257 results about "Quality check" patented technology

A method for quality inspection of an etching paste material based on a spectral response difference, including: identifying a position of density mutation boundary through spectral collection and density gradient analysis. Applying a specific frequency beam to obtain the resonance response parameter, generating stress field reconstruction data. Performing a reverse optical tracking to obtain a defect formation path, generating degradation prediction data. Establishing a self-organizing optical monitoring grid, to form a hierarchical spectral fingerprint library. Constructing a multi-point linked defect blocking network, to form an interconnected optical energy field. Monitoring a cooperative response to obtain network stability data, Identifying abnormal position coordinates based on a material quality grade and the network stability data, obtaining the abnormal position coordinates. Performing a phase adjustment to obtain a phase difference spectral set, conducting a phase comparison marking on the abnormal position coordinates, completing the quality inspection of the etching paste material.

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 provides a 10kV cable accessory manufacturing quality inspection report generation method and system, and relates to the technical field of power equipment quality detection and reliability evidence storage. According to the method, original detection data is acquired and structured in multiple dimensions, and data sources, operators and equipment identifiers are bound, so that context integrity and traceability are realized; initial and enhanced data fingerprints are generated through block classification, standardization and Hash operation, and a dynamic fingerprint chain is constructed by using a chain structure, so that whole-course data has content and process dual integrity identifiers; a multi-level verification sequence is embedded in each stage of report generation, finally, a block chain is utilized to record a data packet abstract, an anchor point on the chain is bound with report metadata, a transaction ID is embedded into a report document, and a checking two-dimensional code is generated. According to the method, the non-tampering performance and the verifiability of the content and the generation process of the detection report and the traceability of the whole circulation process are improved.

The invention discloses a medical review and publication quality control system based on deep learning and medical knowledge, and relates to the technical field of medical information processing, and the system comprises a medical knowledge enhancement module which is used for carrying out the named entity recognition and relation extraction technology based on a multi-source fusion medical knowledge system, constructing a structured medical knowledge graph and carrying out intensive training; the multi-dimensional examination and analysis module is used for performing multi-dimensional evaluation on the manuscript based on the medical knowledge graph; the intelligent interaction and feedback module is used for displaying the evaluation result of each dimension through a visual interface; and the publishing quality management and control module is used for sequentially executing whole-process publishing supervision of standardized monitoring, quality inspection and abnormal intervention on the manuscripts passing the examination and analysis. The manuscript reviewing efficiency is greatly improved, and the requirements of high-frequency and rapid publishing of medical scientific research are met; the problem of review difference caused by subjective factors of a manuscript reviewer is solved, and the stability and reliability of the assessment result of the same manuscript are ensured through a standardized and quantitative assessment system.

The invention relates to the technical field of human resources, and discloses a multi-dimensional automatic background investigation method and system based on Ai assistance. The system comprises a multi-source data acquisition module, an intelligent analysis engine module, a real-time interview processing module, a quality verification control module, a dynamic decision support module, a privacy security protection module and a visual report generation module. According to the method, public data of candidates are acquired through a multi-thread crawler technology, and the authenticity of educational background is verified; analyzing the paper certificate by adopting an optical character recognition technology; constructing a space-time relation knowledge graph to detect work experience abnormality; interview content is transcribed in real time, and emotional tendency is analyzed; dynamically adjusting the evaluation dimension weight; and generating a visual risk assessment report. According to the method, the enterprise employment risk is reduced, the comprehensiveness and depth of background investigation are improved, the accuracy and applicability of a background investigation conclusion are improved, a safe and reliable background investigation environment is constructed, and the enterprise human resource allocation effect is optimized.

Method for sampling product quality based on Industrial Internet of Things, including: obtaining model data, production data, and equipment data, determining a first preset condition, a first sampling parameter, a second preset condition, and a second sampling parameter; obtaining production feature data, and in response to the production feature data satisfying first preset condition, generating a first sampling inspection instruction based on the first sampling parameter and obtaining a first quality inspection result; obtaining historical sampling inspection results and historical model data, and in response to the historical sampling inspection results and / or the historical model data satisfying second preset condition, generating a second sampling inspection instruction based on the second sampling parameter and obtaining a second quality inspection result; adjusting the first preset condition, the first sampling parameter, the second preset condition, and the second sampling parameter based on the first quality inspection result and the second quality inspection result.

The invention relates to the technical field of power system state monitoring and operation and maintenance, and discloses a power distribution network equipment state intelligent sensing and predictive maintenance decision-making method, system, equipment and medium, and the method comprises the steps: collecting equipment operation data, monitoring data and environment data through a power distribution automation system, an online monitoring device, an environment sensor and other channels; performing time synchronization and quality verification on the multi-source data; extracting multi-dimensional characteristic parameters reflecting the running state of the equipment, and performing normalization processing and fusion analysis on the characteristics; establishing an equipment state evaluation model by using a deep learning algorithm, and carrying out quantitative scoring on the current health condition of the equipment; constructing a time sequence prediction model, and predicting the future degradation trend and residual life of the equipment; and establishing a multi-objective optimization model, and generating an optimal maintenance decision scheme. The whole process is from data acquisition, state perception and fault prediction to decision optimization, and intelligent management of power distribution network equipment is realized.

The invention discloses a high-speed rail canopy steel structure corrosion image recognition and evaluation method based on deep learning, and the method comprises the following steps: obtaining and calibrating a multi-view image, and organizing the multi-view image into an observation sequence; performing normalization and quality checking on the observation sequence, and extracting a state vector and an observation driving vector; constructing an energy function and a structure matrix in the Hamiltonian neural network, and promoting state vector time evolution to obtain an evolved corrosion state vector; inputting the evolution result into a decoder, generating a pixel-level corrosion mask and a boundary position, and obtaining a corrosion segmentation result; the corrosion type is judged based on the segmentation result, and corrosion severity indexes are calculated and graded; and extrapolating and predicting the severity index by using time evolution, outputting a predicted maintenance time window and priority, and finally generating a detection report. According to the method, the Hamiltonian neural network is introduced to realize identification, grading and evolution prediction of steel structure corrosion, and the detection stability and the engineering availability are improved.

The embodiment of the invention discloses an image auditing method and device based on a co-creation annotation mode, equipment and a medium. A specific embodiment of the method comprises the following steps: acquiring medical film and television data as initial data; preprocessing the initial data to obtain a task image set; distributing the task image set to at least one student terminal; in response to the detected annotation information sent by the student terminal, generating annotation feedback information according to the annotation information; sending the labeled feedback information to the student terminal; in response to the received annotation task image submitted by the student terminal, performing preliminary quality inspection on the annotation task image; sending the preliminary quality inspection result to an expert end; and in response to received auditing information corresponding to the preliminary quality inspection result sent by the expert terminal, sending the auditing information to the student terminal. According to the embodiment, a co-creation annotation mode is combined with a deep learning technology, so that efficient, accurate and high-quality image annotation auditing is realized, and the overall quality of annotation data is improved.

The invention relates to the technical field of intelligent monitoring management, particularly discloses an intelligent assembly component whole-process management system, and aims to optimize production efficiency and product quality through real-time data acquisition and analysis. The system comprises a data acquisition module which monitors and records the switching time of each process and the quality inspection qualified rate of each component in real time through a sensor and automation equipment on an integrated production line, a production plan evaluation module which calculates a switching time abnormal coefficient, identifies a bottleneck or low-efficiency operation in the process switching time, and evaluates the quality inspection qualified rate of each component according to the bottleneck or low-efficiency operation. And the process switching time is dynamically adjusted based on the specific numerical values of the influence interference factors and the positive and negative characteristics of the influence interference factors, the production quality evaluation module calculates a quality abnormal coefficient according to the quality inspection qualified rate and judges the quality fluctuation condition of each component, and the influence analysis and adjustment module predicts the quality qualified rate by using a gradient enhanced regression model. The process switching time is dynamically adjusted to eliminate potential quality problems.

Techniques of automated quality control for digital pathology whole slide images are presented. The techniques include obtaining a thumbnail image derived from a whole slide image of a pathology slide; determining whether the whole slide image includes an artifact in a first class of artifacts by providing the thumbnail image to an electronic neural network trained to detect artifacts in the first class of artifacts by analyzing a plurality of labeled training thumbnail images; generating a tissue mask representing tissue depicted in the thumbnail image; determining whether the whole slide image includes an artifact in a second class of artifacts by performing a comparison using the tissue mask; and providing an indication of whether the whole slide image includes an artifact in the first class of artifacts or an artifact in the second class of artifacts.

The invention relates to the field of otological disease prediction, in particular to an otological disease prediction method based on multi-modal data fusion and confidence evaluation. According to the technical scheme, the method comprises the following steps: acquiring an otoendoscope digital image, broadband tympanic image measurement data and structured or unstructured clinical information; carrying out integrity verification and quality verification on the collected data, and checking whether the resolution of the digital image of the otoendoscope meets the minimum pixel requirement or not and whether the information is complete or not; preprocessing the otology data and extracting multi-modal features, and extracting an ear endoscope digital image feature vector, a feature vector representing broadband tympanic chamber image data and a clinical information feature vector; fusion is carried out after feature extraction, and confidence evaluation and recurrence risk prediction are carried out after fusion. According to the method, the three-mode data of the image, the physiological signal and the clinical text are deeply fused through the attention mechanism, so that the prediction accuracy of the otological diseases is remarkably improved. The method is suitable for otological disease prediction.

The invention relates to an intelligent risk monitoring and management and control method, and belongs to the technical field of intelligent risk monitoring and management and control. At present, problems such as operation jitter and safety risk are easy to occur. The method comprises the steps of performing cleaning, alignment and quality verification on operation data, equipment data and environment information under a unified time reference to form three types of read-only inputs of an equipment portrait, an environment portrait and a primary topology snapshot; performing abnormal preliminary screening based on internal and external bistable baselines, performing open set identification and rejection on samples crossing a robust band, performing modal consistency check on multi-modal evidence, outputting interval confidence, and generating a structured candidate event record; the method comprises the following steps: performing hierarchical disposal judgment by combining node topology criticality and disposal cooperative sensitivity, performing radial threshold and angle gating mapping on the node topology criticality and the disposal cooperative sensitivity in a polar coordinate domain to obtain a disposal level, and inhibiting jitter of a critical interval through asymmetric hysteresis and minimum residence time. And the accuracy of exception identification, the robustness of decision processing and the security of system operation are effectively improved.

The invention relates to the technical field of building informatization, and particularly discloses a BIM construction quality dynamic monitoring method and system based on edge computing, and the method comprises the steps: S1, collecting the physical parameters of a construction site through an Internet of Things sensor according to a preset initial frequency, and transmitting the original data to an edge computing node; s2, preprocessing the original data through edge computing nodes to generate a structured data set; s3, comparing the structured data set with design parameters in a BIM model database to generate a quality inspection intermediate result; according to the method, automation, refinement and real-time performance of construction quality inspection are realized, the quality management and control efficiency and the problem response speed are remarkably improved, deep association and dynamic updating of quality information and the BIM model are ensured, a strong data driving capability is provided for risk early warning, decision support and archive management in the construction process, and the construction efficiency is improved. And the engineering quality is effectively guaranteed by dynamically optimizing the monitoring resources.

The disclosure is directed to methods to design and revise hydraulic fracturing (HF) job plans. The methods can utilize one or more data sources from public, proprietary, confidential, and historical sources. The methods can build mathematical, statistical, machine learning, neural network, and deep learning models to predict production outcomes based on the data source inputs. In some aspects, the data sources are processed, quality checked, and combined into composite data sources. In some aspects, ensemble modeling techniques can be applied to combine multiple data sources and multiple models. In some aspects, response features can be utilized as data inputs into the modeling process. In some aspects, time-series extracted features can be utilized as data inputs into the modeling process. In some aspects, the methods can be used to build a HF job plan prior to the start of work at a well site. In other aspects, the methods can be used to revise an existing HF job plan in real-time, such as after a treatment cycle, a pumping stage, or a time interval.

Systems and methods for verifying the quality of patient specific implants are disclosed. A system can generate implant data, such as design files or fabrication instructions, for manufacturing the implant. The system can manage access to the implant data based on authentication levels. Based on the determined authentication level of the user requesting access to the implant data, the system can permit the user to access some or all of the implant data. The system can perform a quality check on a manufactured implant by scanning the manufactured implant to identify any errors in the manufactured implant.

The invention discloses a low-altitude field multi-source heterogeneous data unified conversion method and system based on Beidou grid codes, and relates to the technical field of data processing.The method comprises the steps that a constructed multi-source heterogeneous data set is analyzed, and key information is extracted; on the basis of Beidou grid codes, coding precision is dynamically adjusted according to data types, and time-space stamps of the analyzed data are converted into unified Beidou grid time-space coordinates; establishing a mapping relationship between the multi-source heterogeneous data set and a preset uniform data format, and converting the aligned data into data conforming to the uniform data format according to the mapping relationship; and performing quality verification on the converted data conforming to the unified data format to obtain high-quality data conforming to the unified data format, and performing associated storage on the high-quality data and the corresponding Beidou grid codes. According to the method, a data barrier can be broken, efficient fusion and accurate expression of data under a unified framework are realized, and high-quality data support is provided for intelligent management and decision-making in the low-altitude field.

The invention discloses a data governance method, device and equipment, and relates to the technical field of data information management, and the method comprises the steps: carrying out the resource checking operation of data based on a data governance overall plan, and determining a business attribute set and a corresponding technical metadata set; performing ownership confirmation operation based on the business attribute set and the technical metadata set, and determining a management responsibility subject; based on the management responsibility subject, performing standard formulation operation on the service attribute set, and establishing a data standard system; on the basis of the data standard system, performing lake entering operation on the data, and loading the data into a data storage system; based on a preset data quality rule, performing quality verification operation on the data, and outputting a data quality evaluation result; based on a data quality evaluation result, performing capitalization release operation on the data to generate a data asset directory; and based on the data asset catalog, performing service packaging operation on the data assets, and generating a standard data service interface for each business unit to call.

The invention belongs to the technical field of forest seedling traceability, and discloses a forest seedling traceability system and method.The system supports storage and association of unstructured data such as pictures, videos and PDF (such as quarantine evidences), and specifically comprises seedling process evidences: 'archive management 'upload seedling process pictures, quarantine proof upload'quarantine certificate' pictures or PDF files; all the files can correspond to the items to form a traceability file of characters and images; quality inspection certificates: seedling outplanting, supporting self-inspection code calling, automatically extracting key information (such as a quarantine permit number and a qualified rate), and avoiding manual input errors; the problem tracing basis is as follows: if the seedlings have quality problems (such as low survival rate after afforestation, incorrect variety and no result), related archives such as seed / scion sources, seedling culture management, field management, self-checking records and the like can be retrieved through a system, and responsibility links, such as impure camellia oleifera scion variety, too late grafting time and the like, can be quickly positioned.

A method of calibrating an imaging device adapted to characterize a feature of a sample container, such as a cap color or cap type. The method includes providing a calibration tube including an imaging surface at an imaging location of a first imaging apparatus; illuminating the imaging surface with light emitted from multiple front light sources; adjusting a drive current to each of the multiple front light sources to establish a substantially uniform intensity of the imaging surface; recording drive current values for the multiple front light sources; replacing the calibration tube with a calibration tool having a calibration surface of a known reflectance; and measuring target intensity values of the calibration tool at the respective drive current values. Calibration tools, imaging apparatus, quality check modules, and health check methods are provided, as are other aspects.

The invention discloses a multi-modal reasoning algorithm based on bidirectional chain type thinking enhancement. The method comprises the following steps: firstly, performing bidirectional thinking chain generation and quality verification on original multi-modal question and answer data, constructing a multi-modal thinking chain data set (MCD), and providing standardized reasoning process data for a model; secondly, three types of annotation labels of knowledge types, question types and reasoning types are added on the basis of the MCD, and a knowledge-enhanced multi-modal thinking chain dataset (KMD) is obtained and used for model supervision fine tuning (SFT) to constrain model reasoning; and finally, generating multiple candidate outputs by using KMCD in combination with the structured cue word, and obtaining a strong model with complex reasoning ability through reinforcement learning training of two-dimensional adaptive reward strategy optimization (Di-GRPO). According to the method, the problems of low data quality and insufficient training focusing of the existing multi-modal reasoning task thinking chain are effectively relieved, and the reliability and the accuracy of multi-modal reasoning are improved.

The invention relates to a coding and decoding adaptive regulation and control system for a 5G video information marketing scene, and belongs to the crossing field of 5G communication, video coding and decoding and marketing digitization. The system cooperates through four units: a 5G video information marketing video perception acquisition unit captures an interactive video in a directional manner, and generates a structured data set through scene noise reduction and bimodal perception; the multi-dimensional coding and decoding intelligent decision-making unit analyzes and extracts a core fragment with high adaptability, and completes criticality evaluation in combination with an exclusive rule base; the dynamic adaptive iterative optimization unit outputs an adaptive result based on a cross-industry code rate system and a scene migration mechanism; and the multi-terminal compliance output unit optimizes parameters, generates a three-dimensional compliance label, and generates an operation report through quality verification and data integration. According to the invention, the coding and decoding parameters are adaptively adjusted along with a 5G network, a terminal and a scene, the data volume is controlled while the marketing key information is kept clear, an adaptive scheme is provided for a private domain terminal, and the touch efficiency and the conversion effect of 5G visual information marketing are improved.

Techniques of automated quality control for digital pathology whole slide images are presented. The techniques include obtaining a thumbnail image derived from a whole slide image of a pathology slide; determining whether the whole slide image includes an artifact in a first class of artifacts by providing the thumbnail image to an electronic neural network trained to detect artifacts in the first class of artifacts by analyzing a plurality of labeled training thumbnail images; generating a tissue mask representing tissue depicted in the thumbnail image; determining whether the whole slide image includes an artifact in a second class of artifacts by performing a comparison using the tissue mask; and providing an indication of whether the whole slide image includes an artifact in the first class of artifacts or an artifact in the second class of artifacts.

The application discloses a construction quality inspection batch traceability management method based on a blockchain storage record, and belongs to the technical field of construction quality traceability management. The method comprises the following steps: acquiring construction process multi-source monitoring data of a current inspection batch of a target construction project, and constructing an initial digital twin mirror image. By performing deviation analysis on the mirror image and a standard construction specification, a key quality feature node that needs to be stored is accurately positioned. The node feature data is encapsulated into traceability data blocks in timestamp order, the content hash value of the traceability data blocks is calculated, and the content hash value is associated with the block header hash value of a previous inspection batch to calculate the block header hash value of the current inspection batch, thereby forming an unalterable blockchain storage record data chain. Quality traceability is inquired based on the data chain, and a verification result is output. The application combines digital twin visualization mapping and the anti-tampering characteristics of a blockchain, realizes accurate positioning, credible storage and efficient tracing of construction quality, and enhances the transparency of construction quality management and the data credibility.

The present invention relates to a method for operating a project distributed development framework service by using a computer system having a server computer, the method comprising the steps of: (a) receiving new project generation information from a project owner terminal and transmitting the new project generation information to a planning partner terminal; (b) receiving planning document information created according to the new project generation information from the planning partner terminal and transmitting the planning document information to an engineering design partner terminal; (c) receiving engineering design document information created according to the planning document information from the engineering design partner terminal and transmitting the engineering design document information to an API development partner terminal; (d) receiving API development information created according to the engineering design document information from the API development partner terminal and transmitting the API development information to a quality inspection partner terminal; and (e) receiving inspection result information of the API development information from the quality inspection partner terminal and transmitting the inspection result information to the project owner terminal or an operator terminal.

A system and method for authenticating an image is given. The method encompasses capturing a first image comprising at least one first human object and a second ID image; detecting at least one face of one first human object and processing the first human object based on face attribute checks, face quality checks, and face manipulation checks; performing a liveness check of the identification document to crop and align the region of interest; detecting a face of the at least one second human object; performing ID quality checks and ID tampering checks on the region of interest of the identification document; determining the image face feature vector and an ID card face feature vector; determining a match score between the image face feature vector and the ID card face feature vector and a status indicating one of authentication success, authentication failure, or a requirement for manual review of the image.

The embodiment of the invention provides a power grid asset quality management method based on a data center, and belongs to the technical field of power grid data processing. The management method comprises the steps that metadata information of various power grid assets is acquired to form an asset metadata information base, and the metadata information comprises all attribute information in the whole life process of the assets and all attribute information concerned by a business department; constructing an asset quality verification rule base according to the asset meta-database and the business characteristics; carding a source end service system involved in power grid assets, and completing source end data access based on a data intermediate station; calling the asset quality verification rule base, verifying source end data, and judging data which does not accord with the rule as suspected abnormal data so as to form a suspected abnormal data annotation information base; annotating annotation information in an information base according to the suspected abnormal data. According to the management method, a rule base about power grid knowledge can be constructed so as to realize management of power grid assets.

The invention relates to a data quality checking method and device based on graph embedding, and belongs to the technical field of computers.The method comprises the steps that service data nodes and service domain nodes are input into a graph embedding module to obtain service data node vectors and service domain node vectors respectively; inputting the business data node vector and the business domain node vector into a feature conversion module, performing feature extraction, and then outputting an updated business data node vector and an updated business domain node vector; and constructing an edge vector of the updated service data node vector and the service domain node vector through a node aggregation module, performing feature extraction and prediction based on the edge vector, outputting a prediction score of an edge existing in the service data node and the service domain node, and determining a check result of the service data according to the prediction score. According to the method and the device, the automatic quality check of the irregularly input data is realized, the labor cost is saved, and the accuracy and the credibility of the data quality check are improved.

A method of characterizing a specimen to be analyzed in an automated diagnostic analysis system provides a segmentation determination and / or an HILN (hemolysis, icterus, lipemia, normal) determination of the specimen while providing characterization training updates based on the accuracy and / or confidence in the determinations. The method includes identifying an incorrect or low confidence segmentation or HILN determination, forwarding the incorrect or low confidence determination from the HILN network to a database, and providing one or more training images to the HILN network based on the incorrect or low confidence determination. Quality check modules and systems configured to carry out the method are also described, as are other aspects.