277 results about "Mechanism analysis" patented technology

The invention provides a multi-agent system for water quality purification material development, and belongs to the technical field of computer systems based on specific calculation models. The system comprises a data extraction agent, a material design agent, a material evaluation and screening agent, a synthesis method generation agent, a material characterization agent, a working condition matching agent, an effect verification agent, a mechanism mining agent and a material field knowledge base. According to the system, through cooperative work of a plurality of intelligent agents, a development report including structure information, an evaluation report, a synthesis scheme, a characterization scheme, a working condition matching scheme, an effect verification report and a deep mechanism analysis report of a water quality purification material for realizing a target task of the water quality purification material is directly generated according to the target task of the water quality purification material; the method does not depend on manpower and computing power resources, and saves time and labor.

The invention relates to the technical field of semiconductor photoelectronics, and discloses a multi-physics field coupling silicon-based optical interconnection chip degradation model construction method and application, and the method comprises the steps: firstly constructing a multi-physics field coupling analysis model, and quantitatively representing the coupling relation of thermodynamics, optics, electricity and material aging through an inter-field coupling equation and a material aging dynamic model; simulating field distribution of a chip key area under multi-environment stress through finite element analysis, and extracting data; then historical degradation data is collected by combining an accelerated aging test and long-term operation monitoring, and a degradation parameter prediction model is established after data processing based on machine learning; and finally, inputting a finite element result and a prediction result into a material aging dynamic model, and finally forming a multi-physics field coupling degradation model. The problems that the prior art depends on a single physical field, degradation mechanism analysis is incomplete, and service life evaluation is inaccurate are solved, and support can be provided for reliability evaluation, structure optimization and service life prediction of the silicon-based optical interconnection chip.

The invention discloses a driving mechanism analysis method of reservoir group scheduling for flood non-consistent evolution, which comprises the following steps: acquiring a smooth reservoir entering flood sequence by utilizing a topologically coupled physical manifold constraint denoising model, and constructing dynamic topological characteristics representing propagation time lag and sensitivity through a hydraulic propagation mechanism-based Figure ordinary differential equation; constructing a structural causal model comprising a scheduling capability index, ectogenic hydrological driving and topological characteristics, and fitting a nonlinear dependency relationship by using a causal generalized additive model; executing anti-fact inference, and calculating a local causal driving index and a global causal cumulative effect index through a dry budget; and generating an optimal scheduling strategy for suppressing variation based on the causal index. According to the method, systematic analysis from data physical restoration to causal mechanism decoupling can be realized, the driving contribution of a scheduling behavior to flood inconsistency is accurately quantified, and decision support is provided for scientific flood control of a drainage basin.

The invention relates to the technical field of psychological health, and discloses a psychological health multi-modal evaluation method, system and device and computer equipment, and the method comprises the steps: obtaining multi-modal data, and carrying out the feature extraction of the multi-modal data, and obtaining multi-modal features; performing emotion-oriented cross-modal attention mechanism analysis on the multi-modal features to respectively obtain a preset-dimension emotion scale vector, an emotion semantic feature, a multi-dimensional emotion feature and an uncertainty quantitative feature; fusing the preset dimension emotion scale vector, the emotion semantic feature, the multi-dimensional emotion feature and the uncertainty quantitative feature to obtain a multi-modal fusion feature; and obtaining a mental health assessment result based on the multi-modal fusion features and a preset multi-task learning framework. Through a cross-modal attention mechanism, deep semantic fusion of five modals of vision, audio, physiology, text and behavior is realized, and intelligent mapping from original multi-modal data to accurate psychological state judgment is realized through a deep learning technology.

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

The invention relates to a cross-system lattice constant modeling method, device and medium based on Pareto frontier optimization and symbolic regression collaboration, and the method comprises the steps: collecting theoretical calculation and experimental data of a perovskite and spinel cubic phase system, and constructing an initial data set containing geometric and electronic structure parameters; preprocessing the data; performing global search by adopting a Pareto frontier optimization algorithm to improve prediction precision and feature consistency to obtain an optimal feature subset; generating a lattice constant analytical model with physical significance through genetic programming by using a symbolic regression algorithm; the model is finely adjusted through experimental data, and a cross-system prediction general formula suitable for experimental conditions is obtained. Compared with the prior art, the method has the advantages that the global optimal feature subset is efficiently searched in the multi-material system, the explicit quantitative relation between the lattice constant and the key material attribute is established, and an efficient and reliable calculation means is provided for lattice constant prediction and mechanism analysis of the complex material system.

The invention discloses a psychological health assessment system and method based on big data, and relates to the technical field of physical and psychological health assessment, the system comprises a data acquisition module, a data processing module, a psychological mechanism analysis module, an ability quality modeling module, a multi-modal detection module and an assessment result generation module; the technical key points are as follows: in the implementation process of the technical scheme, a data acquisition module synchronously acquires static, dynamic, self-assessment and real-time emotion data of an assessed person in multiple scenes of campus, security and protection and the like; with a campus scene as an example, static information such as homework documents and social platform records of students is collected, dynamic behavior data such as classroom interaction and extracurricular activities are collected through an intelligent camera and voice equipment, and meanwhile, real-time emotion data are obtained by means of a face video stream collection technology to form a multi-dimensional data source; the heterogeneous data are processed into standardized coded data in a unified mode, and the limitation that traditional evaluation depends on a single questionnaire or simple observation is broken through.

The invention relates to an ecological system health space-time driven attribution analysis method based on IGTWR-XGBoost, and the method comprises the steps: obtaining an ecological system health index of a target region, calculating the ecological system health index of the target region according to the ecological system health index, and collecting a multi-source driving factor; constructing an interactive geographic space-time weighted regression model according to the multi-source driving factor, the ecological system health index and a geographic-time weighted regression model; acquiring a multi-dimensional local regression coefficient matrix according to the interactive geographic space-time weighted regression model; correcting a multi-source driving factor according to the multi-dimensional local regression coefficient matrix to form a space-time enhanced feature matrix; and inputting the space-time enhanced feature matrix into an XGBoost model, and obtaining a prediction result of the ecological system health index and global and local contribution values of each driving factor. According to the invention, the accuracy and reliability of ecosystem health driving mechanism analysis can be significantly improved.

The invention belongs to the technical field of vibration control, and provides a fluid-structure interaction numerical calculation method for controlling flutter of a large-span bridge by a submerged heaving plate, which is based on computational fluid dynamics software Fluent, utilizes a dynamic grid technology, compiles a UDF secondary development program and embeds the UDF secondary development program into a solver for calculation. The method comprises the following steps: establishing a motion control equation of a rigid main beam-sling-underwater heaving plate coupling system in a wind field, embedding a system motion solving process into Fluent software by utilizing a user-defined function, and realizing system fluid-solid coupling solving by adopting a mode of alternately solving a rigid main beam and a wind field computational domain and simultaneously solving a heaving plate and a water body computational domain. And vertical and torsional motion responses of the controlled main beam are obtained. Compared with a physical experiment, rich information such as movement time history data of the main beam, the heaving plate and a nearby flow field under the condition of various sizes can be conveniently and safely obtained, vibration suppression mechanism analysis can be conveniently carried out, and the research difficulty, cost and risk are greatly reduced.

The invention relates to the technical field of neural networks, in particular to an intelligent teaching supervision evaluation method and system based on a large model, and the method comprises the following steps: collecting multi-source data of a teacher, carrying out the tagging and normalization processing, constructing a time sequence behavior set and a tag sorting sequence, clustering high frequencies, issuing the clustering high frequencies, and marking conflicts. And extracting a change trend adjustment boundary, sorting score jump dominant tags, connecting behaviors to establish a score linkage path, and outputting a teaching supervision evaluation scheme. According to the method, structural recombination and dynamic expression of teaching behaviors are achieved through multi-source data label construction and time sequence integration, behavior classification accuracy is improved by combining behavior frequency clustering and conflict recognition, key behavior characteristics are extracted by means of trend ratio and score jump alignment, score influence factor sorting and behavior flow direction chains are established, and the behavior classification accuracy is improved. Chained mapping of teaching behaviors and score changes is realized, the evaluation discrimination capability and the data response efficiency are enhanced, and the teaching behavior identification accuracy and the score mechanism analysis definition are improved.

The invention discloses a satellite-ground bistatic synthetic aperture radar (SAR) dual-angle two-dimensional deformation inversion method, which constructs a dual-angle system configuration based on a satellite-ground bistatic SAR, proposes a two-dimensional deformation inversion method under the satellite-ground bistatic SAR dual-angle configuration, and obtains two-dimensional deformation of a kilometer-level region. The limitation that a traditional satellite-ground SAR can only obtain one-dimensional deformation in the equivalent sight line direction is solved, the method is suitable for refined monitoring of complex deformation fields in geological disasters such as landslides and earthquakes, and multi-dimensional data support is provided for disaster mechanism analysis. Observation data of ground double receiving stations are fused, shielding and low correlation areas are masked, the influence of noise phases and orbit error phases is effectively restrained, the problem that the space-variant property of the equivalent sight included angle of the satellite-ground bistatic SAR is large is solved, and the two-dimensional deformation precision reaches the centimeter level.

The invention provides a heat supply system thermal load prediction method based on time sequence compensation and hybrid learning, and the method comprises the steps: constructing a thermal load prediction input variable screening mechanism based on two-dimensional analysis, and achieving the scientific screening of input variables through the dual verification of a Pearson's correlation coefficient matrix and a significance test; a thermal load-temperature time sequence synchronization system based on dynamic time shift compensation is innovatively designed, and the problem of time sequence dislocation caused by instability of manual experience adjustment in a central heating system is solved; an ES-LSTM collaborative prediction architecture is provided, and a three-level prediction model of'trend decomposition-random learning-dynamic weighting 'is established. According to the method, statistics significance test and thermodynamic mechanism analysis are combined in a breakthrough manner, a dynamic compensation system with a time sequence self-correction capability is innovatively researched and developed, a hybrid model collaborative prediction mechanism is constructed, and a complete technical chain from data preprocessing to prediction model architecture is formed.

The invention discloses a debris flow formation mechanism analysis and risk assessment method based on multi-modal data fusion. The method comprises the steps that S1, debris flow multi-modal data are collected and preprocessed; s2, constructing a condition vector; s3, generating debris flow risk scene data according to the condition vector; s4, evaluating the authenticity probability of the generated data by using a discriminator in the conditional generative adversarial network; s5, performing hierarchical modeling on the generated debris flow risk scene data by adopting a multi-scale image convolution layer to obtain updated node feature representation; s6, calculating a global debris flow risk prediction result, and generating a debris flow risk assessment report; s7, finely adjusting the generator and the discriminator according to the actual debris flow event occurrence condition; and S8, performing real-time evaluation and early warning on the debris flow risk. According to the method, an efficient and scientific optimization scheme can be provided in debris flow formation mechanism analysis and risk assessment, and remarkable technical values and economic benefits are brought to practical application.

An analysis method for a metro line steel rail corrugation forming mechanism comprises the steps that numerical model input parameters are obtained, and a wheel-rail three-dimensional coupling numerical model suitable for a vehicle-rail / wheel set of a metro line is constructed; longitudinal and transverse half width lengths of wheel-rail contact spots in the stable running process of the vehicle / wheel are obtained so as to determine a creep force-creep rate relation curve; determining a creep rate saturation critical value to judge the creep saturation state of the wheel-rail system; a macroscopic determination program for determining the structural stability of the wheel-rail system; determining an analysis process of a steel rail corrugation forming mechanism; judging whether the contact interface creep is saturated or not, and analyzing the probability of occurrence of rail corrugation according to the judgment; and determining a wavelength fixing method for rail corrugation in an actual measurement interval, and verifying the effectiveness and applicability of the method. According to the method, the steel rail corrugation phenomenon on a subway line is focused, the analysis method is provided from the two aspects of microcosmic creep saturation and macroscopic structure stability, and therefore the occurrence probability of steel rail corrugation is reflected more accurately.

The invention discloses an overhead transmission line multi-disaster risk early warning method and device based on disaster-pregnant mechanism analysis, and belongs to the technical field of transmission line disaster early warning. The method comprises the steps of data acquisition and disaster-pregnancy mechanism model construction, disaster coupling analysis and risk level judgment, Monte Carlo simulation and single-disaster early warning, and comprehensive risk assessment and model output. According to the method, dynamic and accurate multi-disaster early warning of the overhead transmission line is realized by constructing the disaster-pregnant mechanism model, quantifying the disaster coupling effect, performing Monte Carlo simulation and performing comprehensive risk assessment.

The invention relates to the technical field of fluid mechanics modeling and aerodynamic acoustic analysis, in particular to a spectral orthogonal decomposition-based separated flow wall surface pressure pulsation dominant structure modeling method, which comprises the following steps of: acquiring a whole flow field and / or wall surface unsteady pressure data of a separated flow under a preset working condition; performing spectral orthogonal decomposition on the unsteady pressure data, performing frequency domain decoupling on multi-scale features, and extracting feature values and spatial dominant feature modes under each feature frequency; constructing a complex wave packet physical parameterized model, fitting the spatial dominant feature modals, and compressing the spatial dominant feature modals into sparse physical parameter vectors after nonlinear regression solution; and reconstructing a wall surface pressure pulsation dominant component based on the sparse physical parameter vector and the characteristic value, and carrying out at least one of flow mechanism analysis and pneumatic order reduction modeling according to the dominant component. Therefore, the problems of low reconstruction precision and the like caused by the fact that asymmetric evolution and variable acceleration convection of a large-scale structure in separation flow wall surface pressure pulsation cannot be accurately represented in a frequency domain in related technologies are solved.

The invention relates to the technical field of internal combustion engine superchargers, and discloses a time window dynamic adjustment method and system for fault diagnosis of a diesel engine supercharger, and the method comprises the steps: extracting time sequence data of key characteristic parameters of the diesel engine supercharger through mechanism analysis when the fault diagnosis of the diesel engine supercharger is carried out based on deep learning; after preprocessing, inputting into a fault diagnosis model; and adaptively adjusting a time window of the fault diagnosis model based on the principal component analysis result, so that the time window is adjusted according to the fluctuation change of the feature data. According to the method, the time window of the turbocharger in the actual working condition data can be dynamically adjusted, the concept of time domain-feature domain double closed-loop control is utilized, principal component analysis is improved from a traditional dimension reduction tool to a dynamic system regulator, a quantitative mapping model of the window length and the working condition data is established, and on the premise that feature information is guaranteed, the real-time performance of the turbocharger is improved. And the data processing amount of the window can be effectively reduced.

The invention discloses a control valve accelerated degradation experiment method which comprises the following steps: S1, determining a control valve fault mode according to real working conditions; s2, sorting fault modes of the control valve, and carrying out degradation mechanism analysis; s3, accelerated stress selection and experiment setting; and based on degradation mechanism analysis, determining experiment schemes corresponding to different fault modes. The method comprises the following steps: firstly, according to a real working condition, determining a fault mode which is easy to occur on the control valve under the condition, namely finding out a fault mode with relatively high occurrence frequency; secondly, on the basis of the sorted fault modes, degradation mechanism analysis is carried out on the fault modes, and the degradation mode of the fault is determined according to the mechanism; and finally, based on an analyzed degradation mechanism, selecting a key index which can best represent performance degradation of the control valve in the fault mode, determining acceleration stress according to the key index, and finally determining a specific method for accelerated degradation of the control valve fault by analyzing and clearing the index, thereby collecting more process data in a short time.

The application provides application of grass aconite in preparation of anti-epilepsy drugs and a mechanism analysis method, relates to the technical field of anti-epilepsy drugs, and discloses a "drug active ingredient-key target point-disease-pathway" network diagram of the grass aconite anti-epilepsy by starting from the chemical structure of the effective component of the grass aconite, applying network pharmacology and molecular docking technology, revealing how the active component of the grass aconite plays the drug efficacy through "multi-target point, multi-pathway and multi-pathway" combined regulation, predicting the anti-epilepsy target point and mechanism of the grass aconite, and providing certain theoretical reference for research and development of the grass aconite as an anti-epilepsy drug and a functional food.

The invention discloses a turbine fault characteristic collaborative verification method and system, and belongs to the technical field of turbine fault diagnos.The turbine fault characteristic collaborative verification method comprises the steps that structural component parameters, operation boundary conditions and historical operation data of a target area of a turbine are collected, and a heat-stress-vibration coupled simulation model is established; a working condition disturbance factor is introduced to simulate an unsteady-state operation condition; a simulation model is driven through a typical operation event, a response set is extracted, and an abnormal response mapping domain is constructed; actual measurement monitoring data are collected and reconstructed, and feature response vectors are generated; projecting the simulation response to a feature response space, and constructing a response fusion error tensor; generating a verification weight field, extracting highly-correlated simulation sub-responses, and forming a dynamic weighted matching group; identifying an optimal influence path chain through path backtracking, and outputting a fault judgment result and a collaborative verification index; according to the method, traceable modeling, quantitative verification and response mechanism analysis of fault identification are realized, and the method has high precision, high interpretability and engineering practicability.

The invention belongs to the technical field of fluid control, and particularly relates to a system for predicting nuclear power plant valve opening and flow nonlinear sudden change. The preprocessing and mechanism analysis module is responsible for performing deep processing and consistency review on the data acquired by the sensor, and performing modeling and parameter correction on flow sections of different types of valves in combination with a physical mechanism; the data driving prediction module is responsible for performing XGBoost modeling and reasoning on historical and real-time collected multi-dimensional data on the basis of a physical mechanism analysis result, outputting a fine prediction value of the valve flow and providing a more sensitive response for a nonlinear abrupt change region; and the hybrid model fusion and adaptive updating module is responsible for organically fusing prediction results of the physical mechanism model and the XGBoost model, dynamically adjusting model parameters according to real-time operation data, and realizing high-precision prediction and stable control of the valve flow. According to the invention, real-time and high-precision prediction of non-linear abrupt changes possibly occurring in different opening degree intervals of the valve is realized.

The invention discloses a fan blade icing monitoring method based on mechanism analysis, and the method comprises the steps: 1, constructing a dynamic mechanism model of "pneumatic-thermal-quality" three-phase strong coupling, local micrometeorological conditions and water drop impact characteristics of each airfoil section and feedback effects of icing growth on aerodynamic configuration and subsequent icing in the blade rotation process are calculated in real time; 2, performing icing prediction based on the constructed dynamic mechanism model, wherein the icing prediction comprises local microenvironment calculation, dynamic icing growth calculation and pneumatic feedback calculation; step 3, judging an icing state; step 4, outputting an icing state and early warning information; the problems that a data-based driving method needs to depend on a large amount of high-quality annotation data, when the data size is insufficient or data distribution changes, the generalization ability and prediction precision of a prediction model can be greatly reduced, the internal mechanism of icing cannot be explained, and the reliability requirement in engineering application is difficult to meet are solved.

The invention relates to the technical field of co-seismic landslide risk evaluation, in particular to a near-fault single co-seismic landslide motion range mapping method and system. The method comprises the following steps: acquiring fracture structure data of a research area; positioning a main seismic fracture based on the fracture structure data, and performing fracture mechanism analysis to obtain seismic source parameter data; constructing a fault geometric structure and earth crust medium structure model containing a three-dimensional terrain based on the fracture structure data and the seismic source parameter data to obtain three-dimensional structure model data; and setting fault source parameters and stratum velocity medium parameters based on the source parameter data and the three-dimensional structure model data to obtain seismic simulation input parameters. According to the method, the whole process of near-fault co-seismic landslide from seismic triggering to accumulation is accurately reproduced through an integrated physical driving simulation frame, the three-dimensional motion range and the danger partition are evaluated in a high-precision mode, and a reliable basis is provided for risk evaluation and disaster prevention decision making.

The invention discloses a switch cabinet condensation mechanism analysis method based on a multi-physical coupling field and a fractal theory, and the method comprises the steps: building a three-dimensional simulation model of a switch cabinet based on a solidworks environment; based on electromagnetic-flow-heat-wet coupling field simulation, judging parts easy to damp and condense; establishing a prediction model of condensation distribution in the switch cabinet based on a fractal theory; analyzing the condensation development process of the edge extraction condensation image based on an improved morphological method; the condensation development sequence of insulating parts in the switch cabinet is determined by simulating the diffusion process of wet air in the switch cabinet on the basis of multiple physical fields, the method is simple in principle, the parts, prone to generating condensation, in the switch cabinet can be accurately positioned, and the generation and development process of the condensation can be clearly analyzed and predicted.

The invention relates to the technical field of city construction, in particular to a city built-up environment intelligent evaluation system based on multi-source data fusion, which comprises multi-source data acquisition and processing, a city update potential recognition model, cross-scale quantitative evaluation system construction and built-up environment and city vitality nonlinear interaction mechanism analysis. The construction of the cross-scale quantitative evaluation system specifically comprises the step of establishing a comprehensive evaluation model of facility density, population matching degree and traffic network structure in combination with an AHP-entropy weight method and spatial syntactic analysis. According to the invention, through multi-source data fusion, cross-scale quantitative evaluation, machine learning modeling and interpretability analysis, an accurate, efficient and generalizable city update decision support system is constructed.

The invention discloses a forklift drive-by-wire drum brake control method and device based on a rapid nonsingular terminal sliding mode, and the method comprises the steps: 1) carrying out mechanism analysis for a mechanical structure and dynamic characteristics of a forklift drive-by-wire drum brake system, and building a dynamic model of the forklift drive-by-wire drum brake system; 2) according to the dynamic model established in the step 1, designing a fast nonsingular terminal sliding mode surface and a power reaching law so as to realize fast convergence of a system state; (3) according to the sliding mode surface and the reaching law designed in the step (2), a rapid nonsingular terminal sliding mode control algorithm suitable for the forklift drive-by-wire drum brake system is designed; and 4) based on the Lyapunov stability theory, proving that the control algorithm designed in the step 3 can enable the system state to converge to a balance point within finite time, and performing simulation on an MATLAB / Simulink platform to verify the effectiveness of the control algorithm in the forklift drive-by-wire drum brake system. And the response speed and the control precision of the drive-by-wire drum brake system of the forklift can be obviously improved.

The invention discloses an interactive catalysis method and platform based on molecular simulation and artificial intelligence. According to the method, intelligent design of a catalyst is achieved through data classification, condition optimization, structure modeling and mechanism analysis. Catalytic data uploaded by a user is subjected to standardization processing and then is divided into two types: a data set A is used for recommending an optimal reaction condition by a multi-objective optimization algorithm; a three-dimensional model is constructed after a catalyst structure of the data set B is optimized through quantum chemistry software, a plurality of intermediates and transition state structures are optimized, and geometric / electronic descriptors are calculated. And constructing a dual-model collaborative prediction system of the model E and the model F through a feature screening and dimension reduction technology, and finally screening the high-performance catalyst by utilizing a model F driven molecule generation algorithm. According to the method, flexible adaptation to specific application scenes can be achieved, the urgent requirements of the industry for efficient and accurate catalyst performance prediction and intelligent design are met, cost reduction and efficiency improvement of the industry are effectively assisted, and the research and development process of the catalyst is accelerated.

The invention discloses a data driving and mechanism analysis combined unmanned aerial vehicle power consumption prediction method and device, a medium and equipment, and the method comprises the steps: decomposing the total power consumption of an unmanned aerial vehicle into a parasitic power component, an induction power component and a leaf surface power component, calculating the theoretical value of each component, and obtaining a mechanism feature vector; extracting hidden feature variables of flight state parameters of the unmanned aerial vehicle by using a feature extraction network, performing hierarchical fusion on the hidden feature variables and mechanism feature vectors through feature splicing in a middle feature layer to obtain an unmanned aerial vehicle power consumption prediction model, and performing joint optimization on prediction errors and regularization constraints by using a composite loss function to obtain the unmanned aerial vehicle power consumption prediction model. Training the unmanned aerial vehicle power consumption prediction model to obtain an optimized unmanned aerial vehicle power consumption prediction model; and inputting the flight state parameters of the unmanned aerial vehicle into the optimized unmanned aerial vehicle power consumption prediction model, and outputting an unmanned aerial vehicle power consumption prediction result, and realizing high-precision and high-stability power consumption prediction by fusing the mechanism characteristics of aerodynamics and the deep neural network.

The invention relates to a roadbed and pavement subsidence cause mechanism analysis method based on a combined weight. The method comprises the following steps: acquiring a basic factor data set influencing subsidence of a target road section; classifying data of the basic factor data set into a preset influence factor index system to obtain a structured index set; the preset influence factor index system comprises a target, a plurality of criteria and a plurality of indexes; the structured index set comprises a standardized index value and a grade criterion table corresponding to each index; calculating a subsidence influence factor index comprehensive weight according to the structured standard set to obtain a combined weight vector; and based on the combined weight vector, performing roadbed and pavement subsidence cause mechanism sorting and risk identification of the target road section according to the structured index set, and obtaining subsidence contribution degree sequences of the indexes and road section subsidence levels. By adopting the method, the importance of each index in the subsidence cause mechanism can be quantified through the preset influence factor index system, and reference is provided for subsequent engineering disease treatment measure suggestions.

The invention provides a metal pitting defect degree evaluation method and system based on artificial intelligence, and the method comprises the steps: obtaining microscopic porosity distribution data of a metal plate in a forming process, and an internal fluctuation signal of the metal plate under the action of a directional stress wave; based on the propagation attenuation rate of the internal fluctuation signal, generating geometric parameters of the structure abnormal area corresponding to the micro porosity distribution data; acquiring current density distribution data of the structure abnormal region; inputting the microscopic porosity distribution data, the geometric parameters of the structure abnormal region and the current density distribution data into an artificial intelligence model so as to associate the microscopic porosity distribution data with the current density distribution data, and generating a pore current coupling characteristic matrix of the metal plate; according to the pore current coupling characteristic matrix, generating a quantitative evaluation result of the internal pitting defect degree of the metal plate; according to the technical scheme provided by the invention, dynamic quantitative evaluation on the degree of the pitting corrosion defect in the metal is realized, and the technical bottleneck of a traditional single-index detection method in defect evolution mechanism analysis and quantitative precision is broken through.