453 results about "Process optimization" patented technology

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention provides a production energy efficiency optimization method and system based on industrial big data, and the method comprises the steps: generating an industrial production data set and creating an industrial knowledge graph according to multi-dimensional operation parameters, energy consumption state data and production line constraint information generated by a target factory, mining a causal association relationship among the multi-dimensional operation parameters through ontology reasoning analysis to generate a causal association path; executing a sequential association rule mining operation on the energy consumption state data to obtain association rule mining information of energy consumption fluctuation and operation parameter change, and matching and fusing the association rule mining information and a causal association path to generate a candidate root cause set of energy efficiency abnormality; inputting the candidate root cause set into a bidirectional long short-term memory network, positioning root cause information of energy efficiency abnormity through time dimension relevance modeling and spatial dimension feature reinforcement, and finally generating energy efficiency optimization guidance containing parameter adjustment priority and process optimization suggestions. The accuracy of energy efficiency anomaly root cause positioning and the pertinence of optimization measures are improved.

The invention provides a heat storage heat pump system control method based on a physical information neural network, and belongs to the technical field of heat storage pump system intelligent control. Aiming at the problems that in the prior art, an algorithm is difficult to adapt to dynamic energy consumption requirements, engineering application of a model is difficult due to building space heterogeneity, high-order RC model prediction credibility is weak, engineering feasibility is poor and the like, a solution combining a physical information sequence to sequence neural network technology and a finite-state machine control strategy is provided. On the model level, a 2R2C resistance-capacitance RC model of building temperature change is established, and then a PI-Seq2seq prediction model is proposed based on the physical model. On the control flow optimization level, on the basis of an industrial and commercial time-of-use electricity price policy, an FSM control model is designed, a system state set is defined, parameters and a transfer function are input, and a control rule is constructed in combination with the working period of a building heat pump and the characteristics of a heat storage tank. And finally, energy consumption cost optimization and indoor temperature stabilization under the peak-valley electricity price are realized.

The invention provides a PCB manufacturability intelligent analysis and early warning method and system based on artificial intelligence, and the method comprises the steps: collecting and marking the multi-source time sequence process parameter data in the PCB design and manufacturing process under working conditions, building a dynamic causal graph model with direction and time lag marks through a sliding window and standardization processing by applying a causal discovery algorithm, and carrying out the calculation of the dynamic causal graph model. Dynamic expression of causal relationships among process variables is realized; when manufacturing abnormity is detected, abnormity attribution is carried out by combining a Bayesian back propagation algorithm, high-contribution-degree root dependent variables are screened, the effectiveness of root causes is verified through virtual intervention simulation and statistical test, and finally verification results and a causal mode are stored in a knowledge base to support subsequent rapid matching and reasoning. According to the method, the accuracy, efficiency and interpretability of PCB manufacturing abnormity attribution are improved, and process optimization and preventive intervention are facilitated.

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 provides an MES-based defect detection and quality control optimization method and system. Defect prevention and process dynamic adjustment are realized through a full-process data closed loop. The method comprises the following steps: collecting multi-process data, extracting features such as material batches, process parameters and equipment numbers to construct defect tags, cleaning historical data, constructing time window features, performing multi-class defect classification prediction by adopting an LSTM + attention mechanism model, and analyzing feature contribution degrees in combination with an SHAP value. And setting a multi-stage early warning mechanism, triggering equipment pause and maintenance notification based on a yield threshold, performing graded response according to defect severity, dynamically adjusting equipment parameters, and linking process optimization suggestions. And iterating the model and the rule, returning rework data to generate an optimized sample, mining a defect-parameter association rule in combination with an Apriori algorithm, and updating the equipment health degree evaluation model. The problems of hysteresis quality and staticization of traditional quality control are solved, and an intelligent closed-loop system from defect prediction to process optimization is constructed.

The invention discloses a cement manufacturing equipment knowledge graph construction method and system based on a large language model, relates to the technical field of artificial intelligence, and solves the problems that the prior art is lack of dynamic knowledge updating capability, cannot integrate new knowledge generated by equipment transformation and process optimization in time, and improves the construction efficiency. And the accuracy of the knowledge graph of the cement manufacturing equipment is relatively low. A field data set is generated based on historical multi-source heterogeneous data; generating a tuple generation model and a cement manufacturing equipment knowledge graph based on the field data set; dynamically updating and complementing the knowledge graph of the cement manufacturing equipment based on the multi-source heterogeneous data to obtain a newest knowledge graph of the cement manufacturing equipment; according to the method, the updating and complementing evaluation result is generated based on the newest cement manufacturing equipment knowledge graph, and the existing cement manufacturing equipment knowledge graph is dynamically updated and complemented after new knowledge is obtained each time, so that the accuracy and timeliness of the knowledge graph are improved, and the completeness and decision support capability of the knowledge graph are improved at the same time.

The invention relates to the technical field of numerical control machining and automatic control, in particular to a numerical control machining self-adaptive control system and method based on multi-source data, and the method comprises the steps that FPGA hardware synchronously collects main shaft current, servo current of each feed shaft, cutting vibration and grating ruler pulse, and time bases are aligned and packaged into a machining multi-dimensional state matrix; the multi-physical field error is decoupled through a three-layer cascade machine tool dynamics model, a tool nose contour deviation vector is solved, and servo lag and cutting force deformation factors are fused to construct a dynamic constraint boundary. When the deviation amplitude exceeds the limit, generating a speed feed-forward gain compensation instruction based on an inverse control model to perform instant suppression; when it is monitored that oscillation is eliminated and returns to a steady state, full-time-domain data stream interception logic is triggered, and a process optimization instruction is generated through backtracking. According to the method, the problems of nonlinear time-varying error decoupling and real-time compensation lag under multi-physics field coupling are solved, and the dynamic precision and process stability of numerical control machining under complex working conditions are remarkably improved.

The invention relates to the technical field of ultra-precision intelligent manufacturing, in particular to an ultra-precision machining error compensation method based on multi-modal information fusion, a medium and equipment. The method comprises the following steps: acquiring three-dimensional shape data of a workpiece through an in-situ measurement device, synchronously acquiring temperature field and vibration data, constructing a multi-modal space-time tensor, inputting a physical information neural network to decouple a static geometric error, a time-varying thermal drift error and a dynamic vibration error, and generating a four-dimensional correction tool path to realize reverse compensation. According to the method, physical constraints are embedded through a PINN model, the problem of false thermal drift misjudgment in a traditional method is solved, experiments show that the RMS error of free-form surface machining is reduced to 5 nm or below, and the precision is improved by 70% or above; and meanwhile, error decoupling is endowed with physical interpretability, the small sample generalization ability is enhanced, and a technical basis is provided for process optimization.

The invention provides a Bayesian network granularity prediction method and system based on physical constraints. The method comprises the following steps: collecting historical process parameters and corresponding granularity distribution indexes D10, D50 and D90 of a ternary hydroxide synthesis process; preprocessing the process data to generate an input feature matrix; a Bayesian attention neural network model is constructed, probability distribution and confidence intervals of particle size distribution parameters are output through Bayesian back propagation training, and model output layers correspond to D10, D50 and D90; and embedding physical constraint terms such as range, distribution width, proportion and dynamic process and / or PBE constraint terms in the loss function, and balancing prediction precision and physical rationality and quantifying uncertainty based on the comprehensive loss function. The system comprises a data acquisition module, a data processing module, a model construction module and a prediction module. According to the method, process data, physical constraint and data driving are fused, the accuracy, real-time performance and adaptability of particle size distribution prediction are improved, and intelligent manufacturing and process optimization of the ternary hydroxide precursor are supported.

The invention discloses a ceramic firing performance prediction method based on a GA-BP neural network, and the method comprises the steps: data collection and preprocessing, gray correlation analysis and screening of key process parameters, construction of a GA-BP neural network model, model training and verification, prediction model testing, and firing performance index prediction. By means of unique intelligent algorithm fusion, the problems of high experience dependence, high experiment cost, long period, low efficiency, time consumption, energy consumption, high efficiency and the like of a method for judging the ceramic firing performance through artificial experience in actual ceramic firing production are effectively solved. The technical problem that multi-target collaborative optimization is difficult to realize at the same time in the prior art is solved, remarkable advantages are shown in the aspects of prediction precision, intelligent degree and the like, the requirements of intelligent, green and high-quality ceramic firing process optimization are met, and the method has wide application prospects and important practical value.

The invention relates to a defect analysis and process optimization method based on artificial intelligence multi-mode fusion, and the method comprises the steps: respectively inputting virtual data of a chip or device design layout, an expected process flow and parameters, and an expected electrical performance curve into an image encoder, a process encoder, and an electrical encoder, extracting a corresponding feature vector; performing cross-modal information interaction on the corresponding feature vectors according to a cross attention fusion mechanism; determining a representation image, a defect category, a root cause category and a process parameter correction suggestion; and based on a manufacturing execution system (MES), an electronic design automation (EDA) tool or an equipment interface, process parameters are corrected, and a closed-loop control result from defect analysis to process optimization is obtained. Therefore, the problems of single data dimension, mutual coupling of various defects, influence of background noise, limited machine identification accuracy, overlarge workload of artificial expert judgment and low efficiency are solved.

The invention provides a welding seam quality prediction and process optimization method based on a welding cloud platform. The initial optimization parameters of the weld joint process parameters are determined on the basis of the weld joint quality data and in combination with the preset target requirements, it is ensured that the optimization direction meets the actual production requirements, a scientific basis is provided for real-time adjustment of welding equipment, real-time optimization is conducted through a welding equipment controller according to the initial optimization parameters, and the welding efficiency is improved. The current welding seam quality data in the optimization process are collected in real time and fed back to the welding cloud platform to update the welding seam quality prediction model, quality fluctuation in the welding process is quickly responded, defect expansion is avoided, the welding quality stability is improved, and the welding quality prediction efficiency is improved. The current welding seam quality data in the optimization process is fed back to the cloud platform, and the model is updated, so that the model can continuously adapt to the change of the welding scene, the prediction and optimization accuracy is gradually improved, and the iterative upgrading of the technical scheme is realized.

The invention relates to the technical field of renewable energy source and process industrial intelligent control, and discloses a full-process optimization method and system for synthesizing ammonia through wind-solar hydrogen production, which can realize full-process collaboration and self-adaption to wind-solar fluctuation, and can guarantee system safety and economy in long-term operation. According to the scheme, the method comprises the steps that running state data of a wind-solar power generation system, a hydrogen production system, a hydrogen storage system and a synthetic ammonia system are collected in real time, and the collected data are preprocessed; establishing a device-level adaptive boundary model, and calculating a dynamic safe operation boundary of the key device; inputting wind-solar power prediction of a future time domain, performing rolling optimization based on the multi-objective optimization model according to constraint conditions, and generating load setting instructions of each system; and executing the load setting instruction, collecting real response data of the system, comparing a deviation between a real response and a predicted response, calculating a multi-dimensional deviation index, and when the deviation exceeds a set threshold value, performing feedback calibration on the equipment-level adaptive boundary model.

The invention discloses a micro-channel heat exchanger additive manufacturing process prediction optimization method based on meta-learning, and the method comprises the steps: defining key geometric parameters and an SLM process parameter space through building a micro-channel parameterized model; multi-size sample pieces are prepared through orthogonal experiment design, and morphology data are obtained through high-precision detection equipment and an image recognition method; a meta-learning model based on an improved model-independent meta-learning algorithm MAML + + is constructed, finite element simulation data is fused for training, and process prediction under small sample cross-size experimental data is achieved; establishing an intelligent process optimization model by adopting a depth deterministic strategy gradient (DDPG) algorithm, and realizing autonomous optimization of process parameters by adopting a multi-scale reward function; finally, a closed loop verification system is constructed to achieve intelligent adjustment and optimization of process parameters, through collaborative optimization of meta learning and reinforcement learning, the number of process development experiments of new structure sizes is greatly reduced, and the technical problems that in micro-channel additive manufacturing, the process optimization period is long, and cross-size adaptability is poor are solved.

The invention relates to the technical field of data processing, in particular to an analysis decision-making method, device and equipment based on coal quality data and a medium. According to the method, the coal quality time sequence knowledge graph fused with the time attribute is constructed, so that traditionally isolated test, production and purchase data are systematically integrated, and the fundamental transformation of coal quality management from scattered information to associated knowledge is realized. On the basis, a stable or fluctuating coal quality group is automatically identified through clustering analysis, and an influence path of process parameters on quality is accurately positioned by utilizing association mining, so that a direct basis is provided for process optimization. Meanwhile, the system can carry out dynamic early warning and intelligent matching recommendation based on the atlas, the core targets of improving the coal quality stability, optimizing the production cost and assisting scientific decision making are finally achieved, and the comprehensive benefits of coal production and utilization are comprehensively improved.

The invention discloses a precise aluminum alloy casting parameter intelligent decision-making system and method based on digital twinning, and relates to the technical field of intelligent manufacturing and casting process optimization. The method comprises the steps of initializing a multi-physics field coupling digital twin model, collecting and preprocessing data in real time, dynamically comparing and correcting the model, constructing a multi-objective function for optimization, transmitting optimized process parameters to an actuator, archiving whole-process data and updating a knowledge base. The system comprises a multi-physics field coupling digital twinning construction module, a real-time data perception and acquisition module, a process parameter intelligent decision module, a closed-loop control and execution module and a data management and knowledge base module. By the adoption of the technical scheme, the nonlinear interaction effect in the casting process can be accurately represented, the consistency of a virtual model and a physical entity is remarkably improved, the process adjustment response time is shortened, the comprehensive yield of castings is increased, and the self-learning and generalization ability is achieved.

The invention provides a display backboard stamping process optimization method and system, and relates to the technical field of process optimization. According to the method, multi-area differential mesh division is carried out on a plate finite element model, fine meshes are adopted for a high-strain small-curvature area and a high-strain area, rough meshes are adopted for a trimming allowance area, and medium meshes are adopted for other areas. The deformation behavior of the key area is accurately captured, and the reliability of simulation prediction is remarkably improved under the same computing resource. And then, reverse compensation iterative correction and rapid convergence are performed on the initial molded surface based on a springback prediction result to obtain an optimized molded surface model, and source control is directly performed for springback errors, so that the number of times and period of mold testing which traditionally depends on experience are greatly reduced. According to the method, through linkage of simulation and real-time sensing data, dynamic optimization and closed-loop control of the process window are achieved, and the forming quality and production stability of the display backboard are effectively improved.

The invention discloses a paint spraying process autonomous optimization method and system, a medium and a product, and relates to the field of process optimization. The method comprises the steps of inputting multi-mode sensing data in a paint spraying process into a pre-trained process diagnosis model, and outputting semantic diagnosis description of a current paint spraying process state; according to the semantic diagnosis description, the workpiece information of the to-be-sprayed workpiece and the target paint film quality standard, a control strategy scheme used for eliminating defects or tendencies is generated; and inputting the control strategy scheme into the digital twinborn model for simulation, and based on a simulation prediction result, performing rolling optimization on a collaborative adjustment instruction set in the control strategy scheme through a dynamic optimizer to form a final optimization strategy. By implementing the technical scheme, autonomous optimization of the paint spraying process is achieved.

The invention is applicable to the technical field of process optimization, and particularly relates to a method for generating an anodic oxidation current density adjustment scheme based on mutual information entropy, which comprises the following steps of: obtaining process parameters in real time and carrying out adaptive filtering processing to ensure the accuracy and effectiveness of key characteristic parameter extraction and provide a high-quality data basis for optimization. Determining key feature parameters according to the process parameters and a mutual information entropy value of a preset optimization target; screening current density data to be optimized according to the key parameter influence weight; a to-be-selected current density curve matched with a preset oxidation quality standard is extracted from the to-be-optimized data, the deviation between a current value and the curve is calculated to serve as a to-be-selected optimization parameter, and an abstract standard is converted into a concrete basis. A self-adaptive adjustment scheme is generated in combination with to-be-selected parameters, process changes are dynamically adapted, fluctuation caused by static setting is avoided, high consistency of current density control precision and oxidation film quality stability is achieved, the optimization period is shortened, and production efficiency is improved.

The invention relates to the technical field of die casting intelligent manufacturing, and discloses an online monitoring and process compensation method and system for residual stress and deformation of a die casting. The method comprises the following steps: embedding a distributed temperature-stress composite sensor array in a mold cavity, and synchronously acquiring temperature and stress signals; after the signal is purified, a space-time correlation matrix is constructed to quantify a thermal-mechanical coupling relation; identifying a stress distribution mode through a support vector machine model, and positioning a fluctuation abnormal region; analyzing a defect mechanism based on mutual information and Granger causality test, and calculating a pore formation probability in combination with fluid dynamics simulation data; matching the pore high-risk location with a historical crack correlation model to generate a crack prediction index; and performing inversion optimization on the mold filling speed and pressure parameters by using the potential quality hazard evaluation function. Real-time monitoring of residual stress and deformation in the die-casting process, defect dynamic traceability and process online optimization are achieved, and air hole and crack defects are effectively restrained.

The invention discloses a green low-carbon intelligent assessment method for the welding influence of a steel structure, and relates to the technical field of green construction, and the method comprises the steps: collecting multi-source heterogeneous data in the welding process of the steel structure in real time, and calculating and visualizing a carbon emission dynamic value in the welding process; performing parameter extraction on the multi-source heterogeneous data, and inputting the multi-source heterogeneous data into a pre-trained defect identification model to obtain a weld defect identification result; and on the basis of the carbon emission dynamic value and the weld defect identification result, with minimization of carbon emission and residual stress as targets, iterative simulation is conducted in the twin welding model, and a low-carbon-low-stress process parameter window is obtained through optimization. The technical problems that a traditional steel structure welding related evaluation mode cannot give consideration to the low-carbon requirement and the mechanical property guarantee, the evaluation result is one-sided, and the parameter adaptability is insufficient are solved, and the technical effects that welding low-carbon and low-stress collaborative guarantee is achieved, and green low-carbon evaluation and process optimization of steel structure welding are more accurate and efficient are achieved.

The invention relates to a deep learning-based stretch film manufacturing process optimization method and system, and the method comprises the following steps: S1, obtaining and preprocessing an original process environment, an equipment state and operation record data, and obtaining a multi-modal data set; s2, according to the preprocessed multi-source heterogeneous data set, performing multi-modal deep learning based on a deep learning model to obtain feature representation output by the deep learning model; s3, process-quality relation modeling is carried out based on feature representation output by the deep learning model, and a process-quality causal relation graph is obtained; s4, according to the process-quality causal relationship graph, the quality target and a preset constraint condition, intelligent optimization is carried out on process parameters under multi-target constraint, and an optimal process parameter combination is obtained; and S5, closed-loop control is carried out according to the optimal process parameter combination, and dynamic optimization and adaptive adjustment are carried out according to real-time data.

According to the intelligent alloy quality diagnosis system provided by the invention, a collaborative optimization architecture integrating data acquisition, processing, analysis and service application is constructed, so that multi-source data fusion and intelligent decision-making in the whole alloy smelting process are realized, and quality diagnosis, fault early warning and process optimization can be organically integrated; and an optimization strategy is fed back to a production process control system in real time to form closed-loop control, so that the quality stability of an alloy product, the adaptive capacity of the technological process and the overall optimization level of production benefits are remarkably improved.

The invention provides an intelligent control method and system for steel pipe galvanizing process equipment, and relates to the technical field of production intelligent control. The method comprises the steps of collecting a process operation original data set of galvanized steel pipe production and constructing a plurality of process order groups, generating a plurality of order state sequences and identifying a plurality of order fragments of each order state sequence, and constructing a qualified sample set and a risk sample set, extracting a plurality of statistical features respectively corresponding to each order fragment in the qualified sample set and the risk sample set, performing order type division on the plurality of order fragments, constructing a risk order set and a stable order set corresponding to the plurality of order fragments, traversing the qualified sample set and the risk sample set, and obtaining a stable order set; a plurality of candidate recovery paths of each order state sequence are constructed and fused to generate a plurality of process optimization motif libraries, and control optimization of the steel pipe galvanizing process equipment is achieved based on the process optimization motif libraries. The intelligent control level of the steel pipe galvanized product production process is improved.

The invention discloses a galvanized steel pipe surface characteristic prediction method based on process modeling, and relates to the technical field of intelligent manufacturing process optimization, and the galvanized steel pipe surface characteristic prediction method comprises the following steps: basic data acquisition, process state characteristic modeling, dynamic process diagram modeling, physical data dual-drive mixing and surface characteristic prediction. Adopting a derived feature construction method based on an embedded mechanism kernel equation to form process intermediate state variable enhanced data; by establishing a dynamic process diagram model, the state transfer and evolution process between steel pipe procedures is represented; a residual learning mechanism of a physical model and a data-driven model is combined, physical consistency constraint is introduced, a dual-drive fusion prediction framework is constructed, and multi-target surface characteristic hybrid prediction is realized; according to the method, high-precision and multi-target prediction of the characteristics such as the surface thickness, the adhesive force and the smoothness of the galvanized steel pipe is achieved, and the prediction stability and the physical interpretability under the complex galvanizing process are effectively improved.

The invention belongs to the technical field of process optimization control, and particularly relates to a machining process optimization control method for a supporting shaft of a drive axle of a mining truck. The problems that existing machining depends on empirical parameters, quality fluctuation is large, and traditional PID control cannot dynamically adapt to working conditions are solved. The method comprises the following steps: collecting control parameters such as cutting speed and feeding amount and corresponding supporting shaft mass fractions, and constructing a sample data set; preprocessing the data to eliminate abnormal values and complement missing values; calculating influence weights of the parameters on quality, distinguishing key and non-key parameters, and identifying sensitive intervals of the key parameters; solving an optimal parameter combination by using a non-dominated sorting genetic algorithm in combination with the sensitive interval; and the optimal parameter is used as a PID set value, and the improved PID controller dynamic setting fused with the real-time quality deviation is designed. According to the method, experience dependence is replaced by data driving, the machining quality stability and consistency are improved, and the method adapts to complex working conditions.

The invention provides a glass bottle lightweight forming process optimization system, belongs to the technical field of glass product production, and solves the technical problems that the existing lightweight glass bottle is low in forming efficiency, process parameters cannot be dynamically adapted and the like. Comprising a hardware execution module, a global data sensing module, a digital twinning optimization module, an enterprise-level cloud management module and a man-machine collaborative operation and maintenance module. The global data sensing module comprises an equipment three-dimensional information scanning and collecting unit, an equipment working state sensor network unit, an energy consumption and resource monitoring unit, a data transmission unit, a data storage unit and a carbon footprint accounting unit; the digital twinning optimization module comprises a virtual simulation unit, a software simulation optimization unit and a dynamic feedback optimization unit. According to the invention, an integrated process optimization system integrating an intelligent algorithm, digital twinning, green carbon reduction, flexible production, full-dimensional detection, predictive maintenance and man-machine collaboration is adopted, and efficient, accurate, green and flexible production of lightweight glass bottles is realized.

The invention belongs to the field of industrial process control, and relates to an evaporative crystallization process optimization method and system based on multi-source data fusion. The method comprises the following steps: collecting multi-source time sequence data of an evaporative crystallization system and constructing an evaporation load index; training a working condition decoupling model based on historical health data, and predicting a theoretical health heat exchange coefficient in real time by using the model; a pure scaling loss signal is extracted by calculating the residual error of the theoretical healthy heat exchange coefficient and the real-time observation heat exchange coefficient, and the future trend of the signal is predicted; and constructing a total average loss rate function containing accumulated operation loss and shutdown opportunity loss, and solving a minimum value of the function to determine an optimal cleaning time point. The problem that in the prior art, process loss and physical loss cannot be distinguished, and consequently cleaning decisions are not accurate is solved, and the operation efficiency and economical efficiency of the system are remarkably improved.

The invention discloses a multi-algorithm collaborative optimization water turbine key component additive manufacturing method, and relates to the technical field of additive manufacturing. According to the method disclosed by the invention, a complete intelligent manufacturing system is constructed: in a data acquisition stage, a high-precision laser scanning technology (the precision reaches 0.01 mm) is adopted to realize accurate three-dimensional reconstruction of a damaged area; in the modeling analysis link, thermodynamic behaviors in the restoration process are accurately predicted through finite element simulation of multi-physics field coupling; in the aspect of process optimization, an adaptive differential evolution algorithm, an improved NSGA-II multi-objective optimization algorithm and deep reinforcement learning (DRL) are innovatively and organically combined to form a full-process intelligent decision-making system for coverage deviation analysis, path planning and parameter optimization. Particularly, due to introduction of a deep reinforcement learning algorithm, millisecond-level dynamic adjustment of key process parameters such as wire and powder feeding speed and laser power is achieved, and the stability and the material utilization rate of the manufacturing process are greatly improved.