221 results about "Manufacturing sector" patented technology

Data from manufacturing is highly uncontextualized and siloed, requiring expert knowledge of context and substantial data pre-processing to support meaningful queries and visualizations. To address this problem, data for a number of sources in a manufacturing context can be retrieved and converted into an intermediate representation in a natural language or near-natural language form, which can in turn be ingested by a generative AI engine, along with suitable prompts by the user to summarize, analyze, and make recommendations based on the data.

State of the art systems used for industrial plant monitoring have the disadvantage that they fail to correctly assess reason for dip in performance of the plant and in turn trigger appropriate corrective measures. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a system and method for development and deployment of self-organizing cyber-physical systems for manufacturing industries. The system monitors and collects data with respect to various parameters, from the industrial plant. If any performance dip is detected, the system determines corresponding cause, and also triggers one or more corrective actions to improve performance of the plant and different plant components to a desired performance level.

The invention provides a process digital collaborative management method and system based on a data unified exchange platform, and belongs to the technical field of industrial software and intelligent manufacturing, and the method comprises the steps: constructing the data unified exchange platform, so as to achieve the data exchange among a process management system, a PLM system and a production management system; in the process management system, based on the EBOM obtained from the PLM system, constructing and managing a multi-state process BOM including a basic PBOM, a trial-production PBOM and a production PBOM; and a collaboration mechanism is constructed through a data unified exchange platform, so that business collaboration based on the multi-state process BOM is realized. According to the method, the problems of data consistency and flow collaboration which troubles the manufacturing industry for a long time are solved, and key technical support is provided for enterprises to realize digital transformation and intelligent upgrading through efficiency improvement, quality guarantee, cost control and knowledge precipitation.

The invention relates to the technical field of key distribution, in particular to a multi-cloud architecture-oriented dynamic key fragmentation storage management method and system, which comprises the following steps of: dividing a manufacturing process of the manufacturing industry into a plurality of time sequence stages corresponding to different data access authority levels and security requirements, and constructing corresponding quantum state evolution operators; driving the key state to evolve among different time sequence stages through a quantum state evolution operator according to the progress of the manufacturing process, and adjusting the access authority of the key; quantum entanglement connection is established between supply chain nodes, the trust degree between the nodes is measured and quantized through the association of the quantum entanglement state, and the distribution weight of the key fragments is adjusted based on the change of the trust degree; collecting constraint parameters of production takt, equipment state, logistics delay and quality index in the manufacturing process, and converting the constraint parameters into quantum state constraint conditions; and when it is detected that the constraint condition changes, recalculating the key fragment distribution scheme based on the quantum state constraint condition and the credibility, and synchronously distributing the key fragment distribution scheme to each supply chain node.

The invention discloses a distributed heterogeneous flexible flow shop batch processing scheduling method and system, relates to the technical field of distributed production scheduling in the manufacturing industry, and aims to solve the problems that an existing scheduling method is not comprehensive in constraint consideration, poor in energy consumption optimization and low in algorithm efficiency. According to the method, a mixed integer linear programming model containing multiple constraints such as release time and sequence-related preparation time is constructed, a learning-assisted dual-objective co-evolution framework is established, and the maximum completion time and the total energy consumption are synchronously optimized by combining mixed initialization, global-local search collaboration, decision reinforcement learning operator selection and a collaborative energy-saving strategy. The release time, the sequence-related preparation time, the inter-stage transportation time and the batch processing scheduling are simultaneously considered in the distributed heterogeneous flexible flow shop scheduling for the first time, the established mixed integer linear programming model better fits the actual production scene, and the method fits the actual production scene, is good in energy consumption optimization effect and can be adapted to the non-ferrous metal metallurgy aluminum production process.

The invention provides a quality control system based on a multivariate exponential weighted moving average control chart, and belongs to the technical field of quality management and statistical process control. The system comprises a quality data acquisition module, a data preprocessing module, a statistical process control module, an anomaly judgment module and a quality evaluation and improvement module, and is used for monitoring and controlling the production process of a product with a plurality of quality characteristics in real time. The method comprises the following steps of: acquiring quality data of a plurality of quality characteristics in a production process by a system, preprocessing the quality data, and constructing a multivariate quality characteristic data set; estimating a mean vector and a covariance matrix in a controlled state based on the historical quality data, performing weighted updating on the multivariate quality data by adopting a multivariate exponential weighted moving average method, calculating a corresponding MEWMA statistic and generating an MEWMA control chart; and comparing the MEWMA statistical magnitude with a preset control limit to judge whether the production process is in an out-of-control state or not, and outputting early warning information and a corresponding quality evaluation result and improvement suggestion when abnormality is detected. The method can comprehensively analyze related information among multivariate quality characteristics, improves the sensitivity and accuracy of anomaly detection in the production process, and effectively improves the quality control level of the production process in the manufacturing industry.

The invention discloses a manufacturing industry production data real-time management system and method based on Internet of Things edge computing. Comprising a data acquisition and preprocessing module, a lightweight agent module, an edge computing module, a network transmission module, a platform management module, an application service module, a security protection module, a self-adaptive resource configuration module and a monitoring and diagnosis module. According to the invention, nearby acquisition, preliminary cleaning and real-time analysis of multi-source heterogeneous data from a sensor, a controller and industrial equipment are realized, the cloud burden is effectively reduced, and the overall processing efficiency is improved. A real-time anomaly detection mechanism based on a sliding window and machine learning is constructed, parameter deviation, equipment faults or process anomalies in the production process can be recognized within millisecond-level time, and early warning or control feedback is triggered.

The invention discloses a belt pulley raw material traceability management method and system based on a block chain, and relates to the technical field of manufacturing industry supply chain management, and the method comprises the following steps: in the whole raw material traceability management process, extracting a time anchor and a sequence difference from a raw material warehousing stage to a raw material feeding stage, and recording the transfer nodes corresponding to the raw material batches, and generating a time sequence anchoring list corresponding to the mapping relationship between the parent batch and the child batch based on the time anchor and the sequence position difference. According to the method, time and inheritance synchronous locking of a raw material batch circulation process is realized through extraction of a time anchor and sequence position difference and construction of a father-child batch relay chain and an atomic token sequence, and continuous and consistent uplink data is ensured; through double-track chaining buffering and breathing type phase traction regulation and control, the batch identification phase difference is eliminated, chain segment splicing is adaptively converged, and the time sequence stability and credible integrity of traceability data are improved.

The invention discloses a fault diagnosis system and method based on a production process causal graph model, and the system comprises a data collection and preprocessing module which is used for collecting and processing fault feature data, fault result data and auxiliary data in the operation process of a production process; the agent variable screening module is used for screening fault agent variables by calculating the correlation between auxiliary data and unobserved hybrid variables in combination with domain knowledge; the causal effect estimation module obtains a causal effect by adopting a mode of combining a tool variable method and deep dual machine learning; the causal graph model is constructed according to a causal effect estimation result and a Bayesian network to represent a causal relationship and a conditional dependency relationship among a fault feature variable, a fault result variable and a fault agent variable; the fault diagnosis module; inputting the operation data collected in real time into the constructed causal graph model, and evaluating the influence degree of different factors on the fault diagnosis result; the method effectively overcomes the interference of hybrid variables in the production process of the manufacturing industry, and improves the fault diagnosis precision and reliability.

The invention belongs to the technical field of production testing in the manufacturing industry, and particularly discloses a production testing system and method based on the manufacturing industry, a terminal and a medium, and the system comprises a back-end service layer which is in communication connection with a front-end service layer for information interaction, dividing the business logic of the production test into a plurality of business modules which can be independently managed through a module registration mechanism; the equipment interaction layer comprises an equipment driving interface, at least one equipment driving plug-in and an equipment communication unit; and the system integration layer is used for carrying out information exchange with a manufacturing execution type service system through a standardized data interaction mode so as to obtain and send the production test data. Test data is aligned with a manufacturing execution type business system through a standardized data interaction mode, connection between a production test link and manufacturing process management is realized, and the overall cooperative capability of a production line is improved.

The invention discloses a process planning model pre-training method and system based on multi-processing scene segmented imitation learning, and relates to the field of computer-aided manufacturing, comprising the steps of: for various processing scenes comprising different geometric features, material characteristics and processing requirements, generating processing paths of various strategies by using a mature process database of CAM software; virtual trial cutting is carried out through a processing process world model to generate a large number of initial states, rich and diversified training samples are provided for reinforcement learning pre-training, and on the basis, large-scale pre-training is carried out by adopting a segmented behavior cloning method to obtain a process autonomous planning base agent which learns processing strategies in different processing scenes; in the face of a new processing task, a processing path can be quickly generated through transfer learning. According to the method, the generalization ability is remarkably improved, meanwhile, geometric-physical-control collaborative planning is achieved, and an intelligent solution is provided for efficient planning of the modern manufacturing industry technology.

The invention relates to a discrete industrial agent-based production management method and system, and relates to the field of production management, and the method comprises the steps: collecting a production prediction sample data set and a quality inspection decision sample data set, carrying out the data weight division of the two data sets, and obtaining two sample weight sets; obtaining a production prediction and quality inspection decision path array, a first prediction accuracy rate set and a decision accuracy rate set after integrated training; combining the two path arrays to obtain a discrete industrial agent array, carrying out joint optimization training, and testing to obtain a second prediction and decision accuracy set; obtaining current production basic data, inputting the current production basic data into the agent array, outputting a predicted production yield and a decision quality inspection parameter, performing compensation according to an error between the second prediction and decision accuracy set and the first prediction and decision accuracy set, and obtaining a predicted production yield and decision quality inspection parameter interval for production management. The technical problem that data interaction and business collaboration of a plurality of complex and independent scenes in the discrete manufacturing industry are difficult to realize in production management is solved.

The utility model relates to a power equipment workpiece universal adjusting machine which comprises a support and a working table, the working table is installed on the support, a workpiece adjusting machine is installed on the working table and comprises an axial adjusting unit and a vertical shaft adjusting unit, a leveling mechanism is installed on the axial adjusting unit, and the vertical shaft adjusting unit is installed on the vertical shaft adjusting unit. The leveling mechanism is used for leveling an end connector of the workpiece with the horizontal plane as the base plane and comprises a leveling installation support, a driver and a pull rod, the pull rod is connected to the output end of the driver, a flattening block is formed at the end of the upper section of the pull rod, and the leveling installation support is installed on the end face of the axial adjusting unit. A workpiece joint placing base surface is arranged on the leveling installation support, the pull rod moves upwards or downwards in a reciprocating mode through the driver, the flattening block is made to be close to or away from the workpiece joint placing base surface, and an end joint of a workpiece is leveled and flush with the horizontal direction. And high-quality leveling of the end connector of the workpiece is achieved, and the device is indispensable efficient and accurate machining equipment in the modern manufacturing industry.

The invention relates to the technical field of data processing, and particularly provides a manufacturing industry energy-saving and efficiency-increasing scheme generation method, system and equipment and a storage medium, and the method comprises the steps: collecting the operation state data, energy consumption data and real-time working condition data of industrial equipment in real time through an equipment monitoring module; preprocessing the collected data, establishing an energy efficiency reference, calculating a key energy efficiency index based on the energy efficiency reference to judge the energy efficiency state of the equipment, and executing multi-dimensional root cause analysis when the energy efficiency state is abnormal; according to the energy efficiency state and a root cause analysis result, matching and filtering applicable technical schemes from a technical unit library, and performing quantitative evaluation and comprehensive sorting on the technical schemes to generate a preferential transformation scheme list; and an energy efficiency analysis result is fed back to the equipment monitoring module for state early warning, and a customized proposal is generated based on the transformation scheme. According to the invention, the systematization and automation of industrial energy-saving management are realized by constructing a full-link closed loop generated by data perception, intelligent diagnosis and decision.

The invention discloses an electronic manufacturing industry production line UPH intelligent prediction method, system, device and medium, and the method can learn a deep interaction relationship between a line body and a product through adversarial training through a generator of a collaborative filtering generative adversarial network, so that even if a new product lacks enough historical data, the production line can be predicted more accurately. The generator can also find a mode similar to other line bodies in a potential space and generate a relatively reasonable UPH, and for an old product with a theoretical UPH and an actual UPH, the relatively reasonable UPH can be generated only by correspondingly inputting a current line body feature vector and the theoretical UPH of the product in the corresponding line body; according to the method, the difficulty that in the initial production stage of a new product, due to lack of reference bases, UPH can be generally formulated only according to the same industry standard or the average yield per hour of the production line can be solved; and meanwhile, the problem of UPH updating hysteresis caused by personnel or equipment can be solved.

This invention belongs to the field of intelligent scheduling technology in manufacturing, and particularly relates to a dual-resource flexible job shop scheduling method and system integrating a large language model and deep reinforcement learning. The method includes: constructing a dual-resource flexible job shop scheduling environment model, defining jobs, processes, machines, workers, and their constraints, and constructing a set of state features for reinforcement learning; designing an action space and feasible action masking mechanism, based on process sequence, feasible machine set, worker skill qualifications, and available time constraints, to mask infeasible actions and reduce the probability of selecting low-quality actions; designing a multi-objective reward function, including minimizing completion time and total energy consumption; training the scheduling strategy using the Asynchronous Advantage Actor-Critic (A3C) algorithm, optimizing the policy network and value network through multi-threaded parallel sampling and asynchronous update mechanisms; and triggering the large language model to generate new reinforcement learning definitions based on trend and stability analysis of training feedback, and achieving adaptive iterative optimization through a closed-loop mechanism.

The invention discloses an automatic bending and steering mechanism for resistor assembly, which realizes breakthrough progress in a resistor disc assembly technology: manual intervention is thoroughly eliminated through full-process automation, and the requirement of a single-line operator is reduced to zero; the working procedures are highly integrated, so that the time consumed from feeding to assembling of a single resistor disc is shortened, and the production speed is remarkably increased compared with that of a traditional production line; the systematized precision guarantee ensures that the bending angle qualification rate and the assembly dislocation rate approach to zero, and the technical advantages are converted into remarkable market efficiency and efficiency dimensions in industrial-grade mass production, and the output per unit time is improved; in the cost dimension, the manpower cost is saved, and the repair cost of defective products is reduced; in the aspect of space dimension, the occupied area of equipment is reduced, the flexibility of production line recombination is greatly enhanced, and comprehensively, the relation of resistor assembly production lines is redefined fundamentally, an automatic upgrading normal form with high economical efficiency and replicability is provided for the electronic manufacturing industry, and excellent comprehensive efficiency and industrial value are created.

1. Name of the designed product: robot. 2. Use of the designed product: robot used in service industry, manufacturing industry, special operation, etc. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view 1.

1. Name of the design product: Robot (SCARA horizontal four-joint). 2. Use of the design product: The design product is used to replace human work in the manufacturing industry of automobiles, 3C semiconductors, etc., medical care, daily chemicals, food, logistics and warehousing industries, etc. 3. Design points of the design product: in shape. 4. Picture or photo best indicating the design points: perspective view 1.

The invention discloses a printing order intelligent production scheduling optimization method and system based on an equipment operation and maintenance state, and relates to the technical field of the manufacturing industry, and the method comprises the steps: obtaining a multi-source operation and maintenance parameter set of equipment and work order attribute information during printing, and recognizing a same-type work order interval; on the basis of the same type of work order intervals, dividing loss levels to generate an adaptation result which is used for providing an adaptation basis for equipment and work order distribution; on the basis of the adaptation result and the work order attribute information, the interference influence of work order switching on equipment production is identified by analyzing the correlation strength of the same process work order and the equipment part loss, and disturbance strength data is obtained; identifying a to-be-distributed node through the disturbance intensity data, and obtaining a feasibility result through a redistribution operation of work order switching; and constructing a production scheduling network according to the feasibility result, and obtaining a distribution optimization result after path traversal and path re-planning so as to realize dynamic matching of production scheduling and equipment operation and maintenance states.

The utility model discloses a front pressing type sleeve sheet mold. The front pressing type sleeve sheet mold comprises a mold shell, a mold core, a sleeve sheet, a C-shaped ring and a rear ejection column, a first mounting groove is formed in the front end of the mold shell, a second mounting groove is formed in the rear end of the mold shell, and the second mounting groove penetrates into the first mounting groove. The mold core is arranged in the first mounting groove, and the mold core and the first mounting groove are coaxially arranged. And the sleeve sheet is arranged above the mold core and is coaxial with the mold core. And the C-shaped ring is arranged in the first mounting groove and is arranged on the outer surfaces of the mold core and the sleeve piece in a sleeving manner at the same time. And the rear ejection column is arranged in the second mounting groove, moves in the length direction of the second mounting groove and is used for ejecting the mold core out of the first mounting groove. Due to the unique design of the front pressing type nest plate mold, the limitation of a traditional mold in the aspects of accessory replacement, service life, precision keeping and production efficiency is effectively solved, and a more efficient and reliable mold solution is provided for the manufacturing industry.

The application discloses a kind of 3D target component automatic identification and analysis method and system, method includes: the point cloud of target component is executed voxel segmentation to the space where;The point cloud of target component is divided into corresponding voxel, and the voxel without point cloud is eliminated;Extract the context feature of remaining voxel, and generate the anchor frame of initial prediction structure as suggestion with this;Fusion target component point cloud and context feature obtain feature map, according to the corresponding relationship of anchor frame and feature map obtain the prediction of boundary box;Using part aggregation network, the point cloud of suggestion region of interest is learned, according to the learning result, boundary box is adjusted, and the identification result of target component is obtained;According to the identification result, search in the database of predetermining, obtain the relevant information of target component.The application adds artificial intelligence identification analysis method in traditional manufacturing industry, intelligently identifies target component, reduces classification workload, analyzes its structure, to be used for the automatic production of application manufacturing.

The invention relates to the technical field of employment influence analysis modeling, in particular to an industrial robot employment analysis modeling method based on a bidirectional fixed effect model, which comprises the following steps of: 1, performing non-observation variable significance test, and detecting whether time and regional quality characteristics are significant or not according to F test and R2 test; 2, determining a core variable, selecting industrial robot fixed investment as a core independent variable, and taking the average number of employees in each industry of the manufacturing industry as a core dependent variable; and 3, solving the selected reference industry coefficient by using a bidirectional fixed effect model. And 4, introducing interaction items of other industries (except the reference industry) to research differences among the industries. And 5, checking and summarizing a model calculation result. According to the method, the P values corresponding to the industries are divided and classified into three classes of significant positive correlation, low correlation and significant negative correlation, and the common points of the same class of industries are analyzed from three aspects of production flow, technical requirements and labor force structures, so that guiding significance is generated.

This invention relates to the technical field of product quality prediction in process industries, specifically to a method for constructing a tread product quality prediction model in the tire manufacturing industry. The method includes the following steps: S1: Collecting and organizing multi-source heterogeneous tread production process data and product quality inspection data to establish a structured dataset with a unified format; S2: Using the isolated forest algorithm to detect and remove outliers in the data; S3: Completing sensor time calibration alignment based on the dynamic characteristics of the production process and a numerical sensor time calibration method using timestamps; S4: Extracting features from the tread time-series data; S5: Decomposing the feature set and feeding it into an improved LSINet model to establish a tread quality prediction model, obtaining the final tread quality prediction result. This invention provides a method for analyzing tread quality in the tire manufacturing industry, helping companies trace quality problems of non-conforming products and improve tread product quality and production efficiency.

The invention relates to a technical method for automatically checking converted product names, which belongs to the technical field of automatic processing of data in the manufacturing industry and comprises the following operation steps of: 1, receiving an original product name and a target product name input by a user; 2, automatically calculating and identifying a difference item between the original product name and the target product name based on a natural language processing technology and a feature word extraction rule; and a third step of obtaining related batch data and quality standard data from the BIP system according to the identified difference item code. And 4, automatically checking and analyzing the acquired data by utilizing an automatic checking engine. And 5, generating a checking report containing batch information which does not meet the requirement, and displaying a checking result through a visual interface. The system has the characteristics of automation, intellectualization, integration and precision. The problems that manual operation efficiency is low, errors are prone to occurring, checking items are omitted, and batch information is misread in the traditional transferred product name checking process are solved.

The invention relates to the technical field of automatic chamfering equipment, in particular to tool changing type automatic chamfering equipment for scraper strip machining, and relates to the technical field of automatic chamfering equipment. The tool changing type automatic chamfering equipment comprises a numerical control box and a plurality of tools, and a pair of transverse electric sliding rails is fixedly installed on the upper wall face in the numerical control box; according to the tool changing type automatic chamfering equipment for scraper strip machining, the bearing blocks and the bearing grooves are designed in the tool magazine box, so that multiple tools of different specifications can be contained, the 180-degree switching function of the automatic tool changing device is matched, the tool changing efficiency is improved, and the tool changing efficiency is improved. The special chamfering machine tool can quickly respond to the machining requirements of scraper strips of different specifications, special tools or complex adjusting equipment do not need to be replaced, the product model changing time is greatly shortened, the problem that a traditional special chamfering machine tool is insufficient in flexibility is solved, and the special chamfering machine tool is suitable for the multi-variety and small-batch production mode of the modern manufacturing industry.

The invention relates to the technical field of discrete manufacturing industry production process control, and discloses an automatic collaborative regulation system for a discrete manufacturing industry production process, which comprises a node control unit distributed at a process node and a collaborative control resolving unit, the cooperative control calculation unit converts the node operation rhythm into a time axis distribution parameter in a sequential logic correlation model, calculates a target adjustment vector based on the parameter, and superposes a compensation pulse at the output leading edge of the target adjustment vector by using a feed-forward shaping mechanism, so that an adjustment signal breaks through a physical execution dead zone of a bottom-layer driving execution unit, and the control of the bottom-layer driving execution unit is realized. According to the method, through signal shaping and smoothing of the dynamic impedance difference between the ideal control law and the mechanical actuator, the non-lagging response to extremely fine disturbance is realized, the non-linear amplification of errors in a rigid time sequence frame is blocked, and the stable convergence of a control loop is ensured.

The application relates to a kind of operation and maintenance fault monitoring system modeling method based on knowledge graph and OPC UA protocol. First, the knowledge and previous processing rules in the operation and maintenance field are summarized, the previous operation and maintenance fault event knowledge of workshop is extracted, the fault data in production is sorted out, the corresponding knowledge base is established, and the concept model of workshop operation and maintenance fault entity and relationship is constructed. Then, the operation and maintenance fault model data source is acquired, the data layer is built, and then the ontology, entity, relationship and attribute are perfected from bottom to top, and the knowledge graph is constructed. Further, based on the OPC UA protocol, the information model is abstracted into the software system, and the operation and maintenance fault information model is instantiated. The whole step is simple and clear, can be applied to various discrete manufacturing production systems, solves the fault handling problem in operation and maintenance scheduling, can express the whole content of operation and maintenance fault monitoring at the same time, and does not affect the subsequent production, and has great application value in the operation and maintenance field.

The invention relates to the technical field of power data analysis, in particular to a traditional and emerging industry automatic identification method and system based on power utilization characteristics. The method comprises the following steps: collecting power consumption time sequence data of a target industrial user; calculating a fluctuation coefficient, a seasonal index and a load rate based on the power consumption time sequence data, and extracting a power factor and a unit output value power consumption index; constructing a multi-dimensional feature vector; constructing a traditional manufacturing industry fingerprint database and an emerging industry fingerprint database; respectively calculating weighted Euclidean distances between the multi-dimensional feature vector and the traditional manufacturing industry fingerprint database and between the multi-dimensional feature vector and the emerging industry fingerprint database; determining an industrial type of the target industrial user according to a minimum distance principle, and calculating a confidence coefficient; and outputting the industrial type tag and the confidence score. According to the invention, the accuracy and reliability of industry power consumption characteristic analysis can be improved, and a scientific basis is provided for power planning, industrial policy making, energy conservation and emission reduction.

The invention belongs to the technical field of intelligent manufacturing capability maturity evaluation, and discloses an intelligent manufacturing capability maturity evaluation method and system based on industry characteristics and semantic understanding. The evaluation method comprises the steps of constructing an industry feature knowledge base, generating a personalized evaluation information packet, performing semantic processing on evidence materials, calculating matching degree and similarity, calculating the maturity of the capability subdomains, performing comprehensive rating and the like, and finally integrating maturity scores of all the capability subdomains to form an intelligent manufacturing capability maturity grade result of an evaluated enterprise. Through industry feature matching, semantic vector analysis and dynamic weight calculation, objective, efficient and accurate evaluation of the intelligent manufacturing capability maturity is realized, and the method can be widely applied to manufacturing enterprises and has remarkable economic benefits and popularization values.