37results about "Registering/indicating quality control systems" patented technology

The application relates to the technical field of doors and windows, and provides a door and window frame quality detection data processing method and system, which comprises the following steps: acquiring original size data of a to-be-tested door and window frame and structure information of the to-be-tested door and window frame; acquiring measurement data of a preset reference object on a bearing device of the to-be-tested door and window frame by a measurement device and environmental data of an environment where the measurement device is located; determining a sensor drift compensation value according to the measurement data of the preset reference object and the environmental data, correcting the original size data according to the sensor drift compensation value, and obtaining corrected size data of the to-be-tested door and window frame; determining whether the to-be-tested door and window frame has an assembly risk according to the structure information of the to-be-tested door and window frame and the corrected size data of the to-be-tested door and window frame; and when the to-be-tested door and window frame has the assembly risk, generating a production intervention suggestion of the to-be-tested door and window frame according to the assembly risk of the to-be-tested door and window frame. The scheme can improve the accuracy of window frame quality detection data.

The application discloses a production quality inspection scheduling method, an automatic processing production line and a numerical control machine tool. The production quality inspection scheduling method comprises the following steps: obtaining a production order; performing NLP analysis on the production order to obtain a structured delivery task; scheduling an AGV to transport materials to a processing terminal according to the delivery task to obtain processing materials; judging whether the working state of a quality inspection terminal meets a preset condition; if yes, scheduling the AGV to transport the processing materials to the quality inspection terminal for quality inspection, and then calling the AGV to transport the quality inspection materials to a warehouse; if not, scheduling the AGV to transport the processing materials to a warehouse, and calling the AGV to transport the processing materials from the warehouse to the quality inspection terminal for quality inspection when the quality inspection terminal is idle, and then calling the AGV to transport the quality inspection materials to the warehouse. The application can improve the utilization rate of quality inspection in the production line, further improve the processing efficiency of the production line, increase the yield and save resource costs.

This invention discloses a method, apparatus, electronic device, and storage medium for evaluating the rolling stability of cigarettes. It involves acquiring multiple cigarette samples from different cigarette rolling machines within the same batch or from different batches within the same cigarette rolling machine; processing these samples using a multi-sample tobacco heat treatment online analyzer under a second preset environment to determine the weight loss curves of the multiple cigarette samples; calculating the differential weight loss curves corresponding to each weight loss curve, and determining the differential weight loss standard deviation based on the differential weight loss curves; determining the mean of the differential weight loss standard deviations based on the differential weight loss standard deviations of all cigarette samples; and determining the rolling stability level of the multiple cigarette samples based on the mean of the differential weight loss standard deviations and a preset range. This achieves efficient, accurate, and objective judgment of the cigarette rolling stability level, which is beneficial for subsequent evaluation of cigarette product rolling quality and thus guides the adjustment of cigarette rolling parameters.

This invention discloses a saw blade detection method and system based on multi-source data fusion, specifically relating to the field of saw blade detection technology. By simultaneously acquiring high-speed visual images of the saw blade's front and side views and spindle current signals, the initial tooth profile reference features of the saw blade are extracted. Furthermore, the system monitors in real time changes in the straightness of the projection line, the amount of cutting edge retraction, and abnormal changes in the current signal during the saw blade cutting process. By fusing visual and current data analysis, the normal wear and degradation trend of the saw blade and abnormal tooth loss characteristics are observed, thereby achieving accurate identification of saw blade wear or tooth loss conditions. A comprehensive evaluation is then performed based on the defect rate of sawn products, ultimately outputting an accurate and reliable saw blade replacement control signal. This invention effectively improves the accuracy of saw blade condition detection and the timeliness of replacement decisions, avoiding processing quality problems and ensuring production efficiency and processing safety.

The application discloses a PCB defect correction method and a circuit board manufacturing method. The correction method comprises the following steps: inputting the design document of the circuit board to be detected into an AOI device, wherein the design document comprises the size design information of each object on the circuit board; scanning the circuit board by using the AOI device to obtain a scanning image, and acquiring the pixel unit size currently set by the AOI device; in response to a detection request for a target object on the circuit board, determining the number of pixels occupied by the target object in the scanning image in a requested measurement direction; calculating the size of the target object in the requested measurement direction according to the current pixel unit size of the AOI device and the number of pixels occupied by the target object; comparing the size of the target object in the requested measurement direction with the size of the target object in the design document; if the size difference exceeds a preset difference threshold, it is determined that the target object has a defect, and the process type of the target object is determined and the process parameter of the target object is corrected according to the design document.

The application relates to the technical field of tire manufacturing, in particular to a tire quality defect automatic early warning and analysis rectification system. The system solves the technical problem that the accuracy of a technical scheme defect root cause tracing and the foresight of early warning cannot meet actual production requirements. The system comprises the following modules: a data acquisition module used for acquiring process parameters, equipment state data and finished product detection data of multiple processes contained in tire production; a risk quantification module used for determining an adjustment factor based on the process parameters, the equipment state data and the finished product detection data; a dynamic threshold generation module used for generating a confidence threshold of an association rule based on the adjustment factor; and a rule screening module used for comparing the confidence of the association rule with the confidence threshold, screening out the association rule with a confidence not lower than the confidence threshold as a quality early warning rule. The application is used for a quality early warning scene of tire manufacturing.

This invention relates to the field of digital manufacturing and industrial information management technology, and discloses an information management system for a digital processing workshop, comprising: acquiring equipment abnormal event records to generate a subgraph of equipment fault propagation with observational evidence; calculating process similarity to screen source domain products; fusing source domain prior causal graphs to perform Bayesian causal structure learning to generate a posterior cross-process quality causal subgraph; calculating conditional Granger causal relationships to generate a set of cross-layer causal edges; assembling a two-layer causal graph; establishing an extended structured Bayesian network model; performing structured variational inference to output joint attribution results; and performing propagation path tracing to generate an attribution report with uncertainty quantification. This invention solves the technical problems in related technologies of digital processing workshop fault attribution, such as the inability to effectively integrate multi-source heterogeneous information from the equipment layer and process layer, the difficulty in completing cross-process causal structure learning under limited sample conditions, and the lack of uncertainty quantification in the attribution results.

This invention relates to the field of quality traceability technology in the chemical industry, providing a method and system for resolving emulsifier quality traceability identifiers throughout the entire process. The method includes: generating carrier identifiers resistant to physical corrosion for raw materials; real-time sensing of critical events in emulsifier phase transitions using a multi-mode sensor array deployed on production equipment; automatically triggering the generation of derived identifiers associated with the previous identifier when a preset phase transition condition is detected; dynamically binding key process parameters and quality inspection data from the production process to the identifiers of the current stage after aggregation through edge computing nodes; and storing summary information of the identifiers and their associated data from each stage in a cross-enterprise blockchain consortium network to form an immutable traceability chain. This invention ensures the continuity of identifiers throughout the physical evolution process and achieves reliable interaction and efficient resolution of cross-system data.

The application relates to the technical field of data processing, in particular to an engine part production tracing method and system based on bar code data, which comprises the following steps: acquiring a bar code scanning event sequence of an engine part; reconstructing a link of the bar code scanning event sequence by using an improved time sequence diagram attention network model and outputting a tracing result of the engine part; wherein the improved time sequence diagram attention network model comprises a frequency parameter. The application solves the problems of low accuracy and continuity of the tracing result.

This invention provides a data-driven quality control method for the entire manufacturing process of ultra-high voltage (UHV) equipment, comprising the following steps: using a hybrid algorithm combining attention mechanism and mutual information entropy to screen coupling factors, constructing a multi-stage bidirectional coupling matrix to quantify coupling strength, and introducing the Lyapunov exponent to determine the critical state of deviation amplification; constructing a multi-objective optimization model based on the coupling matrix and Lyapunov exponent, deriving the correction amount of key process parameters for each process, and conducting dynamic intervention throughout the entire process by combining adjustments to the testing process and adaptation of terminal testing accuracy; establishing a manufacturing and maintenance coupling mapping library, mining implicit mapping paths through graph neural networks, adapting to different equipment types using transfer learning, and updating model parameters through bi-cycle iterative updates. This invention can capture the nonlinear amplification risk of small deviations, avoids relying on manual experience to adjust process parameters, and clarifies the correspondence between manufacturing parameter deviations and maintenance failures, thereby achieving traceability of quality defects.

The application discloses a molten steel refining intelligent control method and device based on machine learning, equipment and medium, the method comprises the following steps: collecting the multi-source process data of the whole process of molten steel refining and preprocessing to obtain the whole process data, performing feature labeling on the whole process data to construct a benchmark data set, training a gradient boosting tree and recurrent neural network fusion model using the benchmark data set to obtain a molten steel refining multi-task intelligent control model, inputting real-time whole process data into the molten steel refining multi-task intelligent control model to obtain molten steel state evaluation results, thereby determining key control parameters and generating control instructions to be sent to an industrial control unit to drive the power supply heating system and the alloy feeding system to perform control; since the application accurately evaluates the molten steel state through the fusion model and cooperatively controls power supply and feeding, accurate regulation of composition and temperature is realized, the narrow composition control precision and rhythm stability are improved, the proportion of manual intervention is reduced, and the automation degree is improved.

The application discloses a cement pole product quality improvement-oriented whole life cycle management method and system, and particularly relates to the technical field of product quality management and edge computing, and comprises the following steps: when a cement pole is at a preset necessary process node, obtaining pole body state information, current process action information and current environmental constraint information corresponding to the cement pole at the current node, and generating current risk representation data of the cement pole at an edge node based on the pole body state information, the current process action information and the current environmental constraint information. Through edge computing at the necessary process node on site, latent defects are pre-visualized, immediately judged and immediately disposed, and the subsequent process disposal path is updated in linkage.

The application discloses a steel structure welding quality evaluation system and method based on distributed computing. The system includes a distributed gradient sensing unit, a physical field reconstruction unit, an entropy calculation unit, a chaotic feature extraction unit, a resource scheduling unit, a physical constraint verification unit, a chaotic synchronization analysis unit, a data aggregation processing unit, a quality comprehensive evaluation unit and a knowledge base management unit. By distributed acquisition of multi-physical field signals in the welding process and calculation of spatial gradient, the data transmission amount is significantly reduced; the complete physical field is reconstructed from the gradient information by using the multi-grid method; the potential defect area is identified and the computing resources are dynamically optimized based on information entropy analysis; the nonlinear characteristics of the sound signal are analyzed by using the chaos theory; multi-dimensional cross verification is carried out through physical constraint verification and chaotic synchronization analysis; and the federal learning is used to realize multi-site knowledge sharing. The application realizes real-time, accurate and comprehensive evaluation of the welding quality, and provides reliable technical support for steel structure welding quality control.

This invention relates to the field of cold rolling leveling technology, and discloses a method for predicting the thermal crown of rolls during the cold rolling leveling process. The method includes: Step 1, collecting characteristic parameters; Step 2, collecting rolling process parameters; Step 3, collecting process lubrication system parameters; Step 4, calculating rolling pressure; Step 5, calculating heat of plastic deformation; Step 6, calculating sliding friction heat energy; Step 7, establishing a temperature field calculation model for the roll system; Step 8, calculating thermal crown; and Step 9, predicting the thermal crown of the rolls. This invention establishes models for heat of plastic deformation, friction heat, and the roll temperature field based on parameters, calculates the surface temperature of the rolls during the rolling process, and completes the calculation of the thermal crown of the rolls during the cold rolling leveling process. This allows for accurate prediction of the roll's thermal crown, ensuring the quality of the finished sheet shape. Compared to the traditional method that simplifies the heat source to a single interface friction heat, this method fully considers the dynamic changes of friction heat along the roll length and contact arc length.

The present invention relates to a method for device monitoring, comprising: a) providing a plurality of fleet measurement data for at least one device, wherein the plurality of fleet measurement data comprises a number of fleet device data measurements "Nfleet"; b) providing a plurality of device measurement data for a monitored device, wherein the plurality of device measurement data comprises a number of monitor device data measurements "Ndata"; c) determining a fleet distribution "Fdist" determined as a distribution of differences between consecutive measurements of the plurality of fleet measurement data; d) determining a device distribution "Ddist" determined as a distribution of differences between consecutive measurements of the device measurement data; e) determining an effective number of data measurements "Neff"; f) determining a probability of failure of the monitored device, the determining comprising utilising the fleet distribution "Fdist", the device distribution "Ddist", the number of monitor device data measurements "Ndata", and the effective number of data measurements "Neff".

The embodiment of the application provides a film layer overflow detection method and device based on thin film interference imaging, information is effectively extracted through innovatively designing a feature extraction system, through geometric analysis and texture analysis, a similarity evaluation mechanism is constructed, a reliable detection strategy is established in combination with feature fusion and region positioning, and abnormal analysis is introduced, parameter quantization and level evaluation are used to ensure the accuracy of detection. The method effectively solves the shortcomings of traditional technologies in feature extraction, region positioning and abnormal analysis, and provides technical support for film layer detection.

This invention discloses an in-machine measurement and error compensation system and method for a five-axis form grinding machine, belonging to the field of CNC machining and precision measurement technology. The system includes a five-axis form grinding machine, a rotary table, a contact probe, a data acquisition and processing module, and a CNC controller, integrated into a hardware system for closed-loop control. The corresponding method includes: first, calibrating the probe's pre-stroke error by comparing in-machine and offline measurement data; then, after machining, controlling the probe to perform in-machine measurement along the surface normal, accurately calculating the normal deviation through vector projection; next, correcting the data using calibration parameters, and generating complete surface deviation compensation data through coarse and fine registration and surface comparison analysis; finally, driving the machine tool to perform iterative compensation machining until the accuracy meets the standard. This invention achieves one-time high-precision forming and inspection of complex curved surface workpieces on a machining tool, significantly improving the machining efficiency and consistency of parts such as aero-engine blades.

The application discloses a kind of semiconductor advanced packaging wafer atlas automatic management system and method, it is related to production quality management technical field, including, obtaining the packaging test data and control atlas of semiconductor wafer, and the packaging test data is stored, classified and filed, and the atlas file is obtained;Atlas file analysis rule is generated based on historical atlas file, and the atlas file is parsed using analysis rule, and the standardized atlas is obtained;Quality assessment is carried out to the standardized atlas using control atlas, and any one evaluation result of quality qualified and quality to be reviewed is obtained, and quality management state is recorded, and quality report is generated when quality to be reviewed;The application realizes the self-adapting analysis and standardization conversion of structured record type atlas by correlating, checking and classifying and filing packaging test data and control atlas.

This invention relates to a method and system for detecting abnormal states in the synthetic reaction process of pharmaceutical intermediates, belonging to the field of abnormal state detection technology. It includes the following steps: collecting and labeling reaction process data to construct a dataset; dividing the process into stages based on stage coding, completing variable coupling modeling, dynamic time warping alignment, and feature decoupling within each stage to obtain a reconstructed feature matrix; extracting abnormal patterns across different time spans through temporal convolution and multi-scale dilated convolution, combining channel attention to enhance key features to obtain an encoded feature matrix, performing stage-aware feature aggregation and global temporal aggregation to obtain the final classification feature vector, inputting four independent stage classification heads to output the classification result; and jointly optimizing the training process using labeled smoothed cross-entropy loss and focus loss. This invention can eliminate batch process rhythm differences, distinguish between normal regulation and true anomalies, and accurately capture early, subtle anomalies.

The application discloses a kind of digital modeling optimization methods for large-span steel anchor box manufacturing process.The method generates data source image on GIS vector, CAD drawing, survey database and image data annotation;Collect terminal to obtain paper material, profile, borehole column chart and generate image collection package containing time position;Call data adapter to extract and transcode into unified intermediate representation;The preprocessed data source image and image collection package are input into multi-branch convolutional neural network, and page segmentation, text detection and recognition and key information extraction are performed, and field-level structured results and confidence are output;Based on data source image, field and intermediate representation are aligned and semantic mapping is generated to store standardized data package.Bi-gaussian filter is used for pre-processing, and tau is determined by gray variance mean classification;When the confidence of key field is insufficient or conflicts with image constraints, increase tau and re-filtering to infer and select the optimal output to improve collection efficiency and quality.

The present application relates to the technical field of semiconductor manufacturing, in particular to a wafer yield prediction method, system, device and medium based on LSTM-FFNN combined model; the method firstly preprocesses the obtained semiconductor manufacturing process data; then calls the LSTM model and the FFNN model to construct a feature extraction model, extracts time sequence features and non-time sequence features from the preprocessed semiconductor manufacturing process data; finally, the time sequence features and the non-time sequence features are spliced to obtain fusion features, and the fusion features are input into a full connection layer to obtain wafer yield prediction values; through type processing of time sequence and multi-modal data, higher-precision wafer yield prediction is realized, data support is provided for real-time regulation and parameter optimization of the semiconductor manufacturing process, and the production efficiency is improved while the cost is reduced.

The invention belongs to the technical field of production line monitoring, and particularly relates to a production line operation situation monitoring method and system based on cloud edge data fusion, and the method comprises the following steps: an edge node of each station collects sensor data of the station, counts an abnormal frame proportion deviating from a normal baseline, determines an operation state label of the station according to the abnormal frame proportion, and sends the operation state label to a server; when the operation state label is in the fault state, abnormal workpiece accumulative counting is started, and the operation state label, the current beat duration, the historical reference beat rate and the abnormal workpiece accumulative counting are reported to the cloud; and the cloud end maintains a station state linked list arranged according to the working procedures. According to the method, the cross-station fault propagation process can be converted into a time sequence early warning link capable of being quantitatively tracked, so that an operator can obtain preposed early warning and quantitative risk data before an abnormal workpiece actually arrives at a downstream station, and passive post-event quality detection is converted into active pre-propagation intervention.

The present application relates to gear machining technical field, especially a kind of variable tooth thickness modification machining method for double lead worm gear and worm, including the wave form distortion rate of idle cutting force of hobbing cutter and torsional stiffness coefficient;Real-time monitoring cutting force wave form distortion rate and tool torsional deformation, calculate disturbance intensity index to divide disturbance level and set detection opportunity;Through the tooth thickness curve of actual measurement, tooth thickness change rate coefficient is obtained to judge machining quality;The tooth thickness deviation curve of unqualified tooth groove is analyzed to determine the dominant interference type, and the spindle speed or tool pose is regulated based on disturbance level;The control effect is verified by attenuation coefficient;When not up to standard, attenuation difference is used to optimize control coefficient;Finally, the overall quality of worm is evaluated by the consistency coefficient of tooth thickness change rate of each tooth groove of full tooth surface.The present application solves the problem that the full tooth surface consistency of formed worm is low due to the resonance and deformation disturbance of asymmetric characteristics of hobbing cutter in variable tooth thickness worm machining.

This application discloses a method and system for optimizing plasticizer production processes based on quality fluctuation analysis, belonging to the field of chemical process optimization and control. The method includes: real-time acquisition of the temperatures at the top, sensitive plate, and bottom of a distillation column; calculation of the first temperature difference fluctuation rate between the top and the sensitive plate, and the second temperature difference fluctuation rate between the sensitive plate and the bottom; inputting the first and second temperature difference fluctuation rates and the feed load into a component distribution model constructed based on a three-dimensional lookup table and trilinear interpolation algorithm, outputting the enrichment layers and mass concentration percentages of light component impurities; calculating the target reflux ratio using a PID algorithm based on the identification results, and adjusting the reflux pump frequency using a ramp function control method; simultaneously, calculating the steam flow compensation amount according to the heat balance compensation formula, and synchronously adjusting the steam feed to the reboiler at the bottom of the column. This application has the effect of improving the stability of finished product quality and significantly reducing the energy consumption of the distillation process.

The present application belongs to the technical field of data processing of organic fertilizer production, and particularly relates to a method for low-carbon adaptive production management of organic fertilizer. The present application aims to solve the problem that the fixed process parameters cannot adapt to the real-time reaction state due to non-standardization of raw materials in the existing organic fertilizer production process, thereby causing uncontrolled carbon emission and increased invalid energy consumption. The method of the present application comprises: collecting multi-dimensional time series data in the production process, mapping the real-time data to the standard reaction process using the dynamic time warping algorithm; calculating the morphological difference degree of each dimension, combining the numerical deviation and the trend deviation to construct a global dynamic adaptive deviation index; based on the energy consumption gate feedback mechanism, generating control parameter adjustment instructions only when the real-time energy consumption is higher than the reference energy consumption. The present application realizes real-time dynamic adaptation of the production process and the characteristics of the raw materials, effectively reducing carbon emission and energy consumption in the production process.

The present application provides a kind of monitoring device and monitoring method, which can more appropriately make the judgment about the abnormality of the constitution related to the opening and closing action of the railway vehicle door. The door control device (100) of one embodiment obtains the output of DLS70 for detecting the locking of the door (80) of the railway vehicle (1) or DCS60 for detecting the opening and closing state of the door (80), and the position information of the opening and closing direction of the door (80) is obtained from the encoder (31), and based on the output of the obtained DLS70 or DCS60 and the position information of the opening and closing direction of the door (80), the judgment about the abnormality of the constitution related to the opening and closing action of the door (80) is made.

This invention discloses a saw blade detection method based on multi-source data fusion, specifically relating to the field of saw blade detection technology. It involves real-time acquisition of image data of the rake face, side edge, and cutting edge, as well as spindle current data, during the saw blade's cutting process. By integrating image analysis and current characteristic analysis techniques, the method performs a joint evaluation of the saw blade's visual wear state and current change trends using multi-source data. This accurately detects the saw blade's wear degree and abnormal tooth loss. Furthermore, by combining this with quality indicators such as dimensional deviations, surface finish, and defect rate of the processed product, the method determines the saw blade's wear threshold and tooth loss threshold. This enables automated decision-making for saw blade scrapping or replacement, effectively improving the accuracy and real-time performance of saw blade lifespan monitoring and ensuring the stability of processed product quality.

The application relates to the technical field of semiconductor intelligent manufacturing, in particular to a data-driven integrated circuit manufacturing yield signature method, system, device and medium; the method is based on integrated circuit manufacturing full-process data, realizes accurate signature of the yield of the integrated circuit manufacturing through data preprocessing, feature engineering, yield prediction model construction and dynamic signature standard formulation, improves the accuracy and efficiency of the signature, and reduces the misjudgment rate and the missed judgment rate.

The application provides a glass production line cold end stress detection system and method. It belongs to the technical field of glass production detection. The method comprises the following steps: collecting stress-related spectral data of the cold end region of the glass production line through a multispectral stress sensing array, and preprocessing the collected spectral data to obtain a pretreated spectral data set; extracting spectral features from the pretreated spectral data set to construct a spectral feature vector set; based on the spectral feature vector set, extracting the time-space features of the glass cold end stress by using a time-space correlation analysis algorithm to obtain a stress time-space feature matrix; and collecting spectral data through the multispectral stress sensing array, which avoids the influence of traditional contact detection on the glass product and the production line, and improves the detection accuracy and real-time performance.