Digital-real fusion test method based on object environment mapping mechanism

Abstract

The invention discloses a data-real fusion test method based on an object environment mapping mechanism. The method comprises the following steps: establishing an evaluation index system, determining evaluation parameters corresponding to evaluation indexes, mining an association relationship among the evaluation parameters, and constructing an evaluation parameter relationship graph; an object environment mapping mechanism is analyzed, a test environment twinborn model is constructed, equivalent action distribution of the object environment is analyzed, and equivalent action points of a number-real fusion test are obtained; and configuring a test environment twinborn model, establishing communication connection between a test object and the test environment, determining a linkage mechanism of a digital-real test, and executing a digital-real fusion test process. The method can be provided for the linkage test of the physical test object and the digital test environment, the rapid execution of the digital-real fusion test is realized to a certain extent, and the test cost is saved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of electronic engineering and computer science, and particularly relates to a numerical-real fusion test method based on object environment mapping mechanism. BACKGROUND

[0002] Under the background of rapid development of industrial digitization and intelligent manufacturing, traditional pure physical test mode faces the problems of long test cycle, high cost, high risk, and limited coverage of working conditions. The development of new generation information technologies such as digital twin, artificial intelligence, and Internet of Things promotes the evolution of test mode towards the direction of deep fusion of digitalization and physical entity. Numerical-real fusion test realizes the deep fusion of virtual and real space elements by constructing the real-time interaction mechanism between physical test objects and digital test environment, so that the system performance evaluation is systematically optimized in comprehensiveness, accuracy, reliability, and economy.

[0003] In the prior art, digital twin technology mainly focuses on product lifecycle management and predictive maintenance, and lacks systematic methods for test verification links. Some existing virtual-real combined test methods have the following shortcomings: first, the mapping relationship between the object and the environment is not clear, resulting in a lack of accurate equivalence guarantee between the digital environment and the physical object; second, there is a lack of correlation analysis method between evaluation parameter system and environment parameters, making it difficult to realize accurate configuration of test conditions; third, the linkage mechanism between physical objects and digital environment is imperfect, making it difficult to realize real-time collaboration and data synchronization; finally, the determination of equivalent action points depends on experience and lacks theoretical support and systematic methods. These shortcomings limit the wide application of numerical-real fusion test in practical engineering. SUMMARY

[0004] To solve the above technical problems, the present application discloses a numerical-real fusion test method based on object environment mapping mechanism, which is suitable for tests using numerical-real fusion method. By constructing an evaluation parameter relationship graph, establishing an object environment mapping mechanism model, determining an equivalent action point and a linkage mechanism, accurate coupling and collaborative test of physical test objects and digital test environment are realized, providing an efficient and reliable test means for complex system performance verification.

[0005] The present application solves its technical problems by adopting the following technical solutions:

[0006] A numerical-real fusion test method based on object environment mapping mechanism, comprising the following steps:

[0007] Step (1), for a certain test task, an evaluation index system is established, the evaluation parameters corresponding to the evaluation indexes are determined, the correlation between the evaluation parameters is mined, and an evaluation parameter relationship graph is constructed. This step systematically decomposes the test target, constructs a multi-level evaluation parameter network, and provides a basic data framework for subsequent mapping analysis;

[0008] Step (2): Analyze the object-environment mapping mechanism, construct a twin model of the experimental environment, analyze the equivalent action distribution of the object-environment, and obtain the equivalent action points of the digital-real fusion experiment. This step establishes a quantitative relationship between evaluation parameters and experimental environment parameters, constructs a high-precision digital environment model, and determines the key action positions of physical signal inputs;

[0009] Step (3): Configure the experimental environment twin model, establish the communication connection between the experimental object and the experimental environment, determine the linkage mechanism of the digital and physical experiments, and execute the digital-physical fusion experiment process. This step realizes the real-time interaction between the physical entity and the digital model, completes the experimental execution and data acquisition, and finally obtains the evaluation index results.

[0010] Beneficial effects:

[0011] 1. By constructing an evaluation parameter relationship graph and an object-environment mapping mechanism, this invention establishes a quantitative correlation between the physical object characteristics and digital environment parameters, which greatly improves the mapping accuracy and significantly enhances the equivalence of the digital environment.

[0012] 2. Compared with traditional pure physical testing, this invention can reduce the number of physical prototype tests and shorten the test cycle. In particular, it can avoid damage to expensive physical prototypes, especially for extreme working conditions and high-risk test scenarios.

[0013] 3. This invention reduces overall testing costs by replacing most of the physical testing environment with a digital twin environment, thereby saving on testing site construction costs, energy consumption costs, and equipment wear and tear costs.

[0014] 4. The digital test environment can easily simulate extreme working conditions, boundary conditions and dangerous scenarios, greatly improving test coverage, and can carry out failure mode and effect analysis that is difficult to achieve with traditional methods.

[0015] 5. This invention ensures the consistency and complementarity between physical test data and digital simulation data through precise injection of equivalent action points and real-time synchronization of linkage mechanisms, greatly improving the confidence of test results.

[0016] 6. The experimental environment twin model and parameter relationship map constructed by this invention can be reused in similar experimental tasks, knowledge can be accumulated, and it is easy to extend to new experimental object types. Attached Figure Description

[0017] Figure 1 This is a flowchart of a data-real fusion experimental method based on object environment mapping mechanism according to the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above objectives, this invention adopts the following technical solution.

[0019] like Figure 1 As shown, the present invention provides a data-real fusion experimental method based on object environment mapping mechanism, comprising the following steps:

[0020] Step (1): For a specific experimental task, establish an evaluation index system, determine the evaluation parameters corresponding to the evaluation indicators, explore the correlation between the evaluation parameters, and construct a graph of the relationship between the evaluation parameters.

[0021] Step (2): Analyze the object-environment mapping mechanism, construct a twin model of the experimental environment, analyze the equivalent action distribution of the object-environment, and obtain the equivalent action points of the data-real fusion experiment;

[0022] Step (3): Configure the experimental environment twin model, establish the communication connection between the experimental object and the experimental environment, determine the linkage mechanism of the digital and real experiments, and execute the digital and real fusion experimental process.

[0023] Specifically, step (1) includes:

[0024] Step (1.1) Decomposition of evaluation indicators:

[0025] For a specific experimental task, analyze the corresponding evaluation indicators. Obtain the set of evaluation indicators , where n is the number of evaluation indicators and i is the index of the evaluation indicator.

[0026] For example, in fatigue life testing of aero-engine blades, the system analyzes the objectives and requirements of the test mission, identifies and defines corresponding evaluation indicators. These evaluation indicators should comprehensively reflect the key attributes of the test object, such as performance, reliability, and safety, including fatigue life and stress concentration factor.

[0027] Step (1.2) Evaluation parameter identification and quantification method definition:

[0028] For each evaluation indicator Determine the quantitative calculation method for evaluation indicators. , In the formula For evaluation parameters, Evaluation indicators The required number of evaluation parameters leads to a quantitative calculation method library. and evaluation parameter set The evaluation parameters should be physical quantities or state quantities that can be directly or indirectly measured.

[0029] Taking fatigue life evaluation index as an example, its quantitative calculation method can be adopted using Miner's linear cumulative damage theory, expressed as: = f( , , , , The evaluation parameters are as follows: This represents the maximum stress amplitude (in MPa). The stress ratio is dimensionless. The loading frequency (in Hz) These are the material SN curve parameters (including fatigue strength coefficient and fatigue strength index). This is the temperature effect coefficient (dimensionless).

[0030] The quantitative calculation method for the stress concentration factor evaluation index can be expressed as follows: = f( , , ),in It is the geometric shape factor (dimensionless). The radius of the notch is in mm. The material sensitivity coefficient is dimensionless.

[0031] The quantitative calculation method for vibration characteristic evaluation indicators can be expressed as follows: = f( , , ),in The natural frequency (in Hz). The damping ratio is dimensionless. Modal mass (unit: kg).

[0032] For the evaluation index of crack propagation rate, its quantitative calculation method can be adopted using the Paris formula, which is expressed as: = f( , , ),in The stress intensity factor amplitude ΔK (unit: MPa·m^0.5) is given. For the material Paris formula parameter C (units and) Consistent) Let m be the dimensionless parameter in the Paris formula for the material.

[0033] Based on the above analysis, a quantitative calculation method library and a set of evaluation parameters are obtained. This set should contain all the parameters required for all evaluation indicators, and clearly define the physical meaning, dimensions, and acquisition methods of each parameter.

[0034] Step (1.3) Evaluation of parameter correlation mining:

[0035] Based on the obtained set of evaluation parameters, the system mines the explicit or implicit correlations between the evaluation parameters. Explicit correlations refer to correlations directly determined by physical laws or empirical formulas, such as stress amplitude. With stress intensity factor amplitude The elastic mechanical relationship between them. Implicit correlation refers to the correlation discovered through data analysis, machine learning, or experience, such as the temperature influence coefficient. With material Paris formula parameters The temperature-dependent relationship between them.

[0036] A graph theory-based modeling method is used to construct a graph of the relationship between evaluation parameters. In the graph, nodes represent evaluation parameters, edges represent the relationships between parameters, and edge weights indicate the strength of the relationships. For example, in blade fatigue tests, the following relationship can be established: r( , ) represents the conversion relationship between stress amplitude and stress intensity factor amplitude; r( , ) represents the effect of temperature on the crack propagation parameters of a material; r( , The figure () represents the coupling relationship between vibration response and dynamic stress. This relationship diagram provides constraints and optimization paths for subsequent mapping mechanism analysis.

[0037] Specifically, step (2) includes:

[0038] Step (2.1) Mapping mechanism modeling:

[0039] Evaluation parameter relationship graph based on step (1) Correlation analysis was conducted between evaluation parameters and experimental environment parameters. Experimental environment parameters refer to environmental variables that can be directly controlled and measured during the experiment, such as temperature, pressure, vibration excitation, and airflow velocity. Multiple regression analysis, principal component analysis, or neural network methods were used to establish a mapping mechanism model between the experimental object and the experimental environment. , Here, t represents the required test environment parameters, k represents the number of test environment parameters, and k represents the index value.

[0040] For fatigue testing of aero-engine blades, the test environment parameters may include: The acceleration due to vibration table excitation (in g) The temperature of the high-temperature airflow is expressed in °C. The mapping mechanism can be represented as follows: This indicates that the stress amplitude of the blade is determined by both vibration excitation and temperature. This indicates the mapping mechanism between the temperature influence coefficient and the ambient temperature; This indicates that the natural frequency of the blade is affected by the excitation frequency. These mapping relationships are established through finite element simulation, modal test data fitting, or physical mechanism derivation to ensure the equivalence between the digital environment and physical effects.

[0041] Step (2.2) Construction of the experimental environment twin model:

[0042] Based on the mapping mechanism model, a twin model of the experimental environment is established in the digital environment. This model, based on a multiphysics coupled simulation platform, defines the boundary conditions, initial conditions, and corresponding experimental environment parameters required for the experiment. For blade fatigue testing, the twin model should include: a three-dimensional geometric model of the blade, a material property database (elastic modulus, density, and coefficient of thermal expansion curves as a function of temperature), an aerodynamic load calculation module, a thermo-structural coupling analysis module, and a fatigue damage accumulation calculation module. Through model validation, the prediction accuracy of the twin model should be ensured to deviate from the physical test by less than 5%, thus obtaining a digital testing environment that can replace the physical testing environment.

[0043] Step (2.3) Equivalent action point analysis:

[0044] By analyzing the equivalent effects of the behavioral responses of the test subjects and the test environment, the equivalent action points of the data-real fusion experiment are obtained. , Let p be a specific equivalent action point, and p be the number of equivalent action points. An equivalent action point refers to a key interface location where the digital environment applies excitation to a physical object or the physical object responds to the digital environment. Its selection should satisfy the principles of energy equivalence, response equivalence, and damage equivalence.

[0045] For blade fatigue testing, the equivalent point of action includes: The vibration acceleration injection point is the interface between the blade root and the disk. At this point, the physical excitation of the vibration table and the digital simulation excitation should produce the same displacement response. This is the area where the aerodynamic pressure distribution at the leading edge of the blade is applied to the physical blade in the form of surface pressure through digital simulation. These are the strain response acquisition points on the blade surface, used to feed physical strain data back to the digital twin model in real time for damage calculation and calibration. Each equivalent action point needs to have its spatial coordinates, degree of freedom (e.g., translation or rotation in the X / Y / Z directions), signal type (force, displacement, acceleration, pressure, etc.), and measurement range clearly defined.

[0046] Specifically, step (3) includes:

[0047] Step (3.1) Experimental condition configuration and communication establishment:

[0048] The process involves assembling and integrating physical test objects (such as real blades) with a digital test environment. First, the parameters of each test environment are calculated. Configuration methods in the experimental environment twin model. For example, for parameters. (Vibration excitation acceleration) is set by adjusting the gain coefficient, frequency range, and sweep rate of the vibration table controller; for parameters... (High-temperature airflow temperature) can be set by adjusting the heater power, airflow speed, and temperature control accuracy.

[0049] Determine the equivalent point of action The signal injection method. At the point (blade root), an acceleration excitation is applied using a vibration table and clamps, with the signal type being a continuous sinusoidal sweep frequency signal; At the leading edge of the blade, surface pressure is applied using a piezoelectric force sensor array. A connection and communication system is established between the test object and the test environment twin model. A high-speed data bus based on the TCP / IP protocol is used, with a communication bandwidth of no less than 100 Mbps. The data sampling rate is synchronized with the simulation step size to ensure that the communication delay is less than 1 ms, thus obtaining complete data-real fusion test conditions.

[0050] Step (3.2) Establishment and Experiment Execution of the Linkage Mechanism:

[0051] A linkage mechanism between physical and digital experiments is established to achieve real-time interaction and collaborative evolution between the physical and digital domains. This linkage mechanism includes: a timing synchronization mechanism to ensure precise alignment between the physical experiment timing and the digital simulation timing; and a data interaction mechanism to define the data flow direction and format at equivalent action points, such as... The acceleration command for the point is calculated and generated by the digital twin model and sent to the vibration table controller via a D / A converter. At the same time, the vibration table feeds back the actual acceleration response to the digital model for closed-loop correction. An anomaly handling mechanism automatically triggers the test to pause or terminate when physical parameters exceed the safety threshold or the digital model diverges.

[0052] The digital twin test process employed a progressive loading strategy: a pre-test was conducted first to verify the communication link and synchronization mechanism; then, a full-load formal test was performed. During the test, the physical blade was subjected to dynamic loads in a shaking table and airflow environment. The digital twin model calculated the stress distribution, cumulative fatigue damage, and crack propagation state in real time, and compared the calculation results with physical strain data to dynamically adjust the model parameters. The test continued until a predetermined number of cycles (e.g., 10^7) was reached or visible cracks appeared on the blade, obtaining all the evaluation parameters required for the test mission. The specific value.

[0053] Step (3.3) Evaluation index calculation and result output:

[0054] The evaluation index is quantified according to the method determined in step (1.2). The evaluation parameters collected and calculated during the experiment were used. Calculate the values ​​of each evaluation indicator.

[0055] For the fatigue life evaluation index, Miner's cumulative damage theory is used for calculation: Fatigue life evaluation index = 1 / Σ(n_i / N_i), where n_i is the actual number of cycles at the i-th stress level, and N_i is the material fatigue life at the corresponding stress level, determined by the SN curve. For the crack propagation rate evaluation index, the Paris formula is used to fit the crack propagation curve to obtain the values ​​of parameters C and m.

[0056] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0057] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data-real fusion experimental method based on object-environment mapping mechanism, characterized in that, Includes the following steps: Step (1): For a specific experimental task, establish an evaluation index system, determine the evaluation parameters corresponding to the evaluation indicators, explore the correlation between the evaluation parameters, and construct a graph of the relationship between the evaluation parameters. Step (2): Analyze the object-environment mapping mechanism, construct a twin model of the experimental environment, analyze the equivalent action distribution of the object-environment, and obtain the equivalent action points of the data-real fusion experiment; Step (3): Configure the experimental environment twin model, establish the communication connection between the experimental object and the experimental environment, determine the linkage mechanism of the digital and real experiments, and execute the digital and real fusion experimental process.

2. The data-real fusion experimental method based on object environment mapping mechanism according to claim 1, characterized in that, Step (1) includes: Step (1.1) For a specific experimental task, analyze the corresponding evaluation indicators. Obtain the set of evaluation indicators , where n is the number of evaluation indicators.

3. The data-real fusion experimental method based on object environment mapping mechanism according to claim 2, characterized in that, Step (1) also includes: Step (1.2) for each evaluation indicator Determine the quantitative calculation method for evaluation indicators. , In the formula For evaluation parameters, Evaluation indicators The required number of evaluation parameters leads to a quantitative calculation method library. and evaluation parameter set .

4. The data-real fusion experimental method based on object environment mapping mechanism according to claim 3, characterized in that, Step (1) also includes: Step (1.3) Discover explicit / implicit associations among evaluation parameters The relationship spectrum of evaluation parameters was obtained. .

5. The data-real fusion experimental method based on object environment mapping mechanism according to claim 4, characterized in that, Step (2) includes: Step (2.1) is based on the evaluation parameter relationship graph from step (1). Correlation analysis was conducted between evaluation parameters and experimental environment parameters to establish a mapping mechanism between the experimental object and the experimental environment. , t represents the required test environment parameters, and t represents the number of test environment parameters.

6. The data-real fusion experimental method based on object environment mapping mechanism according to claim 5, characterized in that, Step (2) further includes: Step (2.2) Establish a twin model of the test environment and define the boundary conditions and corresponding test environment parameters required for the test in the digital environment. This results in a digital testing environment that can replace the physical testing environment.

7. The data-real fusion experimental method based on object environment mapping mechanism according to claim 6, characterized in that, Step (2) further includes: Step (2.3) analyzes the equivalent effects of the behavioral responses of the test subjects and the test environment to obtain the set of equivalent action points for the data-real fusion experiment. , Let p be a specific equivalent action point, and p be the number of equivalent action points.

8. The data-real fusion experimental method based on object environment mapping mechanism according to claim 7, characterized in that, Step (3) includes: Step (3.1) involves assembling and integrating the test object with the digital test environment, and configuring the test environment parameters in the test environment twin model. Determine the equivalent point of action. The signal injection method establishes a connection and communication between the experimental object and the experimental environment twin model, and obtains the data-real fusion experimental conditions.

9. The data-real fusion experimental method based on object environment mapping mechanism according to claim 8, characterized in that, Step (3) further includes: Step (3.2) Establish a linkage mechanism between numerical and real experiments, execute the numerical-real integrated experiment process, and obtain all evaluation parameters required for the experimental task. The specific value.

10. The data-real fusion experimental method based on object environment mapping mechanism according to claim 9, characterized in that, Step (3) further includes: Step (3.3) Quantitative calculation method based on evaluation indicators Calculate all evaluation indicators required for the experimental task. The results of the data-real fusion experiment were obtained, and the experimental task was completed.

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