563 results about "Hybrid data" patented technology

The invention provides a network traffic anomaly detection model training method and device and a readable storage medium, and the method comprises the steps: extracting a traffic statistical feature vector according to original network traffic data, and generating an initial mixed data set; generating a confrontation disturbance sample output enhanced feature matrix based on the initial mixed data set; constructing a self-adaptive feature fusion rule based on the enhanced feature matrix, embedding asset association degree parameters into an attention calculation layer of a feature encoder, and outputting encoding features fusing threat intelligence; inputting the coding features fused with the threat intelligence into a pre-constructed initial detection model, generating false report and missing report correction labels based on the suspicious traffic fragments, and outputting an adversarial sample correction data set; and performing adversarial training on the initial detection model through the adversarial sample correction data set to obtain an incremental detection model for network traffic anomaly detection. According to the invention, the detection precision, the anti-interference capability and the real-time defense response capability of the detection model to novel attacks can be improved.

The invention discloses an intelligent fusion analysis system for multi-source heterogeneous data, and the system comprises a dynamic data collection module which is used for carrying out the data collection, and carrying out the processing of a collected mixed data flow; the semantic alignment module is used for constructing a domain ontology knowledge graph according to a preset scene target and carrying out semantic alignment and coordinate alignment on the collected data; the self-adaptive fusion engine module is used for fusing the collected multi-source heterogeneous data; the trusted computing module integrates a secure multi-party computing protocol and a homomorphic encryption algorithm to realize that data is available and invisible; and the intelligent decision-making module constructs a state action reward model based on reinforcement learning according to a preset scene target, and performs analysis and decision-making by using historical data and data acquired in real time. According to the invention, the capability and effect of data processing and decision support are improved.

The invention discloses a substation equipment rare defect simulation and identification method and system and a storage medium. The method comprises the following steps: constructing an equipment reference feature library; marking dynamic features of rare defects in historical inspection according to a spatial-temporal feature enhancement algorithm, generating a knowledge graph, and constructing a dynamic defect learning library; the method comprises the following steps: learning space association and environmental factor influence of defects and equipment through a bimodal generation network, and generating initial defect data matched with a weak area of the equipment; generating high-credibility defect fusion data through physical constraint-intelligent detection double screening; constructing a three-dimensional mixed data set, and screening high-quality training samples through a dynamic defect evolution algorithm and hierarchical cognitive evaluation; and constructing a multi-algorithm collaborative fine tuning network by using a federated learning framework, simulating and labeling defect information, and outputting a multi-dimensional identification prediction report. The method aims at solving the problems that the model is insufficient in rare defect recognition precision and lack of evolution prediction ability, and high-quality simulation of rare defect samples and high-precision recognition of the model are achieved.

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention provides a mixed data precision matrix multiplication and addition unit and a calculation method, the matrix multiplication and addition unit comprises a calculation unit, and the calculation unit comprises a format division module, a multiplication array module, an addition tree module, an accumulator module, a normalization module and a shift register module. The calculation unit converts the first input matrix and the second input matrix into input data in a middle floating point format; executing parallel multiplication operation on the input data to generate an intermediate product result; performing index alignment and accumulation on the intermediate product result to generate an intermediate accumulated value; accumulating the intermediate product result and the value of the third input matrix in a form of accumulating an intermediate accumulated value, and outputting an accumulated result; and converting an accumulation result into a normalized result and outputting the normalized result. The format division module supports various precisions and converts data with different widths into an intermediate floating point format, so that other hardware units can be reused, and the problems that hardware resources are complex and different model reasoning scenes are difficult to meet are solved.

A method may include: providing one or more inputs to a hybrid data generator, wherein one of the one or more inputs is based at least in part on a wellsite location, wherein the hybrid data generator comprises a large language model, and wherein the large language model is based at least in part on a machine learning algorithm; utilizing an information handling system to generate a cement job design based at least in part on the one or more inputs and the hybrid data generator; performing at least a portion of a cementing operation based at least in part on the cement job design; and collecting at least one measurement from at least one sensor during the cementing operation.

The invention discloses a fuzzy density electricity consumption anomaly detection method based on granular ball calculation, and belongs to the field of electric power data analysis, and the method comprises the steps: carrying out the normalization processing of electricity consumption data with mixed attributes, and obtaining standard data; dividing the hybrid power utilization data into pellets according to the data distribution to obtain a pellet set; constructing a particle-ball fuzzy information particle set based on the particle-ball center; calculating particle ball fuzzy density and attribute importance; according to the particle-ball fuzzy density and the attribute importance degree, constructing a particle-ball anomaly score; obtaining an abnormal score of the sample through abnormal score mapping; and finally, judging abnormal conditions of all sample points according to a set threshold value. According to the method, an existing anomaly detection model based on the finest granularity sample is changed, a particle ball generation method of the mixed data is provided, the particle ball fuzzy density is constructed by using the fuzzy information granularity theory so as to describe the polymerization degree of the particle balls, and unsupervised anomaly detection of the mixed power consumption data is realized.

The invention discloses a defect classification and segmentation method and system based on unsupervised and weak supervised combination, and the method comprises the steps: employing a training data set only containing a defect-free sample, extracting the feature representation of the defect-free sample through a feature extractor, and storing the feature representation in a feature memory library; comparing the mixed data set without labels including defect and defect-free samples with the features in the feature memory bank to determine false classification labels; designing a class activation graph-based weak supervision network for extracting features in the image, a semantic segmentation network and a loss function thereof, and training the semantic segmentation network by using a training data set of a defect-free sample and pseudo-classification label data; according to the method, the advantages of weak supervision and unsupervised learning algorithms are combined, and efficient defect detection and positioning can be realized.

The invention belongs to the technical field of data transmission and processing, and discloses a zero-copy-based multi-thread high-throughput data stream channel separation method and system, and the method comprises the steps: directly writing a mixed data stream into a front-end buffer region configured as an annular structure through a data receiving module by adopting direct memory access; then, a multi-thread processing module dynamically allocates a plurality of processing threads from a thread pool to separate channel data in parallel, each thread adopts a zero copy algorithm based on pointer offset, positions the channel data in a memory, creates pointer reference and associates the channel data to a corresponding rear-end buffer area, and logic separation is achieved without physical copy; and finally, the data storage module efficiently writes the separated data into persistent storage in an asynchronous I / O mode. According to the method, zero-copy, multi-thread parallel and two-stage dynamic buffering strategies are combined, the data separation efficiency is remarkably improved, CPU occupation and memory bandwidth are greatly reduced, and the real-time performance and stability of high-throughput data processing are guaranteed.

The invention provides a high-voltage circuit breaker voiceprint denoising method based on data enhancement and a storage medium, and the method comprises the steps: processing a collected original voiceprint data sequence, extracting stable and effective Mel-frequency cepstrum coefficient features, and constructing a two-dimensional feature matrix; a parallel mixed data enhancement strategy is adopted to generate a positive sample pair, and an encoder is trained in combination with a contrast learning mechanism, so that the representation robustness of the model under different voiceprint change conditions is improved. The method comprises the following steps: decomposing an original signal containing noise fringes into a plurality of modal components by using variational modal decomposition, extracting low-frequency effective components, introducing Gaussian white noise, constructing a corrosion target signal as a decoder training target, learning through a denoising automatic encoder, and finally outputting a denoised voiceprint feature signal. The method has the advantages of high robustness, high noise suppression capability, excellent feature expression capability and the like, is suitable for the field of online monitoring and intelligent diagnosis of the state of high-voltage circuit breaker equipment, and has good application prospect and engineering value.

The invention relates to the technical field of project intelligent management and control, and discloses a multi-agent power grid project intelligent monitoring, management and control and evaluation system and method, and the system comprises a mixed data resource library, a multi-agent system, an index calculation module and a process control module. The mixed data resource library stores structured business data and unstructured documents; the multi-agent system comprises an information extraction agent, an index design agent, a code generation and treatment agent and the like, works cooperatively, and converts a high-level natural language management and control rule into an executable code; the index calculation module is responsible for executing codes according to a scheduling strategy and calculating quantitative indexes; and the process control module automatically identifies risks and executes management and control operations such as process locking according to the index result and a preset threshold value. According to the invention, the complex business logic can be automated and coded, the accuracy and timeliness of risk identification are improved, and refined and prospective intelligent management and control of the power grid project are realized.

The invention discloses a mixed data encryption method and system based on multiple algorithm cores, and the method comprises the steps: receiving a mixed encryption request based on a first preset path, carrying out the encryption type analysis and data address analysis of the mixed encryption request, and determining a plurality of target encryption algorithm cores corresponding to an encryption type analysis result; sending a starting instruction to a plurality of target encryption algorithm cores based on a second preset path, and determining a multi-core encryption strategy corresponding to the hybrid encryption request; acquiring to-be-encrypted data based on a first preset path, a first preset data handling engine and the data address resolution result, and calling a plurality of target encryption algorithm cores to perform hybrid encryption processing on the to-be-encrypted data according to a multi-core encryption strategy to obtain a hybrid data encryption result, the delay of data encryption can be reduced, and the flexibility of a data encryption mode and the security of an encryption result can be improved.

The invention discloses a multi-language emotion recognition method based on semantic comprehension, which comprises the following steps: realizing cross-language emotion analysis through a three-stage innovation framework, and separating semantic and emotion features by adopting improved relative position coding; the method comprises the following steps of: constructing an anchor point vector by utilizing a Hofstep culture dimension, and realizing culture sensitive vector space alignment through a loss function; the emotional features are dynamically adjusted in combination with the power distance and personal difference, and the cross-culture adaptability is enhanced in cooperation with an emotional intensity quantification formula. In a SemEval-2023 task, the accuracy of the system is 21.6% ahead of the baseline at 87.3%, the recognition rate of the small language reaches 78.2%, and the culture misjudgment rate is reduced by 43%. And through a multi-language parallel corpus and a mixed data enhancement strategy, the reasoning speed is increased by three times while the model parameter quantity is reduced by 40%, and an efficient solution is provided for a cross-culture NLP task.

A hybrid, redundant, fail-safe architecture provides a unified fail-operational framework for autonomous agents operating across physical and virtual domains. The system employs a multi-modal data source suite, an adaptive hybrid data fusion module, and an intelligent decision-making module. A novel closed-loop interaction enables a health monitoring module that detects an incipient fault in a data source by monitoring ancillary performance metrics. Upon detection, the module generates a fault signature, including a quantitative prognostic estimate of a future failure time, and transmits it to an adaptive data fusion module. The fusion module proactively reconfigures its state estimation algorithm by decreasing reliance on the degrading data source in proportion to the prognostic estimate. This preemptive compensation ensures the system maintains a high-integrity environmental model and achieves true fail-operational continuity. The architecture is applicable to numerous embodiments providing a universal solution for proactive fault management and system resilience.

The invention discloses a temperature prediction method based on a graph neural network and a related device, and belongs to the technical field of cloud computing system data center thermal management, and the method comprises the steps: S1, collecting simulation environment basic information, and building a simulation model and simulation data of a data center machine room; s2, expanding the simulation data to obtain generated data, and merging the simulation data and the generated data to form a mixed data set; s3, constructing a graph topological structure of the data center machine room and a thermal prediction model of the data center machine room, and training the thermal prediction model of the data center machine room to obtain a trained thermal prediction model of the data center machine room; and S4, deploying the trained data center machine room heat prediction model to the data center, and inputting the real-time server power, the air conditioner set temperature and the indoor temperature into the trained data center machine room heat prediction model to obtain the server return air temperature. Not only can a complex heat transfer rule be captured, but also different space structures and dynamic changes can be adapted.

The invention relates to a digital pre-distortion method and system based on a hierarchical iterative neural network, and belongs to the field of digital pre-distortion of power amplifiers. According to the method, the pre-distortion process is decoupled into two stages of off-line generalization training and on-line specialization optimization. In the offline stage, a mixed data set containing multiple modulation modes and bandwidths is adopted to train an offline neural network, and a basic model with high generalization ability is constructed; in the online stage, aiming at a signal with a specific modulation mode and bandwidth, a single configuration data set is utilized to train an online neural network, and the accurate compensation capability of the model on a real-time input signal is enhanced. In practical application, a baseband signal is firstly subjected to targeted preprocessing through the online neural network, then is subjected to deep optimization through the offline neural network, and finally is input into the power amplifier PA. According to the method, through a generalization-specialization double-layer structure, the dynamic response capability and compensation precision of a digital pre-distortion system to a complex signal environment are remarkably improved.

The invention discloses a superconducting cable aging state evaluation system and method based on mixed data, and the system comprises a data analysis module, an aging evaluation module, a physicochemical association module and a report generation module which are in communication connection. Wherein the data analysis module comprises a simulation sub-module, a data acquisition sub-module and a data processing sub-module connected with the data acquisition sub-module, and in the aging state evaluation process of the aging evaluation module, superconducting cable system model parameters are output and calibrated through a physicochemical association model; and then an aging state evaluation fitting function is output through the data processing sub-module, and an aging state evaluation report is automatically generated by the report generation module. According to the method, the operation state of the superconducting cable is evaluated by analyzing the mixed data, aging analysis of the superconducting cable system under multiple working conditions is carried out, and decision support is provided for system maintenance.

The invention relates to the technical field of deep learning, and discloses a deep learning model training data enhancement method, which comprises the following steps: data preprocessing: preprocessing original training data, including image size adjustment, normalization and other operations, to ensure the consistency and stability of input data; gAN data enhancement: using a pre-trained generative adversarial network to generate an image sample similar to original data distribution; enhancing neural style conversion data: combining the content of the original image with images of different styles by using a neural style conversion technology to generate an image sample with a new style; and mixed data enhancement: mixing the image generated by the GAN and the image generated by the neural style conversion to form a final enhanced data set. The deep learning model training data enhancement method aims to combine the generation capability of the GAN and the style migration characteristic of neural style conversion, and can generate enhanced data with diversity and richness.

The invention discloses an energy robot-oriented model training method, and relates to the technical field of energy digitization, and the method comprises the steps: synchronizing a business database to a local metadata knowledge base, and carrying out the business feature labeling of a metadata table in the metadata knowledge base according to the business logic; establishing a business problem knowledge base based on the business requirements; constructing a semantic map model of the robot based on the metadata knowledge base; generating mixed data features based on the labeled metadata knowledge base and the business problem knowledge base, and performing multi-model cooperative training of robot intention recognition, intelligent dialogue and intelligent table splicing according to the mixed data features in combination with a semantic map model; performing distributed incremental training on each model of the robot in response to the user session log, and performing model migration according to containerization to complete deployment of the robot; the intention recognition ability and data retrieval and query efficiency of the robot on the text input by the user are improved, then the question-answer response efficiency of the robot is improved, and efficient business question-answer is achieved.

The invention relates to the technical field of satellite communication networks and on-board buses, in particular to a data deterministic transmission scheduling method for a satellite-borne time-sensitive network. The method comprises the following steps: constructing a satellite-borne network architecture, connecting each terminal and a bus gateway by a TSN management node to form a unified data bus, distributing priority and monitoring link state; performing feature modeling on the data frames to define six-tuple parameters; designing a hierarchical queue architecture comprising a key task queue, a load data queue and a background traffic queue; a dynamic shaping mechanism is adopted, corresponding shaping devices are arranged for different queues, and parameters are set; the invention further provides an anti-disturbance scheduling strategy which comprises dynamic priority improvement and elastic bandwidth allocation. And finally, performing output scheduling according to a strict priority and a weighted fair queue rule. According to the method, by constructing a unified TSN bus and integrating heterogeneous subsystems, low-delay and low-jitter transmission of key task data is realized, the bandwidth utilization rate of scientific load data is improved, the network reconstruction time is shortened, the anti-interference capability is enhanced, and satellite-borne mixed data stream transmission in a high-dynamic and multi-task scene is effectively optimized.

The invention provides an abnormal sound detection method based on multi-scale time-frequency feature perception, and relates to the technical field of acoustic detection for industrial machine state monitoring, and the method comprises the steps: inputting an original sound signal, and carrying out the dual-branch feature extraction to generate a time domain coding spectrogram and a logarithmic Mel spectrogram; executing hybrid data enhancement; splicing the enhanced spectrogram and inputting the enhanced spectrogram into a dense encoder for compression modeling; extracting features through a two-stage multi-scale time-frequency sensing network, processing along a time dimension in the first stage, and processing along a frequency dimension in the second stage; inputting the high-order features into a lightweight classifier to output an abnormal score; and comparing the abnormal score with the gamma distribution threshold to judge abnormity. According to the method, the weak anomaly detection rate and the cross-equipment stability are remarkably improved, and the perception and discrimination capability of the model on the multi-scale time-frequency characteristics is effectively enhanced.

A system for adaptive ETL processes using neural networks / LLMs, comprising: an intelligent data extraction module configured to extract data from multiple sources, including structured, semi-structured and unstructured formats; an adaptive data transformation module configured to use neural networks and large language models (LLMs) for schema-free data transformation and dynamic adaptation to evolving data structures; a real-time anomaly detection and data quality module configured to detect and correct data inconsistencies and ensure data integrity; a dynamic workflow orchestration module configured to manage and optimize ETL workflows using AI-based task scheduling and workload balancing; an AI-driven performance optimization module configured to fine-tune ETL job execution and improve overall system efficiency; an automated security and compliance module configured to ensure data transmission, storage and compliance; a hybrid data loading module configured to support real-time data streams and batch processing; a self-learning continuous improvement module configured to continuously improve transformation logic and performance based on historical data and feedback loops; a monitoring and visualization module configured to provide a real-time dashboard for monitoring system performance, data quality, and anomaly detection.

The invention belongs to the technical field of machine life prediction, and particularly relates to a machine residual life prediction method, system and equipment based on a mixed data driving model, and the method comprises the steps: collecting vibration, temperature and sound data of equipment, extracting time domain and frequency domain features, and carrying out the preliminary prediction of the deterioration degree of the equipment through a support vector machine (SVM) model; and the prediction result and the feature data are input into a neural network model, a fatigue life formula is combined as a physical constraint, the physical consistency of the model is enhanced, and finally the residual life of the equipment is predicted. According to the method, data driving and a physical model are fused, the prediction accuracy and interpretability are improved, and the method is suitable for health management of complex industrial equipment. The system can be deployed on an edge computing device or a cloud, supports real-time prediction and dynamic updating, effectively prolongs the service life of the device, reduces sudden faults, and optimizes a maintenance strategy.

A data processing method, device and apparatus, and a storage medium are disclosed. The data processing method includes obtaining a data packet including multiple raw data; determining a target data type corresponding to each raw datum in the data packet; determining a target data processing mode corresponding to each raw datum based on a data processing requirement for the data packet and the target data type corresponding to each raw datum in the data packet, the data processing requirement including at least one or more of data compression, data storage and data decoding; and performing data processing on each raw datum based on the target data processing mode corresponding to each raw datum. The method can ensure the integrity of compression of mixed data, reduce the bandwidth and storage space occupied by the transmission of the compressed data packet, and can be implemented in a relatively simple way.

The invention relates to the technical field of power grid communication, and discloses an HPLC (High Performance Liquid Chromatography) and HRF (High Radio Frequency) dual-mode communication hybrid data transmission control method, which comprises the following steps of: 1, initializing hardware parameters of an HPLC module and an HRF module, and configuring a frequency band range of a power line coupler and a radio frequency front end; step 2, dynamically calibrating fractional order model parameters of the dual-mode channel based on frequency band ranges of the HPLC module and the HRF module; and step 3, analyzing the service type of the data packet by using the fractional order model parameters, and calculating the four-dimensional QoS demand weight. According to the technical scheme, the technical scheme of four-dimensional QoS dynamic weight distribution and fractional order HJB equation optimization is adopted, and the technical effect of cooperative control and dynamic optimization of multi-dimensional service quality is achieved. The method solves the problems that a traditional method cannot meet the requirements of real-time performance, reliability, energy efficiency and safety at the same time in a time-varying channel environment, and resource allocation is rigid due to fixed weight.

The invention discloses a radiation source individual open set identification method, which comprises the following steps of: acquiring a signal and performing data enhancement to obtain a mixed data set to be identified; calculating the mixed data set through difference short-time Fourier transform to obtain radio frequency fingerprint features; inputting the radio frequency fingerprint features into a pre-trained feature extraction neural network, and outputting a penultimate layer feature vector of the feature extraction neural network; and calculating the penultimate layer feature vector through an extreme value theory method, and outputting an open set recognition result.

The invention discloses an HDF5-based multi-resolution block stacked grid storage and management method, which comprises the following steps of: dividing into a plurality of three-dimensional models according to actual requirements and model complexity and different resolution grades, constructing a mixed data storage structure of a hierarchical K-way tree, fusing and managing the models of different scales, and performing cross-scale unified scheduling, so as to realize multi-scale multi-resolution block stacked grid storage and management. And then, after the three-dimensional model is converted into a sparse stacked grid model, the stacked grid model is stored and managed by utilizing the characteristics that an HDF5 file supports infinite multiple data types and is specially designed for flexible and efficient I / O and large-capacity and complex data.

Embodiments of the present disclosure relate to synchronizing and managing data. A first event is received from a data source. The first event comprises an envelope comprising schema information associated with the first event. Schema drift is detected based at least in part on the schema information. A signal indicative of the schema drift is emitted to a mapping module. An activation ticket is received from the mapping module. The activation ticket corresponds to an updated mapping profile. The updated mapping profile is based at least in part on the signal. A second event is received from the data source. The second event is bound with the updated mapping profile.

The invention discloses a single-step depth estimation method based on conditional feature fusion, and the method comprises the steps: constructing a mixed data set, carrying out the data enhancement operation of an RGB image and a depth label, replacing a CLIP text encoder with a CLIP image encoder in a corresponding task, carrying out the coding of the RGB image and the depth label, and carrying out the coding of an input image through the CLIP image encoder. And carrying out loss value calculation on the deep latent code and the predicted deep latent code, keeping VAE freezing in a training process, training a CLIP image encoder and the de-noised U-Net at the same time, carrying out network hyper-parameter iterative optimization, inputting the predicted deep latent code into a VAE decoder, and carrying out VAE decoding on the predicted deep latent code. According to the method, the depth features of different environments, scenes and objects are learned, so that the accuracy and generalization performance of the model are improved.

The invention relates to the technical field of data sharing privacy protection, in particular to a cross-industry universal data sharing privacy protection method and a cross-industry universal data sharing privacy protection system. Through data request and identity authentication, generation of a mixed data set, modification of a homomorphic encryption algorithm, data segmentation, ciphertext distribution and secure joint modeling, secure sharing and joint modeling of data between cross-industry enterprises are realized. On the premise that data privacy is guaranteed, cross-industry data value is fully exerted, integration and innovation of industries such as medical treatment and finance are assisted, industry data sharing requirements are met, and industrial collaborative development is promoted.