7504 results about "Mathematical model" patented technology

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

The invention discloses an energy management and safety protection cooperation method for a liquid cooling industrial and commercial energy storage system, and particularly relates to the technical field of energy storage system management. A battery electrochemical model, a heat distribution diagram, temperature gradient data and electrical parameters are used as input, and a battery temperature change trend curve is output; a liquid cooling control strategy is set according to the prediction result; fusing the temperature gradient abnormal parameters, the temperature trend risk and the multi-modal environment data abnormal parameters, starting a fire risk assessment model, predicting the fire probability and position, calculating a fire risk coefficient, generating a fire risk report and setting safety protection measures; a multi-objective optimization mathematical model is constructed based on the energy efficiency ratio, the full life cycle income and the battery health degree, energy storage operation data and power grid requirements are combined, a Pareto optimal solution set is generated by adopting a non-dominated sorting genetic algorithm, and a charging and discharging strategy and liquid cooling parameters are optimized; the liquid cooling pipeline layout is optimized through reinforcement learning, and the problem that the battery temperature cannot be effectively managed is solved.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention relates to the technical field of soft soil area building engineering construction monitoring, and discloses a soft soil area static pressure pile soil squeezing characteristic time effect monitoring and analysis method, which comprises the following steps: arranging a multi-source sensor array in a construction area, and collecting signals such as soil displacement and pore water pressure; and after noise filtering, time sequence alignment and data normalization preprocessing, a layered soil body finite element model is established. A mathematical model with the soil squeezing effect associated with time is constructed in combination with time sequence analysis, a prediction model based on machine learning is used for predicting soil body response, and model parameters are adjusted through an optimization algorithm. And judging whether convergence occurs or not according to a model prediction error so as to dynamically adjust the sensor array and form a closed-loop iterative analysis process. The method can comprehensively and accurately monitor and analyze the soil squeezing characteristic time effect of the static pressure pile in the soft soil area, provides a scientific basis for construction decision making, and guarantees engineering safety and quality.

The invention discloses a virtual power plant scheduling method based on a large language model and deep reinforcement learning, and belongs to the technical field of virtual power plant scheduling. Comprising the following steps: constructing a virtual power plant multi-agent cloud edge collaborative scheduling framework based on large language model driving; predicting wind power, photovoltaic power and load power based on a large language model; constructing a mathematical model of virtual power plant optimization scheduling; converting the virtual power plant optimization scheduling model into a Markov game process in combination with a large language model; performing initialization training on the strategy network of the edge layer intelligent agent by adopting imitation learning to obtain a pre-trained edge layer intelligent agent strategy network; and based on the pre-training strategy network of the boundary layer intelligent agent, combining with a large language model and adopting an improved multi-agent near-end strategy optimization algorithm to solve a scheduling strategy.

The invention discloses a single-epoch GNSS-oriented PAR ambiguity precision factor determination method, and relates to the technical field of satellite navigation and positioning. Comprising the following steps: dividing pseudo-range and carrier wave observed quantities in a GNSS double-difference mathematical model into two groups, and constructing a function relationship between a floating point solution # imgabs1 # and a floating point solution # imgabs2 # variance-covariance matrix of an ambiguity vector # imgabs0 #; constructing a single-frequency single-epoch single-system P-ADOP expression and establishing a shrinkage factor k de-inversion transformation analytic expression, and simultaneously obtaining a single-frequency single-epoch single-system GNSS non-inverse transformation P-ADOP approximation formula; and performing analogy derivation on the single-frequency single-epoch single-system non-inverse transformation P-ADOP approximate formula to obtain a single-frequency multi-system or multi-frequency single-epoch GNSS non-inverse transformation P-ADOP general approximate formula. The method has important theoretical value and practical guiding significance for GNSS optimal ambiguity subset rapid selection and positioning and partial ambiguity fixing success rate influence mechanism analysis.

The invention relates to the technical field of process parameter design, and discloses a silver electrolysis process parameter design method and system based on multi-objective optimization. The method comprises the following steps: performing differential time sequence parameter decomposition on operation data of the silver electrolysis process to obtain a sensitive parameter set and a reference parameter set; performing reweighted periodic feature extraction on the sensitive parameter set and the reference parameter set, and establishing a dynamic mathematical model of the silver electrolysis process parameters; based on the dynamic mathematical model of the silver electrolysis process parameters, physical and chemical process numerical simulation is conducted through COMSOLMultiphysics software, and a response curved surface model is obtained; and according to the response curved surface model, a process parameter combination evaluation value sequence is generated, silver electrolysis process parameter multi-objective optimization is carried out based on the process parameter combination evaluation value sequence, and a silver electrolysis process parameter optimization scheme is output. According to the application, multi-physical-field collaborative simulation of electrode reaction, ion transmission, heat conduction and the like is realized, and performance index responses under different process parameters are accurately predicted.

A real-time quantitative characterization method, equipment and a medium for rock mass evolution are disclosed. The method includes: the multiphase field detection and monitoring data is fused; a deep learning model driven by physical principles is established based on the physical principles of fluid density; the deep learning model is trained using real-time fused data as input and corresponding evolution distribution images as output; a trained deep learning model is used to obtain an evolution distribution image based on multi-phase field detection and monitoring data of different time periods and types; a mathematical model is used to quantitatively characterize of physical and mechanical parameters in the whole process of progressive failure of the dynamic evolution of the rock mass based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

The invention provides an intelligent aeration optimization control method and system for a sewage plant, and relates to the technical field of sewage treatment.The method comprises the steps that water quality, water quantity and aeration quantity data are collected in real time; analyzing and processing the data of the water quality, the water quantity and the aeration quantity to form a sewage treatment process data set which comprises the change rate of a water quality index, the fluctuation condition of the water quantity and aeration efficiency information; and according to the sewage treatment process data set, establishing and training a dynamic mathematical model in the sewage treatment process to reflect the relationship among the water quality, the water quantity and the aeration quantity. Real-time monitoring, model prediction and intelligent adjustment technologies are integrated, the aeration control strategy is effectively optimized, the sewage treatment efficiency is improved, the stability of water quality is ensured, and energy conservation and environmental protection are promoted.

The invention relates to the field of thin-wall structure displacement field monitoring, and discloses a coupled functional unilateral inverse finite element cylindrical thin-wall structure displacement field monitoring method, which comprises the following steps of: performing discretization by adopting a four-node inverse shell unit, and arranging a sensor at a geometric center position of each unit; based on the basic mathematical model of the inverse finite element, establishing an overall pseudo-stiffness matrix and an overall pseudo-force matrix; and calculating to obtain a displacement field of the structure by solving a linear equation set formed by the overall pseudo-stiffness matrix, the overall pseudo-force matrix and the global displacement vector. According to the method, the structural displacement field is calculated by using the strain information measured at one side, and the global deformation is accurately and reversely deduced under the condition that only the local strain data is obtained by combining the advantages of finite element analysis and inverse problem solution. By optimizing local strain data solution, high cost and low efficiency of global strain measurement in a traditional method are avoided, and the method has remarkable practical advantages.

The invention discloses a heterogeneous multi-agent entropy regularization resource allocation method for dynamic spectrum sharing, which belongs to the technical field of wireless network communication, and comprises the following steps: loading a basic trainer, and managing subsequent reinforcement learning; the method comprises the following steps: constructing a simulation environment, establishing a mathematical model, initializing parameter analysis, constructing a training environment and an evaluation environment, and creating an intelligent agent; generating initial empirical data by using a random strategy, filling an empirical playback buffer area, returning to a final state after a preset preheating step number is reached, and performing formal training to sample data from the empirical playback buffer area to update a network; starting a formal training process, interacting the intelligent agent with the environment, and storing empirical data to a buffer area; the strategy network is optimized through the strategy gradient, and the evaluation network is optimized through the TD error. By the adoption of the method, the problem of performance bottleneck caused by the fact that the strategy is converged to be suboptimal in spectrum allocation of a traditional DRL method is solved, and resource allocation efficiency close to global optimum is achieved through random strategy optimization.

The present application relates to a multi-objective scheduling method and apparatus for an urban power grid, a computer device, a storage medium, and a computer program product. The method comprises: connecting hydrogen energy vehicles to an original urban power grid operation system to obtain an updated urban power grid operation system; determining an electrical-hydrogen coupling unit from the updated urban power grid operation system; establishing a corresponding mathematical model for each subunit in the electrical-hydrogen coupling unit; determining a first objective function on the basis of operation cost parameters of respective modules of the updated urban power grid operation system, determining a second objective function on the basis of energy supply parameters of each subunit of the electrical-hydrogen coupling unit, and generating a multi-objective optimization and scheduling model on the basis of the first objective function and the second objective function; and solving the multi-objective optimization and scheduling model to obtain a target scheduling strategy.

The invention relates to the technical field of data processing, and discloses a ship enterprise-oriented building and structure carbon footprint fine management and accounting method and a ship enterprise-oriented building and structure carbon footprint fine management and accounting system. The method comprises the steps of establishing a ship enterprise building full life cycle carbon footprint accounting framework, collecting and sorting carbon emission factor data, constructing a matrix mathematical model, developing an accounting tool and performing actual project verification, finally forming a carbon footprint accounting process, a monitoring scheme and an emission reduction strategy, and outputting a management manual and a measure library. And the whole-process carbon emission fine management is realized. According to the invention, on the premise that the calculation accuracy is guaranteed, carbon emission full life cycle management, dynamic monitoring, early warning response and emission reduction decision support are realized.

The invention relates to the technical field of marine disaster early warning, and discloses a digital bay hydrological condition early warning method and system, and the method comprises the steps: collecting historical meteorological data and real-time meteorological data of a to-be-predicted bay region; according to the historical meteorological data, establishing a wind and wave relation model by using a deep reinforcement learning and genetic algorithm, and generating wave prediction data based on the relation model and the real-time wind data of the to-be-predicted bay area; based on the theoretical model wind field and the background wind field, synthesizing a synthesized wind field of the to-be-predicted bay area; performing storm surge simulation by using a hydrodynamic mathematical model and a tidal current mathematical model according to the synthesized wind field and the real-time meteorological data; and on the basis of the wave prediction data and the storm surge simulation result, hydrological condition early warning information is generated in combination with an early warning index threshold value, the prediction precision and early warning timeliness of waves and storm surge in the bay area are improved, and accurate grading response of complex meteorological disasters is realized.

The invention provides a method and device for scoring and questioning according to interview question answers and electronic equipment, and belongs to the technical field of artificial intelligence, in the method, multiple similarity measurement algorithms are integrated to achieve multi-angle quantitative evaluation of applicant answers, a large model scoring link based on deep learning is creatively added, and the evaluation efficiency is improved. Therefore, the language understanding depth which cannot be reached by pure mathematical calculation is compensated. According to the double-track scoring architecture, on one hand, the objectivity and the consistency of scoring are guaranteed by means of an accurate mathematical model; on the other hand, subjective understanding and interpretation of scoring are enhanced by means of a large model with high semantic analysis ability, the accuracy and comprehensiveness of evaluation are improved, the process is automatically achieved, efficiency is high, and the method can rapidly, accurately and comprehensively evaluate the applicant answers. In addition, the scoring reason and the question-asking problem given by the large model provide valuable information for mining the knowledge mastering condition of the applicant.

The invention discloses a bed load particle starting test method and device, and relates to the technical field of hydromechanics, the method comprises the following steps: laying sediment particles in a wave water tank, and adjusting the water flow rate through flow control until the bed surface shear stress is less than the critical shear stress; tracer particles are excited to emit fluorescence through a laser beam, and a camera array is used for synchronously collecting instantaneous flow velocity of a flow field and displacement data of sediment particles; analyzing spatial and temporal distribution characteristics of turbulence energy dissipation and pressure energy dissipation based on a turbulence burst process, and establishing a sediment particle starting mathematical model; experimental data and computational fluid mechanics numerical simulation are combined, and the mathematical model is verified and optimized. According to the technical scheme, the analysis precision and the experimental convergence speed of the sediment starting criterion under the complex hydrodynamic force condition can be remarkably improved, and full-chain efficient cooperation from micromechanical response to macroscopic engineering application is achieved.

The invention provides an intelligent path planning method and system based on a large language model, and relates to the field of path planning. According to the invention, an LLM-based multi-level collaborative optimization framework optimizes an original mathematical model and an algorithm strategy for a new demand described by a natural language by integrating the semantic understanding ability of the LLM, so that the algorithm strategy is optimized on the basis of a constructed first mathematical model and a constructed first genetic algorithm. The optimized second mathematical model and the optimized second genetic algorithm are generated in combination with changing new requirements, so that the problems of expressive limitation of engineering modeling by directly using LLM cue words and insufficient field adaptability of generating a meta-heuristic algorithm can be solved, and the scene adaptability of a traditional framework can be improved. Besides, the code proxy based on the MCP is constructed, and a hierarchical progressive self-correction mechanism of the code proxy is combined with standardized scheduling of the MCP, so that omnibearing automatic processing from a surface code error to a deep mathematical model problem is realized.

The invention belongs to the technical field of material structure optimization, and discloses an enhanced structure design method and system for a three-period minimal curved surface, and the method comprises the steps: modeling a geometric model of the three-period minimal curved surface, and exporting the model; expanding a design domain of the geometric model to obtain a corresponding expanded design space of the three-period minimal curved surface; a design space file is converted into an analysis data structure, a general expression mode is constructed for the three-period minimum curved surface, an equal geometric element stiffness matrix is solved by using a Gaussian integral method and the elastic performance of each point in a design domain and assembled into a global stiffness matrix, and the structural response of the model is solved; constructing a local density distribution function in the Bayesian unit, and assembling all LDDFs to obtain a global density distribution function for presenting structural topology; and constructing a stiffness maximization topological optimization mathematical model for the TPMS, calculating an objective function and analyzing sensitivity, and carrying out iterative updating on design variables in the optimization model based on an optimal criterion method to obtain a reinforced structure configuration.

The invention provides a tracing processing method and system for a disinfection supply center. The method comprises the following steps: recording acquired operator input data, sterilization equipment attribute information, sterilization equipment operation data and identification data of articles to be sterilized; the traceability system carries out proofreading and data summarization on all the collected data according to timestamps to form a unified data set; the traceability system compares records in the data set with preset sterilization parameter data item by item, and generates corresponding comparison identifiers; the traceability system executes inventory information updating according to the consumable warehousing record and the packaging or issuing data, and records an inventory state; and establishing a data association index for the classified and stored record data according to the timestamps and the sequence of each subdivision process. According to the technical scheme adopted by the embodiment of the invention, a multi-level mathematical model is constructed to realize decision making, calculate the safety inventory in real time and trigger graded early warning, information tracing and sharing of the whole process are realized, a complete autonomous chain of data acquisition, anomaly detection, balance evaluation and parameter optimization is formed, and the safety of the system is improved. And a systematic solution is provided for fine management of medical supplies.

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention relates to the technical field of simulation, and particularly discloses a real-time simulation method for detecting photoelectric tracking equipment, which comprises the following steps of: performing feature decoupling processing on photoelectric signals through a dual-channel adaptive neural network architecture, and constructing an equipment mathematical model by adopting a dynamic gating fusion mechanism based on a processing result; the method comprises the following steps: establishing a multi-physics field coupling simulation environment based on a heterogeneous computing architecture, introducing an equipment mathematical model, performing three-field co-evolution through light transmission modeling, electromagnetic field distribution calculation and target motion prediction, and generating a dynamic test scene containing space-time relevance; according to the method, a mode of combining mixed feature analysis and fuzzy reasoning is adopted, multi-dimensional performance indexes are extracted from simulation data, and multi-target dynamic optimization is carried out through a strategy of combining a quantum evolution algorithm and swarm intelligence optimization; by means of a digital twin platform and a hardware-in-loop interface, real-time verification and closed-loop optimization are achieved, and the response speed and tracking precision of photoelectric tracking equipment in a complex environment are greatly improved.

The invention relates to the technical field of electrical cabinet monitoring, in particular to an electrical cabinet active anti-condensation method and system based on a condensation mechanism mathematical model. According to the invention, a mathematical model based on a condensation formation mechanism is constructed, and environmental meteorological parameters, station room environmental parameters, electrical cabinet internal microenvironment parameters and cable trench state parameters are monitored through a four-stage monitoring system; and fusing the multi-source heterogeneous data, inputting a condensation mechanism mathematical model to calculate a condensation risk index, and adopting a hierarchical response mechanism to cooperatively control an execution mechanism according to the obtained condensation risk index value to realize active defense of condensation. According to the method, the condensation phenomenon with the space-time dynamic characteristic is quantitatively described through a mass transfer and heat transfer coupling equation, a grading prevention and control strategy and a cooperative control algorithm are developed by calculating the condensation risk index, setting the threshold condition and judging the condensation formation risk level, the prevention and control intensity is dynamically adjusted according to the risk level, and the control accuracy is improved. And the risk response speed and the resource utilization efficiency under the complex working condition are obviously improved.

The invention provides a state monitoring and fault early warning method for a maglev vehicle system, relates to the technical field of urban rail transit data processing, and solves the problem of how to effectively combine an empirical threshold mode and a machine learning technology and give full play to a combination effect. The method comprises the steps of collecting operation data of a magnetic levitation vehicle system, performing corresponding data preprocessing operation, performing analysis and calculation in a mode of constructing a mathematical model, and evaluating the health state of the magnetic levitation vehicle system; constructing and training a fault prediction model, and inputting the evaluation data into the trained fault prediction model to obtain potential fault data of the magnetic levitation vehicle system; and generating early warning information based on the potential fault data, retrieving an expert experience library by using the early warning information, generating an intelligent decision result, and feeding back the intelligent decision result to the operation and maintenance personnel. According to the method, machine learning and expert experience are effectively combined, so that the real-time performance and the accuracy of fault early warning are improved.

The invention discloses a high-precision plane fitting method for a multi-noise point cloud, and the method comprises the steps: carrying out the plane rough fitting of an original point cloud after down-sampling, taking the longest side of a minimum bounding box of a lower sampling point cloud as a distance threshold value, and calculating the distance weight of each point through a weight mathematical model, then, plane fine fitting is carried out on the weighted down-sampling point cloud, the distance weight of each point is updated after each fitting, plane fitting is carried out on the point cloud of which the weight is updated again, and a fine fitting plane of the down-sampling point cloud is calculated in an iterative manner; and finally, calculating the distance weight of each point in the original point cloud based on the fine fitting plane and the iterated distance threshold value, performing plane fitting on the weighted original point cloud in an iteration mode, and finally obtaining the fine fitting plane of the original point cloud. According to the technical scheme, the method is high in stability and low in calculation amount, can obtain a high-precision fitting plane in a weighted iteration mode under the condition of more noise, and is suitable for different types of point cloud data.

The invention relates to a digital twinning energy storage management method and system, and the method comprises the steps: firstly obtaining the historical operation data and working environment data of a battery unit, analyzing the key feature correlation of the data, and determining the influence weight of the data on the efficiency of the battery unit; a correlation model of the operation state and efficiency of the battery unit is constructed based on the key features, and a prediction formula of the efficiency of the battery unit is obtained; meanwhile, a mathematical model of the electrochemical reaction process in the battery unit is established, and the efficiency change trend under different strategies is simulated; in addition, a mapping relation between electrochemical reaction and a working environment is established, and the influence of the working environment on the performance of the battery unit is quantified; on the basis, a control strategy of the battery unit management system is optimized, and a charging and discharging strategy combination under different conditions is determined; and finally, applying the optimization strategy to an actual system, and dynamically adjusting a charging and discharging strategy so as to improve the operation efficiency of the battery unit, prolong the service life of the battery unit, enhance the safety and provide a scientific basis for efficient management of an energy storage system.

The invention provides a winding insulation residual life prediction method and system, and relates to the technical field of winding insulation residual life prediction. According to the method, firstly, a data set is divided through segmentation threshold values, and on the basis of an Arrhenius equation, a segmentation mathematical model is constructed to describe the degradation process of an insulating material; then, a boundary penalty function of mathematical model parameters is introduced, a neural network model is constructed, the mathematical model and a neural network are jointly optimized, and meanwhile, a weighted loss function is designed in combination with statistics of degradation data; and finally, performing progressive training on the neural network by using each sub-data set, and generating a prediction result through superposition of a theoretical aging component of the mechanism model and a residual compensation component learned by the neural network. According to the method, segmented dynamic modeling and neural network learning are combined to realize accurate prediction of the non-linear time-varying trajectory of the insulation state characteristic quantity, and the characterization capability of the model on the complex degradation process of the insulation material is remarkably improved.

The invention discloses a geological modeling, crack propagation and fluid-solid coupling production prediction integrated evaluation method, and relates to the field of multiple technologies, and the method comprises the steps: constructing a three-dimensional geological model of a deep shale gas reservoir; according to the construction model, the attribute model, the three-dimensional ground stress model and the rock mechanical parameter model, a hydraulic fracture model is constructed; based on the hydraulic fracture model and the three-dimensional geological model of the deep shale gas reservoir, establishing a fluid-solid coupling productivity simulation mathematical model of the deep shale gas reservoir; according to the fluid-solid coupling productivity simulation mathematical model of the deep shale gas reservoir, the fractured horizontal well yield of the shale gas reservoir is predicted, and a prediction result is comprehensively evaluated and optimized. According to the method, multiple links of geological modeling, fracture propagation simulation, fluid-solid coupling production prediction and the like are integrated, and the geological characteristics of the gas reservoir, the fracture propagation rule and the interaction between fluid flow and solid deformation are comprehensively considered, so that comprehensive evaluation and optimal development of the deep shale gas reservoir are realized.

The invention belongs to the technical field of industrial intelligence, and relates to a deep reinforcement learning-based non-correlation parallel machine scheduling method, which comprises the following steps of: constructing a mathematical model suitable for non-correlation parallel machine scheduling; the machining time of each workpiece on each heterogeneous machine is collected, and normalization processing is carried out; initializing a genetic algorithm scheduling population and a depth Q network; constructing a deep reinforcement learning training framework; expressing a state vector by using the average fitness, the optimal fitness and the optimal individual code; operating parameters of the genetic algorithm are controlled by using the action space, and parameters of the deep Q network are updated by using a reward function; and dynamically controlling operator selection in a genetic algorithm iteration process by using the trained deep reinforcement learning model to obtain an optimal scheduling solution and realize scheduling of the non-correlation parallel machine. According to the method, a deep reinforcement learning algorithm is provided for solving similar problems in the manufacturing industry production scheduling field by analyzing a non-correlation parallel machine scheduling problem data model, and the production efficiency is improved.

The invention discloses an industrial chain multi-objective optimization solving method and a computer program product, and belongs to the technical field of data processing method.The industrial chain multi-objective optimization solving method comprises the steps that a mathematical model is constructed, and multiple objective functions of an industrial chain are quantized; constructing a swarm intelligence bat optimization algorithm; parameters in the swarm intelligence bat optimization algorithm are initialized; executing a search process, updating the speed and position of each bat individual, and comprehensively evaluating the fitness value of each bat individual by using a plurality of objective functions; a Pareto dominating strategy is adopted to optimize the multiple objective functions, and a Pareto non-dominated solution set is screened out; using a congestion distance algorithm to calculate a congestion distance between solutions in a Pareto non-dominated solution set, and rejecting over-concentrated solutions to form an optimal solution; carrying out back propagation on the optimal solution and the fitness value, and optimizing a next iteration result; and outputting an optimal solution when an iteration termination condition is satisfied. According to the method, the solving precision, the convergence speed and the global search capability of the industrial chain optimization problem can be improved.

A method for performing an automated additive manufacturing (AM) process comprising obtaining a set of first AM factors associated with performing a first AM process and obtaining a set of first characteristics of a first AM output, wherein the first AM output is generated by performing the first AM process using the set of first AM factors. A predictor is trained using the set of first AM factors and the set of first characteristics, which is a machine learning model, and a mathematical model is generated based on extracting internal parameters of the predictor. The mathematical model comprising represents an estimated relationship between first AM factors and first characteristics. A set of target characteristics is obtained and, using the mathematical model, a set of second AM factors is identified. The set of second AM factors are then used to perform a second AM process, generating a second AM output.