2733 results about "Smart manufacturing" patented technology

The invention discloses a multi-modal fusion AGV dynamic path planning and cluster scheduling system, and relates to the technical field of multi-modal perception and data fusion, and the system comprises a multi-modal perception module which generates a dynamic obstacle confidence map through multi-source data fusion in combination with a hardware-level time synchronization and Transform feature fusion network; the dynamic path planning module adopts an improved rolling window algorithm, integrates an LSTM space-time conflict prediction model and an adaptive weight cost function, and realizes dynamic obstacle trajectory prediction and non-oscillation global path generation; the cluster scheduling control module is used for optimizing multi-AGV task allocation and conflict resolution in combination with a dynamic priority preemption mechanism and digital twinborn simulation rehearsal based on a distributed contract network protocol of edge computing; and the data conflict resolution module is used for triggering a multi-modal re-calibration process through confidence weighting and sliding window time sequence verification. According to the system, in logistics storage and intelligent manufacturing scenes, the dynamic obstacle avoidance success rate and the robustness and operation efficiency of an AGV cluster are improved.

The invention relates to the technical field of intelligent manufacturing, discloses a papermaking equipment fault tracing method and system based on a process knowledge graph, and discloses the papermaking equipment fault tracing method and system based on the process knowledge graph. The method and the system comprise data acquisition and preprocessing, papermaking process knowledge graph construction, fault event detection and matching, fault tracing and propagation path reasoning, and maintenance scheme recommendation and optimization. The method overcomes the limitation that the traditional method is difficult to capture the cross-equipment, cross-process and cross-time sequence deep causal association and fault propagation path of the papermaking equipment. By constructing a comprehensive papermaking process knowledge graph, equipment operation state data, process parameter data, production quality data, equipment structure principle, process flow knowledge, fault mode knowledge, maintenance experience and other heterogeneous knowledge are subjected to deep fusion and semantic association, so that the system can exceed the correlation of the data surface; and an internal mechanism and a propagation chain of the fault are deeply excavated.

The invention relates to the technical field of intelligent manufacturing and numerical control machining control, and discloses a machine tool dynamic characteristic sensing and intelligent machining control method and system based on a knowledge graph and a large model, and the method comprises the following steps: extracting frequency domain parameters through a vibration sensor, obtaining vibration displacement in combination with laser displacement, and comparing the vibration displacement with modal data to construct a graph; processing parameters and displacement are synchronously sampled, time sequence characteristics are extracted to generate tensors, dynamic characteristics are predicted and corrected, and frequency response adjustment rotating speed is matched to generate optimized track control parameters which are converted into G code instructions. According to the method, a dynamic characteristic map is constructed by fusing vibration signals and displacement, a sliding window synchronizes processing parameters and vibration data, LSTM extracts joint characteristics, GRU predicts rigidity and damping ratio, an attention mechanism dynamically corrects weight, characteristic coupling analysis and prediction precision is improved, nonlinear modeling captures dominant frequency offset and harmonic distribution, response speed is enhanced, and the method has the advantages of being high in precision and high in precision. Cutting vibration is inhibited, and the process stability is guaranteed.

The invention discloses a multi-welding-robot collaborative operation system based on digital twinning, and belongs to the technical field of intelligent manufacturing and robot control, the system comprises a physical space module, a virtual space module, a twinning data layer and a control module, and communication connection is established among the physical space module, the virtual space module, the twinning data layer and the control module; the physical space module comprises a plurality of welding robots, a sensor group and a data transmission network; the virtual space module comprises a multi-welding robot twinning body and a workpiece twinning model; the twin data layer is used for connecting the physical space module and the virtual space module; and the control module generates a welding path optimization scheme, a multi-welding-robot collaborative collision avoidance strategy and a welding quality feedback control instruction based on the twin data layer. According to the method, a closed-loop control framework of'physical-virtual 'bidirectional mapping is constructed, so that dynamic collaborative optimization of multi-robot motion trails, welding seam forming and thermal deformation is realized.

The invention provides an aluminum alloy surface oxidation spot defect identification method and device based on machine vision, and relates to the field of intelligent manufacturing and industrial automation, and the method comprises the steps: obtaining an aluminum alloy surface color image, and carrying out the preprocessing of the image, so as to extract a brightness component image; self-adaptive threshold segmentation of local contrast enhancement is carried out on the brightness component image, a defect area binary mask is generated, morphological connected domains are extracted according to the mask, and three basic feature indexes of the area pixel value, the contour Fourier descriptor complexity and the area gray scale standard deviation contrast of each connected domain are calculated; and extracting and marking a connected domain boundary, verifying a boundary closed topological structure, and dynamically generating a curvature-driven self-adaptive sampling point through multi-scale B-spline curvature extreme value detection. Through optical-algorithm-process three-level collaborative innovation, the curved surface reflection false alarm rate is reduced, the pinhole detection rate is increased, and the boundary precision is + / -0.2 pixel.

The invention relates to a self-adaptive control rewinding machine tension and coiled material deviation collaborative optimization method in the field of intelligent manufacturing, and the method comprises the steps: deploying a distributed tension sensor network at a key position of a rewinding machine coiled material path, collecting the tension value of each measurement point in real time, and generating a multi-point tension distribution data matrix arranged according to a time sequence; processing the multi-point tension distribution data matrix by adopting a sliding window time sequence analysis algorithm, detecting tension fluctuation abnormity, and if a tension value exceeds a preset threshold range, recording a tension abrupt change timestamp and a change amplitude, and generating tension abrupt change data; based on the working condition state description, the rolling diameter real-time change data sequence and the tension sudden change data, a prediction model reflecting rolling diameter change and tension fluctuation is constructed in real time, and a predicted tension trend is obtained; and comparing the predicted tension trend with a preset ideal tension range through a model prediction control algorithm, and generating a multi-target optimization instruction which comprises a dynamic torque regulation and control quantity and a floating roller position set value.

Disclosed in the present invention is a lightweight defect detection method based on hybrid multi-scale knowledge distillation. The method comprises: constructing a dataset; constructing a teacher network model and a lightweight student network model; using the dataset to train the teacher network model, and saving a weight file of the trained teacher network model; and loading into the teacher network model the saved weight file of the teacher network model, inputting defect images in the dataset into the teacher network model and the student network model to respectively obtain first multi-scale features and second multi-scale features, respectively inputting the first multi-scale features and the second multi-scale features into a cascaded knowledge blending module to obtain final deeply fused first multi-scale features and final deeply fused second multi-scale features, then calculating a hybrid multi-scale knowledge loss, and in combination with the prediction loss of the student network model, using a backpropagation algorithm to update network parameters, so as to obtain a trained lightweight student network model for implementing defect detection of intelligent manufacturing products. The cognitive ability and recognition performance for defects of different scales are improved.

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

The invention relates to the field of artificial intelligence, discloses a large language model driven complex manufacturing and production scheduling method and system, and aims to solve the problems that a traditional scheduling system depends on a static rule, is difficult to cope with multi-constraint dynamic disturbance, is weak in semantic understanding ability and is poor in execution interpretability. The method comprises the steps that a special large language model base is constructed and manufactured, and work orders, equipment, materials, processes and abnormal event data are packaged in a unified mode through a semantic collection module; a task intention recognition sub-module, a resource matching sub-module and a time sequence conflict detection sub-module are used for jointly analyzing the semantic unit; and the scheduling engine based on reinforcement learning generates an optimal action sequence under the multi-target weighting constraint. According to the method, minute-level high-dimensional scheduling, anti-disturbance re-planning and man-machine cooperative execution are realized through semantic driving and dynamic evolution architecture, the equipment efficiency is remarkably improved by more than 15%, the delivery delay is reduced by 30%, the line change loss is reduced by 20%, and intelligent manufacturing is promoted to evolve from rule driving to semantic self-adaption.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

The invention provides an aluminum film sealing defect real-time detection method and system based on multi-algorithm fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: triggering an industrial camera at a detection station to collect an original image of a pesticide aluminum film sealing on a conveyor belt; performing adaptive equalization operation on the original image through pixel brightness distribution data, eliminating light fluctuation and surface reflection interference, and outputting a standardized image; three types of defect detection are synchronously executed based on the standardized image, a dynamic threshold segmentation algorithm is combined with local area brightness analysis to detect edge damage, a contour extraction algorithm is adopted to calculate bottleneck center offset to recognize seal offset, wrinkle defects are recognized based on a surface texture feature analysis algorithm, and a primary detection result is output. The aluminum film sealing defect detection method is based on multi-algorithm fusion, has strong anti-interference capability, real-time detection performance and data traceability, and provides an efficient and reliable automatic solution for aluminum film sealing quality management and control.

The invention relates to the technical field of intelligent manufacturing, in particular to a milling process intelligent decision-making method based on multi-agent collaboration, which comprises the following steps of: constructing a milling process planning-oriented multi-modal knowledge base, receiving and analyzing a milling processing demand input by a user based on a central large language model agent, and establishing a multi-modal knowledge base; decomposing a process planning task into associated sub-tasks based on knowledge in the multi-modal knowledge base, and distributing the associated sub-tasks to corresponding professional agents; and obtaining related knowledge based on a retrieval enhancement generation technology, and executing the subtask. Through the LLM-driven multi-agent collaborative system and the RAG technology, autonomous dynamic optimization of the process scheme is realized to improve the intelligence level, a distributed architecture is adopted to enhance the flexibility to adapt to frequent changes, a multi-modal knowledge base is constructed to capture and reuse expert implicit knowledge to ensure the consistency of the scheme, and the method has the advantages of being high in practicability and high in practicability. And multi-dimensional knowledge is integrated and deeply applied to cope with complex process requirements, so that the defects in the prior art are effectively overcome.

The invention provides a continuous casting quality control method based on a meta-cognitive coordination architecture agent cluster, and relates to the technical field of ferrous metallurgy intelligent manufacturing. The continuous casting quality control method comprises the steps of data input and standardization, center coordination and intelligent agent cluster operation and maintenance. In the central coordination process, the meta-cognitive coordination agent serves as a core to coordinate six kinds of functional agents including a semantic analysis agent, a data perception agent, a defect prediction agent, a root cause analysis agent, a process optimization agent and a digital twinborn agent, and task scheduling and state monitoring of the whole system are achieved. Three key functions of task decomposition, data scheduling and closed-loop management and control are completed; in the closed-loop management and control step, risk early warning, defect prediction, root cause analysis, process optimization and digital twinborn verification and feedback are carried out for continuous casting quality. Compared with a traditional scheme, optimization is carried out in the aspects of whole process, multiple modes, intelligence, collaboration and the like, the management efficiency is improved, and economic benefits can also be increased.

The invention discloses a wire drawing machine operation state evaluation method and system based on deep learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: deploying a multi-source sensor at a preset part of a wire drawing machine, and forming a multi-dimensional operation data flow; the deep learning evaluation model is trained based on the standardized time series data set, model parameters are optimized through a cross entropy loss function, the model learns the characteristic difference between a normal working condition and an abnormal working condition, and the deep learning evaluation model is trained based on the standardized time series data set. And inputting a multi-dimensional operation data flow collected in real time into the trained deep learning evaluation model to generate a dynamic evaluation report. According to the wire drawing machine operation state evaluation method and system based on deep learning provided by the invention, a visual operation state score can be output, detailed fault type probability distribution is given, and intelligent support is provided for equipment maintenance decision.

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention discloses an intelligent thickness control system for calendering a high-brightness edge sealing sheet, and relates to the technical field of intelligent manufacturing and industrial process control. The method sequentially comprises six steps of multi-modal online acquisition, multi-source fusion drift compensation, digital twinning real-time synchronization, model prediction closed-loop control, high-speed execution fault self-diagnosis and reinforcement learning adaptive optimization. Thickness data are synchronously obtained through laser, terahertz, ultrasound and environment quantity, trusted thickness flow is output through a drift model and a confidence matrix, future thickness is predicted through digital twinning rolling to give uncertainty, first-step control quantity is generated through quadratic programming, millisecond-level execution is conducted through a distributed clock, and self-healing is conducted under shadow driving hot standby and cloud diagnosis. And reinforcement learning agent online iteration improves the energy-saving and stable performance, and full-life-cycle closed-loop control with accurate thickness, low energy consumption and long service life of equipment under complex working conditions is realized.

The invention discloses an acquisition and processing system and method for dry data of a transformer, and relates to the technical field of data processing. Comprising the following steps: step 1, multi-source data real-time acquisition and edge preprocessing; 2, constructing a drying end point prediction model; 3, constructing a composite objective function, and solving the composite objective function by adopting a multi-objective evolutionary algorithm; 4, performing real-time correction and feedback; according to the method, key parameters in the drying treatment process are monitored in real time, an accurate drying end point prediction model is automatically constructed, heating and air exhaust curves are dynamically optimized according to the real-time working condition and the production strategy, and traditional manual blind judgment and static presetting are replaced; and meanwhile, the operation energy consumption is remarkably reduced, the optimal balance of the energy consumption and the production efficiency is realized through a multi-objective evolutionary algorithm and an online Kalman filtering correction closed loop mechanism, the drying quality and the equipment safety are improved, the energy cost is greatly saved, and the dual requirements of modern intelligent manufacturing for flexibility, energy conservation and consumption reduction are met.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention discloses an intelligent manufacturing defect automatic detection and classification method based on machine vision, and particularly relates to the technical field of defect automatic detection and classification, by constructing a high-resolution multi-source sample data set and introducing image preprocessing operation, defect expressions under different manufacturing batches, surface states and illumination conditions are covered, and the defect detection and classification accuracy is improved. Generating a defect probability heat map through an image segmentation network, extracting a primary defect candidate region, calculating a pseudo defect high-frequency interference coefficient by combining a high-frequency pseudo defect feature tensor, and calculating a multi-class defect overlapping coupling coefficient based on multi-classification confidence distribution and semantic adjacency; pseudo defect interference intensity and multi-class defect boundary fuzzy degree in the defect candidate area are accurately described, a sample label pollution risk assessment model is constructed to realize automatic identification and screening of high pollution risk samples in training data, and interference of mistakenly labeled samples on deep model training is significantly reduced; and erosion of error feature-label mapping on the generalization ability of the model is effectively prevented.

The invention discloses an order-driven cross-factory collaborative production system, and relates to the technical field of intelligent manufacturing and supply chain collaboration, and the system obtains the productivity data, logistics cost and tax policies of a plurality of production bases such as Ningbo, Thailand and America in real time, and carries out the intelligent splitting and distribution of orders through a multi-base productivity game algorithm. And dynamic optimal matching of the order and the productivity is realized. Meanwhile, the system integrates WMS inventory data and third-party logistics real-time quotation, a transportation scheme with the lowest total cost is generated by adopting a genetic algorithm, and cross-border logistics and tax expenditure are remarkably reduced. The system overcomes the problems of information isolated island, response lag, extensive cost control and the like in traditional multi-factory production, realizes global productivity collaborative optimization and supply chain integrated intelligent decision, and improves the enterprise order performance efficiency and the overall resource utilization rate.

The invention discloses a multi-agent collaborative decision-making system and method for neural symbol fusion, and relates to the technical field of artificial intelligence, and the system comprises a neural symbol fusion engine which constructs a knowledge double-layer representation architecture, and achieves the organic fusion of symbol reasoning accuracy and neural learning adaptability; the intelligent agent coordination optimizer quantifies the intelligent agent difference through cognitive state mapping, constructs a consensus feasible region, carries out hybrid verification and constraint optimization, and selects an optimal decision scheme; and the adaptive interpretation system constructs a decision evidence chain and realizes continuous optimization of system parameters through feedback learning. The technical challenges of symbol reasoning and neural learning fusion, multi-agent cognitive difference coordination, decision reliability and interpretability and the like are effectively solved, and the method is suitable for complex decision scenes of medical treatment, finance, intelligent manufacturing and the like.

The invention discloses an industrial Internet of Things time sequence self-supervision anomaly detection method and a monitoring and early warning system, and relates to the field of industrial Internet of Things, and the method comprises the steps: S1, constructing an anomaly detection model, and S2, obtaining a training data set; s3, training and optimizing an anomaly detection model; s4, acquiring to-be-detected data in real time; s5, performing anomaly detection analysis on the to-be-detected data, and outputting an anomaly detection result; through a time sequence and relation learning module, a dynamic graph topological structure learning module and an enhancement module, internal characteristics of a time sequence in a time domain and a space domain are deeply mined. The time sequence and relation learning module comprehensively captures a multi-scale time pattern, and the dynamic graph topological structure learning module eliminates dependence on a predefined graph structure; the enhancement module enhances the invariant representation under noise, and improves the recognition capability of the model to a normal mode; through wide experiments, the advancement of the method in detection performance is verified, and reliable support is provided for intelligent manufacturing and infrastructure diagnosis.

The invention belongs to the technical field of intelligent manufacturing and automatic design optimization, and discloses an automatic template generation method and system based on UG software, and the method comprises the steps: capturing a sketch operation event flow, carrying out the deep binding of geometric parameters and tolerance rules, generating a technology enhancement parameter set, and constructing a dual-channel instruction set; synchronously generating a two-dimensional engineering drawing projection and a three-dimensional expansion drawing preview; detecting conflicts in real time, and calling an exception correction plan library for dynamic correction; generating a zero-conflict BREP boundary model and a correction track log, and establishing a log-plan library mapping relation; generating an enhanced BERP model through a template drawing optimization mechanism; further generating a processing path instruction set, and integrating the processing path instruction set into a process compliance template drawing package; generating a cross-platform manufacturing package; constructing a quality index set, and generating an abnormal event association graph; and calculating a rule parameter adjustment amount, dynamically updating the process rule base, generating a global strategy packet, and reversely injecting sketch parameter constraints to form a continuous optimization cycle.

The invention belongs to the technical field of high polymer material design and intelligent manufacturing, and discloses a machine learning driven epoxy resin based composite material thermal-mechanical property aided design method, which comprises the following steps: S1, data acquisition and feature construction; s2, performing feature screening; s3, constructing and training an interpretable prediction model; s4, carrying out reverse design and optimization; and S5, performing closed-loop verification and updating. According to the method, the quantitative relation of structure-process-performance is constructed through an interpretable machine learning model, and the contribution mechanism of each factor is revealed by means of SHAP analysis. And finally, reversely designing an optimal epoxy resin monomer structure and a matched curing process according to the performance target. The limitation of a traditional trial and error method is broken through, collaborative optimization of the material structure and the forming process can be achieved, and the development efficiency of the epoxy resin-based carbon fiber composite material is remarkably improved.

The invention relates to the technical field of intelligent manufacturing decisions, in particular to a digital twinning-based manufacturing system decision analysis method and system, and the method comprises the steps: deploying a plurality of Internet of Things sensors on a physical manufacturing system, and collecting a physical real-time data stream of equipment in real time; the method comprises the following steps: establishing a virtual data acquisition channel aligned with a physical manufacturing system clock, injecting a physical real-time data stream into a digital twinning creation model, and performing data preprocessing based on distributed edge calculation to delay and compress original data acquisition of the physical real-time data stream to 10ms level, the time sequence database and the NTP / GPS clock are synchronized to ensure the state alignment error lt of the physical-virtual system; compared with the prior art, the deep space-time prediction network is fused with a CNN-LSTM-attention mechanism, the accuracy of multivariable coupled KPI prediction is improved, in addition, model failure is recognized in real time through Page-Hinkley inspection, and a prediction error reaches a relatively stable state through an adaptive retraining mechanism.

The invention relates to the field of intelligent manufacturing, and discloses a production process regulation and control method based on big data, which comprises the following steps: collecting original data in a production process according to a sensor group, and carrying out data cleaning on the collected original data to obtain cleaned production process data; performing fusion processing according to the cleaned production process data to obtain a fused production process data set; establishing an initial prediction model according to the working condition classification data to obtain initial prediction model data corresponding to each working condition; the production process is regulated and controlled through big data analysis, the product quality can be monitored in real time, the process parameters are predicted, optimized and adjusted, so that the product quality is effectively improved, the quality deviation is reduced, key factors in the production process are accurately recognized and adjusted through the steps of data cleaning, fusion, classification, model optimization and the like, and the production efficiency is improved. Therefore, unnecessary production waste is reduced, and production efficiency is improved.

The invention relates to the technical field of control or regulation systems, and discloses a cooperative control method for a photovoltaic intelligent manufacturing equipment production line, and the method comprises the steps: a control unit collects the stock and change rate of materials in a physical cache region in real time; establishing and mapping the physical cache region into a virtual viscoelastic dynamic model with non-Newtonian fluid characteristics; calculating a virtual elastic restoring force enabling the stock to return to a balance point and a virtual viscous damping force preventing the stock state from changing based on the model; wherein an asymmetric anisotropic damping generation strategy is executed, and a damping coefficient is dynamically split according to a material flowing trend; and finally, superposing the virtual adjustment correction after vector synthesis to the basic transmission speed to generate a dynamic speed instruction, and by constructing a virtual dynamic field with rheological characteristics and an asymmetric damping mechanism, the problem of nonlinear cascade oscillation in discrete logistics transmission is solved, and differential self-adaptive suppression of accumulation and evacuation risks is realized.

The invention discloses an intelligent manufacturing system and method based on an industrial robot, and relates to the technical field of industrial robot control, and the method comprises the steps: constructing a workpiece posture-space structure topological graph according to a cross-scale visual recognition result, carrying out the space comparison with an original trajectory planning graph, outputting a space deviation mapping relation, and carrying out the calculation of a spatial deviation mapping relation; performing spatial correlation analysis and path reachability evaluation on the workpiece attitude-spatial structure topological graph by using a graph neural network to obtain an end execution path instruction; driving the industrial robot to perform intelligent manufacturing operation through the tail end execution path instruction, and obtaining an actual operation result image; and performing feature alignment and difference comparison processing on the actual operation result image and the process reference image to obtain an operation deviation feature mapping relation, performing operation quality evaluation, and outputting an operation result visual detection label. According to the method, the adaptability and the manufacturing precision of the operation path of the industrial robot are effectively improved.