1047 results about "Data Noise" patented technology

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

The invention discloses an intelligent supervision system and method for environmental monitoring quality control, and particularly relates to the technical field of environmental monitoring, after environmental monitoring data is collected in real time through multiple types of sensors and formatted, the system analyzes fluctuation conditions of various types of data and identifies abnormal fluctuation hidden dangers; if the hidden danger is found, performing drift verification on the corresponding sensor, extracting noise and drift characteristics, and generating a data noise index and an accumulated drift index; based on the indexes, using fuzzy logic to divide the sensors into three classes of serious drifting, moderate drifting and low horizontal drifting; performing correction analysis according to a division result, and selecting local or full-category calibration operation to optimize the quality of the monitoring data; by introducing an intelligent evaluation mechanism, the drifting condition and the data noise level of each sensor can be monitored and analyzed in real time, which sensors need to be calibrated can be dynamically judged, local or full-category calibration is selected according to actual conditions, and long-term accuracy and reliability of environmental monitoring data are ensured.

The invention relates to the technical field of intelligent power distribution networks, and provides a fault self-healing control method for preventing override trip of an intelligent power distribution network. The method comprises the following steps: S1, acquiring data of key nodes of a power distribution network in real time through multiple sensors, and performing fusion processing on edge computing nodes to realize anomaly detection and preliminary fault classification; and S2, performing fault trend prediction on the operation state of the power distribution network based on the deep learning model to obtain a fault risk score. By adopting the technical scheme of multi-sensor real-time data acquisition and edge computing node fusion processing, the technical effects of early fault detection and preliminary fault classification are realized; according to the method, the accuracy and response speed of fault detection are improved through multi-source data fusion and edge calculation, and the problems of inaccurate detection and untimely response caused by data noise and delay in the prior art are solved.

The invention discloses a multi-dimensional information integrated transformer health state monitoring method, which relates to the technical field of power systems, and comprises the following steps: deploying a multi-source sensor, collecting various signals of a transformer, and carrying out signal conditioning and analog-to-digital conversion on analog signals collected by the sensor; performing timestamp alignment on the received multi-source heterogeneous data, and eliminating data noise by adopting a filtering algorithm; key features which are sensitive to the health state of the transformer and complement each other are selected from the extracted feature values to form a health state evaluation index set; setting a sliding time window, obtaining historical data in the window, performing normalization processing on the data, calculating the information entropy and the variation coefficient of each index, and fusing the information entropy and the variation coefficient to obtain the dynamic weight of each index; obtaining a health state index of the transformer according to the normalized value and the dynamic weight; and displaying the health state index and the change trend thereof in real time, and setting an early warning threshold value and an alarm threshold value of the health state index.

The invention discloses a laboratory detection data processing method and system based on a machine learning technology, and relates to the technical field of data processing. According to the method, the signal is decomposed through discrete wavelet transform, the noise standard deviation is calculated based on the median of the highest frequency coefficient, the high signal-to-noise ratio data is reconstructed through the dynamic threshold and the soft threshold function, and the signal quality is improved; a sliding window is used for extracting time sequence signal statistics and FFT frequency domain features, spatial features are extracted in combination with an SIFT algorithm, high-correlation features are reserved through mutual information screening, and redundancy is reduced; constructing a graph convolutional network anomaly detection model and an XGBoost-LightGBM weighted regression model, and eliminating pollution data through an anomaly probability threshold to obtain a normal regression predicted value; predicting a compensation amount according to the environmental parameters by using an LSTM network, and obtaining calibration laboratory data according to the normal regression predicted value and the predicted compensation amount; according to the method, the problems of noise sensitivity, feature splitting, model isolation and environment drifting of multi-source data are solved, and the laboratory analysis precision and robustness are remarkably improved.

The invention relates to the technical field of transformer substation intelligent management, in particular to a modular prefabricated cabin transformer substation intelligent comprehensive management system with a multi-source data integration and collaborative management function. Comprising a multi-source data acquisition unit; the edge intelligent processing unit is used for carrying out local preprocessing, feature extraction and abnormal early warning on the acquired multi-source heterogeneous data of the transformer substation, and realizing data noise reduction, abnormal recognition and equipment health degree evaluation through a lightweight AI algorithm and a hybrid communication protocol carried by the edge computing terminal module; and a data integration collaboration unit. According to the invention, through an adaptive sampling strategy and a multi-modal feature fusion mechanism of the multi-source data acquisition unit, multi-dimensional data such as electric power parameters, power environment, fire protection and security protection and the like are incorporated into a monitoring system, feature layer association analysis is realized by depending on a Jousseme distance and a Dempster synthesis rule, the assessment limitation of single equipment and single-dimensional data in a traditional scheme is broken through, and the method has the advantages of high reliability and high reliability. And a more comprehensive state basis is provided for equipment health degree research and judgment.

The invention relates to the technical field of engine cylinder surface defect detection, and discloses an engine cylinder surface defect detection method and system. The method comprises the following steps: acquiring three-dimensional height information of cylinder surface defect points based on a laser displacement sensor, identifying surface defects by combining an edge detection algorithm with a standard template library, calculating geometrical characteristics of the defects by fitting a polynomial and a least square method, and finally generating a defect report and feeding back the defect report to a central control system. Compared with a detection method in the prior art that fine defects on a complex curved surface are difficult to accurately identify, and especially in a data environment with large noise, accurate detection of micron-sized defects cannot be realized, the detection method has the advantages that noise is filtered through a high-precision laser displacement sensor and Fourier transform, so that the detection accuracy is improved, and the detection efficiency is improved. Precise detection and identification of surface fine defects are realized, so that the problems of missing detection and false detection in the prior art are avoided, and the detection precision and the production efficiency are improved.

The invention provides a construction site safety information supervision system based on the Internet of Things, and relates to the technical field of data processing, the risk assessment accuracy is remarkably improved and the data noise interference is inhibited through a deep reinforcement learning dynamic optimization grading early warning strategy and in combination with a multi-source data compression and federated learning framework; a risk coupling model is constructed based on a complex network theory, a conduction path and a linkage effect between regions are analyzed, a global early warning scheme is optimized, and the adaptability of the system to a complex construction environment is enhanced; the model is continuously optimized through a dynamic weight updating mechanism, high adaptability is kept under the environment change of a construction site, meanwhile, a resource dynamic allocation strategy is adopted, the problem of low high-concurrency processing efficiency is effectively solved, and the overall accident early warning accuracy is improved.

The invention discloses an underwater acoustic signal denoising method based on a time-frequency adaptive dual-path Conformer network, and the method comprises the steps: carrying out the preprocessing of real marine environment noise and underwater acoustic target signals, and generating a multi-signal-to-noise-ratio noisy data set; performing short-time Fourier transform on the noisy data, and extracting real part and imaginary part features to form a feature tensor; extracting features by using a feature encoder, and generating intermediate feature representation; respectively extracting a time path feature and a frequency path feature through a time-frequency adaptive dual-path Conformer network; performing multi-scale convolution processing and weighted fusion on the extracted time-frequency features by adopting a multi-scale fusion dynamic gating network; performing nonlinear mapping on the fused features by using a feature decoder to generate a mask matrix; and restoring the complex frequency spectrum based on the mask matrix, and restoring the denoised underwater acoustic time domain signal. According to the method, time-frequency information is fully mined in combination with underwater sound noise characteristics, and the underwater sound signal denoising effect is improved.

The invention discloses a physics-based method for discovering a control equation from scarce and noise data. According to the method, a physical information neural network and a sparse regression method are combined, and a partial differential control equation of a dynamic system is found from scarce and noisy data. Firstly, the size of a candidate function library is effectively reduced through a dimension verification method. And then modeling a physical system and calculating a candidate function by using the strong nonlinear fitting capability and automatic differential characteristic of the deep neural network. And finally, the form of a control equation and the coefficient of the equation can be obtained through sparse regression, and the coefficient is finely adjusted through DNN. The method not only can discover the control equation from the data, but also can obtain the network model to realize the response prediction of the dynamic system. The method is easy to implement, efficient, high in precision and high in universality. The method can be widely applied to physical knowledge mining, modeling and reasoning of a complex dynamic system.

The invention discloses a transcriptomics spatial domain identification method, and belongs to the technical field of transcriptomics. The objective of the invention is to solve the problems of low data noise reduction precision and poor recognition effect of an existing spatial transcriptional spatial domain recognition method. The method comprises the following steps: firstly, obtaining an undirected neighborhood graph according to a gene expression matrix, obtaining embedded representation of the gene expression matrix by utilizing an encoder, obtaining a corresponding reconstruction matrix by utilizing a decoder, and further determining reconstruction loss; meanwhile, a ZINB model is used for fitting a reconstruction matrix, and a ZINB loss function is obtained; then, an augmented graph is constructed based on the undirected neighborhood graph, respective embedded matrixes are obtained through an encoder, the comparison loss of the undirected neighborhood graph and the comparison loss of the augmented graph are obtained through a comparison representation learning mechanism, and then the neighbor comparison loss is obtained; total target loss is obtained based on all losses, a joint optimization strategy is adopted for training, and after training of the whole model is completed, dimensionality reduction and spatial domain recognition are carried out on a generated reconstruction matrix.

The invention discloses an intelligent health management system and method based on edge calculation and reinforcement learning. The system comprises an old people data acquisition module, an edge calculation module, a multi-modal fusion module, a reinforcement learning intervention module and a block chain evidence storage module. Physiological and environmental data are collected through a wearable device, a millimeter-wave radar, a pressure sensor and the like, data noise reduction and anomaly detection are realized at an edge end, a cloud end adopts a CNN-LSTM neural network to fuse multi-modal features, reinforcement learning is combined to dynamically generate personalized intervention actions, intervention behaviors and results are stored through a block chain technology, and the intervention effect is improved. And safe and credible strategy optimization is realized. According to the invention, continuous monitoring and intelligent intervention of the health state of the old people can be realized, and intelligence and credibility of community health management are improved.

The invention provides a sensor anomaly detection method and device based on a long short-term memory network, electronic equipment and a medium, which are applied to the technical field of fault detection, and effectively reduce data noise and improve the input quality of a model by performing preprocessing of null value removal and feature screening on acquired sensor data. Inputting the preprocessed sensor data into an improved attention mechanism-long and short-term memory network model, capturing a long-term dependency relationship of a time sequence by utilizing a long and short-term memory network, and dynamically distributing weights and focusing on key information by virtue of an attention mechanism, so that the model prediction precision is remarkably improved; and finally, the prediction result is compared with historical data and real-time data, so that the sensor abnormality is accurately judged, the defects of a traditional method in processing complex time sequence data are effectively overcome, the accuracy and reliability of abnormality detection are remarkably improved, and meanwhile, the method has prediction capability and adaptivity and is suitable for popularization and application. And characteristic differences of different sensors can be adapted.

A multi-modal data fusion-based rotor fault diagnosis task-oriented method comprises the following steps of S1, removing original vibration data noise, and converting de-noised vibration data into a recurrence plot; s2, dividing the de-noised vibration data and the corresponding recurrence plot into a training set and a test set; s3, pre-training a time domain feature extraction module and a recursive feature extraction module by using the training set; s4, extracting time domain features and recursive features of the training set, and training a multi-modal feature fusion and fault diagnosis module; s5, inputting the vibration signals of the test set and the corresponding recursion plots into a time domain feature extraction module and a recursion feature extraction module respectively, and extracting time domain features and recursion features; s6, inputting the features extracted in S5 into a multi-modal feature fusion and fault diagnosis module, and outputting a fault diagnosis result; according to the invention, rotor fault diagnosis is carried out by using the multi-modal data of the fault vibration signal time domain and recursion characteristics, and the method has the advantages of high feasibility, high precision and the like.

The embodiment of the invention provides a hail identification and prediction method based on multi-source meteorological data fusion and an attention mechanism. The method is applied to the technical field of meteorology and artificial intelligence processing, and comprises the following steps: collecting dual-polarization radar data, satellite remote sensing data and ground meteorological observation data, and carrying out time sequence alignment, data standardization and feature splicing processing; extracting physical mechanism features and statistical texture features of hail clouds from the dual-polarization radar data, the satellite remote sensing data and the ground meteorological observation data; extracting multiple spatio-temporal features of the hail cloud by using a spatio-temporal feature extraction network; and inputting real-time observation data into the trained spatial-temporal feature extraction network, and outputting a hail occurrence probability, a nuclear region position and intensity grade distribution. In this way, the technical problems that in the prior art, multi-modal data noise, inconsistency and insufficient time-space feature capture are caused, and the precision and real-time performance of hail recognition are limited can be solved.

The invention discloses a data processing and quality evaluation method for efficiency test data of an electric drive system. The method comprises the following steps: acquiring an original electric drive efficiency test data set; performing noise reduction on the original electric driving efficiency test data set through a probability distribution-based data noise reduction method to obtain a noise-reduced electric driving efficiency test data set; carrying out the first abnormity detection and processing of the noise reduction electric drive efficiency test data set through a DBSCAN model, and obtaining a preliminary screening electric drive efficiency test data set; carrying out the second anomaly detection and processing of the preliminary screening electric drive efficiency test data set through an LOF algorithm model and an iForest algorithm model, and obtaining a preprocessed electric drive efficiency test data set; and performing quality evaluation on the preprocessed electric drive efficiency test data set through the constructed six-tuple data quality model. According to the invention, effective noise reduction and abnormity detection can be carried out on the efficiency test data of the electric drive system, so that the data quality is improved.

The invention discloses an information transmission attack event intelligent prediction model training optimization method, which comprises the steps of a data preparation stage, a feature engineering stage, a model selection and construction stage, a model training stage and a model evaluation and optimization stage, and is characterized in that data are collected from multiple channels, noise is cleaned and removed, missing values and abnormal values are processed, and accurate labeling is carried out; then key features are extracted, redundant irrelevant features are removed through selection, standardized transformation is carried out, a proper model is selected based on data features, an integrated learning combination model is adopted, then a training set, a verification set and a test set are divided, a proper optimizer is selected, parameters are adjusted to be over, training is carried out through the training set, and over-fitting is prevented through the verification set; and finally, using index evaluation of accuracy and recall rate, and adjusting hyper-parameters and continuous optimization of the model structure through cross validation. According to the method, data collection, data imbalance processing and data noise interference processing are adopted, data diversity is improved, and interference of data noise is reduced.

The invention discloses an AI model training data protection method and device based on local differential privacy. The method comprises the steps of noise scale determination, wherein the noise scale is determined through a Laplace differential privacy mechanism according to a preset privacy budget value and obtained input data; a data noise adding step: generating Laplacian noise according to the noise scale, and adding the Laplacian noise to the input data; a gradient noise adding step: determining the gradient of each data point in the input data after the Laplacian noise is added according to a preset initial model training parameter, and adding the Laplacian noise to the gradient of each data point to obtain a noise-added gradient value; and a parameter determination step: determining a prediction model training parameter according to the noise-added gradient value. According to the invention, the accuracy and efficiency of model training can be improved under the condition of effectively avoiding privacy leakage of training data.

The invention discloses a water quality monitoring system and method, and relates to the technical field of water quality monitoring, the system comprises a water quality data acquisition unit used for acquiring water quality parameter information; the data noise reduction unit is used for constructing a characteristic component matrix group according to the water quality parameter information and generating water quality characteristic sequence data through threshold filtering and matrix reconstruction; and the abnormal data monitoring unit is used for acquiring a water quality linkage sensitive factor and a water quality unconventional fluctuation index based on the water quality characteristic sequence data, calculating a water quality reference weight of each water quality characteristic sequence, and analyzing a water quality abnormal degree in combination with an abnormal score. Based on fusion analysis of the water quality reference weight and the anomaly detection score, the efficiency and reliability of water quality monitoring are improved.

Embodiments of the present application provide a video generation method and apparatus, a device, and a medium. The method can be applied to the technical field of video content generation, and is used for improving the video generation quality. The method comprises: acquiring a sample video frame sequence from a sample video, determining a first step count and sample original noise, and performing data noise addition processing on the sample video frame sequence to obtain video input data; inputting the video input data, a sample text encoded feature corresponding to sample description text, and first embedding information corresponding to the first step count into an initial generation model; and performing noise prediction on the sample video frame sequence by means of M spatiotemporal residual components and M spatiotemporal attention components in the initial generation model to obtain sample predicted noise, correcting a network parameter in the initial generation model on the basis of the sample original noise and the sample predicted noise, and determining the initial generation model comprising the corrected network parameter as a video generation model.

The invention proposes a hard negative sampling comparison recommendation method based on a graph generation diffusion model, and the method comprises the following steps: coding a user-article interaction graph through a graph convolutional neural network, obtaining a node embedded vector, and carrying out the inner product of the node embedded vector, and obtaining a recommendation probability; the model parameters of the graph convolutional neural network are obtained through the following training steps: S1, generating a comparison view 1 and a comparison view 2 from the node embedding vector through an inline diffusion model view generator and a parameter topological noise reduction view generator; the inline diffusion model view generator comprises an inline diffusion model, and the parameter topological noise reduction view generator comprises a parameter topological noise reduction network; and S2, carrying out comparative learning on the comparative view 1 and the comparative view 2. According to the method, a signal alignment strategy is combined, a double-view generator comparison learning framework is built, and the problems that the model generation capacity is insufficient and the influence of data noise is large are solved, so that the performance of a recommendation system is improved.

The invention relates to the technical field of man-machine interaction, and discloses an LED display screen interaction method and an LED display screen interaction system.The method comprises the following steps that user interaction data are collected through a multi-sensor module, and the user interaction data comprise visual data, touch data, infrared data and environment data; preprocessing the collected multi-modal user interaction data, including data noise reduction, feature extraction and unified coding; performing time sequence modeling on the multi-modal user interaction data based on the dynamic data fusion model to generate a fusion state; and identifying the user interaction intention based on the user interaction intention prediction model. Through the technical scheme of combining multi-modal data fusion and deep learning, all-around acquisition and comprehensive perception of user interaction information are realized by collecting visual, touch, infrared and environmental data of a user, and the defects of limited data dimension, poor environmental adaptability and insufficient interaction precision in single-modal interaction are overcome.

The invention provides a crop planting environment data acquisition and real-time optimization device integrated with the Internet of Things technology, which comprises a data acquisition module, a data cleaning and feature extraction module, an environment optimization strategy generation module, a decision support and execution module, an intelligent learning and optimization module and a monitoring and data visualization module, relates to the field of communication and data processing. The environment data is collected in real time through the distributed Internet of Things equipment, the planting area is fully covered, the problem of insufficient data acquisition in the prior art is solved, and a reliable data source is provided for subsequent optimization. The real-time performance and comprehensiveness of data acquisition are improved, and the accuracy of basic data optimization is ensured. An adaptive signal enhancement model is introduced, complex interference is effectively identified and eliminated, the intensity of weak signals is enhanced, and the problem that the optimization effect is reduced due to data noise in the prior art is solved. The precision and efficiency of data processing are improved, and high-quality input is provided for an optimization model.

The invention relates to the field of knowledge base construction, and discloses a knowledge base construction method for assisting digital employee question answering, and the method comprises the following steps: collecting internal multi-modal data of an enterprise, including document data, voice data and metadata; carrying out information amount analysis on the collected data, and screening high-value data through an information entropy theory; performing interactive verification on the screened multi-modal data to analyze data consistency; dynamically correcting the verified data, and reducing data noise in combination with a prediction model and an observation model; and performing semantic extraction and clustering optimization on the corrected data to generate knowledge entries. Through the closed-loop process of multi-modal data collection, dynamic correction, semantic extraction, distributed updating and intelligent question and answer, the data processing efficiency, the knowledge base quality and the question and answer accuracy are remarkably improved, and the problems that in the prior art, unstructured data processing efficiency is low, updating is lagged, and dynamic optimization capacity is lacked are solved.

The invention relates to the field of power distribution network cable main insulation fault detection and management, and discloses a power distribution network cable main insulation fault detection and management integrated test platform construction method comprising the following steps: a) data acquisition: installing a plurality of sensors including a partial discharge sensor, a current and voltage sensor, a temperature sensor and a vibration sensor; acquiring real-time operation data of the power distribution network cable; b) data transmission: transmitting the acquired cable monitoring data to a data processing system in a wired or wireless communication mode; and c) data preprocessing: denoising, missing value interpolation, standardization or normalization processing is carried out on the transmitted original data so as to improve the quality of the data. The real-time operation state of the power distribution network cable is monitored through various sensors, and various advanced data processing methods are combined, so that the accuracy of fault detection is effectively improved, and identification deviation caused by data noise or sensor errors in a traditional fault detection method is avoided.

The invention provides an aircraft fault early warning method and system based on flight data, and relates to the technical field of aircraft fault early warning. The method comprises the following steps: firstly, mining out an aircraft external data semantic vector and an aircraft internal data semantic vector corresponding to flight data to be processed; secondly, performing semantic enhancement operation on a pre-generated flight data noise vector based on an aircraft external data semantic vector, and outputting a flight data enhancement vector; then, according to the aircraft internal data semantic vector, mining an aircraft internal depth vector; and finally, according to the flight data reinforcement vector, carrying out association mining operation on the aircraft internal depth vector, outputting a target flight data semantic vector, and according to the target flight data semantic vector, analyzing aircraft fault early warning information. On the basis of the content, the problem that in the prior art, the reliability of aircraft fault early warning is relatively low can be solved.

The invention discloses a laser engraving machine temperature compensation control system based on multi-source temperature data fusion, and the system comprises a temperature collection module which comprises a plurality of temperature sensors disposed at key parts of a laser engraving machine and is used for obtaining the overall temperature field distribution data; the algorithm module comprises an improved Kalman filtering algorithm used for eliminating temperature data noise; the multi-point weighted fusion algorithm is used for carrying out spatial distribution fusion calculation on the multi-point temperature data; the thermal deformation prediction algorithm integrates heat conduction, natural convection and radiation heat exchange models and is used for predicting the temperature change of the whole system; a self-adaptive compensation algorithm is adopted, and PID parameters are dynamically adjusted according to real-time error feedback; and the compensation execution module is used for performing real-time position compensation on the laser engraving machine according to the compensation amount output by the algorithm module.

The invention discloses a high-precision real-time positioning system and method for positioning personnel based on multi-mode fusion. The system comprises a multi-source data acquisition module, an intelligent data processing module, a fusion positioning model construction module and a positioning result application module, provides visual monitoring, trajectory analysis and scene customization functions, and supports flexible expansion through modular design. According to the method, satellite positioning, Bluetooth beacon and inertial navigation technologies are integrated, the weight is dynamically adjusted in combination with an adaptive weighted fusion algorithm, and a fusion positioning model supports outdoor (satellite dominant + EKF calibration, the precision being 2-5 meters) and indoor (beacon dominant + fingerprint matching, the precision being lt); according to the invention, intelligent switching between three modes (inertial navigation + ZUPT correction) and signal loss (1 meter) is realized, data noise is optimized by using Kalman filtering, and high-precision real-time positioning in indoor and outdoor complex scenes is realized. According to the invention, the problems of insufficient precision, poor environmental adaptability and weak expansibility of a single positioning technology are solved, and the comprehensive performance of the positioning system is significantly improved.

The invention relates to the technical field of air leakage big data analysis, in particular to a compressed air pipeline air leakage intelligent analysis system and method based on LLM. A plurality of monitoring nodes in a compressed air pipeline are analyzed, and data normal range parameters and adaptive acquisition frequency are preset for each node in the pipeline operation stage; the clear definition standard avoids misjudgment of original data due to fuzzy threshold values, then data noise is effectively reduced by analyzing the data synchronization degree of each monitoring node and intelligently repairing the problems of missing, deviation and the like, the repaired data better fits the actual operation state of the compressed air pipeline, the missed detection and mispositioning risks are reduced, and the reliability of the compressed air pipeline is improved. And finally fusing the data of the gas leakage node and the adjacent node to supplement multi-dimensional information, and optimizing the fusion data quality. The LLM model depends on high-quality fusion data, deep analysis is carried out in combination with compressed air pipeline multi-monitoring-node correlation characteristics and historical experience, deviation caused by data defects is reduced, and the accuracy of an intelligent air leakage analysis result is greatly improved.

The invention discloses a dynamic identification method for unusual cells before young tumors based on multi-omics data, and relates to the technical field of cell unusual identification. A dynamic correlation intensity matrix and a cumulative effect contribution matrix are constructed, a differentiation screening strategy is implemented according to individual response characteristics, and the unusual cells before young tumors are identified. And the abnormal dynamic high-fidelity identification of the young tumor pre-cells is realized. And aiming at individuals of different response types, an instant path, a long-term path or a double-path fusion strategy is respectively adopted, key behavior data is accurately screened, and the input quality is improved. According to the method, redundant interference is effectively eliminated, the simulation capability of the model on key processes such as immunosuppression and DNA damage accumulation is enhanced, the biological rationality and prediction precision of a cell state evolution sequence are remarkably improved, and the problems of model response lag, low calculation efficiency and output distortion caused by data noise in the prior art are solved; and a reliable technical support is provided for early warning and individualized intervention of precancerous lesions.