805 results about "Data governance" patented technology

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention relates to the technical field of natural language processing, and discloses a power generation industry data intelligent treatment method, device and equipment based on a large model, and the method comprises the steps: constructing a multi-source heterogeneous task data set covering structured and unstructured information, completing the fine tuning training of a plurality of industry sub-fields based on a language model, forming a large language model set with specific scene adaptability; constructing a multi-view semantic representation structure for actual input data, integrating modeling task intention, application scene and model adaptability, predicting an optimal target model and a Top-K candidate model, and generating a unified semantic embedding vector; and realizing accurate matching of the structured knowledge fragments through the graph neural network. The problems that an existing model is insufficient in semantic understanding, inflexible in model selection and inaccurate in knowledge calling in the data management process are solved, the requirements of diversified tasks for accuracy and specialty are met, and then management of data assets is facilitated.

The invention provides a data privacy protection method for a data governance system, and belongs to the technical field of data governance, and the method comprises the steps: carrying out the cross verification of multi-source feature data and the unique identification information of an object collected on site, generating an original data set, carrying out the sensitive information recognition and grading, and generating the preprocessing data with a sensitive grade label; core identification information in the preprocessed data is disassembled to generate standardized desensitized data conforming to privacy protection, a bidirectional encryption mapping relation is established between object unique identification information collected on site and the standardized desensitized data, an encryption index is formed, the encryption index and preset multi-dimensional compliance data are fused, and a data fusion result is obtained; generating standardized fusion data; and based on the access token, generating a differential authorization data set divided according to permission granularity, performing privacy disclosure risk assessment, generating a risk level, performing privacy processing on the risk level, and outputting the risk level to a risk control system. And the data management efficiency is improved.

The invention relates to the technical field of information and data processing, in particular to an enterprise digitalization-oriented data management and application method, which comprises the following steps of: acquiring multi-source heterogeneous data and recording metadata; establishing a cross-department management system to standardize the treatment process; building an asset catalog based on the business section and the data field classification; formulating unified data description of a multi-level data standard system; designing a quality control rule to filter low-quality data; constructing a business index system and embedding the business index system into a business system; identifying a cross-system and cross-type association mode through a metadata association technology and a rule engine; and dynamically adjusting the treatment process by adopting a multi-objective optimization algorithm in combination with service feedback and treatment parameters. According to the method, the defects of existing data governance in comprehensiveness, depth and business adaptability are overcome, standardized governance and potential value mining of multi-source data are achieved, and effective data support is provided for intelligent decision making of enterprises.

The invention belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

The invention discloses a hydraulic engineering construction operation parameter optimization and data management method and system. The method comprises the steps of 1, multi-source data access and vectorization; step 2, time sequence feature extraction and multi-modal fusion; step 3, identifying interpretable influence factors; 4, performing multi-task dynamic anomaly prediction; 5, risk weighted fusion and uncertainty quantification are carried out; 6, carrying out digital twinborn simulation verification; step 7, governance strategy optimization and security auditing; and step 8, feedback acquisition and model adaptive updating. The method has the advantages that multi-modal unified representation is constructed by fusing structured data such as water level and flow with unstructured information such as meteorological images and operation and maintenance logs, time sequence feature extraction is carried out in combination with the space-time diagram neural network, the dynamic sensing and prediction capability of the complex hydrological process is remarkably improved, and the method is suitable for being popularized and applied. According to the invention, the conversion from static threshold alarm to multi-task and prospective abnormal early warning is realized, and the adaptability of the system to multiple scenes is enhanced.

The invention relates to the technical field of data management and information, and discloses a data asset identification system and method based on multi-dimensional rule engine blood relationship analysis, and the method comprises the steps: obtaining static code information and dynamic operation tracking information of to-be-analyzed data; constructing a theoretical data blood relationship map based on the static code information; constructing a real-time data blood relationship map based on the dynamic operation tracking information; generating a fusion data consanguinity map by adopting a time window alignment strategy; constructing a five-dimensional data asset identification rule model; starting a blood relationship driven multi-dimensional rule engine; executing the multi-dimensional rule engine, and evaluating and determining the nature of the to-be-analyzed data; in the evaluation process, a conflict resolution mechanism is started. According to the method, the multi-dimensional rule engine is driven through dynamic and static combined blood relationship analysis, and conflict judgment is performed by using blood relationship information.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

The invention discloses a data governance agent system and an implementation method, and relates to the field of data processing, and the system comprises a data analysis module, a data storage module, a user response module, a quality monitoring and feedback module and a field large language model module. The data analysis module performs unified analysis modeling on structured and unstructured data; the data storage module adopts a graph database and a vector database to store knowledge data; the user response module supports natural language interaction and intelligent question and answer; the quality monitoring and feedback module optimizes data quality through closed-loop feedback; and the field large language model module constructs an optimization model adaptive to the vertical field. Through multi-modal analysis, knowledge graph construction, double-library collaborative storage and model dynamic optimization, a data barrier is broken through, manual dependence is reduced, the data treatment efficiency, quality and intelligent level are improved, and the method is suitable for specialized data treatment requirements of industries such as manufacturing and finance.

The invention provides an automatic data management method and system based on a multi-modal large model, and the method comprises the steps: collecting multi-source heterogeneous industrial data, and carrying out the standardization processing, and forming standardized multivariable time series data; constructing a process knowledge base, and performing semantic embedding coding on a process knowledge text and storing the process knowledge text; constructing and finely tuning a KTSF multi-modal large model, and fusing process knowledge semantics and multivariable time sequence data through a cross-modal attention mechanism to generate joint semantic representation; based on prediction of a KTSF multi-mode large model, outputting a residual error with actual data, and dynamically identifying abnormal data; performing attribution analysis; based on an attribution result, calling a KTSF multi-mode large model to generate a repair value, and performing intelligent correction on the abnormal data; the design quality evaluation and feedback learning module is used for calculating a data quality score and driving incremental updating of the model; and the design rule self-learning module is used for automatically extracting the governance rule through clustering analysis and updating the knowledge base.

The invention discloses a medical data management method and system based on multi-modal fusion and privacy protection, and the method comprises the steps: receiving multi-modal medical data, and executing cross-modal embedding learning to generate a joint embedding vector; constructing a patient multi-modal graph based on the joint embedded vector, and detecting abnormal inconsistency between the cross-modal data by using a graph attention network to generate an abnormal detection result; a privacy protection strategy is dynamically adjusted according to the anomaly detection result and the access context information, and desensitization processing is conducted on the medical data; and constructing a global model through security aggregation based on the desensitized medical data to generate an optimal governance action and apply the optimal governance action to a data management process. Through organic cooperation of multi-modal fusion, anomaly detection, adaptive privacy protection, security aggregation and dynamic strategy optimization, a closed-loop medical data governance platform is successfully constructed, and the problems of multi-modal data fragmentation, privacy compliance conflict and dynamic governance deficiency are effectively solved.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention relates to the technical field of intelligent agriculture, in particular to a multi-source heterogeneous big data governance method and device.The multi-source heterogeneous big data governance method comprises the following steps that S1, multi-source heterogeneous data collection and semantic alignment are conducted, hidden semantic conflicts are eliminated; s2, performing dynamic data source adaptation and quality monitoring; s3, performing cross-domain permission penetration control; s4, building an industrial model driven by federal learning; and S5, double-chain block chain evidence storage and traceability are carried out. The method is used for disambiguating dialect term conflict, federated learning cross-domain privacy modeling and double-chain block chain evidence storage traceability through a dynamic semantic alignment neural network, and systematically solves the problems of dialect semantic segmentation, data islands and sensitive information leakage caused by small scattered distribution of peasant households in the field of traditional Chinese medicinal material planting. And a trust closed loop of government-enterprise-peasant household three-party cooperative treatment is constructed.

The invention discloses a data governance method and device based on an MCP protocol and a medium, and the method comprises the steps: obtaining a language instruction of a user through a data center regulation and control MCP client, determining a data governance demand according to the language instruction, and determining an obtaining request according to the data governance demand; and sending the acquisition request to the MCP server to acquire the data resource through the MCP server, and performing data processing according to the data resource. The data governance process is started through the natural language instruction, user operation is facilitated, and the use threshold is lowered. And the data governance demand and the acquisition request are accurately determined through the analysis instruction, so that the governance pertinence is improved. And resources are obtained and processed by means of the MCP server, so that efficient data management is realized. According to the process, user requirements and data acquisition and processing links are integrated, so that data management is more convenient, accurate and efficient.

The invention provides a data weaving platform-oriented self-service data preparation method, which comprises the following steps of: accessing original data and associated structured metadata of a target data source into a data weaving platform to generate standardized accessed original data and associated structured metadata; based on a semantic model of the data weaving platform, performing classified semantic annotation on the associated structured metadata, and positioning a transfer node of the associated structured metadata to generate a data blood relationship record; performing bidirectional quality verification on the data consanguinity record according to semantics and path continuity, and matching with a federated governance rule of a data weaving platform to generate a metadata governance list; and generating a unified metadata directory based on the metadata governance list, so that a user of the data knitting platform can obtain key segments containing complete data consanguinity records. According to the method, the problem of insufficient accuracy and efficiency in self-service data preparation can be effectively relieved, and the efficient preparation requirement of a data weaving platform for multi-source data is met.

The invention relates to the technical field of data processing, in particular to an integrated multi-mode culture resource intelligent data governance and management system. The system comprises a data acquisition module, an intelligent processing module, a data management module, a knowledge organization module and an interactive display module. Synchronous acquisition of videos, audios, images and three-dimensional point clouds is realized through camera equipment, audio acquisition equipment and a laser scanner, and cultural elements are extracted through action recognition, speech recognition model processing dialect transcription, image semantic segmentation and named entity recognition by adopting a convolutional neural network. The system realizes structured description and semantic mapping of data on the basis of non-abandoned domain ontology, and constructs a knowledge graph by means of a graph database and a graph neural network. And finally, realizing three-dimensional visual display of the cultural resources by combining a virtual reality technology. According to the method, the problems of non-abandoned multi-modal data processing splitting, insufficient semantic organization and weak display interactivity in the prior art are solved, and the digital governance level and the propagation capability of cultural resources are improved.

The invention relates to the technical field of new energy power data governance, in particular to a multi-source fusion cleaning and closed-loop quality control method and device for wind power data and a medium, and the method comprises the steps: generating an initial data set through collecting SCADA time sequence data and at least one type of external meteorological data of a wind turbine generator; based on the initial data set, executing physical consistency check, grouping adaptive statistical detection, unsupervised machine learning detection and time sequence residual analysis in parallel, and generating four independent exception judgment vectors; inputting the four abnormal judgment vectors into a preset fusion decision model, and outputting a comprehensive abnormal label; obtaining a repaired data set according to the comprehensive abnormal label; and calculating a multi-dimensional quality score representing data quality based on the repaired data set, and triggering a quality control closed-loop operation according to the multi-dimensional quality score and a comparison test result. Through the arrangement, multi-dimensional fusion detection and self-adaptive intelligent repair can be realized, and integrated cleaning of quality quantitative evaluation and closed-loop verification can be realized.

The invention relates to a database table field description intelligent generation and dynamic maintenance method and device based on RAG and a large model. The method comprises the following steps: constructing a domain-based dynamic knowledge base system through multi-source corpus collection, domain adaptation vectorization model processing and a dynamic knowledge base construction and updating process; receiving a description generation request of a table or a field, recalling related knowledge from the knowledge base through an RAG technology, assembling cue words, and then calling a large model for reasoning to generate a description result; the generated description result is confirmed through manual auditing, the knowledge base is updated according to confirmation feedback, and continuous optimization of the knowledge base is achieved. According to the method provided by the invention, the problems of low accuracy, high maintenance cost, poor interpretability and the like in a traditional method are effectively solved by constructing a field-enhanced RAG architecture and combining a three-layer knowledge system and a self-adaptive prompt framework which can be dynamically adjusted according to a business scene, and a new technical implementation path is provided for table and field description in intelligent data governance.

The invention provides a dynamic data traceability method and system oriented to network security, and relates to the technical field of network security and data governance crossing, and the method comprises the steps: carrying out the real-time visual monitoring of a user interaction interface of a data transaction platform, and capturing the visual behavior information related to data calling; extracting feature parameters based on the visual behavior information to obtain a visual feature data set, and performing structured conversion on the visual feature data set to obtain a structured call log record containing visual behavior associated information; based on the structured call log record, extracting a time sequence, a frequency and a spatial distribution feature of a call behavior as basic indexes; a plurality of feature sampling states are dynamically selected in a monitoring time window, and a dynamic feature evaluation set is constructed based on the feature sampling states. According to the invention, the accuracy and controllability of network security protection in a data transaction scene can be improved.

The invention discloses a data label generation and quality control method and system based on a pre-training large model, and relates to the technical field of data management. Aiming at the problems of low efficiency, poor label consistency and difficulty in continuously guaranteeing quality of an existing label generation mode, the adopted scheme comprises the following steps: collecting multi-source heterogeneous data based on a data governance demand; performing preprocessing and vectorization operation on the collected data, then outputting a pre-training large model, and realizing automatic generation of data labels by the pre-training large model in combination with rule constraints; performing quality detection of accuracy, consistency and coverage on the generated labels; performing optimization adjustment on the pre-trained large model or rule according to a quality detection result to realize feedback optimization of a label generation strategy; and outputting a label set passing the quality detection, and binding the label set with the original data to form a traceable label management record. The method is used for subsequent data management and large model training. The method is used for data governance and large model training.

The invention discloses a large model-knowledge graph driven data blood relationship traceability reasoning method and system. According to the method, through a closed-loop framework of KG-driven dynamic decomposition, small-model lightweight reasoning and visual credibility verification, the defects are systematically solved, and an interpretable, low-cost and easy-to-expand data consanguinity depth traceability scheme is provided for scenes with high credibility requirements such as data governance, compliance auditing and root cause analysis. According to the system, aiming at complex problem requirements, after a complex problem is input, the problems of no interpretation, low reliability and the like in data blood relationship traceability are solved through two modules, namely problem reasoning and verification presentation. The modular design not only conforms to the technical decoupling principle, but also facilitates independent optimization, and is suitable for medical treatment, scientific research and other scenes with high requirements for interpretability.

The invention relates to the technical field of data governance, and provides a data platform metadata annotation completion method, system and equipment, and the method comprises the steps: collecting metadata basic annotations in a multi-source heterogeneous database through a standard application program interface; collecting a logic model definition document and a data standard specification document, and constructing a local knowledge base through vectorization processing; when a user triggers annotation completion, calling a local knowledge base through a large language model, and generating metadata intelligent annotations; and returning the collected basic annotation of the metadata and the generated intelligent annotation of the metadata to the user as annotation completion results. According to the data platform metadata annotation completion method, system and equipment, metadata collection normalization, multi-source knowledge fusion, knowledge base construction and intelligent annotation completion can be achieved, cost reduction and efficiency improvement are achieved, the metadata annotation quality is remarkably improved, and the use threshold is lowered.

The invention discloses an Internet of Things multi-source heterogeneous data management method based on artificial intelligence, and the method comprises the following steps: collecting voice data and text data in an Internet of Things system, and constructing a voice and text pair; inputting the voice data into an improved Whisper model, and outputting an enhanced transliteration text; inputting the enhanced transliteration text and the text data into a semantic encoder based on dynamic weighting to generate a fused semantic embedding vector; calculating semantic similarity between the fused semantic embedding vectors, and screening semantic consistent sample pairs; performing event aggregation on the semantically consistent sample pairs; performing semantic level coding and confidence adaptive fusion processing to generate a unified semantic fusion vector; and constructing a backmarking training set based on expert feedback, and executing periodic increment optimization on the updatable parameters. The voice and text multi-modal data fusion quality and semantic consistency recognition precision are improved, and the method has good expandability and is suitable for intelligent data governance tasks in a complex Internet of Things environment.

The invention relates to the technical field of data processing, and discloses a mine production-oriented multi-source data acquisition and edge calculation fusion device, which comprises a clock synchronization access module, a data governance control module, a flow pipeline backpressure module, an AI reasoning optimization module, a rule closed-loop control module, a message safety uplink module and a center training iteration module, according to the method, the problem of difficulty in data fusion of multi-source equipment is solved, and the consistency of heterogeneous data in time and semantic dimensions is ensured; the stability of an edge computing system under a complex working condition is improved, and data loss and processing delay are avoided; collaborative decision-making of the AI model and the rule engine is realized, and the risk of false report and missing report is reduced; the security and reliability of the data transmission process are ensured, and local autonomy when the network is abnormal is supported; a model automatic iteration mechanism is established, and the ability of the system to adapt to different working conditions is improved.

The invention provides an automatic data management method and system based on a large model, and the method comprises the steps: obtaining a native data set corresponding to a to-be-managed data field, carrying out the cross-domain feature association mining of the native data set based on a pre-trained large model, generating a semantic feature map of data entities and an implicit association link between the data entities, and carrying out the data management through the implicit association link. Calling a pre-trained large model, performing quality dimension evaluation based on the semantic characteristic spectrum and the implicit association link, outputting a quality defect vector of a data entity, performing source tracing analysis based on the quality defect vector, and determining a data quality defect type existing in a to-be-treated data field and a cross-entity propagation path of the data quality defect; a strategy evolution module in the pre-training large model is driven to generate a self-adaptive governance strategy containing defect positioning anchor points, and the self-adaptive governance strategy is deployed to a data governance execution environment to complete automatic data governance, so that the governance strategy can better adapt to different data quality scenes; and the pertinence and effectiveness of data management operation are improved.

The invention provides an intelligent agent-based automatic verification and rule matching system for insurance-waiting evaluation reports. The intelligent agent-based automatic verification and rule matching system comprises a report acquisition module which pulls to-be-verified and historical insurance-waiting reports; the auditing processing module calls an attention Bi-LSTM model which is subjected to reinforcement learning fine adjustment and is fused with knowledge graph embedding, and scores the report sentence by sentence; the rule management module is used for storing and analyzing an equal-guarantee 2.0 structured machine rule and carrying out secondary accurate matching on low-confidence sentences; the knowledge graph module is used for maintaining a multi-dimensional association graph of a standard family, a control measure and an evaluation item and providing graph reasoning; the agent decision-making module generates actions of passing, returning, manual rechecking or automatic correction according to the auditing state; the feedback learning module collects artificial recheck differences, an incremental training model, a strategy network and atlas vectors; and the storage module stores reports, models, rules, maps and logs in a multi-modal manner through a relation library, a map library and a vector library, and implements data governance and access control. According to the invention, the audit efficiency, accuracy and standard consistency of the evaluation report can be improved.

The invention discloses a unified search and page configuration rendering method and system based on a data model. The method comprises the following steps: S1, data acquisition and model mapping; s2, indexing and synchronizing data; s3, unified search request processing; s4, processing and returning a result set; and S5, dynamically rendering the page. The system comprises a data source layer, a data unification layer, an index layer, an application service layer and a display layer. The invention provides a set of end-to-end integrated solution for solving the problems that multi-source heterogeneous data is difficult to integrate, the cross-system search efficiency is low, page rendering lacks flexibility and the like. According to the scheme, by constructing a standardized data model, a unified search engine index system and a dynamic page rendering mechanism driven by configuration, a complete technical link from data source access to front-end display is achieved, and the efficiency and expansibility of the system in the aspects of data management, information retrieval and page display are remarkably improved.

The invention relates to the technical field of big data management and artificial intelligence application, in particular to an automatic data quality inspection system and method based on a big model and data flow orchestration, and the method comprises the following steps: system architecture construction: employing a three-layer architecture design which comprises a data access layer, an intelligent quality inspection layer and a flow orchestration layer; the method has the beneficial effects that the full-link automatic quality inspection of structured and unstructured data is realized by integrating the intelligent analysis capability of a large language model (LLM) and the data processing pipeline of a low-code process arrangement tool. The system combines a rule engine and large model dynamic reasoning, supports customized quality inspection rule generation, abnormal data intelligent identification and quality inspection process adaptive optimization, improves the automation level and complex scene adaptability of data quality detection, and is suitable for data governance scenes of multiple industries such as government, finance, medical treatment, e-commerce and the like.

The invention provides an intelligent solitary policy processing method based on a neural network and machine learning, and relates to the field of data processing. According to the method, strategy byte codes and execution contexts are generated by extracting business rules, supervision calibers and field dependency relationships of the single insurance policy, discovery, repair and arbitration of field abnormity are achieved in combination with a dynamic data governance model, an account-falling confirmation receipt is output through a voting game and a compensation transaction, and a replayable trace and a rare mode index are generated. And a high-confidence label and an error correction prior increment are obtained in combination with triggering verification, and finally, full-link evidence is solidified through continuous distillation, chain type evidence storage and an auditable snapshot library, so that a traceable record of the single insurance policy is formed. By implementing the technical scheme provided by the invention, full-link data processing can be conveniently carried out on the single insurance policy, so that the processing efficiency is effectively improved.

The invention provides a redundant material data cleaning system based on a large model, belongs to the technical field of industrial intelligence and data governance, and realizes end-to-end semantic cleaning by utilizing the deep semantic understanding capability of a pre-trained large language model. Material description is converted into a high-dimensional semantic vector, semantic similarity is accurately calculated in a unified vector space, materials with the same essence are recognized beyond literal difference, and redundant items are automatically merged through intelligent clustering. The method not only can deeply capture semantic association between core attributes and effectively neglect noise, but also has the potential of fusing structured attributes to perform more robust judgment, significantly improves cleaning precision and efficiency, and overcomes inherent limitations of a traditional assembly line method.