7107 results about "Element model" patented technology

The invention provides an intelligent maintenance decision-making method and system for a numerical control machine tool based on digital twinning, and relates to the technical field of equipment maintenance, and the method comprises the steps: obtaining the structural data and simulation precision requirements of the numerical control machine tool, determining the optimal grid size and distribution parameters of a finite element model, obtaining operation conditions, and adding the operation conditions to a multi-physics coupling modeling module, generating a machine tool digital twinborn model, fusing the machine tool digital twinborn model with the physical field, generating a machine tool operation degradation model, and optimizing to obtain a self-evolution digital twinborn model; obtaining machine tool operation parameters, performing preprocessing to obtain multi-modal perception data, extracting multi-view potential features, determining potential correlation features, and obtaining a machine tool health state evaluation result; the method comprises the following steps: generating a multi-level fault prediction result, determining a candidate maintenance scheme, adding the candidate maintenance scheme and the multi-level fault prediction result into a knowledge graph construction module, generating a semantic association network, and solving to obtain an optimal maintenance decision in combination with a multi-objective optimization algorithm.

The invention discloses a wind power bolt state detection method and system, and belongs to the technical field of mechanical structure testing and health monitoring, and the method comprises the steps: obtaining the real-time strain data of each monitoring point; acquiring operation condition data of the unit in real time; dynamically generating a finite element model of the tower drum structure according to the operation condition data; the finite element model and the real-time strain data are combined to generate a stress distribution map of the whole tower cylinder bolts; inputting the stress distribution atlas into a preset fatigue damage model, calculating a bolt life attenuation rate and positioning an external load abnormal source; and when it is detected that the life attenuation rate exceeds a preset threshold value, generating a grading early warning signal and outputting a maintenance strategy according to a positioning result. According to the method, a wireless strain sensor network, dynamic finite element model correction and multi-dimensional fatigue damage analysis technology are adopted, a whole tower barrel stress distribution map is generated in real time, external working condition data are associated, the bolt service life attenuation rate can be accurately evaluated, a load abnormal source can be positioned, and a graded maintenance strategy can be output.

The invention relates to the technical field of bridge structure health monitoring and early warning, and discloses a bridge support static displacement overrun early warning correction method based on a prediction model, and the method comprises the following steps: S1, simulating the dynamic response of a support and an overall structure under various working conditions through building a coupling finite element model of the bridge support and the overall bridge structure; s2, displacement, vibration and temperature data of the support are collected in real time through a multi-source sensor, and data fusion and noise reduction processing are carried out; s3, establishing a multi-level prediction model based on a physical model and a data driving model, and predicting a future displacement trend of the support; and S4, monitoring a prediction error in real time through an error feedback mechanism. Prediction errors are corrected in real time through recursive Kalman filtering and an error feedback mechanism, an early warning threshold value is dynamically adjusted through Bayesian updating, the precision and reliability of early warning are improved in combination with a double triggering mechanism, the method adapts to changes under complex working conditions, and false alarms and missing alarms are reduced.

The invention relates to a bridge safety early warning method and system based on construction monitoring, and belongs to the technical field of bridge engineering.The early warning method comprises the steps that multi-dimensional monitoring data collected by sensor networks deployed on a bridge girder and a bridge key section in real time are obtained; performing edge calculation processing on the multi-dimensional monitoring data to obtain a cleaned monitoring data set; based on the cleaned monitoring data set, combining a finite element model to construct a three-dimensional dynamic stress field, and generating a dynamic stress nephogram matrix; dynamically correcting a pre-configured stress allowable threshold value according to the environment monitoring data; predicting a stress prediction matrix in a future time window based on historical stress data and the dynamic stress nephogram matrix; the real-time stress value and the stress prediction matrix are compared with a preset stress allowable threshold value, and an early warning score is calculated; and determining a corresponding early warning level according to the early warning score and triggering a maintenance control strategy. According to the invention, the accuracy of bridge structure risk early warning can be improved.

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention relates to the technical field of heat exchange modules, and discloses a fault diagnosis method and device for a heat exchange module, and the method comprises the steps: constructing a finite element model of the heat exchange module, collecting a temperature signal, a pressure signal and a flow signal, and carrying out the division to obtain a source domain data set and a target domain data set; performing time sequence space feature enhancement processing to obtain a sample enhanced feature image; constructing a feature extraction network; performing training and error reconstruction to obtain a pre-training diagnosis model; fixing shallow feature extraction parameters, inputting the domain data set into a pre-training diagnosis model, and performing enhanced training on fault features to obtain a trained fault diagnosis model; and inputting real-time operation data into the trained fault diagnosis model to carry out fault type probability distribution calculation and fault degree evaluation, and generating a fault diagnosis report, so that the feature fusion effect among the multi-source signals of the heat exchange module is improved, and the recognition capability of different types of faults is enhanced.

The invention provides a dynamic method and system for thermal fatigue evaluation of integrated circuit packaging, and relates to the technical field of integrated circuits, and the method comprises the steps: employing a genetic algorithm to determine the arrangement position of a temperature sensor, and collecting a temperature data sequence of a temperature sensor array and the corresponding time information; establishing a thermal-mechanical coupling analysis model comprising a geometric model, a material constitutive relationship and a load boundary, and performing mesh generation on the thermal-mechanical coupling analysis model to obtain a finite element model; based on the finite element model, periodically solving by adopting a nonlinear iterative algorithm to obtain a welding spot stress-strain field data sequence and time information corresponding to the welding spot stress-strain field data sequence, establishing a corrected Coffin-Manson model considering a temperature-stress-time coupling effect, calculating a fatigue damage increment of the welding spot, and accumulating the fatigue damage increment in real time; and performing thermal fatigue state evaluation on the integrated circuit packaging structure according to the change rate of the accumulated fatigue damage value.

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention provides an intelligent networked automobile chassis drive-by-wire simulation method and system based on multi-domain modeling, and relates to the technical field of adaptive control, and the method comprises the steps: obtaining structural parameters and kinetic parameters of an intelligent networked automobile chassis, constructing a finite element grid, building a multi-flexible-body kinetic model corresponding to the intelligent networked automobile chassis, and constructing a multi-flexible-body dynamic model corresponding to the intelligent networked automobile chassis; introducing a coupling coordination equation, establishing a multi-flexible-body kinetic equation, and solving to obtain vibration response and stress-strain distribution under different working conditions; generating a plurality of finite element models, performing multi-physical field fusion and multi-error source fusion models, performing online error identification and active compensation, performing all-condition drive-by-wire chassis closed-loop control, and generating a drive-by-wire chassis closed-loop integrated simulation model; constructing a road three-dimensional parameterized model, generating a vehicle-road coupling model, generating a simulation test case, identifying a performance short board, constructing a knowledge graph and an optimization rule base, and generating an optimal control strategy.

The invention provides a 3D model multi-scale lightweight method and system based on topological optimization, and relates to the technical field of model lightweight, and the method comprises the steps: carrying out the mesh generation of a target 3D model, and generating a finite element model; a multi-scale topological optimization model is established, density distribution is adjusted through a density filtering function, and a macroscopic lightweight structure is obtained in combination with minimum size constraint and gray scale gradient projection processing; based on a macroscopic lightweight structure, a skeleton structure is obtained, based on an adaptive polygon segmentation rule, a porous filling unit is constructed, a multi-parameterized model is fused, a parameterized representation method of three variables of density, size and shape is established, and an intermediate scale structure is obtained through a multi-target genetic algorithm in combination with a sensitivity analysis technology; and embedding the intermediate-scale structure into the macroscopic lightweight structure, generating a multi-scale structure in combination with cascade optimization, calculating equivalent material attributes by adopting a homogenization method, iteratively adjusting model parameters, and finally obtaining a multi-scale lightweight result.

The invention relates to the technical field of water photovoltaic flexible support pile foundation construction data processing, in particular to a process for a water photovoltaic flexible support pile foundation, which comprises the following steps: fusing meteorological, hydrological and pile deformation multi-source data through a dynamic weight distribution mechanism, and generating a space-time associated dynamic load spectrum by adopting wavelet packet transformation and entropy feature extraction; a collaborative analysis architecture of a finite element simulation and machine learning agent model is constructed, an anchoring depth mapping relation and a real-time design parameter adjustment instruction are output in parallel, and macroscopic fluid parameters and microscopic material degradation characteristics are associated through a data cascade interaction correction mechanism; full-life-cycle data version management is achieved through the block chain technology, and finite element model re-calibration and protection strategy dynamic updating are triggered. The dynamic load response analysis precision and long-term stability of the pile foundation are improved, and the deformation coordination requirement of the flexible support in the complex water area environment is met.

The invention provides a high-precision five-axis rotary table cooperative control method and system based on multi-closed-loop feedback, and the method comprises the steps: obtaining a real-time acceleration signal of a five-axis rotary table, carrying out the spectrum analysis of the acceleration signal, determining a current resonant frequency drift range and a main frequency component, and carrying out the frequency spectrum analysis; dynamically simulating the turntable structure by adopting a pre-established finite element model, and predicting the vibration mode change trend of the structure by comparing the deviation between the modal frequency obtained by simulation and the actually measured signal spectrum; according to the vibration mode change trend, matching similar vibration mode modes from a pre-established rotary table vibration mode database, obtaining corresponding vibration energy transmission path distribution, and determining an energy transmission abnormal region by analyzing the distribution of energy on each transmission path to obtain vibration energy transmission characteristics; according to an optimized motion instruction of a five-axis turntable, the dynamic response of the turntable is estimated, vibration frequency domain characteristics are extracted, and frequency domain compensation parameters are generated in combination with a preset frequency domain error matrix.

The present invention relates to the technical field of machine tool structure optimized design. Disclosed is a method for the optimized design of a headstock structure of a vertical machining center. The method mainly comprises: parameterizing to establish a headstock three-dimensional model; establishing a finite element model of a headstock structure; according to limit working conditions in an actual machining process, performing static and dynamic characteristic analysis on the headstock structure; performing modal test testing on a headstock, carrying out comparative analysis with a simulation result, and correcting a finite element model of the headstock; using a sensitivity analysis method to select a key dimension of the headstock as an optimized design input parameter, while stress strain, low-order inherent frequency, and quality are target output parameters, and establishing a mathematical model for the optimized design of the headstock; and on the basis of a response surface model constructed by a second-order polynomial and a genetic algorithm, performing optimized design of the headstock until an optimal headstock structure which has higher performance and is lighter is obtained. According to the present invention, design efficiency is improved, prototype testing costs are reduced, and a better headstock structure is obtained.

A method and apparatus for evaluating vulnerability of monopile foundations of offshore wind turbines are provided. The method includes collecting offshore wind farm location data and wind-wave characteristic data, and simulating a wind-wave time course according to the offshore wind farm location data and the wind-wave characteristic data; determining a wind-wave dynamic load based on the wind-wave course; obtaining lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters, and inputting the wind-wave dynamic load into a 3D finite element model; using the lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters as boundary conditions of the 3D finite element model; and giving a limit state of monopile foundations, and determining vulnerability of monopile foundations on basis of the dynamic response result of the monopile foundations and the limit state of monopile foundations.

The invention discloses a terrain-level real-scene three-dimensional entity and element dynamic updating method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring data of a target area by an acquisition mechanism based on entity and element type priorities, and preprocessing the acquired data to obtain model data of the target area; s2, change detection: comparing newly collected model data with historical model data, and identifying a change region and type; s3, entities and elements are extracted and updated, wherein the entities and the elements of the change area are processed to obtain updated models; s4, model fusion and optimization: fusing the updated entity and element model with the original model, and realizing efficient acquisition and preprocessing of target area data by using the multi-source surveying and mapping equipment and an acquisition mechanism based on entity and element type priorities; by dividing the small regions according to the region characteristics and correcting the acquisition frequency, the resource allocation is optimized, and the pertinence and efficiency of data acquisition are improved.

The invention discloses an intelligent prediction method for fatigue damage evolution of an aero-elastic sheet based on digital twinning, and the method comprises the following steps: S1, constructing a finite element model, carrying out the modal calibration, and building a digital twinning model; s2, acquiring real-time monitoring data; s3, constructing a crack-induced potential energy tensor field to obtain a crack development potential energy probability distribution diagram; s4, generating a probability prediction map by adopting a potential energy driven map evolution coupling algorithm; s5, constructing a topological damage mapping function, and predicting a dynamic evolution process of topological damage along with load time; s6, applying a time folding algorithm in the dynamic evolution process to obtain a fatigue state submerged space representation; and S7, inverting the topological damage state of the current structure based on the contrast twinborn variational self-encoding network, updating the digital twinborn model, and finally outputting the structural damage evolution trend. According to the method, digital twinning and graph evolution algorithms are fused, and intelligent prediction of the fatigue damage of the aero-elastic sheet is realized.

The invention relates to a nonlinear system reduced-order model generation method fusing physical constraint and digital twinning, and belongs to the technical field of digital twinning. The method comprises the steps of performing sparse sampling on high-dimensional dynamic response data, extracting a dominant mode of high-fidelity model data, and retaining a mode of a physical abrupt change region through physical field gradient constraint in the extraction process; the high-fidelity finite element model and the low-fidelity simplified model are combined, and a mixed order reduction framework based on a multi-fidelity agent model is constructed; fusing a data driving error and a physical mechanism residual error in data-physical joint driving model training and optimization based on a mixed order reduction framework, and updating a primary function of a dominant mode based on online stream data; and performing cross validation on the output of the high-fidelity finite element model and the reduced-order model, and improving the precision of the reduced-order model through an iterative optimization strategy. The intelligent order reduction problem of the model is realized, and the accuracy and rapidity of the order reduction model are ensured.

The invention relates to the technical field of chip heat dissipation, and discloses a photoelectric co-packaging heterogeneous chip heat dissipation integration method, device and equipment, and the method comprises the steps: carrying out the finite element modeling analysis of a photoelectric co-packaging heterogeneous chip, and obtaining a three-dimensional thermal characteristic model; performing signal acquisition and frequency domain characteristic analysis on each functional module of the photoelectric co-packaged heterogeneous chip to generate power consumption prediction data; performing temperature field distribution calculation on the photoelectric co-packaging heterogeneous chip according to the power consumption prediction data and the three-dimensional thermal characteristic model, and constructing a temperature outer ring control model and a heat flow inner ring control model; and performing heat dissipation structure layout optimization calculation based on the temperature outer ring control model and the heat flow inner ring control model, and generating a hybrid heat dissipation layout scheme. Dynamic distribution of heat dissipation power and automatic threshold adjustment are realized, frequent mode switching is avoided, and the thermal characteristic stability of a chip is improved.

The invention relates to a method for establishing and optimizing a multi-scale prediction model for constitutive parameters of a fiber reinforced composite material. Comprising the following steps: constructing a fiber-matrix microscopic RVE model, applying a boundary condition and a load, determining a failure criterion, and calculating equivalent mechanical parameters of a fiber bundle; a fiber bundle-matrix mesoscopic RVE model is generated, fiber bundle equivalent mechanical parameters serve as material attributes of fiber bundles in the mesoscopic RVE, a viscoelastic model is adopted for a matrix, and fiber reinforced material unit cell equivalent mechanical parameters are calculated; constructing a macroscopic fiber reinforced composite material finite element model, endowing a macroscopic model with unit cell equivalent mechanical parameters, defining an orthotropic material card, simulating an explosion impact process by adopting an explicit dynamics algorithm, outputting structural displacement, a damage area and stress distribution, and comparing calculation and test data. And correcting the microscopic RVE model and the mesoscopic RVE model through reverse optimization iteration to obtain a final macroscopic prediction model. The explosion-proof performance of the fiber composite structure is remarkably improved.

The invention provides an additive manufacturing residual stress optimization method and system, and the method comprises the steps: searching a global optimal solution enabling the residual stress of an additive manufacturing part to be minimum from a technological parameter space through the combination of multi-physics field numerical simulation and an intelligent optimization algorithm; the multi-physics field numerical simulation comprises the steps of sampling from a process parameter space by using an optimization algorithm to obtain process parameters, inputting the sampled process parameters into a finite element model, performing thermal analysis on the process parameters based on the finite element model, and obtaining transient thermal distribution through thermal analysis. And performing structural analysis by using transient heat distribution to obtain residual stress under the current process parameters, and transmitting the obtained residual stress to an intelligent optimization algorithm for realizing process parameter optimization. The method has the advantages of high precision, low cost and the like, and high-precision collaborative optimization of multiple types of process parameters can be realized.

The invention relates to an oil bearing carrying capacity evaluation method based on virtual simulation, and the method comprises the steps: obtaining working condition data samples of an oil bearing in a plurality of typical operation states under a complex working condition of a composite load, and carrying out the marking of bearing structure partitions, so as to build a working condition partition mapping table; establishing a multi-physics field sensitive area identification model for each partition, and performing dynamic parameter adjustment on the global finite element model and the local high-precision infinitesimal sub-model by using a multi-physics field coupling calibration factor; and performing time sequence feature extraction on a multi-physical field joint simulation result after dynamic parameter adjustment to obtain a multi-dimensional parameter sequence related to the typical failure mechanism of the oil bearing.

The invention provides a multi-mode tailing pond dam break digital twinning emergency deduction and decision optimization method, which comprises the following steps of: firstly, constructing a high-precision three-dimensional scene model, performing simulation calculation on the high-precision three-dimensional tailing pond model based on a finite element model to obtain a dam body stress-strain field, and performing simulation calculation on the dam body stress-strain field based on ANSYS + reinforcement learning. And predicting a dam break probability distribution diagram by using the dam body stress-strain field and the current environment state, generating a rescue scheduling scheme based on the dam break probability distribution diagram and the resource distribution data, building a dynamic virtual deduction scene for exercise, and verifying and optimizing the rescue scheduling scheme according to exercise record data. And a final rescue scheduling scheme is obtained. According to the method, the ANSYS + reinforcement learning hybrid engine is utilized to reinforce the deduction precision, dam break path prediction, optimal resource scheduling and plan self-optimization closed loop are realized, the problems of data fusion distortion, deduction stiffness, decision lag and the like in the traditional technology are solved, and the method is particularly suitable for variable working conditions such as tailing pond seepage field sudden change and extreme weather.

The invention provides an underground water dynamic evolution prediction method, and belongs to the technical field of underground water prediction based on deep learning. Hydrological parameters and geographic position data of monitoring points are collected, and a regional hydrogeological input tensor is constructed; then establishing an initial finite element model to carry out space extrapolation and flow field completion on underground water level distribution; based on an initial finite element model structure, designing a global optimization algorithm based on target decomposition, carrying out real-time structure optimization on the finite element model, then simulating underground water evolution of a non-monitoring area through a finite element numerical solution, and generating multi-node physical consistency hydrological time series data after resolution is improved; and inputting the generated hydrological time series data into a designed perceptual water level prediction model, and outputting underground water level prediction results at multiple moments and multiple positions in the future and corresponding underground water level thermodynamic diagrams. According to the method, more accurate, continuous and interpretable high-precision prediction of the underground water system is realized, and reliable support is provided for scientific management of underground water resources.

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 provides a vibration analysis method of a reactor vibration isolation system, a program product and electronic equipment. The method comprises the following steps: acquiring a vibration signal of a vibration isolation bracket in a working state; extracting vibration characteristics from the vibration signals by using time domain analysis and frequency domain analysis; according to the vibration characteristics, determining association between the structural characteristics of the vibration isolation bracket and the vibration characteristics to obtain first association; according to the finite element analysis and the first relevance, a finite element model of the vibration isolation support is established; according to modal analysis and a finite element model, the inherent frequency of the vibration isolation support is determined, the correlation between the inherent frequency and the external excitation frequency is determined, and second correlation is obtained; according to the first relevance, the second relevance and a finite element model, the influence of different structural features on the vibration propagation path of the vibration isolation support is simulated, and the damping effect is obtained; and according to the damping effect, the vibration isolation support is optimized. The problem that it is difficult to guarantee the damping effect and the good vibration isolation performance of a vibration isolation support at the same time in the prior art is solved.

The invention discloses a pre-twisted damper fatigue life prediction method for an extra-high voltage ground wire, and particularly relates to the technical field of power transmission line vibration prevention. Collecting multi-source vibration and environment data, and constructing a vibration characteristic time sequence; amplitude, frequency and strain energy are extracted based on the multi-frequency vibration response, and a multi-frequency collaborative vibration model is established; obtaining a damper structure and material parameters, establishing a finite element model, coupling the finite element model with a vibration model, and simulating local stress response; a Rainflow counting method and a Miner damage criterion are adopted to construct a fatigue damage factor distribution matrix; predicting the residual fatigue life of the damper based on the damage evolution trend, and evaluating the replacement opportunity; according to the method, accurate fatigue life prediction and optimal replacement strategy recommendation of the pre-twisted damper under complex working conditions can be realized, and the method has high precision, high adaptability and engineering practicability.

The present invention relates to the technical field of mechanical property testing and characterization. Disclosed are a method and apparatus for calculating a crack propagation life of an engine structure in vibration fatigue, which method and apparatus aim to obtain reliable fracture mechanics parameters and a crack propagation rate curve and provide an accurately quantified crack propagation life, so as to provide methodological guidance for the life assessment of reusable engines. The calculation method comprises: acquiring a time-domain random stress spectrum of stress responses in an assessment section in a finite element model of an engine structure; on the basis of the time-domain random stress spectrum, performing finite element analysis processing on a crack model of the engine structure, so as to determine fracture mechanics parameters of a cracked structure; performing a vibration fatigue performance test on a crack propagation rate model, which is set up according to a preset crack propagation mode, and determining, by using parameter identification, a target parameter value corresponding to the crack propagation rate model; and in combination with the parameter value corresponding to the crack propagation rate model and a preset critical crack length value, performing crack propagation life calculation, so as to obtain a target crack propagation life.

The invention discloses an internal defect detection method and system for an automobile injection molding part and a storage medium, and relates to the technical field of injection molding part defect detection.The method comprises the steps that scanning imaging is conducted on a to-be-detected automobile injection molding part, abnormal area identification marking is conducted, and N initial injection molding part abnormal areas are obtained; activating secondary defect detection equipment, and collecting N multi-source detection images of abnormal areas of the N primary injection molded parts; carrying out registration alignment and multi-scale fusion to obtain N region multi-source fusion images, carrying out defect classification positioning, and determining an injection molding part region defect feature set; and establishing an injection molding part finite element model, and generating an injection molding part internal defect detection result. The technical problem that the detection accuracy and reliability are insufficient due to the fact that the internal defects of the automobile injection molding part are difficult to comprehensively detect in the prior art is solved, and the technical effects of comprehensively detecting the internal defects of the automobile injection molding part, evaluating the influence of the defects and improving the detection accuracy and reliability of the internal defects of the automobile injection molding part are achieved.

The invention discloses an online health monitoring system suitable for a large-span roof truss steel structure, and the system comprises a structure analysis module which is used for building a three-dimensional finite element model of the roof truss steel structure, carrying out the application of load and boundary conditions, stress-strain analysis and key position recognition, and providing a monitoring point arrangement suggestion; the real-time measurement module is used for selecting and arranging sensors, collecting and transmitting real-time data, preprocessing and storing the data and providing real-time monitoring and alarming functions; and the intelligent analysis module is used for processing and cleaning the collected data, executing anomaly detection and fault diagnosis, carrying out trend analysis and prediction, optimizing the monitoring and analysis process by utilizing machine learning and artificial intelligence technologies, and generating a visual report. According to the invention, comprehensive, online, real-time and accurate health state monitoring of the roof truss steel structure is realized, the safety and stability of the large-span roof truss steel structure are ensured, the service life is prolonged, the maintenance cost is reduced, and the method has important practical application value.

The invention discloses a comprehensive performance prediction and structural parameter optimization method for a hemispherical resonator, and belongs to the technical field of aerospace inertia. According to the method, a high-precision finite element simulation model of the ideal hemispherical harmonic oscillator is established based on a multi-region grid uniform division method; aiming at the condition that each structure parameter corresponds to different structure sizes, calculating through a finite element model of a control variable to obtain a data set of different performance parameters of the hemispherical resonator; based on a BP neural network algorithm optimized by a particle swarm optimization algorithm, after the data set is divided into a training set and a test set, a comprehensive performance parameter prediction model of the hemispherical harmonic oscillator is established; and taking the PSO-BP neural network model as the input of a second-generation non-dominated sorting genetic algorithm, and performing multi-objective optimization on the structural parameters of the hemispherical harmonic oscillator by taking higher thermoelastic damping and larger minimum frequency difference between the interference modal and the PSO-BP neural network model as optimization objectives. The use performance of the hemispherical resonator is improved, and the performance requirement of the aerospace technology field on the high precision of the hemispherical resonator gyroscope is met.