204 results about "Simulation algorithm" patented technology

The invention discloses an industrial MBSE model generation and evaluation method based on a large language model, and the method comprises the steps: collecting and sorting an aerodynamic model, assembly design rules and related basic design parameters, and carrying out the formatted expression of the rules applied in MBSE, so as to form a special enhanced knowledge base in the industrial field; the method comprises the following steps: converting a demand described by a natural language of a user into a standard MBSE model code based on a large language model through a cue word engineering technology, selecting an optimization algorithm and a simulation algorithm through a function call technology, performing real-time optimization on the model according to a design target, and applying the generated MBSE model to propulsion device design and verification test. And determining availability and model consistency. Reconstruction and acceleration of digital design are realized through automatic configuration based on requirements and components. The method has remarkable advantages in the aspects of reliability, effectiveness and adaptability.

The invention provides a BIM construction progress optimization method and system based on multi-agent autonomous decision, and belongs to the technical field of building information. The BIM construction progress optimization method comprises the steps that a BIM construction progress model is constructed based on multiple construction agents, and the BIM construction progress model comprises a multi-agent construction simulation algorithm model; in the multi-agent construction simulation algorithm model, a plurality of construction agents determine the current construction action according to the real-time field state of the construction progress; wherein each construction agent represents one construction resource in the construction progress and autonomously decides to execute a construction task; the multiple construction intelligent agents independently execute construction actions and output simulation result data corresponding to execution of the construction actions; and updating the model state of the BIM construction progress model according to the simulation result data. The problem that in the prior art, a one-way static deduction mode is difficult to respond to field changes in the construction progress can be solved.

The invention relates to the field of constructional engineering, and discloses an intelligent construction management and control method for constructional engineering, which comprises the steps of collecting multi-dimensional data of a building construction site, including construction progress data, personnel positioning information, an equipment operation state and site environment parameters, and constructing a construction behavior feature data set in combination with a multi-source data fusion algorithm; performing automatic structured analysis on the construction behavior feature data set, constructing a construction process modeling framework based on a hierarchical feature clustering method, and introducing a dynamic feedback learning mechanism to perform adaptive optimization on a modeling result; judging whether the optimization process is stable or not according to the evolution trend of the process model, and if so, recording the stage state of the current construction process feature; and based on the corrected construction plan path, performing disturbance factor correction on the prediction progress by applying a multi-scale construction simulation algorithm, extracting corresponding control parameters, and performing intelligent adjustment on prediction nodes. The method has the advantage of improving intelligent construction management.

The invention discloses an aircraft end ablation simulation algorithm structure and method based on a meshless method. The structure comprises a pneumatic calculation module, an ablation calculation module and an SPH boundary movement / structure heat transfer module. The method comprises the following steps: dividing a flight trajectory into a plurality of discrete flight state points according to the flight trajectory of reentry flight of the aircraft; ballistic data, a geometric shape and an atmosphere model of the aircraft end are obtained; according to the incoming flow parameter and the geometric shape, calling a pneumatic calculation module to calculate a wall surface pneumatic flow field parameter of the end; calculating an effective heat flow and a linear ablation rate; calling a meshless SPH structure heat transfer / boundary movement module program; according to the method, characterization through particles replaces characterization through grids, so that characterization of the end appearance after ablation does not need grid movement, the length-width ratio of the grids is changed to cause singularity of the grids, calculation of a temperature field in the ablation process cannot be continuously carried out, and calculation can be stably carried out in the large-deformation ablation simulation process.

The invention provides a wire harness crimping quality intelligent detection and self-adaptive control method and system, and the method comprises the steps: calculating a stress distribution value of a crimping point through the obtained internal metal flow distribution characteristics in combination with a mechanical strength simulation algorithm, and obtaining a mechanical strength evaluation index; if the mechanical strength evaluation index is lower than a preset threshold value, triggering an alarm signal, extracting deviation type data from the alarm signal, and judging a quality judgment result; generating a feedback control instruction according to the judged quality judgment result, transmitting the feedback control instruction to crimping equipment to adjust crimping force parameters, and obtaining optimized crimping process data; updating a real-time monitoring database by adopting the obtained optimized crimping process data, and determining a geometric parameter reference value of a subsequent crimping batch; and matching the determined geometric parameter reference value with a vehicle safety requirement to obtain a final quality verification report.

Various methods and processes, apparatuses or systems, and media for predicting probability of hallucination before generation for a query imposed to a Large Language Model (LLM) are disclosed. A processor causes a trained generative model to receive a query from a user via a user interface operatively connected to the generative model; perturbs the received query n times into unique variations that retain the original semantic meaning of the received query yet significantly diverge lexically; implements n+1 independent agents to sample an output from each query including the original received query; applies the simulation algorithm on the sampled outputs; derives an empirical estimate into an expected rate of hallucination for the original received query as a ground truth for the encoder; and outputs a probability of hallucination value for the query received by the generative model before the LLM generates an output.

The invention relates to a continuous punch forming method of a high-hardness alloy thin-wall lock case in the field of intelligent manufacturing, which comprises the following steps: according to a stress distribution diagram, adjusting a die gap by adopting a self-adaptive algorithm, dynamically optimizing gap parameters for a local stress concentration area, and determining real-time gap configuration; if the temperature deviation of any area in the temperature field exceeds 10 DEG C, recalculating heat source distribution through a heat flow simulation algorithm, and determining optimized heating parameters; acquiring surface topography data of the annealed thin-wall lock case through an optical scanner, and analyzing fracture and wrinkling defects by adopting an image processing algorithm to obtain defect distribution characteristics; if the proportion of the fracture area in the defect distribution characteristics exceeds 2%, the forming failure probability is predicted through a neural network model, and whether the stamping speed is adjusted or not is determined; and according to the defect distribution characteristics and the softening consistency data, a data fusion algorithm is adopted to comprehensively evaluate the forming stability, and optimized operation parameters of the continuous stamping production line are obtained.

The invention discloses an intelligent water operation management and control system based on the Internet of Things, and relates to the technical field of industrial Internet of Things crossing, and the system comprises the steps: collecting multi-dimensional operation data in a water supply network, carrying out the preliminary time mark alignment and preprocessing, and generating a multi-source synchronous perception data set; importing the comprehensive risk list into a digital twinborn body, dynamically injecting corresponding fault and abnormal scene parameters in a digital twinborn environment, and performing large-scale parallel deduction through a Monte Carlo simulation algorithm to generate a rehearsal consequence data set; and carrying out multi-objective decision analysis on the rehearsal consequence data set, carrying out prediction comprehensive plan utility scoring based on a preset safety weight and an economic weight, and calculating an optimized management and control plan through iterative optimization. According to the invention, the multi-source heterogeneous data is deeply fused through the graph neural network of the risk identification module, the dynamic impedance characteristics of the pipe network are accurately extracted, the early abnormality is identified, and advanced diagnosis of the multi-modal coupling risk is realized.

The invention discloses a deep sea color inversion method fusing satellite radiation simulation under a multi-level sea color background, and the method comprises the steps: carrying out the simulation through employing a simulation algorithm based on an OSOAA radiation transmission model, obtaining simulation data including simulation wave band apparent reflectivity, and constructing a multi-level sea color data set; secondly, constructing a deep sea color inversion network with the orientation and wave band feature joint characterization capability, including a residual network and a binary attention mechanism, and performing sea color inversion based on multi-level sea color data; and finally, evaluating by using a test part of the data set, collecting real satellite remote sensing and sea color data, and testing. According to the method, the problems of low robustness and reliability of deep learning in a complex scene and the like caused by scarcity of current sea color marking data are solved, the problem of end-to-end inversion of sea color parameters such as chlorophyll concentration is solved, and the inversion efficiency is improved.

The invention provides an engine virtual simulation training system based on a mixed reality technology, and the system comprises a central processing unit which is used for executing a simulation algorithm and storing simulation data; the at least one head-mounted display is used for providing immersive visual experience; the handles are used for user interaction and operation; the at least two displays are used for displaying simulation results and operation interfaces; and the at least one tablet device is used for assisting display and control. According to the invention, a comprehensive immersive engine virtual simulation training system can be provided, the training efficiency and skill level of maintenance personnel are improved, and the risk and cost in actual operation are reduced.

The invention discloses an engineering machinery industry green factory evaluation method and system. The method comprises the steps of obtaining a pre-formulated green factory evaluation index system; calculating the subjective weight of each secondary index relative to the target layer; introducing a Monte Carlo simulation algorithm, and calculating an objective weight and an evaluation matrix of each secondary index corresponding to each Monte Carlo simulation relative to the target layer; based on a minimum relative information entropy principle and a Lagrange multiplier method, combined with the subjective weight and the objective weight of each secondary index relative to the target layer, calculating the comprehensive weight of each secondary index relative to the target layer corresponding to each Monte Carlo simulation; on the basis of the comprehensive weight and evaluation matrix of each secondary index relative to the target layer, constructing a fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation; and obtaining a green factory grade evaluation result based on the fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation and a preset score interval corresponding to each comment grade. According to the method, the accuracy and objectivity of green factory evaluation can be remarkably improved.

The invention provides a dynamic load prediction and optimal scheduling method and system for charging and discharging of an energy storage power station, and belongs to the technical field of industrial big data. According to the method, power grid operation related parameters such as power grid demand signals, market electricity price and generating capacity are obtained and subjected to time sequence feature processing to generate a time sequence feature sequence, whether the fluctuation amplitude of the time sequence feature sequence exceeds a preset fluctuation amplitude threshold value or not is judged, and if yes, uncertain factors are quantized by adopting a Monte Carlo simulation algorithm, and the power grid operation related parameters are obtained. The problem that parameter fluctuation uncertainty is neglected in traditional load prediction is effectively solved, the constructed load prediction model is more suitable for an actual power grid operation scene, and the reliability and accuracy of a load prediction result are greatly improved. Moreover, based on the comparison between the residual capacity percentage of the energy storage equipment and the preset percentage threshold, the charging and discharging operation is dynamically decided, so that the risk of insufficient power supply capability when the residual capacity of the energy storage equipment is too low is avoided, and resource idling when the residual capacity of the energy storage equipment is too high is prevented.

The invention relates to a man-machine interaction user action intention recognition method, device and equipment and a medium, and the method comprises the steps: for continuous wave trough values of a wave trough value cluster, according to a first time index corresponding to a wave peak value in a wave peak value cluster and a second time index corresponding to the wave trough value in the wave trough value cluster, calculating the continuous wave trough values of the wave trough value cluster; finding out a middle crest value between the continuous trough values to segment a plurality of action segments in the gesture action; inputting the to-be-recognized gesture action into the gesture action recognition model to determine probability distribution corresponding to the gesture action category of the to-be-recognized gesture action; and adopting a preset Monte Carlo simulation algorithm to perform multiple random sampling on the probability distribution corresponding to the gesture action category, and taking the gesture action category with the maximum probability as the user action intention of the gesture action to be recognized so as to complete the recognition of the user action intention of human-computer interaction. According to the method and the device, smoother, more personalized and more immersive control experience can be provided for the user.

The invention belongs to the field of infusion monitoring, and particularly relates to a target container liquid level visual detection and early warning method and system.The method comprises the steps that an enhanced image and a three-dimensional point cloud sequence are obtained by collecting a target container deformation image, a three-dimensional point cloud and flexibility attribute data and conducting polarized light enhancement preprocessing; obtaining a deformation feature space and a deformation liquid level line feature sequence of the target container at different time points by means of a spatial-temporal feature extraction model, and constructing a three-dimensional dynamic deformation model in combination with three-dimensional modeling, finite element analysis and a NeoHookean model; based on the model, an undeformed standard model and a simulation algorithm, obtaining a correlation mapping function of the liquid level line deviation and a deformation characteristic space; the correlation mapping function is fed back to the spatial-temporal feature extraction model, the height and coordinates of the corrected deformation-free liquid level line are obtained in combination with the real-time target container deformation image, real-time monitoring and early warning in the infusion process are finally achieved in combination with a preset early warning rule, and the accuracy and reliability of infusion monitoring are improved.

The invention discloses a robot intelligent manufacturing workshop energy consumption optimization method based on the Internet of Things, and belongs to the field of energy consumption optimization, and the method comprises the steps: optimizing a path based on a genetic algorithm, and generating a candidate path set containing diversified paths; aiming at a time sequence scheme, predicting energy consumption uncertainty caused by task change and equipment aging by adopting a Monte Carlo simulation algorithm, and generating prediction distribution containing an energy consumption fluctuation range; if the predicted distributed energy consumption fluctuation range exceeds 10% of a preset threshold value, a path set and a time sequence scheme are adjusted through a reinforcement learning algorithm, and an optimized path and time sequence combination is generated; otherwise, the current path and the time sequence scheme are reserved, and a stable energy consumption distribution scheme is obtained; and extracting a task execution sequence and equipment operation parameters from the final adaptive energy consumption optimization scheme, and transmitting the task execution sequence and the equipment operation parameters to a workshop control system to complete real-time deployment of energy consumption optimization.

The invention particularly relates to a spatial analysis method based on large model agent driving. According to the spatial analysis method based on large model agent driving, syntactic analysis and intention recognition are carried out on a spatial problem described by a natural language through a large model, and spatial-temporal clustering, network analysis and a field simulation algorithm module are dynamically matched; packaging a heterogeneous algorithm into a standardized service component based on a model context protocol (MCP), and automatically constructing an analysis process chain; and integrating a GIS engine and a visual template library, generating a dynamic thematic map according to the processed spatio-temporal data through the visual engine, and superposing multi-dimensional analysis annotations. According to the spatial analysis method based on large model agent driving, the full-chain intelligent capability from data processing to decision support is constructed, the efficiency and precision of spatio-temporal evolution modeling are remarkably improved, meanwhile, the system integration and maintenance cost is reduced, the method is suitable for high-complexity scenes needing real-time spatio-temporal data analysis, and the method is suitable for popularization and application. And the method has remarkable technical innovation and industrial application value.

The invention belongs to the technical field of data processing, and particularly relates to a real-time control simulation system for air cooling island freezing prevention, and the system obtains a temperature field, a back pressure field and a fan operation state in a target area in real time through a monitoring module; the analysis module constructs a space-time distribution field of the temperature and the back pressure according to the temperature and the back pressure, and further fuses the space-time distribution field into a temperature-back pressure causal distribution mapping field; the inversion module carries out fault type discrimination based on the mapping field and a back pressure abnormity identification model, and generates an inversion mapping space containing an early warning level, starting time, air volume, air speed and air direction in combination with a fan operation inversion function; the response simulation module performs real-time anti-freezing control by applying a strengthening adjustment strategy and a simulation algorithm, so that the temperature and the back pressure are maintained in a standard interval; according to the invention, intelligent identification, accurate prediction and self-adaptive regulation and control of the air cooling island freezing injury risk are realized, and the accuracy of anti-freezing control and the reliability of system operation are effectively improved.

The invention relates to the field of building information model delivery, and particularly discloses a BIM delivery self-examination method based on a Revit plug-in, and the method comprises the steps: S1, extracting core attribute data in a model component; s2, exporting the model as a. Ifc format file; s3, establishing a matching mechanism of dictionary values and delivery specifications; s4, executing component logic normative verification of geometric legality, topological relation consistency and field dependency legality; s5, carrying out area conformity detection, calculating room and building areas, and comparing a deviation rate with a platform standard area rule through a simulation algorithm; s6, performing three-dimensional visualization feedback, visually presenting a detection result in the three-dimensional model, and providing a problem component ID; and S7, exporting a self-examination problem report and an operation log. According to the method, attribute integrity verification, geometric structure detection, standard rule examination and interactive three-dimensional feedback are realized, a problem report and a log can be automatically generated, and the model delivery quality, efficiency and passing rate are remarkably improved.

The invention discloses a metal structural member manufacturing quality online monitoring system, and particularly relates to the technical field of quality monitoring, and the system comprises an on-site perception and edge processing unit, a cloud digital twin unit, a cloud decision and optimization unit and a closed-loop control unit. The field unit collects multi-modal physical signals in real time and carries out preprocessing and defect preliminary classification based on a lightweight AI model; the digital twin unit drives a high-fidelity virtual model based on field data, and simulates a defect evolution and repair strategy through finite element simulation; the decision-making unit fuses classification and simulation results to generate an executable repair instruction, and performs collaborative optimization on an AI model and a simulation algorithm based on feedback data; the closed-loop unit controls instruction issuing and field execution, and real-time regulation and control and system self-optimization of the repair process are achieved. According to the system, full-process online monitoring, intelligent diagnosis and self-adaptive repairing of the manufacturing process are achieved, and the production quality and efficiency of the metal structural part are remarkably improved.

The invention relates to an intelligent mold calibration optimization method based on big data analysis. The method comprises the following steps: firstly, collecting current state data of a mold, including shape, size, wear degree and production environment parameters, and simultaneously obtaining ideal state data of a product; and constructing a mold-product correlation model by utilizing a physical simulation algorithm, and simulating a product forming process under different production parameters. Key control parameters are determined based on simulation results, the parameters are optimized through a machine self-learning algorithm, and an intelligent calibration optimization strategy is generated. Actual product data is collected in real time and compared with ideal state data, a product data difference value is generated, and an optimization strategy and key control parameters are dynamically adjusted according to the product data difference value. And finally, the production parameters of the mold are automatically adjusted according to the optimization strategy, intelligent calibration optimization is achieved, and the product quality and the production efficiency are improved.

According to the BDIM-based propeller cavitation streaming simulation algorithm provided by the invention, the particularity of a propeller structure is considered, a hyperbolic tangent encryption function is introduced into a near-wall region of the propeller structure to carry out non-uniform grid division, and high-gradient flow and boundary layer development of a blade tip region can be effectively captured, so that the resolution of a cavitation source region and a vortex core is improved; in multi-phase interface tracking, a VOF method is adopted to carry out high-resolution reconstruction on fluctuation behaviors of a vapor-liquid interface, interface diffusion distortion can be avoided, interface definition and mass conservation can be taken into consideration in combination with double-field evolution of a volume fraction function and an intermediate velocity field, and the method is suitable for being applied to multi-phase interface tracking. The method is particularly suitable for dynamic capture of a cavitation primary area and a complex gas-liquid phase mixing area in a propeller wake, and the precision and reliability of simulation on the cavitation occurrence position, the time scale and the form can be improved.

The embodiment of the application provides a kind of welding path planning method and device, method includes: by machine vision algorithm to the object to be processed is three-dimensional scanning, obtains three-dimensional point cloud data, by the segmentation algorithm based on deep learning to three-dimensional point cloud data is data analysis, and the geometry feature is identified;With the geometry feature is constructed into a undirected graph, and the optimal connected path of the weld center line is found on the graph by minimum spanning tree algorithm, and the optimal connected path is constructed to obtain the fine three-dimensional model of processing object;According to the three-dimensional coordinates of weld search site, current welding gun attitude and target weld length, determine the optimal search height, search depth, inclination angle and offset of welding gun, generate corresponding welding path planning based on search height, search depth, inclination angle and offset by welding process simulation algorithm;The application can effectively improve the precision and efficiency of welding path planning.

The invention relates to a hybrid flow shop scheduling robust optimization method and device based on a hybrid genetic algorithm, which can realize the improvement of the robustness of a hybrid flow shop scheduling scheme, emphasizes the mean value performance while paying attention to the worst situation, and has relatively strong capabilities of global search and jumping out of a locally optimal solution. The method comprises the following steps: determining a hybrid flow shop scheduling problem mathematical model based on robust optimization; constructing a first optimization algorithm based on a genetic algorithm according to the constraint of the hybrid flow shop scheduling problem; constructing a second optimization algorithm based on a simulated annealing algorithm framework; constructing a scheduling scheme evaluation method based on the stochastic simulation algorithm and considering the worst situation and the average performance; and fusing the first optimization algorithm and the second optimization algorithm to obtain a target optimization algorithm, and performing scheduling scheme optimization based on the target optimization algorithm and the scheduling scheme evaluation method to obtain an optimal scheduling scheme.

The invention belongs to the field of spatial design optimization, and particularly relates to an exhibition streamline optimization method and system based on a viewpoint thermodynamic diagram and a spatial syntax, and the method comprises the steps: firstly constructing an initial three-dimensional layout model, carrying out the dynamic discretization of the initial three-dimensional layout model into visual or path-connected spatial units, and calculating the integration degree, understandability and other indexes of the units in combination with the spatial syntax. Generating a theoretical streamline sequence; collecting viewpoint data of the test group along a preset path through eye movement tracking, and generating a viewpoint thermodynamic value distribution diagram in combination with a thermodynamic diagram algorithm, group interest distribution and visual attention characteristics; based on the theoretical streamline and the thermodynamic diagram, the coupling coordination degree of the space unit is calculated through a coupling algorithm, a preset optimization strategy is called after a defect unit is recognized, and an optimized exhibition streamline scheme is generated by combining the initial model and simulation algorithm adjustment. And the exhibition viewing experience and the space use efficiency are effectively improved.

A numerical control cutting process digital twinning implementation method based on three-dimensional reconstruction comprises the steps that a digital twinning model and a physical logic twinning model of a numerical control machine tool and a geometric model of a workpiece and a cutter are constructed respectively, and real-time synchronization of a physical machine tool model and a digital twinning machine tool model is achieved by transmitting motion data and machine tool state data in real time; the cutting state of a workpiece and a cutter is calculated in real time through a cutting simulation algorithm, collision detection simulation is carried out, a workpiece model constructed based on a one-way line cluster model is converted into a standard STL triangular mesh model based on a three-dimensional reconstruction algorithm, and the triangular mesh model is mapped into a digital twinning monitoring scene through coordinate system conversion. According to the method, a high-precision digital twinborn model is constructed, real-time data synchronization and cutting process simulation are combined, and rapid conversion between a simulation data model and a display model is realized based on a composite block algorithm.

The invention relates to the field of golf, in particular to a portable multi-eye golf real-time detection and measurement method and equipment and electronic equipment. The method comprises the following steps: carrying out coupling calibration on a laser line direction and a camera coordinate system, and establishing a reference direction of measurement; acquiring a ball position based on the reference direction, judging whether the ball position is within a preset ball hitting range or not, and if yes, adjusting each camera and each auxiliary lighting device to be in a preset first working mode according to the ball position and a preset ball hitting mode; the ball hitting process is monitored, state information at different moments is screened according to a preset comparison rule, multi-view image data associated with state change moments are stored, and the state information comprises club state information, ball state information and environment state information; according to the multi-view image data and a preset image processing algorithm, calculating a three-dimensional space position including a sphere, a ball rod and an environment; and according to the three-dimensional space position and a simulation algorithm, acquiring flight data of the sphere and outputting the flight data.

The invention discloses a power distribution network dynamic partitioning method and system based on multi-objective ant colony optimization, and relates to the technical field of power system optimization, and the method comprises the steps: carrying out the power grid topology parameter set synchronization and three-dimensional field intensity mapping through a dynamic state estimation algorithm and a finite element electromagnetic field simulation algorithm based on a dynamic data flow, and generating a virtual power grid mirror image; a multi-target ant colony optimization algorithm is embedded in the virtual power grid mirror image, new energy sudden drop and harmonic resonance disturbance are injected into the virtual power grid through Monte Carlo simulation, and a Pareto optimal solution set is generated; and based on the Pareto optimal solution set and in combination with real-time power grid multi-dimensional state data, dynamically adjusting pheromone parameters of a multi-target ant colony optimization algorithm through a depth deterministic strategy gradient algorithm, and generating optimization parameters. Through virtual mirror image generation and parameter dynamic adjustment, deep coupling of data-strategy-execution is realized, and the technical bottlenecks of dynamic data synchronization misalignment and optimization parameter stiffness in a traditional method are solved.

The application relates to the technical field of acoustic signal processing. A three-dimensional sound event accurate identification and positioning method based on AI and a multi-microphone array is provided, which comprises the following steps: acquiring a multi-channel sound signal; performing frame windowing processing on the multi-channel sound signal to obtain time-frequency domain information; generating mel feature information; performing time-space joint feature extraction to generate an initial event category and a three-dimensional direction code; generating multi-angle virtual sound field data by combining the attenuation relationship between the sound source direction and the microphone angle through a simulation algorithm; inputting the real-time collected multi-channel sound signal into an optimized three-dimensional sound detection model for reasoning, and outputting an optimized event category and a three-dimensional direction code; performing dynamic calibration to generate a real-time calibrated three-dimensional sound detection model, so as to solve the problems of insufficient multi-dimensional distribution characteristic capturing capability of a three-dimensional sound field, limited model generalization capability caused by single data training and static environment assumption, and insufficient real-time adaptability under dynamic noise interference and long-term performance attenuation.

The invention relates to an aero-engine simulation method, device and equipment, a storage medium and a program product. The method comprises the following steps: acquiring description file information of an external dynamic link library, wherein the external dynamic link library is a packaged simulation algorithm; generating a target adaptation function according to the description file information, wherein the target adaptation function is used for being compatible with an input and output format of a simulation system and an input and output format of an external dynamic link library; if it is determined that the target adaptation function needs to be compiled according to the historical compiling condition, a target compiler is obtained, and an adaptation dynamic link library is generated according to the target compiler and the target adaptation function; data interaction with an external dynamic link library is completed according to the adaptive dynamic link library, and simulation of the aero-engine is achieved. By adopting the method, the expansibility of an aero-engine simulation system can be improved.

The invention belongs to the technical field of data processing, and particularly relates to a performance prediction method and system based on aluminum product element proportions and technological parameters, and the method comprises the steps: obtaining and preprocessing sequences of historical element proportions, technological parameters and the like of a target aluminum product, and constructing a technological proportion node network in combination with a graph algorithm; generating a ratio-performance forward prediction function and a performance-ratio backward reasoning function by using a multiple regression and factor analysis algorithm; according to the real-time performance requirement, an initial parameter sequence is obtained through a parameter disassembly matching model and a node network, prediction performance and a secondary parameter sequence are obtained through forward and reverse function operation, forward and reverse cycle adjustment is carried out by means of reinforcement learning and a simulation algorithm based on parameter and performance deviation until the deviation meets a threshold value, and the prediction performance is obtained. Outputting an optimal element ratio and a process parameter sequence; according to the method, accurate prediction of the aluminum product performance and intelligent optimization of the process parameters are realized, and the production efficiency and the product quality are improved.