369 results about "Sensitive analysis" patented technology

The invention discloses a multi-drive process multi-factor agricultural non-point source pollution prediction method based on coupling meteorological numerical forecasting. The method comprises the following steps: firstly, introducing numerical weather forecast data, and constructing a high-precision weather driving field with kilometer-level space and hour-level time resolution by combining WRF dynamic downscaling, DEM terrain correction and conservation resampling; secondly, establishing a multi-drive process coupling model system which comprises a meteorological drive layer, a hydrological response layer, a pollutant migration layer and a crop feedback layer and is used for simulating runoff production, sediment erosion, nitrogen and phosphorus migration and transformation and crop transpiration and root nutrient absorption processes; thirdly, performing precision verification on a simulation result by using observation data, and identifying key meteorological and hydrological factors through an error transfer matrix and a sensitivity analysis method; and finally, realizing parameter adaptive correction by adopting a long short-term memory network, finishing parameter optimization in combination with a multi-target genetic algorithm, packaging the model chain through a containerization technology, and realizing cross-platform deployment and visual output of a pollution load result. The method can be used for agricultural non-point source pollution prediction and management.

The invention discloses a risk assessment method and system based on a Bayesian network and an evidence theory, and relates to the technical field of risk intelligent assessment, and the method comprises the steps: determining an assessment dimension and an assessment index of a high-investment low-income risk of an educational institution in MOOC learning according to a human-cargo-field model and an industry report; constructing a MOOC learning risk assessment index system according to the assessment dimensions and the assessment indexes; constructing a Bayesian network structure according to the MOOC learning risk assessment index system; obtaining questionnaire data and expert opinions according to the MOOC learning risk assessment index system, and determining Bayesian network parameters based on the questionnaire data and the expert opinions; and inputting the Bayesian network parameters into the Bayesian network structure to obtain an assessment result of the high-investment low-income risk, the assessment result including a risk prediction result, a key risk factor and a sensitivity analysis result. According to the method, the high-investment and low-income risk in MOOC learning can be accurately evaluated.

The invention relates to the technical field of ship navigation risk assessment, in particular to a ship navigation risk assessment system based on multi-source heterogeneous data fusion, which comprises a multi-source data integration module, a spatial-temporal feature mapping module, a dynamic risk detection module, a linkage decision control module and a feedback optimization module. According to the method, standardized operation data is generated through multi-source data cleaning and fusion, a spatial-temporal feature distribution map is generated by using a multi-dimensional dynamic clustering algorithm, a risk index set is extracted in combination with adaptive boundary adjustment and a nonlinear optimization algorithm, and accurate path planning and real-time regulation are realized. In addition, a global sensitivity analysis framework and an early warning module are introduced into the system, and the ship navigation safety and reliability are improved. According to the method, the risk prediction accuracy can be remarkably improved, the navigation accident probability is reduced, the navigation efficiency is optimized, and safe operation of the ship is guaranteed.

The invention discloses a high-capacity composite material hydrogen storage cylinder layering design method considering the strength of a transition section, and relates to the technical field of hydrogen storage cylinders. Establishing a finite element model of the hydrogen storage cylinder, inserting a cohesion unit with zero thickness between adjacent layers, and performing damage evolution on the cohesion unit to obtain a hydrogen storage cylinder simulation result considering the interlayer failure in the composite material layer; according to the fiber stress of the composite material layer and the damage state of the cohesion unit, determining a weak position under the current layering design, determining to-be-optimized parameters of the weak position, and formulating a layering optimization strategy; according to the method, sensitivity analysis of layering parameters is carried out on a transition section, so that a layering optimization strategy of the transition section is formulated; and according to the layering optimization strategy, parameters to be optimized in the layering design are gradually adjusted until no fiber damage exists in the whole composite material layer. According to the invention, the layering design of the high-capacity composite hydrogen storage cylinder considering the strength of the transition section is realized.

The invention discloses a multi-unmanned aerial vehicle cooperative task allocation and path planning method based on a genetic algorithm, and relates to an unmanned aerial vehicle path planning method. And operating a Dijkstra algorithm to accurately solve the shortest feasible path between all airports and task points, and finally generating a global path cost matrix for quick query. A complete and safe flight path that each unmanned aerial vehicle starts from an airport, sequentially accesses task points and returns is graphically displayed, a convergence curve of an algorithm, population diversity changes and performance comparison data under different task scales, unmanned aerial vehicle numbers and obstacle densities can be output, and a parameter sensitivity analysis function is supplemented. And the effectiveness, the stability and the practical value of the proposed method in a static urban environment are comprehensively verified.

The invention relates to the technical field of energy storage battery management, and discloses a capacity recovery method based on the full life cycle of a lithium ion battery. According to the method, the uncertainty of power grid demand and environment temperature is modeled through a scene tree generation algorithm, a low-sensitivity temperature region is identified through sensitivity analysis to generate a robust temperature parameter candidate set, and a recovery time window is identified based on a battery aging characteristic prediction sequence. Constructing a dual-objective optimization problem of power grid auxiliary service income and battery full life cycle value, solving a conditional optimal decision scheme under each scene through a dynamic programming algorithm, selecting a decision scheme with a maximum worst condition target value by using a robust optimization criterion, extracting an execution instruction, outputting the execution instruction to a control system, and performing power grid auxiliary service income and battery full life cycle value optimization. And the decision scheme is adjusted in real time through a rolling optimization mechanism. According to the method, the robustness of balancing economic benefits and battery health in an uncertain environment by a capacity recovery decision is improved.

The invention provides a water quality abnormity early warning system and method based on multi-source data fusion, and relates to the technical field of abnormity early warning, and the method comprises the steps: recognizing a sensitive region in which the water quality is gradually changed and abnormal in a target water area through the time-space correlation characteristics of each piece of water quality multi-source data; performing sensitivity analysis on the response intensity of the water quality gradual change abnormity in the sensitive area to obtain a water quality sensitivity index of the sensitive area; determining an early warning response period when the water quality at the monitoring point is gradually changed and abnormal; performing confidence correction on a water quality abnormal fluctuation early warning threshold value at the monitoring point through the water quality sensitivity index and the early warning response period to obtain an abnormal early warning confidence coefficient of the water quality at the monitoring point; and when the abnormity early warning confidence exceeds a preset confidence threshold, sending a water quality gradual change abnormity verification instruction to an intelligent sensor at the monitoring point, and generating risk alarm information of water quality gradual change abnormity based on feedback verification data. According to the invention, risk early warning can be carried out under the condition that the water quality has gradual abnormal fluctuation.

The invention discloses a parameter identification method for an electrochemical-thermal-micro short circuit coupling model. The method comprises the following steps: constructing the electrochemical-thermal-micro short circuit coupling model; collecting multi-working-condition data of battery operation, and clustering the multi-working-condition data; performing feature extraction on the clustered data to obtain a feature data set; performing sensitivity analysis on parameters in the electrochemical-thermal-micro short circuit coupling model, and estimating the influence degree of the parameters on the characteristics; and parameters with high influence degrees are preferentially considered, probability influence evaluation is carried out on the parameters, and a parameter identification result is obtained. According to the method, on the premise of accurately judging the potential influence of the parameters, the accurate identification of the operation characteristic parameters of the battery system when the micro short circuit occurs can be realized.

The invention discloses a method and a system for improving carbon dioxide mineralization and storage efficiency. The method comprises the following steps: building a microfluidic experiment platform integrated with an online CO2 mineralization and storage monitoring system; on a microfluidic experimental platform, simulating a formation temperature and pressure condition to carry out a CO2 mineralization corrosion experiment to obtain pore scale reaction kinetic parameters and a pore structure evolution rule; macromineralization efficiency data are obtained through an indoor physical simulation mineralization reaction experiment of the core scale; a CCM-GEM reaction flow numerical model is constructed; determining a Pareto optimal solution set by using a CMG-GEM reaction flow numerical model; and based on the pore scale reaction kinetic parameters, the pore structure evolution law, the macromineralization efficiency data and the Pareto optimal solution set, determining a CO2 mineralization storage efficiency improvement path of the target rock sample. According to the method, global sensitivity analysis and automatic optimization of multiple parameters are rapidly completed, the research and development period is shortened, the economic cost and the time cost are reduced, and the research efficiency is improved.

The invention relates to the technical field of graph neural networks, in particular to an ideological and political education video content compliance examination system based on a dynamic graph neural network. The module comprises three layers of progressive innovation units: a semantic space mapping unit maps text data into a semantic space of a dynamic dimension to form a semantic unit, and can adaptively adjust the dimension according to content complexity; the keyword network construction unit constructs a keyword association network containing a hierarchical subgroup structure based on a group theory principle, so that the network is kept stable and can be flexibly evolved; the network sensitivity analysis unit performs sensitivity analysis on the keyword association network based on the chaos theory and maps the network state to a predefined compliance attractor pattern library, the advanced mathematics theory is applied to the field of ideological and political content auditing, the auditing efficiency and accuracy are remarkably improved, the manual intervention requirement is reduced, and the auditing efficiency is improved. And the adaptability and sustainability of the system are enhanced.

The invention relates to the technical field of water pump energy efficiency optimization, and discloses a water pump dynamic model calibration and efficiency optimization method. The method comprises the following steps: constructing a water pump dynamic characteristic model comprising a plurality of operation parameter nodes, an association relationship among the parameter nodes and characteristic information of each parameter node, and updating the model based on real-time operation data such as flow change, pressure fluctuation, rotating speed feedback and environment temperature; key influence parameters, low-efficiency parameter combinations and deviation nodes in the model are identified through a parameter sensitivity analysis algorithm; generating an initial calibration strategy set comprising a parameter compensation scheme, an operation condition adjustment scheme and a priority scheduling scheme based on the information; receiving a user configuration signal to determine a calibration operation type, and dynamically correcting the initial calibration strategy set according to the calibration operation type; the corrected strategy is executed, and the water pump dynamic characteristic model is updated based on execution result feedback. According to the method, collaborative implementation of model calibration and efficiency optimization in the operation process of the water pump is achieved.

The invention relates to the technical field of water turbine design and simulation, in particular to a water turbine guide vane structure optimization design method, system and device and a medium, and the method comprises the steps that a multi-physical field coupling simulation model of a guide vane working environment is built and solved, and guide vane multi-working-condition performance data is obtained; on the basis of the multi-working-condition performance data, a mixed agent model for predicting guide vane performance statistical characteristics is constructed in combination with self-adaptive test design and uncertainty quantitative analysis; performing multi-objective collaborative optimization on the design parameter set of the guide vane and sealing system by using the mixed agent model to generate a robustness optimization solution set; based on the robustness optimization solution set and guide vane dynamic characteristics extracted from the multi-working-condition performance data, dynamic modeling and parameter calibration are carried out on the sealing system; and performing simulation verification and sensitivity analysis on the robustness optimization solution set, and outputting a parameterized design result. The method aims at improving simulation fidelity, optimization efficiency and robustness of optimization design of the guide vane structure of the water turbine.

The invention discloses a power distribution network operation parameter optimization identification method based on optimal power flow calculation, and relates to the technical field of power distribution systems, and the method comprises the steps: collecting historical operation parameters of each node of a power distribution network, screening to obtain a data collection priority of an optimal power flow output index, and reasonably distributing node sampling frequencies according to the data collection priority; measuring and collecting operation parameters of the power distribution network according to the distributed sampling frequency, carrying out pre-detection on the collected data, and meanwhile, carrying out adaptive anomaly judgment and sampling frequency dynamic adjustment and carrying out multi-scale decomposition and sensitivity analysis on node residual errors, so as to realize optimal power flow calculation time window adjustment driven by dynamic parameters; and the load model parameters are verified to ensure the accuracy and reliability of the parameter identification result. Through combination of historical data, real-time measurement and residual sensitivity information, transient disturbance, medium-term fluctuation and long-term trend can be considered in the operation parameter identification of the power distribution network, and the online precision of a load model and line parameters is improved.

The invention relates to the technical field of hydroelectric power generation equipment structure reliability evaluation, and particularly provides a water turbine top cover bolt time-varying reliability evaluation method and device based on parameter sensitivity analysis. According to the technical scheme provided by the invention, key variables are highlighted through parameter sensitivity analysis, and the response surface method and Monte Carlo simulation are combined, so that the calculation efficiency is remarkably improved while the precision is ensured, and the reliability evolution rule of the bolt in the whole life cycle can be scientifically represented; and an important basis is provided for maintenance decision and structure optimization of the water turbine top cover bolt.

The invention discloses a model intelligent verification and parameter correction method, which comprises the following steps of 1, building an unmanned ship task high-value verification point set based on expert experience and a large model collaboratively, screening boundary points and extreme points, and performing simulation-actual measurement data consistency verification; if the normalization error of the simulation data and the actual measurement data exceeds a threshold value, triggering correction; step 2, constructing a sensitive factor set according to global sensitivity analysis and a parameter association relationship mining result, screening high-sensitivity parameters as a priority correction target, and avoiding redundancy optimization; and step 3, optimizing the high-sensitivity parameters, and forming a verification-correction-update closed loop by verifying and iteratively updating the priority of the factors to realize the intelligent correction of the parameters of the unmanned ship model.

A method for parameter sensitivity analysis based on a large language model comprises the steps that a first text is received, and the first text comprises a sensitivity analysis problem aiming at a first key index; and determining a first measurement and calculation model based on the first text through a large language model, the first measurement and calculation model being used for mapping a first parameter set into the first key index. Determining a plurality of measurement and calculation parameters and a plurality of pieces of parameter information corresponding to the measurement and calculation parameters through a large language model according to the first parameter set and the first text; the parameter information at least comprises a reference value and a value range. For each measurement and calculation parameter, within the value range of the measurement and calculation parameter, based on a reference value, performing sensitivity analysis by using the first measurement and calculation model to obtain the sensitivity of the measurement and calculation parameter; the sensitivity is used for representing the influence degree of the measurement and calculation parameter on the first key index.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a parameter security re-calibration and response method, device, equipment and medium, and the method comprises the steps: obtaining parameters of a basic model and a fine-tuned model, and comparing the parameters to obtain differential parameters; performing gradient sensitivity analysis on the differential parameters to generate an abnormal correlation map; detecting the fine-tuned model by using an abnormal association map to determine unsafe parameters; unsafe parameters are processed, safe re-calibration is completed, and basic model parameters are fused to form a re-calibration model; verifying the re-calibration model based on the scenarized security verification set and obtaining feedback; according to the feedback updating strategy, re-calibration is carried out again, and a security application model is generated; and processing the task request through the security application model to obtain a security response result. According to the method, unsafe parameters are identified and corrected through differential analysis and closed-loop recalibration, the safety and the compliance are enhanced while the model performance is kept, and reliable application in a complex scene is ensured.

The invention discloses a multi-objective optimization design method and system for an axial-flow circulating pump, and belongs to the technical field of evaporative crystallization systems.The method comprises the steps that design variables are selected to construct a multi-objective optimization model, the design variables are associated with geometric parameter sizes of an axial-flow pump full-flow-channel parameterization model, initial fluid dynamics calculation is executed, and a multi-objective optimization model is constructed; obtaining an initial data set containing a design variable-performance response mapping relation, and performing parameter sensitivity analysis on the initial data set to screen key design variables; on the basis of the initial data set, an optimal approximation model is adaptively selected for the key design variables, multi-objective optimization is carried out, and a Pareto optimal solution set is determined; uncertain factors are introduced for robustness evaluation, a target design scheme is determined, and after fluid dynamics verification is carried out, the design scheme of the design variables of the axial flow type circulating pump is obtained. According to the method, an optimization process with high integration, automatic iteration and intelligent learning is constructed, and rapid, accurate and steady multi-objective optimization design of the axial flow pump hydraulic model is realized.

The application provides a pretreatment and detection method of tetrabromobisphenol A and ten common derivatives thereof in sludge, which comprises the following steps: (1) sample pretreatment: after mixing the frozen and dried sludge sample and the isotopic internal standard, oscillation extraction is carried out, and the extraction liquid is obtained after centrifugation by a centrifuge; the extraction liquid is purified by solid phase extraction to obtain a sample to be detected; (2) the sample to be detected obtained in the step (1) is detected by high performance liquid chromatography-tandem mass spectrometry; (3) the content of tetrabromobisphenol A and ten common derivatives thereof in the sample is calculated according to the internal standard method. The application provides a rapid and sensitive analysis and quantitative method of tetrabromobisphenol A and ten common derivatives thereof in sludge samples.

The invention discloses a self-adaptive configuration method for an optimal working point of a concrete hidden crack ultrasonic thermal excitation system, and the method comprises the steps: collecting multi-physical field response data of the system under different parameter combinations through constructing a parameter space containing initial coupling pressure and working frequency; constructing an excitation efficiency evaluation index based on the active power response or thermal image temperature difference of the transducer, and locking an initial optimal working point by identifying extreme value features of an index response function; performing second-order sensitivity analysis on the optimal point neighborhood by using a Hessian matrix, and constructing a tolerance elliptic model to quantify a parameter control boundary; and meanwhile, frequency and pressure drift are monitored in real time based on a power-phase decoupling mechanism of voltage and current. According to the method, the optimal working parameters can be adaptively matched for loading devices with different rigidities, the engineering tolerance is scientifically set, and the energy conversion efficiency and the detection robustness of the ultrasonic thermal excitation system are improved.

The invention discloses a tree safety risk assessment method, system and device based on a key index priority mechanism, and a storage medium. The method comprises the following steps: constructing an inference model according to a fault tree model of a street tree and a fuzzy Bayesian network; based on the inference model, through reverse inference, sensitivity analysis and most approximate cause chain analysis, screening out a key index set and a conventional index set from a preset risk candidate index set; and based on a hierarchical risk assessment principle, respectively assessing the key index set and the conventional index set to obtain an assessment result. According to the inference model, key factors having significant contributions to the overall risk of the border tree are identified from a large number of risk indexes; a conjoint analysis method of reverse reasoning, sensitivity analysis and most approximate cause chain analysis is adopted, so that false alarm and missing alarm are effectively reduced; according to the hierarchical risk assessment principle, key factors are assessed preferentially, it is ensured that the street tree with structural fatal defects can be recognized and controlled in time, and the reliability and timeliness of assessment are improved.

The invention discloses a cognitive function screening system and method based on an MMSE (Minimum Mean Square Error) prediction model, and relates to the technical field of data analysis, the method comprises the following steps: collecting physical examination index data, removing missing records, adopting a multiple interpolation method for interpolation, and obtaining multiple sets of complete data sets; a continuous prediction model is constructed, MMSE continuous prediction values are obtained, and a sensitivity analysis report is generated; constructing a first-stage classification model, adaptively dividing an optimal threshold combination, and dividing a sample into a high-confidence region, a to-be-discriminated region and a low-confidence region; if the sample size of the to-be-discriminated region is higher than a preset training threshold value, constructing an enhanced feature set, and constructing a second-stage classification model; when the prediction probability reaches the optimal re-discrimination threshold value, the classification result in the first stage is corrected, and otherwise, the classification result is maintained; if not, maintaining the classification result; and integrating the classification results to obtain a final classification result of all the samples.

The invention discloses an end face tool sharpener control system, and relates to the technical field of numerical control systems. The end face tool sharpener control system comprises a data preparation and recording module, a tool type parameter dynamic modeling module, a multi-axis linkage control module, a grinding wheel pressure self-adaption control module, a grinding wheel abrasion compensation module and a technological parameter optimization module. By comprehensively collecting multi-dimensional historical processing data and constructing a set framework, automatic classified storage of parameters is realized; the NURBS curve interpolation algorithm is adopted, and the machining precision and smoothness of the complex shape are guaranteed; instructions are synchronized in real time, errors are compensated, vibration is monitored in combination with an acoustic emission sensor, and machining stability is improved; a fuzzy PID model and machine learning are utilized to dynamically adjust parameters and optimize the parameters in advance; through machine vision and digital twinning, abrasion is accurately recognized, and the finishing period is predicted. And based on the hybrid model and reinforcement learning, predicting a processing result, dynamically adjusting parameters, and performing error compensation and sensitivity analysis.

The invention provides a sewage biological treatment model prediction control method. According to the method, firstly, parameters are sorted through global sensitivity analysis, and high-sensitivity parameters are emphatically processed; dynamically evaluating the local sensitivity of the parameters based on a multi-scale time window; the DDQN network based on sensitivity guidance is fused with data-driven learning and physical mechanism knowledge; and the model prediction control system realizes multi-objective optimization of stable and up-to-standard effluent quality and energy-saving operation of the system based on the calibrated ASM3 model. According to the method, a complete closed-loop system from sensitivity analysis to parameter correction to prediction control is constructed, and compared with a traditional off-line parameter correction and experience control method, the model prediction precision, the control response speed, the operation stability, the energy efficiency level and the like are remarkably improved; and a complete technical support is provided for intelligent operation of an activated sludge process.

The invention provides a multi-scheme collaborative yield forecasting method based on data assimilation and model parameter optimization, and belongs to the technical field of agricultural information, and the method comprises the steps: obtaining historical and real-time data of a target region and a target crop growth season; constructing a plurality of combined simulation schemes of the WOFOST model; carrying out data assimilation on the leaf area index and the soil humidity by utilizing an ensemble Kalman filter (EnKF) method and combining a Gaussian disturbance strategy; performing sensitivity analysis and optimization on photosynthetic parameters of the WOFOST model, determining an optimal photosynthetic parameter combination and operating the model; improving a water stress function; constructing a rolling updating yield prediction framework, and dynamically optimizing a yield prediction result; and dynamically selecting an optimal simulation strategy to simulate and forecast the yield. According to the method, the yield simulation precision and forecasting stability of the crop model under different moisture years are remarkably improved, and a reference is provided for developing a new meteorological year adaptive dynamic simulation framework of the crop model.

The invention discloses a composite material storage life prediction method based on variable transformation and a genetic algorithm, and relates to the technical field of composite material performance evaluation and life prediction, and the method comprises the steps: obtaining performance degradation data at different temperatures through an accelerated aging test, and building a semi-empirical performance degradation model; specific variable transformation is introduced, a nonlinear optimization problem is converted into a linear regression problem, partial model parameters are rapidly solved by adopting a least square method, and meanwhile, key nonlinear parameters are globally optimized by using a genetic algorithm, so that the defect that a traditional method is easy to fall into local optimum is overcome. And establishing a temperature-service life relationship by combining with an archenius model, and realizing storage life prediction at normal temperature. The method also integrates sensitivity analysis and uncertainty quantification functions, and provides reliability evaluation for a prediction result. The method fully considers the influence of the environment temperature, meets the requirement of efficient prediction, and improves the precision and stability of life prediction.

The invention provides a motor stator potting structure failure risk assessment and optimization design method. The method comprises the following steps: firstly, establishing a simplified numerical calculation domain based on a potting structure, carrying out grid and time step length independence verification, carrying out parameter sensitivity analysis and experimental measurement, and establishing a high-precision thermal-mechanical coupling numerical calculation model; then, in combination with the simulation result and the temperature change constitutive characteristics of the ultimate strength of the material, a node-level failure risk calculation method is provided, failure risk indexes of different stress forms are obtained, and a weighted comprehensive failure risk evaluation standard is determined according to the contribution degree of the stress forms to failure behaviors; and finally, proposing a parameterized structure optimization scheme according to a dangerous area and a dominant failure mechanism, carrying out quantitative evaluation and optimization on the candidate scheme under a weighted comprehensive failure risk evaluation standard, and determining a final structure optimization scheme. The method has the advantages of closed flow loop and quantifiable indexes, and is suitable for reliability design and life evaluation of the motor encapsulation structure.

The invention relates to the technical field of seismic exploration, in particular to a method for determining structural parameters and sweep frequency signals of a controllable impact seismic source, which is used for carrying out an indoor physical test of a controllable impact seismic source model machine and ensuring the effectiveness of a design scheme of the controllable impact seismic source. Constructing and verifying a multi-body dynamics numerical model of single impact, performing single variable sensitivity analysis on key structure parameters, and determining an optimal structure parameter combination by taking a frequency domain of output seismic waves as an evaluation index; constructing a multi-impact model, performing a simulation test on the sweep frequency control parameter, and determining an optimal sweep frequency signal parameter based on the main lobe amplitude and the main lobe-side lobe amplitude ratio of the output signal autocorrelation function; through forward modeling and cross-correlation-scattered wave inversion imaging processing, an application method of a controllable impact seismic source is established, and validity is verified. The technical problems that a traditional trial and error method is low in efficiency, main lobe energy of seismic source output signals is dispersed due to an unknown parameter influence mechanism, side lobe interference is large, and the signal-to-noise ratio is low are solved.

The invention discloses a method and a system for predicting throttling performance of a waterproof hammer air valve based on multi-working-condition simulation, and relates to the technical field of waterproof hammer air valves. The method comprises the following steps: establishing a simulation reference model containing adjustable parameters for the waterproof hammer air valve; defining a multi-working-condition driving matrix, performing global sensitivity analysis on each working condition, and extracting dominant and redundant parameters; the reference model is equalized by redundant parameters, and working condition configuration and calibration are completed based on dominant parameters to generate a matching model; and real-time working conditions are collected and matched with corresponding models to complete throttling performance prediction. The technical problems of low calculation efficiency and insufficient prediction precision caused by an undefined parameter influence mechanism and model redundancy in a multi-working-condition scene of a traditional waterproof hammer air valve throttling performance prediction method are solved. The technical effects of accurately quantifying parameter influence through working condition global sensitivity analysis, equivalently processing redundant parameters to construct a lightweight working condition simulation model, and realizing high-precision and high-efficiency throttling performance real-time prediction under multiple working conditions are achieved.

The invention provides a laying hen feed raw material sample screening method and system based on multi-source variability, and relates to the technical field of data processing analysis, and the method comprises the steps: obtaining multi-source attribute data and conventional component content data of raw materials, mapping the attribute data into tensor modal dimensions through multi-source variability tensor construction processing, and obtaining a multi-source variation tensor model; the component data is used as a characteristic component, and a multi-source variability tensor is obtained through decoupling and compression. Variability spectrum decomposition processing is carried out, local rank spectrum decomposition is carried out along producing areas, time and component dimensions, and a variability spectrum vector set is obtained; and identifying a candidate modeling sample set through multi-scale extremum and sparsity screening. And evaluating the contribution degree and sensitivity of the sample to a standard ileum amino acid digestibility prediction equation through a leave-one-out method and sensitivity analysis, and screening out an optimal modeling sample. According to the method, the multi-source variation information of the raw materials can be integrated, the variation spectrum is comprehensively covered with the minimum sample size, and the precision and generalization ability of the prediction model are remarkably improved.