1406results about How to "Reduce redundancy" patented technology

The invention relates to the technical field of medicine quality management, in particular to a medicine quality management method and system based on big data and artificial intelligence, and the method comprises the steps: obtaining the quality influence parameters of a medicine product in the production, transportation and storage processes through a multi-modal data collection device; noise is removed through data cleaning, data dimension reduction is carried out through principal component analysis and an auto-encoder, the analysis efficiency is improved, and the efficiency problem of mass data processing of a traditional method is solved. Time sequence analysis is carried out through the long-short-term memory network, abnormal nodes are recognized in time, manual sampling inspection is replaced, and accurate early warning is achieved. The graph neural network constructs a path tracking model, analyzes the relevance between nodes, traces the quality problem, and reckons the batch of affected drugs. Based on a traceability result, an XGBoost model generates quality risk scores of all links by analyzing historical and real-time data and sets a threshold value, and quality risks are early warned in real time.

The invention provides a complex geothermal system digital twinning online experiment method based on multi-source data deep fusion, and solves the problems of precise construction of geothermal system digital twinning and low-delay visualization of virtual-real interaction. The method comprises the following steps: firstly, building a typical geothermal reservoir multi-source heterogeneous data parallel acquisition system, and carrying out fine inversion, high-precision identification and continuous correction on geothermal reservoir parameters by using a large amount of field experiment test data; then, a complete physical model of the geothermal system is established, online simulation is conducted on an actual system through high-performance scientific calculation, and mutual calibration is conducted on the actual system and test data; and finally, building a geothermal system digital twinning on-line experiment platform based on real-time acquired test data, a reliable mechanism model and an artificial intelligence method, and realizing accurate measurement and on-line monitoring of a geothermal exploitation process and iterative evolution of a geothermal reservoir exploitation scheme. According to the method, multi-source data of the geothermal system are deeply fused, high-precision online simulation and low-time-delay virtual-real interaction in the geothermal experiment process are achieved, and geothermal storage and exploitation details can be comprehensively transparentized. And furthermore, intelligent and rapid inversion is performed on the multi-scale seepage and heat transfer process of the geothermal reservoir by carrying out a virtual online experiment, so that the scientificity and efficiency of full-life-cycle geothermal exploitation are greatly improved. Meanwhile, a multi-agent collaborative optimization regulation and control framework provided by the method has relatively high expandability and flexibility, and an optimal well spacing design scheme and an optimal experiment scheme of the geothermal well group can be automatically excavated through adaptive optimization and iterative evolution of a system regulation and control strategy; and the method has a very strong application prospect in the distributed optimization field of the geothermal storage virtual experiment.

The invention discloses a retrieval method and system for efficiently collecting global accurate potential customer information, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data from an e-commerce platform, social media, an enterprise official website, exhibition information, an industry database and customs; generating standardized data through spatio-temporal feature extraction and multi-dimensional credibility evaluation; establishing a dynamic topological structure by adopting a characteristic decomposition reconstruction and value network construction method; based on adaptive classification and multi-dimensional trend analysis, customer value prediction and accurate recommendation are realized; and through system quality evaluation and feedback optimization, dynamic updating and optimized display of the result are realized. According to the method, the accuracy and timeliness of customer information retrieval can be effectively improved, the information redundancy is reduced, and the reliability of the recommendation effect is improved.

The invention relates to the technical field of model construction, and discloses a neural network-based frozen soil unfrozen water content detection model construction method and system. The method comprises the following steps: carrying out segmented sampling test on a clay sample in a temperature interval of 0 DEG C to-20 DEG C to obtain a multi-dimensional original data set, and carrying out standardization and principal component analysis on the data set to obtain a standardized feature vector; and performing three-stage classification marking on the feature vectors to obtain a training data matrix. A deep neural network is adopted to carry out five-fold cross validation training, and a prediction model of three hidden layers is constructed. And performing correction calculation on model output according to soil mineral composition and porosity parameters, and finally identifying an abnormal value through root-mean-square error analysis and a three-time standard deviation principle to obtain a standardized prediction result. According to the method, the mapping relation between the thermal parameters and the unfrozen water content is established, correction is carried out in combination with the soil body characteristic parameters, and the technical problem that the prediction precision of the unfrozen water content is not high in the prior art can be solved.

The invention relates to the technical field of wire harness intelligent evaluation, in particular to a wire harness connection performance intelligent test method and system. The method comprises the following steps: collecting wire harness test data generated by a wire harness test device and a wire harness sensor in real time through an edge computing node; performing electrical performance detection extraction and mechanical performance detection extraction according to the wire harness test data to respectively obtain wire harness electrical performance detection data and wire harness mechanical performance detection data; performing electrical performance deep evaluation according to the wire harness electrical performance detection data to obtain wire harness electrical performance evaluation data, and performing mechanical performance deep evaluation on the wire harness mechanical performance detection data to obtain wire harness mechanical performance evaluation data; and performing environment pressure cross reference according to the wire harness electrical performance evaluation data and the wire harness mechanical performance evaluation data to obtain wire harness connection performance test evaluation data. According to the method, the accuracy of wire harness performance evaluation is improved, and reliable performance data can be obtained in different application fields.

The invention relates to the technical field of data processing, in particular to a real estate surveying and mapping data processing method and system based on intelligent data. The method comprises the following steps: acquiring multi-source real estate surveying and mapping data; performing space-time synchronization on the multi-source real estate surveying and mapping data to generate multi-source real estate surveying and mapping space alignment data; real estate space feature extraction is carried out on the multi-source real estate surveying and mapping space alignment data to obtain real estate space feature data; performing cross-domain feature fusion on the multi-source real estate surveying and mapping data based on the real estate spatial feature data to generate real estate surveying and mapping fusion spatial feature data; public surveying and mapping point confirmation is carried out on the multi-source real estate surveying and mapping data through the real estate surveying and mapping fusion spatial feature data, and real estate surveying and mapping public surveying and mapping point data are obtained. Through multi-source data space-time synchronization, feature fusion, distortion correction, high-dimensional space reconstruction, semantic tag association and chain storage, the efficiency and accuracy of real estate surveying and mapping data processing are improved.

The invention relates to the technical field of image classification, in particular to a fetal heart monitoring image classification method and system based on a neural network. The method comprises the following steps: acquiring a fetal heart monitoring image, generating a standard monitoring image through Gaussian noise elimination and spatial domain normalization, then performing multi-channel convolution on the standard image to extract image convolution features, obtaining standard fetal heart features through compression regular optimization, performing morphological reconstruction to generate a fetal heart dynamic model, and finally obtaining the fetal heart dynamic model. Baseline features are identified based on historical data, then periodic variation quantification and safe floating range derivation are performed on fusion features, and finally a multi-scale discrimination framework is constructed to realize intelligent classification of fetal heart monitoring images. The fetal heart monitoring image classification method is more stable and more accurate.

The invention discloses a surveying and mapping geographic information data management method and system based on cloud computing, and relates to the field of geographic information, and the method comprises the steps: constructing a semantic segmentation model based on a convolutional neural network Deep Lab, and carrying out the feature extraction and semantic annotation of a surveying and mapping geographic data set through the semantic segmentation model, and obtaining a geographic element set; performing spatial division and indexing on the geographic element set by adopting an R tree spatial indexing method to obtain a hierarchically organized geographic element tree; constructing a graph neural network model based on a graph convolutional network GCN, and performing topological relation modeling on the geographic element tree by using the graph neural network model to obtain a time-space attribute graph containing geographic semantics and topological relations; and constructing a space-time prediction sequence model based on the space-time diagram neural network STGCN, and performing evolution trend prediction on the space-time attribute diagram by adopting the space-time prediction sequence model. Aiming at the problem that dynamic prediction of geographic elements is difficult to realize in the prior art, the spatial-temporal evolution trend of the geographic elements is predicted, and dynamic prediction is realized.

The invention relates to the field of energy storage frequency modulation prediction, and discloses an energy storage frequency modulation instruction prediction method and system based on a two-stage method, and the method comprises the steps: obtaining an energy storage frequency modulation instruction, and carrying out the variational mode decomposition; judging mutual attraction or mutual repulsion of the subsequences according to a variation mode decomposition result; if it is judged that the two subsequences attract each other, sequence fusion operation is carried out, and if it is judged that the two subsequences repel each other, sequence generation operation is carried out; obtaining a new sub-sequence set through sequence fusion operation and sequence generation operation; and respectively inputting the sequences in the new sub-sequence set into the recurrent neural network to obtain a final prediction result of the energy storage frequency modulation instruction. By optimizing the frequency modulation instruction in advance, the energy storage system can respond to the frequency change requirement more quickly and accurately, the frequency modulation efficiency and economic benefits of the power system are remarkably improved, and meanwhile the stability of the system and the operation efficiency of the power plant are enhanced.

The invention discloses a micro-grid network topology flexible configuration method based on matrix topology, and relates to the field of micro-grids, and the method comprises the steps: constructing a heterogeneous database of a micro-grid network and equipment; mapping a micro-grid network and equipment into a graph structure, and constructing a graph neural network model; setting mapping strategies of different heterogeneous hardware units according to heterogeneous characteristics of the different heterogeneous hardware units, and constructing a scheduling model of the heterogeneous hardware units by adopting a reinforcement learning algorithm; inputting the preprocessed data into a graph neural network model to obtain an incidence relation between a micro-grid network topology structure and equipment parameters, and taking the incidence relation as a constraint condition; inputting the preprocessed data into a scheduling model to obtain a scheduling strategy of the heterogeneous hardware unit, and taking the scheduling strategy as an optimization target; constructing a multi-objective function of micro-grid network topology optimization; and solving the multi-objective function of the microgrid network topology optimization by adopting an intelligent algorithm, and generating a microgrid network topology scheme. According to the invention, the robustness of the topology scheme is improved.

The invention discloses a large language model parameter fine tuning method, system and device, a storage medium and a product, and relates to the field of natural language processing. The fine tuning method comprises the steps that a pre-training model and a parameter matrix of the pre-training model are acquired, a parameter fine tuning module is arranged on a self-attention layer of the pre-training model, the parameter fine tuning module comprises LoRA modules of different task types and a router, each LoRA module comprises a general feature matrix and a specific feature matrix, and the general feature matrixes of the multiple LoRA modules are shared; obtaining a fine tuning data set; loading and freezing a parameter matrix of the pre-training model, and initializing parameters of a parameter fine tuning module; and performing fine tuning on the parameters of the parameter fine tuning module based on the fine tuning data set to obtain a fine-tuned large language model. Parameters of the MoE architecture are remarkably reduced, it is ensured that the model captures differences of various tasks to the maximum extent, and the cross-task generalization of the model is ensured.

The invention relates to the technical field of medical image processing, in particular to a medical image enhancement processing method and system based on deep learning, and the method comprises the steps: selecting an image enhancement model according to whether the number of samples needs to be expanded or not; if the number of samples needs to be expanded, performing image enhancement by adopting a generative adversarial network, otherwise, performing image enhancement by adopting an auto-encoder; a federal learning architecture is adopted to train a model, and the model is trained on a plurality of scattered devices or servers. The generative adversarial network is used to generate diversified medical image samples, and the diversity of the samples is improved. And the auto-encoder extracts key features and generates new samples, so that the diversity of data is increased. Through a federal learning architecture, by using dispersed data sources, the diversity of training samples of the model is enhanced, and meanwhile, data privacy is protected. And the diversity of samples is effectively increased, so that the generalization ability of the model is improved, and the method has higher robustness and accuracy in practical application.

The invention relates to the technical field of pressure sensing, and discloses a pressure distribution data acquisition method and device, and the method comprises the steps: carrying out the calibration and signal preprocessing of a pressure sensor array, and obtaining an initial state matrix, a preprocessing parameter set, and a response curve database; calculating a pressure change rate to obtain a pressure sampling dynamic trigger threshold value and a pressure sampling control matrix; performing partition control on the pressure data acquisition process to obtain a pressure acquisition partition control model; collecting target pressure data and inputting the target pressure data into the time weighted processing network model for feature extraction to obtain multi-modal pressure distribution feature data; the multi-modal pressure distribution characteristic data is input into a closed-loop control system, and the pressure sampling parameters and the pressure processing parameters are updated to obtain a pressure acquisition control parameter set, so that high-precision pressure distribution acquisition is realized, and the limitation of a traditional method in processing non-linear and time-varying pressure signals is solved.

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous big data processing system, which comprises the steps of dividing data into structural sub-data and non-structural sub-data, performing feature extraction on the non-structural sub-data to obtain multi-modal features, so that the data can be compared in a unified feature space, and then, performing feature extraction on the non-structural sub-data to obtain multi-modal features. The similarity and relevance between the data are identified through pattern matching, it is ensured that the data of the same entity are identified to be directly fused, redundancy is reduced, accuracy is improved, then the most similar data combination is selected through an optimization algorithm, and the actual relation type between the data is determined through relation deduction; and finally, the most suitable fusion mode is selected according to the relationships. According to the method, the accuracy and effectiveness of the data fusion process are ensured, and a reliable basis is provided for deeper analysis and decision making.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-assisted adaptive sickbed control system and method, and the method comprises the steps: carrying out the noise reduction and normalization preprocessing of original physiological data collected by a multi-modal sensor, and obtaining standardized multi-modal data; performing time sequence fusion and dimension reduction to obtain low-dimensional fusion features; performing feature extraction through a multi-branch convolutional neural network to obtain generalization feature representation; the optimal posture demand of the patient is predicted through a long-short term memory network model with an attention mechanism, and an optimal posture demand prediction result is obtained; a layered sickbed adjustment strategy is generated through a double-time-scale reinforcement learning network comprising a priority experience playback mechanism; according to the intelligent sickbed control method and system, the multi-mode sensing data can be comprehensively utilized, the adaptive learning ability is achieved, the intelligent sickbed can be accurately controlled, and the performance and the application effect of the intelligent sickbed are improved.

The invention belongs to the field of network security of smart grids, and discloses a method for detecting an antagonistic network attack in a smart grid based on an iterative algorithm, and the method comprises the steps: generating an antagonistic attack sample through iteration, so as to avoid the protection of a detection system; performing feature extraction and reconstruction of input data by using the constructed automatic encoder network model, and capturing complex time sequence features through convolution, pooling and up-sampling operations; a dynamic parameter adjustment strategy is adopted to adaptively optimize the training optimization momentum coefficient and the training optimization target reconstruction error in the training process, and the system robustness is improved; and finally, verifying the concealment and stability of the resistance attack sample under different scaling conditions through multi-scale evaluation so as to optimize the automatic encoder network model. According to the invention, the security protection level of the smart grid system in a complex network attack scene is improved, and stable operation of the power system is ensured.

The invention relates to the technical field of battery charging and discharging optimization, and discloses a charging and discharging optimization method, device and equipment for a robot energy storage battery, and the method comprises the steps: collecting a working condition feature data set of the robot energy storage battery; performing state cooperative calculation on the working condition characteristic data set to obtain battery operation state data; establishing a mapping relation model of load power and charge-discharge efficiency, and determining a battery working interval boundary value to obtain charge-discharge characteristic data; inputting the charge and discharge characteristic data into a deep gating cycle unit network, and obtaining a battery performance prediction model through dynamic modeling; based on a battery performance prediction model, a gain factor self-tuning technology is adopted to construct a closed-loop controller, online optimization calculation is carried out on control parameters through a gradient descent algorithm, a control instruction containing a charging and discharging current limit value and a voltage threshold value is generated, and then accurate prediction of the battery performance is achieved. And stable operation of the charging and discharging control system is ensured.

The invention relates to the technical field of battery charging and discharging optimization, and discloses an energy management system data analysis method, device and equipment and a storage medium, and the method comprises the steps: collecting a working condition feature data set of a robot energy storage battery through an energy management system; performing state cooperative calculation on the working condition characteristic data set to obtain battery operation state data; establishing a mapping relation model of load power and charge-discharge efficiency, and determining a battery working interval boundary value to obtain charge-discharge characteristic data; inputting the charge and discharge characteristic data into a deep gating cycle unit network, and obtaining a battery performance prediction model through dynamic modeling; based on a battery performance prediction model, a gain factor self-tuning technology is adopted to construct a closed-loop controller, online optimization calculation is carried out on control parameters through a gradient descent algorithm, a control instruction containing a charging and discharging current limit value and a voltage threshold value is generated, accurate prediction of battery performance is further achieved, and stable operation of an energy management system is ensured.

The invention relates to the technical field of financial data processing, in particular to a data processing method and system based on financial information bills. The data processing method comprises the following steps: acquiring multi-modal financial information bill data; and performing bill modal decomposition on the multi-modal financial information bill data to generate financial information bill modal decomposition data. Bond information price income prediction needs a large amount of financial data, such as market interest rate, bond expiration date, face interest rate and the like, the data are similar to the data form of bill modal decomposition, and a more accurate model can be obtained through standardization and feature extraction of multi-modal data. Therefore, valuable input is provided for bond price and income prediction. According to the invention, through multi-modal data fusion, counterfeited feature comparison modeling, abnormal bill clustering analysis and dynamic risk assessment, the security and accuracy of financial bill identification are improved.

The invention discloses a multi-object and multi-model deployment and control early warning system. The multi-object multi-model deployment and control early warning system comprises a multi-source data fusion module, an efficient algorithm and model optimization module, a real-time data processing and analysis module, a multi-model cooperation and decision module, an adaptive learning and evolution module and a security and privacy protection module. And a more comprehensive and deep judgment result is provided, so that the police officers can make more accurate decisions in the operation process. The multi-model structure of the system realizes cooperative work through the support of each module, and finally achieves the goals of accurate data, timely analysis and reliable early warning. And due to the close relationship between each module and multiple models, the system has higher decision-making ability and adaptability when facing a complex dynamic environment.

The invention discloses a fault root cause analysis method based on multi-source equipment data fusion. According to the method, multi-source heterogeneous data such as equipment operation data, maintenance records and environment data are collected and efficiently fused, a fault mode is automatically recognized from fused data, and time sequence evolution characteristics related to faults are mined. And based on the established knowledge base of the fault modes and reasons, and in combination with interactive Bayesian network reasoning and manual feedback, the root cause of the fault is deduced. According to the method, innovative technologies such as active learning and transfer learning are introduced, and the intelligent level, accuracy and efficiency of fault diagnosis are improved. And a visual interface visually presents an analysis process, supports man-machine interaction correction, and enhances interpretability. The method is suitable for the fields of industrial equipment, transportation tools, medical equipment and the like, and the system stability and usability are improved.

The invention belongs to the field of power equipment monitoring, and discloses an electrical signal acquisition system based on intelligent dynamic sampling, which comprises a signal acquisition module, a signal conditioning module, an abrupt change detection module, an impedance calculation module and an upper computer, and is characterized in that the signal acquisition module comprises a first ADC module, a second ADC module, a first MCU module and a second MCU module. Short-circuit impedance real-time calculation and waveform abrupt change detection are adopted for cooperative judgment, and when equipment operates stably, the sampling rate of the system is kept low; when impedance changes or waveform changes suddenly, the sampling rate is improved automatically, important signal changes are recorded accurately, and waveform abrupt change detection is carried out by adopting an edge detection algorithm improved based on an FIR (Finite Impulse Response) filter. The system ensures the time sequence consistency of data under different sampling frequencies through multi-channel data synchronization and timestamp management, and meanwhile, smooth transition is performed by applying an interpolation algorithm during sampling switching, so that the continuity of a data sequence is ensured. The data redundancy is reduced; and the integrity of key information is ensured.

The invention belongs to the field of natural language processing, and particularly relates to a causal atlas formation model construction method based on adaptive context learning, a causal atlas formation model comprises a searcher and a large language model, and the construction method is divided into two stages of searcher pre-training and adaptive context learning fine tuning. A sample retriever based on self-supervised learning is constructed by utilizing contrast learning loss, and text vector representation is optimized; besides, cue words containing sample examples are designed, a self-adaptive context learning strategy is combined, and the large language model is finely adjusted, so that the large language model captures event causal relationship modes in similar samples, and the accuracy of implicit event causal relationship extraction is improved. According to the method, the affair logic knowledge in the text can be fully mined, and the extraction accuracy of the implicit causal event relationship is improved, so that a high-quality causal affair graph can be automatically and efficiently constructed, and the development of downstream tasks such as causal knowledge question answering and reasoning is promoted.

The invention provides a remote sensing image super-resolution reconstruction method and system based on residual hierarchical Transform, and relates to the technical field of digital image processing, and the method comprises the following steps: obtaining a low-resolution image; performing shallow feature extraction on the input low-resolution image through convolutional layer operation to obtain a shallow feature image; secondly, on the basis of the design concept that residual blocks are nested in residual groups, a multi-scale feature calibration mechanism, a Transform space-channel attention mechanism based on a hierarchical window and a space-gate feed-forward network are combined, and a depth feature image is obtained; and finally, performing fusion and up-sampling on the shallow feature image and the depth feature image through convolution and sub-pixel convolution operation to obtain a final remote sensing image super-resolution reconstruction result. By means of the scheme, the remote dependency relationship in the remote sensing image can be effectively modeled, the feature extraction capacity can be improved, and the problem that large-scale window self-attention calculation is high in complexity in a Transform mechanism can be solved.

The invention relates to the technical field of data management, in particular to a data asset sorting method, which comprises the following steps: preliminarily classifying all data assets in an enterprise, performing quality evaluation on the preliminarily classified data assets, generating a quality evaluation report, and performing data redundancy detection and optimization based on the quality evaluation result. Cleaning redundant data by adopting a deduplication algorithm and an optimization algorithm, carrying out data correlation analysis, generating a data correlation graph through a graph model and a statistical model, and determining the relationship between data sets; and finally, according to a correlation analysis result, integrating the data sets with high correlation into a unified data set by adopting a data fusion algorithm, and reducing a storage space through a data compression technology. According to the method, the accuracy of data classification and management can be improved, the data redundancy is reduced, the data storage and access efficiency is optimized, and the data security and privacy protection are enhanced through security evaluation and protection measures.

The invention relates to the technical field of data processing, in particular to a multi-modal recommendation method based on an information bottleneck principle. The method comprises the following steps: constructing a data set # imgabs0 # according to a user-article historical interaction record in an Amazon data set; constructing a user-article graph for each mode according to the data set, and performing multi-layer graph convolution calculation on the embedding of the user and the article to obtain the final expression of the user and the fusion embedding of the article; an article-article similarity graph is constructed for each mode of the articles, neighbors of the first k articles with the highest similarity are reserved, article semantic signals are grouped according to the contribution degree of the article modes to downstream recommendation tasks, and final expressions of the articles are obtained; and performing optimization through denoising, loss calculation and an Adam optimizer. According to the method, key information can be extracted from multi-modal data, redundancy and noise can be removed, and the contribution degree of article characterization to downstream recommendation tasks in different modals can be calculated.

The invention provides an influence maximization method and system based on deep reinforcement learning and a graph neural network, and belongs to the field of influence maximization in social network analysis. The method aims to optimize selection of seed nodes through a reinforcement learning technology so as to realize maximum propagation of information in a network, and the graph neural network can accurately capture an interaction relationship between nodes in a graph structure by virtue of a message transmission mechanism of the graph neural network, which is highly matched with an information propagation process in a cascade propagation model. Therefore, in the influence maximization model based on the deep reinforcement learning and the graph neural network, the graph neural network is combined with the deep reinforcement learning, the embedded representations of the nodes are learned by using the graph neural network, and the embedded representations are used as the input of the deep Q network so as to fully capture the complex structure and node relationship in the network. In addition, a multi-head attention mechanism is introduced to aggregate information of each round, expression of key features is enhanced, and information redundancy is effectively reduced.

The invention discloses a scaffold steel plate net building method based on BIM, and relates to BIM modeling. The method comprises the steps that feature parameters of a scaffold steel plate net are obtained; according to the characteristic parameters, establishing a three-dimensional entity model of the scaffold steel screen by using a parametric modeling method; performing grid division on the three-dimensional solid model by adopting a self-adaptive grid division algorithm to obtain a finite element grid; identifying a stress concentration area in the finite element grid by adopting a self-adaptive grid encryption algorithm, and performing grid encryption on the stress concentration area to obtain an encrypted finite element grid; performing structure optimization on the encrypted finite element mesh by adopting a topological optimization algorithm to obtain an optimized three-dimensional entity model of the scaffold steel plate mesh; and performing component arrangement optimization on the three-dimensional entity model after structure optimization by adopting a discrete topological optimization algorithm to obtain a final scaffold steel plate net BIM model. In order to solve the problem that in the prior art, scaffold steel plate net erecting redundancy is large, the redundancy design is reduced.

The invention provides a circuit breaker mechanical fault voiceprint feature extraction method, which belongs to the technical field of circuit breaker mechanical fault voiceprint, and comprises the following steps: collecting sound signals of a circuit breaker in normal operation and various mechanical fault states; performing filtering and noise reduction on the collected sound signal to obtain a preprocessed sound signal, and extracting an abnormal waveform segment set; processing the preprocessed sound signal by using a CUDA parallel processing stream of the GPU, and processing an abnormal waveform segment set by using an input stream of the CPU; performing time-frequency domain feature extraction on the preprocessed sound signal to obtain a first feature matrix; clustering the first feature matrix to obtain a plurality of clustering centers as typical sound features, and constructing a second feature matrix; extracting features of the abnormal waveform segment set to obtain a third feature matrix, and performing feature fusion and dimension reduction on the third feature matrix and the second feature matrix; and finally, a mechanical fault voiceprint feature matrix is obtained. The technical problem that a traditional serial processing mode is low in efficiency is solved.

The invention relates to the technical field of data storage, and discloses a lossless compression algorithm-based data storage method, which comprises the following steps of: receiving to-be-stored data through a data interface, automatically identifying a data format by using a data analysis tool, and performing feature analysis to obtain the size, the type and the complexity of the data; through deep analysis of data features, the method can flexibly select a proper Huffman coding processing mode, and optimizes a compression process according to different data types and complexity. In addition, a context modeling optimization mechanism is introduced, the coding mode is dynamically adjusted, and the compression ratio and the storage efficiency are further improved; efficient storage and reliability verification of data are ensured through a block storage strategy in combination with metadata management; the problem that an existing compression algorithm is low in efficiency is solved, the integrity and reliability of data storage are improved, and the method is suitable for storage requirements of large-scale data and diversified data.