63418results about How to "Increase productivity" patented technology

The invention provides an on-line quality monitoring method and system based on optical multispectral fusion, and relates to the technical field of multispectral fusion, and the method comprises the steps: collecting visible light and infrared multispectral images of an object to be measured, carrying out the preprocessing and registration, inputting the images into a variational auto-encoder, and carrying out the probability distribution modeling, and obtaining a spectrum basis vector and an initial abundance graph; and constructing a multi-scale morphological feature pyramid, performing graph segmentation in combination with spatial information to obtain a local consistency region, and fusing the local consistency region with the initial abundance graph to obtain an enhanced abundance feature. And fusing the spectrum basis vector and the enhanced abundance feature by adopting a multi-head cross attention mechanism to obtain a multi-spectrum fusion feature. And inputting the fusion features into a dynamic neural network, analyzing a topological connection relationship between nodes, obtaining a structured representation, performing discriminative enhancement, calculating node significance distribution, determining a quality defect area, analyzing a formation mechanism of the quality defect area, and finally generating a quality monitoring report.

The invention provides a die-casting die heat balance distribution optimization method and system based on an energy model, and relates to the technical field of die-casting dies, and the method comprises the steps: collecting heat distribution data, and building a heat energy transfer mapping relation; extracting space-time correlation features by using a graph attention network; constructing a heat distribution dynamic evolution model; constructing a temperature field predictor based on a deep belief network; training a predictor by using a multi-agent coevolution algorithm; constructing a temperature field optimization strategy network; generating an optimal temperature field control sequence; establishing a regional adaptive control strategy; constructing a temperature field controller based on deep reinforcement learning; and a dynamic fuzzy immune optimization algorithm is adopted to adjust cooling control parameters, and intelligent optimization control over heat distribution of the die-casting die is achieved.

The invention relates to the technical field of optical detection, in particular to a workpiece surface defect detection method and system based on the optical technology, and the method comprises the following steps: S1, generating a curvature model of a workpiece; s2, dynamically adjusting the irradiation angle and the light spot shape of a light source according to the curvature model in the S1; s3, adjusting the focal length of the detection equipment in real time; s4, imaging acquisition is carried out on the workpiece at the same time through multiple angles; s5, correcting image distortion caused by surface curvature change, and fusing the multi-angle images into a high-precision workpiece surface image; s6, defect features of the surface of the workpiece are extracted, defects are classified, positions are marked, and a defect detection report is generated; according to the invention, by dynamically adjusting the light source and the focal length and combining multi-view imaging and advanced image processing technologies, efficient and accurate defect detection is realized, and the product quality and the production efficiency are remarkably improved.

The invention discloses an industrial product quality detection method and system based on machine vision, and relates to the technical field of quality detection.During operation of the system, through an industrial camera and an optimized light source layout, high-resolution image acquisition is performed on electronic components in real time by adopting multi-angle shooting, a series of optimization processing is performed on the acquired images, and the quality of the electronic components is detected. The method comprises the following steps: extracting shape, texture and color features according to geometric characteristics of an electronic component to form key parameters for judging defects, identifying potential defects of the electronic component by using a deep learning model algorithm, calculating to obtain a quality evaluation index Q, and calculating the quality evaluation index Q according to calculation results of feature extraction and defect detection. And comprehensively evaluating the quality of each component, comparing the quality evaluation index Q with a first preset threshold value M and a second preset threshold value N to obtain a quality evaluation grade, feeding unqualified detection information back to a production line, and removing unqualified components.

The invention discloses a multi-task collaborative robot scheduling method and system oriented to an intelligent factory, and the method comprises the steps: receiving the input data of a plurality of tasks to be executed by the intelligent factory, the input data comprising the task type, the dependency relationship between the tasks, the task priority, the resource demand of each task, and the expected completion time of each task; determining an initial task execution sequence of the tasks according to the task types, the dependency relationships and the task priorities; obtaining current resource state data of all available robots in the intelligent factory; according to the resource demand of each task, the expected completion time of each task, the initial task execution sequence and the current resource state data of all available robots, a scheduling algorithm is adopted to generate a multi-task cooperative scheduling rule; and controlling the plurality of target robots to execute the plurality of tasks according to the multi-task cooperative scheduling rule. According to the method, the resource utilization rate of the robot in the intelligent factory can be effectively improved, and the task execution efficiency is optimized.

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention provides a predictive maintenance management method and system for an industrial robot, and relates to the technical field of equipment maintenance management, and the method comprises the steps: obtaining multi-modal heterogeneous data of the industrial robot, carrying out the intelligent preprocessing, obtaining first preprocessing data, carrying out the cross-modal correlation fusion of the first preprocessing data, generating fusion data, and carrying out the prediction of the industrial robot; extracting complementary redundant information and constructing a semantic feature map; constructing a health state prediction model, adding the semantic feature map into the health state prediction model, mapping the semantic feature map to an embedding space through linear transformation, extracting time sequence information, carrying out feature interaction to obtain interaction features, and carrying out transformation and feature enhancement through a feedforward neural network layer to obtain health state probability distribution; comprehensive health state distribution is generated through Bayesian reasoning fusion, an optimal maintenance strategy is generated in combination with a detection model, an abnormal mode and a key turning point, collaborative maintenance is conducted on the industrial robot, a maintenance result is uploaded to a server, and the current state is stored.

The invention relates to the field of circuit board quality detection, in particular to an aluminum circuit board surface treatment quality detection system and method. The method comprises the following steps: acquiring a real-time thermal imaging view of the aluminum circuit board; performing multi-point sampling calculation on the real-time thermal imaging view of the aluminum circuit board, and performing temperature distribution analysis so as to generate temperature distribution data of the aluminum circuit board; performing temperature rise distribution trend reconstruction based on the temperature distribution data of the aluminum circuit board, and performing abnormal region positioning so as to obtain a coating abnormal region; carrying out multidirectional ultrasonic pulse detection on the coating abnormal area, and collecting a multi-array sound wave signal; carrying out low-pass filtering processing on the multi-array sound wave signals, then carrying out three-dimensional projection remodeling, and constructing a three-dimensional sound wave projection drawing; and acoustic wave penetrating circuit board attenuation inversion is carried out on the three-dimensional acoustic wave projection drawing, and then surface defect analysis is carried out, so that an aluminum circuit board surface defect area is obtained. The method for accurately and efficiently detecting the surface defects of the aluminum circuit board is realized.

The invention discloses a visual inspection system for appearance defects of a pressure sensor, and relates to the technical field of visual inspection of defects. Comprising a light source control module, an image acquisition module, an image preprocessing module, a feature extraction module, a defect identification module, an evaluation grading module, a result display module and a man-machine interaction module which are integrated on the appearance defect AOI equipment, and all the modules are connected through electric signals. According to the visual inspection system for the appearance defects of the pressure sensor, through the high-precision industrial camera and the advanced image processing technology, precise recognition of tiny defects on the surface of a product is achieved, compared with a traditional manual inspection mode, higher inspection precision is achieved, tiny scratches and sunken defects which are difficult to perceive by human eyes can be found, and meanwhile, the visual inspection system is convenient to use. The automatic detection process greatly shortens the detection time and improves the production efficiency, and the system can detect a large number of products in a short time.

The invention discloses an AI spraying system control method capable of achieving autonomous 3D scanning, and the method comprises the steps: obtaining three-dimensional surface data of a to-be-coated workpiece through high-precision 3D scanning equipment, constructing a surface model of the workpiece according to the data, and extracting surface feature parameters. Coating parameters set by a user are received, the coating parameters comprise the viscosity, the chemical property and the film thickness requirement of paint, the surface characteristic parameters and the coating parameters are input into the spraying optimization model, and an initial spraying track and technological parameters are generated through calculation in combination with historical data. Environment parameters are collected and associated with the initial process parameters, and the adjusted spraying track and the adjusted process parameters are generated through dynamic environment compensation. And through a robot control system, coating operation is executed according to the adjusted parameters, the spraying effect is optimized in real time, and it is ensured that the surface of the workpiece is evenly coated. Efficient automation of the spraying process is achieved, the coating quality and consistency are remarkably improved, and the automatic spraying device is particularly suitable for coating of workpieces with complex curved surfaces or changeable shapes.

The invention, which relates to the technical field of the industrial internet, discloses an intelligent equipment data interaction system based on the industrial internet, comprising an industrial internet platform. The industrial internet platform is in communication connection with a data acquisition and preprocessing module, an edge calculation module, a data transmission module, an anomaly analysis module, an intelligent decision module, a feedback control module and a man-machine interaction module, and all the modules are in electric signal connection. According to the intelligent equipment data interaction system based on the industrial internet, the production efficiency is remarkably improved by monitoring and optimizing the production process in real time, data are collected through a sensor and an equipment state monitor, and abnormal states in the production process are quickly recognized and responded through preprocessing and analysis of an edge calculation module; and the intelligent decision-making module generates an optimization control scheme according to an analysis result and feeds back the implementation scheme of the control module, so that the risks of equipment failure and production interruption are reduced.

The invention discloses a temperature correction method and system for a hot press, and particularly relates to the technical field of hot press temperature control, and the method comprises the steps: obtaining and preprocessing multi-dimensional operation data, including a temperature time sequence, pressure setting and material attributes; constructing a coupling model based on the multilayer neural network, and predicting temperature distribution of different process stages; performing sensor drift compensation, analyzing temperature deviation characteristics, judging whether sensor drift or material thermal diffusion abnormity exists or not, and generating a local distortion mark; determining a compensation mode according to the distortion mark, and starting a local compensation mode or a global balance control mode if the distortion is recognized as over-limit distortion; and comparing with the output of the coupling model, verifying the temperature correction effect, and if the deviation is reduced and the distortion mark is converged, outputting a stable temperature correction result. The problems of temperature distortion and sensor drift are effectively solved, and the long-term precision and stability of the hot press under complex working conditions are improved.

The invention relates to the technical field of sand making process optimization, in particular to an SCM sand making process optimization system based on the Internet of Things, and provides the following scheme that the system comprises a self-adaptive crushing module, an intelligent screening module, a grinding control module, an intelligent conveying module and a feedback optimization module. And the self-adaptive crushing module adjusts working parameters of crushing equipment in real time according to material characteristics through material identification and equipment adjustment logic. The intelligent screening module achieves material granularity monitoring and blockage prediction and adjustment in the screening process through granularity monitoring and material blockage prediction logic. And the grinding control module dynamically adjusts the pressure and the rotating speed of the grinding equipment by monitoring the dust change in real time. The intelligent conveying module achieves optimization of the speed and the inclination angle of the conveying belt through material flow monitoring and conveying parameter adjustment. And the feedback optimization module optimizes and adjusts parameters of each module of the system according to the finished product quality data. Through intelligent regulation and control, the efficiency of the sand making process and the product quality are improved.

The invention provides a concrete production management method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: collecting the health monitoring data of concrete production equipment in real time through a plurality of sensors, and collecting the full life cycle event data of the concrete production equipment; preprocessing the health monitoring data at an edge end, extracting state features of the equipment, performing real-time diagnosis by using a pre-trained fault prediction model based on the state features of the equipment, and constructing an equipment multi-level digital twinborn model at a cloud platform; the method comprises the following steps: fusing a pre-constructed physical degradation model and a data driving model based on a multi-level digital twinborn model, constructing an equipment residual life prediction model, and dynamically adjusting a concrete production strategy in the digital twinborn model according to an obtained prediction result to obtain an optimal concrete production scheduling scheme. And the information is converted into an equipment control instruction, so that automatic issuing and execution of a concrete production scheduling scheme are realized.

The invention provides a wafer factory-level real-time scheduling method in semiconductor manufacturing based on deep reinforcement learning, which relates to the technical field of semiconductors, and comprises the following steps: constructing a state space vector by collecting equipment state, batch information and process flow information based on deep reinforcement learning, inputting a pre-trained deep reinforcement learning network model, and constructing a state space vector; the model includes a value assessment network and a policy network. The model generates matching probability distribution of batches and devices, a hierarchical reinforcement learning method is adopted to decompose the distribution into two sub-tasks of batch selection and device allocation, and a plurality of candidate matching schemes are generated. And then evaluating the candidate schemes by applying a multi-target reward and punishment function, selecting an optimal scheduling scheme in combination with a Monte Carlo tree search algorithm, and issuing a processing instruction. And real-time processing data is collected in the working procedure processing process to update the state space vector, so that real-time scheduling is realized.

The invention provides an electromagnetic field composite powder feeding type plasma arc additive manufacturing formability regulation and control method and system, and relates to the technical field of plasma arc additive manufacturing. The flowing direction and speed of liquid metal in a molten pool in the deposition process are regulated and controlled through the target composite electromagnetic field, and the morphology and the internal organization structure of a deposition layer are controlled; in the process of regulating and controlling the liquid metal in the molten pool, process parameters and molten pool dynamic image data in the deposition process are collected in real time, and a pre-trained state recognition model is used for conducting self-adaptive adjustment on the process parameters of a subsequent deposition layer; and after deposition of each layer is completed, a laser scanning system is adopted for measuring the actual geometric size and surface appearance of the deposition layer, deposition manufacturing of the next layer is executed based on the optimized composite electromagnetic field control parameters, the corrected layered slicing data and the process path, and the execution is executed circularly till manufacturing of the whole workpiece is completed.

The invention provides a digital twinning method and system based on industrial automation, computing equipment and a storage medium. The method comprises the following steps: acquiring real-time operation parameters from a physical system; based on the real-time operation parameters and a preset physical model, constructing a digital twinborn body of the physical system in a virtual environment; simulating the operation state of the physical system by using the digital twin, and predicting the performance of the physical system under different working conditions to generate a simulation result; comparing the simulation result with the obtained actual operation data of the physical system so as to evaluate the digital twin body and obtain an evaluation result; adjusting the physical model according to the evaluation result so as to update the digital twin; and performing fault prediction and / or optimizing operation parameters of the physical system by using the updated digital twin. According to the technical scheme provided by the invention, the digital twin can be continuously optimized and the physical model can be adjusted.

The invention relates to the field of production fault analysis, in particular to a fault monitoring analysis method and system for lubricating oil production. The method comprises the following steps: acquiring a multi-dimensional working monitoring parameter and an omnibearing working image; performing abnormal outlier filtering processing on the multi-dimensional working monitoring parameters, and performing time sequence evolution simulation to generate time sequence state evolution data; performing continuous frame optical flow motion analysis on the omnibearing working image, performing dynamic parameter mapping based on time sequence state evolution data, and constructing a dynamic production twinborn model; obtaining a preset production standard demand log; carrying out dynamic process deviation detection based on a preset production standard demand log dynamic production twinborn model, and carrying out production fault analysis so as to obtain stage production fault data; performing potential fault traceability speculation on the staged production fault data to obtain an abnormal fault potential node; and constructing a potential fault mark twinborn model based on the abnormal fault potential nodes. According to the invention, efficient and accurate fault analysis of lubricating oil production is realized.

The invention provides a degradable plastic production method which comprises the following steps: acquiring an optimal raw material proportioning scheme, and controlling an intelligent batching system to perform automatic batching; temperature and humidity parameters of raw material storage and conveying links are obtained, and raw material pretreatment process parameters are dynamically adjusted in combination with time sequence data analysis; performing quality detection on the preprocessed raw materials, identifying defects and impurities of the raw materials by adopting machine vision and a deep learning algorithm, and removing unqualified raw materials; various raw materials are accurately weighed through an industrial robot and mixed according to the optimized formula proportion; a thermal infrared imager is adopted to monitor temperature field distribution of a plastic extrusion forming mold in real time, and finite element simulation analysis is combined to optimize mold structure design and a heating and refrigerating system; the extrusion molding process is monitored on line, material component changes are analyzed through a chemometrics model, and if degradation products exceed the standard, extrusion process parameters are adjusted or a mold is replaced.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention provides a textile woven structure anomaly detection method, which comprises the following steps: acquiring real-time tension monitoring data of warps and wefts in a jacquard textile weaving process, and establishing a tension distribution model in a normal weaving state according to the real-time tension monitoring data; if the classification result is in an abnormal state, spraying a pre-prepared chemical reagent on the surface of the abnormal position of the jacquard textile, and collecting spectral data of the surface of the textile after spraying; performing feature extraction and classification on the spectral data, and obtaining a comprehensive anomaly detection result by comparing a classification result with a tension position anomaly detection result; according to the comprehensive anomaly detection result, an abnormal area is accurately positioned and marked, and a sample in the abnormal area is cut and conveyed to a sample analysis device; and carrying out microstructure analysis on the cut sample, extracting fiber arrangement characteristics, judging the abnormal degree of fiber arrangement, and obtaining a microstructure abnormality evaluation result.

The invention discloses a screening efficiency online monitoring method, device and system based on a vibrating screen, and relates to the technical field of vibrating screen efficiency monitoring, and the screening efficiency online monitoring method comprises the following steps: collecting working parameter data of the vibrating screen in real time through a sensor arranged on the vibrating screen; the accuracy and stability of screening efficiency are improved by monitoring key parameters such as humidity, particle size distribution and vibration frequency in real time and combining a screening effect model and a dynamic correction algorithm. The system automatically adjusts equipment parameters, equipment overload or screen damage is avoided, the material passing rate is optimized, and production losses are reduced. Meanwhile, manual intervention is reduced through an automatic control mechanism, the automation level of a production line is improved, human errors are reduced, the equipment fault and shutdown time is shortened, the production efficiency is improved, and the service life of the equipment is prolonged.

The invention relates to the technical field of building information management, in particular to a BIM (Building Information Modeling)-based prefabricated part design quality management method, which comprises the following steps of S1, establishing a prefabricated part three-dimensional information model with multi-dimensional parameters by using BIM software; S2, collecting real-time production data by using a sensor in a prefabricated part production process; s3, comparing the production data with initial design parameters in the BIM model to identify deviations exceeding a set tolerance range; s4, feeding back the identified deviation information to a production link, and adjusting operation parameters of production equipment according to the deviation condition; s5, after production is completed, whether the indexes meet the requirements or not is determined; and S6, adjusting the design and process standard of subsequent production. According to the invention, through real-time data acquisition, deviation analysis and feedback, optimization suggestion generation and closed-loop management, continuous improvement of precision and quality of a prefabricated part production process is ensured, and effective combination of design and production is realized.

The invention relates to the technical field of cable production automation control, and discloses a control method, system and device of cable extrusion equipment and the extrusion equipment, the method comprises the steps that sensor parameters are acquired, and the sensor parameters comprise the cable extrusion temperature and the extrusion speed; performing time delay correction on the sensor parameter to obtain a corrected parameter value; inputting the correction parameter value into a pre-trained extrusion speed and extrusion temperature dynamic compensation model to generate an adjustment instruction; and sending the adjustment instruction to equipment to adjust the heating temperature and the extrusion speed of the cable. Compared with the prior art, the method has the following effects that the heating temperature and the extrusion speed of the cable can be accurately adjusted in real time through combined use of time delay correction and an extrusion speed and extrusion temperature dynamic compensation model, and the waiting time and the adjustment time in the production process are reduced, so that the control precision and the production efficiency of cable extrusion equipment are improved.

The invention relates to the technical field of quality traceability management, in particular to an organic fertilizer quality safety traceability management method based on multi-source data. The method comprises the following steps: collecting organic fertilizer source data in real time from links of organic fertilizer production, circulation, storage and use, performing standardization processing, and performing production batch synchronous set processing and traceability association connection file establishment to generate an organic fertilizer multi-source data traceability association connection file; and carrying out production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection file, carrying out organic fertilizer quality index evaluation analysis and quality index error limitation analysis, carrying out organic fertilizer quality safety abnormity traceability processing and abnormity decision support processing, and generating an organic fertilizer quality safety abnormity problem decision support report. Therefore, the corresponding organic fertilizer quality safety abnormity traceability problem management work is executed. The scientificity and accuracy of organic fertilizer quality safety management can be improved.

The invention discloses a mold parameter adjustment method and system based on a multi-objective optimization algorithm, and relates to the technical field of mold manufacturing. The method comprises the following steps: calling a production database, carrying out production state division, and establishing a production state division result; executing process data acquisition, and synchronizing a process data acquisition result to a data processing center through an MQTT protocol; matching a process data acquisition result with a production state division result by using a data processing center, and constructing a real-time dynamic weight; updating the multi-objective evaluation function by using the real-time dynamic weight, and establishing a configuration optimization priority; adjusting and optimizing the mold parameters according to the multi-objective evaluation function and the optimizing priority, and optimizing the mold parameters based on an adjusting and optimizing result. The technical problems that in the prior art, the die production efficiency is low, and the quality is unstable are solved, the die parameters are adjusted through the multi-objective optimization algorithm and the real-time dynamic weight, and the technical effect of improving the die production efficiency and the manufacturing quality is achieved.

The invention is suitable for the field of photovoltaic technology, and provides a battery assembly and a photovoltaic system, and the battery assembly comprises a plurality of battery pieces, each battery piece comprises a chamfer edge and a straight edge which are parallel to each other, and the plurality of battery pieces are partially overlapped in a first direction to form a battery string. The plurality of battery strings are partially overlapped along the second direction to define a plurality of closed areas, the plurality of battery pieces are partially overlapped to eliminate the piece spacing, and the plurality of battery strings are partially overlapped to define a plurality of closed areas to reduce the string spacing; according to the photovoltaic module, more battery pieces can be arranged in a limited area, the utilization rate of the space area and the power generation power of the module are improved, in addition, due to the formation of the closed area, the light leakage condition of the battery module can be improved, and the power generation efficiency of the photovoltaic module can be improved by utilizing light rays in the intervals of the battery strings; the photovoltaic module is better in overall attractiveness, and the production efficiency can be improved.

The invention discloses an intelligent sensing algorithm and system for crop growth vigor, computer equipment and a medium, and the algorithm comprises the following steps: 1, carrying out the image collection of a farmland, and collecting the spectral data of different wavebands in the farmland; 2, preprocessing the collected images and spectral data, and extracting multi-dimensional feature information of the crops; 3, fusing the extracted multi-dimensional feature information of the crops to obtain comprehensive and accurate crop growth feature information; 4, performing data analysis on the crop growth feature information through deep learning, and constructing an intelligent decision model; through the implementation of the invention, the characteristics and beneficial effects of multi-information fusion, high-precision perception, cost reduction, quality improvement, wide application and the like are realized through the crop growth vigor intelligent perception algorithm, and the method plays an important role in improvement of agricultural production and agricultural product quality.

The invention relates to the technical field of industrial internet, in particular to a smart energy management method and system based on the industrial internet, and the method comprises the following steps: S1, data collection: collecting energy consumption data and environment data in a production process in real time; s2, data fusion: carrying out fusion processing on multi-source data by adopting a data fusion technology; s3, distributed energy optimization: optimizing the energy consumption of each node on the distributed nodes in real time by using an edge computing technology; s4, intelligent load balance optimization: dynamically adjusting the operation load of each industrial production device; s5, multi-level fault prediction: performing fault prediction on the industrial production equipment; and S6, control strategy generation: generating a self-adaptive control strategy in combination with the fault prediction result of the industrial production equipment. According to the method, the lowest energy consumption of the optimization result is ensured, the load is uniformly distributed, local overload and energy waste are avoided, and the overall energy utilization efficiency is improved.

The invention provides a chip production logistics network optimization method and system based on a graph neural network model, and relates to the technical field of chip production logistics optimizing.The method comprises the steps that logistics data of a plurality of process nodes of a chip production line are collected, node criticality scores are calculated, node types are determined, and the node types are determined; constructing a multi-level logistics network diagram comprising a material transmission edge and a priority-based resource competition edge; the method comprises the following steps: dividing logistics sub-networks, determining association strength among the sub-networks, constructing a sub-network state predictor and a cooperative scheduling module, and inputting a network topology structure and node features into a graph neural network for training to obtain an optimization model; and predicting a sub-network state change based on the optimization model to generate a resource allocation scheme and a node scheduling sequence, triggering an adjustment mechanism when a prediction state exceeds a threshold value, re-planning a material allocation and transmission path, and finally converting the scheme into an equipment control instruction and issuing the equipment control instruction to a process node.