372 results about "Quality check" patented technology

The invention discloses a product traceability direction management and control system based on full-process monitoring, and relates to the technical field of product traceability management. The full-process management and control platform is in communication connection with a product data acquisition module, a product identification analysis module, a product data management module, a data traceability analysis module, a product abnormity management module, a process optimization module and an interaction management module. According to the product traceability direction management and control system based on full-process monitoring, through comprehensive monitoring and recording of raw material sources, production dates, processing details and quality inspection results, it is ensured that products meet preset quality standards in each production link, once any deviation is found, the system can immediately give out early warning, and the product traceability direction management and control system is convenient to use. In addition, a large amount of data is analyzed through a machine learning algorithm, key factors influencing the product quality can be recognized, and therefore a scientific basis is provided for quality improvement.

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 relates to the technical field of quality inspection, in particular to a complex steel structure construction quality detection method and system based on three-dimensional laser scanning, and the method comprises the steps: obtaining three-dimensional laser scanning data and corresponding design data of a steel member in a machining quality detection stage, a virtual pre-assembly stage and an on-site actual assembly stage of the steel member; preprocessing the obtained three-dimensional laser scanning data to obtain point cloud data corresponding to a real object, constructing the obtained design data into a BIM model and converting the BIM model into point cloud data corresponding to the model, carrying out point cloud registration on the two parts of data through a registration algorithm, obtaining an error between the two parts of data through comparison, and obtaining the point cloud data of the real object according to the obtained error information. And determining the actual quality of the steel member.

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.

Provided are system, method, and device for performing validations of a system. According to embodiments, the system may include: a memory storage storing computer-executable instructions; and at least one processor communicatively coupled to the memory storage, wherein the at least one processor may be configured to execute the instructions to: receive at least one software part from a user; determine whether the received at least one software part passes a quality check; in response to determining that the received at least one software part passes the quality check: add the received software part to the list; and perform a first validation of the list; wherein each of the plurality of software parts specified in the list include one or more indication of a type of the quality check which a respective one of the plurality of software parts in the list passes.

The invention provides a tubular pile production appearance quality inspection method and system, and relates to the technical field of tubular pile quality inspection.The method specifically comprises the steps that the temperature, appearance size and material parameters of a tubular pile are collected in real time, and a first evaluation coefficient is generated according to the collected temperature and appearance size of the tubular pile; generating a second evaluation coefficient according to the collected material parameters; constructing a quality evaluation model of the tubular pile, and calculating a quality evaluation index; and comparing the quality evaluation index with a preset quality evaluation threshold value, and generating a production appearance quality grade of the current to-be-evaluated pipe pile according to a comparison result. According to the invention, by introducing real-time data acquisition and dimensionless processing technologies, comprehensive evaluation of the appearance quality of pipe pile production is realized. By constructing the quality evaluation model and integrating the quality parameters of different dimensions, the quality evaluation index can be efficiently and accurately generated, so that the quality risk can be better controlled in production and construction links, and the overall safety and reliability of the pipe pile are improved.

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.

Systems and methods for quantitative seismic integrated modelling (QSIM) are disclosed for integrating the one, two and three-dimensional (1D, 2D, 3D) data from different geoscience domains within a framework in order to produce hi-resolution geocellular models that simulate realistic sub-surface reservoir properties. The QSIM systems and methods accurately leverage the seismically derived reservoir rock properties, integrating the geophysical, geological and engineering information through an optimum rock physics models and takes in consideration all the empirically constrained templates to correct, validate and quality check all the input data.

The invention discloses a data auditing method and device, electronic equipment and a nonvolatile storage medium. The method comprises the following steps: acquiring cross-modal data to be audited; a verification rule set corresponding to the multi-modal knowledge base is determined, first verification processing is carried out on the cross-modal data based on the verification rule set, a first verification result is obtained, and the first verification processing is used for verifying whether the cross-modal data has exceptions which do not conform to rules in the verification rule set or not; the large auditing model is adopted, second verification processing is conducted on the cross-modal data, a second verification result is obtained, and the second verification processing is used for detecting whether logic contradictions exist in the cross-modal data or not based on the semantic reasoning ability of the large model; and determining an exception report corresponding to the cross-modal data according to the first verification result and the second verification result. According to the method and the apparatus, the technical problem of poor content quality inspection effect of cross-modal data in related technologies is solved.

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 invention relates to the technical field of automobile accessories, and discloses a manufacturing lean production management system based on transfer card work reporting, and the system comprises a data collection module which is used for collecting production data in real time through scanning a code transfer card; the production plan and circulation card configuration module is used for making a production plan and generating a circulation card matched with the production plan; the production process tracking module is used for dynamically monitoring the production state of the transfer card and the use condition of equipment; and the quality inspection and batch tracing module is used for binding the quality inspection data with the circulation card to realize quality problem tracing. By scanning a code transfer card, collecting production data in real time, and updating the state and task progress of each station in the production process to the system in real time, a manager can master the current production situation at any time, find potential problems in time and quickly take countermeasures, the response speed of sudden problems and the production efficiency are effectively improved, and the production efficiency is improved. And the production lean degree is improved.

The invention discloses a quality inspection system for a production process of an amomum tsao-ko mixture for guiding qi downward and dredging collaterals, and aims to realize comprehensive monitoring and control of the production process through multiple modules and improve the product quality and the production efficiency. The image data acquisition module is used for acquiring raw material and production process images and spectral analysis images. The timing module records and judges the production process time. The multi-source data acquisition module collects production environment data and raw material weight. The raw material proportion control module monitors a raw material proportion. And the data processing module is used for analyzing and processing images and environment data in the production process. And the user interaction module provides an interface for real-time quality detection results and user modification commands. Through comprehensive data acquisition and processing, intelligent proportion control, accurate time management and efficient environmental condition evaluation, the production quality and safety of the amomum tsao-ko mixture for guiding qi downward and dredging collaterals are remarkably improved, human errors are reduced, and the production efficiency is 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 provides a method for automatically generating a quality check rule based on a data standard, and relates to the technical field of data governance, and the method comprises the steps: defining the data standard, matching a database field with the data standard, and determining a check range to generate a mapping relation; based on a predefined checking rule template, the data standard and the mapping relation, generating a quality checking rule; and triggering and executing the quality checking rule according to a preset period or event.

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.

Provided in the embodiments of the present disclosure is a wireless communication method, which includes: a terminal device acquires a third data set, where the third data set includes identification information of spatial filters of K3 time units and link quality information of K3 time units; inputs the third data set into a second network model, to output second prediction information, where the second prediction information includes identification information of spatial filters of F3 time units and link quality information of F3 time units, and at least one checkpoint is set within the F3 time units; and determines, according to a link quality check result of a first checkpoint, whether to use spatial filters output by the second network model within n time units after the first checkpoint, wherein the first checkpoint belongs to the at least one checkpoint.

The invention belongs to the technical field of graphite crucible production, and particularly relates to a quality inspection device and method for graphite crucible production, and the device comprises a detection table, the top surface of the detection table is fixedly provided with a plurality of sliding rods, the top ends of the plurality of sliding rods are fixedly provided with the same top table, and the plurality of sliding rods are slidably provided with a lifting table; the lifting table is located above the detection table, a U-shaped lifting plate is fixedly installed on the bottom surface of the lifting table, fixed plates are fixedly installed on the two sides of the interior of the U-shaped lifting plate, connecting plates are rotatably installed on the two fixed plates, and movable plates are rotatably installed on the top surfaces of the two connecting plates. The other adjusting assembly is started to drive the other movable plate to rotate, the lower pressing block is made to rotate to the position above the graphite crucible, at the moment, the lower pressing block is driven to descend through the lifting assembly, and due to the fact that the pressure sensor is arranged at the bottom of the lower pressing block, the pressure sensor can monitor the pressure resistance of the graphite crucible in real time.

The present invention discloses a quality control management system for project supervision, which relates to the technical field of project management, and includes a control management platform, wherein the control management platform is communicatively connected with a project information management module, a project progress management module, a project cost management module, a quality inspection and evaluation module, a project quality grading module, and a resource allocation management module, wherein the modules are electrically connected; the project information management module is used to clarify the quality control objectives of the project. The quality control management system for project supervision ensures that the project reaches the predetermined quality standards in each link by setting the quality grade standard and the evaluation process, and conducts a comprehensive and objective evaluation of the project quality through quality inspection and evaluation, quality evaluation coefficient calculation, and quality grade determination steps, thereby timely discovering and solving potential quality problems, so as to improve the reliability and safety of the project.

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.

Systems and methods are described for replacing an object being presented in a livestream with a secondary object that is personalized to a user that is consuming the livestream and rendering the livestream with the secondary object are described. The methods identify a target object in a livestream based on certain selection factors. A secondary object that is contextually related to the target object and selected based on selection factors is selected. A 2D-to-3D conversion of the scene description is performed to generate a 3D model. A replacement option is selected. The attributes of the secondary object based on its 3D model are matched with attributes of the target object based on its generated 3D model. Quality checks and scalability options are explored. The livestream is re-rendered with the secondary object having replaced the target object.

The invention relates to a load resource data management system based on a quality checking rule. The load resource data management system comprises a historical database, a type indexing module, an interference generation module, a classification execution module, a classification training module and an abnormity monitoring module. The model is trained, a classifier tree is established to classify the load feature types of the production devices, electricity utilization information collection and summarization are facilitated, abnormal data can be captured in time when appearing and not limited to the classification of the devices, a scene for classification in the electricity utilization dimension is established, and the classification efficiency of the production devices is improved. And powerful data support is provided for subsequent analysis of power grid load data.

Systems and methods for collecting evoked responses (ERs), and using the same to guide neuromodulation are disclosed. An exemplary system includes an electrostimulator to provide electrostimulation to a neural target, a sensing circuit to sense ERs, and a controller circuit to collect first ERs sensed from a group of sensing electrodes in response to electrostimulation through first one or more stimulating electrodes. The controller circuit performs a quality check of the first ERs, determine or update an ER sampling routine including timings or an order of performing multiple stimulation and ER collection tests, and sequentially execute the multiple stimulation and ER collection tests, and collect second ERs according to the determined or updated ER sampling routine. When the second ERs satisfy an acceptance criterion, a recommendation can be provided to a user to reposition the lead or to set or adjust a stimulation setting.

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.