712 results about "Sensitivity analysis" patented technology

The invention relates to the technical field of SRAM (Static Random Access Memory) memory anti-radiation testing, and discloses an SRAM memory anti-radiation testing method and system based on running state injection, and the method comprises the following steps: system initialization, mapping preparation and time sequence baseline establishment; carrying out model quantitative compiling, sensitivity analysis and physical bit candidate generation; generating a running state injection plan and loading a test case; performing operation state execution, disturbance injection and synchronous monitoring acquisition; multi-source triggering, weight read-back and fault decoupling judgment are carried out; and performing quantitative evaluation on anti-radiation performance, constructing a degradation curve and outputting a report. The system corresponds to the method. According to the invention, a hardware-in-the-loop operation state injection and online evaluation technology is constructed, and the technical problems of guidance and position limited disturbance / equivalent irradiation injection based on weight bit level sensitivity and logic-physical mapping are solved.

The invention discloses a static database self-adaptive fuzzification desensitization implementation method for highly sensitive data in the power industry. The method comprises the following steps: a data preprocessing module, a sensitivity analysis module, a fuzzification algorithm module, a self-adaptive mechanism module and an effect evaluation and verification module. Compared with the prior art, the method has the advantages that 1, self-adaptive dynamic adjustment is performed: a desensitization strategy is automatically optimized according to data characteristics and use scenes, and privacy protection indexes (such as k-anonymity values) are improved by 30%-50%; 2, multi-dimensional balance: the information loss rate is reduced by 10%-20%, the query accuracy is improved by 5%-10%, and safety and service requirements are considered; 3, system expansibility: a plug-in architecture supports dynamic loading of new algorithms and rules, and a configuration management module realizes strategy real-time adjustment; and 4, compliance guarantee: through a law and regulation rule base and a compliance check algorithm, the compliance is ensured to meet the standard.

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 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 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 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 network dynamic topology anomaly detection and defense method, device and equipment. The method comprises the following steps: dividing a large-scale network into a plurality of sub-networks based on community discovery; constructing a Laplacian matrix based on the sub-network, and obtaining a Laplacian spectrum of the sub-network through the Laplacian matrix; obtaining the time difference of the Laplacian spectrum of the sub-network, and judging whether the sub-network is credible or not according to the time difference of the Laplacian spectrum of the sub-network; performing node sensitivity analysis on the untrusted sub-networks to obtain multi-dimensional features of the nodes; inputting the multi-dimensional features into a BP neural network model, outputting node credibility scores, and screening out untrusted nodes of which the node credibility scores are higher than a threshold value; and local access view control is set for the untrusted nodes, so that the accuracy of identifying low-frequency and long-term hidden penetration attacks is improved, and the effectiveness of defending node attacks is improved after abnormal nodes are identified.

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 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 drilling-measurement constrained three-dimensional geothermal geologic attribute modeling and quantification method, and relates to the technical field of data analysis and processing, and the method comprises the following steps: 1, constructing a drilling-measurement constrained three-dimensional geothermal geologic voxel basic model, and forming a three-dimensional geothermal geologic voxel basic model covering a modeling range; 2, constructing an implicit signed distance field on the three-dimensional geothermal geologic voxel basic model to express fracture geometry, generating a unified implicit geothermal geologic voxel field, and redividing voxel categories according to the unified implicit geothermal geologic voxel field; and step 3, executing moving cube surface reconstruction and prior sensitivity analysis on the unified implicit geothermal geologic voxel field to give a space confidence body and a volume interval, and dividing the space confidence body and calculating the volume interval according to the voxel occurrence frequency. Evaluation of the geothermal body is changed from traditional single volume estimation to interval and confidence expression, and a reliable basis is provided for well position deployment, risk management and development planning.

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 discloses a truss type load-bearing structure time domain dynamics topological optimization method and system based on an intrinsic orthogonal decomposition method, and relates to the technical field of truss type load-bearing structure topological optimization. The method is provided for solving the problems of high calculation power and the like caused by large-scale space-time response iteration of original transient problems and intensive calculation of dual problem sensitivity analysis in the prior art. According to the method, a local geometric control strategy for the cross interference problem of rod piece units is introduced, and a transient dynamics full-order topological optimization model of the truss structure is established through an equal geometric stiffness diffusion method. Based on an intrinsic orthogonal decomposition method, a transient dynamics reduced-order model of a truss structure is constructed, a reduced-order model base is updated in real time through an incremental singular value decomposition method, and then efficient solving of a transient dynamics equation and an adjoint equation in sensitivity analysis is achieved. And updating an iterative design variable by adopting a penalty function method, and seeking the optimal layout design of the truss structure. The invention provides a time-domain dynamics topological optimization method of the truss type load-bearing structure based on an intrinsic orthogonal decomposition method, and the method can be used for efficiently solving the dynamics layout optimization problem of the truss structure under the action of a transient load.

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 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 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 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 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 tourism highway landscape segmentation method and device and a readable storage medium, and the method comprises the steps: firstly, presetting visual sampling points along a highway, employing a multi-azimuth visual collection strategy, and collecting image data and space coordinate information of vegetation, a water body, an artificial structure and a natural landform under a preset illumination condition; dividing the landscape resources into a natural class, a humanity class and a composite class through a classification model based on the visual feature parameters; performing level division on landscape resources by adopting a multi-dimensional quantitative evaluation system and combining visual uniqueness, ecological integrity and culture identification degree; calculating spatial buffer distances of different levels of landscape resources through a landscape sensitivity analysis model; and finally, based on the spatial continuity, the level distribution characteristics and the spatial buffer distance of the landscape category, generating a landscape segmentation boundary of the tourist highway through a GIS spatial analysis tool. According to the method, the scientificity and reasonability of segmentation are improved, and technical support is provided for tourism highway landscape planning.

The invention provides an axial flux motor multi-objective optimization method based on an improved homing religious algorithm. Firstly, key structure parameters are screened through sensitivity analysis, and the optimization range of the key structure parameters is determined; then, sample data is generated by adopting improved Latin hypercube sampling, and a data set is constructed through finite element simulation; then, performing automatic optimization on smooth factors of the generalized regression neural network based on a He-horse optimization algorithm, and establishing a high-precision agent model; improving a horizon algorithm by adopting Tent chaotic mapping, non-dominated sorting and congestion degree distance, and combining the horizon algorithm with the proxy model to carry out multi-objective optimization to generate a Pareto solution set; and finally, selecting an optimal motor parameter according to the target weight. According to the method, parameterized finite element analysis, an optimization agent model and an improved multi-target algorithm are deeply fused, so that the global optimization capability is ensured, the optimization efficiency and precision are remarkably improved, and an effective solution is provided for rapid and accurate design of the axial flux motor.

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.

The invention provides a medical multi-modal data fusion analysis method and system based on cross-modal alignment, relates to the field of artificial intelligence, and solves the technical problems of uneven data quality, inflexible semantic alignment steps and fusion weight distribution and the like in the prior art. The method comprises the steps of obtaining multi-modal medical data and performing quality grading evaluation to obtain a modal quality score of each modal; performing semantic alignment on the multi-modal data after quality grading evaluation by adopting a dual-stage alignment algorithm of intra-modal feature enhancement and cross-modal semantic calibration to obtain a semantic alignment result; performing feature fusion on the semantic alignment result by adopting a dynamic weight model based on the knowledge graph correlation degree, the clinical guide priority and the modal quality score; and generating a structured diagnosis report for the fusion result through an artificial intelligence generation content AIGC model, performing clinical compliance verification and sensitivity analysis, and feeding back verification and analysis results to the feature fusion step for optimization.

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.