3760 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 provides a battery assembly process defect real-time detection and classification method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: carrying out the standardization preprocessing of collected battery surface images and three-dimensional point cloud data; a distributed edge computing architecture is adopted for feature extraction, the distributed edge computing architecture comprises a defect propagation prediction module based on a graph neural network, inter-process defect features are constructed into a dynamic graph structure, a defect evolution rule is learned through the graph network, and feature extraction parameters are dynamically adjusted; inputting the extracted features into a deep learning detection model, carrying out feature adaptive mapping and alignment through a multi-modal spatial-temporal feature coding layer, constructing a process association graph through a dynamic reasoning layer based on causal reasoning, and calculating a propagation influence degree; carrying out hierarchical precision detection on different defect evolution probability regions by a prediction perception detection layer with a self-adaptive threshold value, and outputting a defect detection result; and classifying defect detection results based on defect classification standards.

The invention discloses a cross-modal adaptive interaction-based few-sample industrial anomaly detection method, which comprises the following steps of: calculating total loss according to aligned final features, normal semantic projection, abnormal semantic projection, optimized visual features and optimized text features, and updating parameters of an industrial anomaly detection model by using the total loss. A trained industrial anomaly detection model is obtained; and inputting a to-be-detected industrial image into the trained industrial anomaly detection model to obtain the aligned final feature, the anomaly semantic projection, the optimized visual feature and the optimized text feature for judging the anomaly condition of the to-be-detected industrial image. According to the method, under the condition that only a small number of normal samples are needed, the distinguishing capacity of the model for normal and abnormal features can be remarkably enhanced, dependence on labeled data is reduced, and an industrial anomaly detection solution which is high in precision, low in cost and capable of being rapidly deployed is provided for intelligent manufacturing.

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent production scheduling method and system for a manufacturing system, and the method comprises the following steps: collecting an equipment state, a material neat rate and an order emergency degree in real time based on dynamic production data, and calculating an equipment availability coefficient, a material guarantee index and an order priority score through feature analysis; and forming a dynamic production feature set. The system constructs a production scheduling optimization model through work order-equipment matching degree calculation and process priority optimization, and performs multi-objective optimization solution by adopting an NSGA-III algorithm to realize the optimal combination of equipment utilization rate, order delivery rate and inventory balance. Meanwhile, an arbitration mechanism is introduced to coordinate process priority conflicts, and the scheme is verified through time, space and dynamic adaptability, so that feasibility and stability of production scheduling execution are ensured. According to the invention, the production scheduling efficiency of the manufacturing system can be improved, the equipment utilization rate can be improved, the order delivery delay rate can be reduced, and the adaptive ability to the change of the production environment can be enhanced.

The invention discloses a casting surface treatment quality evaluation method and system, and relates to the technical field of intelligent manufacturing and quality control, and the method comprises the steps: carrying out the multi-modal data collection of a casting, and carrying out the preprocessing; analyzing the preprocessed data, and constructing a comprehensive quality evaluation data set; a quality evaluation model is constructed, abnormal conditions in surface processing are identified, and a quality evaluation result is generated; performing quality classification on the casting according to a quality evaluation result, generating an evaluation report, and providing quality state judgment and related optimization suggestions; the casting surface treatment process is adjusted and optimized, the overall manufacturing quality is improved, and continuous improvement of quality evaluation is achieved. According to the method, the detection precision of the casting surface treatment quality and the process optimization capability are improved. By adopting data-driven closed-loop control, quality abnormity can be accurately identified, optimization suggestions can be provided, intelligentization, stability improvement and long-term quality improvement of the manufacturing process are realized, the defect rate is effectively reduced, and the manufacturing consistency is improved.

The invention discloses a complex curved surface geometric self-adaptive machining error compensation and optimization method, and belongs to the technical field of numerical control machining and intelligent manufacturing, and the method comprises the steps: dividing a composite curved surface into a plurality of regions according to an equal-area mode, and collecting the precision data of each region after machining in real time during curved surface machining; based on the precision data after the region processing is completed; machining parameters are automatically adjusted according to an error result, error compensation is achieved, a compensation coefficient in the error compensation process is obtained, and the error compensation capacity is judged; processing parameters are globally optimized by utilizing an optimization algorithm according to specific requirements of a processing task, so that double improvement of processing efficiency and processing quality is realized; according to the method, the curved surface machining process is monitored in real time, error judgment is carried out, the machining parameters are automatically adjusted according to the error result, effective control over the machining errors is achieved, the machining parameters are globally optimized through the optimization algorithm, and the machining precision, efficiency and stability of complex curved surface parts are improved.

The invention relates to the technical field of intelligent manufacturing, in particular to a manufacturing industry digital twinning three-dimensional visual management and control method and system, and the method comprises the following steps: collecting manufacturing equipment operation data, converting the data into a time sequence, analyzing the trend characteristics of each dynamic subsequence in the time sequence, and generating a dynamic operation state sequence set. According to the method, the time sequence characteristics in the operation data of the manufacturing equipment are dynamically analyzed, so that the trend change of the operation state of the equipment can be accurately captured in multiple time dimensions, and potential abnormal states are deeply mined and predicted. In combination with prediction of future operation state parameters and comparison of a normal state range, an abnormal trend region and a corresponding fault level can be clearly marked, and the risk assessment capability of the equipment health state is effectively improved. And carrying out three-dimensional space mapping on the marked risk area, and accurately positioning an abnormal part and a deviation area in combination with gradient change and deviation distribution analysis of three-dimensional coordinates of equipment.

The invention provides a precision injection mold accessory production quality traceability management method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting material, process, equipment and environment data in real time through a distributed sensor; fusing multi-source data based on dynamic material characteristic parameters and process stability indexes, and quantifying melt flow and process fluctuation characteristics; constructing a mixed kernel function anomaly detection model to identify quality deviation; establishing a cross-process association map to reveal the space-time relationship among the raw materials, the process and the finished product; generating a three-dimensional traceability identifier containing the material hash, the process compression code and the block chain address; a block chain enhanced database is adopted to realize tamper-proof storage; and generating a visual traceability report through reverse analysis. Through dynamic modeling, cross-process association and block chain technologies, the problems of data isolation, detection lag and low traceability credibility in a traditional method are solved, the quality traceability efficiency and precision are remarkably improved, and precise injection molding full life cycle management is supported.

The invention provides a VDSL ultra-low time delay communication method and system, and relates to the technical field of communication, and the method comprises the steps: encrypting a clock synchronization channel through a quantum key distribution protocol, dynamically dividing micro time slot resources in an orthogonal frequency division multiplexing symbol period, and generating a dynamically adjusted micro time slot resource distribution result; calculating an optimal phase offset matrix of the metasurface intelligent reflecting surface through a depth deterministic strategy gradient algorithm to obtain an optimized electromagnetic wave propagation path; generating a global optimization check matrix by aggregating the locally trained lightweight error correction model gradient of each node to obtain a compensated data stream; and constructing a causal graph model dynamic pruning high-entropy path to minimize causal entropy, through multi-agent reinforcement learning, taking time delay-energy efficiency as a game target to decide an optimal modulation order and a subcarrier switching strategy, and obtaining an optimized stable communication link. According to the invention, high-reliability and low-delay communication basic support is provided for high-precision intelligent manufacturing.

The invention relates to the technical field of equipment fault diagnosis, in particular to an extruder equipment fault recognition method and system based on artificial intelligence, and the method comprises the following steps: collecting key fault features of an extruder in real time based on a multi-mode sensor network, optimizing the signal quality through data preprocessing and feature decoupling, and obtaining a fault recognition result; and the generalization ability of the model is improved by using cross-device feature mapping and transfer learning, a hybrid neural network is combined, a physical constraint layer is embedded on the basis of a data driving layer, a feature incidence matrix conforming to the dynamic characteristics of the extruder is constructed, and a fault prediction model can be adjusted in real time through a dynamic weight distribution mechanism and dual-target loss optimization, so that the fault prediction efficiency is improved. The method adapts to the change of the operation state of the equipment, and realizes the real-time detection, graded early warning and precise operation and maintenance of faults in combination with an intelligent early warning mechanism and a multi-target optimization decision. According to the invention, the operation stability and maintenance efficiency of the extruder equipment are obviously improved, and the method is suitable for equipment health management in the field of intelligent manufacturing.

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

The invention 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 discloses an artificial intelligence-based industrial production line dynamic scheduling method and system, and relates to the technical field of intelligent manufacturing and industrial intelligent scheduling, and the method comprises the steps: collecting real-time state data, carrying out the preprocessing, taking the maximization of productivity, the shortest delivery time and the lowest energy consumption as optimization objectives, constructing a multi-objective reinforcement learning model, and carrying out the optimization of the multi-objective reinforcement learning model; scheduling priority data is generated, and operation distribution of each process node is adjusted in combination with a current equipment load threshold value and production bottleneck node information; when an abnormal condition is detected, triggering a rescheduling mechanism according to scheduling priority data, and updating an operation sequence and a resource allocation result; and synchronously feeding back the updated job allocation result and execution effect to the multi-target reinforcement learning model, and carrying out iterative optimization on the multi-target reinforcement learning model through a priority experience playback mechanism to realize continuous optimization of a scheduling strategy. According to the method, through a mode of combining multi-target reinforcement learning and dynamic scheduling, the learning efficiency and the optimization effect are improved.

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 discloses an intelligent film-drawing and unwinding control method and system based on real-time tension, and relates to the field of automatic control and intelligent manufacturing, and the method comprises the following steps: ensuring normal starting of equipment and system states through equipment self-inspection and technological parameter input; tension and speed data are collected in real time and preprocessed, and support is provided for fuzzy PID control; the parameters of the fuzzy PID controller are dynamically adjusted by calculating the tension error and the change rate of the tension error so as to adapt to different membrane material characteristics and unwinding working conditions; an improved genetic algorithm is used for optimizing parameters of a PID controller, overshoot is minimized, the adjusting time is shortened, and the robustness and the anti-interference capability of the system are improved; a closed-loop control system is constructed, and the stability of unwinding tension is ensured through real-time feedback signals; and the system state is monitored in real time in combination with an anomaly detection algorithm, and a control strategy is automatically adjusted or fault protection is performed. The control method disclosed by the invention can be widely applied to the fields of film unwinding and automatic production lines, and has a relatively high intelligent level.

The invention relates to a real-time monitoring method and related equipment for an intelligent manufacturing digital twinning system, and the method comprises the following steps: constructing a three-dimensional digital twinning model based on a real-time image sequence; based on the three-dimensional digital twinborn model, analyzing whether an abnormal phenomenon exists in the production field, and if yes, performing abnormal region identification in the three-dimensional digital twinborn model through a preset abnormal identification algorithm to obtain abnormal state data; performing abnormity deduction on the production field based on the abnormal state data to obtain an abnormal evolution trend; and performing risk assessment on the production field based on the abnormal evolution trend to obtain a risk assessment result, and selecting a corresponding risk control strategy based on the risk assessment result, thereby solving the problem that a traditional method is difficult to provide enough details to comprehensively know reasons behind an abnormal phenomenon, and improving the risk control accuracy. And subsequent risk assessment and selection of control strategies are influenced.

The invention provides a digital workshop production process management platform for intelligent manufacturing, and relates to the technical field of data processing, and the platform comprises a production data collection module which is used for obtaining the real-time state data of production equipment in a workshop; the resource state evaluation module is used for analyzing the current state of the production resources; the task decomposition module is used for decomposing the production task into a plurality of mutually associated process tasks; the dynamic task allocation module is used for dynamically allocating equipment resources for each process task; the abnormal data processing module is used for detecting abnormal data in the process of executing the process distribution plan; the task optimization adjustment module is used for updating the resource state information in the resource state evaluation module; the execution instruction issuing module is used for converting the newest process allocation plan into a control instruction and sending the control instruction to corresponding equipment for execution; according to the invention, the autonomy and accuracy of resource allocation in the production process are improved.

The invention discloses a furniture processing control system based on artificial intelligence, and relates to the technical field of intelligent manufacturing and industrial automation. According to the system, cutter vibration frequency spectrum, plate texture features and environment temperature and humidity data are collected in real time through a distributed sensor array, multi-source data are fused through a space-time attention mechanism, and a joint feature matrix is generated. And based on the joint feature matrix, utilizing a depth map neural network to deconstruct a topology constraint relation of non-standard customization requirements, and generating an initial processing parameter set. And driving the digital twin model to perform virtual processing according to the initial processing parameter set, and predicting a processing node deformation error and generating a compensation vector in combination with the three-dimensional laser point cloud and infrared thermal imaging data. The compensation vector and real-time working condition data are received, a path cost function is evaluated through Monte Carlo tree search and a time sequence convolutional network, the cutter feeding speed and the cutting depth are corrected, and dynamic regulation and control of cutter path parameters are achieved.

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.

The invention relates to the technical field of industrial automation and intelligent manufacturing, in particular to a rubber tube production line monitoring method and system.The method comprises the steps that multi-mode production data of a production line is collected and preprocessed, a virtual production line model is generated through a dynamic twin model and compared with actual data, and virtual-real difference data is generated; performing anomaly detection on the virtual-real difference data based on a classification algorithm and a rule matching structure, and optimizing parameters of an anomaly detection model; generating abnormal trend prediction data by using the optimized model, and proposing a system correction suggestion according to the prediction data; and dynamically adjusting the operation parameters of the equipment through a closed-loop feedback mechanism. According to the invention, through innovative dynamic twin modeling, deep learning optimization and closed-loop control technologies, real-time monitoring and intelligent adjustment of the production line are realized, and the production efficiency and the product quality are effectively improved.

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 an industrial equipment intelligent operation and maintenance management system and method based on 5G-MOM, and belongs to the technical field of industrial internet and intelligent manufacturing. The system comprises a multi-source heterogeneous data acquisition layer deployed in industrial equipment, an edge computing node cluster based on 5G, a cloud intelligent analysis platform and a man-machine collaborative operation and maintenance terminal. The method comprises the following steps of collecting equipment vibration, temperature and current multi-dimensional working condition data in real time through a 5G network; performing data cleaning and feature extraction by using edge computing nodes, and constructing an equipment operation digital twin model; a cloud deep neural network is adopted to carry out fusion analysis on the multi-dimensional time series data, and self-adaptive diagnosis and residual life prediction of a fault mode are realized; a dynamic maintenance strategy is generated based on an MOM system, and field personnel are guided to execute precise maintenance through an AR terminal. According to the invention, 5G ultra-low time delay communication and an industrial mechanism model are creatively combined, and real-time visual management and predictive maintenance decision optimization of the equipment health state are realized.

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 discloses a numerical control machining equipment state monitoring system based on big data analysis, and relates to the technical field of intelligent manufacturing. The method is used for solving the problems of correlation analysis and real-time feedback between tool wear and machining quality in the machining process. A time sequence correlation feature vector is extracted through a vibration signal, an acoustic emission signal and main shaft axial micro-displacement data, a machining quality degradation index is calculated in combination with a nonlinear regression model, and the machining quality is monitored in real time. And on the basis of adaptive feature weight adjustment of the types of the processing materials, feature fusion during processing of different materials is enhanced, and the precision of quality evaluation is improved. And establishing a nonlinear mapping relation between the tool wear and the surface roughness by utilizing a gradient lifting tree model, and realizing accurate prediction of the tool wear and the machining quality. And finally, through a closed-loop dynamic adjustment mechanism, the cutting speed and the feeding amount are automatically adjusted according to the machining quality degradation index and the fault positioning result, and the stability of the machining process and the product quality are ensured.

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent defect detecting and repairing method and system for a vacuum isothermal forging based on multi-mode sensing data. The method comprises the following steps: respectively acquiring image data, heat distribution data and stress monitoring data of the vacuum isothermal forging through a visual sensor, a thermal sensor and a stress sensor, and preprocessing the data to generate a time-space aligned multi-modal data set; performing fusion processing on the multi-modal data set, extracting multi-dimensional features of defects through a deep learning network, and generating a defect feature sequence; optimizing a repair path planning model based on the defect feature sequence in combination with the stress distribution features, and generating a repair task sequence; and inputting the repairing task sequence into the intelligent repairing equipment to execute repairing operation. According to the method, the multi-mode sensing technology and the deep learning algorithm are combined, high-precision detection, classification and repair of the defects of the vacuum isothermal forging are achieved, the production efficiency and the product quality are remarkably improved, and the defective rate and the repair cost are reduced.

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 wire harness manufacturing management system and method based on artificial intelligence, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: inputting a structured feature vector and a process constraint condition into a multi-target reinforcement learning model, and generating a process parameter instruction; the process parameter instruction is executed, the product yield is monitored in real time, and cross-process quality tracing is carried out on the abnormal product yield; historical data of abnormal batches in crimping, assembling and testing procedures are traced, the defect root cause probability of each procedure is calculated based on a Bayesian causal network, and primary and secondary root causes of quality defects are obtained and analyzed; and dynamically updating process constraint conditions according to primary and secondary root cause analysis results, adjusting a reward function of the multi-target reinforcement learning model, and re-optimizing a process parameter instruction. According to the invention, the operation state of the production equipment is accurately captured, the production process is optimized and adjusted in real time, and a closed-loop management process is formed, so that the production flexibility, efficiency and product quality are remarkably improved.

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.