721 results about "Evaluated data" patented technology

The local intrinsic dimensionality (LID) for a diffusion model with respect to a particular data sample is determined by using the diffusion model's diffusion process to apply noise to a data sample and evaluate how the estimated log probability of the data sample changes at different levels of noise. Particularly, the differential of change in noise to change in log probability can be used to determine the local intrinsic dimensionality. This may be determined by evaluating the log probability at several noise levels and determining a slope of the difference. In additional examples, the differential is evaluated directly at a selected noise level. The selected noise level can be optimized by calculating the estimated LID for various data samples at a variety of noise levels and selecting the LID that corresponds to a “knee” where the estimated LID sharply changes.

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

The invention relates to the technical field of edge computing, and discloses a network data efficient caching method and system based on edge computing, which realize dynamic identification of hotspot data and optimal caching position decision by analyzing edge node data flow characteristics and network topology states in real time. Firstly, data packet meta-information is extracted from an edge node, a content feature vector is generated, and data popularity is evaluated; secondly, analyzing a network topology structure and a link state, and screening candidate cache nodes; then, factors such as service priority, network reliability and topology stability are fused, and an optimal cache position is determined by adopting a reinforcement learning algorithm; and finally, the cache content is updated, the cache state is synchronized between the edge nodes, and the cache distribution is optimized. The data access efficiency and the resource utilization rate of the edge network can be effectively improved, and the method adapts to dynamically changing network environments and user requirements.

The invention relates to the technical field of power equipment monitoring and diagnosis, in particular to a GIS partial discharge high-sensitivity monitoring and microdefect diagnosis system, which comprises a signal acquisition module, a signal processing module, a defect identification module and a data fusion module, wherein the signal acquisition module is used for acquiring partial discharge signals and related environment data in real time; the signal processing module is used for carrying out noise suppression and filtering processing on the partial discharge signals from the signal acquisition module and extracting feature data of the partial discharge signals; the defect identification module is used for diagnosing potential micro-defects and performing defect level evaluation; the data fusion module is used for generating a multi-dimensional equipment health condition report; according to the invention, through multi-dimensional data fusion and accurate signal processing and defect evaluation, the diagnosis precision of the partial discharge signal of the GIS equipment is improved, and comprehensive evaluation and fault early warning of the health state of the equipment are realized.

The invention belongs to the technical field of agricultural Internet of Things, and discloses an Internet of Things-based intelligent agricultural cloud platform data acquisition and management system, which comprises the following steps of: acquiring plant rhizosphere microenvironment information, generating rhizosphere original data and performing correlation analysis to obtain rhizosphere electrochemical interaction data; collecting canopy microclimate information, and performing temperature and humidity gradient quantification processing and pore dynamic response characteristic extraction to obtain pore response characteristic data; collecting field illumination information, and carrying out multispectral distribution analysis and photoperiod sensitivity differentiation analysis to obtain a crop photoperiod sensitivity spectrum; performing multi-dimensional association fusion to generate crop physiological status comprehensive evaluation data; agricultural measure analysis and resource allocation optimization are carried out, and finally a resource allocation instruction is generated and agricultural operation scheduling is carried out; according to the invention, intelligent acquisition, analysis and management of data in each link of agricultural production are realized, and the utilization efficiency of agricultural resources and the production accuracy are improved.

The invention relates to the technical field of data processing, and discloses a large steel structure hoisting precision real-time monitoring method and system. The method comprises the following steps: scanning and collecting the edge of a steel structure through a distributed sensor network, and obtaining structure data to obtain an original monitoring data set; extracting and analyzing structure transient response features based on the original monitoring data set, and generating a state feature matrix; analyzing a coupling relationship between local deformation and overall torsion to obtain a dynamic response prediction matrix; performing space-time registration and data fusion on the monitoring data, and constructing a state evaluation data set; carrying out optimization calculation on the hoisting parameters to generate a hoisting control data packet; and performing intelligent reasoning analysis in combination with historical case data, and outputting a control instruction sequence. Real-time monitoring of the coupling relation between local deformation and overall torsional deformation in the hoisting process of the large steel structure is achieved, and the technical defect that in the prior art, the structural deformation development trend cannot be accurately recognized and predicted is overcome.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention discloses a test evaluation method and device for an industrial park integrated management system. The method comprises the following steps: acquiring a performance index data set of the industrial park integrated management system; the performance index data set comprises a performance index test data subset of each subsystem; the performance index test data subset comprises a test data sequence of each performance index; pre-processing the performance index data set to obtain a to-be-evaluated data set; and performing test evaluation processing on the to-be-evaluated data set to obtain a performance evaluation result value of the industrial park integrated management system.

The invention discloses an automatic software function testing and evaluating method based on a multi-modal large model, and particularly relates to the field of non-embedded software function testing and evaluating, which comprises the following steps: collecting software texts, images and structured data, generating corresponding feature vectors, aligning through a cross-modal comparison algorithm, and constructing a multi-modal testing and evaluating data set; deep features of all modes are extracted through a mode exclusive encoder, and a unified multi-mode function vector is output based on information entropy dynamic weighted fusion; constructing triple training data, and optimizing the multi-modal large model by combining a joint loss function with an enhanced training strategy; and finally, deploying the model at a cloud end and an edge node, carrying out real-time triggering test by docking a CI / CD process, and realizing functional integrity verification, interactive compliance detection and accurate defect positioning through semantic vector comparison, graph isomorphism rate calculation and cross-modal attention backtracking. According to the scheme, the test comprehensiveness and precision are improved, and the rapid iteration requirement of software is met.

Systems and methods for dynamically evaluating metadata consistency and data reliability in a data management system are disclosed herein. The system may retrieve first metadata and second metadata. The system may retrieve a metadata ruleset. Based on the metadata ruleset, the system may generate a first metadata consistency metric indicating a first measure of consistency. The system may determine to process each record of the first metadata as a batch. The system may generate a second metadata consistency metric indicating a second measure of consistency. The system may determine to process each record of the second metadata independently.

The invention discloses a construction monitoring system for a horizontal warehouse arched roof based on multi-modal data fusion, relates to the technical field of construction monitoring, and is used for solving the problem of poor structural construction anomaly recognition. The method comprises the steps of collecting multi-modal data and completing synchronous preprocessing, setting a sampling frequency according to modal characteristics, dividing construction stages, constructing a stage response function, dynamically updating during stage switching, fitting an actual observation value and a prediction value, extracting a modal response deviation degree, constructing a credibility function and evaluating data reliability. Further analyzing a residual trend, adjusting an abnormal score in combination with credibility, identifying a collaborative abnormal behavior between modals, systematically constructing a modal causal graph, identifying an abnormal propagation path, extracting modal conduction characteristic information, and triggering a risk response strategy according to the modal causal graph, so that dynamic tracking of the structural abnormality in the construction process of the horizontal warehouse arched roof is realized; and the accuracy of construction process monitoring identification is improved.

The invention relates to the technical field of data resource management, in particular to an online evaluation method for data resource value, which comprises the following steps of: constructing a multi-dimensional evaluation index set, and acquiring metadata and market dynamic information of to-be-evaluated data in real time through a distributed interface; carrying out weight distribution on the index set based on a dynamic weighting algorithm, generating a weight coefficient set and constructing a value calculation model; performing standardized preprocessing on the collected original data, inputting the data into the model, and calculating an initial estimated value; and dynamically correcting the initial value estimation value based on the timeliness attenuation factor and the data scarcity factor, and generating a final value estimation value and an estimation report. Through multi-dimensional index construction, a dynamic weighting algorithm, standardized preprocessing and two-factor correction, automatic evaluation of the data value is realized, the problems of single dimension, static weight and neglect of timeliness scarcity of a traditional method are solved, the real-time performance and scientificity are improved, and a quantitative decision basis is provided for data capitalization.

The invention relates to the technical field of sensors, and discloses a detection and management method and system for purification equipment. The method comprises the following steps: carrying out three-phase coupling dynamic modeling on a gas phase flow state, a particle phase movement track and a sensor phase interface characteristic in the purification equipment to obtain a gas mass transfer characteristic parameter; performing response characteristic analysis on a real-time detection signal of a sensor array in the purification equipment to obtain a sensor calibration compensation parameter; based on the sensor calibration compensation parameters and the gas mass transfer characteristic parameters, deposition kinetics analysis of pollutants on the surface of the sensor is carried out, and equipment operation state evaluation data are obtained; and performing spatial layout optimization on the sensor array according to the equipment operation state evaluation data and the gas flow consistency constraint condition to obtain an optimal layout scheme and generate a staged adaptive management control instruction. According to the invention, self-adaptive adjustment and intelligent switching of the sensor management strategy are realized, and the detection performance and reliability of the purification equipment sensor system are significantly improved.

Various embodiments include methods and systems for automatically evaluating data structures and / or data management documentation for compliance with relevant standards, requirements, and policies. Various embodiments provide a framework for automating compliance reviews of data structures and / or data management against the latest version of standards, requirements, and policies. Methods may include transforming input requests for analysis and data structures and / or data management documents into formats suitable for processing by a large language model (LLM), prompting the LLM to identify concepts and / or elements that are likely subject to a standard, requirement, or policy, submitting a query to a vector database to identify standards, requirements, or policies relevant to the requested analysis and documents, prompting the LLM to assess compliance the data structures and / or data management documents with standards, requirements, or policy, and producing a report on compliance of the data structures and / or data management documents with the relevant standards, requirements, or policies.

The invention discloses a multi-level dynamic isolation data processing system and method, and relates to the technical field of data security, the system comprises a data access module supporting a cloud native environment and a hybrid deployment mode, a strategy matching module, a sandbox execution module and an audit analysis module; the data access module receives the data, evaluates the sensitivity level and the purpose of the data, and labels the data based on an evaluation result; the strategy matching module reads the data label, searches an isolation strategy and an auditing rule which are matched with the data label from a strategy library, and automatically loads the strategies to the sandbox execution module once the matched strategies are found; the sandbox execution module selects an isolation mode according to the sensitivity level of the data, and processes and analyzes the data in an isolation environment according to a preset rule and algorithm; and the auditing analysis module records all operations on the data in real time, and analyzes the operation compliance based on a preset auditing rule. The problems of large resource consumption, insufficient strategy flexibility, coarse behavior audit granularity and the like in the existing data sandbox technology can be solved.

The invention discloses a bidding and tendering risk early warning method and system based on artificial intelligence, and relates to the field of artificial intelligence risk assessment. Bidding and tendering related data and bidding and tendering enterprise credit data are collected from multiple angles and multiple sources, and are stored in a bidding and tendering database after format alignment is completed; calling bidding and tendering data in the bidding and tendering database, generating a text data set, analyzing financial data based on the text data set, extracting trend characteristics, and calculating a financial ratio; cyclically traversing the text data set, and obtaining keywords and key sentences with bidding and tendering relevance higher than a preset threshold in the text data set through text similarity and keyword feature extraction; and constructing a risk prediction model, inputting the financial ratio, the keywords and the key sentences into the risk prediction model, outputting an early warning value, and determining a current bidding and tendering risk level. According to the invention, the accurate threshold value is preset according to the project and the history, and the evaluation data is updated in real time, thereby realizing real-time accurate early warning to assist related parties in reducing loss.

The invention provides an identification detection method and system for a radio frequency connector, and the method comprises the steps: obtaining the initial appearance image data of the surface of the radio frequency connector, and carrying out the processing of the image data, and obtaining a first appearance image; extracting an appearance feature set and an identification area image from the first appearance image; performing character segmentation and feature extraction on the identification area image, obtaining a preliminary manufacturing information text in combination with a pre-established text recognition model, verifying the preliminary manufacturing information text and associating the preliminary manufacturing information text with the appearance feature set to generate an associated data set; comparing the appearance feature set with a preset standard appearance database to generate defect marking data, fusing the associated data set with the defect marking data to generate comprehensive analysis data, and generating a visual chart according to the comprehensive analysis data; and further analyzing performance index change and defect severity according to the visual chart, generating risk assessment data, and generating a final detection analysis file according to the risk assessment data.

The invention belongs to the technical field of new energy electric power, and discloses a new energy bearing intelligent evaluation and regulation system and method, the system is composed of a data acquisition module, an intelligent evaluation module, a prediction module, a regulation strategy module and an execution and feedback module, and the data acquisition module acquires meteorological, historical operation, load and power grid state data in real time; the time sequence of the prediction module and a deep learning algorithm are combined, short-term to medium-and-long-term prediction is carried out on new energy output and load change, output change is captured in real time, regulation and control lag is avoided, power grid risks are quantified through a multi-model fusion algorithm, and node accessible capacity and a risk assessment result are output; and the regulation and control strategy module generates a regulation and control instruction by utilizing optimization algorithms such as a genetic algorithm based on the evaluation result and the prediction data, and realizes closed-loop control through the execution and feedback module, so that the evaluation data directly guides regulation and control actions.

The invention relates to the technical field of background investigation, and particularly discloses a talent background investigation method based on multi-source data evaluation, which comprises the steps of constructing a dynamic talent knowledge graph, performing semantic alignment processing on multi-source cross-modal data, evaluating data quality based on a multi-dimensional confidence model, and constructing a confidence closed-loop correction mechanism. Performing intelligent correction on the low-confidence data, and reversely updating the credibility weight of the data source according to a correction result; an event-driven incremental update architecture is adopted, and dynamic events of multi-source data are captured in real time through a distributed event awareness engine. According to the method, the dynamic knowledge graph is used as a unified carrier, and the multi-source data is upgraded from fragmented storage to a semantic association network, so that the talent background survey data is converted from static piling to a dynamically evolved knowledge network, and the accuracy and timeliness of evaluation are remarkably improved.

The invention relates to the field of industrial data detection, in particular to an industrial production data anomaly detection system, which comprises a production monitoring module used for detecting a production control difference parameter and a set drift parameter and determining a production data state of a time sequence training data set; the window analysis module is used for responding to the window evaluation condition to determine a window setting strategy of the time sequence training data set; the extraction execution module is used for performing training execution window extraction based on the window setting strategy determined by the window analysis module; the training execution module is used for training the anomaly analysis model based on the acquired training execution windows; and the anomaly evaluation module is used for carrying out reconstruction anomaly analysis on each window analysis data of the to-be-evaluated data to determine a weighted anomaly index of each window analysis data, and responding to the early warning evaluation condition to determine anomaly detection data, and the anomaly detection efficiency of the multivariate time series data is improved.

The invention discloses an intelligent prediction and uncertainty analysis method for pose errors of a six-degree-of-freedom motion platform, and the method comprises the steps: taking an expected pose in a high-precision six-degree-of-freedom motion platform control system as network input data, and obtaining an actual pose through employing a binocular vision measurement system; collecting multiple groups of data, normalizing the data, and dividing the data into a training set and a test set; constructing a pose error prediction network model taking a gated circulation unit neural network as a basic framework, designing a translation and rotation feature coupling attention module, introducing a sparse attention mechanism guided by domain knowledge, and improving the capturing capability of the model for the real mechanical coupling characteristics of the six-degree-of-freedom platform; and inputting a test set into the trained model, and evaluating data of the test set by adopting a Monte Carlo Dropout method to obtain a pose error prediction result and an uncertainty analysis result. According to the method, the static pose precision of the six-degree-of-freedom motion platform can be effectively improved, a visual and reliable network prediction confidence level is provided, and the precision measurement requirement in the metering field is met.

The invention provides an internet right product agent management system, and relates to the technical field of product agent management. The system comprises a product management module, an order management module, an agent management module, a database and a data analysis module. The system processes on-off and stock of products through the product management module, the order management module calculates commission, and the agent management module achieves credit evaluation. The data analysis module predicts requirements through a multi-layer architecture; a data input layer integrates internal and external data; the analysis engine layer adopts feature coding and a time attention mechanism to model space-time correlation, and outputs a demand prediction formula; and the decision output layer generates an agent personalized report through inventory calibration, credit grading and regional weighting strategies, recommends products and early warns risks. The system realizes intelligent acquisition and sales decision making, and improves the operation capability and market influence of the system.

The invention belongs to the technical field of tunnel construction risk assessment, and provides a shield tunnel excavation face instability disaster risk quantitative assessment method and system. The evaluation method comprises the following steps: constructing a shield tunnel excavation face instability disaster risk evaluation system and converting the system into a Bayesian network topological structure; calculating the membership degree of each disaster evaluation index according to the digital feature calculation of the forward cloud generator and the to-be-evaluated data, and converting the membership degree into a Bayesian network prior probability; according to the historical data of the instability disaster of the excavation face of the shield tunnel and the Bayesian network topological structure, parameter learning is conducted on the historical data of the instability disaster of the excavation face of the shield tunnel, and the conditional probability of a Bayesian network model is calculated; according to the Bayesian network prior probability, the Bayesian network model conditional probability and the Bayesian network topological structure, calculating a quantitative evaluation result of the instability disaster risk of the excavation face of the shield tunnel; and according to the quantitative evaluation result of the instability disaster risk of the excavation face of the shield tunnel, performing adjustment decision on shield tunneling control parameters.

The invention discloses an intelligent safety management and control system for data desensitization and risk assessment, relates to the technical field of data analysis, and aims to automatically identify and classify sensitive data by introducing artificial intelligence and big data analysis technologies, dynamically assess safety risks in data use and transmission processes in combination with an intelligent risk assessment model, and improve the safety of data desensitization and risk assessment. Comprising a data acquisition module, a feature extraction module, a risk assessment module, a dynamic early warning module, a rule optimization module and a data desensitization processing module, the processing process of sensitive data can be monitored, managed and controlled in real time, it is ensured that regular denial of service attacks are prevented, the data security and compliance are effectively improved, and through accurate risk assessment and dynamic adjustment of a desensitization strategy, the security and compliance of the sensitive data are improved. According to the method, the efficiency and accuracy of the data processing flow are optimized, the safety risk is remarkably reduced, and the system stability is improved.

The invention discloses a raw coal quality data optimization and reconstruction method based on clean coal ash content feedback, and belongs to the technical field of computers. Data completion is achieved through the generative adversarial network; a generator predicts missing raw coal data according to the high-sampling-frequency clean coal ash content, and a discriminator evaluates the authenticity of the data. According to the method, the characteristic that the clean coal ash sampling rate is higher in the dense medium separation process is used for guiding data reconstruction; meanwhile, a composite loss function including adversarial loss, reconstruction loss and regression loss is designed, and the generation quality is improved. The method effectively solves the problem of raw coal quality data missing in the coal dressing process, and has high engineering application value.

The invention provides a financial risk prediction method based on a multi-objective ensemble learning algorithm, and aims to solve the problems of multi-risk type comprehensive evaluation, data complexity and nonlinear relation processing, dynamic change adaptability and the like in risk prediction in the financial field. According to the method, a financial time sequence is divided into a plurality of time blocks through a space-time block data preprocessing technology, and a heterogeneous model library is independently trained, so that the adaptability and prediction precision of a model are improved. Meanwhile, an NSGA-II multi-objective optimization algorithm is combined with Gibbs distribution to dynamically distribute weights, the prediction precision and the engineering practicability are balanced, and dynamic optimization of economic indexes and engineering indexes is achieved. In addition, a time decay and mixed attention mechanism is designed, multi-model prediction results are fused, market dynamic changes are further captured, and the accuracy and real-time performance of risk prediction are improved. According to the method, the precision and the real-time response capability of financial risk prediction are effectively improved.

The invention discloses a method for training a large model in the field of satellite communication, and aims to solve the problem that a general large model is insufficient in processing capability in the professional field of satellite communication. The method comprises the specific steps that multi-modal professional corpora (structured and unstructured data) in the satellite communication field are collected; generating more than ten thousands of incremental pre-training and fine-tuning corpora through data preprocessing, performing data enhancement on the corpora by adopting a plurality of mixing strategies, and dividing a training set and a test set according to a general proportion; based on low-rank self-adaptive fine tuning technologies such as LoRA, incremental pre-training and supervised fine tuning are carried out on the general large model; a professional evaluation data set is constructed manually, a self-established evaluation data set is used for comparing semantic comprehension and generation capabilities of the satellite communication large model and the general model in the satellite communication field, and model training hyper-parameters are iterated according to an evaluation result; an RAG framework is fused, a self-built private database, a self-defined text partitioning method and a knowledge base calling strategy are integrated, knowledge base data are accurately called, and question and answer content conforming to rules and specifications is generated.

The invention discloses a silica sol preparation process optimization method and system based on data analysis, and belongs to the field of data processing.The method comprises the steps that operation data of an ion exchange column are obtained, meanwhile, quality parameters of silica sol are obtained, and evaluation data about the health state of the ion exchange column are calculated based on operation data of an inlet and an outlet of the ion exchange column; and then evaluating the health state index of the ion exchange column, predicting the quality parameters of the silica sol based on the operation data of the ion exchange column, estimating the influence of the column state on the quality of the silica sol, calculating the index relative error based on the comparison of the prediction index value and the actual measurement value of the silica sol, and adjusting the collection frequency of the silica sol. Meanwhile, the residual effective operation time of the ion exchange column is evaluated by combining the column saturation and the health state index, so that the stability of silica sol production is improved, and the production efficiency is improved.

The invention provides a flow battery energy storage power station full life cycle health degree assessment management method. The method comprises the following steps: constructing an initial health reference library, collecting electrochemical parameters through a reference electrode, collecting electrolyte physical parameters and environmental parameters through a sensor, synchronously recording operation and maintenance data, and carrying out time-space synchronization processing; introducing a hierarchical fusion assessment mechanism, assessing data integrity and constructing an attention mechanism model, calculating a dynamic weight according to a parameter relative change rate and a health degree correlation coefficient, generating a weighted feature vector, and predicting a short-term health degree index by adopting a bidirectional long-short-term memory network Bi-LSTM; a particle filter (PF) algorithm is introduced to be combined with a physical parameter drift distance to correct a long-term health degree index, and then a real-time health degree index is output through fuzzy comprehensive evaluation. The method can achieve the linkage evaluation of the health state in the operation period and the full life cycle trend, corrects the long-term SOH through the PF algorithm in combination with the physical parameter drift distance, and reduces the error of long-term prediction.

The invention discloses a yarn uniformity analysis method based on image processing, and relates to the technical field of image data processing, and the method comprises the steps: employing a convolutional neural network model to carry out the deep learning analysis of the surface features of a yarn through a preliminary feature distribution diagram, carrying out the adaptive feature weighting processing of a complex texture region, and obtaining a feature distribution diagram; obtaining a more accurate third characteristic distribution diagram; if the characteristic values of some areas in the third characteristic distribution diagram exceed a preset abnormal threshold value, performing local amplification analysis on the areas, and further extracting fine defect boundaries in combination with an edge detection algorithm to obtain a defect candidate area set; and generating a visual analysis report according to the multi-dimensional evaluation data, visually presenting yarn uniformity distribution by applying a thermodynamic diagram technology, marking specific position information for a defect high-incidence area, and outputting a final detection analysis result. The problems of yarn image acquisition and defect detection under complex illumination can be effectively solved, and the accuracy and efficiency of yarn uniformity detection are improved.