223 results about "Sensitivity analyses" patented technology

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, and the method comprises the steps: constructing an accident chain knowledge graph and a physical trigger graph, and building a causal relationship and physical constraints among equipment, states, events and consequences; gathering and mapping historical records, expert rules and online observation data into map entities and relationships, forming a baseline accident scene and calculating a baseline index; generating candidate paths by utilizing graph reasoning and coupled multi-physics field simulation, and realizing a closed loop of graph reasoning and physical solution through consistency check; performing scoring and disturbance simulation analysis on the candidate paths, and screening robust target paths; and carrying out high-fidelity simulation on the target path, identifying key nodes in combination with sensitivity analysis and a minimum cut-off set, and generating disposal suggestions and action priorities. According to the method, high-credibility prediction of accident evolution and emergency response closed-loop linkage are realized, and the method has high precision, high robustness and engineering implementability.

The invention relates to a machining path planning system, in particular to the technical field of intelligent manufacturing and numerical control machining, and overcomes the defect that a thermal coupling effect is ignored in traditional geometric planning by constructing an evolution model fused with a physical law. A dynamic decoupling algorithm is utilized to extract a real cutting load from multi-source data and reconstruct a rigidity field changing along with material removal in real time, the problem that a physical boundary is difficult to perceive under a complex working condition is solved, and the system effectively eliminates size out-of-tolerance caused by heat accumulation through transient deformation advanced prediction. According to the method, the non-linear error is actively counteracted in cooperation with a reverse compensation strategy based on sensitivity analysis, the machining precision and surface quality of the thin-wall weak-rigidity part are remarkably improved, the rejection rate and trial cutting cost are reduced, and intelligent closed-loop control over the manufacturing process is achieved.

The invention relates to the technical field of big data analysis, and discloses a prospecting target area positioning method and system based on big data, and the method comprises the steps: collecting multi-source exploration data in real time through distributed nodes, completing coordinate normalization, semantic alignment and time synchronization through a spatial heterogeneous data flow engine, and generating a standardized incremental data block; performing local feature sensitivity analysis based on the historical model library, identifying a newly added feature dimension, and performing parameter increment updating by adopting a sliding window gradient descent method; inputting the updated model into a target evolution model driven by a Bayesian space-time probability field, and dynamically calculating the metallogenic probability of each space grid in combination with a stress field, an element migration path and historical verification data; and generating high, medium and low three-level target area maps according to probability sorting, and pushing the high, medium and low three-level target area maps to a three-dimensional visual decision terminal. According to the method, minute-level dynamic response of the target region under triggering of newly-added data is realized, computing resource consumption is reduced to be less than 5% of that of an original system, and prospecting efficiency and abnormal region identification timeliness are improved.

The invention discloses a gear machining multi-source thermal error compensation system based on tensor compression edge deployment, which fuses physically guided multi-source error modeling, tensor-based model compression, edge deployment and bandwidth sensing signal scheduling to realize low-delay and high-precision error compensation. The method comprises the following steps: firstly, establishing a three-layer system architecture comprising a cloud layer, an edge layer and a sensing layer, supporting distributed model training, edge reasoning and error compensation so as to support closed-loop operation for realizing adaptive scheduling with extremely low delay, and mainly comprising: (1) a multi-source error model, thermal and geometric errors are fused into coupling high-order representation for capturing space-time interaction through an analytical method; and (2) establishing a sensitivity analysis model based on a physical guidance tensor mode decomposition method (PG-TMDM), identifying a key error source through a three-order tensor structure, and calculating a physical perception contribution rate to retain physical interpretability. And in order to realize real-time deployment, deploying to the edge layer through model compression, model packaging and an edge deployment strategy.

The invention discloses a distributed power supply-containing power distribution network coordinated scheduling method, which relates to the field of power distribution network coordination, and comprises the steps of S1, multi-flexibility resource refined modeling and interaction characteristic analysis, S2, power distribution network linear load flow calculation and sensitivity analysis, and after the power distribution network linear load flow calculation and sensitivity analysis in S2 are completed, carrying out power distribution network coordinated scheduling. And S3, generating an uncertainty scene based on the generative adversarial network. According to the coordinated dispatching method for the power distribution network containing the distributed power supply, differences of various resources in control modes, response speeds and adjusting capabilities are analyzed through refined modeling, complementary characteristics of the resources are fully utilized by a collaborative optimization model, an overall optimization effect is achieved, a single type of resources are not optimized in an isolated mode, and the method is suitable for large-scale popularization and application. All adjustable resources such as a distributed power supply, an energy storage system, a reactive power compensation device, a flexible load and an intelligent soft switch are incorporated into a unified modeling and optimizing framework, and deep fusion and collaborative interaction of flexible resources can be realized.

The invention discloses a carbon dioxide geological sequestration quantity prediction method, system and device and a storage medium, and the method comprises the steps: obtaining a mathematical expression of carbon dioxide sequestration capacity through combining a coupling model of a flow field, a mechanical field and a thermal field with random parameters, and introducing a leakage risk coefficient; taking the weighted sum of the expected value of the storage capacity and the variance as an optimization objective function, and according to the mathematical expression of the carbon dioxide storage capacity, carrying out optimal solution solving on the carbon dioxide storage capacity to obtain an optimal storage strategy; performing spatial discretization on the control equation by adopting a finite element method, discretizing time by adopting a semi-implicit difference method, discretizing random variables by combining a random Galkin method, and solving a coupling model of a flow field, a mechanical field and a thermal field to obtain a prediction result of the carbon dioxide sequestration amount; and according to a prediction result of the carbon dioxide sequestration amount, outputting an expected value and a variance of the sequestration capacity, and generating a sequestration amount distribution diagram, a risk assessment diagram and a sensitivity analysis diagram.

The invention provides a sewage treatment process modeling control optimization method based on mechanism-data dual drive, and the method comprises the steps: collecting basic data of a sewage treatment process, constructing an ASM2d model of a sewage treatment plant, carrying out the sensitivity analysis and parameter calibration of ASM2d model parameters, constructing an environment and a data set, screening input features according to the data set, and carrying out the modeling control optimization of the sewage treatment process. A training set and a test set are divided according to a proportion, an ASM2d-LSTM mixed water quality prediction model is established based on an ASM2d model, the ASM2d-LSTM mixed water quality prediction model is trained based on the training set, an MPC control framework is integrated on the basis of the trained ASM2d-LSTM mixed water quality prediction model, an optimized operation sequence is generated by using the model, and the operation sequence is optimized. And evaluating the control effect of the optimization strategy by taking the collected real control parameters as a reference. According to the method, through a series of programs such as ASM2d-LSTM sewage quality prediction model construction based on mechanism-data dual drive, MPC control based on energy consumption and carbon emission reduction target optimization, key index online optimization strategy output in the sewage treatment process is realized.

The invention discloses a multi-objective optimization method for improving the comprehensive performance of a hydrostatic guideway, and the method comprises the steps: 1, carrying out the theoretical analysis and calculation of the performance indexes of the hydrostatic guideway based on a hydrostatic bearing theory, obtaining a mathematical model of each performance index, and determining design parameters which affect the working performance of the hydrostatic guideway; step 2, on the basis of a Sobol sensitivity analysis method, obtaining sensitivity indexes of each design parameter on the comprehensive performance of the hydrostatic guideway, determining key design parameters with relatively great influence, and analyzing an influence rule of each key design parameter on the performance of the guideway; and step 3, based on an NSGA-II multi-objective optimization algorithm, constructing a hydrostatic guideway comprehensive performance multi-objective optimization model, and obtaining a Pareto optimal solution set. And step 4, based on an AHP-TOPSIS method, selecting an optimal optimization scheme of the hydrostatic guideway in a Pareto optimal solution set obtained through a targeted optimization algorithm.

The invention discloses a construction method of a synthetic rock mass digital model containing a three-dimensional master control fracture, and belongs to the technical field of rock mass numerical simulation. The method comprises the following steps: collecting a complete rock mass and a crack-containing rock sample on site, and preparing a coal rock sample; reconstructing a rock core through CT (Computed Tomography) scanning and Avizo software, and segmenting a mineral matrix and fractures; based on a Fisher statistical distribution method, performing standardization processing on the orientation characteristic parameters of the three-dimensional fracture of the rock; carrying out grouping and dominance analysis on the fractures; the method comprises the following steps: carrying out coal sample fracture analogue simulation by adopting a DFN in software 3DEC, and generating a DFN-Voronoi block model; a DFN-Voronoi SRM model is manufactured; carrying out a compression experiment on the model to monitor stress and strain; and carrying out DFN-Voronoi SRM model parameter sensitivity analysis based on a single-factor experimental method, and proposing a parameter calibration method. According to the method, the mechanical property and the dominant fracture group of the rock sample are considered, and a parameter acquisition and calibration method of the model is completely given in the aspect of model calibration; fracture network construction and model synthesis are optimized, and DFN-Voronoi SRM model parameter sensitivity analysis and calibration are summarized.

The invention discloses a ship sensor measuring point optimization method and system, and relates to the technical field of sensor measuring point optimization, and the method comprises the steps: generating a fractal hierarchical weight matrix through a fractal geometric model based on pre-obtained ship navigation working condition data, and carrying out the load sensitivity analysis through the combination of pre-obtained sea area observation data, obtaining a dynamic load sensitive matrix; a morphological load coupling weight matrix is obtained, a preset ship body layout node set is combined, a layout optimization algorithm is utilized to carry out sensor measuring point layout of guide type search, and candidate measuring point layout is obtained; and carrying out time domain dynamics simulation by using the simulation model, carrying out ship whole-field structure state reconstruction in combination with a state inversion method, and optimizing the candidate measuring point layout to obtain an optimized measuring point layout. The spatial-temporal variability of external excitation is fully considered, and the problem of high-rigidity low-response misjudgment possibly caused by a traditional method only depending on structural rigidity or mass distribution is avoided.

The invention provides a low-bit large model quantification method based on sensitivity analysis and abnormal value processing, and the method comprises the steps: dividing a model layer into a high-sensitivity layer, a medium-sensitivity layer and a low-sensitivity layer through employing a hierarchical mixing precision quantification strategy based on sensitivity analysis, and executing different quantification operations; carrying out weight abnormal value suppression and weight layer-by-layer quantization execution by using a quantization method based on two-stage combination; and carrying out activation outlier offline smoothing processing based on equivalent transformation. According to the method, key weights sensitive to quantization errors are identified based on a hierarchical mixed precision quantization strategy of sensitivity analysis, so that a differentiation strategy is adopted in the quantization process, the precision of high-sensitivity weights is reserved, and more aggressive quantization is performed on low-sensitivity weights to minimize model performance loss; the two-stage weight quantization collaboratively solves the problem of abnormal value and error accumulation: the quantization difficulty is migrated from activation to weight offline, the friendliness of activation quantization is improved, outliers do not need to be dynamically detected during operation, and the reasoning speed is prevented from being influenced.

The invention relates to a vibration suppression-oriented electromagnetic actuator data driving collaborative optimization design method, and belongs to the technical field of electromechanical equipment optimization design. Sample data are collected through Box-Behnken experimental design, a Gaussian process regression agent model is constructed based on the data, and a nonlinear complex mapping relation between each objective function and a design parameter is accurately represented. The influence degree of the design parameters is quantitatively evaluated through sensitivity analysis, the main design variables and the secondary design variables are distinguished accordingly, and the optimization efficiency is improved. Multi-objective optimization is carried out for the main design variables, and a design scheme with the optimal comprehensive performance is selected from a Pareto solution set through an objective decision-making mechanism based on an ideal point method in combination with the optimization result of the secondary design variables. According to the method, limitation of a traditional physical model is broken through through data-driven modeling, global optimization balance of electromagnetic performance, loss and volume is realized, and an efficient and quantifiable evaluation method is provided for design of the high-performance electromagnetic actuator.

The invention provides a multi-level agricultural ecological barrier construction and evaluation method and system, and relates to the technical field of ecological barrier construction. The method comprises the following steps: firstly, constructing a multi-objective optimization model and an agricultural ecological unit layering system, and solving based on a random generation and evolution algorithm to obtain a barrier configuration scheme considering ecological process adjustment, ecological service supply and cost control; and further constructing a structure, process and service multi-level evaluation index system of the field layer, the operation subject layer and the drainage basin or region layer, forming a structure sub-index, a process sub-index and a service sub-index through standardization and weighting calculation, and fusing the structure sub-index, the process sub-index and the service sub-index into a multi-level agricultural ecological barrier comprehensive index. Key configuration parameters are identified through sensitivity analysis, parameter boundaries are adjusted, a search strategy is optimized, iteration updating is conducted on the multi-target optimization model, and an agro-ecological barrier configuration scheme with the maximum comprehensive benefit and the reasonable spatial pattern and a quantitative evaluation result are obtained.

The invention provides a multi-objective structure optimization method and system suitable for a wheel excavator. Comprising the steps that a three-dimensional model which has the same actual size as an experimental test prototype of the wheel excavator and comprises a cab, an upper frame and other related parts is established; and comparing frequency domain response curve results obtained by experimental tests and verifying the accuracy of vibration performance prediction of the finite element model. Sensitivity analysis is carried out, and parts finally used for follow-up multi-target optimization are screened out according to the influence degree on the performance of the multiple targets; and performing test design according to the screened parts to obtain sample points, and importing the sample points into a finite element model for simulation to obtain a sample response data set. And fitting the agent model according to the sample data, optimizing by using a genetic algorithm to obtain an optimal solution, and sorting according to the magnitude of an acceleration root mean square value to select an optimal scheme. And importing the optimal scheme obtained according to the steps into a finite element model, and comparing the optimal scheme with an original scheme to verify the rationality of a new optimization scheme.

The invention provides a small sample AI proxy model construction method based on sensitivity analysis and supplementary sampling, and the method comprises the steps: carrying out the global sensitivity analysis through the automatic simulation process of parameterized parts of wind power equipment in combination with test design, and removing insensitive variables, thereby guaranteeing the accuracy and standardization of a sample, avoiding the invalid consumption of irrelevant variables, and achieving the automatic simulation of the parameterized parts of the wind power equipment. Latin hypercube sampling is adopted based on key variables, a core design area is uniformly covered, the number of initial simulation times is greatly reduced, the cost is controlled, the problem that the generalization ability is poor due to uneven small sample distribution is solved, a deep neural network regression model is trained through a training set, the strong nonlinear fitting ability of the deep neural network regression model adapts to a complex mapping relation, the core law is efficiently learned, and the robustness is high. Based on initial model prediction precision and sensitivity information, samples are accurately supplemented in weak areas, samples are not increased blindly, investment is reduced, prediction blind areas are made up, errors are gradually reduced through iterative training closed-loop optimization, and finally the small sample AI proxy model meeting preset requirements is obtained.

The invention discloses a protection setting calculation and optimization method based on an intelligent power plant management platform, and relates to the technical field of power system protection. By constructing a platform and equipment communication link, integrally collecting and preprocessing internal and external parameters, and dynamically updating topology and model parameters in combination with a switch state, the problem that a traditional static model is difficult to adapt to operation changes is solved, the timeliness and accuracy of the model are improved, a multi-scene database is constructed in a classified mode, labels are labeled, and the efficiency is improved. Fault current distribution is accurately calculated, constant value adaptability verification is carried out, constant value optimization is carried out in combination with a multi-objective optimization algorithm and sensitivity analysis, limitation of single-scene constant values is avoided, constant value conflicts under different scenes are reduced, reliability of initial constant values is guaranteed, sensitivity analysis is superposed, it is ensured that the constant values are still reliable during parameter fluctuation, and the reliability of the constant values is improved. The risk of protection operation refusal and misoperation is reduced, a constant value is issued, the device state is monitored in real time, evaluation indexes are calculated, the constant value is controllable in the whole process, and stable operation of the device is guaranteed.

The invention relates to the technical field of power distribution network toughness demand response, in particular to a power distribution network toughness improvement optimization method, system and device based on response prediction and a storage medium. Training an integrated decision tree model based on historical data to predict a response intention value of the cooling and heating load user, and determining a temperature regulation boundary and a response frequency upper limit according to the response intention value; generating an initial scene set through source load uncertainty sampling, extracting a typical scene by adopting a transportation distance scene reduction method, and introducing an overall offset constraint and a single-point extreme value constraint to perform two-dimensional limitation on scene probability distribution; a scene probability combination enabling the load recovery value to be minimum is searched in a probability distribution domain, and a scheduling scheme enabling the key load recovery value to be maximum is solved under the condition that the power flow constraint, the cold and heat power balance constraint and the temperature regulation boundary constraint are met; and carrying out probability weighting on the load recovery values of different scenes to obtain a toughness evaluation value, and carrying out sensitivity analysis.

A method for optimizing the natural frequencies of a paddy field grader structure based on dimensional linkage and response surface methodology is disclosed. The method comprises the following steps: S01: Establishing a simplified 3D model of the paddy field grader structure; S02: Parametrically configuring the model; S03: Linking SolidWorks and Workbench to establish dimensional relationships and a set of dimensional parameters; S04: Performing modal analysis to obtain the total deformation and the first six natural frequencies of the grader structure, setting some output results as output parameters and adding them to the dimensional parameter set; S05: Performing sensitivity analysis on the dimensional parameter set to select input parameters with higher overall sensitivity values; S06: Performing multi-objective response surface optimization to obtain the optimal design parameters; S07: Reconstructing the model based on the optimal parameters and performing modal analysis to confirm the optimization effect. This invention can accurately and efficiently optimize structural parameters using response surface methodology to improve their natural frequencies and reduce resonance risk, belonging to the field of agricultural machinery structure optimization technology.

The invention provides a dynamic pneumatic test method and system for a commercial vehicle, and the method comprises the steps: dividing a plurality of dynamic working condition clusters through employing a preset clustering algorithm in combination with aerodynamic resistance sensitivity analysis, and extracting the aerodynamic feature threshold value of each dynamic working condition cluster; constructing a mapping relationship between the simulation data and the measured data, and migrating correction parameters of the reference working condition to each dynamic working condition cluster by adopting a transfer learning algorithm based on the mapping relationship to generate a corresponding initial correction coefficient; pre-adjusting boundary conditions and turbulence model parameters of a preset simulation model according to the initial correction coefficient and the aerodynamic characteristic threshold value so as to generate corresponding initial correction simulation data; and adopting a Hash index algorithm to quickly match the actually collected aerodynamic parameters with the initial correction simulation data so as to calculate a corresponding target deviation value, and outputting corresponding target correction simulation data according to the target deviation value and the initial correction simulation data. The test efficiency can be effectively improved.

The invention discloses an SOP locating and sizing optimal configuration system and method based on sensitivity analysis. Comprising a source network data acquisition unit, a sensitivity analysis and calculation unit, an SOP locating and sizing optimization unit, a configuration scheme evaluation unit, a data storage unit and a visual monitoring platform. Synchronously acquiring and processing power distribution network data; selecting candidate branches according to sensitivity analysis; establishing a planning model, embedding multiple types of constraint conditions, obtaining an SOP optimal capacity through a particle swarm optimization algorithm, and determining an SOP configuration scheme; a multi-dimensional evaluation index system is constructed, after simulation verification, an analytic hierarchy process is adopted for weighted scoring, a comprehensive score is output, and an evaluation report is generated and sent to a data storage unit and a visual monitoring platform; through cooperative application of sensitivity analysis and an intelligent optimization algorithm, the SOP locating and sizing problem is efficiently solved, the operation economy, reliability and stability of the power distribution network can be remarkably improved, and powerful support is provided for scientific configuration of the SOP.

The invention discloses an efficient identification method for connection rigidity of a steel pipe pole of a power transmission line, which relates to the field of structural health monitoring, and comprises the following steps: constructing a multi-scale finite element model of the steel pipe pole, acquiring displacement and frequency response of the structure to carry out parameter sensitivity analysis, and screening key correction parameters; constructing a key parameter-structure response sample data set, and training and optimizing an FEA-Net neural network model; and constructing a Bayesian correction framework, solving posterior probability distribution by adopting a TMCMC algorithm, and realizing the probabilistic identification of the connection stiffness and the systematic correction of the finite element model. By constructing a local refined contact model, a bolt pretightening force-connection rigidity corresponding relation is established, and an effective means is provided for verifying whether the rigidity of the corrected model is matched with the rigidity of an actual structure or not. The structure rigidity damage position can be accurately recognized, the rigidity degradation degree can be quantified, and a high-credibility model basis is provided for safety evaluation, digital twinborn construction and operation and maintenance decision making of engineering structures such as electric transmission line steel pipe poles and the like.

A system for analyzing sports science data with integrated financial management, consisting of: a data acquisition module configured to ingest heterogeneous data streams from multiple sources, including digital advertising platforms, customer relationship management systems, ticket databases, sponsorship activation protocols, broadcast audience statistics, and point-of-sale systems for goods; a telemetry harmonization processing unit configured to encode temporal, categorical, and numerical marketing and engagement signals into structured tensors, correcting inconsistencies in sampling frequency and missing values; an attribution modeling processor comprising a neural sequence encoder selected from a bidirectional long-term-short-term memory network or a transformer-based attention model, coupled with a causal inference submodule that generates attribution scores by unraveling overlapping influences of concurrent marketing campaigns; an explainability controller configured to generate interpretable attribution outputs by applying Shapley value decomposition, local surrogate model explanations, counterfactual simulation, and temporal sensitivity analysis, with the outputs visualized for end users in real time; a financial simulation engine that is operationally coupled with the attribution modeling processor and configured to translate incremental marketing contribution signals into structured financial reports, including profit and loss statements, balance sheets, and cash flow forecasts, using stochastic financial models to predict key indicators under variable marketing scenarios; a secure audit logging module that includes a blockchain anchoring layer configured to record allocation decisions, explainability results, and financial simulation results in tamper-proof records to ensure traceability and compliance; and a machine interface device consisting of a processing unit, a storage unit, a visualization subsystem and an interaction console, displaying mapping, explainability and financial simulation results on interactive dashboards.

The invention relates to the technical field of large models, in particular to a method and system for improving the memory amount of a large model, and the method comprises the steps: deploying a monitoring module in a training or reasoning process, carrying out the statistics of the number of memory errors and the total task amount of each component, and calculating the initial memory performance of the components; based on the performance evaluation result of each component, quantifying the overall initial memory amount of the model as an optimization starting point; utilizing a dynamic sensitivity analysis algorithm to mine the influence degree of each component parameter on the memory performance, and calculating a dynamic sensitivity factor; and in combination with the sensitivity factor, the current parameter state and the memory performance, model parameters are adaptively adjusted through dynamic parameter optimization and an iterative lifting algorithm, an iterative verification mechanism is introduced, and the memory ability of each component and the whole machine is subjected to closed-loop optimization. According to the method, fine quantization and parameter-level dynamic regulation and control on the memory performance of the model are realized, and the memory stability and long-term dependence processing capability of the large model in tasks such as knowledge keeping and context understanding are effectively improved.

The application provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, which comprises the following steps: constructing an accident chain knowledge graph and a physical trigger graph, establishing the causal relationship and physical constraints among equipment, state, event and consequence; mapping the historical records, expert rules and online observation data into graph entities and relationships to form a baseline accident scene and calculate baseline indicators; generating candidate paths by using graph reasoning and coupled multi-physical field simulation, and realizing the closed loop of graph reasoning and physical calculation through consistency checking; scoring and perturbation simulation analysis on the candidate paths, screening the robust target path; carrying out high-fidelity simulation on the target path, identifying key nodes combined with sensitivity analysis and minimum cut set, and generating disposal suggestions and action priority. The application realizes high credible prediction of accident evolution and closed loop linkage of emergency response, and has high precision, high robustness and engineering implementability.

This invention discloses a drought early warning method based on energy flux, causal relationship analysis, and machine learning. The method includes the following steps: S1, acquiring energy flux and drought indicators, determining the optimal lag time through causal reasoning, and constructing a multi-order lag feature set; S2, establishing a tree model, using regression / kernel methods and a time series model candidate set, optimizing hyperparameters through particle swarm optimization, integrating two layers in a stacked manner, adaptively optimizing the performance of comprehensive regression and event recognition, and outputting a predicted sequence; S3, setting multi-level early warning rules according to drought thresholds, mapping drought levels, and evaluating effectiveness through statistical precision and recall; S4, calculating contribution using an additive feature attribution algorithm, identifying nonlinear thresholds to form sensitivity analysis, and improving interpretability. This invention achieves a 7-11 month early warning prediction of drought based on energy flux, with a drought early warning recall rate of 66.67%-75.86%, significantly improving the accuracy and interpretability of drought early warning.

The invention discloses a suspension system dynamic model calibration method based on an optimization algorithm, and belongs to the technical field of software. The invention aims to solve the problems of existing Kamp; and C, a performance calibration method is low in efficiency and long in period, and the problem that calibration work is repeated and failed due to a parameter interaction effect exists. Therefore, the invention provides a method which comprises the following steps: carrying out a sample vehicle suspension Kamp; c, testing to obtain test data; the method is based on sample car parameters and Kamp; c, establishing a whole vehicle model according to test data; a suspension Kamp is used; carrying out DOE calculation and parameter sensitivity analysis by taking the characteristics C as variables, and selecting Kamp sensitive to the whole vehicle performance; c characteristics are used as benchmarking items; a suspension rigid-flexible coupling dynamic model is established in ADAMS software, and a secondary development script is written to realize automatic operation; carrying out Kamp by taking model part parameters as variables; c, performing DOE calculation and sensitivity analysis on each working condition, and selecting each key Kamp; c, taking characteristic sensitive part parameters as benchmarking variables; kamp of simulation and test is used; c curve matching is taken as an optimization target, an optimization function is established, and Kamp is carried out; the model C is subjected to benchmarking solution, and a high-precision suspension Kamp is output; and C model. Compared with the prior art, the method has the advantages that the optimization algorithm is introduced, so that the suspension Kamp is realized; due to automation and high precision of C model calibration, the working efficiency and benchmarking precision are greatly improved, and a high-precision suspension dynamical model can be output.

A laminate cooling structure performance optimization method adaptive to uncertainty analysis comprises the following steps: firstly, establishing a parameterized finite element model of a flame tube laminate cooling structure, and realizing flow heat exchange mechanism analysis of the laminate cooling structure by adopting a solid-thermal coupling mode; carrying out a DOE experiment by using Latin hypercube sampling LHS, generating a parameter set, importing the parameter set into a parameterized finite element model, obtaining a data set of a training proxy model BP-NN by combining a laminate cooling structure solid-heat coupling heat exchange method, and carrying out uncertainty quantification in a design space of the parameters of the laminate cooling structure by using an MC simulation method; using a Sobol sensitivity analysis method to analyze the uncertainty of the laminate cooling structure; the training agent model is combined with a multi-target particle swarm optimization algorithm to carry out multi-target optimization on the laminate cooling structure parameterization, and a laminate cooling ideal structure is found in the Pareto frontier; according to the method, the calculation cost and time of uncertainty analysis are reduced, and the calculation cost of laminate cooling uncertainty analysis is greatly reduced.

This invention discloses a linear guide rail SOMC design method, equipment, medium, and program product, including: (1) constructing a simulation model and SOMC design model with static stiffness as the target and weight and maximum contact stress as constraints based on the structural characteristics and static load analysis of the linear guide rail; (2) constructing a design space, generating an initial population and establishing a database, and obtaining a set of high-influence continuous variables and a set of high-influence discrete variables through SRC and CEA sensitivity analysis methods; (3) generating corresponding candidate sets based on the two sets of variables, and obtaining a complete set of candidate guide rails through individual pairing; (4) establishing a random forest prediction model, screening excellent subsets and selecting the best offspring guide rails; (5) updating the database and prediction model after simulation evaluation, returning to step (3) until the indicators meet the standards, and outputting the optimal parameter values. This invention can effectively balance the SOMC process with stiffness as the optimization target and weight and maximum contact stress as constraints, achieving higher accuracy and better overall performance.