705 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 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 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 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 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.

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 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.

The invention discloses an unmanned aerial vehicle target identification method based on multi-modal information fusion, and mainly relates to application research of an artificial intelligence technology in the field of unmanned aerial vehicle target identification. The method is based on a multi-modal large model, radar, optical, radio and other multi-modal unmanned aerial vehicle detection data are input at the same time for co-training, processing and reasoning, few-sample reasoning of downstream tasks is realized by using rich priori knowledge, and a small unmanned aerial vehicle target identification result is returned. According to the method, the features of the unmanned aerial vehicle are comprehensively described from different dimensions by utilizing mutual complementation and mutual promotion of different modal information, so that the problem of multi-modal data missing can be solved, high-quality multi-modal data are integrated, data noise is better coped with, and the accuracy, availability and robustness of reasoning of an unmanned aerial vehicle target recognition algorithm are improved.

The present invention is a parameter estimation method for compartment model based on physics-informed neural networks. Starting from a physical model, the method extracts information from an AIF and a small amount of measurement data to obtain kinetic parameters, thereby greatly improving the scanning efficiency of a measuring instrument, and reducing occurrence of inaccurate estimation results due to patient movement. In addition, the present invention has the robustness to AIF noise and measurement data noise, and can flexibly arrange the time of data acquisition, reduce an error of inaccurate estimation caused by long time 10 acquisition and the patient movement, and improve the efficiency of data acquisition of the instrument. Experimental results show that the present invention is more stable and has less errors. Meanwhile, the present invention does not require the setup of training datasets, and is superior to an end-to-end supervised reconstruction method U-net network with fewer samples.

The invention belongs to the technical field of wastewater treatment, and provides a wastewater treatment equipment remote control system based on the Internet of Things, which aims at solving the problems that the traditional fixed DO concentration control cannot adapt to water inlet load fluctuation, the effluent is easy to be substandard or the aeration energy consumption is high, the monitoring data noise is large, and the time sequence is misplaced. According to the scheme, a multi-dimensional real-time sensing network is constructed, flow, COD, BOD, ammonia nitrogen, water temperature, distributed DO and sludge activity sensors are deployed, discrete wavelet transform is adopted for noise reduction, and a data time sequence is aligned; a three-layer load-DO-energy consumption dynamic correlation model is designed, a basic layer predicts a load trend through LSTM, a middle layer quantifies a DO demand through a microbial metabolism model, and an optimization layer outputs an optimal DO set value through a PPO algorithm; and developing a dynamic decision-making system, adaptively generating a DO interval according to a load state, and adjusting fan parameters and number in a linkage manner. According to the invention, load dynamic adaptation is realized, effluent ammonia nitrogen is guaranteed to reach the standard, aeration energy consumption is reduced, and remote control precision and equipment operation efficiency are improved.

The invention relates to a user operation risk dynamic monitoring method and device based on data consanguinity and a medium, and the method comprises the following steps: obtaining operation behaviors of all users in real time, and extracting a segment of continuous operation behaviors of each user as a group of behavior fragments with semantic relevance; a plurality of continuous behavior segments of each user are matched with a preset behavior template, so that a plurality of intermediate behavior blocks are obtained through dimension reduction; according to a time sequence, traversing and extracting a predetermined number of intermediate behavior blocks from the plurality of intermediate behavior blocks, and respectively combining to generate a behavior fragment sequence; and respectively inputting the behavior fragment sequences into a pre-trained neural network model to obtain a risk classification result or a risk probability distribution result corresponding to each behavior fragment sequence. The method has the advantages that each operation behavior can be subjected to structured expression, the potential behavior chain can be reconstructed through the combination strategy, and the high-risk behavior path hidden in the data noise is recognized by means of the deep model.

The invention discloses a data security sharing method and system, and relates to the technical field of data sharing. The method comprises the steps that a data provider uploads original data, a privacy budget value is calculated through a differential privacy algorithm, noise is added to obtain noisy data, and the noisy data is processed through an anonymous privacy protection algorithm based on maximum dissimilarity degree clustering to obtain desensitized data; encrypting the desensitized data by adopting a block chain decentralization-based ciphertext policy attribute-based encryption method, obtaining an encrypted data ciphertext, uploading the encrypted data ciphertext to a cloud server, and obtaining a content addressing hash value; encrypting the hash value through an elliptic curve encryption algorithm, and storing the encrypted hash value to a block chain account book; and when an access request of a data requester is received, the cloud server verifies the authority by using a non-interactive zero-knowledge proof protocol and returns an encrypted hash value after passing the verification, and the requester decrypts to obtain the hash value and decrypts the encrypted ciphertext to obtain desensitized data, thereby realizing data sharing.

The invention relates to a PINN-CNN target impact damage nephogram prediction model establishment method based on MHA enhancement. Comprising the following steps: acquiring data: acquiring a target impact condition data set and a damage cloud picture data set, and dividing the data into a training set, a verification set and a test set after preprocessing; building a network model based on a CNN-MHA neural network algorithm; integrating a PINN physical constraint module into the established network model to obtain a depth prediction network model; updating hyper-parameters of the deep prediction network model to obtain an optimal model; and performing impact damage prediction. According to the method, hierarchical characterization of spatial features of the damage cloud picture is realized through CNN, time sequence correlation and cross-regional coupling effect of damage evolution are captured by means of an MHA mechanism, and generalization ability of a physical constraint enhancement model introduced by PINN is combined, so that the damage cloud picture can be identified in a complex impact condition scene with large data noise or missing data. The high-precision prediction from the current damage state to the future multi-moment damage cloud atlas has important engineering application value.

The embodiment of the invention discloses a data noise reduction method and device for multi-level magnetotelluric data and a medium, and relates to the technical field of noise reduction processing, and the method comprises the steps: collecting an original magnetotelluric data sequence, determining first noise reduction data, carrying out the variational mode decomposition of the first noise reduction data, and determining second noise reduction data; determining a first signal-to-noise ratio corresponding to the second noise reduction data, and when the first signal-to-noise ratio is smaller than a preset signal-to-noise ratio reference value, performing dynamic fusion on the residual signal to determine a fused residual signal; performing noise reduction processing on the fused residual signal, determining fused residual noise reduction data, performing residual recovery based on the fused residual noise reduction data, and determining current noise reduction data after residual recovery; when the second signal-to-noise ratio corresponding to the current noise reduction data is not smaller than the signal-to-noise ratio reference value, the current noise reduction data is output, the signal recovery requirement of weak effective information in the magnetotelluric data can be met, the signal fidelity is enhanced, and the deep noise processing and weak signal protection capabilities are improved.

The invention discloses an emergency linkage control system for oil depot fire hazard real-time monitoring, and relates to the technical field of safety monitoring, the emergency linkage control system comprises an emergency linkage control center, the emergency linkage control center is in communication connection with the following modules: a multi-source data fusion sensing module used for constructing a multi-mode sensor network, and collecting multi-source heterogeneous data in real time, carrying out fusion analysis, and generating a hidden danger dynamic distribution diagram. Real-time high-frequency acquisition of oil depot environment, equipment and disaster evolution data is realized by constructing a multi-mode sensor network and integrating various sensors, data noise is eliminated and a hidden danger dynamic distribution diagram is generated in combination with a spatio-temporal data fusion engine, so that the monitoring data covers the whole area of the oil depot and the time resolution reaches a second level; compared with a traditional threshold triggering mode, the system can dynamically capture multi-source heterogeneous data of the key area of the oil depot, discover the hidden danger evolution trend in advance, shorten the disaster early warning time and remarkably improve the active suppression capability.

The invention provides a bridge design method and system based on pushing displacement and rigidity inversion, and belongs to the technical field of bridge design. A theoretical formula among the axial force of each span of steel box girder, the displacement of each pier and the anti-pushing rigidity of the piers under the condition of system temperature change after each pushing in the walking pushing construction process is deduced, and actual monitoring data of the beam length change of the steel box girder in the walking pushing construction process are combined; numerical analysis software is used for carrying out inversion analysis on the anti-thrust stiffness of each pier, the curve trend of the anti-thrust stiffness changing along with the temperature difference under the theoretical condition is obtained, the theoretical analysis of the influence of the system temperature effect on the longitudinal bridge direction in the walking incremental launching construction process is perfected, and data support is provided for implementation of measures such as pier stiffness adjustment and displacement release in the follow-up process; by calculating the sparse parameters of the missing data, the adaptability to unbalanced data and the data noise redundancy are higher, and the method is suitable for missing data supplementation in the field of bridge design which is susceptible to natural factors.

The invention provides a few-sample multi-label Android malicious software detection method based on LLM, and solves the major challenge of keeping stable malicious software detection performance under the condition of data noise and label inconsistency. Two main innovations are introduced into the provided LeoDdroid framework to deal with the challenges. Firstly, a complex core set strategy is realized, representative samples are carefully selected, and the influence of noise is reduced to the maximum extent. Secondly, the advanced reasoning ability of a large language model is utilized through a customized prompt project. In addition, a novel Multi-Sample-ACC measure is introduced, and the measure provides more meaningful evaluation for the multi-label classification performance in the malicious software detection context. The method is characterized in that a consistent MS-ACC (Maximum Sequence-Adaptive Cracking Code) score, which is realized by the LoDandroid on an anonymmouscept data set, a Drebin data set and a VirusShare data set, is higher than 0.93. Due to the powerful framework, the framework is superior to a traditional machine learning method by more than 300% in an anonymmousert data set. These results verify the framework's ability to maintain high detection accuracy with varying degrees of noise and data quality.

The invention provides an industrial time sequence generation method based on a time sequence decomposition conditional diffusion model. The method comprises the steps that industrial time sequence data are collected and preprocessed to obtain a data set; introducing conditional variables to construct a conditional diffusion model, and injecting noise through forward diffusion; designing a time sequence decomposition and reconstruction UNet module, performing feature extraction on noisy data and conditional variables to obtain an intermediate state, performing time sequence decomposition on the intermediate state, and performing feature reconstruction by using a decoder; the Sinkhorn distance is used as a regularization term to be fused into conditional noise prediction loss to construct a loss function for training; and randomly generating pure Gaussian noise, inputting the pure Gaussian noise into the trained TDA-CDM model, obtaining predicted noise of the current time step, calculating noisy data of the next time step, performing cyclic operation until a time sequence without noise is obtained, and accelerating sampling by using a denoising diffusion implicit model in circulation. According to the method, coexisting multi-scale dynamic features in the complex industrial MTS can be carefully and effectively captured and restored, and the comprehensive quality of generated data is remarkably improved.

The invention discloses a credit risk prediction method based on data and dynamic feature optimization and Bagging integration, and the method comprises the following steps: S1, obtaining an initial data set, carrying out the data preprocessing, balancing the data set through an ATGA algorithm, and reducing the data noise; s2, using LightGBM as an agent model, calculating importance scores of all features, gradually screening key features, adaptively adjusting feature screening standards according to data distribution through a dynamic threshold algorithm, gradually screening feature subsets, and inputting the screened features into a subsequent model; and S3, constructing a TabPFN model by using a Bagging integration algorithm, introducing a dynamic weight distribution mechanism, adaptively adjusting the weight of each base model according to sample features, and finally realizing credit risk prediction through the TabPFN model. According to the method, the credit risk prediction process is comprehensively optimized in three aspects of data set optimization, feature screening optimization and model performance optimization, so that indexes such as the accuracy rate and the rate of return are improved.

The invention relates to the technical field of intelligent water conservancy and automatic control, in particular to a polder area multi-station water conservancy data real-time collection and intelligent fusion processing method and system, and the method comprises the steps: a multi-dimensional collection system construction step: collecting time sequence state, fluid field and environment boundary data, and executing time base unified cleaning; an ideal model construction step: constructing an ideal water tank model based on the law of conservation of mass and generating an ideal water level change curve; a parameter injection simulation step: generating a simulation fault water level curve according to injection efficiency attenuation and medium leakage parameters; a double-track difference calculation step: respectively carrying out difference on actual measurement and simulation data and an ideal curve to generate real and theoretical residual vectors; a similarity judgment and control step: calculating sequence similarity to judge a real physical fault or sensor data noise, and generating a fusion control instruction; according to the invention, the technical problem of distinguishing the noise of the sensor from the real physical fault is effectively solved, and false alarm or missing alarm is avoided.

The invention discloses a method for suppressing magnetotelluric mixed noise in a shallow water area. The method comprises the following steps: reading a noisy ocean MT time sequence; setting phase space reconstruction parameters; constructing a phase-space vector to obtain a noisy data matrix; noise priori information is obtained through noise pre-estimation; constructing an original noisy data matrix after noise whitening; performing singular value decomposition to obtain a feature vector and a feature value matrix of a data covariance matrix after noise whitening; selecting a low-order principal component after noise adjustment principal component transformation to reconstruct a denoised data matrix; recovering the denoised data matrix into a time sequence; carrying out Fourier transform to obtain an ocean MT magnetic field component amplitude spectrum, and combining to obtain an ocean MT four-component amplitude spectrum; detecting the frequency point of the pulse in the four-component amplitude spectrum; and setting all amplitudes corresponding to pulse frequency points in the four-component amplitude spectrum to be 0, and performing inverse Fourier transform to obtain a processed four-component time sequence. According to the invention, the ocean magnetotelluric signals can be better processed, and the data quality is improved.

The invention discloses a thermal power generating unit initial pressure dynamic optimization control system based on big data, and relates to the technical field of industrial automation, and the system comprises a data preprocessing module which is used for obtaining multi-dimensional parameter records from a historical operation database of an industrial unit, carrying out the unified dimensional processing of original data through a standardization method, and storing the processed data in a data storage module; meanwhile, data noise interference is reduced through a wavelet transform denoising technology, if data missing is found, missing points are complemented through a linear interpolation method, and a processed complete historical data set is obtained; according to the thermal power generating unit initial pressure dynamic optimization control system based on big data, the operation risk of an industrial unit can be effectively predicted, the early warning accuracy is improved, the prediction model performance is continuously improved through adaptive optimization, and a powerful guarantee is provided for safe and stable operation of industrial equipment.

The invention relates to the technical field of granulation control, and discloses a powder granulation control method and system based on fuzzy control, the system comprises a data acquisition unit, a data processing unit, a data analysis unit and a data adjustment unit; by collecting key parameters such as powder humidity in real time, instant and accurate data are provided for control; through fine processing such as filtering, normalization and feature extraction, data noise is removed, the data scale is unified, and parameter dynamic change features are deeply mined; by means of a preset fuzzy rule, the parameter features are converted into fuzzy linguistic variables for reasoning, and the equipment adjustment amount can be scientifically and reasonably determined; in the execution link, a final adjustment value is determined through weighted average, and precise regulation and control of the granulation equipment are achieved; the rich and practical fuzzy rule base covers various parameter deviation combinations, so that the system can adapt to different granulation working conditions, and the granulation efficiency and the product quality are effectively improved.

The invention discloses a rainfall prediction method based on LightGBM and a variable attention mechanism, and belongs to the technical field of weather prediction. The objective of the invention is to solve the problem of poor prediction effect of an existing model when noise or missing exists in data. According to the method, the LightGBM and the variable attention mechanism are combined, the variable attention mechanism is used for accurately capturing cross-variable dynamic association and multi-scale time dependency in meteorological data, the model can flexibly adapt to the relative dependency between different time steps through relative position coding, and the modeling capacity for long-time-sequence data is improved; by adopting a split weighting mechanism based on LightGBM, different types of exogenous variable data can be weighted, large-scale data can be effectively processed, and feature selection can be carried out, so that the model is helped to pay more attention to variables having great influence on meteorological prediction in the training process, and the robustness of the model to noise data and missing data is enhanced. The method can be applied to rainfall prediction.

The invention discloses an exhaust valve self-adaptive noise reduction method based on environmental noise recognition, and belongs to the technical field of exhaust valve data noise reduction. The problem that noise reduction parameters of an exhaust valve cannot be automatically generated according to the actual environment noise condition at present, and then signals cannot be effectively processed is solved. The method comprises the following steps: acquiring a noisy exhaust valve audio signal, preprocessing and standardizing the signal, then sending the signal into a deep learning model for identification to obtain an environment category of a valve audio, and acquiring a binary mask corresponding to a prior environment category according to the environment category; performing short-time Fourier transform on the preprocessed audio to obtain a frequency amplitude spectrum Yn (f) and a phase phi n (f) of a signal; and performing point-by-point multiplication operation on the Yn (f) and a binary mask Mv (f) corresponding to an environment category to obtain a signal, and performing inverse STFT based on the phase phi n (f) to recover a time domain signal # imgabs0 # to synthesize a continuous waveform through an overlap-add method.