387 results about "Virtual test" patented technology

The invention discloses an interaction test method for gait simulation of a humanoid robot, which is applied to a test platform. Comprising the following steps: controlling a current gait simulation model corresponding to a virtual robot to perform a gait simulation test in a virtual test environment in a current preset time period; the virtual test data is sent to the physical robot; controlling the physical robot to execute corresponding gait operation according to the virtual test data in the actual test environment, and sending the actual test data to the virtual robot; updating the current gait simulation model based on the virtual test data and the real test data; and continuing to perform the gait simulation test based on the updated gait simulation model, and ending the updating operation until the gait simulation test is ended, so as to generate a complete gait scheme. Therefore, by constructing a closed-loop interaction mechanism between the virtual robot and the physical robot, dynamic adjustment of the gait simulation model is achieved, the gait of the physical robot is restored, and the stability and precision of the gait of the physical robot are improved.

The invention discloses a good product production real-time monitoring system of vehicle gauge equipment, which belongs to the technical field of quality control and comprises a sensing module, an enhanced test module and a decision module. The sensing module is used for acquiring physical parameters and performance test physical data of a vehicle gauge product in a production process in real time, constructing a batch deviation matrix, calculating a quality deviation degree, generating an edge identification table and screening key control parameters influencing physical performance; the strengthening test module is used for carrying out virtual test on a target edge part, utilizing a parameter coupling matrix, generating an equipment control scheme, dynamically adjusting parameters and comparing virtual and real curves to calibrate a twinborn body so as to optimize the digital twinborn body, and issuing the parameters again; and the decision module is used for constructing a tracing file of the target edge component, defining decision parameters, determining a matrix through multiple dimensions, calculating a comprehensive decision score, and executing differentiated decisions according to scenes so as to generate an equipment control instruction and accurately identify the edge component of which the performance is close to a qualified threshold value.

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 provides an HIL simulation test system of a commercial vehicle domain controller. The system comprises an HIL system software platform, an HIL system simulation model, an HIL system simulation hardware platform and a vehicle body system special simulation assembly which are electrically connected. The HIL system simulation hardware platform is used for providing hardware support and executing the hardware-in-the-loop simulation test; the HIL system software platform is used for controlling a simulation test process, managing a test task and automatically generating an interface model; the HIL system simulation model is used for constructing a virtual test environment of a controller and performing signal interaction with the HIL system simulation hardware platform; the simulation assembly special for the vehicle body system is used for simulating vehicle body control, air conditioner control, gateway and vehicle control functions so as to improve the test coverage degree, and efficient development and iteration of a commercial vehicle domain controller are facilitated.

The invention discloses a digital pre-distortion method, system and device and a storage medium, and relates to the technical field of aerospace communication. The method comprises the following steps: acquiring a first input signal and a first actual output signal of the power amplifier, and acquiring a thermal state characteristic; constructing a three-dimensional dynamic baseline; superposing the detection sequence into the first input signal to obtain a second input signal, obtaining a corresponding second actual output signal, and predicting an ideal output signal of the second input signal; extracting a harmonic component generated after the detection sequence passes through a power amplifier, and determining a distortion early warning moment; recording a third input signal after the distortion early warning moment; constructing a virtual test signal according to the third input signal, and predicting to obtain a reference output signal; and comparing the difference between the distortion output signal and the reference output to obtain a distortion feature vector, constructing a dynamic increment compensation function, and correcting the signal processing flow of the power amplifier based on the dynamic increment compensation function. And the accuracy of digital pre-distortion is improved.

The invention belongs to the technical field of virtual automobile exhibition, and particularly relates to a virtual automobile exhibition system based on digital twinning. The system comprises a digital twinning construction module which generates a lightweight model and supports first-person visual angle dynamic updating; the user behavior analysis module is used for mapping data such as a sight track into a weighted directed graph, and predicting vehicle types and configuration requirements by using a multi-head graph attention network; the personalized configuration selection module is used for generating a configuration scheme in combination with digital twinborn parameters; the virtual test driving module is used for providing immersive experience based on the configuration scheme and the scene; and the background data analysis module integrates the data to generate a user portrait so as to optimize the exhibition content. According to the system, dynamic virtual-real mapping, accurate demand prediction and data-driven intelligent exhibition are realized, and the user interaction depth and the car purchase efficiency are improved.

The invention discloses a real-time motion control algorithm simulation debugging method, which relates to the technical field of real-time simulation crossing, and comprises the following steps of: acquiring real-time track data and multi-scale simulation parameters and preprocessing the real-time track data and the multi-scale simulation parameters; based on the multi-scale simulation parameters, a multi-scale simulation environment is constructed, and multi-physics field coupling control parameters are obtained; performing multi-physical field simulation according to the real-time trajectory data in a multi-scale simulation environment, and obtaining trajectory error data with a safety verification identifier; based on the trajectory error data with the security identifier, filtering out high-frequency components and separating low-frequency contour deformation components by adopting empirical mode decomposition to obtain low-frequency contour deformation features; and based on the optimal parameter adjustment instruction set, updating the multi-physics field coupling control parameters in real time and generating a simulation debugging report. According to the invention, a multi-scale simulation environment is constructed, dynamic configuration and synchronous simulation of multi-physical field coupling control parameters are realized, and a high-fidelity virtual test platform is provided for motion control under complex working conditions.

The invention discloses a frequency conversion vector control method for a digital energy-saving pump station, which belongs to the technical field of vector control and comprises the following steps: constructing a coupling vector model, simulating the running states of a pump unit under different working conditions through a virtual test signal, and automatically correcting the coupling vector model; operating data of a pump set are collected in real time, an operating situation feature set is constructed, predictive vector control is carried out, an optimal voltage vector sequence is generated, and meanwhile load fluctuation of a pipe network is predicted; demand pre-judgment is carried out on the starting and stopping time and the action amplitude of the anti-backflow device, a bidirectional disturbance suppression mechanism is constructed, current vector parameters are pre-adjusted, and calculus correction is carried out based on deviation judgment; when multiple pumps operate in parallel, the instantaneous operation efficiency point of the pump set is calculated in real time, the efficiency change trend is predicted, a load distribution scheme is generated, the comprehensive energy efficiency is evaluated, energy efficiency self-optimization is carried out, the comprehensive energy efficiency of the system is improved, and control experience reuse is achieved.

The invention relates to the technical field of circuit assembly type selection, and particularly discloses an optimized matching circuit assembly rapid type selection method and system, which are applied to a circuit assembly characteristic database, and comprises the following steps: obtaining electrical parameter information corresponding to a plurality of candidate circuit assemblies; a virtual test model is constructed based on the electrical parameter information, virtual circuit operation is carried out based on the virtual test model, virtual operation information is generated, and the virtual operation information comprises a plurality of virtual dynamic operation parameters corresponding to a plurality of preset test time points; and obtaining operation performance information of virtual circuit operation according to the virtual operation information. According to the method, in the evaluation link, the voltage, temperature, power and other parameters are subjected to normalization processing, the multi-dimensional correlation change parameters are generated, the problem that traditional model selection neglects the component performance correlation relation is solved, the comprehensive performance of the component in a complex scene can be accurately evaluated, and a comprehensive basis is provided for model selection.

The invention provides an anthropomorphic virtual test scene multi-background vehicle collaborative decision-making method, which comprises the following steps of: constructing a dynamic traffic map of multiple vehicles based on a graph structure, and constructing a parameter-shared generative adversarial imitation learning framework, inputting the node features optimized by the graph attention network to a reinforcement learning network to generate a serialized control instruction, and comparing the generated control instruction with a state-action pair of expert data by using a discriminator sharing scoring parameters to iteratively optimize a control instruction generator; potential factor variables are introduced to represent driving styles, and strategy personification is enhanced in combination with explicit rule punishment and implicit confrontation awards; and a highly anthropomorphic multi-background vehicle sequence cooperative control instruction is generated through multiple rounds of iteration. According to the method and the system, the adversarial imitation learning is iteratively optimized and generated by utilizing potential factor variables and reward enhanced imitation learning, and a decision with diversity, compliance and personification is generated, so that the reliability of an automatic driving simulation test is improved in multiple aspects.

The invention discloses an operation ticket intelligent analysis and sequence control logic closed-loop verification method and system, and belongs to the field of power system automation technology and power transformation operation and maintenance, and the method comprises the steps: obtaining a typical interval sequence control operation ticket and an SCD file; identifying the typical interval sequence control operation order through a BERT-ERNIE multi-head text extraction model, and generating a corresponding test script; querying corresponding object path information in the SCD and completing association; constructing a closed-loop acceptance environment; simulating a local operation scene and initiating sequence control operation according to the associated test script to carry out correctness acceptance; on the basis of a similar interval operation ticket generation tool, carrying out one-key replacement on part of description information of the test script passing acceptance, generating similar interval sequential control operation tickets to be accepted, and carrying out correctness acceptance; and simulating a sequence control master station address in the closed-loop acceptance environment, issuing a sequence control instruction to a sequence control host based on the sequence control operation order passing correctness acceptance, and automatically virtualizing the test environment to perform forward and reverse closed-loop automatic acceptance.

The invention provides an unmanned aerial vehicle route planning operation safety monitoring system and method based on virtual-real fusion, belongs to the technical field of unmanned aerial vehicle safety monitoring, and aims to solve the problems that the test training difficulty of an unmanned aerial vehicle is increased and the experiment cost of the unmanned aerial vehicle is high due to interference and terrain transformation in the task execution process of the unmanned aerial vehicle. The safety monitoring system comprises a ground distributed semi-physical virtual-real fusion simulation platform and an air experiment module. The ground distributed semi-physical virtual-real fusion simulation platform is used for constructing a virtual test scene of task execution of the unmanned aerial vehicle, optimizing a waypoint planning decision according to feedback information in a task execution process, and performing safety judgment and real-time obstacle avoidance on the current waypoint planning decision so as to optimize the waypoint planning decision; and the space-time experiment module is used for monitoring the flight state of the unmanned aerial vehicle and executing the optimized waypoint planning decision.

The invention provides a rock mineral analysis test management method and system, and the method comprises the steps: collecting rock mineral sample multi-modal data, and constructing a digital twinborn model; constructing a geological knowledge map, and injecting and fusing the two knowledge to generate a virtual test data set; performing self-supervision pre-training by using the data set to obtain a self-adaptive mineral phase recognition model; deploying the model to a block chain-Internet of Things double-chain traceability system to obtain an identification result and an optimization instruction; and executing a sample preparation process and removing unqualified samples. According to the method, diffusion steps are adjusted through adaptive noise scheduling and a space-time alignment algorithm, data are integrated, a high-reality-sense virtual test data set is generated, and a self-supervised pre-training and knowledge distillation technology is combined, so that the model has space-time feature extraction and dynamic optimization capabilities; and finally, full-process closed-loop control is realized through a double-chain traceability system and a modular robot, and the recognition precision of a complex scene is improved.

The invention provides a Unity3D-based multi-agent collaborative integrated navigation virtual simulation system and method, belongs to the technical field of integrated navigation, and solves the problems that a traditional integrated navigation experiment method is high in cost and risk and is difficult to simulate various complex actual working conditions, and the simulation system comprises a display layer, a business layer and an underlying service layer; the display layer provides an operation and monitoring platform for a user; the business layer is used for performing control logic, queue management, path planning and artificial intelligence training functions on the agents in the simulation environment; and the underlying service is used for performing agent machine learning model training and project version management. The invention provides a low-cost and high-safety virtual simulation scheme for intelligent agent motion control and collaborative operation efficiency optimization, an improved A * search algorithm and an artificial intelligence training interface are integrated, and an economical and efficient virtual test platform is provided for research and development of an intelligent agent control algorithm.

The invention discloses a virtual accompanying test system for an unmanned surface vehicle, and relates to the field of unmanned surface vehicle testing, and the system comprises an integrated case, and a task scene simulation module, a sensor simulation module, a pose mapping module, a collision avoidance safety evaluation module and a test display control interface which are integrated in the integrated case. The task scene simulation module constructs a three-dimensional virtual test water area scene based on the static geographic information data, and is provided with a dynamic obstacle and a virtual unmanned ship; the sensor simulation module generates simulation data of the detected unmanned ship environment sensing sensor in the scene; the input end of the pose mapping module is connected with a position and attitude measuring unit which is externally arranged on the case and is arranged on the measured unmanned ship through a data bus, receives real-time position and attitude data of the position and attitude measuring unit, and drives the pose of the virtual unmanned ship to change; the collision avoidance safety evaluation module calculates a total safety evaluation score according to interaction data of the virtual unmanned ship and the dynamic obstacle; and the test display control interface is used for man-machine interaction, test process display and result display.

The embodiment of the invention provides a steer-by-wire test method, device and equipment for vehicle entering and exiting a curve and a medium, and belongs to the technical field of vehicle test. Comprising the steps that a vehicle basic model is obtained, a vehicle test model is built based on the vehicle basic model, and the vehicle test model is a vehicle parameterized model comprising a steer-by-wire system; constructing a test scene, wherein the test scene at least comprises a bend entering scene entering a ramp and a bend exiting scene leaving the ramp; based on the vehicle test model, virtual tests are executed in the bend-in scene and the bend-out scene respectively, the virtual tests are that a driver carries out simulated driving in the test scene, and objective test data and subjective evaluation of the driver are obtained; and determining objective evaluation according to the objective test data, and determining a test result of the steer-by-wire system according to the objective evaluation and the subjective evaluation. The invention aims to improve the testing accuracy of the steer-by-wire system so as to improve the design quality of the steer-by-wire system.

The invention discloses a humanoid robot motion control method and device based on an enhanced contrast learning algorithm. The method comprises the following steps: establishing a training environment of a humanoid robot; constructing an initial neural network model and an enhanced contrast learning algorithm framework; determining a domain randomization parameter and a reward function of a training environment; based on the reward function and the domain randomization parameter, training the initial neural network model in a training environment through a reinforced contrast learning algorithm framework to obtain a trained neural network model; establishing a virtual test environment, and deploying the trained neural network model to the virtual test environment for model test optimization to obtain a target neural network model; and deploying the target neural network model into the humanoid robot to perform motion control on the humanoid robot. According to the method, the motion control stability of the humanoid robot is improved, meanwhile, the domain randomization parameters are used for simulating the parameters of the real environment, and the generalization and robustness of the model are improved.

The invention discloses a liquid rocket engine digital test run platform, relates to the technical field of liquid rockets, and aims to solve the problem that a liquid rocket engine digital test run system in the prior art cannot perform multi-dimensional simulation. The liquid rocket engine digital test run platform comprises a one-dimensional simulation module, a three-dimensional simulation module, a one-dimensional and three-dimensional joint simulation module and a virtual test run display module. The one-dimensional simulation module is used for performing one-dimensional simulation on the liquid rocket engine under different working conditions; the three-dimensional simulation module is used for performing three-dimensional simulation on a target part in the liquid rocket engine under different working conditions; the target component at least comprises a thrust chamber and a flow regulator; the one-dimensional and three-dimensional joint simulation module is used for performing one-dimensional and three-dimensional joint simulation on the target component; and the virtual test run display module is used for visually displaying the test run process of the liquid rocket engine. Therefore, the multi-dimensional comprehensive simulation of the digital test run of the liquid rocket engine can be realized.

The invention belongs to the technical field of robot testing, and provides an intelligent robot testing method and system based on a virtual environment, electronic equipment and a storage medium. The method comprises the steps of building data acquisition, virtual test environment reconstruction, digital twin robot construction, multi-dimensional test task setting, quantitative evaluation formula system construction, virtual test and quantitative evaluation. According to the method, the three-dimensional reconstruction model is improved, the multi-dimensional point cloud data is utilized for collaborative reconstruction, and the actual remote sensing image is utilized for dynamic adjustment of the reconstruction structure, so that a large amount of manual reconstruction data is not used, the situation that the model reconstruction effect is reduced due to unsupervised reconstruction is avoided, and the test scene reduction degree is greatly improved; the deviation between test and actual operation is reduced; by coupling and binding the digital twin robot and the target robot, the problem that the reliability of the test result is low due to excessive idealized setting is avoided, and the test result better fits the actual working condition.

The invention provides a power plant fuel blending combustion optimization method based on multi-source data and deep learning, and the method comprises the steps: detecting key characteristic indexes of incoming fuel, and constructing a dynamically updated fuel characteristic database; based on the fuel characteristic database, the boiler operation parameters and the load prediction data, a real-time fuel blending scheme is generated through a multi-objective optimization algorithm; performing a virtual combustion test on the real-time fuel blending scheme by using the three-dimensional combustion model of the boiler, and adjusting the opening degree of a secondary air door in the real-time fuel blending scheme; the method comprises the following steps: recording a data chain of fuel in a whole life cycle from factory entry to combustion through a block chain technology, generating a tamper-resistant carbon emission report, and when unconventional fuel is subjected to blending combustion, adapting combustion characteristics of the unconventional fuel through a transfer learning algorithm and generating a safe blending combustion proportion threshold value. And a real-time fuel blending scheme is generated and optimized through virtual testing, so that the fuel blending combustion optimization efficiency is effectively improved.

The invention discloses a driver direct view calculation model and a test method. Comprising the following steps: firstly, acquiring virtual test scene data corresponding to a to-be-tested vehicle; and inputting the virtual test scene data into the trained calculation model. The calculation model comprises a view region segmentation sub-model and a visible volume calculation sub-model, and a visible region and a shielding region in a standard evaluation volume in the virtual test scene data are accurately segmented through the view region segmentation sub-model to obtain a corresponding binary segmentation mask. And extracting spatial distribution characteristics strongly related to the visible volume from the segmented binary mask through a visible volume calculation model, fusing, screening and compressing the extracted spatial distribution characteristics, and finally mapping the spatial distribution characteristics into four types of visible volumes of the near side, the front side, the far side and the total of the tested vehicle. Therefore, a large entity test scene is not needed, and the test efficiency is improved. And when the standard is updated, only a data set needs to be updated and model parameters need to be calculated, and the overall architecture does not need to be redeveloped.

The method is mainly applied to the technical field of vehicle engineering. The invention discloses a virtual calibration method, device and equipment based on a driving simulator and a medium. A virtual test environment is constructed through a vehicle model of a target vehicle, a virtual test scene model and a chassis electric control system model. And executing driving operation and acquiring test data by utilizing human-computer interaction of the driving simulator, and further evaluating the vehicle performance. And when the performance parameters do not reach the preset indexes, optimizing the parameters of the chassis electric control system model according to the manual adjustment instruction until all the parameters reach the standard. And after reaching the standard, further adjusting the parameters by combining the subjective quantitative indexes of the driver, and determining the optimal performance parameters. The scheme is carried out in a virtual environment, so that the risk of real testing is avoided, and the safety is ensured; scenes and parameters can be quickly switched, and the calibration efficiency is improved; various working conditions can be simulated, and comprehensive evaluation is realized; lots of real vehicles and sites are not needed, and the cost is reduced.

The invention provides a method and a system for testing compatibility and performance of multiple frameworks. The method comprises the following steps: creating a virtual test environment according to environment information of different autonomous controllable platforms based on a virtualization technology; each virtual test environment simulates the combination of different environment information; obtaining a to-be-tested operator based on the operation information of the different autonomous controllable platforms, and generating a test case based on the characteristic information of the operator and the environment information of the different autonomous controllable platforms; distributing each test case to a corresponding virtual test environment for execution based on the test environment configuration information, and obtaining a test execution result and performance index data in the execution process of each test case; and determining compatibility evaluation results and performance analysis results of the operator under different autonomous controllable platforms based on the test execution result and the performance index data. According to the embodiment of the invention, the efficiency, accuracy and universality of operator compatibility and performance testing are improved.

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.

A comprehensive SOTIF testing system includes a scene database, a virtual testing module and an analyzing module. The scene database includes a plurality of scene groups, and the scene groups respectively correspond to a plurality of source types. Each of the scene groups includes a plurality of scenes, and each of the scenes corresponds to at least one of a plurality of trigger conditions and a plurality of function limitations. The virtual testing module is configured to decide a plurality of to-be-tested members from the source types based on demands of a to-be-tested system, to choose a plurality of chosen members from the scene groups, to generate a plurality of virtual testing scenes based on the scenes of the chosen members, to test the to-be-tested system on the virtual testing scenes, and to confirm whether at least one fail member is present in the virtual testing scenes.

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.

The invention belongs to the technical field of automobile electronic testing, and particularly relates to an intelligent OTA automatic testing method and system in a multi-dimensional environment, and the method comprises the steps: a testing management platform configures a testing scene and a testing case, schedules a testing task to a cloud server to simulate a real OTA cloud service, and cooperates with a multi-dimensional environment simulation cluster to construct a virtual testing environment; deploying a real vehicle test agent to execute a test case and collect real vehicle data, and utilizing a digital twin synchronization engine to realize bidirectional data synchronization of a simulation environment and a real vehicle environment; collecting heterogeneous data of each key node of the simulation environment and the real vehicle environment in real time, and uploading the collected heterogeneous data to the big data platform for storage; and based on heterogeneous data stored by the big data platform, intelligent analysis is performed by using an intelligent analysis and evaluation engine, and system closed-loop optimization is performed according to an analysis result.

The invention provides a material constitutive parameter inversion method which can be applied to the technical field of artificial intelligence. The material constitutive parameter inversion method comprises the following steps: acquiring an elastic modulus and an actually measured load depth curve of a to-be-tested material in a single nanoindentation test; performing extrapolation translation preprocessing on the actually measured load depth curve to obtain an equivalent ideal curve; carrying out characterization processing on the equivalent ideal curve to extract input characteristic parameters which comprise press-in rigidity, unloading rigidity and plastic work proportion; the indentation rigidity, the unloading rigidity, the plastic work ratio and the elastic modulus are input into a pre-trained indentation constitutive mapping model to predict the yield strength and the strain hardening index of the to-be-tested material, and the indentation constitutive mapping model is a full-connection back-propagation neural network pre-trained through a finite element virtual test sample. The invention further provides a material constitutive parameter inversion system and device, a storage medium and a program product.

The invention relates to the technical field of aero-engine whole machine air environment simulation, and discloses an aero-engine air environment simulation test run platform system and method. Inputting assessment parameters of an engine test process into the virtual test run platform digital model and the engine digital model, and driving the models by real-time test data of equipment and an engine; according to the engine digital model, the assessment parameters are analyzed, defective pixels of the data are removed, abnormal data points caused by factors such as sensor errors and data transmission anomalies are effectively removed, and therefore the accuracy and reliability of data processing are improved; and finally, the states of the engine and the equipment are predicted, so that comprehensive monitoring and fault early warning of the air environment simulation test run process of the aero-engine are realized.