1117 results about "Quality data" patented technology

The invention relates to the technical field of die-casting optimization, and discloses a die-casting process parameter optimization method and system based on digital twinning, and the method comprises the steps: arranging a sensor to collect the operation parameters of die-casting equipment and the quality data of a die casting in real time, and forming multi-source die-casting production data; according to multi-source die-casting production data, a multi-physical field simulation model is established, and a digital twinborn model is constructed. And comparing the virtual prediction result with the actually measured quality data, and constructing a virtual-real difference compensation network to correct the parameters of the digital twin model. And performing a multi-target reinforcement learning method based on the compensated digital twin model to generate optimal die-casting process parameters. And applying the optimal die-casting process parameters to die-casting equipment for verification, and updating the virtual-real difference compensation network according to a verification result. Intelligent optimization and continuous self-evolution of the die-casting process parameters are achieved, and the casting forming precision, the energy efficiency utilization rate and the production stability are improved.

The invention discloses a marketing content generation method and system based on big data, and relates to the technical field of big data processing and artificial intelligence, and the system comprises a multi-source data collection and fusion module which is used for receiving original multi-source data from different behavior sources, content sources and business scene sources, and performing structured processing, time relationship coordination and feature fusion on the original multi-source data to generate a fused portrait data set for describing the relationship between the user and the product. In the invention, through the multi-source data acquisition and fusion module, deep integration of heterogeneous multi-source data and unified representation of multi-modal features are realized, a high-quality data basis is provided for subsequent knowledge graph construction and content generation, and the problems of isolated island and insufficient feature utilization of traditional marketing data are solved; through a ternary knowledge graph construction module, information such as users, products, scenes and the like is constructed into a structured knowledge graph, and the semantic understanding ability and relevance of content generation are greatly enhanced.

The invention relates to the technical field of civil engineering structure health monitoring, in particular to a concrete bridge crack abnormity intelligent monitoring and early warning method based on GAF image classification and GRU prediction.The method comprises the steps that an intelligent monitoring framework integrating GAF image coding, CNN classification and recognition and GRU time sequence predication is constructed, high-frequency noise is removed through wavelet packet decomposition, and then the GRU time sequence predication is carried out; smoothing the crack-temperature coupling time sequence data; encoding the image into a two-dimensional image through a GAF method, and enabling the CNN to recognize an abnormal mode; and training a GRU model based on the high-quality data set after abnormity elimination, and realizing accurate modeling of crack width evolution under temperature driving. According to the method, residual error approximate normal distribution is predicted, the crack width early warning decision coefficient (R) is stabilized to be more than 0.93, a + / -3 sigma dynamic residual error threshold early warning mechanism is combined, structural damage trends under different disturbance scenes can be identified in a graded mode, and the method is suitable for online monitoring and maintenance decision support of bridge crack diseases in actual engineering.

The invention provides a low-voltage power distribution network load change trend intelligent prediction and management method, belongs to the field of low-voltage power distribution network load prediction, and is used for solving the problems of large load fluctuation, insufficient prediction precision and high cloud deployment delay of a hybrid industry transformer area in related technologies. The method is deployed at an edge node of a transformer area, high-quality data is output through multi-modal data anomaly detection and scene completion, a four-dimensional dynamic load portrait is constructed based on the high-quality data, model super-parameter self-adaptive parameter adjustment is realized by combining transfer learning and Bayesian optimization, and accurate load data is output through three-dimensional linkage resource scheduling and dynamic fusion residual error correction. The method improves the load prediction precision and efficiency, reduces the response delay, and can effectively support the real-time scheduling of the power distribution network.

The invention provides a construction and use method of a potential diffusion model for SAR image super-resolution. The construction and use method comprises the steps of obtaining an original SAR image and inputting the original SAR image into a real degradation model to generate a degraded SAR image; inputting the degraded SAR image into an automatic encoder to generate a structure-enhanced submerged space feature map; and inputting the structure-enhanced latent space feature map and the degraded SAR image into a potential diffusion model to generate an SAR super-resolution image. The method has the beneficial effects that a two-stage training strategy is adopted, different optimization targets are focused in stages, the training efficiency is improved, and meanwhile, the learning ability of the model to SAR image features is enhanced; sAR imaging key degradation factors are comprehensively covered, so that a generated low-resolution sample is closer to a real scene, high-quality data support is provided for model training, and model learning is prevented from being separated from an actual degradation rule; sAR specific interference such as speckle noise is effectively simulated, and the anti-noise training effect of the model is enhanced.

Data is the “fuel” that powers the machine learning “engine” for Artificial Intelligence. However, identifying high quality data that can catalyze smarter AI, AGI, and SuperIntelligent systems is becoming an increasingly challenging bottleneck for machine learning. This invention not only describes novel methods for identifying the most valuable data, but it also presents an entirely new framework for understanding the information content of AI-relevant datasets. The methods can be used by intelligent systems autonomously or in collaboration with humans. Novel methods for accelerating AI learning, and for updating the knowledge of AI systems in real-time, are also disclosed. Consistent with the view that human survival may depend on the fastest path to AGI also being the safest path, the invention describes catalysts which help maximize alignment between the values of AGI and humans. These innovative catalysts increase not only the intelligence, but also the safety, of AI systems.

The invention discloses a real-time cleaning and aligning method for multi-source heterogeneous data, and particularly relates to the technical field of source heterogeneous data, a self-adaptive interface adapter is compatible with multiple types of data, and a semantic index atlas is generated through four-dimensional classification labeling; constructing a cloud-edge collaborative streaming processing framework, preprocessing edge nodes, and performing accurate cloud alignment; a dynamic rule cleaning and semantic-dimension-entity three-layer progressive alignment mechanism is adopted, and a four-dimensional quality evaluation system is combined for real-time monitoring; and through closed-loop optimization, homomorphic encryption, a block chain and other security mechanisms, data security and traceability are ensured. According to the real-time cleaning and aligning method for the multi-source heterogeneous data, the real-time performance and accuracy of data processing are effectively improved, the method is adaptive to multiple service scenes, and high-quality data support is provided for data value mining.

The invention relates to the technical field of machine vision and industrial automation, and discloses an industrial sewing machine stitch defect detection and self-adaptive compensation method based on machine vision. The technical problems that stitch quality control of an existing industrial sewing machine depends on manpower, efficiency is low, and defects cannot be intervened in real time are solved. After synchronously capturing a stitch image, segmenting a stitch graph through image processing; extracting quality data of the stitch length and the average width value from the graph; comparing the quality data with preset parameters to diagnose defect types; a compensation instruction is generated according to the diagnosed defect type, and the cloth feeding speed and the upper thread tension of the sewing machine are adjusted in real time. By extracting the geometrical characteristics of the stitches and performing quantitative analysis, the defects of stitch skipping, non-uniform stitch length and abnormal stitch tightness are automatically identified; by establishing a closed-loop feedback mechanism of defect types and specific compensation actions, the follow-up stitches are rectified in real time, so that the defective rate is reduced, and the manual dependence is reduced.

The invention discloses a block chain and digital twinborn integrated production whole process credible tracing method, and belongs to the technical field of intelligent manufacturing and digital tracing. The system comprises a digital twin construction module, a block chain evidence storage module, a tracing verification module and a data acquisition module. The method comprises the following steps: creating a digital twinborn body for each product instance, and mapping material, process, equipment and quality data of the whole manufacturing process in real time; recording the twinborn key state hash value to a block chain in real time through an intelligent contract; credible tracing verification is provided based on block chain evidence storage and digital twin data; industrial Internet of Things equipment is used for collecting manufacturing data in real time to drive twin updating. According to the method, millisecond-level slice tracing in the manufacturing process is realized, the deep description capability of digital twinning and credible guarantee of the block chain are fused, the problems of insufficient data depth and low credibility of a traditional tracing system are solved, and judicial-level evidence support is provided for quality disputes.

The invention discloses a sensitive data protection-oriented generative model differential privacy leakage prevention method and system, and belongs to the technical field of artificial intelligence and data privacy protection. Constructing a forward diffusion and reverse sampling process based on sample information and a diffusion generation model framework; calculating the semantic sensitivity of the current generation state in each step of reverse sampling through a self-adaptive differential disturbance regulation algorithm, and regulating the noise intensity of the current step according to self-adaption; privacy expenditure brought by each round of disturbance is monitored through a verifiable privacy budget tracking mechanism, and real-time tracking privacy budget consumption is obtained; identifying high-similarity samples as high-risk areas in each round of generation by generating a risk perception feedback optimization strategy; and the model is guided to be far away from the privacy sensitive area by dynamically enhancing the disturbance intensity of high-similarity samples. According to the method, the privacy protection capability of the model in the sensitive field is effectively improved while high-quality data generation is realized.

The invention relates to the field of computer vision algorithms, in particular to a defect detection data screening method based on a Pontryagin maximum principle, which comprises the following steps of: training a training data set and testing an evaluation data set to obtain a model reference index; based on a preset proxy data set, respectively calculating definition, labeling integrity and distribution deviation degree indexes to obtain an initial quality weight vector; iteratively updating the basic model parameters and reversely iteratively updating the target vector to obtain a sample quality score; a training scoring device scores and sorts full samples of the training data set; and dividing the sorted full samples into a plurality of candidate screening intervals, and screening high-quality data to train a final model. According to the method, the entropy weight method is adopted to weight the three dimensions to obtain the initial quality weight, so that the initial weight of the sample can reflect the own basic quality difference, the problem that the traditional uniform weight ignores the sample quality difference is avoided, and the accuracy of the sample quality score is further improved.

The invention discloses a collaborative data processing method and system based on source-network-load-storage integration. The collaborative data processing method and system are used for data acquisition, processing and optimal scheduling of multi-source heterogeneous equipment. The method comprises the following steps: registering a power generation side device, an energy storage side device, a power distribution network device and a load side device, obtaining self-description information, automatically identifying a communication protocol and loading a corresponding drive; a standardized data stream is generated through protocol conversion and semantic mapping, edge nodes execute real-time feature extraction and preliminary calculation, and a cloud platform performs global state estimation, prediction analysis and optimization scheduling; establishing a time delay monitoring and task priority mechanism to realize edge and cloud dynamic collaboration; performing quality evaluation and anomaly repair on the transmission data to generate a high-quality data set; a probability distribution model is established based on data, an uncertainty weight is calculated, feature fusion and weighted dimension reduction are performed through a collaborative data processing engine, a weighted confidence matrix is formed and input into optimal scheduling, system robustness is enhanced, and the method is suitable for a power system with a high new energy proportion and large load fluctuation.

The invention discloses a geometric constraint fitting point cloud filtering method for sea surface three-dimensional reconstruction, and belongs to the technical field of computer vision and three-dimensional reconstruction. The objective of the invention is to solve the problem of insufficient subsequent three-dimensional reconstruction precision caused by interference of reflection noise, mismatching points and the like in sea surface point cloud. The method specifically comprises the following seven steps: firstly, acquiring sea surface original point cloud through three-dimensional data acquisition equipment; a filtering technology is adopted to obtain a to-be-fitted point cloud; fitting a quadric surface through an improved RANSAC (Random Sample Consensus) algorithm to solve an initial parameter; constructing a comprehensive error function, and optimizing the model through gradient descent; effective inner points are screened through quadratic term coefficient constraint and a distance threshold value; and finally, iterating until a termination condition is met, and outputting an optimal effective point cloud. The method is high in noise rejection rate, the point cloud fits the sea surface form, and high-quality data support can be provided for sea surface fitting, sea wave simulation and unmanned ship control.

The invention discloses a cascade reservoir scheduling method and system based on an artificial bee colony algorithm, and the method comprises the following steps: the system collects multi-source hydrological data in real time through a hydrological perception and preprocessing module, and introduces a large language model to carry out the semantic judgment and anomaly labeling of an abnormal hydrological time sequence; inputting the processed high-quality data into a reservoir model construction module, establishing a cascade reservoir optimal scheduling system, and setting corresponding boundary conditions and operation constraints in combination with reservoir scheduling regulations; the scheduling optimization module receives model input, adopts a variable structure taking a water level as a core to construct an optimization individual, and completes population initialization, disturbance generation and fitness evaluation based on a potential solution guide mechanism in an improved artificial bee colony algorithm; the system transmits the scheduling sequence optimized and output by the scheduling optimization module into an LLM intelligent auxiliary module; and the intelligent text interpretation generated by the LLM and the scheduling optimization solution enter a result evaluation and visualization module together. According to the scheduling method and system, a high-quality and physically feasible scheduling scheme can be output within reasonable calculation time.

The invention discloses a convective cloud in-cloud temperature gradient joint inversion method based on a laser radar, and relates to the technical field of meteorological detection, and the method comprises the steps: synchronously collecting multi-modal meteorological remote sensing data through a multi-modal meteorological remote sensing device; constructing an adversarial unsupervised correction network, inputting the multi-modal meteorological remote sensing data into the adversarial unsupervised correction network, and generating corrected multi-source observation data; mapping the corrected multi-source observation data to a uniform space-time grid to form a space-time aligned multi-modal data set; and based on the vertical profile of the temperature gradient, in combination with the radar reflectivity factor and the fusion feature vector, calculating the vertical distribution of the liquid water content, the water vapor density and the particle effective radius in the convective cloud, and generating a quantitative analysis report of the macro and micro characteristics of the convective cloud. According to the invention, by constructing the antagonistic unsupervised correction network, errors in multi-source observation data can be effectively corrected, and a high-quality data basis is provided for fusion and inversion in meteorological service.

The invention discloses a dynamic active learning and knowledge graph agent-based airworthiness document review method, which comprises the following steps of: collecting and processing a multi-modal heterogeneous data source to obtain a standardized knowledge base; based on the standardized knowledge base, constructing an aviation domain knowledge system by using dynamic active learning and a man-machine cooperation mechanism; constructing a knowledge graph and performing knowledge enhancement; and dynamically constructing a review context based on a retrieval enhancement generation method in combination with the knowledge graph, and injecting an intelligent agent to perform intelligent data review and report generation. According to the method, automatic review of airworthiness documents is realized through a dynamic active learning-agent review method, and the manual review efficiency is remarkably improved; domain knowledge is injected into an intelligent agent by utilizing an RAG technology, so that the accuracy of a general large language model in an airworthiness review task is improved; through knowledge semantic driving and human-in-the-loop knowledge enhancement, the problem of scarcity of high-quality data in the field is relieved; models of different parameter scales are cooperatively utilized, and computing power resources are saved.

The invention relates to the technical field of freeze-drying control, and discloses an automatic control method and system for freeze-drying, rotating and pre-freezing. According to the method, a freeze-drying process dynamic model is constructed by monitoring operation parameters in a rotary pre-freezing stage, and characteristic indexes of the freeze-drying process dynamic model are analyzed; comparing the index with a standard reference value to generate a deviation analysis result, mining abnormal signals, screening key control points, monitoring environmental parameters in real time, and calculating response coefficients of the environmental parameters influencing the key control points; evaluating a potential fault mode based on the response coefficient, positioning a fragile link in the process, and carrying out performance detection to find out a specific problem point; formulating a control planning target according to the problem point, analyzing a corresponding control mode, querying a standard of the control mode, and generating a control operation instruction according to the standard; after the instruction is applied to equipment execution control, freeze-dried product quality data is collected for consistency verification, and finally a process quality report is generated. According to the method, multi-dimensional monitoring, fault early warning and accurate regulation and control of the freeze-drying, rotating and pre-freezing process are achieved.

The invention provides a blended yarn process intelligent design system combining digital twinning and multi-objective optimization, relates to the field of textile process optimization, and improves the efficiency of blended yarn process design and the product quality stability. The method comprises the following steps: firstly, collecting real-time data, establishing a digital twinning model, simulating multiple groups of process parameters in a virtual environment, predicting yarn quality data, and obtaining a feasible parameter space; thirdly, calculating the influence weight of the process parameters on the yarn quality data in the feasible parameter space, establishing a multi-objective optimization function to generate a Pareto optimal solution set, and further obtaining the optimal process parameters; and finally, calculating and adjusting the priority according to the parameter deviation, sequentially adjusting the process parameters, comparing the predicted yarn quality data with the actual yarn quality data, and updating the digital twinning model. Through the synergistic effect of digital twinning pre-screening, constrained multi-objective optimization and a model self-learning mechanism, the intelligent level, optimization efficiency and long-term adaptability of blended yarn process design are remarkably improved.

The invention discloses an intelligent network security risk assessment method and system, and belongs to the technical field of network security, and the method comprises the steps of network security related data collection, network security related data processing, network security risk assessment model construction and intelligent assessment. According to the scheme, basic features and time sequence features are combined and expanded into polynomial feature vectors, fluctuation verification sampling and weighted error marking are combined, normal data are dynamically screened, abnormal data are removed through a self-adaptive residual error threshold value, and high-quality data support is provided for network security risk assessment; the method comprises the steps of generating confrontation features and comprehensive physical risk features, obtaining multi-source fusion features based on decision coefficient weighted fusion, constructing a physical topological graph and a dynamic association graph, and carrying out spatial-temporal feature coding and risk prediction decoding through a time multi-head attention mechanism, double-view graph convolution and attention fusion. And the evaluation efficiency and accuracy of the complex network security risk are comprehensively improved.

The invention discloses a multi-dimensional feature fusion-based high-speed optical module life prediction and fault early warning method. The method comprises the steps of constructing a physical-signal double-layer high-frequency feature space containing bottom-layer physical state data and upper-layer signal quality data; a full-life-cycle self-adaptive physical constraint network model is constructed and trained, a degradation stage self-recognition module is arranged in the model, and the weights of physical constraint items such as monotonicity and curvature in a loss function can be dynamically adjusted according to the running-in period, the stable period or the aging acceleration period of the optical module; and finally, performing graded early warning based on the quantized prediction uncertainty. According to the invention, the physical constraint can be adaptively applied according to the life cycle stage of the optical module, the problem of inaccurate prediction caused by poor adaptability of the traditional static constraint in different stages is solved, and the accuracy and robustness of life prediction are remarkably improved.

The invention relates to the technical field of industrial process control and data analysis, in particular to a polyacrylamide production process monitoring data analysis system based on reinforcement learning, which comprises a data acquisition step: acquiring process data at a first frequency, and receiving quality data at a second frequency; a strategy generation step: outputting an action by using a neural network, and calculating an instant agency reward by using an agency model; an asynchronous experience playback step: constructing a nonlinear storage structure, and mapping lagging quality data back to historical process data fragments to complete a causal closed loop; a model updating step: calculating a delay correction reward, and updating network parameters based on the mixed reward; according to the invention, decoupling of real-time decision and delay calibration is realized through a dual-time scale architecture, and the response capability and high-precision control of a test vacuum period are ensured.

The invention discloses a defect detection method and system based on insulator multi-modal image fusion, and relates to the technical field of electrical equipment defect detection, multi-modal image fusion is performed based on an attention mechanism improved RFN-Nest image fusion model, fusion of infrared image temperature anomaly features and visual image structure detail features is enhanced, and the defect detection accuracy is improved. The information entropy, mutual information and other indexes of the generated fusion image are remarkably superior to those of an original model and a traditional fusion method, high-quality data support is provided for a detection task, then defect detection is carried out based on an attention mechanism improved YOLOv8 target detection model, the defect feature discrimination capability and the anti-interference capability are improved, and the detection efficiency is improved. The problems of missing report and false report of insulator defects in a complex scene are effectively solved, the performance is remarkably improved compared with a traditional independent link design scheme, and the method can be directly applied to an actual electric power inspection scene.

The invention provides an agricultural data processing method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source agricultural data, and coding the multi-source agricultural data into a multi-source time sequence feature vector; performing intra-modal time sequence feature extraction on the multi-source time sequence feature vectors to generate a multi-modal feature sequence aligned with the unified time axis; performing time step-by-step cross-modal fusion on the multi-modal feature sequence through a gated cross attention mechanism to generate a fused feature sequence; and performing global context coding on the fused feature sequence, and compressing to generate a full-growth-cycle feature vector. According to the method, the problem of effective fusion of multi-source heterogeneous data can be solved, the multi-source heterogeneous agricultural data is converted into the global feature vector which is highly concentrated in information and rich in spatio-temporal context and causal semantics, and the global feature vector can improve the accuracy and reliability of downstream agricultural intelligent tasks; and a high-quality data basis is provided for precise decision-making of intelligent agriculture.

The invention provides a welding seam quality prediction and process optimization method based on a welding cloud platform. The initial optimization parameters of the weld joint process parameters are determined on the basis of the weld joint quality data and in combination with the preset target requirements, it is ensured that the optimization direction meets the actual production requirements, a scientific basis is provided for real-time adjustment of welding equipment, real-time optimization is conducted through a welding equipment controller according to the initial optimization parameters, and the welding efficiency is improved. The current welding seam quality data in the optimization process are collected in real time and fed back to the welding cloud platform to update the welding seam quality prediction model, quality fluctuation in the welding process is quickly responded, defect expansion is avoided, the welding quality stability is improved, and the welding quality prediction efficiency is improved. The current welding seam quality data in the optimization process is fed back to the cloud platform, and the model is updated, so that the model can continuously adapt to the change of the welding scene, the prediction and optimization accuracy is gradually improved, and the iterative upgrading of the technical scheme is realized.

The invention discloses an intelligent man-hour management method and device based on multi-source data, relates to the technical field of man-hour management, and mainly aims to realize quantitative association of man-hour data and working quality through multi-source data fusion and provide intelligent decision support for cost-reducing and efficiency-increasing refined man-hour management of enterprises. According to the main technical scheme, the method comprises the steps that a man-hour filling request is received and analyzed, and a target object and a statistical period are obtained; acquiring original man-hour data of the target object in the statistical period from the multi-source heterogeneous data source platform; inputting the original man-hour data into a preset man-hour filling model to obtain a man-hour distribution list, the man-hour distribution list comprising accumulated man-hours, task details and task priorities, and the task details comprising work places and task states; according to the man-hour distribution list, calculating an efficiency value and a post adaptation degree of the target object in the statistical period to obtain post quality data; and generating a man-hour report based on the man-hour distribution list and the post quality data.

The invention provides a multi-source data fused water quality pollution monitoring method and device and a storage medium, and relates to the technical field of water quality monitoring. According to the method, through the key steps of data preprocessing, cross-modal feature fusion, pollution concentration estimation, graph neural network traceability and the like, multi-source water quality data and monitoring point space topology information are deeply integrated, and a whole-process automatic monitoring system is constructed. According to the method, end-to-end processing from data standardization to pollution emission source positioning is realized, a multi-source heterogeneous data barrier is effectively broken, the complementary value and the time-space association rule of different modal data are fully mined, the integrity, accuracy and efficiency of water quality pollution monitoring are improved, the emission source position and the pollution diffusion path can be accurately positioned, and the water quality pollution monitoring efficiency is improved. Full-link and targeted technical support is provided for water quality pollution control and treatment decision, and the problems that traditional monitoring data is low in utilization rate and insufficient in traceability accuracy are solved.

The invention discloses a processing method and system for rejecting and hanging work order data, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the rejecting and hanging work order data and a real label corresponding to the rejecting and hanging work order data; inputting the rejected work order data into a trained basic text model to obtain a reasoning result, the reasoning result comprising a prediction label and a confidence coefficient; screening out the rejected work order data of which the confidence coefficient is smaller than a preset value and the predicted tag is inconsistent with the real tag from the reasoning result as low-quality data; determining a quality problem type of the low-quality data based on a quality problem determination rule; performing iterative optimization on the basic text model by adopting a corresponding optimization strategy based on the quality problem type to obtain a multi-label classification model; and inputting the to-be-improved work order rejecting and hanging data into the multi-label classification model to obtain an optimal reasoning result, thereby facilitating solving the problem that the reasoning result of the work order rejecting and hanging data cannot be accurately obtained in the prior art.

The invention provides a federal retrieval enhancement generation method oriented to multiple data sources. The method comprises the following steps: initializing the reliability of federal data sources; dividing a user query field to obtain the reliability of the federal data source to the user query field; selecting a target data source from the federated data source; performing local retrieval on the target data source to obtain candidate answers; estimating the confidence of the candidate answers; according to the confidence coefficient, filtering the candidate answers, and generating a user query answer; and according to the confidence, updating the reliability of the federal data source, and performing confidence estimation on the candidate answers, so that a confidence estimation result has rich semantic information and can be well identified and utilized by a large language model, thereby being beneficial to solving the conflict problem between exogenous knowledge and model endogenous knowledge, and improving the reliability of the model. And through federal data source quality detection of feedback iteration, reliability evaluation of the data source is continuously converged to the domain knowledge accuracy, efficient and accurate detection of the data source quality is realized, and query of invalid data sources and low-quality data sources is effectively reduced.

Embodiments described herein comprise an advanced Software as a Service (SaaS) platform addressing challenges including trust, governance, quality, data accuracy, and transparency, ensuring full visibility of enterprise data warehouse data pipelines. These embodiments seamlessly integrate with cloud-based and legacy data platforms, enhancing visibility through monitoring data quality, tracking metadata changes, and generating exceptions for review. Data quality management allows for custom script creation to validate pipelines and business rules. Cloud-native connectors facilitate integration with one or more cloud-based services streamlining data delivery processes. Personalized notifications aid administrators in responsive action, while data profiling capabilities identify quality issues and provide distribution insights. Actionable data intelligence for privacy, security, and governance is enabled through data classification. Automation features encompass data quality checks, metadata monitoring, profiling, and classification, orchestrated by a flexible scheduler. The architecture leverages a scalable serverless design with independent microservices, dynamically scaling based on demand.

The invention discloses a piece placing sequence mistake proofing device which comprises a multi-dimensional sensing module, an intelligent control module, a reconfigurable positioning module, an execution intervention module, a block chain tracing module and a man-machine interaction module, and all the modules communicate through an industrial Ethernet. The multi-dimensional sensing module and the intelligent control module adopt a time-sensitive network protocol to guarantee the certainty of data transmission, the multi-dimensional sensing module integrates an AI vision assembly, an ultrahigh frequency radio frequency identification read-write unit and a polarized light detection assembly, and parallel acquisition and cross validation of cutting piece multi-dimensional information are realized through a time sequence synchronous triggering mechanism. According to the method, a full-dimensional recognition system can be constructed through cooperation of multi-modal sensing fusion and an intelligent algorithm to avoid a single detection blind area, multi-variety production requirements are adapted by relying on AI incremental learning and rapid remodeling design, non-tampering tracing of quality data is realized in combination with a block chain technology, and the method is suitable for mass production. And meanwhile, reliable operation of the system is guaranteed through redundancy control and emergency design.