4885 results about "Correlation coefficient" patented technology

The invention discloses a stationary pollution source data acquisition and calibration method and system, a medium and a program product, and the method comprises the steps: carrying out the segmentation processing of a plurality of concentration change data, and obtaining a plurality of data segments; determining the concentration change trend of various pollutants, and calculating a correlation coefficient matrix; the Euclidean distance value between the correlation coefficient matrix of the current data segment and each correlation coefficient matrix before sorting is calculated; when the Euclidean distance is greater than a preset threshold value, marking the corresponding data segment as an abnormal data segment; determining the pollutant as a target pollutant, and establishing a material balance equation; based on the material balance equation, generating a theoretical concentration change curve of the target pollutant; calculating a deviation value, and when the deviation value exceeds a preset dynamic error limit value, determining a sensor correction coefficient according to the process parameters; and carrying out calibration processing on the acquired data of the target pollutant according to the sensor correction coefficient. According to the invention, the accuracy of data acquisition and calibration of the stationary pollution source is improved, so that the influence on formulation and execution of pollution prevention and control measures is reduced.

The invention discloses a typhoon wave forecasting method based on integrated machine learning. The method comprises the following steps: firstly, integrating historical typhoon wave data, meteorological data and marine environment data; preprocessing the data, including integration, cleaning, vacancy filling and standardization, and performing multi-source data completion by adopting a K-nearest neighbor algorithm and a spline interpolation method; secondly, screening key characteristic parameters through a Pearson's correlation coefficient, and reinforcing nonlinear correlation representation in combination with a mutual information method; then, constructing an integrated prediction model containing an LSTM (Long Short Term Memory), an XGBoost (X Goose Boost) and a Transform; and finally, dividing a training set and a verification set by adopting a dynamic time sequence division strategy, optimizing model hyper-parameters, and completing training and testing of the typhoon wave height prediction model. According to the method, the data sparsity problem is solved through multi-source data fusion and feature selection optimization, the generalization ability is improved through an integrated model architecture, and compared with a traditional single model, the training period is remarkably shortened, and the forecasting precision and timeliness are improved.

The invention discloses an explosion-proof junction box fault diagnosis method and system based on a sensor, which are used in the field of fault diagnosis, and the method comprises the following steps: obtaining historical multi-source sensor data reflecting the state of an explosion-proof junction box; extracting a time sequence sample of the preprocessed historical multi-source sensor data by using a sliding time window method; multi-domain features reflecting fault types are extracted from the time sequence samples, and a fault diagnosis model is constructed based on the feature combination with the optimal diagnosis performance; performing feature extraction on the multi-source sensor data acquired in real time, and inputting the extracted features into a fault diagnosis model for fault diagnosis; and the operation state and the diagnosis result of the explosion-proof junction box are displayed in real time. According to the method, the subtle change of the fault signal is captured from multiple angles, the Pearson correlation coefficient is used for screening the features significantly related to the fault type, and the dimension reduction processing is carried out through principal component analysis, so that the operation efficiency and accuracy of the model are improved.

The invention relates to a machine room environment intelligent regulation and control method fusing multi-source sensing information, and the method comprises the steps: collecting the temperature and humidity data of different regions in a machine room through a temperature and humidity sensor, and collecting the movement track and stay time data of a person in the machine room through a person positioning device, transmitting the collected machine room environment data and personnel activity data to a data fusion module; in the data fusion module, machine room environment data and personnel activity data are aligned according to a time sequence, wrong data and outliers are eliminated, and a data set in a unified format is obtained; a correlation coefficient matrix among the characteristic parameters of the computer room is analyzed through correlation, the characteristic parameters with correlation higher than a preset correlation threshold value are screened out, and the characteristic parameters comprise temperature, humidity, personnel access frequency and staying duration parameters.

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 relates to the technical field of intelligent process monitoring, and discloses an intelligent process monitoring and feedback method and system for resourceful treatment of construction waste, and the method comprises the steps: carrying out the process parameter collection and standardization processing of a plurality of monitoring points of a construction waste treatment production line, and obtaining a preprocessing data set; constructing a graph data structure, calculating a correlation coefficient between nodes to determine a node connection relationship, and performing dynamic optimization through a capability awareness graph neural network to obtain a process data association graph; inputting the process data association graph into a process parameter association model for graph convolution operation and attention feature extraction to obtain a process parameter feature vector; the technological parameter feature vectors are input into a multi-constraint non-convex optimizer, combined optimization of the processing efficiency, the resource utilization rate and the product quality is calculated, and a technological parameter regulation and control strategy is obtained, intelligent technological monitoring of construction waste resourceful treatment is achieved, and the optimal control strategy under different working conditions is effectively integrated.

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, which relates to the technical field of social emotion analysis, discloses a university social negative emotion characterization analysis system comprising a data acquisition and preprocessing module, a feature extraction and anomaly detection module, an intelligent perception and negative emotion characterization module, and an anomaly risk assessment and response module. Emotional feature information is acquired in real time based on a multi-dimensional data source, and the acquired emotional feature information is cleaned and preprocessed. According to the method, by calculating the data consistency offset coefficient and the strong correlation coefficient, the multi-collinearity problem among multi-dimensional data features is solved, and instability of the model regression coefficient is avoided. And the system identifies abnormal correlation in real time and combines risk threshold classification processing to ensure robust model output. An analysis set is established by continuously collecting data, long-term emotion trend analysis is supported, personalized responses such as reminding, intervention or emergency measures are provided according to risk levels, and accurate management and timely adjustment of student emotional states are achieved.

The invention discloses a shield tunneling intelligent auxiliary method based on stratum types. The method comprises the steps that parameters of a current shield tunneling machine in the tunneling process are collected; through Pearson's correlation coefficient calculation, correlation coefficients of all the parameters and the total thrust and the cutterhead torque are obtained respectively, and a feature combination is obtained; utilizing a K-means algorithm to calculate and obtain a stratum classification result which is clustered by taking a basic combination consisting of the total thrust and the cutterhead torque and a feature combination as a feature set; a cooperative training algorithm is adopted, and dug stratum classification based on the basic combination and the feature combination is obtained under semi-supervised learning; a machine learning algorithm is utilized to carry out learning training on types of excavated stratums, unknown stratum conditions in the tunneling direction are identified, and a reference range of total thrust and cutterhead torque is given. The method can be circularly executed in the shield tunneling process, the excavated stratum classification result is continuously updated, the method has the characteristics of practical feasibility, real-time performance and the like, and important auxiliary decision-making information can be provided for shield tunneling.

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 discloses a transformer fault diagnosis method based on multi-sensor multi-domain feature fusion, and the method comprises the following steps: 1, obtaining a vibration signal data set of a transformer box, and carrying out the decentralization of a collected signal; 2, decomposing the decentralized transformer vibration signal by adopting a dung beetle optimized ICEEMD method, calculating a Pearson correlation coefficient between an IMF component and an original signal, and selecting a strongly correlated component to reconstruct the transformer vibration signal, so as to filter and denoise the transformer vibration signal; step 3, extracting time domain, frequency domain and space domain features in the transformer vibration signals by adopting an MVAR model, VMD-BIS decomposition and Riemannian manifold, and constructing a joint feature vector; 4, performing fusion dimension reduction by adopting a kernel principal component analysis algorithm to obtain a fusion dimension reduction feature vector; and a fifth step of constructing a kernel extreme learning machine model, training parameters of the kernel extreme learning machine model in combination with the time-frequency-space fusion features, and optimizing hyper-parameters of the kernel extreme learning machine model in combination with a Harris eagle optimization algorithm so as to obtain an optimal kernel extreme learning machine classification model. According to the method, the problems of misjudgment, missed judgment and low diagnosis rate caused by unbalanced distribution due to nonlinear characteristics of vibration signals in transformer fault diagnosis are effectively solved.

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.

A cooling capacity prediction method and system for heating, ventilation and air conditioning and a related device. The method comprises: by means of a denoising algorithm, decomposing an original signal into multiple intrinsic modes and a residual, the multiple intrinsic modes constituting an intrinsic mode function; using the Pearson correlation coefficient to classify the intrinsic modes into a first intrinsic mode dominated by an effective signal and a second intrinsic mode dominated by noise; decomposing the second intrinsic mode by means of wavelet soft thresholding denoising to obtain a third intrinsic mode; reconstructing the first intrinsic mode and the third intrinsic mode to finally obtain denoising data; taking the denoising data as an input of a preset neural network to finally obtain a cooling capacity prediction model, and using the cooling capacity prediction model to predict the cooling capacity of heating, ventilation and air conditioning to obtain a prediction result of the cooling capacity of the heating, ventilation and air conditioning. Compared with the prior art, the present invention solves the problems of nonlinearity, coupling, and long-term dependency in a cooling capacity prediction process, and increases the speed and accuracy of the cooling capacity prediction model.

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 the technical field of intelligent power grids, and discloses an abnormal power consumption behavior real-time monitoring and positioning method and system, and the method comprises the steps: carrying out the transformation of an existing collection device at an edge end, achieving the serial communication with the edge collection device, and carrying out the dynamic adjustment according to the collection condition and business demands of a transformer area, and forming a new collection strategy; clustering the electricity meters by using a Pearson's correlation coefficient and a DBSCAN clustering algorithm, dividing electricity meter clusters, and calculating the load condition of each cluster; according to the load condition and the acquisition weight of the electric meter cluster, the acquisition sequence and the acquisition frequency are adjusted, and the acquisition strategy is iterated and optimized periodically; and monitoring ammeter data in real time, identifying an abnormal electricity consumption behavior, and outputting an abnormal prompt. According to the invention, the algorithm is built in the edge computing platform through the big data algorithm analysis of the historical power consumption behaviors of the user, so that the dependence on manual judgment is effectively reduced, and the automation degree in the abnormal power consumption behavior monitoring, analyzing and processing process is improved.

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 relates to the technical field of water ecological environment remote sensing monitoring, and provides a water body turbidity remote sensing inversion method and system, a medium, equipment and a program. The water body turbidity remote sensing inversion method comprises the following steps: converting actually measured hyperspectral remote sensing reflectivity into satellite equivalent remote sensing reflectivity, and combining each wave band of the satellite equivalent remote sensing reflectivity to obtain a wave band combination; calculating a Pearson correlation coefficient between each single wave band and each wave band combination and the turbidity data, and screening the wave band combination meeting a set threshold value; encoding the wave band combination and the water environment data meeting a set threshold value, respectively obtaining wave band features, time features and water body type features, and constructing feature vectors; and by taking the feature vector as input and the turbidity data as output, constructing an objective function, and training a turbidity remote sensing inversion model, so as to minimize the objective function and select an optimal hyper-parameter combination in each round of iteration. The method can improve the prediction precision and reliability.

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 a multi-end line pilot protection method and system based on time domain model identification, and relates to the technical field of electrical engineering, and the method comprises the steps: obtaining a three-phase voltage sampling value, a three-phase current sampling value and a switching value of each end of a multi-end line; judging whether each end of the multi-end line meets a protection starting condition or not; if yes, differential voltage variation and differential current are calculated, and correlation coefficients of the differential voltage variation and the differential current are calculated; judging whether the correlation coefficient is within a preset fault threshold range or not; if the correlation coefficient is not within the preset fault threshold range, determining that the multi-terminal line has an external fault, and enabling the protection device not to act; and if the correlation coefficient is within a preset fault threshold range, determining that the multi-terminal line has an internal fault, and enabling the protection device to act to remove the fault. According to the invention, the influence of capacitive current and a power electronic power supply on pilot protection performance is overcome, the operating characteristics of a relay protection device are improved, and safe and stable operation of a power system is ensured.

The embodiment of the invention provides an X-band weather radar non-rainfall data quality control method and system. The method is applied to the technical field of weather radars and comprises the following steps: acquiring X-band weather radar data; identifying and eliminating the electromagnetic interference echo according to the continuity characteristic of the electromagnetic interference echo and the distribution condition of the correlation coefficient CC and the differential reflectivity factor ZDR; judging whether the difference value of the reflectivity factors of each echo point before and after hardware filtering is greater than a preset threshold value or not, and if so, marking the echo point as a rainfall echo; if not, fuzzy logic recognition elimination based on characteristic parameters is carried out; wherein the non-precipitation echo probability PNPR and the precipitation echo probability PPR of each echo point are calculated according to the characteristic parameters, whether the PNPR is larger than the PPR or not is judged, and if yes, the echo point is marked as a non-precipitation echo; if not, non-rainfall weak echo discrimination is carried out; whether the echo is a non-rainfall echo is determined by comparing the reflectivity factor mean value # imgabs0 #, the correlation coefficient mean value # imgabs1 # of the elevation angle of the whole layer of the echo and the ratio ValCC of the effective distance library number of the correlation coefficients with a judgment threshold value, and if the echo is the non-rainfall echo, the echo point is marked as the non-rainfall echo; and if not, continuously filtering scatter and plaque noise around the echo point by using the reflectivity factors in the radial direction and the azimuth to obtain the rainfall echo. In this way, the quality of weather radar data can be improved.

The invention relates to the technical field of sewage treatment control, in particular to a sewage treatment control system based on data analysis. The system comprises a data acquisition module, a water quality prediction module, a dynamic adjustment module and an energy-saving feedback module. The data acquisition module acquires water quality historical data, environmental factor data and sewage flow data, the water quality prediction module is used for establishing a long-short term memory network model for predicting the water quality change trend, and the dynamic adjustment module is used for analyzing the relationship between water quality indexes and equipment operation parameters by using a Pearson's correlation coefficient analysis method. An energy-saving feedback module is adopted; a genetic algorithm is adopted to find an optimal operation combination among the devices; feedback adjustment is performed on the aeration rate, the water pump flow rate and the stirring speed parameters of the dynamic adjustment module, so that when the water quality changes, the operation parameters of the devices are adjusted in time, and the sewage treatment effect is ensured; and energy consumption is reduced.