394 results about "Manufacturing data" patented technology

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

The invention provides a robot cable manufacturing process optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting cable manufacturing data, constructing a digital twin model, and achieving the process flow simulation through a graph network; building a deep reinforcement learning environment by taking the manufacturing data and the simulation data as state input; analyzing a causal dependency relationship of process parameter adjustment; constructing an industrial knowledge graph expert system to generate an optimization strategy; and a process optimization closed loop is formed. According to the invention, self-adaptive optimization of the cable manufacturing process is realized, and the manufacturing efficiency and quality are improved.

The invention belongs to the technical field of data quality management, and particularly discloses a power transmission and transformation equipment manufacturing full-process data checking method and system, a terminal and a medium, and the method comprises the steps: collecting original business data of each stage of power transmission and transformation equipment manufacturing, and carrying out the standardization, format normalization and preliminary abnormity elimination; uploading the processed data to a cloud processing module, and executing semantic fusion and field mapping processing; calling a corresponding checking rule set according to the manufacturing state, and executing field-level and record-level quality checking on the structured data; generating an abnormal identifier for the unqualified data, feeding back the abnormal identifier to a client interface, and storing the qualified data to the block chain module to realize credible evidence storage; and finally, generating a data quality checking report and performing structured visual display. According to the method, the problems that existing manufacturing data semantics are not uniform, rules cannot be dynamically adapted, abnormity is difficult to identify with high confidence, and data evidence storage is missing are solved.

The invention relates to an aviation equipment manufacturing digital main line engine system supporting multi-source heterogeneous data access, and belongs to the technical field of aviation manufacturing data management. The system comprises a multi-source heterogeneous data access adaptation module, a data fusion processing module and a full-process data link construction module. The multi-source heterogeneous data access adaptation module passes through a data interface protocol and format conversion component; the data standardization and fusion processing module completes association fusion through an aviation heterogeneous data collaborative computing framework by means of an aviation manufacturing field data element standard library, a unified heterogeneous data model is generated, and the framework comprises a data fragmentation layer, a parallel node layer and a result aggregation layer; and the full-process data link construction module constructs a data link covering the full life cycle of the product based on a time sequence association algorithm and a product unique identifier mapping mechanism. The system can effectively solve the integration and management problems of multi-source heterogeneous data in aviation equipment manufacturing, and improves the data processing efficiency and the whole-process data tracing capability.

The invention discloses a surface grinding machine self-adaptive control optimization method for mold component manufacturing, and belongs to the technical field of intelligent manufacturing. The method comprises the steps that machining prediction modeling is conducted on manufacturing design information through the digital twinning technology; a mold component manufacturing data set of the surface grinding machine is collected and obtained, and retrieval matching is conducted on the basis of the manufacturing design information and the mold component manufacturing data set; carrying out processing production and real-time monitoring on the target mold component based on the target grinding machine processing parameters; performing processing prediction on the real-time processing data of the mold component by using the digital twin model of the mold component; and adaptive optimization is carried out on the target grinding machine machining parameters based on the mold component prediction manufacturing information, and adaptive optimization control is carried out on the target mold component through the grinding machine optimization machining parameters. The technical problems that in the prior art, die component machining parameters cannot be dynamically optimized, and machining precision and efficiency are difficult to meet at the same time, so that machining quality is unstable, and trial and error cost is high are solved.

The invention relates to the technical field of chip yield prediction, in particular to a GPU chip yield prediction method and system based on multi-dimensional data fusion, and the method comprises the steps: firstly obtaining multi-dimensional data in a GPU chip manufacturing process; performing time sequence calibration and alignment on the multi-dimensional data based on a process flow, and generating high-order interaction features by using an implicit feature interaction technology; then generating a multi-dimensional abnormal association vector through yield bottleneck projection; then generating a local risk value based on the multi-dimensional abnormal association vector and the manufacturing process node, and constructing a global yield map; and finally, constructing an anomaly propagation and accumulation calculation framework, calculating along manufacturing process nodes to obtain an accumulation risk value, and obtaining a final GPU chip yield prediction value through global damage projection. According to the method, the multi-dimensional manufacturing data is subjected to time sequence fusion and feature crossing, the global yield map is constructed, and abnormal propagation and accumulation risks are calculated, so that GPU chip yield prediction is realized.

The invention discloses a sleep thermal comfort perception and indoor sleep environment regulation and control method and device, and belongs to the technical field of computer vision and heating ventilation air conditioning control, and the method comprises the steps: obtaining video data, physiological data and environment data, which are measured in advance, of a subject during sleep; performing data annotation, feature extraction and data fusion on the video data, the physiological data and the environmental data to make a data set; inputting the manufactured data set into a pre-constructed sleep thermal comfort perception model to obtain a sleep thermal comfort perception prediction result; regulating and controlling the indoor sleep environment according to the obtained sleep heat comfort perception prediction result; according to the method, by predicting the sleep thermal comfort of the person, the operation parameters of the heating ventilation air conditioning system can be correspondingly adjusted, the influence of contact type equipment on a sleeper is avoided, the thermal comfort of the sleeper can be ensured, the energy use efficiency can be remarkably improved, and therefore the total energy consumption of a building is reduced, and energy conservation and emission reduction are achieved.

Methods and computer systems for methods, computer systems, and computer-readable memory media for determining a warp function. An as-designed watertight spline model of an object is received. A set of sweep points along a sweep trajectory for manufacturing the object are used to determine a sweep surface. Model points are sampled from the as-designed watertight spline model and projected onto nearest as-executed points on the sweep surface. The projected as-executed points are used to construct an as-executed model of the object. A warp function is determined based on a difference between the as-designed watertight spline model and the as-executed model. The warp function is a continuous function approximating differences between the as-designed model and the as-manufactured object. The warp function is stored in a non-transitory computer-readable memory medium.

The invention provides an overlay mark asymmetry compensation method and device for a multi-mode deep learning network, and the method comprises the steps: obtaining manufacturing data of different modes, inputting the manufacturing data into the multi-mode deep learning network for processing, and obtaining an overlay mark asymmetry prediction error; according to the multi-modal deep learning network, feature extraction is performed on manufacturing data of different modals, feature fusion is performed on vector features corresponding to the different modals, and prediction errors with asymmetric overlay marks are obtained through association combination of physical parameters of actual production in the manufacturing process and parameters observed by equipment. Therefore, the actual overlay mark is compensated in actual production. By introducing a multi-modal learning network, various data features are fully mined and utilized, the generalization ability of the model is enhanced, meanwhile, the intelligence of equipment is realized by means of a machine learning technology, and an effective traceability and positioning solution is provided for process problems in wafer production.

The invention discloses a high-precision intelligent quality inspection system for diamond grains, and relates to the technical field of defect detection and contour measurement, and the system comprises a multi-mode sensing device which is used for obtaining a two-dimensional image, a three-dimensional shape and spectral data of the grains; the module is used for receiving manufacturing process parameters and constructing a theoretical three-dimensional model; the interface is used for acquiring actual manufacturing process data; the unit is used for processing the fused measured data and comparing the fused measured data with a theoretical model to generate deviation data; the hierarchical artificial intelligence analysis engine comprises a data analysis and coordination control AI model based on multi-head attention, the AI model is coupled with an attribution analysis layer, and the engine fuses and analyzes measured data, deviation data, process parameters and actual manufacturing process data. And performing deep cross-modal association, trend analysis, manufacturing data association analysis and accurate attribution by using an attention mechanism, and finally generating a quality evaluation result containing application performance prediction and process optimization suggestions with attribution information.

The invention provides an optical proximity correction method and system based on mask error model optimization, and a terminal, and the method comprises the steps: building a data sample set containing a plurality of process conditions through obtaining a large amount of mask manufacturing data under a specific photoetching process; and based on the sample set, constructing a physical optical sub-model and a machine learning sub-model to form a mask error model for outputting a final mask key size value. And then, according to a plurality of preset alternative weight coefficient combinations, reconstructing an objective function which introduces a mask error term, calculating corresponding objective function values, and correcting the photoetching pattern by using an OPC algorithm corresponding to each objective function value to obtain performance index data so as to determine an optimal weight coefficient combination. And finally, optimizing the OPC algorithm by using the target function of the optimal combination, thereby realizing the accurate correction of the photoetching pattern, and improving the precision and yield of semiconductor manufacturing. According to the method, the mask error model comprehensively considering physical optics and machine learning is established, and the OPC process is introduced, so that the influence of the mask error can be predicted and compensated more accurately, the critical dimension deviation and the like are reduced, and the device performance and the yield are improved. The method can adapt to different process conditions, and parameters and weight coefficients can be dynamically adjusted. And moreover, rework can be reduced, automatic adjustment is realized through closed-loop feedback control, the production stability and efficiency are improved, and the cost is reduced.

A method that includes obtaining, from one or more stations, embedding vectors that embed features of the stations, obtaining measurement vectors and associated measurement names, generating a text array of the measurement names utilizing a language model, concatenating the text array and the measurement vector at one or more cross-attention modules configured to encode one or more measurement arrays to one or more latent embedding vectors, generating one or more latent embedding vectors associated with the measurement vector and corresponding measurement names via the cross-attention module and a fixed-size station embedding vector, outputting the latent embeddings; generating a query vector; generating key vectors value vectors utilizing a latent embedding vector; decoding the latent vectors utilizing the key vector and value vector; utilizing the cross attention modules and query vectors, decoding the latent embedding vectors; and output a predication.

The invention belongs to the technical field of industrial detection, and discloses a structured knowledge-driven industrial defect detection method and system, and the method comprises the steps: obtaining manufacturing data related to a product, and constructing a knowledge graph which reflects the relation between the defect characteristics of the industrial product and the machining process and environment through the manufacturing data; regarding the established industrial defect knowledge graph as a heterogeneous information network, and performing deep feature aggregation on various entities and relationships in the industrial defect knowledge graph by using a heterogeneous graph neural network technology to form a high-dimensional feature vector for guiding coupling of multiple environmental factors; establishing an industrial image illumination model by using a deep transfer learning technology, and simulating image features of industrial products under different acquisition conditions; an industrial product defect data enhancement framework based on a conditional diffusion model is constructed by training a conditional noise prediction network, a real and diversified industrial product image is generated according to the industrial product defect data enhancement framework, and industrial product defect detection is completed.

A computer-implemented method and apparatus for managing physical and digital products' customization using container format are disclosed. In some embodiments, the method comprises receiving a container file with serialized objects, deserializing these objects to form a hierarchical object structure, and interacting with a database to obtain real-time configuration data. Custom attributes of the products are defined by binding attribute / value pairs to the hierarchical structure. A visualization of the customized product is generated based on these pairs, and manufacturing instructions are determined considering the attribute / value pairs and manufacturing constraints. The apparatus comprises a memory and a processor to execute instructions for deserializing the container file, retrieving configuration data, organizing objects into a hierarchical tree, negotiating product customizations, and outputting manufacturing data. This system enables efficient and accurate production of customized products by managing data, rendering graphics, and incorporating manufacturing constraints.

Provided herein are methods, apparatuses, computer program products, and systems for a manufacturing data analysis system assistant. One method can include receiving, by an agent of a manufacturing data analysis system and from a user interface of the manufacturing data analysis system, an input related to manufacturing data; generating, by the agent, a prompt input based on context information related to the manufacturing data and a task identified for the input; providing, by the agent, the prompt input to a large language model (LLM); receiving, by the agent and from the LLM, a response that is based on the prompt input; and providing, by the agent, the response for display in the user interface.

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

The invention discloses a new energy lithium battery surface defect visual detection method based on a YOLOv8 model, and belongs to the field of new energy lithium battery manufacturing and detection.The new energy lithium battery surface defect visual detection method comprises the steps that S1, a stable data acquisition platform is built; s2, optimizing optical parameters; s3, surface defect images of a plurality of lithium ion batteries are collected for data preprocessing, and a standardized data set is constructed; s4, adopting an improved network architecture deep learning detection engine based on YOLOv8; and S5, inputting the manufactured data set into an improved YOLOv8 model for model training. According to the intelligent detection system based on machine vision and the YOLOv8 model provided by the invention, the precision and robustness of defect identification are remarkably improved while efficient detection is ensured.

The invention provides a manufacturing process optimization control method and system for electronic components of a main control board, and relates to the technical field of manufacturing process optimization, and the method comprises the steps: obtaining a historical manufacturing database of the electronic components in the main control board of a steering wheel; extracting a first historical record in the historical electronic manufacturing data records; analyzing the first manufacturing process record to obtain an optimal historical manufacturing scheme, and taking the optimal historical manufacturing scheme as an initial optimal solution; optimizing the initial optimal solution according to a first constraint in predetermined optimization constraints to obtain a first optimal candidate solution; performing optimization analysis on an optimal solution set established based on the first optimal candidate solution; and designing and manufacturing the multi-layer PCB for the electronic component according to the target optimal solution. According to the invention, the technical problem of signal interference caused by unreasonable wiring in the PCB design in the prior art can be solved, and the technical effects of improving the performance of the circuit board and enhancing the stability and reliability of the system are achieved.

A method synchronizes a plurality of mobile devices from different vendors by pairing mobile devices with a hub; positioning the hub in an area accessible to group members; automatically copying data structures with characteristics of the mobile device and extracting data from the mobile devices when each of the group members are within a range of the hub; uploading the copied data structures to a remote server; parsing the data based on each respective mobile manufacture data structure; and synchronizing user data with a cloud database. In implementations, the method can be used for fitness training of teams or for selecting optimal team members for a task. The hub's awareness of connected devices in a building can be used to reduce energy consumption by turning off building sectors based on occupants therein.

The invention discloses a data annotation supply chain collaborative management system and method based on big data analysis, and relates to the technical field of supply chain collaborative management, and the system comprises a data annotation presetting module, a manufacturing progress analysis module, a supply progress analysis module, a distribution progress analysis module and a supply chain completion degree analysis module. The method comprises the steps of setting a preset data label for a supply chain, marking a supply chain name on the preset data label, collecting a product manufacturing process of the supply chain, analyzing the manufacturing process and then converting the manufacturing process into a manufacturing data label, collecting a total product sales progress of the supply chain, analyzing the total sales progress and then converting the total sales progress into a sales data label, and collecting a product sub-sales progress of the supply chain. After the sub-sales progress is converted into the sub-sales data annotation, the sub-sales data annotation is filled with the preset annotation, the unique data annotation of any supply chain is generated, and the predicted production capacity of the next production cycle is stipulated through the data annotation, so that a mode of predicting the production capacity required by the supply chain is provided, the market demand is met, and the product retention time is shortened.

The invention relates to a manufacturing process quality tracing and root cause analysis system based on a data graph, and belongs to the technical field of manufacturing process quality control. The system performs cleaning, duplicate removal and standardization processing on multi-source heterogeneous data in the whole manufacturing process through a data processing module to generate structured data; the data graph construction module fuses the manufacturing domain knowledge ontology, constructs a manufacturing process digital twin semantic model and a multi-dimensional manufacturing data graph, introduces a dynamic association weight updating mechanism and mines node potential implicit association; the quality tracing module positions map nodes based on query semantic vectors, and reconstructs an entity state change track by using a digital twin semantic model to generate a tracing link; and the root cause analysis module screens candidate abnormal factors, locates a root cause in combination with dynamic weight and multi-scene simulation verification, and generates a quality optimization scheme. According to the invention, full-link accurate tracing and root cause efficient positioning of the manufacturing process quality are realized, and the intelligence and refinement level of manufacturing quality management and control is improved.

The invention relates to the technical field of knowledge maps, in particular to a steel manufacturing knowledge map construction method based on multi-source heterogeneous data, which comprises the following steps: converting message data into a standardized semantic triple through a semantic mapping rule, and complementing missing attributes by using physical and chemical component balance logic to form a standardized entity; meanwhile, a semantic processor is used for extracting unstructured report text logic, cross-modal coupling is carried out on the unstructured report text logic and SEM image features, and a failure mechanism enhanced entity is generated. Then, full-process dynamic parameters are collected and regularized into equal-interval time sequence parameters; and finally, taking the normalized entity as an index feature and the mechanism enhanced entity as an association constraint, executing semantic fusion by utilizing a topological feature mapping engine, and constructing a dynamic weight graph structure by combining with a dynamic probability distribution correction operator iteration parameter weight. According to the method, efficient association and semantic organization of manufacturing whole-process multi-modal data are realized, and the retrieval precision and knowledge discovery capability of steel manufacturing data are greatly improved.

The invention relates to a management method and device for manufacturing a dry electrode of an intelligent manufacturing digital twin system. The method comprises the steps of collecting manufacturing data generated in the manufacturing process under the condition that the dry electrode is manufactured through a physical manufacturing device; wherein the manufacturing data comprises manufacturing state data of the physical manufacturing device and manufacturing product data of the dry electrode; constructing a production line model corresponding to the dry-type electrode according to the manufacturing data through a digital twin model device; wherein the production line model comprises a simulation object of the physical manufacturing device; and analyzing the manufacturing data and simulation data generated by the production line model through the information physical cloud integration device to obtain a manufacturing result of the dry-type electrode. By adopting the method, the manufacturing process of the dry electrode can be detected in real time, the corresponding production line model is constructed to simulate a manufacturing scene, and reasonable and effective data analysis is performed, so that problems in production can be found in time, production interruption is reduced, and the production efficiency is improved.

The invention discloses a manufacturing industry processing equipment information digitization system comprising a data acquisition layer which acquires data through a sensor which is connected to a data acquisition terminal in a wired or wireless mode; the data acquisition terminal is used for receiving the data acquired by the data acquisition layer, respectively marking the acquired data as key monitoring parameters and non-key monitoring parameters, and then transmitting the key monitoring parameters and the non-key monitoring parameters to the server; the invention relates to the technical field of manufacturing industry data processing, and can realize equipment fault point judgment when equipment fails, can more comprehensively capture subtle changes of equipment operation states in fault prediction, greatly improves the fault feature identification degree, and improves the production continuity by predicting the fault occurrence time and type. And the risks of capacity loss and sharp rise of maintenance cost caused by faults are reduced. Impact of faults on equipment is reduced, and the service life of the equipment is prolonged.

The invention relates to a tire character recognition method, in particular to a tire character recognition method based on three-dimensional point cloud, and the method comprises the steps: calibrating an industrial 3D camera, managing and controlling the connection and communication of the camera through a tire point cloud data acquisition module, and visualizing and storing an acquisition result; performing combined filtering on the tire point cloud data by using voxel downsampling and a statistical filtering method, and then making a tire data set; putting the manufactured data set into a PointNet + + network added with a self-attention mechanism for training, and generating a training weight; and visualizing a tire identification result and storing a prediction result file. The invention relates to the technical field of three-dimensional recognition, geometric characteristics of the surface of a tire are more accurately captured by using point cloud, the influence of factors such as light and tread stains is reduced, hierarchical feature extraction of PointNet + + is combined with a Transform self-attention mechanism, detail features are more sufficiently extracted by using the relationship between different hierarchical features, and the recognition accuracy is improved. And the robustness of tire character recognition in a complex environment is improved.

The invention discloses a sonar image generation method fusing stable diffusion and style migration, and belongs to the field of underwater sonar image generation and image processing. The method comprises the following steps: firstly, making a data set for training a composite image model; thirdly, finely adjusting a large-scale stable diffusion model by using the manufactured data set, so that the large-scale stable diffusion model has the capability of synthesizing sonar images in a specific field; and finally, synthesizing a sonar image with specified content by using a style injection method in a reasoning stage by utilizing the generation capability of the fine tuning weight model. And quantitative and qualitative evaluation analysis is carried out on the synthesized image. According to the image generation method established by the invention, the generated image is closer to the performance of real sonar data, the diversity of the sonar data is remarkably improved, and richer and more reliable data support is provided for subsequent sonar image analysis and application.

The invention relates to the technical field of structural part injection molding, in particular to a manufacturing method and equipment for a continuous fiber reinforced structural part, and aims to solve the problem that a structural part manufactured in the prior art cannot meet the requirements for light weight and high strength at the same time. Comprising the following steps: heating a thermoplastic composite material sheet and putting the thermoplastic composite material sheet into a mold; designing an original material formula, preparing materials according to the formula, and mixing the materials into a uniform melt; conveying and injection molding of melt materials are controlled through a reversing valve; the manufacturing data is associated with the product. The equipment comprises an extruding and mixing mechanism for melting, plasticizing and mixing each material in an original material formula, an injection molding mechanism for realizing the injection of a melt material, an injection molding machine mold locking unit for completing molding and curing, a control system for coordinating the whole process and an information tracing mechanism for ensuring the traceability of a product. The scheme is suitable for manufacturing functional parts with extremely high requirements on performance and safety in aerospace and automobile industries.

Control, assessment and optimization techniques for manufacturing secondary electrochemical devices to improve the quality, throughput, and safety of cells produced and to facilitate the finishing (formation / aging / sorting / grading) process. A system for dynamic control and optimization of secondary battery finishing process includes a closed-loop process control module that is configured to process real-time in-line manufacturing data derived from at least one of electrochemical impedance spectroscopy (EIS), self-discharge analysis (SDA), amperometric, or potentiometric battery measurements. Cell formation can be reduced from the several days with prior art technology to less than 24 hours, and aging can be reduced from 2-3 weeks to less than an hour. A control module provides real-time feedback to predecessor operations / materials for confirmations, refinements, corrections, and / or recognition or isolation of better / worse performance characteristics. It also provides feedforward information of aspects recognized that may indicate performance deviations from the norm.

Methods and systems for executing tracking and monitoring manufacturing data of a battery are disclosed. One method includes: receiving, by a server system, sensing data of the battery from a sensing system; generating, by the server system, mapping data based on the sensing data; generating, by the server system, identification data of the battery based on the sensing data; generating, by the server system, monitoring data of the battery based on the sensing data, the identification data, and the mapping data; and generating, by the server system, display data for displaying a simulated electrode of the battery on a graphical user interface based on the monitoring data of the battery.

The invention relates to a welding problem tracing method and device, computer equipment and a storage medium. The method comprises the following steps: performing data acquisition on process data, service information system data and quality manufacturing data of a terminal industrial personal computer, and performing data rough classification according to relevancy of the data and process quality to form rough classification data; performing data cleaning and data standardization on the rough classification data to obtain various welding process data sets and storing the welding process data sets in a database; selecting process parameters according to the various welding process data sets to construct a welding problem traceability model, and training the welding problem traceability model based on a welding process quality traceability process; and the trained welding problem traceability model is utilized to carry out influence factor positioning and sorting, traceability is carried out to obtain process parameters generated by unqualified welding products and the proportion of the influence factors, and the process parameters and the proportion are output. According to the method, efficient and accurate prediction can be provided for the welding process quality problem, and the traceability precision of the welding process quality problem can be improved.