215 results about "Mechanism analysis" patented technology

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 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 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 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 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 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 discloses a vortex numerical analysis method based on transport equation decomposition. The method comprises the following steps: 1) constructing a three-dimensional geometric model; 2) exporting a computational domain established by the model into ICEM CFD to establish a model grid, and simulating a vortex generation condition in analogue simulation software; 3) extracting fluid data of each grid unit; 4) Liutex vector acquisition and expression: calculating the Liutex vector of each unit based on a given velocity field; 5) selecting a proper vortex kernel threshold value; (6) calculation of Liutex transport equation decomposition is carried out; 7) extracting a result after decomposition calculation of each grid; according to the method, the transport equation of the Liutex vector is constructed, decomposition and quantification of different physical effects in the evolution process of the Liutex vector are completed, and mechanism analysis is achieved.

An intelligent compensation method for coupling errors of a dynamic gravimeter comprises the following steps: selecting typical environment interference influencing the dynamic gravimeter in a complex measurement environment, and analyzing an influence mechanism of each typical environment interference on the measurement accuracy of the dynamic gravimeter; according to the influence mechanism under each typical environment interference, analyzing a coupling error influence mechanism between the typical environment interferences; based on the coupling error influence mechanism, constructing a coupling error comprehensive compensation model; adding the coupling effect loss into a data-driven loss function, and defining a model coefficient constrained by physical information; and performing model training by using the training data, and calculating the total loss of the verification set on the obtained model to obtain a final coupling error comprehensive compensation model. According to the invention, the coupling error of the dynamic gravimeter in a complex environment is compensated based on coupling error mechanism analysis and a physical constraint long-short-term memory model, and the measurement precision and environmental adaptability of the dynamic gravimeter under complex multi-factor interference conditions are improved.

The present application relates to a kind of carbon emission space refinement simulation and driving mechanism analysis method based on multi-source data and machine learning, including obtaining night light remote sensing, population density data, multi-source data such as interest point density, nonlinear spatial fitting model is constructed and trained by using information entropy weight method to carry out multi-source data fusion and construct economic energy comprehensive index, use city land use classification data as industry space restriction, combined with the total carbon emission of each industry calculated to build carbon emission space simulation base map, based on extreme gradient boosting tree, introduce SHAP framework for post-analysis, from global, local and spatial three scales, the nonlinear driving mechanism of carbon emission of multi-source city form element is analyzed, overcome the over-saturation defect of single night light data in city core area, realize the leap from pure space fitting to mechanism explanation, provide scientific basis and method support for high-density city accurate locking emission hotspot and making low-carbon policy.

The application provides an amorphous alloy magnetocaloric performance prediction method and system based on interpretable machine learning, relates to the technical field of material science and engineering, and comprises the following steps: step 1, constructing an amorphous alloy magnetocaloric performance data set, wherein the data set comprises alloy composition data, physical and chemical descriptors, external conditions and structural characteristics of a plurality of amorphous alloy samples and corresponding experimental values of maximum magnetic entropy change and Curie temperature; step 2, screening and optimizing the features in the data set, eliminating the collinearity between the features and performing recursive feature elimination to determine the final feature subsets for predicting the maximum magnetic entropy change and the Curie temperature respectively; and step 3, establishing machine learning models for predicting the maximum magnetic entropy change and the Curie temperature respectively through the final feature subsets, and training and optimizing the models to obtain the trained prediction models. The application realizes a closed loop of high-precision performance prediction, physical mechanism analysis and directional composition design.

The invention provides a high-frequency parasitic parameter extraction and electromagnetic interference source modeling method for a vehicle high-voltage high-power IGBT (Insulated Gate Bipolar Transistor) module, which comprises the following steps of: extracting parasitic capacitance to ground and lead inductance of each pole of the IGBT module by adopting a finite element method, and combining entity port impedance measurement with a simulation mode to obtain parasitic parameters among the poles of the IGBT; corresponding high-frequency equivalent circuit models are established for electromagnetic interference sources such as bridge arms, laminated busbars and cables in sequence, finally, a double-pulse simulation and entity test platform is established by integrating parasitic parameters and the models, and the accuracy of modeling of the IGBT electromagnetic interference sources can be effectively verified by comparing simulation and test results under the same working condition. And theoretical support can be provided for analysis and optimization of an electromagnetic interference mechanism of a high-voltage system of the new energy automobile.

The application discloses a kind of urban waterlogging rapid prediction and disaster-causing mechanism analysis method.The method obtains dynamic rainfall characteristics and the static physical properties of multiple computing nodes;Determine the spatial cluster to which the computing node belongs based on the static physical properties of the computing node, call the preset expert model corresponding to the spatial cluster, generate the waterlogging prediction result of the computing node based on the dynamic rainfall characteristics and the static physical properties;Based on the expert model, calculate the feature contribution value of dynamic rainfall characteristics and static physical properties to the waterlogging prediction result, aggregate it according to the physical system attribution of static physical properties, obtain the system dominance corresponding to each physical system;According to the comparative relationship of the system dominance of each physical system, determine the disaster-causing mechanism type of the computing node.The application solves the problems of low computing efficiency and poor physical interpretability, realizes the second-level prediction of urban waterlogging and the quantitative analysis of disaster-causing mechanism.

A kind of zirconium alloy water environment corrosion microscopic mechanism analysis method based on ab initio molecular dynamics, 1, construct the multi-phase interface model of metal substrate model and corrosion medium layer model combination, obtain the size information of simulation box;2, set simulation parameters for multi-phase interface model;3, carry out geometry structure optimization to multi-phase interface model and eliminate local internal stress, then carry out ab initio molecular dynamics simulation, obtain atomic evolution track and corrosion product structure;4, carry out data post-processing and statistical analysis to ab initio molecular dynamics simulation result, obtain corrosion elementary reaction path and calculate reaction energy barrier, the interaction mechanism of corrosion group and surface is analyzed by DFT method.The method can complete accurate zirconium alloy corrosion process dynamic simulation under periodic boundary condition, has higher calculation precision and is more reasonable to the description of interatomic force in system, and can also analyze the interaction mechanism between alloy and corrosion medium from atomic electronic scale.

The invention provides an atmospheric pollution driving factor decoupling attribution method and device. The method aims to solve the problems that in the prior art, atmospheric pollution prediction neglects a space process, consequently, precision is insufficient, and static background and dynamic meteorological driving factor contribution cannot be quantitatively decoupled. Comprising the following steps: training a prediction model with spatial information perception capability based on multi-modal input features, and learning a spatial dependency relationship of pollutant concentration and outputting a prediction value by extracting spatial feature data containing a target position and neighborhood information of the target position as input by the model; and calculating the contribution value of each input feature by applying an attribution algorithm, and grouping and aggregating the contribution values based on preset feature group division to obtain the aggregated contribution amount of the feature group, thereby realizing the quantitative decoupling of the driving factor. According to the method, contribution of driving force of different time scales is quantitatively distinguished, so that global high-resolution accurate prediction is realized, and mechanism analysis and decision support is provided for scientific pollution control.

The embodiment of the invention provides a reliability evaluation method based on mechanical equipment component-level mechanism analysis. The method comprises the following steps: analyzing an equipment failure mode; key components are decomposed; evaluating the failure rate of the component; determining a multiplier factor; and summarizing equipment failure rates. According to the embodiment of the invention, dependence on equipment experiment data is eliminated, the influence of environmental conditions on the parts is determined by issuing the operation environment faced by the equipment to the part level, the reliability of the mechanical equipment is evaluated under the condition that the experiment data is incomplete and insufficient, the evaluation precision is improved, the early failure is early warned, the maintenance cost is reduced, and the reliability of the mechanical equipment is improved. By establishing a'material-component-system 'multi-scale model, the modeling barrier between a micromechanism and macroscopic performance of a traditional method is broken through, the component failure rate is expressed in a multiplier factor mode, and quantitative calculation of the component failure rate is achieved.

The invention belongs to the technical field of bridge wind vibration control, and discloses a fluid-solid coupling numerical calculation method for studying the control of a friction energy consumption device on the flutter of a large-span bridge, which comprises the following steps: compiling a user-defined function secondary development program by using a dynamic grid technology based on computational fluid dynamics software Fluent, and embedding the program into a solver for calculation. And a motion equation of a rigid main beam-friction energy consumption device coupling system in the wind field is established, fluid-solid coupling solving is achieved in the mode that rigid main beam motion and flow field motion are alternately solved, and vertical and torsional motion responses of the controlled main beam are obtained. Compared with a wind tunnel test method, the method has the advantages that various parameters and stress modes of the rigid main beam and the friction energy consumption device can be accurately set, full-scale simulation can be realized, rich information such as movement time history data of the main beam and a nearby flow field under various parameter conditions can be conveniently and safely obtained, vibration suppression mechanism analysis can be conveniently carried out, and the method is suitable for popularization and application. And the research difficulty, cost and risk are greatly reduced.

The invention relates to the technical field of data processing, in particular to a spinal disease intelligent confirmation method, device and equipment, and the method comprises the steps: firstly constructing a multi-modal spatio-temporal data set fusing a static structure, a dynamic function and clinical data, and carrying out the standardization processing; the method comprises the steps that firstly, through a pre-trained HMC-VSPF heterogeneous modal collaborative awareness and centrum sequence conduction fusion network, quantitative prediction of symptom severity is achieved, the contribution degree of structural lesions and dysfunction to symptoms is quantified, finally, through unsupervised clustering fused with biomechanical constraints, intelligent typing of pathogenic modes is achieved, and the probability of pathopoiesis is lowered. And an individualized diagnosis and treatment data product is generated through three-level quality control. Through multi-modal data deep fusion, causal mechanism analysis and intelligent typing, the diagnosis and treatment accuracy and interpretability are improved, individualized precise diagnosis and treatment of spinal diseases are achieved, and a quantitative basis is provided for clinical decision making.

This invention discloses a tool axis vector optimization method for wear uniformity, belonging to the field of CNC machine tool technology. The method includes inputting workpiece, tool, and machining path information, and initializing the tool axis vector model; defining and calculating the tool-workpiece meshing area, and determining the cutting edge participation; for the cutting edge segment participating in cutting, using a wear mechanism analysis model, calculating the increment of the lateral wear volume at the current time step and the lateral wear width of the cutting micro-element at the next time step, and superimposing these values ​​to calculate the subsequent tool face wear width; iteratively adjusting the tool axis vector to achieve uniform wear distribution on the cutting edge; collaboratively optimizing cutting parameters to control surface roughness; and outputting a wear-uniformity machining scheme. This invention, by dynamically optimizing the tool axis vector and collaboratively adjusting cutting parameters, achieves active uniformization of tool wear and stable control of surface roughness, thereby significantly extending tool life and ensuring machining quality.

The invention discloses a silicon wafer V-shaped groove polishing positioning deviation management and control system, and particularly relates to the field of semiconductor material manufacturing. Related parameters of a silicon wafer V-shaped groove, a positioning block and a Notch grinding part are collected, and polishing abnormity inducement parameters are obtained through calculation; pEEK material parameters are called, a novel positioning block is designed in combination with the positioning requirement and the installation space, and the precision is verified through CAD modeling; positioning block parameters are collected to fit the wear trend, a check period is set, wear parameter comparison threshold values are detected, and a replacement mechanism is established. A novel positioning block structure is designed by selecting a PEEK material to improve the wear resistance, the positions of a grinding part and a V-shaped groove are calibrated in combination with parameter detection and operation, a positioning block regular inspection and replacement mechanism is established, polishing abnormity inducements are quantitatively judged through production data statistics, material object detection and mechanism analysis, a standardized management and control process is formed to replace experience management and control, and the production efficiency is improved. And the polishing abnormal reworking, management and control process and material cost are reduced.

The invention discloses a load frequency coordination optimization control method, and belongs to the technical field of power system load frequency control technologies. The method comprises the following steps: obtaining a frequency response state space model of a hybrid energy storage-containing two-region interconnected power system through mechanism analysis, performing discretization processing, converting the model into a discretization model, and further establishing a discretization prediction model of the system; analyzing constraints existing in the system, and converting the constraints into a linear matrix inequality; and finally, establishing an objective function of the system, performing online rolling optimization on the control quantity, and designing a multi-region controller, thereby realizing a load frequency coordination optimization control effect of the interconnected power system containing hybrid energy storage. The problems of insufficient flexible resources and poor adjusting characteristics of frequency modulation of an electric power system are solved, coordination and cooperation of battery energy storage, pumped storage and a traditional thermal power generating unit are achieved through coordination control, the frequency response performance of the system is improved, the nonlinear constraint problem existing in the system is solved, and potential safety hazards caused by overload work of equipment are avoided.

The invention discloses an earthquake damaged building prediction method and related equipment, and aims to solve the technical problems that the building damage condition cannot be quickly identified and predicted after an earthquake and damage mechanism analysis is lacked in the prior art. The method comprises the following steps: constructing a building damage prediction model by using a gradient lifting algorithm, and realizing rapid prediction by inputting near-real-time multi-source influence factor data which can be easily acquired after an earthquake; and meanwhile, an SHAP framework is adopted to carry out interpretability analysis on the model, the contribution degree of each influence factor is quantified, and a building damage mechanism is disclosed. According to the method, the problems of low efficiency, insufficient prediction precision and lack of mechanism analysis of a traditional evaluation method are effectively solved, and reliable technical support is provided for earthquake emergency response and post-disaster rescue.

The application relates to the technical field of tunnel drilling and blasting construction, in particular to a drilling and blasting method tunnel construction scheme simulation method and system based on digital twinning, which comprises the following steps: collecting geological and rock mass data, historical blasting data, site equipment data, site personnel data, real-time environment and working condition data; constructing a digital twinning model of a tunnel to be blasted, and updating the digital twinning model; designing an original drilling and blasting plan based on historical experience and mechanism analysis, and obtaining an optimal drilling and blasting construction scheme by using the digital twinning model and a hierarchical collaborative strategy; performing a field drilling and blasting experiment by using the optimal drilling and blasting construction scheme, and obtaining real blasting data to optimize the digital twinning model. The application realizes auditing and screening of a blasting construction scheme by constructing a high-fidelity tunnel digital twinning model and using a hierarchical collaborative strategy, and unifies efficiency and precision while obtaining the optimal drilling and blasting construction scheme.