, file contents can be comprehensively evaluated from multiple dimensions of semantic understanding, risk evaluation, value classification and the like, and the evaluation efficiency is improved. And an accurate comprehensive evaluation result and a rule matching reference are generated, so that the comprehensiveness and scientificity of file content evaluation are improved.

The invention provides a short-term photovoltaic power prediction method and system, computer equipment and a medium, and belongs to the field of photovoltaic power generation output power prediction.The method comprises the steps that short-term photovoltaic power and meteorological working condition data samples are obtained, and a Gaussian mixture model is used for conducting multi-modal clustering processing on the meteorological working condition data samples to obtain membership probability embedded vectors; through a Pearson's correlation coefficient weighting and sliding window mechanism, extracting features from the data sample and the membership probability embedding vector, and constructing a multi-modal time sequence feature tensor; a multi-head attention mechanism in a traditional Transform network is replaced with a class domain fusion self-attention mechanism, and a class domain fusion attention model is formed; inputting a multi-modal time sequence feature tensor to train a class domain fusion attention model to obtain an initial prediction value; and residual error estimation is carried out on the initial prediction value by using a residual error learning error compensation strategy, a short-term photovoltaic power prediction result is output, and the accuracy and robustness of the model are improved.

The invention relates to the technical field of monitoring management, in particular to an intelligent water affair monitoring management system based on the Internet of Things, the system comprises a processor and a memory, and the processor executes a computer program stored in the memory to realize the following steps: acquiring data correlation coefficients and differential correlation coefficients corresponding to different monitoring moments, obtaining a target abnormal coefficient of the first clustering cluster and a target abnormal coefficient of the second clustering cluster according to a data volume ratio and a variable coefficient in the first clustering cluster and the second clustering cluster obtained by clustering the data correlation coefficient and the difference correlation coefficient corresponding to the monitoring moment; and obtaining a target abnormal index value corresponding to the current monitoring moment according to the data correlation coefficient corresponding to the current monitoring moment and the target abnormal coefficient of the cluster to which the differential correlation coefficient belongs, and monitoring the water affair system according to the target abnormal index value. And the timeliness and the reliability of abnormal monitoring and early warning of the water affair system can be improved.

A system for extracting glacier boundaries using multiparametric analysis, consisting of: a data acquisition module configured to acquire Synthetic Aperture Radar (SAR) data of a glacial region; a data processing unit configured to process the SAR data to generate coherence images and backscatter intensity maps, to extract terrain parameters including slope and curvature information from a digital elevation model (DEM), to apply thresholds to the coherence images, backscatter intensity maps, and terrain parameters, to generate a multi-band stack comprising the thresholded coherence images, the thresholded backscatter intensity maps, the thresholded slope information, and the curvature information, and a principal component analysis module configured to standardize the multi-band stack, calculate a covariance matrix from the standardized data, calculate eigenvalues and eigenvectors of the covariance matrix, and transform the original data into a principal component space with reduced dimensionality; a texture feature extraction module configured to calculate first- and second-order statistics from the output of the principal component analysis; generating a texture feature set comprising at least one of the following: sum average, entropy, difference entropy, sum entropy, variance, difference variance, inverse difference moment, contrast, correlation, information measures of correlation, and maximum correlation coefficient; a connected component segmentation module configured to: convert the set of textural features into a binary format that distinguishes glacier ice from the background; group spatially connected pixels of similar intensity into segments; extract a vector shape file corresponding to the glacier boundary; an output generation module configured to generate a glacier boundary delineation output; and a user interface having a display configured to display the output generated by the output generation module.

The invention relates to the technical field of power grid state monitoring, in particular to an intelligent monitoring and early warning method and system for a high-voltage power grid, and the method comprises the steps: collecting the multi-dimensional parameter data of a power grid node, and obtaining the multi-dimensional parameter data of the power grid node based on the relative deviation of the data of each dimension in a local window and the mean value of the data of each dimension in combination with the correlation coefficient of the data of each dimension; calculating parameter fluctuation attention at a target moment so as to correct parameter data of each dimension; processing the data points through a clustering algorithm to obtain a plurality of clusters, and selecting the cluster center of the cluster with the most data points as a power stability index; and calculating the relative deviation between the data point and the index, and generating a state early warning coefficient so as to estimate and evaluate the abnormality of the power grid node region and generate an early warning signal. According to the method, parameter fluctuation is accurately quantified by fusing the deviation degree and correlation of the multi-dimensional data of the local window, and a foundation is built for monitoring and early warning.

The invention relates to the technical field of computers, and discloses an integrated information system for clinical test archives and a data processing method thereof, and the method comprises the steps: constructing a standardized data model covering a whole process; multi-source data dynamic structured intake and integrity verification are carried out; performing multi-dimensional semantic association analysis based on a Bayesian network and a correlation coefficient; block chain type version tracing and difference comparison are carried out; performing role-sensitivity-operation-context four-dimensional access control; and task-driven collaborative sharing and auditing log records. The system comprises a unified modeling module, a data intake module, a semantic analysis module, a version tracing module, an access control module and a collaborative auditing module. The problems of data islands, semantic segmentation, poor dynamic adaptability, weak safety protection and the like in the prior art are solved, intelligent management, semantic interconnection, safety controllability and efficient collaboration of the whole life cycle of clinical test files are achieved, and data quality, audit compliance and multi-role collaboration efficiency are remarkably improved.

The invention discloses a data processing method and system for micro-motion detection array station distribution, and relates to the technical field of micro-motion detection, and the method comprises the steps: according to the actually measured starting point coordinates and the azimuth angle of a measuring line, setting the measuring point distance, and the radius and number of nested triangle circumcircles, generating field station coordinates in batches; collecting and preprocessing station waveform data; calculating a spatial autocorrelation coefficient of any center station and a non-center station; dBSCAN clustering is carried out based on the station distance; performing spatial averaging on the spatial autocorrelation coefficient according to a clustering result; fitting the averaged spatial autocorrelation coefficient and a zero-order Bessel function to obtain a frequency dispersion spectrum, picking up a surface wave phase velocity in the frequency dispersion spectrum, and calculating an apparent shear wave velocity; inverting the shear wave velocity of the waveform data by adopting a self-adaptive gradient inversion strategy; and generating a velocity comprehensive profile of a depth domain and a frequency domain according to the surface wave phase velocity, the apparent shear wave velocity and the inverted shear wave velocity. The invention provides a new method for station distribution to data processing for micro-motion detection.

The invention discloses an intelligent pipe network leakage detection method based on edge calculation, and relates to the technical field of pipe network monitoring and leakage detection, and the method comprises the steps: carrying out the feature extraction of a preprocessing data set through an edge analysis terminal, and obtaining a pressure stability coefficient, a sound wave correlation coefficient and a flow mutation coefficient; inputting the pressure stability coefficient, the sound wave correlation coefficient and the flow abrupt change coefficient into a leakage probability evaluation model, and outputting a leakage risk index; and the edge decision terminal generates a leakage level signal according to the leakage risk index and triggers a positioning instruction. The pressure stability coefficient, the sound wave correlation coefficient and the flow abrupt change coefficient are extracted through the edge analysis terminal and input into the leakage probability evaluation model, the leakage risk index is output, accurate quantitative evaluation of the leakage risk is achieved, the model can adapt to changes of different working conditions through introduction of the dynamic weight, and the reliability of the model is improved. And the adaptability and accuracy of evaluation are improved.

The invention discloses a sub-industry load prediction method based on time sequence decomposition and multi-core Transform fusion. Firstly, industry loads are divided into a new energy dominant type, an industrial dominant type, a meteorological sensitive type and a mixed type through a clustering algorithm in combination with correlation coefficients of new energy output, policy factors and meteorological elements; secondly, decomposing the load data into trend components and day, weekly and monthly periodic components by adopting a time sequence decomposition layer, and respectively extracting features through a multi-kernel Transform architecture: capturing short-term fluctuation features by a time convolution kernel, capturing long-term trend features by a Transform layer based on a multi-head self-attention mechanism, fusing and outputting sub-industry load prediction results, and finally, accumulating four types of industry load prediction results to obtain a system load prediction result. According to the method, the problems of periodic component aliasing, difficulty in multi-factor coupling classification, insufficient long-term and short-term feature fusion and the like of a traditional method are solved, and the precision and robustness of sub-industry load prediction are improved.

The invention discloses an analysis method and system for a dynamic recrystallization structure form of a titanium alloy based on machine learning and a medium, and belongs to the technical field of metal material microstructure quantitative characterization, a Gleeble thermal compression test is processed on a titanium alloy to be tested, multi-modal data is collected, a DRX probability graph is output based on a DRX segmentation model, and then the characteristics of DRX are extracted. And respectively inputting the DRX features into the first-level classifier, carrying out PCA dimension reduction processing, splicing and fusing the prediction probability and the features after dimension reduction, and inputting the spliced and fused features into a second-level classifier to output a two-dimensional DRX segmentation map. Based on the FIB-SEM tomography sequence image, reconstructing a three-dimensional model of the DRX crystal grain so as to carry out consistency verification on the two-dimensional DRX segmentation image; and evaluating a correlation coefficient of the three-dimensional model and the two-dimensional DRX model, and finally carrying out three-dimensional visualization on the space aggregation of the DRX crystal grains. According to the method, the automation degree and efficiency of dynamic recrystallization proportion and type identification are remarkably improved.

The invention relates to the technical field of electric power energy storage, in particular to an energy storage demand analysis method of a high-proportion new energy power grid, which comprises the following steps of: obtaining a power predicted value and a measured value through a prediction platform to calculate deviation, performing sliding window segmentation, dynamically adjusting length based on volatility to extract variance characteristics, and calculating the energy storage demand of the high-proportion new energy power grid; and the center number is optimized through K-means clustering, a classification result is output, a capacity adjustment value is calculated according to a matching grade adjustment coefficient, time points are extracted and sorted according to a load and output correlation coefficient, and an energy storage capacity space-time distribution table is generated. According to the invention, through prediction of deviation sequence sliding window segmentation and variance feature extraction, dynamic identification of new energy output fluctuation intensity, construction of a deviation fluctuation and energy storage capacity mapping model, quantification of load and output coupling intensity, and construction of a multi-dimensional energy storage correction system, energy storage and source load dynamic matching is realized, and response sensitivity is improved. The risk of resource mismatching is reduced, the robustness of the system is enhanced, and the cooperative efficiency of charging and discharging strategies is optimized.

The invention provides an equipment log fault trend prediction method and system based on deep learning, and relates to the technical field of fault prediction, and the method comprises the steps: building equipment, fault and maintenance entity nodes and associated edges thereof through obtaining equipment operation historical data; and performing information propagation and aggregation on the node attribute information and the time sequence attribute information of the edge by using a graph neural network to obtain a node representation vector, performing time sequence segmentation by using a sliding time window to obtain a node dynamic feature, calculating a time sequence autocorrelation coefficient to identify a fault rule and an evolution mode, and constructing a fault prediction model to output a prediction result. According to the method, the graph structure and the time sequence information are fused, the accuracy of fault prediction is improved, and effective guidance can be provided for equipment maintenance decisions.

The invention discloses a high-low voltage linkage line loss comprehensive intelligent diagnosis system and method. The system comprises a distributed storage and high-performance calculation module, a data fusion and processing module, a multi-dimensional feature construction module, a high-low voltage linkage analysis module and a line loss intelligent diagnosis module. The method is used for carrying out line loss comprehensive intelligent diagnosis based on the system, and comprises the following steps: processing multi-source data in real time through the distributed storage and high-performance calculation module; bus-line-user archive data are fused, and a line loss index is calculated; extracting time, space and electrical three-dimensional characteristic indexes; performing high-low voltage linkage analysis based on the Pearson's correlation coefficient and the DTW distance; and fusing the system state model and the isolated forest anomaly detection model to output an anomaly diagnosis result. The method is suitable for accurate analysis and treatment of line loss in an intelligent power grid environment, and the efficiency and quality of line loss management can be 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.