316 results about "Manufacturing sector" patented technology

The invention provides a discrete MES-oriented intelligent production scheduling system, method and equipment and a medium, and belongs to the technical field of discrete manufacturing industry production scheduling. Data is acquired through a sensor and serves as production scheduling data; establishing a material inventory data association order ID and establishing an index; determining a process sequence constraint, a calculation equipment productivity constraint, a material supply constraint and an order priority constraint; initializing a population based on a genetic algorithm, randomly generating N groups of process sorting schemes, calculating a utilization rate index, and taking a comprehensive score as a fitness value; outputting a better solution set; the optimal solution of the genetic algorithm is used as initial pheromone distribution, high-quality path pheromones are enhanced according to the actual production effect of the completion scheme, and if the preset number of iterations is reached, the operation is stopped, and an optimized production scheduling scheme is output; and checking the production scheduling plan through a graphical interface. Through continuous optimization of the procedure sorting scheme, the equipment utilization rate is effectively improved, the total order completion time is shortened, the production resource configuration is optimized, and the production efficiency is improved.

The invention discloses a federated learning-driven cross-domain supply chain elastic inventory optimization system and a method thereof, and aims at realizing inventory data collaboration among same-level enterprises or regional nodes through transverse federated learning and ensuring data security by adopting a self-adaptive differential privacy protection mechanism. The method comprises the steps of constructing a transverse federated learning network, locally performing data preprocessing, adding differential privacy noise, iteratively training a global model based on federated deep reinforcement learning, generating a transverse inventory allocation and replenishment decision, and performing model adaptive adjustment in real time based on key performance indicators. According to the method, multi-target balance is considered, inventory configuration is dynamically optimized through a multi-target reward function, the inventory turnover rate is remarkably increased, the inventory holding cost is reduced, the service level is improved, and the method is suitable for various scenes such as retail chain, manufacturing industry distributed storage and cross-regional logistics distribution; and global optimal inventory configuration is realized on the premise of ensuring data privacy.

The invention belongs to the technical field of industrial intelligence, and relates to a deep reinforcement learning-based non-correlation parallel machine scheduling method, which comprises the following steps of: constructing a mathematical model suitable for non-correlation parallel machine scheduling; the machining time of each workpiece on each heterogeneous machine is collected, and normalization processing is carried out; initializing a genetic algorithm scheduling population and a depth Q network; constructing a deep reinforcement learning training framework; expressing a state vector by using the average fitness, the optimal fitness and the optimal individual code; operating parameters of the genetic algorithm are controlled by using the action space, and parameters of the deep Q network are updated by using a reward function; and dynamically controlling operator selection in a genetic algorithm iteration process by using the trained deep reinforcement learning model to obtain an optimal scheduling solution and realize scheduling of the non-correlation parallel machine. According to the method, a deep reinforcement learning algorithm is provided for solving similar problems in the manufacturing industry production scheduling field by analyzing a non-correlation parallel machine scheduling problem data model, and the production efficiency is improved.

The invention provides an industrial manufacturing industry-oriented carbon asset full life cycle management method and system, and the method comprises the steps: carrying out the cross-link feature fusion processing through obtaining an original carbon activity data set of an industrial manufacturing whole process, and generating carbon footprint feature data; and calling a pre-trained deep learning model to perform carbon asset state evolution analysis on the carbon footprint feature data, predicting a carbon asset evolution path, generating a carbon asset optimization strategy set based on a prediction result, including equipment energy efficiency regulation and control and a production plan recombination strategy, and pushing the carbon asset optimization strategy set to an industrial manufacturing execution system. And triggering an automatic control instruction reconstruction operation to realize automation and intelligence of carbon asset management, thereby comprehensively and accurately mastering a carbon asset state and an evolution rule, formulating a scientific and effective optimization strategy, reducing carbon emission, improving energy utilization efficiency, and improving green production capacity of an enterprise.

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.

The invention discloses a manufacturing industry ERP dynamic resource optimization configuration system based on reinforcement learning, and relates to the technical field of manufacturing industry ERP resource configuration, and the system comprises an ERP state data perception module which is in butt joint with a manufacturing industry ERP system, collects multi-source heterogeneous data in real time, fuses the multi-source heterogeneous data in real time by using a Kalman filtering technology, detects an abnormal value, and sends the abnormal value to the ERP state data perception module; an improved Robust Scaling method is adopted for standardization processing, and initial data of a unified structure are output and transmitted to the feature extraction and integration processing representation module; multi-source heterogeneous data are collected in real time through the ERP state data sensing module, fusion is carried out through the Kalman filtering technology, dynamic changes in the production environment can be captured in time, strategies can be continuously adjusted according to the real-time dynamic change data through the reinforcement learning method, and the real-time dynamic change data can be obtained. And outputting an optimal resource allocation action adapting to a dynamic environment, so as to greatly improve the adaptability of the system to a complex production environment.

The invention is suitable for the technical field of production quality detection, and provides an intelligent detection method and system based on an aluminum alloy production line, and the method comprises the steps: firstly responding to a gradienter starting instruction, obtaining real-shot image information through an industrial camera, and then carrying out the image detection processing of the real-shot image information according to an image detection algorithm, according to the method, laser datum line information and workpiece edge contour line information are determined, then multiple pieces of actual measurement distance information are effectively generated according to the laser datum line information and the workpiece edge contour line information, and finally detection result information is accurately generated according to the multiple pieces of actual measurement distance information. According to the invention, the flatness of multiple positions of the aluminum alloy workpiece can be rapidly and accurately detected, the detection efficiency is remarkably improved, and the production requirements of the modern aluminum processing and manufacturing industry for efficient and high-precision detection are met.

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 relates to the technical field of information retrieval, in particular to a part three-dimensional model retrieval method and system based on feature extraction. The part three-dimensional model retrieval method comprises the following steps that a manufacturing industry part design drawing is obtained; geometric structure feature extraction and material feature extraction are carried out according to a manufacturing industry part design drawing, and geometric structure data and material data are obtained; constructing a part three-dimensional model based on the geometric structure data and the material data; performing process processing feature extraction according to a manufacturing industry part design drawing to obtain part process processing data; and performing process treatment, including surface treatment, heat treatment and welding treatment, on the part three-dimensional model according to the part process treatment data to obtain surface treatment data, heat treatment data and welding treatment data. Based on the information retrieval technology, the part three-dimensional model retrieval precision and the assembly reliability are improved, and the part design optimization rate and the production quality control efficiency are remarkably improved.

The invention relates to the technical field of information, in particular to a manufacturing industry-oriented multi-modal data deep fusion and cognitive intelligent system, which comprises a data acquisition module for acquiring a first type of physical field signals by deploying a distributed sensor network, and deploying second type of physical field signal acquisition equipment in the same area; the signal preprocessing and modal activation module is used for preprocessing the first type of physical signals, generating an activation instruction after judging that abnormity exists, and triggering second type of physical field signal acquisition equipment to enter an acquisition mode by the instruction; the data fusion module is used for carrying out space-time calibration on signals of the first type of physical field signal acquisition equipment and the second type of physical field signal acquisition equipment and constructing a complete evidence chain; and the anomaly cognition module is used for calculating the spatial distribution overlap ratio of the temperature anomaly region and the acoustic emission sound source positioning region, and judging structural damage according to a preset coefficient in combination with surface deformation characteristics. The first type and the second type of sensors are synchronously deployed, the problem of single information blind areas is solved, and the comprehensiveness of abnormal features is ensured.

The invention relates to the technical field of data processing, and discloses a manufacturing industry data intelligent analysis method and system based on deep reinforcement learning. The method comprises the following steps: carrying out time sequence processing on manufacturing industry equipment state, order characteristics, inventory level and quality index data to obtain a four-dimensional production data matrix, carrying out strategy learning through an LSTM-Actor-Critic algorithm to obtain a manufacturing decision strategy network, carrying out classification processing according to a production cycle to obtain a hierarchical data set, constructing an intelligent experience playback buffer area, and carrying out intelligent experience playback. And carrying out collaborative optimization on order scheduling, inventory replenishment and equipment task allocation decisions to obtain a manufacturing industry data intelligent analysis result. The technical problem that an existing manufacturing industry data analysis method lacks adaptive learning ability and cannot process multi-domain collaborative decision optimization is solved.

The invention discloses a manufacturing industry supply chain processing method and system, and relates to the technical field of data processing, and the system comprises a capacity demand prediction end, a supply chain risk prediction end and an alternative strategy coping end. The capacity demand prediction end is used for reasonably formulating a supply chain execution strategy according to the supply demand of the prediction result, and balancing the capacity utilization of a manufacturing industry supply chain in real time by adopting a game theory; the supply chain risk prediction end is used for acquiring the delivery punctuality rate of suppliers in the manufacturing industry in real time and judging and predicting whether the supply of the suppliers is abnormal or not in real time; and the alternative strategy coping end is used for tracking the supply chain strategy deviation in real time and performing alternative strategy adjustment in real time according to the deviation. According to the manufacturing industry supply chain processing method and system, the situation of unbalanced capacity utilization is avoided, the safety and reliability of supplier supply during supply chain processing are ensured, and the alternative scheme of the supply chain is adjusted in time.

The invention discloses a Beidou-based manufacturing industry contract logistics data fusion and scheduling method and system, and belongs to the field of data processing methods specially suitable for management. In the method, a basic geography and road network data module obtains basic geography data; the Beidou dynamic monitoring data module collects transport vehicle data; the cargo and contract data module stores cargo and contract data; the logistics node data module obtains logistics node data; the data fusion processing module performs spatial matching on the basic geographic data, the transport vehicle data, the cargo and contract data and the logistics node data to obtain a manufacturing industry logistics data association table; the data fusion processing module performs data fusion based on the manufacturing industry logistics data association table to obtain a unified data set; and the intelligent scheduling module generates an optimal scheduling control instruction based on the unified data set and a preset constraint condition, and sends the optimal scheduling control instruction to the corresponding vehicle-mounted terminal. The method and the device are used for improving the accuracy of a scheduling scheme in a complex manufacturing industry scene.

The tempering furnace comprises a base, a furnace body is arranged on the upper end face of the base, temperature equalizing mechanisms are arranged in the furnace body and on the base and used for keeping temperature balance of all areas of the furnace body, and a supporting frame is transversely arranged in the furnace body in a penetrating mode; sliding positioning assemblies used for positioning the racks are symmetrically arranged in the supporting frame. The device is simple in structure and reasonable in design, dynamic support is provided for the lower end face of the corresponding rack through continuous symmetrical sliding of the supporting rollers in the heat treatment process, the conditions of uneven tempering hardness, local stress concentration and the like of the rack due to the single clamping or supporting position are avoided, the overall performance of the rack is improved, and the service life of the rack is prolonged. And the actual use requirements in the field of the current high-end equipment manufacturing industry are better met, meanwhile, the temperature difference is eliminated, the performance balance of the rack in each area in the furnace body after tempering is guaranteed, and the tempering quality of the rack is further improved.

The invention relates to the field of manufacturing industry, in particular to a pomegranate peeling production line-oriented multi-process collaborative scheduling optimization method, which comprises the following steps of: acquiring and processing a first business data stream from an internet of things interface, and inputting historical sensor data into a business prediction model to generate a second business data stream; generating a third service data stream by using the data of the multi-mode sensor array; constructing a multi-target business cost function through a business decision optimization engine; starting an iterative optimization business simulation process to obtain an optimized scheduling scheme; and generating a corresponding digital production work order according to the optimized scheduling scheme. According to the method, equipment state, equipment health degree cost and quality indexes are analyzed through fusion of three business data streams and a multi-objective cost function construction method, comprehensive commercial value evaluation is generated, iterative optimization simulation and closed-loop optimization are started when conditions are met, scheduling optimization of timeliness and value fluctuation in perishable material scheduling is achieved, and the scheduling efficiency of perishable materials is improved. And the commercial value creation and self-adaptive scheduling capability of production is improved.

The invention discloses an automatic welding method, device and equipment, a storage medium and a product, and relates to the technical field of welding automation. According to the method, the three-dimensional point cloud data and the two-dimensional image data of the welding area are collected, the three-dimensional point cloud data and the two-dimensional image data are fused, and the fused image is obtained. The rough welding area is determined through semantic segmentation based on the two-dimensional information contained in the fused image, and the target welding seam is determined in the rough welding area based on the depth information contained in the fused image, so that the precision and robustness of welding seam positioning are remarkably improved by utilizing the characteristics of the fused image; the system can stably operate in a complex factory environment with uneven illumination, strong reflection or partial shielding; and the dependence of the algorithm on environmental conditions is reduced, so that the algorithm is more suitable for the application scene of an actual manufacturing industry factory. And on the basis of more accurate positioning, the welding gun is controlled to weld the target welding seam, and the accuracy of automatic welding can be remarkably improved.

The invention provides an enterprise production index monitoring method based on electric power data, and relates to the technical field of enterprise production index monitoring. The method comprises the following steps: acquiring real-time power data, water consumption, heat / gas energy consumption data and equipment sensor data of an enterprise, including vibration, temperature and pressure sensor data; calculating a production trend index and a product energy efficiency index by using the dynamic weight, and generating a comprehensive energy efficiency index CEI in combination with the equipment health coefficient; and when the CEI is lower than a threshold value, fault diagnosis is carried out based on the Bayesian network and early warning is triggered. And meanwhile, the electric power data is stored through a block chain technology, and data credibility verification and exception recheck are realized in combination with financial data cross validation. The system realizes multi-dimensional production state monitoring, has the advantages of dynamic adaptation to production fluctuation, intelligent fault diagnosis, data credibility guarantee and the like, can effectively improve enterprise production management efficiency, reduces energy consumption and fault loss, and is suitable for digital transformation requirements of high-energy-consumption industries such as chemical industry and manufacturing industry.

The invention relates to a dynamic intelligent stamping production line real-time monitoring strategy based on digital twinning, and adopts the technical scheme that a sensing layer acquires production total factor data related to a stamping production line in real time through multi-sensor fusion and an RFID (Radio Frequency Identification) technology, performs primary processing by utilizing edge calculation, and transmits the data to a real-time reaction layer; the real-time reaction layer quickly processes and analyzes multi-source data through edge calculation and a real-time database, detects faults in real time by using a multi-modal fusion technology, triggers an alarm mechanism, and feeds back to the interaction layer; and the interaction layer integrates intelligent production scheduling and NLP voice interaction through data interaction processing, error verification and multi-protocol adaptation to realize virtual factory visualization and equipment health real-time monitoring. Multi-modal data fusion is realized, an adaptive incremental learning and early warning mechanism is realized, an information island is broken, and millisecond-level response is realized. The strategy can improve the production efficiency, reduce the operation and maintenance cost, prolong the service life of equipment, and provide an efficient solution for intelligent transformation of the manufacturing industry.

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 relates to the technical field of intelligent production and manufacturing, in particular to an energy-saving fuzzy cascade scheduling method and system for regional gathering cooperative production, and aims to solve the composite problems of supply chain cascade scheduling heterogeneous factory resource allocation, multi-stage time accumulation effect, uncertainty interference and the like in regional cooperative transformation in the manufacturing industry. According to the method, a double-layer collaborative optimization framework is assisted through integrated learning, the uncertainty of quintuple interval fuzzy quantization processing, transportation and assembly time is adopted, an initial Q value matrix is generated through a pre-training layer, self-adaptive operator selection is achieved in combination with a dynamic decision-making layer, and local search, damage recombination and genetic operation are executed by multiple sub-groups. According to the method, the energy-saving second-class fuzzy distributed flow shop and multi-flexible job shop cascade scheduling problem model is effectively defined, three-segment coding and full-process energy consumption calculation are supported, and the overall scheduling efficiency and the energy efficiency balance capability of the regional aggregation industry are remarkably improved.

The invention relates to the technical field of data management, in particular to a supply chain-based manufacturing industry big data management system, which comprises an event attribution module, a data integration module, an inventory trend module, a batch judgment module and a parameter tracing module. According to the method, automatic association analysis of multi-dimensional parameters such as process, logistics, inventory and environment is realized through time sequence synchronous acquisition of multi-source data and dynamic linkage of business events, and accurate circulation of whole-process data is promoted by adopting an event-level feature recognition and parameter screening mechanism; furthermore, positioning and traceability of an abnormal fluctuation process are supported in a process traceability and trend discrimination mode, information interconnection and parameter relation chain establishment of each manufacturing link are enhanced, synchronous processing and hierarchical clustering of multi-parameter heterogeneous data are realized, and the real-time decision support capability and traceability precision of the data are improved; and the response speed of an abnormal process and inventory fluctuation in a manufacturing scene and the intelligent level of data application are effectively enhanced.

A method and a system for ensuring product quality in a process for producing a product from a material in a manufacturing plant via a software performed by a computer comprising the following steps of Establishing a data pipeline including a data transformation originated from different source systems, like an ERP, LIMS, or Data Historian, to the computer, wherein the data pipeline provides updated datasets to maintain up-to-date parameter information; Preparing the datasets for a multivariate analysis calculation and performing the multivariate analysis calculation via the software, wherein all relevant parameters, like quality parameter, process parameter, sub-suppliers' quality parameters, are included in the calculation; Visualizing the result data of the multivariate analysis calculation as a 2D plot on a display connected to the computer, wherein the software identifies and marks new production batches and reference batches, time series plots for the relevant parameters and principal components, and selected top parameters which contribute to a principal components' deviation from the new production batch; and Performing quality processes and workflows to improve the production process and product quality by using the visualized result data.

The invention discloses a manufacturing industry process parameter optimization method based on a knowledge graph, and belongs to the field of manufacturing industry intellectualization. The technical problems that in the prior art, parameter optimization depends on artificial experience, systematic management is lacked, intelligent reasoning is difficult to conduct, and knowledge retrieval precision is low are solved. The method comprises the steps of firstly collecting and preprocessing manufacturing industry process data; then, constructing a semantic vector embedding model, and converting the process description into vector representation by adopting a pre-trained bidirectional encoder; defining a process knowledge ontology model, and establishing a parameter association relationship through semantic coding to form a knowledge graph; storing the coded knowledge vector into a distributed database and establishing a retrieval index; user requirements are received, and parameter matching is carried out through cosine similarity calculation; optimizing the matching parameters based on the knowledge graph association relationship; and finally collecting feedback to continuously update the knowledge graph. Through continuous optimization of the feedback learning mechanism, the precision and efficiency of technological parameter optimization in the manufacturing industry are improved.

The invention relates to the field of industrial automation, in particular to an industrial robot system based on high-precision control and intelligent operation and maintenance, and provides a multi-source information fusion algorithm to precisely regulate and control robot operation, so that the precision of tasks such as welding is greatly improved. The intelligent fault diagnosis and prediction model realizes efficient diagnosis and accurate prediction of faults by virtue of deep learning and Bayesian reasoning, and the fault shutdown time is greatly reduced. According to the multi-robot cooperation algorithm, the cooperation efficiency is remarkably improved through dynamic task allocation. The self-adaptive path planning algorithm assists the robot in flexible obstacle avoidance and optimizes the carrying path. The integrated platform integrates operation and maintenance management, the production management efficiency is comprehensively improved, and intelligent upgrading of the manufacturing industry is powerfully promoted.

The invention discloses a knowledge graph alignment method and system based on a large language model and an agent and oriented to the field of the manufacturing industry. The method comprises the following steps: enhancing a key relation weight by adopting a relation perception graph network, fusing a semantic vector of the large language model and a vector of a domain term library to correct a name conflict, and dynamically adjusting learning parameters; implementing a dynamic bucket dividing strategy, high-confidence scene simplification candidates, low-confidence scene fusion term retrieval and a big language model virtual name generation mechanism according to the embedding quality; based on an agent and large language model hierarchical interaction architecture, precise prediction is realized through coarse-grained screening and domain knowledge enhanced fine-grained matching, and an intelligent iteration termination condition is set; and executing lightweight incremental training to update the embedded representation, combining reinforcement learning to dynamically adjust and optimize the strategy library, and synchronizing and automatically expanding the term library. According to the method, cross-language alignment precision jump, data noise interference suppression, efficiency and accuracy optimization balance and system continuous autonomous evolution capability enhancement are realized.

Intelligent assistance system for worker guidance and quality control of manual activities at industrial manual workstations. In the manufacturing industry, many production processes are carried out at manual workstations. The high quality standards of modern production require employees to have precise knowledge of the process steps to be performed and maintain a consistently high level of concentration. The assistance system is intended to facilitate work at such manual workstations, improve the quality of work results, and thus increase productivity. The invention guides employees step by step through the production process using output devices and uses sensors to check for error-free execution at each step. If an error occurs, the operator is immediately warned and prompted to correct it.

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.