224 results about "Performance curves" patented technology

The invention discloses an intelligent building energy-saving management method and system based on a digital twinning technology, and relates to the field of digital twinning technologies, and the method comprises the steps: building a three-dimensional building model based on a building information model and three-dimensional laser scanning point cloud data, and generating a twinning database through multi-source equipment data fusion; performing energy consumption simulation modeling according to the lamp dimming parameters, the air conditioner performance curve and the elevator operation log in the twin database to generate an energy consumption reference report; acquiring personnel density and environment parameters, and generating a building environment state matrix mapped with the twin database after space-time calibration; and establishing a hierarchical threshold value control strategy library based on the building energy consumption reference report and the building environment state matrix, and generating an optimization control strategy set through the equipment linkage relation matrix. According to the method, by embedding a Pareto optimal solution screening mechanism and equipment mutual exclusion rule verification, the daily average energy consumption is reduced on the premise that the comfort reaching rate of the optimal control parameter packet is ensured.

The invention provides a high-efficiency intelligent teaching resource scheduling algorithm and an implementation method thereof, and the method comprises the steps: constructing a judgment matrix according to a learner capability difference evaluation result, cooperation efficiency analysis data, knowledge graph coverage rate statistics and learning style matching degree score, carrying out the calculation to obtain a group heterogeneity index, solving the weight of each index through a feature value method, and carrying out the calculation of the group heterogeneity index. Carrying out weighted summation to obtain a group complementarity comprehensive index; acquiring a historical learning data sequence of a learner, extracting time sequence features, combining a current individual progress deviation monitoring result, individual resource demand evaluation and individual learning content suitability analysis, constructing an individual development trajectory prediction model, and predicting a future individual learning performance curve; and aiming at the group complementarity comprehensive index and a future individual learning performance prediction result, constructing a multi-objective optimization model of resource allocation, setting a group complementarity objective function and an individual satisfaction objective function, and solving to obtain a resource allocation scheme for balancing group and individual benefits.

The embodiment of the invention provides a centrifugal compressor performance data determination method and a centrifugal compressor performance optimization method, and belongs to the technical field of centrifugal compressors. The method comprises the steps that the inlet guide vane opening degree and the compressor rotating speed are combined to obtain an operation mode combination; grouping the original compressor operation parameters based on the operation mode combination to obtain grouped operation parameters, wherein the grouped operation parameters comprise evaporator parameters, condenser parameters, refrigerating capacity of a refrigerating machine and unit operation efficiency parameters; calculating a flow coefficient and a pressure ratio of a compressor according to evaporator parameters, condenser parameters and refrigerating capacity of a refrigerator; the isentropic compression efficiency of the compressor is calculated according to the condenser parameters; calculating the isentropic compression efficiency of the compressor unit based on the unit operation efficiency parameter and the isentropic compression efficiency of the compressor; and constructing a performance curve based on the compressor flow coefficient, the pressure ratio, the compressor isentropic compression efficiency and the compressor unit isentropic compression efficiency. According to the embodiment of the invention, the performance data of the centrifugal compressor can be accurately obtained, and the accuracy of performance optimization is improved.

The invention provides a defective part reprocessing decision-making method based on multi-target balance, which comprises the following steps: acquiring real-time defect rate data and part distribution information from production, analyzing a defect rate change trend and a part quantity fluctuation condition, and setting a standard defect threshold according to an industrial standard and historical data; if the defect rate exceeds a standard defect threshold value, identifying and fitting part performance parameters to obtain an average performance curve, calculating slope change and abrupt change amplitude of the curve, and determining time and influence range of a variation point; acquiring a factory processing load, combining the priority distribution sequence with the target part grouping scheme and the factory processing load to obtain a reprocessing flow configuration scheme, and identifying a balance point of resource consumption and a reprocessing coverage rate through simulation; and acquiring a real-time ratio of the reprocessing coverage rate and the resource consumption corresponding to the balance point, comparing the real-time ratio with a historical optimal ratio, and if the real-time ratio is not smaller than the historical optimal value, determining that a performance bottleneck risk exists, and analyzing a risk cause.

The invention belongs to the technical field of refrigeration control, and discloses a refrigeration system optimization method and device based on error correction, and the method comprises the steps: obtaining a target required cooling capacity and a current environment parameter, inputting an optimization model, and obtaining an optimal control parameter and prediction power; constructing a performance curve; the performance curve comprises a temperature refrigeration curve, a temperature correction curve and a load correction curve; inputting the optimal control parameters into the performance curve to obtain actual power; subtracting the actual power from the predicted power to obtain a power error value; judging whether the absolute value of the power error value is smaller than a preset error threshold; if yes, the refrigerating system is made to operate according to the optimal control parameters; and if not, the cooling water inlet temperature in the optimal control parameters is adjusted according to the power error value. According to the method and the device, the error of the optimal control parameter output by the optimization model can be corrected, and the refrigerating system can be ensured to operate at the optimal power energy consumption while the target required cooling capacity is met.

The invention relates to the technical field of intelligent manufacturing, in particular to a nodular cast iron alloy component self-adaptive optimization method and system based on artificial intelligence, and the method comprises the following steps: obtaining furnace temperature and component parameters, generating a stable comparison legend, screening offset abnormal combinations to form an available set, extracting performance curve characteristics to construct a trend chart, and obtaining a nodular cast iron alloy component self-adaptive optimization result. And superposing the structural parameters and the element distribution positioning segregation area, and outputting feeding correction parameters to generate an optimization scheme. According to the method, normalization processing is carried out on furnace temperature, carbon-silicon ratio and other multi-factor offset values, a component offset map is constructed, trend feature extraction and sorting of response curves under multiple working conditions are combined, component combinations with stable performance are screened out, a mapping relation is established through the structural curvature, the wall thickness slope and the injection rate, and a spatial coincidence map is generated through coincidence analysis. And the feeding direction and the injection rate are inversely calculated, dynamic adaptation of components and the structure is achieved, and the structure uniformity and the performance consistency are improved.

The invention provides an AI-based floor mopping machine production equipment abnormity prediction system and method, and the method comprises the steps: monitoring operation parameters of floor mopping machine production equipment in real time, comparing the operation parameters with historical data, and separating periodic and non-periodic components; using a clustering algorithm to identify an unexpected fluctuation mode, and mining through association rules to obtain key factors; constructing a reference performance curve by combining performance data of components of the same type, and evaluating a potential abnormal risk level by using a fuzzy logic system based on the deviation degree of an actual performance curve; and adopting a Bayesian network to predict possible faults in a specific time period in the future, and generating a maintenance suggestion report. The maintenance efficiency and reliability of the floor mopping machine production equipment are improved.

The invention discloses a simple high-precision wind tunnel compressor variable-speed aerodynamic performance parameterized modeling method, and relates to the field of wind tunnel operation characteristic digital modeling, and the method comprises the steps: obtaining a plurality of equal-speed total pressure ratio performance curves of a wind tunnel compressor through measurement; adding a virtual working point to obtain a plurality of virtual constant-rotating-speed total pressure ratio performance curves; constructing a first parameterized model, and obtaining parameters in the first parameterized model; obtaining a mapping relation; constructing a second parameterized model corresponding to the given rotating speed; calculating to obtain an equal-rotating-speed total pressure ratio performance curve of the wind tunnel compressor corresponding to the given rotating speed; the total pressure ratio of the wind tunnel compressor corresponding to the converted mass flow of the given wind tunnel compressor is obtained through calculation based on the equal-rotating-speed total pressure ratio performance curve of the wind tunnel compressor corresponding to the given rotating speed; the method solves the problem that a conventional wind tunnel compressor parametric modeling method depends on mass real aerodynamic performance data to achieve simple and high-precision wind tunnel compressor variable-speed aerodynamic performance digital modeling.

The invention relates to the technical field of computer aided design, in particular to a fan model generation method and system. The method comprises the following steps: acquiring basic design parameters and constraint conditions of a fan, and performing fan model simulation generation and aerodynamics analogue simulation to generate aerodynamics simulation fields corresponding to different fan models; performing fan blade power curvature and airflow vibration evaluation analysis on the aerodynamic simulation fields corresponding to the different fan models, and performing fan airflow efficiency calculation at the same time to obtain fan air flow efficiency corresponding to the different fan models; performing operation acoustic noise evaluation analysis and performance curve simulation analysis on the corresponding fan three-dimensional models to generate airflow efficiency-noise performance curves corresponding to different fan models; a fan optimization model is generated by obtaining a corresponding fan constraint optimization target and performing time sequence energy efficiency ratio optimization design. The design effect and efficiency of the fan model can be improved.

The invention discloses a heat exchange station circulating pump characteristic adaptive optimization control method and system, and relates to the technical field of circulating pump control, and the method comprises the steps: building a building terminal heat transfer model and a building energy balance model based on the temperature distribution of the surface of a radiator, and then building a building load prediction model; performing numerical modeling on the performance curve of the heat exchange station circulating pump, and establishing a circulating pump performance model; based on a depth deterministic strategy gradient algorithm, establishing a neural network control model for self-adaptive control of the circulating pump of the heat exchange station; establishing a state, an action and a reward function of an agent of the neural network control model; and training the neural network control model to obtain a trained control model, and performing real-time control on the heat exchange station circulating pump based on the trained control model. The technical problems that a traditional control method of the circulating pump is low in operation efficiency and serious in energy waste are solved.

The invention discloses a supply chain network toughness dynamic evaluation method. According to the method, an improved cascade failure model is constructed, normal operation, damage and interruption states of nodes are defined, interruption propagation is simulated, and node state changes are tracked; counting the number of normal operation nodes at each moment to generate a structural performance curve, constructing a maximum flow model with the maximization of the total delivery amount as a target, and solving the total delivery amount at each moment to generate an operation performance curve; according to the structure performance curve and the operation performance curve, the maximum performance decreasing amplitude, the recovery duration and the average performance maintenance rate are calculated respectively, and the supply chain network toughness is quantified from the three dimensions of absorption force, recovery force and adaptability. The invention further discloses a supply chain network toughness dynamic evaluation device, electronic equipment, a storage medium and a computer program product. The method can dynamically evaluate the toughness of the supply chain network facing the interruption risk, provides a stable and reliable basis, and is suitable for toughness evaluation and optimization of the supply chain network.

The invention relates to a surge margin prediction method and device of a gas compressor, equipment and a storage medium. The method comprises the steps that key parameter point sets obtained through load step iteration of the gas compressor in all the operation stages are obtained, all the key parameter point sets are mapped to performance diagrams of temperature identification to obtain corresponding mapping performance diagrams, discrete points in the same temperature layer in all the mapping performance diagrams are fitted, and the corresponding mapping performance diagrams are obtained; and the thermal state performance curves of the gas compressor in all the operation stages are determined, the surge critical points of all the thermal state performance curves are obtained, the predicted surge margin of the gas compressor in all the operation stages is determined according to the surge critical points of all the thermal state performance curves and all the key parameter point sets, and load step iteration is achieved based on multi-physics field coupling of the gas compressor. By adopting the method, the surge margin of the gas compressor in different operation stages can be predicted based on the key parameter point set obtained by load step iteration realized by considering multi-physics field coupling in each operation stage of the gas compressor, so that the accuracy of a prediction result is improved.

The invention relates to an operation debugging method and device of a pump set system, and belongs to the field of pressure supply medium equipment. Calculating the pipeline impedance coefficient of the pipeline between the nodes under the rated flow; respectively determining a valve impedance coefficient and an equipment impedance coefficient; establishing a pump set performance equation according to the pump set performance curve; establishing a flow distribution mathematical model according to the principle that flow conservation and parallel branch pressure drop are the same; calculating the flow distribution coefficient of each pipeline according to the pipeline impedance coefficient, the valve impedance coefficient and the equipment impedance coefficient, and establishing a system main pipeline characteristic equation; the main pipeline characteristic equation and the pump set performance equation are combined, and a pump set operation condition point is calculated; calculating the pressure of each node according to the operating condition point of the pump set and the flow coefficient of each pipeline; and the valve opening is calculated according to the pressure and flow requirements of all the nodes, and debugging is conducted by controlling the valve opening. The method can effectively reduce the calculation duration and the calculation amount.

The invention relates to the technical field of compressors, in particular to a compressed air energy storage compressor all-working-condition modeling method which comprises the steps that the modeling requirement of a compressor to be modeled is obtained; determining a first rotating speed performance curve of the to-be-modeled compressor, determining at least one state variable according to the modeling demand, building an initial compressor model based on the at least one state variable and a preset constraint relationship, and operating the initial compressor model to obtain a second rotating speed performance curve of the to-be-modeled compressor; a first performance curve is determined according to the first rotating speed performance curve, a second performance curve is determined according to the second rotating speed performance curve, a third performance curve is determined according to the first performance curve and the second performance curve, and the performance curve of the full-working-condition rated rotating speed is obtained through combination. Therefore, the problems of inaccurate modeling, poor universality and low economical efficiency of the compressor are solved, the method is simple and high in economical efficiency, and all-working-condition modeling can be carried out on the compressor with unknown performance.

The invention relates to the technical field of aero-engines, and discloses a wheel disc fatigue acceleration test stress coefficient determination method and system, and an application of the wheel disc fatigue acceleration test stress coefficient determination method and system. Analyzing by combining a performance curve of a wheel disc material to obtain a first upper limit threshold value of a wheel disc fatigue acceleration test stress coefficient; then carrying out a characteristic simulation piece fatigue test on the key part of the wheel disc to obtain a second upper limit threshold value of the stress coefficient of the wheel disc fatigue acceleration test, and reasonably determining the maximum value of the stress coefficient of the wheel disc fatigue acceleration test by comparing the first upper limit threshold value with the second upper limit threshold value; the service life damage equivalence and the same failure mechanism before and after the fatigue acceleration test of the wheel disc are ensured, the situation of wheel disc service life verification deviation danger caused by non-equivalent damage due to unreasonable stress coefficient in the existing method is avoided, the safety and the reliability of the wheel disc in the service life are improved, and the service life of the wheel disc is prolonged. The fatigue life of the wheel disc can be effectively verified.

The invention relates to a target detection performance curved surface progressive sampling method based on Gaussian process multi-core fusion. The invention relates to the technical field of target detection, in a target detection process, multi-level search space construction is carried out, and efficient kernel function combination and optimization are realized in a complex performance curved surface scene; a progressive sampling optimization strategy of a multi-scale collaborative adaptive kernel system is established, a kernel function combination structure and parameters are optimized step by step, and the fitting precision of a Gaussian process model to a target detection performance curved surface is improved. The method is mainly used for solving the problem of model performance evaluation under a multi-dimensional interference condition, and the performance evaluation efficiency and accuracy of the target detection model in a complex environment are improved by optimizing kernel function combination and parameter tuning.

The invention relates to an IGBT module junction temperature estimation method and system, and the method comprises the following steps: obtaining a performance curve set of an IGBT module under different working conditions, and carrying out the fitting optimization, and obtaining an optimal performance curve set; constructing a transient thermal resistance curve model according to a transient thermal resistance principle, and constructing a second-order Foster thermal network model based on the transient thermal resistance curve model; performing Laplace transformation and discretization processing based on a backward difference method on the second-order Foster thermal network model to obtain a junction temperature estimation model; collecting a phase current instantaneous value, and calculating the total power loss of the IGBT module in real time in combination with the optimal performance curve set; and finally, the junction temperature of the IGBT module is obtained in real time by using the total power loss of the IGBT module obtained through real-time calculation and the junction temperature estimation model, and junction temperature estimation is completed. According to the method, the effect of improving the power loss precision of the IGBT module is achieved by optimizing the performance curve, and then the junction temperature estimation precision is improved.

The invention relates to a defect analysis and process optimization method based on artificial intelligence multi-mode fusion, and the method comprises the steps: respectively inputting virtual data of a chip or device design layout, an expected process flow and parameters, and an expected electrical performance curve into an image encoder, a process encoder, and an electrical encoder, extracting a corresponding feature vector; performing cross-modal information interaction on the corresponding feature vectors according to a cross attention fusion mechanism; determining a representation image, a defect category, a root cause category and a process parameter correction suggestion; and based on a manufacturing execution system (MES), an electronic design automation (EDA) tool or an equipment interface, process parameters are corrected, and a closed-loop control result from defect analysis to process optimization is obtained. Therefore, the problems of single data dimension, mutual coupling of various defects, influence of background noise, limited machine identification accuracy, overlarge workload of artificial expert judgment and low efficiency are solved.

A computer-implemented method for detecting mechanical damage to a machine based on a performance indicator present at a rotation speed of the machine is provided, including the steps of: receiving a number of calibration data points during a calibration period, each comprising the rotation speed of the machine and a performance characteristic value associated with the rotation speed, calibrating an AI-based Gaussian process regressor, by inputting the calibration data points received during the calibration period and outputting an estimated performance curve and an uncertainty range assigned to each value of the performance curve for all rotation speed values, receiving a data point to be evaluated, and comparing the data point to be evaluated with the estimated performance curve, and outputting an anomaly message if the data point to be evaluated is outside a specified limit value, which is dependent on the uncertainty range of the performance characteristic value.

The invention discloses a flow instrument test-free air flow calibration method for a VGT axial nozzle ring supercharger, and particularly relates to the technical field of turbochargers, which comprises the following steps: preparing equipment, calculating required air flow requirements, obtaining a performance curve relation graph according to analog simulation analysis, forming a theoretical performance curve relation graph by analog calculation values, and calibrating the performance curve relation graph according to the theoretical performance curve relation graph. The method comprises the steps of gas source connection, displacement gauge installation and positioning, initial measurement of automatic program detection, test out-of-tolerance fine adjustment and repeated measurement of automatic program detection. According to the invention, the matching relation among the actuator control pressure, the actuator valve rod displacement, the VGT axial nozzle ring blade opening degree and the rocker arm rocker rotation angle is simulated and calculated, and the pneumatic test bench and the displacement sensor are utilized at the same time; the actual matching condition of the actuator valve rod displacement, the VGT axial nozzle ring blade opening degree and the rocker arm rocker rotation angle of a VGT structure supercharger product in the production process can be monitored on line, the air flow calibration technology free of flow instrument testing is achieved, and the design requirement is met.

The invention relates to the technical field of gas compressors, in particular to a gas compressor stall and surge prediction method based on Riemann-Invariant boundary condition optimization, and the method comprises the steps: constructing gas compressor grids at different regions; the target outlet pressure and the target outlet mass flow rate of the gas compressor close to the stall working condition are obtained according to the steady-state gas compressor performance curve; defining Riemann-Invariant boundary conditions according to the target outlet pressure and the target outlet mass flow rate when the gas compressor is close to the stall working condition and the pressure when the mass flow rate is zero; and the stall working condition and the surge working condition of the gas compressor are predicted on the basis of Riemann-Invariant boundary conditions. According to the method, the transient behavior of internal flow of the gas compressor is accurately simulated by combining a CFD numerical simulation technology with Riemann-Invariant boundary conditions, and the prediction precision and calculation stability of stall and surge phenomena are remarkably improved.

The invention discloses a vibration fatigue life prediction method and system based on a working condition adaptive spectrum. According to the method, load information of a target structure under various service conditions is obtained, and frequency domain load descriptions corresponding to different working conditions are subjected to weighted fusion according to working condition weights, so that a working condition adaptive load spectrum capable of reflecting real service characteristics is constructed; on the basis, frequency domain stress response of the structure under the random vibration condition is obtained in combination with a structural dynamics analysis model, equivalent stress description of dominant fatigue damage is further determined through a multi-axis equivalent criterion, and fatigue damage distribution is calculated through a frequency domain fatigue analysis method; and finally, predicting the vibration fatigue life of the structure in combination with a material fatigue performance curve and an environment-related performance correction relation. Compared with the prior art, the method has the advantages that the multi-working-condition load superposition effect and the service environment influence can be effectively considered, and the accuracy and engineering applicability of vibration fatigue life prediction are improved.

The invention discloses a charging pile axial flow fan blade parameter optimization method based on a genetic algorithm, and the method comprises the steps: extracting the geometrical shape of an existing blade, calculating a performance curve of an existing fan structure, and carrying out the verification through an experiment; drawing a hub line and a wheel cover line in a meridian plane; reconstructing a three-dimensional model of the moving blade; keeping the molded lines of the constructed hub and axle unchanged, widening the molded lines of the front and rear edges of the meridian plane blade to enable the width to be matched with the axial height of the blade, and constructing a single flow channel; 5, performing three-dimensional calculation by using the obtained three-dimensional moving blade model, and verifying whether data features are matched or not; step 6, establishing a meta-model; performing genetic algorithm multi-target optimization; according to the method, the genetic algorithm is utilized, and the axial flow fan blade with the minimum fan power and the maximum static pressure efficiency is rapidly and accurately obtained; in addition, the blade of the axial flow fan is researched through parameterization, and the blade with changed geometric parameters can be verified more easily.

The present invention discloses a grid-scale watershed flood resilience evaluation method and system based on the function and tolerance of underlying surfaces. The method includes obtaining high-resolution remote sensing image data and POI data, obtaining a fine classification result through multi-level classification processing, and calculating the function weight values of various underlying surfaces in combination with socioeconomic data; performing statistical analysis and grid calculation on the fine classification result and historical disaster data to obtain the tolerance capacity values of grid cells; constructing a coupled hydrodynamic model for simulation calculation to obtain grid inundation depth and inundation time data; calculating the system performance curve, extracting the resilience characteristic values, and obtaining a set of grid resilience characteristic values; calculating the comprehensive resilience based on the set of resilience characteristic values, and performing spatial integration in combination with the function weight values of underlying surfaces at different times to obtain the resilience evaluation results at different spatio-temporal scales. The present invention provides accurate decision-making support for urban flood control and disaster reduction.

Disclosed in the present application are a turbine device simulation model evolution method and apparatus, a medium and a computing device. The turbine device simulation model evolution method comprises: acquiring current operating data of a turbine device corresponding to a condition in which inlet parameters are design parameters; on the basis of the current operating data, pre-testing a turbine device simulation model; when a pre-test result shows that the turbine device simulation model needs to evolve, preprocessing the current operating data, so as to obtain a combined data set, the combined data set comprising input data and output data corresponding to the input data; on the basis of the combined data set, building a mapping model and, by means of the mapping model, outputting an updated performance curve with the inlet parameters being the design parameters; and replacing a historical performance curve of the turbine device simulation model with the updated performance curve, so as to evolve the turbine device simulation model. Turbine device simulation models can evolve on the basis of the degree of degradation of turbine devices, so as to realistically reflect operating characteristics of the turbine devices at different life cycle stages.

The invention relates to the technical field of image processing, in particular to a computer image processing system based on deep learning, which determines a first contour boundary of a target object through a preprocessing module according to a deep learning model, and draws brightness expression curves of pixel points of each sub-region in different direction dimensions through a feature extraction module. Marking a non-confidence sub-region in the aggregation analysis region through a feature aggregation module, obtaining brightness expression curves of pixel points subjected to exposure adjustment in different direction dimensions through a local processing module so as to determine contour boundary fitting points, and fitting contour boundary sections of the non-confidence sub-region, and updating the first contour boundary through the output end and outputting a second contour boundary of the target object. According to the method, local region optimization is carried out aiming at insufficient local features in a target object contour boundary extraction process under a complex illumination condition, and high-precision extraction and dynamic updating of a target object contour in a complex environment are realized.

The invention discloses a compressor shutdown analysis and fault diagnosis method and system, a medium and a product, and relates to the technical field of compressors, and the method comprises the steps that first parameter data within a preset first duration before shutdown of a compressor and second parameter data within a preset second duration after shutdown of the compressor are obtained; according to the first parameter data within the preset first duration and the second parameter data within the preset second duration, a compressor performance curve is determined; based on the compressor performance curve, the shutdown state of the compressor is determined; if the shutdown state of the compressor is an abnormal shutdown state, shutdown reason analysis is conducted on the compressor, and a fault diagnosis report is obtained; and according to the shutdown reason in the fault diagnosis report, matching the improvement scheme with the highest relevancy in a preset improvement scheme database. By implementing the method, the comprehensiveness and accuracy of the shutdown diagnosis result of the compressor can be improved.

Disclosed are systems, apparatuses, methods, and computer readable medium for a real-time feedback and machine learning system for downhole environments. A method includes: receiving first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment; determining a performance curve based on the first measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity; and providing the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment.

The invention discloses a frequency selective surface performance prediction method and related equipment. The method comprises the following steps: constructing a pre-training data set; constructing a variational auto-encoder model, and extracting potential common features of a performance curve by using a variational auto-encoder as a generative model; training a variational auto-encoder model by using the pre-training data set; preprocessing the data of the target frequency selective surface scene, extracting resonance characteristics of the performance curve, and obtaining a characteristic data set; constructing a resonance sensing variable-length proxy model; and sampling the characteristic data set to train a resonance sensing variable-length proxy model, and using the trained model to predict the performance of the frequency selective surface. According to the method, physical priori knowledge is embedded in the agent model training process to improve the curve representation capability, a resonance point sensing curve generation mechanism is introduced, the physical consistency modeling and high-quality reconstruction of the performance curve are realized from the resonance point characteristics of the curve, and the expression capability of the model to the complex resonance characteristics is remarkably improved.

The invention relates to the technical field of gas compressor test design, in particular to a test method for verifying the influence of gas compressor blade tip clearance on performance, which comprises the following steps: constructing an input data sequence formed by inlet temperature and rotating speed; a theoretical model of the inlet temperature and the blade tip thermal-state gap is constructed; acquiring an actual blade tip thermal clearance corresponding to the rotating speed of one group of input data of the gas compressor, and acquiring a correction model; obtaining a final inlet temperature and a final rotating speed corresponding to each to-be-verified blade tip thermal-state gap according to the corrected correction model; an actual performance curve of the gas compressor at the final rotating speed is obtained; and evaluating the influence value of the blade tip thermal-state clearance on the performance of the gas compressor. By obtaining the performance characteristics of the gas compressor under different blade tip gaps, the problems of repeated disassembly, assembly, coping and loading and unloading caused by a traditional method are avoided, so that the test efficiency is improved, and the development period is shortened.