163 results about "Production optimization" patented technology

The invention relates to the technical field of abalone peptide production, and discloses an abalone peptide production process optimization and intelligent decision-making method and system, and the method comprises the steps: collecting production data through a multi-source sensor, and extracting features through a convolutional neural network; the genetic algorithm optimization model is combined with an adaptive mechanism and a multi-target fitness function to generate a process parameter optimization scheme; and the multi-objective production optimization model outputs optimal production process parameters by means of an improved particle swarm algorithm. In the hierarchical intelligent decision-making model, the planning layer realizes global resource allocation, the scheduling layer adjusts a process in real time based on a Bayesian network and the like, and the operation layer controls equipment through fuzzy logic and constructs quality closed-loop control. According to the method, the problems that parameter control depends on experience, data processing is difficult, optimization is one-sided, decision making is not intelligent and the like in traditional production are solved, data can be accurately processed, the process is optimized, decision making is intelligent, production efficiency, the raw material utilization rate and product quality are effectively improved, cost is reduced, and the application prospect is wide.

A method and device for optimizing comprehensive production of an oil reservoir. The method comprises: on the basis of historical production data, performing regional level recognition, single-well level recognition, and production optimization model recognition, and updating oil reservoir dynamic recognition; on the basis of the historical production data, using the following steps to perform cyclic historical fitting on model parameters of a plurality of production optimization models: performing historical fitting on the model parameters, and performing well-to-well communication relationship calibration on the plurality of production optimization models; determining, on the basis of the updated oil reservoir dynamic recognition, a constraint condition corresponding to each production optimization model, solving an objective function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; obtaining a comprehensive development regulation scheme of an oil reservoir; and updating the oil reservoir dynamic recognition on the basis of a regulation effect obtained by real-time monitoring of a development operation.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention relates to the technical field of automobile part manufacturing, and discloses an automobile injection molding part production process control system and method, and the system comprises the following modules: a data collection module which is used for collecting technological parameters, molds, raw materials and equipment operation original data, attaching timestamps, and storing the data in a database; the process parameter prediction module is used for reading original data to construct a time sequence data set, inputting the time sequence data set into a TFT model to obtain a pre-training model, and predicting a short-term process parameter fluctuation range in combination with current production working condition parameters; and the quality risk index acquisition module is used for inputting the process parameter data and the mold data into a quality risk index calculation formula to obtain a quality risk index. Through the system, data-driven comprehensive production optimization is realized, the process control accuracy and adaptability are improved, the quality control scientificity and reliability are enhanced, the intelligent level of the production process is improved, the production efficiency is effectively improved, the defective rate is reduced, and the product quality is stabilized. The problem that process control lacks system intelligence is solved.

The invention provides a graphene antibacterial polyester yarn production control platform based on intelligent algorithm optimization, and relates to the technical field of automation control, and the graphene antibacterial polyester yarn production control platform comprises the steps that a quality control analysis module obtains a production operation parameter set of graphene antibacterial polyester yarns for quality control analysis, and sets product performance indexes; the optimization model building module is used for building an objective function and root and building a multi-objective optimization model; the process optimization module performs process optimization by using a multi-objective optimization model to obtain production optimization parameters; the operation change analysis module performs operation control monitoring based on the production optimization parameters and draws a production parameter change trend chart; the product quality prediction module performs product quality prediction through reinforcement learning; the control strategy making module makes a production control strategy based on the product quality prediction data set. The technical problems of product quality fluctuation and cost control difficulty caused by lack of real-time accurate adjustment means for production parameters in the prior art are solved, and the production cost is reduced while the product quality is improved.

The invention discloses a vehicle frame production information management method and system, and relates to the technical field of production optimization. A vehicle frame production information management system comprises a sensing module, a data storage module, a data processing module, a production optimization model module, a plan management module, a quality inspection management module and a data analysis and decision support module. Based on a machine learning model and a neural network technology, historical operation data and real-time parameters of the equipment are analyzed, and the future fault probability and the fault type of the equipment are accurately predicted; and when the equipment fault risk exceeds the preset threshold value, early warning is triggered, and a maintenance or replacement suggestion is generated, so that the shutdown risk caused by the equipment fault in the production process is remarkably reduced, and intelligent and predictive maintenance of equipment management is realized.

The invention relates to the technical field of digital factories, and discloses a production data analysis method and system based on a digital factory, and the method comprises the steps: carrying out the space-time alignment and standardization processing of high-precision sensor data and low-precision sensor data in the digital factory, and obtaining a target production data tensor; carrying out production rule constraint fusion to obtain physical consistency production data and process parameter estimation values; performing reliability analysis to obtain a production data set with a confidence interval; a multi-stage production optimization problem is constructed according to the production data set with the confidence interval, the multi-stage production optimization problem is solved, an optimal production parameter strategy and an equipment adjustment scheme are obtained, the method achieves remarkable improvement in the aspects of product quality stability, energy efficiency, equipment adjustment frequency and comprehensive production efficiency, and the method is suitable for popularization and application. And effective technical support is provided for intelligent production management of a digital factory.

The invention provides an intelligent factory production optimization method and system based on AI scheduling, and belongs to the technical field of intelligent factory production management.The method comprises the steps that firstly, process disassembling is conducted on batch production orders, an order task atlas is obtained, a dynamic resource pool is constructed according to the real-time resource state of a factory, the order task atlas is input into a pre-trained scheduling AI model, and a scheduling AI model is established; a dynamic resource pool is combined to generate multiple groups of initial scheduling paths, conflict detection and conflict point identification are performed on the initial scheduling paths, an adaptive adjustment module is called to correct parameters based on conflict point types and influence ranges, a final production optimization scheme is generated and imported into a factory execution system, and intelligentization and high efficiency of production scheduling are realized. And the production efficiency and benefits are improved.

The invention discloses an electric toothbrush head plastic mold forming control device and method, and relates to the technical field of mold forming control. The device comprises the steps that the production requirement of an electric toothbrush head is obtained, plastic mold design is conducted, and brush head plastic mold design parameters are obtained; according to the brush head plastic mold design parameters, a plastic mold production line is activated, and a target brush head plastic mold is obtained; constructing a brush head molding process control space to obtain a brush head injection molding process solution set; carrying out traversal search optimization by taking the production demand of the brush head of the electric toothbrush as a constraint parameter, and outputting a forming process control parameter; and performing a brush head pre-forming test to obtain brush head forming test parameters, and performing brush head production optimization control. The technical problem that in the prior art, the product quality and the production efficiency are low in the whole brush head production process is solved, and the technical effects that precise control and optimization from the production requirement to the whole brush head production process are achieved, and the product quality and the production efficiency are improved are achieved.

The invention discloses a feed online production intelligent monitoring method and system based on data analysis, and relates to the technical field of intelligent monitoring, original data RAW is collected and preprocessed to obtain an operation feature vector VPS, and a data foundation is laid for accurate analysis. Based on the operation feature vector VPS and a historical database HIS, a predicted quality feature vector VPQ containing specific indexes such as starch gelatinization degree Ysg is generated in real time, a comprehensive feed quality index SIFQ is calculated, a decision signal Sig is generated, multi-dimensional overall evaluation of product quality is achieved, and a clear basis is provided for regulation and control. And when the decision signal Sig indicates that the quality does not reach the standard, an adjustment instruction AIR is generated, and scientific and efficient process adjustment is ensured. And adjusting equipment according to an adjustment instruction AIR, continuously monitoring and updating a historical database HIS, and based on this, carrying out iterative optimization on an inherent coefficient set SCA, endowing the system with self-learning and continuous improvement capabilities, and ensuring long-term adaptability of the model and stable production optimization.

The invention discloses an oil reservoir comprehensive production optimization method and device, and the method comprises the steps: carrying out the region level recognition, single well level recognition and production optimization model recognition according to the production historical data, and updating the oil reservoir dynamic recognition; based on production historical data, performing cyclic historical fitting on model parameters of the various production optimization models by adopting the following steps: performing historical fitting on the model parameters, and performing inter-well communication relation calibration on the various production optimization models; determining a constraint condition corresponding to each production optimization model according to the updated reservoir dynamic cognition, solving a target function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; an oil reservoir comprehensive development regulation and control scheme is obtained; and according to the regulation and control effect obtained through real-time monitoring of the development operation, oil reservoir dynamic understanding is updated. According to the invention, the efficiency and precision of oil reservoir production optimization can be improved.

The invention discloses a heparin production process optimization method and system based on environmental analysis, and aims to improve the purity and quality of heparin production. The method comprises the following steps: acquiring historical environmental monitoring data and multispectral data of an enzymolysis reaction kettle, and determining substance generation paths under different environmental conditions; establishing a nonlinear mapping model of environmental parameters and production purity based on a random forest algorithm, and calculating the influence weight of each parameter; generating an environment-adaptive enzymolysis reaction optimization scheme according to the weight; the method comprises the following steps: detecting molecular weight distribution data of a heparin crude product, and dynamically adjusting enzymolysis reaction parameters in combination with vibration spectrum and molecular conformation change characteristics; and finally, carrying out multi-target matching on the environment optimization scheme and the reaction parameters to generate a production optimization scheme containing an environment-process parameter mapping relation. The method can effectively improve the heparin production efficiency and quality, and is suitable for industrial production optimization.

The invention discloses a garment factory production intelligent management method and system based on artificial intelligence, and belongs to the technical field of production management. The method is used for solving the technical problems that in an existing scheme, the autonomous supervision and analysis effect of tailored fabric generated by garment production in a garment factory is poor, and the autonomous optimization and management effect of fabric tailoring is poor. Local tailoring supervision of tailored fabrics is performed on batch production of different garments, and local tailoring supervision analysis data in different aspects are integrated, calculated and analyzed, so that local tailored fabric states corresponding to different tailored fabrics are obtained; and performing targeted production optimization management on subsequent production of the clothes according to all the abnormal tailored fabrics obtained through analysis. According to the method, data processing and calculation analysis in different aspects are carried out on all tailored fabrics in garment production, simultaneous analysis is carried out on data analysis results in different aspects, the overall tailoring state of different tailored fabrics is determined, and targeted tailored fabric production optimization management is carried out.

The invention discloses a data self-acquisition system for a pig casting machine and an intelligent cast iron production line, and relates to the technical field of intelligent cast iron production, the data self-acquisition system comprises a data acquisition module, an image acquisition module, an edge calculation module and an AI data analysis module, the data acquisition module and the image acquisition module are connected with the edge calculation module through a CAN bus, the edge calculation module is connected to the AI data analysis module through database sharing, and the AI data analysis module is used for carrying out production optimization control according to the collected production parameters. By installing the AI data analysis module, AI analysis and prediction functions of multi-dimensional production parameters of the pig casting machine are achieved, the problem that in traditional cast iron production, deep data analysis of production working conditions and equipment states is lacked is solved, manual judgment errors are reduced, the accuracy of the production process is improved, and the production efficiency and the product quality are improved.

The invention relates to the technical field of feed production, and discloses a feed production control method and system based on deep learning. Aiming at the problems of low production efficiency and unstable product quality caused by uncertain factors such as raw material quality fluctuation, equipment state change and the like which are difficult to deal with by a traditional feed production method, an improved CrossFormer deep learning model is adopted, and feed production process data are acquired and preprocessed in real time; and inputting the preprocessed data into a model to carry out validity evaluation on the current production strategy, and giving an adjustment instruction. And automatically adjusting a feed production strategy according to an output result of the model and an adjustment instruction, and recording all implemented adjustment measures, production data comparison before and after adjustment and equipment monitoring information to form a production optimization log. According to the method, the intelligent level of feed production is remarkably improved, the production cost is reduced, and the product quality and the market competitiveness are improved.

The embodiment of the invention provides an injection-production scheme optimization method and system based on the dominant water flow direction between injection-production wells, and belongs to the technical field of complex oil reservoir development. The method comprises the following steps: collecting geological detection information and production state information in a target well group range to form fitting basic data, and preprocessing the fitting basic data; based on the preprocessed fitting basic data, reservoir geologic feature recognition and dynamic response feature recognition are executed respectively; executing main control factor identification based on the dominant water flow identification result; and generating a corresponding injection-production optimization scheme based on the identified main control factors, and pushing the injection-production optimization scheme to a user side. According to the scheme, the problems that in the prior art, dominant water flow direction judgment is not accurate, and an optimization scheme lacks pertinence are effectively solved, the water injection development effect is remarkably improved, resource allocation is optimized, and finally efficient utilization of reserves and maximization of the recovery ratio are achieved.

The invention provides a production optimization method and system for an anylayer core board layer, and relates to the field of equipment manufacturing, the characteristics (such as the size, the material and the batch number) of a core board and the processing parameters (such as the PTH linear speed, the copper deposition liquid concentration, the temperature, the current density and the spraying pressure) of the core board are used as input, and the compatibility requirements of the core board and horizontal line equipment are automatically analyzed, so that the production optimization of the core board layer is realized. Key parameters such as the thickness, the strength and the connection mode of the adaptive band plate are generated; then, the band plates are accurately screened based on the calculation result, physical coupling of the band plates and the core plate is achieved through the pressing plate clamp, and a rigidity-enhanced integrated structure is formed; in the process, equipment adjustment does not need to be manually intervened, the problems of core plate bending, plate clamping and the like can be effectively solved only through parameterized matching and intelligent transmission assistance, and finally stable transmission and smooth production of the ultrathin core plate in the complex electroplating process are ensured.

The invention provides a station-level production optimization scheduling system for a steel plant, which comprises a steelmaking MES subsystem, a crown block logistics subsystem, an equipment management subsystem and a process control subsystem in an input layer, and further comprises a real-time communication subsystem, a production rhythm tracking subsystem and a production optimization scheduling subsystem in a middle layer, the system further comprises an intelligent bun preparing subsystem, a vehicle dispatching subsystem and a working procedure process control subsystem of the output layer. Wherein the production optimization scheduling subsystem receives a production plan or adjustment of the MES subsystem, and obtains a process package arrival / departure signal of the crown block logistics subsystem, a process smelting process of the process control subsystem, an equipment maintenance plan of the equipment management subsystem and trigger scheduling dynamic adjustment at the same time. According to the system, production plan scheduling is taken as a research object, ordered production of a production plan is realized through static scheduling and dynamic adjustment, and meanwhile, consistent scheduling of the production plan scheduling, vehicle scheduling and molten steel tank package allocation is established.

The invention relates to the technical field of Internet of Things, and discloses an FPC production information tracing method and system based on a product identification code, and the method comprises the steps: determining a tracing demand of an FPC product, defining a coding rule of the FPC product, coding the FPC product through the coding rule, and obtaining an FPC product code; a production detection device of the FPC product is defined, a tracing integrity coefficient of the production detection device to the FPC product is calculated, and a tracing block chain network of the production detection device is constructed; associating the production flow data with the FPC product codes to obtain a flow data-code set, and establishing a product tracing library of the FPC products; obtaining tracing information of a target FPC product corresponding to the tracing instruction, and analyzing a tracing abnormal state of the target FPC product by using a trained tracing abnormal analysis model; and positioning an abnormal node of the target FPC product, analyzing a production optimization parameter of the abnormal node, and executing information tracing optimization of the FPC product based on the production optimization parameter. According to the invention, the tracing effect of the FPC production information can be improved.

The invention provides a gas reservoir gas storage injection-production optimization method based on block pressure balance control. The method comprises the following steps: acquiring target gas reservoir basic data; establishing a gas reservoir gas storage production operation dynamic optimization model objective function; constructing a gas reservoir gas storage production operation dynamic optimization model constraint condition; and solving the gas reservoir gas storage production operation dynamic optimization model, and generating a gas reservoir gas storage optimization injection-production scheme. On the basis of the injection-production scheduling optimization problem of the multi-block gas reservoir type underground gas storage, the corresponding optimization model is established with the purpose of minimizing the pressure difference degree between the gas reservoir blocks in the injection-production process, and the injection-production scheduling scheme is successfully solved through the established optimization model and the solving method. According to the method, the deviation degree of the formation pressure between the blocks can be effectively reduced under the condition that the requirement for the gas injection and production amount of the gas storage is met, extreme high-pressure blocks can be effectively avoided, and safe and stable operation of the gas storage is further guaranteed.

The invention discloses a multi-agent collaborative production optimization method and system based on an industrial large model, and relates to the technical field of production control, and the method comprises the steps: connecting an industrial knowledge base platform, carrying out the platform retrieval and interpretation reconstruction of a target production scene, and determining a production line interaction chain; aiming at a production line interaction chain and a driving agent construction interface, calling and splicing optimization of a code fragment library are carried out, lightweight construction of agents is carried out, multi-agent integration is carried out by introducing an intelligent interaction contract, and an industrial large model is established; and deploying the industrial large model in an industrial production system, establishing interaction with production line equipment, triggering the industrial large model along with the start of a target production scene, and performing automatic production management of the production line equipment through multi-agent cooperative control driven by an intelligent interaction contract. The technical problem that in the prior art, an industrial production system lacks intelligent and multi-dimensional collaborative optimization is solved, and the technical effects of improving the production efficiency, the resource utilization rate and collaborative work of production line equipment are achieved.

The invention discloses a part flexible dynamic production optimization method and device based on a low-carbon target, and relates to the field of intelligent machining. The method comprises the following steps: firstly, aiming at an autonomous production station process and an outsourcing production station process of part production, constructing a multi-objective optimized Petri net model; secondly, respectively calculating the production cost, the carbon emission and the production cycle of each path in the Petri network model based on the decision variable group; and finally, performing multi-objective optimization through a marine predator algorithm according to the priorities of the three objectives of the production cost, the carbon emission and the production cycle. On the basis of a traditional part production and manufacturing organization mode, multiple targets such as the product production cycle, the production cost and the carbon emission in the production process are considered, a part flexible dynamic production organization optimization model is constructed, and dynamic flexible distribution of autonomous production and outsourcing production is achieved. The carbon emission in the production process can be estimated in the product design stage, and effective measures are taken to reduce the carbon emission when production tasks are distributed.

The invention relates to the technical field of production, in particular to an intelligent batch production optimization method, device and equipment. The method comprises the following steps: establishing a simulation scheduling model of a welded pipe production process, and generating an initial batch amount scheme based on historical scene offline training; inputting the scheme into a digital twinning simulation environment for high-precision simulation, and collecting whole-process data; analyzing feedback and dynamically adjusting a reward function by using a large language model in combination with the data, and generating a new batch amount scheme after optimizing the model; the new scheme is put into a digital twinborn environment for multi-round closed-loop verification, and a verification result and a dispatcher natural language instruction are analyzed into structured constraints; and finally, the constraint is injected into the optimization model to generate a production scheduling optimization scheme. Dynamic optimal configuration of production resources and precise closed-loop management and control of the manufacturing process are achieved, and the effect in intelligent scheduling of the welded pipes is remarkable.

The invention discloses a crowd-sourcing scheduling method and system for photovoltaic module multi-process collaborative production optimization, and the method comprises the steps: constructing a multi-agent system, and determining the acquaintance relationship between agents; the warehouse agent is used for acquiring order information in real time and disassembling the processing task according to the order information; the scheduling agent adjusts priority parameters of the machining tasks, and allocates the adjusted machining tasks to a workpiece agent; the workpiece intelligent agent sends a bid invitation book to all production intelligent agents with an acquaintance relationship, and the production intelligent agents judge whether to respond to the bid invitation book or not according to the states of the production intelligent agents; the scheduling agent performs bid evaluation on the production agent responding to the bid invitation book based on deep reinforcement learning, and the task acquaintance relationship degree with the workpiece agent is considered in the state space of the production agent; and carrying out production scheduling according to the bid evaluation result. By adopting the acquaintance library priority screening, the formatting bidding book and the bidding strategy based on the perception coefficient and the credibility, the communication redundancy is reduced, and the production efficiency is improved.

The invention discloses a parallel hyper-heuristic injection-production optimization method based on an agent model, which belongs to the technical field of oil reservoir injection-production optimization, and comprises the following steps: step 1, constructing a three-dimensional geologic model and an oil reservoir injection-production model; step 2, constructing a water-drive reservoir injection-production optimization model and an initial database; step 3, constructing an agent model; 4, generating a filial generation population through parallel evolution by using four algorithms, and evaluating filial generation individuals by using an agent model to obtain respective candidate solutions of each evolution algorithm; 5, constructing a hyper-heuristic guide search strategy and an online parameter return method, and adjusting the search behavior of the evolver based on the candidate solution; step 6, repeating the step 3 to the step 5 until a preset maximum optimization iteration number is reached; and step 7, outputting an optimal Pareto frontier scheme and a corresponding economic net present value. According to the method disclosed by the invention, the search direction is dynamically adjusted by utilizing a hyper-heuristic guide search strategy and an online parameter return method while the injection-production optimization efficiency of the water-drive reservoir is improved.

The invention belongs to the technical field of industrial anomaly detection, and particularly relates to an industrial part anomaly detection method based on SCFlow, which takes a normal sample image as input, and extracts multi-scale features through a pre-trained feature extractor. The SCFlow receives the multi-scale feature maps and carries out probability density estimation. A layer-by-layer increasing feature layer-by-layer enhancement module StepFlow is utilized to independently encode each layer of feature map so as to ensure that the bottom layer captures detail features, the middle layer enhances the feature expression ability, and the top layer combines a global attention mechanism to realize effective integration of features; the feature fusion module ConvergeFlow effectively fuses feature maps from different scales, and improves the comprehensive analysis capability of the model for anomalies. Product defect detection in the industrial manufacturing process is a key link for guaranteeing the product quality, and the solution provided by the invention can improve the accuracy and efficiency of industrial anomaly detection, thereby providing support for applications such as industrial quality control, equipment maintenance, production optimization and the like.

The invention relates to the technical field of corrugated board production, and discloses a corrugated board production optimization method and system based on data mining and a medium. The method comprises the steps that corrugated board production full-link multi-dimensional data are collected and stored in a classified mode; performing cleaning, integration and feature engineering processing on the multi-dimensional data in sequence to obtain a standardized data set; key features and rules influencing quality and efficiency are mined based on the data set; constructing a quality and efficiency prediction model by adopting a gradient lifting decision tree algorithm; by taking model output as a constraint, optimizing through an NSGA-II algorithm to obtain an optimal process parameter, and issuing and executing the optimal process parameter; and acquiring an execution result in real time and comparing the execution result with a threshold value, and if the execution result exceeds the threshold value, triggering secondary optimization. Through data mining and closed-loop optimization, the problems of existing production parameter solidification and optimization passivity are solved, and the production quality and efficiency are improved.

The invention discloses a production line optimization method and system based on digital twinning, and the method comprises the steps: carrying out real-time simulation according to a production line simulation model, obtaining a real-time simulation result, and dynamically adjusting production control parameters through a preset parameter optimization strategy according to the simulation result and a preset production optimization target; the optimized control parameters of the at least one production section are applied to the digital twin model, and the optimization effect is predicted and evaluated; and after the effect reaches the expectation, the optimized production control parameters are applied to the physical production line. The production control parameters are dynamically adjusted through the production line data, the production control parameters can be dynamically adjusted according to the three production optimization targets of the minimum takt time, the minimum load fluctuation between workstations and the minimum adjustment cost, and the accuracy and effectiveness of adjustment of the production control parameters are improved.

The invention provides a job shop scheduling method based on decomposition type multi-objective reinforcement learning, and solves the problems that multi-objective optimization is difficult to balance, the strategy is unstable and the optimization efficiency is low in a complex scheduling environment. According to the method, weight configuration is dynamically adjusted, target values of candidate weights are predicted by using a hyperbolic tangent model, model complexity is reduced in combination with regularization, a comprehensive evaluation index is introduced to screen a solution set, and adaptive learning of features and stable updating of a strategy are realized. By optimizing the weight selection and strategy updating mechanism, a plurality of conflict targets can be efficiently balanced, the efficiency and accuracy of scheduling decision making are improved, the energy consumption is reduced, and the production efficiency is improved. In the industrial automation and intelligent manufacturing process, the multi-target job shop scheduling problem can be effectively solved, and technical support is provided for production optimization.

The invention relates to the technical field of concrete electric pole manufacturing, and discloses an optimized steel fiber anti-crack ultra-high performance concrete centrifugal electric pole manufacturing method and system. The method comprises the following steps: dividing an electric pole manufacturing process into a plurality of manufacturing units according to production batches, and collecting material component data, process setting data and online detection data in each manufacturing unit to form a multi-source production data set; calculating an anti-cracking performance index and a strength performance index based on a multi-source production data set, and dynamically outputting an initial process correction value through a dual-threshold monitoring mechanism and a performance adjustment model; performing adaptive adjustment on the initial process correction value of each manufacturing unit according to the production correlation between the adjacent manufacturing units to generate an adjusted process correction value; and carrying out serialization processing on the adjusted process correction value, and outputting a production optimization instruction which is applied to a manufacturing execution process. According to the method, electric pole manufacturing whole-process data integration and dynamic process optimization are achieved, and the production efficiency and the product quality stability can be improved.