204results about "Registering/indicating quality control systems" patented technology

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

The invention relates to a multi-dimensional aging test data analysis system and method, and aims to realize real-time acquisition and intelligent analysis of multiple performance indexes in the aging process of electronic products. The system comprises an information acquisition module, a test scheduling module, a data processing module, an analysis module, an abnormity judgment module, a report module and a communication module. Cooperative work among the modules is realized based on task driving and data circulation, accurate association of test data is realized through bar code binding, and unified analysis is performed in combination with multi-source information such as USB read-write performance, network transmission state and display resolution. The system realizes multi-task concurrent acquisition through a resource-aware scheduling strategy, and the analysis module identifies performance abnormity and outputs a structured report by combining preprocessing, feature extraction and time sequence prediction. According to the method, the test automation level and the anomaly recognition accuracy are improved.

The invention relates to the technical field of intelligent manufacturing and industrial digital twinning, in particular to a latex paint packaging full-process digital twinning method and system. The method comprises the following steps: collecting multi-source data in real time through a sensor network, and preprocessing to form a standard data stream; a coupled digital twinborn model fusing material characteristics and process parameters is constructed, and real-time prediction is realized by combining physical laws and data driving residual correction and online parameter self-adaption; constructing a multi-scale abnormal index by comparing model output with actual data, identifying an abnormal mode by adopting PCA, calculating local and overall health indexes, and predicting a trend; and generating a preliminary control strategy according to the abnormal mode and the health trend, forming closed-loop adaptive control in combination with health state weighted optimization and look-ahead adjustment, and issuing execution and feeding back an update strategy. According to the invention, the quality control qualification rate and the energy-saving efficiency of the whole process of emulsion paint packaging are improved.

The invention relates to the technical field of carbon fiber mold optimization, and discloses a carbon fiber box mold optimization method and system based on multiple targets. A multi-target definition library of the system stores a design constraint set composed of a geometric accuracy threshold value, a material strength threshold value and thermal deformation tolerance; the real-time sensing network collects data of layering tension distribution, temperature gradient and resin flow front edge; the topological dependency analyzer constructs a dependency relationship graph of the design parameters and the real-time sensing data; the material characteristic library stores carbon fiber layering parameters and resin curing kinetic parameters; the optimization calculation scheduler dynamically allocates multi-target optimization calculation task nodes; an iterative optimization engine generates a layering path sequence and curing temperature curve combination scheme; and the result verifier compares the combination scheme with the design constraint, and outputs a scheme deviation index. According to the system, the design standardization, the production monitoring comprehensiveness and the optimization verification accuracy of the carbon fiber box mold can be improved, and the product quality and the production stability are guaranteed.

The invention discloses a dynamic optimization method and system for a boron diffusion process of a photovoltaic cell and electronic equipment. Comprising the following steps: collecting process parameters and sheet resistance data, and associating the process parameters and the sheet resistance data to form a process parameter set; optimizing a process parameter set, removing noise data and incomplete data, and extracting an available feature data set; dividing the feature data set into a training set and a verification set, and training to obtain a nonlinear regression model for predicting resistance data under different feature data; carrying out validity verification on the nonlinear regression model, and constructing an optimization function to reduce a difference value between a predicted value and an actual value; and deploying a nonlinear regression model in the diffusion process, adjusting the process parameters of the diffusion process in real time according to the predicted value, and optimizing the obtained sheet resistance data. The problem that the boron diffusion process is difficult to accurately adjust by a traditional process control method can be solved.

The invention discloses a fruit juice beverage production monitoring and early warning method and device, and relates to the technical field of food production, and the method comprises the steps: carrying out the correlation division of production nodes on a fruit juice beverage production line, and obtaining a heat and oxidation abnormal correlation node set; configuring a temperature sensor array at each thermal association node to monitor real-time temperature-time data and generate a first early warning signal; and monitoring dissolved oxygen data, a material turbulence state and pipeline pressure at each oxidation associated node, establishing an oxidation intake map sequence, predicting the total oxidation intake and distribution uniformity, and analyzing the influence of oxidation on quality indexes to generate a second early warning signal. The technical problems that in the fruit juice beverage production process, heat imbalance can damage raw material nutrition and affect the sterilization effect, and oxidation can cause color and luster deterioration, taste decline and shelf life shortening are solved, and accurate monitoring, abnormity analysis and early warning of quality related key factors in the fruit juice beverage production process are achieved; and the stable product quality is effectively guaranteed.

The invention discloses an MES-based aluminum alloy casting production management method and system, and relates to the technical field of production management, and the method comprises the steps: firstly, synchronously collecting time sequence data streams of core process parameters such as pressure, speed, position and the like, and carrying out the multi-channel data alignment; and a deep learning auto-encoder model is introduced to carry out nonlinear dimension reduction and feature extraction on high-dimensional data, and reconstruction errors are utilized to realize real-time evaluation and anomaly detection of the quality of the casting process. And for the abnormal state exceeding the preset threshold value, key influence factors causing quality fluctuation are further traced through an intelligent diagnosis mechanism, autonomous decision is made based on a diagnosis result, an optimal parameter adjustment strategy is formed, and finally a closed-loop control instruction is generated to drive the production equipment to perform adaptive adjustment. In this way, the problem of unstable quality caused by parameter fluctuation in the casting process can be effectively solved, and the stability and the intelligent level of the aluminum alloy casting process are remarkably improved.

The invention discloses a coating quality prediction and optimization method and system based on multi-modal data fusion, and the method comprises the steps: S1, fusing coating parameters, environment data, equipment states and historical quality data, and generating a global feature matrix; s2, inputting the matrix into a GBDT model containing physical prior constraints, and outputting a quality risk probability and a key index; s3, target parameters are generated through a genetic algorithm, Q-Learningg and Pareto screening; s4, performing closed-loop feedback on updated data; s5, cross-line adaptation is achieved through transfer learning and domain adaptation; and S6, iteratively training the model in batches. The system comprises a data fusion module, a quality prediction module, a parameter optimization module, a closed-loop control module, an overline adaptation module and a self-evolution module. The method aims at solving the problems that traditional coating overline parameters are difficult to reuse, small sample modeling cost is high, prediction is separated from process logic, and parameter adjustment lags, so that overline adaptability and prediction stability are improved, small sample cost and energy consumption are reduced, and the method is suitable for high-end coating scenes such as automobiles and aviation.

The invention relates to the technical field of glass bottle detection, and discloses a glass bottle bottom defect detection method which comprises the following steps: building a detection platform, and adopting a multi-view imaging module of an annular LED and oblique light supplement combined light source, a vertical camera and 3-4 oblique cameras; after the to-be-detected glass bottle is positioned, synchronously collecting a bottle bottom full-view image set; graying, adaptive median filtering, CLAHE histogram equalization and Otsu binarization preprocessing are carried out on the image; extracting geometric and textural features of the image, inputting the geometric and textural features into an improved YOLOv5 model for identification and classification, and judging defects by combining with multi-view result fusion; and sorting the glass bottles according to a detection result and generating a traceable report. The identification rate of tiny defects is larger than or equal to 99%, the detection time of a single bottle is smaller than or equal to 0.5 second, and the method is compatible with glass bottles with the diameter of 30-100 mm, is suitable for large-scale quality control in the fields of food and beverage, medicine packaging and the like, and has extremely high practical value.

The invention discloses a tea making monitoring method and system based on a knowledge graph, and the method comprises the steps: integrating variety characteristics, processing technology parameters, equipment operation states and environmental factors into a structured data set in a unified format through a data fusion technology, and constructing a processing technology knowledge graph through a graph representation learning model; the method comprises the following steps: representing correlation between process links and parameters by nodes and edges, calculating a parameter deviation vector in real time, triggering cross-link influence propagation simulation, identifying a potential influence path, extracting an equipment related node sequence, analyzing an abnormal fluctuation mode, generating an abnormal index set, tracing the contribution degree of a preorder link parameter to abnormity, and generating a parameter adjustment vector; and finally, the optimized sequence output of the machining process parameters is realized. According to the invention, through fusion of the knowledge graph and the abnormity traceability, the problem of dynamic optimization of the processing parameters is solved, and the precise control capability of tea processing and the efficiency of equipment abnormity processing are significantly improved.

The invention discloses a manufacturing process optimization method and system based on multi-agent autonomous collaboration, and the method is applied to a central coordinator, and comprises the steps: transmitting a received production demand to a task planning agent for the task planning agent to carry out the analysis and decomposition, and forming task information; receiving task information, wherein the task information comprises sub-tasks after the task planning intelligent agent analyzes and decomposes the production demand; the sub-tasks are sent to a plurality of intelligent agents, so that the intelligent agents perform bidding according to self capability evaluation functions and cost estimation to generate bidding information; and bidding information is received, the bidding information comprises a self-ability evaluation function and cost estimation of each agent for each sub-task, the sub-tasks are allocated to the multiple agents according to a total execution cost minimization principle, and a production plan is generated and executed. According to the method provided by the invention, planning and execution of the production task can be autonomously completed in a complex manufacturing scene, and the production efficiency and the product quality are improved.

The invention belongs to the technical field of instrument board production, and discloses an instrument board tubular beam assembly integrated welding quality control and detection system which comprises the following steps: S1, design stage optimization and simulation prediction; s2, the welding process is precisely controlled; s3, material and equipment collaborative management; the design stage optimization and simulation prediction comprises structure topology optimization, weld joint layout and a welding deformation prediction model, and the welding process precise control comprises a low-heat input welding process and a dynamic parameter adjustment system. Through structural optimization and intelligent process regulation and control, welding deformation and residual stress are remarkably restrained, and high-precision forming of light-weight materials is ensured. A dynamic reverse compensation mechanism and a low-heat input welding technology cooperate to effectively avoid air holes, cracks and other defects, and the strength and fatigue life of the connector are improved. A multi-dimensional material pretreatment and equipment calibration system breaks through the reliability bottleneck of dissimilar material interface bonding, and provides guarantee for stable production under complex working conditions.

The invention provides a laser additive manufacturing defect monitoring method, system, medium, equipment and device, and belongs to the technical field of laser additive manufacturing defect recognition. A laser additive manufacturing defect monitoring method comprises the steps that X-ray image information and sound signal information in the melting process of a laser powder bed are obtained; preprocessing the X-ray image information and the sound signal information to obtain preprocessed X-ray image information and sound signal information; marking the preprocessed X-ray image information and sound signal information to construct a data set; inputting the preprocessed X-ray image information and sound signal information into a multi-modal teacher model for training so as to obtain defect identification and positioning capability based on multi-modal information; inputting the preprocessed sound signal information into a single-mode student model to obtain a trained single-mode student model; and migrating the defect identification and positioning capability based on the multi-modal information to the trained single-modal student model through a knowledge distillation strategy.

The invention relates to the field of data processing, in particular to a carton production line monitoring method and system, and the method comprises the steps: collecting and preprocessing multi-dimensional time sequence data of a production process; constructing a standard hidden Markov model (HMM) based on historical normal data, and defining the hidden state of the HMM as a microscopic operation mode under a macroscopic process; extracting procedure context features, and constructing a procedure conformity evaluation model (PCAM) to calculate a conformity score; dynamically adjusting the emission probability of the standard HMM in combination with the score to form an improved IHMM; and calculating the log-likelihood probability of the real-time observation sequence by using the improved IHMM, and comparing the log-likelihood probability with a preset threshold to judge the production abnormality. According to the invention, the accuracy and reliability of abnormity monitoring can be effectively improved.

The invention relates to a dynamic lag compensation method for inclined hole machining of a base of a water jet cutting machine. The dynamic lag compensation method comprises the following steps that S1, machining parameters are obtained; s2, establishing a dynamic lag vector model; s3, based on the target axis vector of the inclined hole, establishing an asymmetric taper prediction model; s4, coupling the dynamic lag vector model and the asymmetric taper prediction model to generate a comprehensive compensation model; s5, real-time reverse compensation is conducted on a preset inclined hole cutting theoretical tool path; s6, the high-pressure abrasive water jet device is controlled to conduct inclined hole machining on the cutting machine base according to the execution path; and S7, in the cutting process, water jet pressure, abrasive flow and nozzle abrasion state parameters are built in real time. The method has the following advantages that accurate compensation of the dynamic hysteresis effect and effective suppression of the asymmetric taper are achieved, the hole axis and position precision is remarkably improved, and the high-quality hole wall shape is obtained.

The invention belongs to the technical field of intelligent manufacturing traceability, and provides a battery part production traceability management system based on the Internet of Things, which comprises the steps of collecting battery part information, setting double identifiers for each battery part, storing the battery part information, calculating a qualified index of the battery part for preliminary evaluation, and determining whether the battery part is qualified or not. And for the preliminarily qualified battery parts, constructing and training an attenuation prediction model to predict the service lives of the battery parts to obtain the predicted actual service lives of the battery parts, dividing the battery parts into inferior products, non-defective products and superior products, constructing and training a defect analysis model to identify the defect root causes of the inferior products, and determining the actual service lives of the battery parts. The method comprises the following steps: determining steps causing defect root causes, setting risk mark values, recording the number of times of causing the defect root causes in a batch, sorting each step in a production line, obtaining a tracing priority sequence, calculating the inferior product rate of battery parts in the batch, and judging whether to trigger forward recall or reverse tracing.

The invention provides an arc-shaped lithium battery intelligent forming method and system fusing digital twinning, and relates to the technical field of digital twinning. According to the method, a twin covering geometry, material evolution, heat engine coupling and equipment dynamics is established, unified modeling is carried out on a forming target, process data and image data, millisecond-level collection is carried out on the edge side, deviation vectors are output, parameter identification and time delay compensation are driven, a formula is generated through online optimization, and closed-loop execution is carried out; and when the judgment result is unqualified, triggering re-calibration and re-optimization, and filing and updating design parameters, formulas and measurement and judgment results to form a cross-batch learning and knowledge base. According to the invention, experience dependence and tracing fracture are overcome, and real-time controllability, full-link traceability and cross-batch optimization are realized.

The application discloses a kind of abrasive grain flow microchannel polishing quality reliability analysis systems, including abrasive grain flow polishing system, quality detection system and reliability analysis system, the abrasive grain flow polishing system is used in the polishing processing of microchannel in microfluidic chip;The quality detection system is used to carry out quality detection to the microchannel after processing, and obtains detection value R1;The reliability analysis system carries out reliability analysis to processing quality, and the information basis of analysis is processing parameter P and the microchannel processing quality detection value R1 obtained by detection;The reliability analysis system transmits adjusted processing parameter to the abrasive grain flow polishing system, and is used to control microchannel polishing.The application also discloses a kind of abrasive grain flow microchannel polishing quality reliability analysis method.The application utilizes new learning function, and the correlation coefficient introduced by new learning function, improves the accuracy of model in threshold value vicinity, and can avoid the accuracy in low correlation area, ensures the accuracy of reliability analysis.

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.

This invention discloses a production quality control system for mold products, belonging to the field of production control technology. Specifically, it includes: establishing a dynamically updated quality status benchmark library and constructing a directed network reflecting the propagation relationship of quality characteristics between processes to achieve real-time monitoring of the production process; calculating the state offset of each process node, collecting process entropy and result entropy data when an anomaly is detected, and accurately distinguishing between material characteristic anomalies and tool status anomalies by analyzing the cooperative jump characteristics of the dual entropy sequence and the time-series pattern of entropy change sensitivity ratio, and generating corresponding process parameter adjustment instructions or tool maintenance instructions. This invention overcomes the limitations of traditional quality control systems in identifying the root causes of anomalies, achieving accurate diagnosis and classification of quality problems, and significantly improving the scientific nature and effectiveness of production quality management.

The invention provides a scrap steel quality inspection APP pressing block two-end dismounting prompting method, and relates to the technical field of scrap steel quality inspection in the iron and steel industry, and the method comprises the following steps: S1, material type selection: an acceptor selects a to-be-inspected scrap steel pressing block type in a scrap steel quality inspection APP system according to an actual condition; s2, inputting to-be-detected information: inputting specific information of a to-be-detected briquetting vehicle; and S3, two-end unpacking prompting: according to the input layer number, row number and column number of the to-be-detected briquette vehicle, dynamically generating an unpacking instruction by the APP, and prompting an acceptor to carry out unpacking detection. According to the scrap steel quality inspection APP pressing block two-end-disassembly prompting method, an unpredictable and tamper-resistant random unpacking mark generation system is constructed, dynamic generation of an unpacking instruction is achieved, the sampling rule is transparent, man-made interference is resisted, and the unpacking efficiency is improved. Therefore, the problems that the inspection efficiency is reduced due to full-amount disassembly of the two ends and the internal quality of the pressing block is possibly covered due to lack of randomness are solved, and the accuracy and the high efficiency of scrap steel inspection are effectively improved.

The invention relates to the technical field of production monitoring, and discloses a quality monitoring method and system for a non-woven fabric production line, and the method comprises the steps: synchronously capturing the linear speed information, gram weight information and appearance image information in the non-woven fabric production line; performing frame alignment on the gram weight information and the appearance image information to obtain synchronization feature data; fusing the texture anomaly candidate region and the gram weight information fragment in the synchronized feature data to obtain a fused defect description; matching a combination mode of image morphological features and surface density statistical distribution features in the fusion defect description to obtain a defect category; reversely mapping to a process flow diagram, and positioning primary process links causing defect categories to obtain a judgment conclusion; integrating the judgment conclusion, the fragment of the synchronization feature data and the identifier of the primary process link to obtain a quality monitoring report; the quality monitoring efficiency of the non-woven fabric production line can be improved.

A method and a device for processing product manufacturing messages, and an electronic device are disclosed. The method for processing product manufacturing messages includes: monitoring a plurality of product manufacturing messages; establishing a product defect analysis task queue based on the plurality of product manufacturing messages; distributing product defect analysis tasks to product manufacturing assisting devices based on the product defect analysis task queue, wherein the product defect analysis tasks include a task of identifying product defect content based on a defect identification model; wherein the product defect content includes any one or more of: product defect type, product defect location, and product defect size.

This invention relates to a replaceable filter solenoid valve and a method for determining filter specifications. The solenoid valve body has several grooves, each groove containing an oil inlet and a control oil inlet. An elastic filter is embedded within each groove, covering both the control oil inlet and the oil inlet. The elastic filter has an opening. Because the elastic filter is elastic and has an opening, it can be inserted into or removed from the groove by expanding the opening, thus greatly improving the convenience of replacing the elastic filter.

The invention provides a printed book and periodical automatic cutting management method and system based on image recognition, and belongs to the technical field of image recognition, and the method specifically comprises the steps: determining a quality inspection processing strategy of a cutting result of an image recognition model according to adjustment data and time sequence distribution data of an adjustment time period, and carrying out quality inspection processing on the cutting result by utilizing a quality inspection processing strategy, determining abnormal cutting data under different adjustment modes according to a quality inspection processing result when it is determined that management and control processing of maintenance of the printing mechanism needs to be carried out according to the quality inspection processing result and the correlation degree of the adjustment data between the adjustment time periods, and carrying out management and control on the maintenance of the printing mechanism according to the quality inspection processing result. And based on the abnormal cutting data and the change data of the adjustment mode of the cutting mechanism, the management and control processing method of the maintenance processing of the printing mechanism is determined, and the reliability degree of the cutting processing is improved.

The invention relates to a network abnormity early warning method and device based on digital linkage, equipment and a medium, and belongs to the technical field of semiconductor processing monitoring. The method comprises the following steps: acquiring multi-dimensional production data in a semiconductor cutting process, and carrying out analytical calculation on the multi-dimensional production data to obtain an abnormal data set in the semiconductor cutting process; inputting the exception data set into a pre-trained multi-dimensional exception analysis model to obtain an exception prediction result of each link of semiconductor cutting production; and based on the anomaly prediction result, inputting a pre-trained multi-agent collaborative early warning mechanism, and generating corresponding early warning information. By adopting the method, artificial experience judgment and data recording can be replaced, real-time production monitoring and accurate abnormity early warning are realized, the semiconductor cutting production quality and efficiency are guaranteed, and the potential risk of production is reduced.

The invention relates to an industrial visual defect detection system and method, and the system comprises an image collection module which is used for collecting industrial product images on a production line in real time; the detection reasoning module is used for performing multi-dimensional defect detection through a trained defect detection model according to the industrial product image to obtain multi-dimensional defect information of the industrial product, and the multi-dimensional defect information comprises defect type information, defect position information and defect shape information; and the output control module is used for executing different levels of early warning processing according to the multi-dimensional defect information and generating a corresponding quality inspection report. By means of the method and device, multi-dimensional defect detection of the industrial products on the assembly line is achieved, automatic recognition of various complex defects is completed based on the types, positions and shapes of the defects, early warning processing of different levels is executed, the refinement level of quality control is improved, and the problem of how to improve the defect detection reliability of the industrial products is solved.

The application discloses a quality detection system and method for glue injection laser profile, and belongs to the technical field of quality detection. The system comprises the following steps: obtaining a reflected point cloud signal of a glue injection finished product, constructing a point cloud data stream, setting an image reconstruction method, constructing a glue layer three-dimensional model, synchronously generating a pseudo-color gray scale image, and realizing visual representation of the surface texture of the glue layer; constructing a three-layer feature extractor, setting a quality preliminary judgment method, cross-layer fusing features of different scales, solving the missed detection problem of traditional single feature detection on complex defects, automatically identifying basic defects, and outputting a quality preliminary judgment result; judging defects of the glue injection finished product that does not meet the preliminary judgment, setting a defect detailed judgment method, voxel-level segmenting a three-dimensional image, calculating features of the defects, and judging whether the defects affect the use performance of the product, so as to generate a detailed judgment result, realize accurate positioning of complex defects, and improve the defect type recognition accuracy.

The invention relates to the technical field of digital manufacturing and industrial information management, and discloses an information management system for a digital processing workshop, which comprises the following steps: acquiring an equipment abnormal event record to generate an equipment fault propagation sub-graph with observation evidence; calculating process similarity and screening source domain products; fusing the source domain priori causal graph to execute Bayesian causal structure learning to generate a posterior cross-process quality causal sub-graph; calculating a conditional Granger causal relationship to generate a cross-layer causal edge set; assembling a double-layer causal map; establishing an extended structured Bayesian network model; executing structured variational inference to output a joint attribution result; executing propagation path tracking to generate an attribution report with uncertainty quantization; according to the method, the technical problems that multi-source heterogeneous information of a device layer and a process layer cannot be effectively fused in the fault attribution process of the digital processing workshop in the related technology, cross-process causal structure learning is difficult to complete under the condition of a small number of samples, and the attribution result is lack of uncertainty quantification are solved.