401 results about "Mechanism analysis" patented technology

A rolling bearing combined fault diagnosis method based on a multi-domain feature construction is provided, including: collecting vibration signals of rolling bearings, extracting time-domain and frequency-domain features of fault-sensitive vibration signals of the rolling bearings, and obtaining an in-domain feature set; performing an improved empirical mode decomposition on the vibration signals of the rolling bearings to obtain time-frequency features; constructing a multi-domain fault sample set based on the in-domain feature set and the time-frequency features; performing a fault mechanism analysis on the multi-domain fault sample set to obtain modulation features under each single fault; carrying out a feature engineering on the time-domain and frequency-domain features, and screening out relevant features, redundant features and irrelevant features; performing an unsupervised hierarchical clustering on test set fault samples, and then inputting the test set fault samples into a KNN model for a classification, and obtaining rolling bearing combined fault diagnosis results.

The invention discloses an asymmetric fault transient stability analysis method and system based on a network-forming inverter. Relates to the technical field of power system transient stability analysis. According to the scheme, a mathematical model of hybrid synchronous control before and after a fault is derived according to a power grid system model of a hybrid synchronous control inverter connected to a power grid, and a transient instability mechanism based on the hybrid synchronous control inverter under an asymmetric fault is analyzed from the aspect of legal quantity by means of a power angle curve and a phase plane. The influence rule of the positive and negative sequence current on the stability is disclosed; based on mathematical modeling, a power angle curve and a phase diagram method, the influence of positive and negative sequence component coupling under an asymmetric fault is considered, and transient instability mechanism analysis of hybrid synchronous control is realized; the boundary of instability caused by positive and negative sequence current can be determined, a converter transient stability analysis method considering positive and negative sequence coupling caused by asymmetric faults under network construction type control is perfected, and a reference is provided for improving the transient synchronization stability of the converter under large disturbance; meanwhile, the researched current loss stability boundary can provide reference for a current limiting reference value.

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 discloses a submarine pipeline stability discrimination method based on submarine scour mechanism analysis, and relates to the technical field of submarine facility protection, basic data of a target sea area are obtained, and the basic data comprise geological information of an area where a submarine pipeline is located, hydrological information of a sea area near the pipeline and pipeline information; performing feature analysis on the basic data of the target sea area, and extracting feature data related to water flow scouring, wave scouring and soil particle movement; according to the method, the risk area which is easily influenced by scouring around the pipeline is accurately identified through seabed scouring mechanism analysis, and then key monitoring and early warning are performed on the risk area, so that protection measures are strengthened in a targeted manner, and the influence of scouring on the stability of the pipeline is dynamically evaluated through continuous monitoring and analysis; the problems of pipeline deformation and stress concentration caused by scouring can be found in time, a maintenance strategy is adjusted according to the problems, the pertinence of safety protection is improved, resources can be effectively saved, and the maintenance cost is reduced.

The invention belongs to the technical field of vibration control, and provides a CFD numerical simulation-based TMDI control bridge rigid main beam low-frequency vertical vortex vibration research method, which comprises the following steps of: establishing a rigid main beam-TMDI vertical coupling vibration equation in a wind field, carrying out order reduction processing on the coupling vibration equation by constructing a state equation, embedding a solving process into Fluent software by utilizing a user-defined function, and carrying out low-frequency vertical vortex vibration research on a rigid main beam-TMDI control bridge rigid main beam. And the vertical vortex-induced vibration response of the controlled main beam is obtained by alternately solving the flow field domain and the structural domain. Compared with a theoretical method, the fluid-solid coupling effect can be directly simulated, a vortex-induced force model does not need to be assumed, and the result is more reliable; compared with a wind tunnel test method, various parameters of the structure and the TMDI can be accurately set, physical space constraint is avoided, machining errors do not exist, the method is not limited to low wind speed, and the vibration suppression effect can be verified more conveniently; compared with an on-site actual measurement method, the method does not need to passively wait for vertical vortex vibration of a real bridge for a long time, can obtain richer information, facilitates the development of vibration suppression mechanism analysis, and greatly reduces the research difficulty and cost.

The invention discloses a thermal error prediction method for a spindle system of a high-speed precision machine tool under an inclined working condition, and develops a comprehensive thermal error compensation method for the spindle system of the high-speed precision machine tool running under the inclined working condition by utilizing error mechanism analysis. The thermal error is modeled as an exponential function which depends on the steady-state thermal error, the time constant of the thermal error balance process and the working time, derivation of the exponential function is supported by error mechanism analysis, and the exponential function is used for accurately identifying the time constant of the steady-state thermal error and the time constant of the thermal error balance process through thermal-structural interaction. Influences of inclined working conditions are incorporated into a quasi-static mechanical model of the bearing, and the problems of bearing heating and changes of axial and radial rigidity are solved. The invention further discloses a thermal error compensation system.

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 relates to the technical field of wind power grid-connected operation control, in particular to a wind power grid-connected system impedance modeling method and device considering wind power active output, equipment and a medium, and the method comprises the steps: building a machine side model of a direct drive fan, building a grid side small disturbance dynamic model of the direct drive fan, and building a grid side initial impedance model; on the basis of the initial impedance model of the grid side, taking the active power output of the fan as an independent variable, perfecting an impedance model, and establishing a wind power grid-connected system impedance model containing a fan output level influence factor; calculating the equivalent impedance of the wind power grid-connected system under different active power output conditions, and evaluating the stability of the system under different active power output conditions; the difference of the output of each fan is considered into the stability analysis of the wind power grid-connected system, so that the active output is used as an independent variable to directly influence an impedance model of the wind power grid-connected system, and modeling support is provided for the influence research and mechanism analysis of parameters such as the wind power active output on the stability of the grid-connected system.

The invention provides a fault early warning method and device for a variable pitch system of an offshore wind turbine generator. The method comprises the following steps: constructing a health data set and a first fault data set; inserting a plurality of balance data into the first fault data set to obtain a second fault data set; carrying out fault mechanism analysis on the offshore wind turbine generator variable pitch system, extracting a fault characteristic value corresponding to a fault mechanism, obtaining a third fault data set, calculating a mahalanobis distance between the data sets, constructing a health score sequence and a first sliding window, analyzing an abnormal value of the health score sequence in the first sliding window, and obtaining an abnormal value of the health score sequence in the second sliding window; obtaining a health assessment result of the offshore wind turbine generator variable pitch system; and carrying out fault early warning on the deterioration of the operation state of the variable-pitch system of the offshore wind turbine generator. According to the method, the fault mechanism is analyzed to obtain the occurrence probability of the abnormal data, then the health degree of the offshore wind turbine generator variable pitch system is predicted and evaluated, and the fault early warning reliability of the offshore wind turbine generator variable pitch system is improved by obtaining the reliable health evaluation result.

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 provides a fault early warning method and system for an offshore wind turbine generator gearbox, and the method comprises the steps: carrying out the fault mechanism analysis of the offshore wind turbine generator gearbox based on the historical operation data of the offshore wind turbine generator gearbox, and obtaining a fault monitoring index; based on a preset first algorithm and the fault monitoring index, obtaining an oversampling fault sample; obtaining a key fault feature index based on a preset second algorithm and the oversampling fault sample; based on a preset third algorithm, calculating a weighted mahalanobis distance value of the key fault feature index to obtain an actual health score of the offshore wind turbine generator gearbox; and based on the actual health score of the gear box of the offshore wind turbine generator, evaluating the health state of the gear box of the offshore wind turbine generator, and sending out a corresponding fault early warning. According to the method, the fault mechanism of the gear box of the offshore wind turbine generator is analyzed, and the health state of the gear box of the offshore wind turbine generator is evaluated, so that the reliability of fault early warning is improved.

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 discloses a ship electromechanical equipment fault diagnosis method based on a causal relationship network, and the method comprises the following steps: 1) collecting electromechanical equipment monitoring data, and carrying out the data preprocessing; 2) according to related design and mechanism analysis of the electromechanical equipment, judging a causal relationship existing in monitoring variables of the electromechanical equipment, and obtaining a determinable causal relationship existing among part of monitoring characteristic variables; 3) mining the causal relationship between the feature variables according to the preprocessed data, and constructing a corresponding causal relationship network; 4) taking the preprocessed monitoring data and the constructed causal relationship network as input, and establishing a fault diagnosis model; 5) training the fault diagnosis model, and optimizing and adjusting model parameters; and 6) performing fault diagnosis by using the trained model. Compared with an existing fault diagnosis method based on correlativity, the fault diagnosis method based on the correlativity has higher interpretability by mining the causal relationship between the monitoring characteristic variables.

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 relates to a numerical mapping-based road hidden danger sensitive element feature factorization method. The method comprises the following steps of: splitting a road network in a preset region to obtain road network data containing a plurality of road analysis grids; performing road collapse hidden danger mechanism analysis on the road network to obtain road hidden danger sensitive elements and characteristic factors; constructing an element attribute-factor knowledge graph based on the attribute information and the characteristic factors of the road hidden danger sensitive elements; establishing a corresponding relationship between each road analysis grid and the corresponding road hidden danger sensitive element based on the spatial position information of the road hidden danger sensitive element and the road network data; and by taking the road analysis grids as units, based on the element attribute-factor knowledge graph and the corresponding relationship, performing assignment processing on the feature factors according to a preset quantification rule, and mapping the feature factors subjected to assignment processing to the corresponding road analysis grids. According to the invention, the problem of how to quantitatively evaluate the susceptibility of pavement collapse is solved.

The invention discloses a method for analyzing an action mechanism of a frozen soil active layer on decomposition of falling leaves of dahurian larch. The method specifically comprises the following steps: S1, analyzing the influence of the frozen soil active layer on forest stand and soil characteristics of the dahurian larch; s2, analyzing the response of the leaf characters of the dahurian larch and the phyllospheric microflora characteristics of the dahurian larch to a frozen soil active layer; s3, analyzing an action mechanism of a frozen soil active layer on decomposition of fallen leaves of the dahurian larch forest; according to the method, forest stand characteristics, soil physicochemical properties, leaf traits, phyllospheric microorganism characteristics, litter leaf traits and decomposition rates of the dahurian larch under frozen soil active layers with different thicknesses are investigated. The invention aims to analyze the structural characteristics of microflora at the phyllosphere of the dahurian larch under frozen soil active layers with different thicknesses, reveal a response strategy adopted by the leaf characters of the dahurian larch to adapt to environmental changes, explore the decomposition difference and main influence factors of the litter leaves, and clarify the action mechanism of the frozen soil active layers on the decomposition of the litter leaves.

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.

A ceramic matrix composite micro-hole drilling quality and material removal mechanism analysis method comprises the following steps: determining a damage model and performance parameters of carbon fibers, a damage model and performance parameters of a SiC matrix, and a damage model and material attributes of an interface layer according to ceramic matrix composite micro-holes with the diameter phi of 0.5-2mm; a drill bit model and a ceramic matrix composite macroscopic model are established in simulation software, macroscopic finite element simulation is carried out, and a macroscopic finite element simulation result is obtained; a drill bit model and a ceramic matrix composite microscopic model are established in simulation software, microscopic finite element simulation is carried out, and a microscopic finite element simulation result is obtained. Carrying out a drilling test to obtain a test result; and comparing the test result, the macroscopic finite element simulation result and the microscopic finite element simulation result, and verifying the effectiveness of the macroscopic finite element simulation result and the microscopic finite element simulation result.

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 a uniaxial compression performance test analysis method of HFGPC under quasi-static and seismic strain rates, relates to the technical field of concrete material performance testing, and is characterized in that test data is collected and analyzed while a test piece is manufactured to carry out an experiment. The uniaxial compression performance of the HFGPC under the quasi-static state and the seismic strain rate can be comprehensively and systematically researched, including the aspects of failure form, stress-strain curve, mechanical property parameters, deformation parameters and the like, and abundant data support is provided for deeply knowing the mechanical behavior of the HFGPC. The test results are analyzed in detail and deeply, including strain rate effect analysis and mechanism analysis, so that the performance change rules and internal reasons of the HFGPC under different strain rates can be disclosed, a scientific theoretical basis is provided for the application of the HFGPC in practical engineering, and the engineering design can be optimized and the safety and durability of the structure can be improved.

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.