409 results about "Physical test" patented technology

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention discloses a valve vibration impact test method and system based on digital twinning, and belongs to the technical field of industrial equipment test and evaluation. The method comprises the following steps: firstly, deploying a multi-source sensor network, constructing a physical entity layer of a valve vibration impact test, collecting and preprocessing various physical monitoring data, and constructing a digital twinborn body layer; secondly, real-time data assimilation with a physical test bed is realized through parameter calibration and updating of a digital twinborn model based on various physical monitoring data and the digital twinborn layer; and finally, executing a virtual vibration impact test, realizing prediction and evaluation of global response and key part information of the physical entity according to a test result, and finally realizing interaction and performance prediction. And the valve vibration impact test is realized through the valve vibration impact test system. According to the method, the fault diagnosis capability, the early damage identification capability and the valve state prediction and evaluation capability can be remarkably improved, and the defect of insufficient field monitoring information is overcome.

The invention discloses a mold and formwork design system based on virtual simulation, and particularly relates to the field of mold design, the mold and formwork design system comprises a multi-mode perception fusion module, a digital twin modeling module, a multi-physics field coupling simulation module, a hybrid intelligent optimization module, a digital twin closed loop verification module and a data center module, and each module forms a design closed loop through a data center. The multi-modal sensing fusion module adaptively collects and fuses multi-source signals to generate high-credibility data; the digital twin modeling module constructs and iteratively corrects a model based on the data; the multi-physics field coupling simulation module realizes multi-solver co-simulation under a dynamic boundary; the hybrid intelligent optimization module accelerates to generate an optimal solution through secondary optimization and an agent model; the digital twin closed-loop verification module detects defects and generates a correction instruction; the data center module is responsible for data management, cross-module scheduling and precision control; the system improves the design precision and efficiency of the mold frame, reduces the physical mold testing cost, and is suitable for a high-precision mold development scene.

The invention discloses a multi-view child motion coordination ability evaluation system and method based on deep learning, belongs to the technical field of motion evaluation, and solves the problems that child motion evaluation in the prior art mainly depends on manual observation and simple physical testing, and multiple angles and key details of child actions are difficult to synchronously track. The method comprises the following steps: training to obtain a skeleton point detection model and an athletic ability evaluation model, acquiring real-time video streams of personnel entering a field based on an acquisition camera, identifying skeleton key points in a preprocessing data set by the skeleton point detection model, performing multi-person detection on a personnel matching result based on an athletic area division method, and evaluating the athletic ability of the personnel. The exercise ability evaluation model carries out quantitative analysis on multi-person detection results; according to the invention, visual identification and motion state detection technologies are combined, a front view angle and side view angle dual-camera layout is adopted, and a deep learning algorithm is matched, so that automatic children dynamic motion evaluation is realized. The action process can be completely captured, and the detection accuracy and efficiency are improved.

The invention discloses a coordinated regulation and control system and method for dynamically testing pod environmental parameters, and the system comprises a multi-source sensor array which comprises a plurality of sensors used for collecting various environmental parameters in a pod in real time; the central controller is used for fusing sensor data and digital twinborn prediction information in real time through an FPGA + GPU heterogeneous architecture, dynamically generating and optimizing a regulation and control strategy based on a deep reinforcement learning algorithm, and synchronously coordinating an execution mechanism group to realize multi-physics field coupling control; the digital twinning interface is used for synchronizing the physical test environment and the bidirectional data flow of the digital twinning interface in real time, calculating and predicting the environment parameter evolution trend based on multi-physics field coupling, and feeding back closed-loop correction model parameters through reinforcement learning; and the execution mechanism group is used for adjusting the environmental parameters according to the control instruction. The system has the testing environment control functions of multi-parameter coordinated regulation and control, low-delay dynamic response and intelligent optimization capability, and the accuracy and testing efficiency of complex working condition simulation can be improved.

The invention relates to the technical field of wind power, in particular to a dynamic response test method for a wind power variable pitch system. Comprising the steps of building a high-fidelity digital twinborn model, performing virtual testing and case optimization, performing physical self-adaptive testing execution, performing real-time analysis and decision-making driven by machine learning, dynamically adjusting testing stress, performing data feedback and model calibration and generating a comprehensive health report. Compared with the prior art which mainly depends on a fixed physical test process and artificial experience judgment and has the defects of low test efficiency, large resource consumption and difficulty in comprehensively covering extreme working conditions, the method innovatively introduces a high-fidelity digital twinning technology and a virtual test optimization process; according to the method, massive tests are executed in the virtual environment in a risk-free manner, and the enhanced test cases are intelligently generated, so that test forward movement and accurate optimization are realized, the pertinence and efficiency of physical tests are remarkably improved, and the test cost and period are greatly reduced.

The invention discloses a data-real fusion civil aircraft environment control system fault positioning test method, and belongs to the field of civil aircraft system fault digital positioning test, and the method comprises the steps: S1, real: carrying out fault positioning based on a physical test; s2, performing digital twinning-driven fault location deduction based on the physical test data obtained in the S1; s3, number-real fusion: based on the physical test result in S1 and the digital twinborn deduction result in S2, performing Bayesian-based fusion analysis and iterative optimization: calculating the deviation between the physical test and the digital twinborn simulation result; if the deviation exceeds a threshold value, optimizing parameters of the digital twinborn model by using a Bayesian method, adjusting a physical test scheme, and iteratively executing S1-S2 until the deviation meets the requirement; and outputting a final fused fault positioning result. According to the method, the precision of civil aircraft environment control system fault positioning is improved, and reliable technical support is provided for fault positioning and maintenance decision making.

The invention discloses a power monitoring system mimicry defense architecture based on dynamic heterogeneous redundancy, which relates to the field of smart grid network security protection and comprises a physical layer, a twinborn layer and a simulation layer. All the layers communicate through encryption channels, a collaborative architecture of a physical layer, a twinborn layer, a simulation layer and an application layer is constructed, accurate mapping of a virtual environment and a physical power monitoring system is achieved by means of the digital twinborn technology, and a virtual carrier fitting the reality is provided for anomaly detection and attack simulation. The adaptability of the defense architecture to the power business logic is improved through analysis and simulation support on the power dedicated protocol; each layer ensures that the normal operation of the power monitoring system is not interfered through encryption communication and reasonable resource control; meanwhile, the digital twinborn body is used for replacing the traditional physical test environment, so that the construction, operation and maintenance cost of a defense system is reduced, the management efficiency is improved, and an adaptive service, efficient and reliable basic framework is laid for the safety protection of the power monitoring system.

The invention discloses a pipeline plugging test system and method for a plugging device, and relates to the technical field of plugging test, and the method comprises the steps: carrying out the first physical test of the plugging device, and determining first test data; a mechanical environment is introduced through simulation of flow field circulation, a test module is established through plugging simulation of a plugging device, and a discrimination assembly based on plugging characteristics is introduced and is deployed in a test system in an embedded manner; the judgment component judges the first test data, determines a first test result, triggers the test module, executes a two-order mechanical test, determines second simulation data, judges based on the judgment component, and determines a second test result; and performing terminal interface display on the second test result. The technical problems that in the prior art, a single physical testing means is relied on, the plugging performance is difficult to comprehensively evaluate, and an intelligent evaluation mechanism and standard uniformity are lacked are solved, and the technical effect of systematized and intelligent testing of the plugging device performance is achieved.

The invention discloses a simulation method for a corresponding relation between a baffle tongue position and a flow field of a semi-spiral suction chamber, and relates to the technical field of double suction pump design and numerical simulation. According to the method, a three-dimensional flow channel model of multiple groups of baffle tongue reference position parameters is constructed, multi-working-condition simulation and quantitative analysis are combined, a mapping relation model of baffle tongue positions and flow field characteristic parameters is established, and a baffle tongue region vortex generation rule, turbulence dissipation and pressure pulsation characteristics are accurately captured; the optimal baffle tongue parameter interval is determined by combining the flow field uniformity and energy loss minimization principle, the flow loss is effectively reduced, the vortex strength and energy dissipation are reduced, and the flow field uniformity is improved, so that the pump efficiency is improved, the vibration noise and the cavitation phenomenon are inhibited, and the optimal baffle tongue parameter interval is obtained by adopting adaptive grid encryption, a multiphase flow cavitation model and a test verification mechanism. The mapping model is corrected by combining measured data, so that the optimization result can directly guide the baffle tongue design under different working conditions, the research and development period is remarkably shortened, and the physical test cost is reduced.

The invention relates to a wind power blade hoisting load simulation analysis method which comprises the following steps: defining a plurality of simulation working conditions corresponding to a plurality of hoisting scenes according to a blade hoisting scheme of a wind generating set and the plurality of hoisting scenes; a multi-body coupling model established on the basis of a blade hoisting system is adopted, hoisting load simulation analysis is conducted on the basis of the multiple simulation working conditions, hoisting load data of the multiple simulation working conditions are obtained, and the multi-body coupling model comprises blades, a draught fan main engine, a hoisting tool and environment wind conditions. The invention further provides a wind power blade hoisting load simulation analysis device and a storage medium. The simulation analysis of the hoisting load of the wind power blade is realized, the load level of the blade and the whole machine in the hoisting process of the wind power blade can be evaluated, physical tests can be reduced, and the hoisting safety of the wind power blade can be enhanced.

The invention discloses a ventilation cooling optimization method for an all-hydrogen-cooled gas turbine generator, and relates to the technical field of all-hydrogen-cooled generator cooling, and the method comprises the steps: analyzing cooling demands, calculating loss in combination with a high-frequency start-stop peak regulation working condition, and setting a stator RTD temperature, an iron core temperature and a temperature rise target; an end ventilation structure is designed, the diameter of the fan is optimized, and bar gaps, wind shield holes and inner supporting ring ventilation grooves are formed; optimizing variable air ducts of the iron core, and adjusting the width of the air ducts according to thermal load partitions; cFD coupling simulation verification is carried out, and a loss data simulation flow field and a temperature field are imported; carrying out physical test iterative optimization, manufacturing a prototype model, testing, and adjusting parameters until errors are qualified; operation monitoring and dynamic adjustment are carried out, and a sensor is installed to collect data in real time and adapt to working condition requirements. Cooling uniformity and efficiency are improved, peak regulation start and stop working conditions are adapted, hydrogen parameter change influences are balanced, vibration and cooling are cooperatively controlled, operation and maintenance convenience is improved through modular design, and long-term stable operation of the generator is guaranteed.

An engineering machinery equipment performance degradation prediction method based on multi-model stacking fusion is characterized in that design calculation, a simulation model and a data driving model are combined, physical test data under different carrying equipment and different working conditions are used as training materials, confidence weight distribution of sub-models is completed, and a fusion total model is obtained. And when real object live test data under certain conditions are lacked, the environmental conditions are used as input, a final prediction value is obtained through a model stacking weight distribution mechanism, and the prediction result of the performance degradation of the engineering mechanical equipment is optimized.

The invention discloses a land-based virtual-real symbiosis experiment system for traveling cable swing, which comprises a land-based cable experiment table carrying a driving and loading module, and a mapping module for collecting real parameters of a traveling cable based on the driving and loading module to establish a mechanical model, the driving and loading module comprises driving equipment arranged on the land-based cable experiment table, the mapping module defines boundary conditions of the mapping module in a fixing mode of the land-based cable experiment table, and the boundary conditions serve as constraints of a dynamic model in the mapping module. By establishing real-time data transmission between the physical experiment table and the digital twin model, the problem of mapping lag is solved on the basis of real parameters collected by the driving and loading module, a simulation result can be fed back to the physical experiment table, parameter adjustment is guided, a digital research platform is provided for trailing cable reliability research in a land-based environment, and the reliability of a trailing cable is improved. And an effective guarantee is provided for ensuring absolute safety of cable running.

The invention provides a tire model parameter optimization method and device, a storage medium and a program product, and the method comprises the steps: obtaining the physical test data of a tire under a standard working condition, the physical test data comprising an input condition corresponding to the standard working condition and the actual mechanical behavior data of the tire; according to the input condition and the initial tire model parameters, simulating the tire model to obtain simulation mechanical behavior data of the tire model under the standard working condition; based on the difference between the simulated mechanical behavior data and the actual mechanical behavior data, constructing a multi-objective function for model parameter optimization; the optimization of the multi-objective function is taken as an objective, a multi-objective evolutionary algorithm is applied, a Pareto optimal solution set of the tire model is iteratively determined, and the Pareto optimal solution set comprises multiple groups of tire model parameters; and taking the Pareto optimal solution set as a tire model parameter optimization result. The method is used for achieving the effects of reducing manual intervention, achieving full-process automation and further improving efficiency and accuracy.

The invention discloses an aerospace equipment performance evaluation method based on digital test data fusion, and belongs to the field of electronic engineering and computer science, and the method comprises the steps: designing a physical test and a digital test, selecting related indexes of aerospace equipment, and carrying out corresponding physical and digital tests around the related indexes of the aerospace equipment; performing preprocessing and multi-source data integration on the physical test data; performing data quality evaluation and virtual data division on the digital test data; and performing double fusion on the result through an adaptive fusion algorithm to obtain a performance evaluation result. The advantages of the digital test and the physical test can be fully exerted, and comprehensive and accurate evaluation of the aerospace equipment performance is realized through deep fusion of the digital test data and the physical test data.

The invention discloses a heat pump drying experiment method based on 1D-3D joint simulation, particularly relates to the crossing field of heat energy engineering and simulation modeling, and aims to solve the problem of insufficient precision caused by disconnection of a heat pump system and cavity flow field simulation in a heat pump drying process in washing equipment such as a dish-washing machine. The method comprises the following steps: establishing a heat pump system one-dimensional dynamic model and a cavity three-dimensional flow field model, dynamically setting one-dimensionally output air supply temperature and humidity parameters as a three-dimensional inlet boundary, and meanwhile, feeding back three-dimensional air return parameters to a one-dimensional evaporator adjusting boundary to form closed-loop coupling iteration, so as to obtain a three-dimensional flow field model; dynamic high-precision simulation of the whole drying process is achieved, the structure optimization efficiency and the energy consumption evaluation reliability are effectively improved, part of physical tests are replaced, and the development cost is reduced.

The invention provides a multi-objective optimization method for material performance, and belongs to the technical field of material science and artificial intelligence, and the process of the method provided by the invention takes a small sample learning model as a core, combines an active learning optimization strategy, and realizes quantitative optimization of multi-objective performance indexes in material research and development in an extreme service environment. According to the method, limited test data are fully utilized for learning, and the number of physical tests is remarkably reduced; through intelligent optimization search, the efficiency and the success rate of searching for the material design scheme meeting the multi-target performance requirement are improved, and the method has important significance in accelerating development of new materials in a severe environment.

The invention discloses a chip temperature and humidity aging test system, which relates to the field of chip temperature and humidity aging test and comprises a core control and digital twinborn fusion module, a three-dimensional temperature and humidity field active construction module, a multi-physical field coupling stress loading module, an energy self-adaptive supply module and a full life cycle data traceability and intelligent evaluation module. According to the chip temperature and humidity aging test system, through the core control and digital twinning fusion module, chip cross-scale digital twinning modeling containing process deviation and deep closed loop of digital rehearsal, physical test and feedback regulation can be realized, aging key nodes can be pre-judged in advance, the physical test can be accurately guided, and the chip temperature and humidity aging test efficiency can be improved. The perspectiveness and the accuracy of the test are greatly improved; the three-dimensional temperature and humidity field active building module can build a three-dimensional non-uniform temperature and humidity field which is precisely regulated and controlled in a layered mode and driven by a dynamic flow field, the three-dimensional non-uniform temperature and humidity field is closer to a heat and humidity distribution and airflow disturbance scene in actual application of a chip, and temperature and humidity stress simulation is more real.

The invention relates to the field of titanium alloy mechanical property prediction, and discloses a titanium alloy yield strength prediction method for machine learning based on a thermal expansion curve, and the method comprises the following steps: 1, designing a multi-component titanium alloy, and carrying out a thermal expansion test in a sintering process and a mechanical property test after sintering to obtain a titanium alloy original database; 2, carrying out feature screening and screened data preprocessing on the obtained data in the titanium alloy original database; 3, selecting a proper machine learning model, dividing existing data into a training set and a test set, substituting the training set and the test set into the machine learning model for training and testing, and finally selecting an optimal model; and 4, performing a thermal expansion test on the titanium alloy of which the yield strength needs to be predicted in the sintering process, extracting curve characteristic data, and substituting the curve characteristic data into the selected optimal model to output the yield strength. The invention aims to predict the reliable titanium alloy yield strength by combining a thermal expansion method which is a physical test method which is simple to operate and short in time consumption with machine learning.

The invention relates to the technical field of three-dimensional digitization, in particular to a physical property information fusion analysis method based on the three-dimensional digitization technology, and the method comprises the steps: obtaining multi-source physical property data from different sources of geological exploration, rock and soil physical testing and seismic exploration, and carrying out format standardization processing to obtain a preprocessed data set; performing time sequence alignment on the preprocessed data to generate a time-space synchronization data sequence; performing cross-modal fusion and noise reduction processing based on the time-space synchronization data sequence to obtain multi-modal fusion data; and finally, inputting the multi-modal fusion data into a three-dimensional modeling module to generate a three-dimensional physical property information model, and performing visualization processing to obtain a three-dimensional display result. By accurately synchronizing and fusing the data of various sensors, the method overcomes the differences of different modal data in time, space and resolution, realizes seamless integration of physical property information in a three-dimensional space, and has a wide application prospect.

The invention belongs to the technical field of escalator braking performance detection, and particularly discloses an elevator braking performance intelligent detection method and system.The method comprises the steps that a parameterized simulation model is built based on mechanical topology and dynamic characteristics of an escalator, and firstly, model initial calibration is completed through a no-load braking test; then, a gradient simulation load scheme is designed in combination with actual operation load distribution, advanced calibration is performed on the model through physical test data under multi-stage loads, and finally, accurate prediction of the full-load braking performance is realized by relying on a high-fidelity calibration model, so that actual measurement of full-load weights is not needed, the detection safety and efficiency are remarkably improved, and the detection efficiency is improved. And the problem of model mismatching caused by factors such as equipment aging and abrasion can be effectively solved through a data-driven iterative calibration mechanism, the long-term prediction precision is ensured, and reliable technical support is provided for normalized and intelligent evaluation of the braking performance.

The invention relates to the technical field of automobile intelligent driving, in particular to an intelligent network connection automobile digital twinning evaluation method and system for extreme scenes. The evaluation method comprises the following steps: constructing a knowledge graph; generating an extreme scene; the automatic driving system is tested in a virtual environment generated by the digital twin test platform based on an extreme scene; predicting a safety risk score in real time, and if the safety risk score exceeds a safety threshold, sending an intervention instruction to the digital twin test platform; performing anomaly detection on the full-quantity test log, deriving a scene and injecting the scene into a scene library if a performance inflection point is found; and calculating a root cause based on the weight vector, the system fault cause and effect graph and the test data, and generating a diagnosis result. According to the method, extreme scenes can be covered, the system can be scored from multiple dimensions, the performance bottleneck can be accurately positioned, and the closed loop of the test scene can be realized. According to the system, two-way closed loop and common evolution of physical test data and a virtual test environment are realized through a cloud side end architecture, and continuous synchronization and two-way interaction with real world data are realized.

The invention discloses a construction method and device of a bucket load spectrum generation model, equipment and a medium. The method comprises the steps that a physical test bed is controlled to apply multi-dimensional dynamic loads to all stress point positions of the hydraulic excavator, and the hydraulic excavator is controlled to execute simulation operation according to working condition types; instantaneous loads acting on the bucket after the loads are transmitted to all the stress point positions are recorded in real time, and all the instantaneous loads are gathered to form a three-dimensional load spectrum of the bucket; collecting dynamic response data generated by each stress point position, associating the three-dimensional load spectrum with the dynamic response data to form training sample pairs, aggregating the training sample pairs to form a training data set, performing characterization processing, inputting the training data set into an original deep learning model for iterative training, and obtaining a three-dimensional load spectrum; and taking the deep learning model meeting the iteration termination condition as a hydraulic excavator bucket load spectrum generation model. According to the embodiment of the invention, the virtual rack is constructed by a physical test rack of a small number of excavator samples, and a basis is provided for efficiently generating reliable bucket load spectrums for batch excavators.

Methods and systems are described for predicting application performance on a hardware system, without the need for physical testing or use of the subject hardware. This can be done with a set of profiling and analysis tools. In some embodiments an Application Profiling Tool can receive information about the possible hardware architectures that an application source code supports, application and user requirements, configurations, parameters, and / or application specific libraries. Information about common libraries can be stored and retrieved from a database. The results provided by the Application Profiling Tool can comprise representations of the code together with other derived metadata based on the inputs. The performance metadata can then be used directly or later (and possibly, in a different location) to estimate the WCET for a specified hardware and architecture.

The invention provides a reinforced concrete member damage assessment method, system, equipment and medium, and belongs to the technical field of structural damage assessment, and the method comprises the steps: constructing a fire response feature knowledge base containing fire physical test data and simulation data of a reinforced concrete member, training the machine learning agent model by taking component design parameters and fire working condition parameters of the reinforced concrete component in the knowledge base as input parameters and taking post-fire component response data as output parameters to obtain a prediction model for predicting direct physical damage indexes of the post-fire reinforced concrete component; constructing a fire vulnerability curve representing the relationship between the failure probability and the fire intensity based on the prediction model; and receiving parameters of a to-be-evaluated component, and generating and outputting a damage evaluation result of the component based on the prediction model and the fire vulnerability curve. According to the invention, through data and model collaborative optimization, high-efficiency and accurate post-fire reinforced concrete member damage evaluation is realized.

The invention relates to the technical field of teenager physical health management, and discloses a teenager physical health management system based on physical test results, and the system comprises a data collection terminal, a health intervention device and a cloud management platform which are in communication connection. The data collection terminal collects teenager physical test motion indexes and physiological signals in real time, and the health intervention device receives an instruction of the cloud management platform to adjust the training intensity. The data monitoring unit records an index abnormal starting point and a recovery termination point to determine a health risk interval, and controls the equipment to operate at a safety threshold and generate a physical characteristic curve for physical prediction mapping; the evaluation processing unit corrects the intervention path to generate an execution strategy; and a conventional regulation and control unit and an emergency regulation and control unit of the health management unit control equipment to execute corresponding operation under normal and abnormal working conditions respectively. According to the system, real-time monitoring, accurate evaluation, scientific intervention and risk processing of teenager physical health are realized, teenager physical test safety is effectively guaranteed, and the physical health condition is improved.

The invention relates to an interactive intelligent test analysis system based on a natural language. The system comprises a natural language processing and dialogue management module used for receiving and presenting natural language information, analyzing user input, identifying operation intention and extracting key parameters, an intelligent parameter recommendation and execution module used for connecting and controlling the physical test instrument according to a language understanding result, and an intelligent parameter recommendation and execution module used for importing from the instrument or a file, the working area and management module is used for storing and managing test data; the data processing and analysis engine module is used for automatically selecting and executing a data analysis algorithm according to user intention and context; the natural language processing and dialogue management module analyzes and generates a structured intention based on a pre-trained large language model and a domain knowledge base; and the intelligent parameter recommendation and execution module automatically recommends and sets optimal instrument parameters according to the structured intention based on a pre-trained large language model and a domain knowledge base. According to the invention, complex signal processing is simplified into natural dialogue interaction with experts.

The invention discloses an SSD full-automatic aging test system and method, and the system comprises a physical test cluster layer which comprises one or more test bins, a central control and data processing unit, a task scheduling and resource management engine, a self-adaptive stress field generator, a data lake, and a digital twinborn model. The system comprises a data processing module for establishing a corresponding virtual instance and a model calibration and synchronization interface for each SSD body, a predictive analysis module for predicting the residual life and the potential fault mode of the SSD body based on accelerated operation of a digital twin model, and a reinforcement learning module for establishing a model calibration and synchronization interface for each SSD body, and keeping the state of the virtual instance consistent with the state of the physical SSD body by using information of a data lake. The self-adaptive stress field generator works cooperatively with the self-adaptive stress field generator; and the feedback and evolution layer is used for calculating a test efficiency index TEI and periodically optimizing a self-adaptive stress field generation algorithm and a strategy network of a reinforcement learning agent by utilizing the accumulated TEI and data.

The invention discloses a real-time mixed fire test method for a large-scale substructure of a bridge cable, and belongs to the technical field of bridge fire resistance test. A full bridge is divided into a physical test substructure and an LSTM numerical substructure, the numerical substructure is constructed based on multi-scale gating residual LSTM, and second-level high-precision prediction of the boundary force or displacement of a cable system in a fire disaster is achieved through a parallel multi-branch network, a dynamic feature selection gate and residual cross-layer connection. By combining an LSTM-PID intelligent control system, displacement / stress and a temperature field are converted into loading signals according to a prediction instruction, a time delay compensation mechanism is synchronously integrated, a center hole jack, a combustor and a fan are driven in real time, a non-uniform wind-fire coupling scene is reproduced, and a full-bridge three-dimensional visual fire response is formed. According to the method, the speed bottleneck of traditional finite element calculation is broken through, second-level thermal coupling interaction is achieved, and a high-precision and high-reliability test method and platform are provided for large-span bridge fire resistance design.