2830 results about "Predictive methods" patented technology

The invention relates to a dynamic adjust gyroscope life forecasting method based on gray model. By data collection of vibration effective value, random drift and environmental temperature parameter which are preprocessed using radial neural networks, influence of environmental temperature on vibration effective value and random drift is eliminated and random drift and effective value just related to time are obtained by subtracting drift constant value term, then trend term of vibration effective value and random drift are extracted by using wavelet transformation and gray model are built separately for their trend term. The smaller data in two values of life predicted of dynamic adjust gyroscope unless two predicted values exceeding performance parameter limitation when dynamic adjust gyroscope is considered losing effect. The invention uses performance parameter of life probative period of product to predict its life, showing discipline of performance parameter and life of dynamic adjust gyroscope. It is easy and convenient economical and reliable.

The invention relates to the technical field of robot anomaly prediction, in particular to a robot anomaly prediction method and system based on multi-dimensional fusion and causal inference. The method comprises the steps of performing multi-scale depth state characterization based on acquired robot multi-joint sensing data, and performing dynamic causal graph fusion based on the multi-scale depth state characterization. Comprising the steps of priori knowledge graph construction based on a kinematics chain, dynamic association attention mechanism construction based on data driving, state fusion of knowledge and attention guidance and global state vector generation. Performing hierarchical space-time dependency prediction based on the fused features, wherein the hierarchical space-time dependency prediction comprises robot joint topological graph construction, spatial dependency dynamic modeling, long-range time evolution prediction and future robot health state prediction; the method shows excellent performance in a plurality of core dimensions such as prediction precision, early warning timeliness and diagnosis interpretability, and has extremely high actual deployment value and engineering popularization potential.

The invention relates to the field of agricultural information processing and artificial intelligence, in particular to a farmland yield prediction method and system based on a heterogeneous graph neural network, and the method comprises the steps: obtaining multi-source farmland data, and extracting an initial feature vector of a farmland plot node; on the basis of the initial feature vector, constructing a heterogeneous graph structure containing multiple semantic relationships; performing node feature updating on the heterogeneous graph structure by using a heterogeneous graph neural network, and dynamically aggregating information of multiple types of neighbor nodes through relation-aware message passing and an edge propagation gating mechanism; performing enhancement processing on the node features by using a space-time dependency enhancement mechanism and a knowledge-guided reasoning mechanism; and outputting a regression prediction result of the farmland yield through a prediction module based on the enhanced node features. The invention aims to realize modeling and intelligent yield prediction based on multi-source heterogeneous data in an agricultural system, and improve the environmental adaptability, prediction generalization ability and interpretability of farmland yield prediction.

The present invention relates to the technical field of very-short-term photovoltaic power forecasting, and in particular to a very-short-term photovoltaic power forecasting method and system for real-time control, which intend to improve precision and real-time performance in photovoltaic power forecasting. The method comprises the following steps: performing normalization processing on meteorological data, and performing a feature correlation analysis; using a BP neural network to perform short-term photovoltaic power forecasting, inputting the meteorological data and historical output data, and outputting a short-term forecasting value with a resolution of 15 minutes; and performing spline interpolation and outlier removal on an upper-layer result of the BP neural network, and using same as a long short-term memory recurrent neural network input, so as to improve a temporal resolution of forecast data and obtain very-short-term photovoltaic power forecast data with a resolution of 1 minute. The method comprehensively considers meteorological factors and uses advanced neural network models and data processing techniques to achieve photovoltaic power forecasting on a very short temporal scale while ensuring forecasting precision, making the method suitable for the real-time control and optimized operation of photovoltaic power stations.

The invention discloses a low-altitude wind field prediction method and system based on a space-time diagram convolutional network, and relates to the technical field of weather forecast and wind energy utilization, and the method comprises the steps: collecting wind field observation data and physical field data of all nodes of a target region, a dynamic space-time diagram is constructed based on a flow function-vorticity theory through a dynamic diagram construction module; extracting spatial information through a graph attention network to obtain a spatial feature tensor; the spatial feature tensor and the physical field data are processed by a PhysFusion-TransTCN encoder to obtain the deep spatial and temporal features of the wind field; performing hierarchical feature aggregation on the wind field deep spatial-temporal features through an output module to obtain a wind field prediction result of the target area; a wind field physical mechanism is deeply fused, multi-scale spatial-temporal feature fusion is realized, and prediction result precision and physical rationality are ensured.

The invention relates to a water plant intelligent chemical adding amount prediction method based on data preprocessing, and belongs to the technical field of deep learning and intelligent chemical adding. Calculating a theoretical dosage based on historical flow, pH, water temperature and turbidity; dividing a plurality of clusters and splicing to query historical dosage data; weighting and fusing the theoretical dosing amount and the inquired historical dosing amount as a pre-treatment dosing amount; learning the relationship among the flow, the pH, the water temperature, the turbidity and the pretreatment dosage to perform forward feedback optimization; building an alumen ustum image recognition model, classifying alumen ustum, and associating the alumen ustum with corresponding dosage to form a dosage feedback algorithm model; building a dosage feedback model based on the sedimentation tank outlet water quality monitoring data; and correcting the weight in the preprocessed dosage based on the adjusted data of alumen ustum identification and water quality feedback on the dosage. The method can effectively reduce the influence of the dosage data error on the effectiveness of the model, can reduce the complexity of the algorithm model, and improves the robustness of the model.

The invention discloses a supply chain data-oriented enterprise upstream and downstream collaborative risk control prediction method and system, and relates to the technical field of supply chain financial risk management, and the method comprises the steps: extracting a payment time deviation value between enterprises to construct a payment behavior sequence, calculating a supply chain collaborative credit rating based on a fluctuation amplitude and a historical default record, and recognizing a risk source enterprise; adopting a recursive partition time window to extract a period index and a sudden change index of the payment behavior, and constructing a payment behavior portrait; identifying associated enterprises through the phase overlapping degree of the payment behavior track to form a risk conduction sequence; and based on the evolution law and the risk conduction sequence of the payment behavior portrait, predicting a risk triggering node, and generating a risk prevention and control scheme including a credit line regulation and control instruction and a behavior monitoring strategy. The method can effectively identify the supply chain risk source, predict the risk conduction path, and improve the financial risk control capability of the supply chain.

The invention provides a multi-source heterogeneous data integrated coronary heart disease accurate prediction method, which comprises the following steps: acquiring various physiological signals of a patient in real time, the physiological signals comprising ST segment change characteristics and basic cycle function parameters in electrocardiosignals, and obtaining a multi-source signal data set; according to the joint data set, analyzing instantaneous fluctuation characteristics of a vascular resistance index in a postprandial hyperlipemia window period, extracting a vascular resistance fluctuation amplitude from the instantaneous fluctuation characteristics, and if it is detected that the fluctuation amplitude exceeds a preset threshold range, marking the window as a high-risk time window; if the score value exceeds a preset threshold value, triggering a coronary heart disease early warning signal according to the comprehensive risk score value in combination with a feature mode of a coronary heart disease hidden period in historical data; and storing the current high-risk time window characteristics and the blood sugar and blood fat curve change trend through a triggered coronary heart disease early warning signal to obtain structured risk archive data.

The invention relates to a regional distributed photovoltaic power prediction method and system based on multivariate data cross-modal fusion, and belongs to the technical field of photovoltaic power prediction. Firstly, adaptive extraction of meteorological data spatial features and power data spatial heterogeneity is realized through a dynamic perception convolution kernel of AG-CNN, and a high-quality spatial basis is provided for cross-modal fusion; secondly, utilizing a two-dimensional dynamic attention mechanism of a DR-Transform to deeply mine cross-modal association of'historical power time sequence-future weather driving ', adapting to fusion requirements in different scenes through dynamic weight distribution, and capturing association evolution in a long period; thirdly, designing a cross-modal feature bridging module; and finally, verifying the robustness of the model in a typical scene, and ensuring that the model meets the precision requirement of multi-scale scheduling of the power grid.

Disclosed in the present invention is a deep learning-based power distribution network load prediction method, comprising: acquiring regional load data and renewable energy power generation data to form a data set, and preprocessing the data set to obtain a first data set; using a convolutional neural network to extract a time series feature in the first data set, and converting the time series feature into a data form of a deep learning model by means of an embedding layer to obtain one-dimensional time series data; performing fast Fourier transform on the one-dimensional time series data to obtain a frequency curve, extracting amplitude values on the frequency curve to calculate corresponding periods, and selecting the corresponding periods to slice the one-dimensional time series data and form same into two-dimensional matrixes; using a two-dimensional convolutional network to perform feature extraction, reshaping the two-dimensional matrixes that have undergone feature extraction into one-dimensional arrays, and performing adaptive fusion on the one-dimensional arrays to obtain a first time series feature; and inputting the first time series feature into a fully connected layer for weight calculation and linear transformation to obtain a power distribution network load prediction result, thereby improving the stability and reliability of electric power supply.

The invention provides a thyroid cancer auxiliary diagnosis and metastasis risk prediction method based on deep learning, and relates to the technical field of artificial intelligence auxiliary medical treatment, and the method comprises the steps: extracting ultrasonic image multi-scale features through a self-adaptive neural architecture search network, combining clinical examination data, fusing diagnosis and treatment knowledge through a neural symbol inference device, and carrying out the prediction of the metastasis risk. Generating a knowledge enhancement feature map; constructing a feature propagation field by using a dynamic neural field network, solving a dynamic evolution equation, and generating a spatial-temporal feature field representing the dynamic change of focus features; constructing a tumor diffusion kinetic model by using an implicit neural representation network and a nerve ordinary differential equation network, calculating a transition probability based on an optimal transmission algorithm, solving an optimal control equation, and outputting a metastasis risk prediction result of each organ; the thyroid cancer diagnosis accuracy and metastasis risk prediction reliability can be effectively improved, and doctors can be assisted in accurate diagnosis and treatment.

A rock mass mechanical parameter prediction method based on an underground powerhouse of a pumped storage power station relates to the technical field of mechanical parameter prediction, and comprises the following steps: identifying an embedding position and distribution characteristics of a sulfur-containing shale interlayer by constructing a three-dimensional geologic structure model and lithologic information of a construction area, and establishing a structural domain partition parameter prediction area system; constructing a nonlinear regression function model taking infrared spectrum characteristic factors and environment control parameters as input and taking elasticity modulus and the like as output by combining infrared spectrum characteristic data and a historical evolution track; the model is embedded into a regional system for dynamic assignment, and a parameter space-time change atlas is constructed; the laser point cloud data and the micro-seismic monitoring data are fused to invert the actual response of the surrounding rock; when the monitoring value deviates from the prediction map, triggering parameter degradation function re-calibration to complete parameter dynamic updating and prediction closed loop; according to the method, the accuracy of rock mass mechanical parameter prediction of the pumped storage power station underground powerhouse can be improved.

The invention discloses a low-voltage cabinet fault prediction method and system based on artificial intelligence, and relates to the technical field of low-voltage electrical equipment state monitoring, and the method comprises the steps: obtaining operation data, such as current and voltage, collected by a sensor in a low-voltage cabinet; a multi-dimensional tensor structure containing spatial topological codes is constructed through preprocessing such as normalization; inputting a fault prediction model to obtain fault type probability and trend evaluation parameters, and generating a dominant abnormal factor index set through an interpretable module; analyzing a root cause path by using a fault causal knowledge graph in combination with the data, and determining a fault evolution chain; and finally generating interpretable early warning prediction information. The technical problems that a traditional low-voltage cabinet fault detection mode is difficult to process multi-dimensional complex data, cannot efficiently mine key fault information and is difficult to accurately predict are solved, the prediction model is constructed by using the deep learning technology, and then efficient processing of the multi-dimensional complex data of the low-voltage cabinet and deep mining of the key fault information are achieved. The technical effect of accurate fault prediction is achieved.

The invention relates to the technical field of photovoltaic prediction, in particular to a distributed photovoltaic power prediction method and system based on high-dimensional gridding numerical weather forecast, and the method comprises the steps: carrying out the standardization of the numerical weather forecast data and photovoltaic power historical data of a target region, and achieving the time-space alignment based on a preset grid, generating a gridding data set; utilizing convolution processing to extract local space features, and converting and fusing the local space features into a feature sequence containing space and historical time sequence information at the same time; modeling is carried out through an encoder-decoder architecture, an encoder excavates historical power dependence, and a decoder dynamically couples future meteorological characteristics with historical power through an attention mechanism and outputs a grid-level predicted value; aggregating to obtain a system total power prediction result; by establishing a unified space-time grid, refined alignment of data is realized, cross-space-time dynamic fusion is performed in combination with convolution and an attention mechanism, and prediction precision and stability can be kept in complex weather.

The invention discloses a sewage treatment plant effluent prediction method based on multi-task learning. The method comprises the following steps: acquiring sewage treatment data; based on the sewage treatment data, establishing an effluent prediction model; the input of the effluent prediction model is inflow water quality data, process data, environmental data and sewage treatment unit data, and the output of the effluent prediction model is predicted effluent index data; predicting the water outlet index data in future time based on the water outlet prediction model and the input of the water outlet prediction model; according to the method, the water outlet prediction model is constructed in combination with multi-task learning, and the model can comprehensively consider the time sequence dependence and mutual influence relationship among the input data to perform prediction, so that the calculation redundancy is reduced, and the prediction demand that an actual process needs to cooperatively consider multiple targets is met; the effluent quality prediction precision and the process regulation and control efficiency are remarkably improved, and a solid foundation is laid for promoting intelligence of operation management of a sewage treatment plant.

The invention discloses a photovoltaic module service life prediction method and a health management system. The life prediction method comprises the following steps: acquiring degradation data of a photovoltaic module, and segmenting the degradation data based on a sliding window; reconstructing the degradation data in each sliding window in combination with Kalman filtering and an RTS smoothing algorithm to obtain a degradation track; according to the degradation track, based on a degradation model and a preset failure threshold value, probability distribution of the remaining life of the photovoltaic module is determined; wherein the degradation model is constructed based on a Wiener process, and posterior distribution of parameters is estimated by using a variational inference method. According to the invention, accurate evaluation of the running state of the photovoltaic module and high-precision prediction of the residual life can be realized, so that reliable support is provided for intelligent operation and maintenance and predictive maintenance of a photovoltaic power station.

The invention provides a user behavior prediction method and system applied to an online marketing service platform, and the method comprises the steps: firstly obtaining a multi-scene user interaction record set which comprises browsing, searching, collecting and ordering interaction records in the online marketing service platform of a user; constructing a scene behavior association mapping table for recording user interaction behavior triggering association relationships in different marketing scenes, and performing dynamic tendency evolution modeling based on the scene behavior association mapping table to obtain a user behavior tendency evolution model; according to the method, a user behavior tendency evolution model is established, marketing scene behavior prediction adaptation is performed according to the user behavior tendency evolution model to obtain a user behavior prediction scheme, and finally the user behavior prediction scheme is transmitted to a platform marketing decision module to generate marketing strategy execution guidance, so that user behaviors can be comprehensively and accurately predicted, and the platform marketing accuracy is improved.

The invention discloses a continuous time dynamics prediction method, system, terminal and medium fusing a diffusion model and a graph frequent differential equation, and relates to the technical field of dynamics prediction.The method comprises the steps that a multi-node time sequence is obtained, network structure inference is conducted on the multi-node time sequence through the diffusion model, and a potential graph structure between nodes is obtained; carrying out continuous time dynamic modeling by adopting a Scheng ordinary differential equation, and predicting a node state at any time point; diffusion reconstruction loss, dynamic prediction errors and structure sparsity constraints are constructed, total loss is established, network structure inference based on a diffusion model and dynamic modeling based on a Shenzheng differential equation are coupled based on the total loss, and collaborative training optimization is achieved. According to the method, the potential graph structure of the system can be stably recovered in a complex noise environment, high-precision and continuous prediction can be carried out on dynamic evolution of the potential graph structure, and the limitation that structure inference and continuous time modeling cannot be considered in the prior art is overcome.

The invention discloses a cascade system online monitoring and prediction method based on a sparse self-attention mechanism, and the method comprises the steps: S1, obtaining and preprocessing multi-working-condition data of a cascade system, and constructing a time series data set; s2, performing embedded conversion and position coding on the data, and capturing sequence position information; s3, pre-fusion of adjacent time step information is realized through one-dimensional convolution; s4, a multi-head sparse attention mechanism is introduced, and a ReLU2 activation function is adopted to replace softmax so as to reduce calculation overhead; s5, completing information fusion through one-dimensional convolution, ELU activation and maximum pooling; s6, constructing an encoder containing a multi-head sparse attention mechanism; s7, designing an autoregressive decoder, and combining self-attention with cross attention; s8, adopting a HuberLoss loss function to train the model; and S9, carrying out reverse normalization on the model output to obtain a final prediction value. According to the invention, by optimizing the Transform architecture, 60 s effective prediction of the key parameters of the cascade system is realized, the prediction error is significantly reduced, and the intelligent early warning capability and the operation stability of the system are improved.

The invention discloses an aircraft aerodynamic parameter prediction method based on multi-view airfoil profile concept correlation learning, and belongs to the technical field of aerodynamics, deep learning and computer vision crossing, and the method comprises the steps: converting a three-dimensional model into a multi-view image sequence through a hybrid projection strategy combining orthogonality and surround view; utilizing an optimal transmission theory to map visual features to a learnable airfoil profile concept space to realize semantization and discretization of the features; in combination with flow field working condition coding, aerodynamic parameters are regressed under the constraint of aerodynamic physical laws; and finally, by displaying the matching relationship between the input sample and the airfoil profile concept, the inherent interpretability based on the instance is provided.

Disclosed are an ultra-short-term wind power forecasting method and system, relating to the technical field of artificial intelligence. The method comprises: obtaining an original dataset of a wind farm, processing the original dataset, and performing training on the basis of processed original data; decomposing wind speed data in the trained original data, calculating each decomposition component, and constructing a feature matrix on the basis of the calculation results; and introducing a residual attention mechanism to reconstruct the feature matrix, using the reconstructed result to establish a network model, performing secondary training, and forecasting ultra-short-term wind power. The present invention improves the accuracy and reliability of ultra-short-term wind power forecasting and achieves significant advances in algorithm optimization, thereby providing effective support for the stable power supply of renewable energy sources such as wind farms and for power grid operation.

The invention relates to the technical field of blood glucose prediction, in particular to a blood glucose fluctuation prediction method based on historical data. The method comprises the following steps: acquiring corresponding historical blood glucose monitoring data, daily behavior data, physiological status data and medical intervention data of a user, performing time axis alignment and abnormal value cleaning treatment, and extracting basic features and associated influence features corresponding to blood glucose fluctuation; performing space-time dimension fusion on the basic features and the associated influence features, and generating a blood glucose fluctuation prediction curve of the user in a future preset time period; performing comparative analysis on the blood glucose fluctuation prediction curve and actual blood glucose monitoring data of the user, calculating a prediction deviation degree and a trend goodness of fit, and generating a corresponding deviation analysis result; feature weight distribution of the dynamic prediction model is optimized based on the deviation analysis result, and a personalized blood glucose fluctuation prediction report is generated and comprises a risk early warning threshold value and an intervention suggestion triggering condition. According to the method, prediction efficiency and personalized management of blood glucose fluctuation can be realized.

The invention provides a cell growth dynamics prediction method and device based on a space-time image sequence, and belongs to the technical field of computer vision and deep learning. The method comprises the following steps: acquiring a multi-focal-plane space-time image sequence containing a cell complete growth process; preprocessing and normalizing images in the image sequence to obtain a final test image sequence; and inputting the test image sequence into a cell growth dynamics prediction model consisting of a multi-scale feature extraction module and a time sequence modeling module which are connected in sequence to obtain a cell growth stage identification result corresponding to each time point image in the sequence so as to realize cell growth dynamics prediction. The method can effectively solve the problems of poor adaptability, insufficient stability, high labeling cost and the like when a traditional model is used for processing the dynamic medical image sequence, and has remarkable advantages and application prospects in the aspect of dynamic prediction of the cell growth space-time image sequence.

The invention provides a cardiovascular trend prediction method and system based on time sequence medical health data, and relates to the technical field of medical health data analysis. The method comprises the following steps: collecting time sequence medical health data of a patient through a multi-source sensor, wherein the time sequence medical health data comprises dynamic physiological indexes such as heart rate, blood pressure and oxyhemoglobin saturation; performing time calibration and feature extraction on the acquired multi-source data; constructing a time sequence feature model based on the time sequence features, and fusing the time sequence feature model with the static information of the patient to generate a comprehensive feature vector; using a deep learning model to train historical data, and learning a dynamic change rule of cardiovascular health indexes; predicting the change trend of the cardiovascular health indexes in real time, and evaluating the risk level of cardiovascular diseases; the model parameters are optimized through online learning, the prediction precision is improved, the change trend of cardiovascular health indexes can be predicted in real time, and support is provided for early discovery and personalized medical treatment of cardiovascular diseases.

The invention discloses an aero-engine combustion chamber ignition state prediction method and system based on deep learning. The method comprises the steps that a combustion chamber ignition parameter data set is collected; analyzing sample distribution to determine an augmentation amount, generating a new sample based on KNN interpolation, injecting adaptive Gaussian noise, and referring to physical relevance between a fuel-air ratio and a temperature-pressure ratio of a real sample during augmentation of a special working condition; an AttResVGG deep neural network is constructed, gradient disappearance is prevented by adopting residual connection, a multi-head self-attention mechanism is embedded to capture a long-range dependency relationship among parameters, and multi-scale features are integrated through a global feature fusion module; double classifier training is designed, a main classifier adopts category weighted cross entropy loss, an auxiliary classifier adopts label smooth loss, and model parameters are jointly optimized; and inputting working condition parameters for real-time prediction. According to the invention, parameter dependence of a traditional empirical model is broken through, and adaptive prediction of complex working conditions is realized; the calculation time consumption is reduced, and the real-time decision demand is met; and the device adapts to various combustion chamber structures, and re-modeling is not needed.

The invention provides a TLF-GV signal correlation earthquake magnitude prediction method and system, and a medium, belongs to the technical field of earthquake prediction, and aims at solving the problems that a traditional magnitude prediction model lacks physical constraints, TLF-GV features are not fully utilized, the interpretability is poor, and cooperation with a preorder technology is lacked. Comprising the following steps: extracting a multi-source feature vector of a TLF-GV signal containing a gravity component absolute amplitude, a vibration component relative amplitude and a signal phase; based on the G-R law, constructing the positive correlation physical characteristics log10 (GVCIpeak) and BF * Tanomal of the abnormal intensity and the magnitude of the TLF-GV signal; weighting the features according to a Bayesian factor BF value; fitting according to historical samples to obtain a physical empirical formula of positive correlation of GVCI peak logarithm and magnitude as a physical constraint, and constructing a loss function by taking the physical constraint as a regularization item and combining with a mean square error of a magnitude prediction task; and constructing an XGBoost model, and training according to the training set to obtain a prediction model for earthquake magnitude prediction. According to the method, quantitative prediction of earthquake magnitude is realized based on multi-source features of TLF-GV signals in combination with a physically constrained XGBoost regression model.

The invention discloses a physical-data dual-drive bench blasting effect prediction method in the technical field of blasting construction and intelligent mining, which comprises the following steps of: dynamically calibrating a fractal dimension field, and constructing a rock blasting classification model by combining blasting indexes such as fractal gradient, rock density, uniaxial compressive strength and drilling speed; performing three-dimensional reconstruction on the geometric morphology of the step before blasting by using an unmanned aerial vehicle high-precision modeling technology, and importing a finite element simulation model to predict a key blasting effect; collecting an actual blasting effect after blasting, and correcting parameters of the numerical simulation prediction model; and constructing a data set of actual lumpiness distribution of the muck pile, establishing a mapping relation between the rock detonability grade and the charge density, and optimizing the differential time sequence and the single-hole charge amount. According to the invention, through fractal dimension field dynamic mapping and real-time feedback control, accurate adaptation of blasting energy and a rock mass structure is realized.

The invention discloses an intelligent fishing point dynamic prediction method and system based on multi-source marine environment data fusion. The method comprises the following steps: step 1, access, space-time alignment and pre-screening of multi-source heterogeneous marine environment data; step 2, priori knowledge base construction and suitability modeling based on target fish ecological habits; 3, constructing a fishing point prediction model fusing the multi-time-sequence environmental characteristics and deep learning; step 4, fusing two-channel prediction results under the Bayesian framework and quantifying uncertainty; 5, generating a dynamic mask of a real-time sea condition safety threshold value and fishery regulation space constraint; 6.1, constructing a comprehensive scoring function of the risk perception function. According to the method, multi-source heterogeneous data is constructed, a target fish ecological suitability model and a depth time sequence prediction model are combined, the fishing point posterior probability is generated through a Bayesian fusion mechanism, the prediction uncertainty is quantified, and dynamic fishing point recommendation with risk perception and compliance safety is realized.

The invention discloses an equipment fault prediction method in a power transmission and transformation system, and relates to the technical field of power system operation and maintenance, and the fault prediction method comprises the following steps: data collection: collecting the data of power transmission and transformation equipment through a sensor network, and forming a multi-source heterogeneous data set; data preprocessing: performing abnormal value elimination, missing value filling and standardization processing on the multi-source heterogeneous data set; feature extraction: mining associated features and time-space evolution laws of data of different dimensions by adopting a multi-modal fusion network based on an attention mechanism; performing fault prediction, and constructing a dynamic threshold early warning model in combination with an equipment aging curve; and correcting the result to generate a final fault prediction report. The equipment fault prediction method in the power transmission and transformation system solves the problems that traditional prediction depends on single data, the fixed threshold value false alarm is high, and the advance is insufficient, improves the fault prediction accuracy and reliability, and is suitable for operation and maintenance early warning of power transmission and transformation equipment such as transformers and circuit breakers.

The invention discloses an NAND block health degree prediction method and system based on read interference perception. The method comprises the steps that read interference event counts of an NAND flash memory block are collected in real time; dynamically triggering multi-dimensional parameter acquisition to generate multi-dimensional parameter data with timestamps; extracting a dynamic change rate, a distribution entropy value and a growth slope feature based on the multi-dimensional parameters, and generating a compression feature matrix; inputting the compressed feature matrix into a pre-trained graph neural network-Hamiltonian Monte Carlo hybrid model, outputting a health degree score and recording a low-confidence sample; based on the health degree score and the historical health degree attenuation trajectory, generating an early warning level signal through a dynamic threshold engine; executing a corresponding block maintenance strategy according to the early warning level, and recording strategy execution effect data; and model parameters are updated through knowledge distillation by utilizing a low-confidence sample and strategy execution effect data, so that level-by-level and cross-level health risks of the NAND flash memory based on read interference perception are truly and accurately reflected.