1834 results about "Smart manufacturing" patented technology

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 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 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 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 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 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 relates to the technical field of industrial process automatic control, and discloses an intelligent manufacturing flexible production line cooperative beat control method. The method is used for solving the technical problem that dynamic adjustment of the real-time rhythm of the flexible production line is difficult to achieve under the extreme uncertainty condition in a traditional method. The method comprises the following steps: firstly, collecting operation data and external uncertainty signals of each station of the flexible production line, and carrying out preliminary integration through a central control module; classifying the data, and distinguishing internal deviation and external interference according to priorities; constructing a dynamic mapping relation based on a classification result, and associating uncertain factors to beat parameters to form a temporary adjustment framework; correcting station beat parameters step by step by using a frame, and transmitting an instruction from upstream to downstream; synchronously monitoring a response state during correction, and returning information to update a mapping relation; the updated mapping is applied to acquisition and processing of the next period to form closed-loop iteration; according to the method, the problem of real-time rhythm dynamic adjustment under the extreme uncertainty condition is solved.

The embodiment of the invention provides a dynamic flexible workshop scheduling method and related equipment, and belongs to the technical field of industrial intelligent manufacturing and production scheduling. The method comprises the steps of obtaining current state information of a workshop in response to a scheduling event; inputting the state information into a pre-trained scheduling decision model for processing; the model outputs state feature embedding through a two-stage feature extraction network: in the first stage, feature extraction is performed on a heterogeneous disjunction graph by using a graph attention network, and in the second stage, expert output is dynamically fused through a hybrid expert model; and finally, outputting and executing a process-machine pairing decision by the actor network. Wherein the model is trained by adopting a near-end strategy optimization algorithm based on multiple commentators; and a meta-learning framework is integrated during training, so that the model obtains strong generalization ability. According to the method, the defects of an existing scheduling method in the aspects of state characterization, multi-target tradeoff and environmental adaptability are effectively overcome, and the efficiency, quality and robustness of dynamic flexible workshop scheduling are remarkably improved.

The invention belongs to the related technical field of product detection, provides a multi-view zero sample anomaly detection method and system based on cross-modal prompt reasoning, and aims at solving the problem of multi-view zero sample anomaly detection by constructing core technologies such as multi-view pose estimation and alignment, static-dynamic prompt collaboration, vision-language progressive fusion, feature space semantic enhancement and the like. And a set of end-to-end anomaly detection and reasoning system is formed. Particularly, a collaborative mechanism of a dynamic learnable prompt pool and a static attribute prompt library is designed, deep fusion of prompts is realized through cross attention, and multi-view feature compression and semantic decoding are performed by adopting a visual angle self-adaptive hybrid expert model. Zero sample anomaly detection and visual question and answer performance is further improved on multiple industrial public data sets, and the method can be widely applied to industrial precision part quality inspection, intelligent manufacturing and other complex scenes needing high-precision and multi-view perception and semantic reasoning.

The invention discloses a multi-modal measurement system and method of a three-coordinate measuring machine, relates to the cross technical field of precision measurement, computer vision and intelligent manufacturing, and is used for solving the problems of low automation degree, strong subjective dependence and lack of assembly performance prediction capability in automobile covering part characteristic line detection. According to the method, point cloud data and positioning coordinates are synchronously collected through a multi-mode sensor, and a fusion point cloud sequence is generated through time sequence synchronization; constructing a high-precision three-dimensional point cloud model through noise filtering, registration and curved surface reconstruction; automatically extracting a feature line path based on curvature analysis and generating an analysis section; calculating a sharpness index through contour fitting, and generating a quality evaluation coefficient in combination with a CAD ideal model contour tolerance deviation; and finally, defects are identified through virtual assembly analysis, a sensor remeasurement path instruction is generated, and a quality report is output in combination with a quality coefficient, so that the detection precision, the efficiency and the assembly quality prediction capability in the measurement process of the three-coordinate measuring machine are improved.

The invention discloses a thermal spraying coating thickness online optimization control method based on digital twinning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: synchronously collecting multi-modal online observation data and process control observation data in a thermal spraying process, carrying out the time-space alignment, and generating a multi-modal observation set; performing adaptive optimization of multi-modal uncertainty gating on the candidate control set according to the credible thickness field confidence distribution map to generate a control instruction sequence; and the control instruction sequence acts on the thermal spraying process, response feedback data are continuously collected in the execution period, a new multi-mode observation set is generated according to the response feedback data, and real-time closed-loop optimization control is kept. According to the method, the thickness field with high credibility and the credible thickness field confidence distribution diagram can be generated through forward generation and uncertainty quantification of physical guidance, and depth coupling of multi-modal data and physical rules and accurate mapping of uncertainty are achieved.

The invention discloses a temperature and humidity cooperative control method for an air source heat pump drying room, and relates to the technical field of energy conservation, environmental protection and intelligent manufacturing. The method comprises the following steps: acquiring gas heat state parameters and material and environment characteristic parameters in an air source heat pump drying room, and constructing a multi-source state vector; calculating a dynamic response coefficient group and a material evaporation influence coefficient based on continuous sampling data, and establishing a cross response model; a control compensation signal is generated and corrected according to the disturbance estimator, and the comprehensive control quantity is input into an execution module of the air source heat pump system; a dynamic response coefficient group and a lag correction parameter are updated through a periodic feedback data set, and dynamic coordination between temperature and humidity variables is achieved; the response stability of temperature and humidity control can be maintained under the condition that the material moisture content, the stacking density and the external environment condition change, system oscillation and energy consumption deviation are reduced, and the energy efficiency level of the air source heat pump drying process and the product drying uniformity are improved.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

The invention discloses an edge cloud collaborative industrial equipment health management and predictive maintenance method, and the method comprises the steps: forming a closed-loop system through four deep coupling steps: edge adaptive fusion perception, cloud knowledge enhancement reasoning, edge cloud collaborative self-evolution prediction, and risk-driven maintenance decision. The edge end dynamically adjusts a sensor acquisition and feature fusion strategy according to the equipment health state, the cloud end performs causal reasoning and situation evaluation by using a physical-data mixed knowledge graph, and the edge cloud collaboratively optimizes a prediction model through federal element learning and bidirectional knowledge distillation; maintenance decision is based on multi-objective optimization, the actual effect is fed back to the perception and prediction link, the method achieves accurate assessment of the equipment health state, early fault prediction and maintenance intelligent decision, the availability of the equipment is remarkably improved, the maintenance cost is reduced, and core technical support is provided for intelligent manufacturing.

The invention relates to the technical field of intelligent manufacturing, and discloses an industrial processing production line adaptive control method and system based on reinforcement learning. The method comprises the following steps: collecting a processing monitoring signal and environment state data to construct a comprehensive processing state vector; deducing the comprehensive processing state vector by using a preset depth time sequence learning model to obtain an error evolution trend vector; determining an initial strategy parameter and a strategy matching state according to the error evolution trend vector; according to the strategy matching state and the initial strategy parameter, parameter iteration optimization based on a preset comprehensive error prediction model is carried out, and a target compensation control quantity is obtained; machine tool dynamics constraint verification is carried out according to the target compensation control quantity, and a final compensation instruction set is obtained; and performing instruction mapping and execution according to the final compensation instruction set to obtain processing feedback data, and performing reinforcement learning updating on the model based on the processing feedback data. According to the invention, high-precision self-adaptive compensation under complex dynamic working conditions can be realized.

The invention discloses a scraper conveyor middle trough production line operation state monitoring method based on digital twinning, and belongs to the technical field of industrial intelligent manufacturing. The method comprises the following steps: constructing digital twin bodies in one-to-one correspondence with physical production line elements; establishing a real-time data driving channel between the digital twin and the physical elements; based on the channel, driving the digital twin to synchronously map the real-time operation parameters, and generating a virtual operation state of the production line; based on the virtual operation state, performing time sequence deduction on the processing flow in the digital twin according to the processing technology logic and the equipment performance parameters to obtain a production line pre-estimation state of a future time point; and comparing the parameters in the pre-estimated state with a preset threshold range to generate prediction information. According to the invention, real-time data driving and bidirectional interaction of the physical production line and the virtual model are realized, the problems of data isolation and feedback lag of a traditional monitoring mode are overcome, the prediction capability is provided, and the operation reliability and intelligent management of the production line are effectively improved.

The invention relates to the technical field of intelligent manufacturing, and discloses a multi-objective optimization method and system for an aviation airborne product, so as to improve the overall performance of the aviation airborne product. The method comprises the steps of obtaining an initial population based on a series of initial model files of a target aviation airborne product, and determining a constraint condition and a target function of the target aviation airborne product; the constraint condition comprises the range of each iteration parameter; in each iteration process of obtaining the global optimal Pareto frontier according to the constraint condition, performing non-dominated sorting and congestion degree calculation on the tth generation of population through an algorithm, selecting individuals with excellent performance from a sorting result, and applying gradient-guided Gaussian disturbance to generate a (t + 1) th generation of new solution; the tth generation of population comprises cross progenies of the (t-1) th generation of population; and when the target iteration termination condition is met, iteration is terminated, and the global optimal Pareto front is obtained.

The invention provides a servo press control system and method based on a time-sensitive network, and relates to the technical field of intelligent manufacturing and industrial automation control, and the system comprises a time-sensitive network TSN which is used for providing global time synchronization and deterministic data transmission; a main controller of the multi-axis servo control unit is used for issuing a motion control instruction and a feeding and discharging control instruction through a TSN. A robot controller in the robot feeding and discharging unit is used for driving an industrial robot to execute plate taking and placing operation under the action of the feeding and discharging control instruction. The visual monitoring unit comprises an industrial camera accessed to the TSN and is used for collecting images and transmitting image data to the edge computing node through the TSN; the edge computing node is used for processing the image data to generate a compensation instruction and feeding back the compensation instruction through the TSN; and the upper monitoring system is used for subscribing real-time data published by the edge computing nodes, so that the control range of the servo pressure control system is expanded, and the convenience of data transmission and interaction is improved.

The invention relates to the technical field of intelligent manufacturing and industrial digital twinning, in particular to a latex paint packaging full-process digital twinning method and system. The method comprises the following steps: collecting multi-source data in real time through a sensor network, and preprocessing to form a standard data stream; a coupled digital twinborn model fusing material characteristics and process parameters is constructed, and real-time prediction is realized by combining physical laws and data driving residual correction and online parameter self-adaption; constructing a multi-scale abnormal index by comparing model output with actual data, identifying an abnormal mode by adopting PCA, calculating local and overall health indexes, and predicting a trend; and generating a preliminary control strategy according to the abnormal mode and the health trend, forming closed-loop adaptive control in combination with health state weighted optimization and look-ahead adjustment, and issuing execution and feeding back an update strategy. According to the invention, the quality control qualification rate and the energy-saving efficiency of the whole process of emulsion paint packaging are improved.

The invention relates to the technical field of intelligent manufacturing and production optimization scheduling, and particularly discloses a thermal fuse flexible production-oriented order scheduling resource scheduling optimization method, which comprises the following steps of: generating a structured vector by extracting inherent and dynamic characteristics of an order, and constructing a production scheduling dynamic environment model by fusing a device resource state time sequence; the order comprehensive process switching cost and the resource constraint tightness are calculated, order clusters are divided through multi-target constraint clustering, and a pre-arrangement sequence is generated; key resource conflict early warning is completed through time sequence prediction; constructing a multi-target scheduling model by taking the order cluster as a unit, and generating a look-ahead window scheme by combining a rolling optimization solidification plan baseline; and finally, on the basis of a real-time execution data updating model, through dynamic priority preemption and local rescheduling adjustment when abnormity occurs, the problems of high cost of small-batch mixed arrangement, conflict identification lagging, easy damage of a thermosensitive process steady state and poor compliance constraint adaptation are solved, and collaborative optimization of the scheduling efficiency, the production yield and the compliance is realized.

The invention discloses an intelligent manufacturing workstation safety control system oriented to man-machine cooperation, and belongs to the technical field of intelligent manufacturing and man-machine cooperation. The system comprises a multi-modal sensor array, a personnel state sensing module, a robot state monitoring module, a dynamic safety area adaptive calculation module, an intention prediction and risk assessment module, a cooperative control decision module and an execution feedback module. The dynamic safety area self-adaptive calculation module constructs geometric representation of a man-machine interaction space based on a topological manifold theory, and calculates a dynamic safety boundary in real time; the intention prediction and risk assessment module is fused with multi-source information to predict a future trajectory of a person and assess a collision risk; the cooperative control decision module performs multi-objective optimization with safety and efficiency as double objectives to generate an optimal adjustment strategy, the system realizes prospective safety control based on intention prediction, the cooperative efficiency is improved by 25%-45% on the premise of guaranteeing safety, and the working safety accident rate is reduced to be close to zero.

The invention discloses a full-link quality tracing method driven by batch of a process card, relates to the technical field of intelligent manufacturing and quality tracing, and aims to automatically generate a dynamic process card batch number through PDA code scanning and work reporting and realize full-process seamless association and tracing from material picking, film spraying, bag making to inspection. According to the method, a defect propagation path prediction mechanism is innovatively introduced, all associated batches are automatically locked when a certain batch is detected to be bad based on the BOM topological relation, the traditional tracing time is compressed from the hour level to the second level, and the quality problem response speed and accuracy are greatly improved. Meanwhile, the system intelligently recommends the optimal reworking path in combination with historical reworking data, the reworking time can be reduced by about 40%, and the production efficiency and the resource utilization rate are remarkably improved. According to the technology, real-time acquisition, dynamic analysis and intelligent decision support of quality data are realized, and core technical support is provided for an enterprise to construct an efficient, transparent and traceable full-link quality management system.

Systems and methods for intelligent data sanitization employing PCM and AI / ML are provided. The idea uses AI / ML to detect specific facts that needs sanitization rather than full properties in incoming records. Data sanitization is optimized using this focused method, saving computational resources. To properly manage changing data volumes, PCM shifts between Logical 0 and Logical 1 states. Logical 0 processes smaller volumes with high resistance and low conductivity, while Logical 1 processes large volumes with low resistance and high conductivity. The AI / ML module organizes and directs data to maximize resource and processing efficiency. The PCM processes data in-memory and directly overwrites, eliminating erasure. AI / ML and PCM integrate to sanitize data quickly, efficiently, and securely, improving system performance and data integrity without a central repository. The system dynamically adjusts to changing data patterns, protecting and optimizing data.

The invention provides a visual positioning and grabbing force control method for a palletizing robot in the intelligent manufacturing industry, and relates to the technical field of intelligent manufacturing. The multispectral visual positioning module at least comprises a visible light imaging unit, a near-infrared imaging unit and a structured light imaging unit; s2, on the basis of the material three-dimensional information, fitting a material motion track through a motion trend prediction model, and calculating a target grabbing position in advance; through visible light + near infrared + structured light multispectral fusion, the problem of interference of dust and haze on visual positioning is solved, and the positioning error is reduced to be within 1mm in combination with an adaptive weight algorithm; and meanwhile, the grabbing position is calculated in advance through the motion trend prediction model, the dynamic positioning response speed is increased, compared with the prior art, the performance is higher, and the high-speed dynamic stacking requirement is met.

The invention provides an unmanned aerial vehicle distribution task scheduling optimization method considering an endurance state, and belongs to the field of intelligent manufacturing. The method comprises the following steps: aiming at the problems of takeoff sequence, task allocation and endurance state change of an unmanned aerial vehicle in a distribution task, constructing a power consumption model based on flight stage division; under constraint conditions of unmanned aerial vehicle electric quantity, loading capacity, safe flight height and the like, converting the scheduling scheme into a three-layer integer sequence including a take-off sequence, a task allocation sequence and an execution sequence for expression and optimization; a task interruption and battery replacement decision-making mechanism and a battery replacement station selection cost function under the condition of insufficient electric quantity are introduced, and a swarm intelligence algorithm is adopted for solving, so that the optimization target of minimizing the total task completion time, the total battery replacement frequency and the flight distance standard deviation is achieved.

The invention relates to an intelligent process research and development system and method based on AI model closed-loop iteration, and belongs to the field of intelligent manufacturing and artificial intelligence. In order to solve the problems that in existing process research and development, due to the fact that links of knowledge analysis, model optimization and experimental verification are separated, the efficiency is low, and manual experience is highly relied on, the method comprises the steps that unstructured process knowledge is automatically analyzed into machine executable scripts, and new process parameters are recommended according to experimental data through a process optimization model. The core of the method is that the system automatically feeds back verification results of new parameters, updates the verification results to a data set, and drives a model to carry out next round of iterative training, so that an automatic closed loop from knowledge to the model to an experiment is constructed, and continuous self-evolution and efficient convergence of process research and development are realized.

The invention relates to the technical field of crossing of artificial intelligence and intelligent manufacturing, discloses a position self-adaptive AI regulation and control method and system for continuous heat treatment of a fastener, and aims to solve the problems of non-uniform temperature field, high hardness dispersion, increase of quenching cracking rate and the like caused by lack of position perception and dynamic compensation lag in the prior art. According to the method, in-furnace coordinates of fasteners are tracked in real time through a high-speed visual positioning device, a thermal demand prediction neural network model with space-time convolution mixed with a gating circulation unit is constructed in combination with the conveying speed, thermal environment parameters and material characteristics, and an instantaneous thermal power instruction is output; the system comprises a high-speed visual positioning module, a motion state monitoring module, a thermal environment sensing module, a thermal demand prediction module and the like, and drives a high-density partitioned infrared heating array to realize millisecond-level local thermal compensation. According to the method, the temperature deviation of the junction of the temperature zones can be controlled within + / -8 DEG C, the hardness dispersion and the cracking rate are remarkably reduced, and the heat treatment consistency and the finished product yield are improved.

The invention discloses a product production process scheduling method and system based on intelligent manufacturing, and belongs to the technical field of intelligent manufacturing. According to the method, rapid local rescheduling is realized through fault influence domain identification, a three-level response strategy and real-time equipment health assessment. When an equipment fault signal is detected, a to-be-executed task on the fault equipment is identified, a subsequent task chain is traced through the technological process dependency relationship, the boundary of a fault influence domain is determined, and the influence domain relates to 10%-20% of tasks in the whole production plan. And selecting a corresponding response strategy according to the predicted repair time, adopting a task delay strategy when the predicted repair time is less than 30 minutes, performing local redistribution on tasks in the influence domain when the predicted repair time is less than 30 minutes to 2 hours, and expanding the influence domain to the whole process for comprehensive optimization when the predicted repair time is more than 2 hours. Meanwhile, monitoring data such as equipment vibration, temperature, current and rotating speed are collected to calculate a health degree score, and 5%-10% of productivity redundancy is reserved for high-risk equipment.

The invention relates to the technical field of flexible job shop scheduling in intelligent manufacturing and production scheduling, in particular to a flexible job shop scheduling optimization method for solving and adjusting resource constraints. Comprising the steps of initializing parameters and randomly generating an initial population; sequentially using a crossover operator and a mutation operator to evolve the current population; executing a hybrid decoding strategy on the current population; sorting the individuals in the current population from small to large according to the maximum completion time to form an elite population, and updating the elite population through question specific local search; and judging whether an evolution condition is met or not, if so, executing CP-based mathematical evolution, and outputting a final solution when the running time reaches the total running time. The method has the positive effects of reducing the resource waiting time, improving the machine utilization rate and improving the resource utilization efficiency and the scheduling performance of the whole workshop production.

The invention belongs to the technical field of man-machine cooperation. The invention provides a self-adaptive man-machine cooperation task scheduling method for an intelligent manufacturing scene. According to the embodiment of the invention, an extensible context state encoder is designed, scene features, task dependency relationships, resource states and the like are fused based on a graph neural network, and multi-dimensional constraint state representation is generated; an experience-driven task distributor is constructed, a cross-scene cooperation mode is mined through historical scheduling knowledge, and an initial distribution scheme is generated to reduce the search space dimension; an autonomous learning optimizer based on element reinforcement learning is developed, and field differences and scale elastic changes are quickly adapted through multi-scene element strategy pre-training and online lightweight fine tuning.