A method and system for constructing a digital-real fusion test process model for the whole life cycle of aerospace equipment

Abstract

The application discloses a kind of space equipment oriented to whole life cycle of number-real fusion test process model construction method and system, belong to aerospace digitization and computer science technology field.The method includes: the unified test process framework and state monitoring model of whole life cycle such as covering conceptual demonstration, design, test, operation and maintenance are constructed;Establish the data interaction process model of multiple number-real fusion modes, improve compatibility and scalability;Consistency maintaining mechanism of three layers of data, model and execution is constructed, to ensure the stability and synchronization of multiple system parallel test;Finally, through modular integration and management, form reusable, scalable test process system.The application solves the problem that traditional test process relies on manual experience, is difficult to reuse and dynamic optimization, significantly improves the consistency, coverage, efficiency and traceability of test.

Description

Technical Field

[0001] This invention belongs to the field of aerospace digitalization and computer science technology, specifically relating to a method and system for constructing a data-real fusion test process model for the entire life cycle of aerospace equipment. Background Technology

[0002] As a highly complex, multidisciplinary, and reliability-critical system engineering project, aerospace equipment has a long development cycle, high technical difficulty, and operates in extreme environments. Testing and verification work is carried out throughout the entire life cycle from conceptual design to decommissioning, and is a key link in ensuring equipment performance and safety. However, traditional aerospace equipment testing procedures mainly rely on manual experience and are mostly presented in document form. This results in problems such as low standardization of procedures, information fragmentation across stages, and difficulties in data sharing. Consequently, test plans have poor reusability, low resource allocation efficiency, and difficulty in continuously accumulating and optimizing test data.

[0003] With the development of digital twins, big data, and intelligent decision-making technologies, data-physical integration has become an important direction for improving testing efficiency. Its essence is to construct digital models corresponding to physical entities, enabling interactive iteration and closed-loop feedback between virtual and physical data, thereby enhancing the predictability, flexibility, and coverage of the testing process. However, data-physical integration still faces many challenges in current aerospace equipment testing: First, there is a lack of a unified process modeling method throughout the entire lifecycle, resulting in loose connections between testing tasks at different stages and making it difficult to form a continuous and consistent testing chain; second, the data-physical interaction mechanism is not unified, with differences in data formats, interface standards, and semantic understanding, leading to difficulties in collaboration between multi-source heterogeneous systems; third, there is a lack of effective consistency maintenance mechanisms, which can easily lead to data drift, model mismatch, and asynchronous execution in cross-regional, multi-system parallel testing environments, affecting the reliability and engineering applicability of test results.

[0004] Therefore, it is urgent to build a systematic, structured, and reusable data-real fusion test process model to achieve standardized definition of the test process, flexible adaptation of multi-mode data-real interaction, and consistent maintenance of data, models, and execution logic throughout the entire life cycle, thereby improving the overall efficiency, collaborative capabilities, and intelligence level of aerospace equipment testing. Summary of the Invention

[0005] To address the aforementioned technical challenges, this invention provides a method and system for constructing a data-real fusion testing process model for the entire lifecycle of aerospace equipment. By analyzing testing activities at each stage of the lifecycle, a process framework covering the entire process of requirements, design, verification, and evaluation is constructed. Based on this, a multi-mode data-real fusion interactive process model is studied to achieve efficient flow and fusion of data. Finally, through a consistency-maintaining process model, the problems of information asynchrony and model drift in multi-system collaboration are solved, ensuring the stability and reliability of the testing system under long-term operation and parallel operation in different locations, thereby improving the consistency, coverage, efficiency, and traceability of the entire lifecycle testing.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for constructing a data-real fusion testing process model for the entire lifecycle of aerospace equipment, including:

[0008] Step S101: Systematically sort out the test tasks at each stage to form a clear overall process framework, as well as an error correction model and a status monitoring model.

[0009] Step S102: Construct a data interaction process model that can adapt to multiple data-physical fusion modes, and define the interaction mechanism and quality assurance measures between the digital domain and the physical domain.

[0010] Step S103: Establish a data, model, and execution consistency maintenance process model, and use the state monitoring model to perform state tracking to ensure the stability and consistency of the testing process throughout the entire lifecycle and under multi-system parallel operation;

[0011] Step S104: Systematically integrate the overall process framework, data interaction process model, and consistency maintenance process model to form a scalable and reusable management system.

[0012] Furthermore, in step S101, forming a hierarchical overall process framework includes: systematically reviewing the testing tasks of aerospace equipment in each stage of concept demonstration, scheme design, prototype integration, ground testing, joint testing before orbital insertion or first flight, on-orbit or in-service operation and maintenance, upgrade and modification, and life assessment and disposal; dividing the testing tasks into whole-machine level, subsystem level and component level; and performing structured modeling of the logical relationships between stages; and constructing a unified process model framework driven by testing task objectives and centered on information and decision-making collaboration.

[0013] Furthermore, in step S101, constructing the error correction model includes: standardizing the input, output, interface relationships and feedback mechanisms of each process link of the data-real fusion test to form a process system with clear hierarchy and traceable structure; by embedding feedback paths in each test link, ensuring dynamic adjustment and optimization of the test process, and realizing real-time correction of deviations through data-real fusion, accumulating verification experience and reverse correction of the design stage.

[0014] Furthermore, in step S101, constructing the state monitoring model includes:

[0015] Establish a unified indicator system and use the interval normalization method to transform the original indicators of different dimensions into a unified interval;

[0016] Define a process phase readiness index, which is calculated based on multiple normalized phase indicators and their weights, and is used for the admission determination of process phases.

[0017] Set the traceability rate from requirements to test cases, and set admission criteria including readiness threshold and coverage threshold to objectively determine whether to pass the current process stage.

[0018] Furthermore, step S102 includes:

[0019] Construct a unified data interaction process model to clarify the dominant proportions, interaction directions, and decision-making methods of the digital and physical domains;

[0020] A data interaction quality monitoring model is constructed to monitor the quality of data acquisition, transmission, processing, fusion, and feedback.

[0021] The time delay is estimated by calculating the maximum value of the cross-correlation function between the digital and measured output sequences, and time alignment is performed to eliminate sampling misalignment;

[0022] A real-time dynamic closed-loop feedback model is established, and data fusion is performed based on the state estimation method of weighted least squares. Parameter identification and model correction are then performed based on the fusion residuals to optimize the testing strategy.

[0023] Furthermore, in step S103, establishing the data, model, and execution consistency maintenance process model includes:

[0024] From a data perspective, a cross-system, cross-timescale data synchronization process model is established. Through time synchronization, precision matching, and anomaly detection strategies, the consistency of timing and dimensionality in the data fusion process is ensured.

[0025] At the model level, a model update strategy based on the difference analysis between simulation output and measured data is constructed to correct model parameters or boundary conditions in real time.

[0026] From the execution level, a collaborative control process model for distributed test tasks is constructed, and a unified control mechanism for task scheduling, instruction distribution, and status feedback is established.

[0027] Furthermore, in step S103, a composite consistency index is used to quantitatively evaluate and alert on the consistency maintenance effect. The composite consistency index is a weighted combination of time consistency, statistical consistency, and precision consistency.

[0028] When the composite consistency index exceeds the preset alarm threshold, an alarm is triggered and the consistency correction process is initiated.

[0029] The efficiency and effectiveness of the correction process are evaluated by calculating the test stop recovery time and the correction deviation convergence rate.

[0030] On the other hand, this invention provides a data-real fusion testing process model construction system for the entire life cycle of aerospace equipment, including:

[0031] The data-real fusion test process model building module is used to systematically sort out the test tasks at each stage, form a clear overall process framework, as well as an error correction model and a status monitoring model.

[0032] The data interaction process model construction module is used to build a data interaction process model that can be adapted to various data-real fusion modes, and to define the interaction mechanism and quality assurance measures of data between the digital domain and the physical domain.

[0033] The consistency monitoring and correction process model building module is used to establish a data, model, and execution consistency maintenance process model, and to use the state monitoring model to track the state, ensuring the stability and consistency of the test process throughout its entire lifecycle and under multi-system parallel operation.

[0034] The process integration and management module is used to systematically integrate the overall process framework, data interaction process model, and consistency maintenance process model to form a scalable and reusable management system.

[0035] Thirdly, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for constructing a data-real fusion test process model for the entire life cycle of aerospace equipment.

[0036] Fourthly, the present invention provides a computer-readable storage medium storing executable instructions thereon, which, when executed by a processor, enable the processor to implement the aforementioned method for constructing a data-real fusion test process model for the entire life cycle of aerospace equipment.

[0037] The beneficial effects of this invention are as follows:

[0038] Improved the standardization and reusability of the testing process: A unified process framework covering all stages of the entire lifecycle was constructed, enabling hierarchical and structured modeling of testing tasks. By standardizing process elements and key nodes, the limitations of traditional processes relying on manual experience and documented procedures were overcome, supporting rapid migration and reuse of test solutions across models and platforms, significantly improving the standardization and execution efficiency of the testing process.

[0039] The adaptability and system compatibility of data-real integration have been enhanced: a data interaction process model adaptable to various modes such as "data-assisted reality, data-supplemented reality, data-real integration, data-led reality, and data-replaced reality" has been proposed, and a unified data semantic mapping and quality monitoring mechanism has been established. This enables the system to flexibly respond to different testing scenarios and integration requirements, effectively improving the data interoperability and collaborative operation capabilities between multi-source heterogeneous systems.

[0040] Stability and consistency are guaranteed in complex testing environments: A multi-layered consistency maintenance mechanism is constructed, encompassing data synchronization, model calibration, and execution collaboration. Through time alignment, difference analysis, and distributed task control, the stability and reliability of the testing process are ensured under conditions of parallel operation of multiple systems and long-term operation, effectively suppressing data drift and model mismatch, and improving the accuracy and reliability of test results.

[0041] A scalable and manageable process system has been formed: through modular integration and version management, the process model has been systematically integrated and managed throughout its entire lifecycle, supporting the continuous optimization of the testing process and the accumulation of knowledge, and providing a solid foundation for the digital and intelligent transformation of aerospace equipment testing. Attached Figure Description

[0042] Figure 1 This is a flowchart of a method for constructing a data-real fusion testing process model for the entire life cycle of aerospace equipment according to the present invention;

[0043] Figure 2 This is a module division diagram of a data-real fusion testing process model construction system for the entire life cycle of aerospace equipment according to the present invention. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0045] like Figure 1 The diagram shows a flowchart of a data-real fusion testing process model construction method for the entire lifecycle of aerospace equipment according to the present invention. A typical aircraft is used as an example for illustration. The method specifically includes:

[0046] Step S101: Construction of the data-real fusion test process model framework for the entire life cycle of aerospace equipment: Systematically sort out the test tasks at each stage, form a clear overall process framework, as well as an error correction model and a status monitoring model.

[0047] Step S102, Construction of Data Interaction Process Model for Multi-mode Data-Real Fusion Test of Aerospace Equipment: Construct a data interaction process model that can adapt to multiple data-real fusion modes, define the interaction mechanism and quality assurance measures between the digital domain and the physical domain, and improve the compatibility and scalability of data-real interaction;

[0048] Step S103: Construction of the consistency maintenance process model for data, model, and execution fusion testing of aerospace equipment: Establish a consistency maintenance process model for data, model, and execution, and use the state monitoring model to track the state to ensure the stability and consistency of the testing process throughout its entire life cycle and under the parallel operation of multiple systems;

[0049] Step S104, Integration and Management of Data-Real Fusion Testing Process Model for Aerospace Equipment: Systematically integrate the overall process framework, data interaction process model, and consistency maintenance process model, and incorporate them into a unified management system to form an scalable and reusable management system.

[0050] The specific implementation of step S101 includes:

[0051] This paper constructs a data-driven and data-integrated testing process framework for the entire lifecycle of aerospace equipment. It systematically reviews the testing tasks at each stage: concept demonstration, scheme design, prototype integration, ground testing, pre-flight joint testing, in-service operation and maintenance, upgrades and modifications, and life assessment and disposal. The testing tasks are categorized at the system level, subsystem level (e.g., flight control system, avionics system, hydraulic system, landing gear system, propulsion system, environmental control system), and component level. Combining typical tasks, load conditions, and interdisciplinary coupling characteristics, the paper analyzes the typical features of aerospace equipment throughout its lifecycle in terms of requirements analysis, scheme design, test implementation, data processing, and performance evaluation, identifying common data-driven and data-integrated testing process elements and key control nodes. For the entire lifecycle, the paper structures and models the logical relationships between stages, thereby constructing a unified process model framework driven by testing task objectives and centered on information and decision-making collaboration. Specifically:

[0052] A data-real fusion test error correction model is constructed. Based on the above framework, the input, output, interface relationships, and feedback mechanisms of each process step in the data-real fusion test are standardized and modeled to form a hierarchical and traceable process system. The boundaries of each test step are clearly defined, and the dynamic adjustment and optimization of the test process are ensured through the embedding of feedback paths. This ensures that deviations are corrected in real time during the test activities through data-real fusion, verification experience is accumulated, and the design phase is corrected in reverse.

[0053] Construction of a data-real fusion testing process status monitoring model. Establish a unified indicator system, connect the information transmission links between various stages throughout the entire life cycle, and realize the inheritance, continuation, and dynamic updating of data and models between different stages. This enables the data-real fusion testing system for aerospace equipment to have global consistency and evolution capabilities, thereby providing a unified operational process benchmark for subsequent multi-mode interaction and consistency maintenance.

[0054] To uniformly convert various performance indicators (such as temperature, pressure, response time, etc.) with different testing stages and different physical meanings into the interval [0, 1], this invention defines interval normalization:

[0055] ,

[0056] In the formula, These are the original indicators; This establishes boundaries for the project. This aims to eliminate dimensional differences and lay the foundation for the fusion and comprehensive evaluation of multi-source indicators.

[0057] Based on this, a process phase readiness index is defined for admission determination. Its calculation method is as follows: a weighted sum of multiple normalized phase indicators is performed, where the sum of the weight coefficients is 1, meaning all weights satisfy the condition that... At the beginning of each stage of the process, define a process stage readiness index. Used for admission determination For the number of indicators; For the first Weight of each indicator; For the first Each stage indicator The normalized value; for The engineering boundary; the process phase readiness index quantifies whether the current stage is ready to enter the next stage by weighting and synthesizing multiple key indicators.

[0058] Simultaneously, the traceability rate from requirements to test cases is defined to measure the completeness of test coverage. The traceability rate from requirements to test cases is... express, For the entire set of requirements, This is the set of requirements that have already been covered.

[0059] Finally, by setting admission criteria, the system can objectively and automatically determine whether a process stage is passed. , The ready threshold, To cover the threshold, Indicates whether this stage of the process has been completed.

[0060] The process status monitoring model establishes information transmission links between various stages throughout the entire lifecycle, enabling the inheritance, continuation, and dynamic updating of data and models across different stages. This allows the aerospace equipment data-real fusion testing system to possess global consistency and evolution capabilities, thereby providing a unified and quantifiable operational process benchmark for subsequent multi-mode interaction and consistency maintenance.

[0061] The specific implementation of step S102 includes:

[0062] Construct a data interaction process model adaptable to various data-physical fusion modes. Addressing the differences in data-physical fusion levels across various mission scenarios in aerospace equipment testing activities, this model clarifies the dominant proportions, interaction directions, and decision-making methods of the digital and physical domains under each mode. It establishes a unified data interaction process model adaptable to typical modes such as "data-assisted physical, data-supplemented physical, data-physical fusion, data-led physical, and data-replaced physical." Corresponding configurations and constraints are provided for each mode to ensure consistency between data processing methods and evaluation indicators, thereby guaranteeing the comparability and uniformity of results. Specifically:

[0063] Construct a data interaction quality monitoring model. This involves building a process model encompassing five stages: data acquisition, transmission, processing, fusion, and feedback. Data quality monitoring and delayed transmission mechanisms are implemented at each stage to ensure the timeliness and accuracy of data interaction. To address the inherent time asynchrony between the digital domain (simulation model) and the physical domain (real physical test system) due to the data acquisition, transmission, and processing stages, time offset estimation calculations are required.

[0064] ,

[0065] in, Output sequences of numbers and actual measurements. For time indexing, It is a cross-correlation function. For time-shifted variables, For the maximum search half window, To estimate latency, linear interpolation or window averaging is used to eliminate sampling misalignment after time alignment. By standardizing data interface definitions and information format structures, data interoperability and structured sharing among the physical testing system, simulation model, and control module are achieved. Simultaneously, considering the characteristic differences of heterogeneous data sources, a unified semantic model and recognition system are designed. A calibration matrix and calibration bias are used to linearly transform the original observation vectors, mapping them to a unified dimension and semantic space. This allows various types of test information to be fused and invoked under a unified standard, improving the compatibility and scalability of data-physical interaction. The semantic mapping is achieved by… express, Represents the original observation vector. This represents the observation vector after dimension unification. Represents the calibration matrix. This indicates the calibration bias.

[0066] A dynamic closed-loop feedback model for data and reality is established to enable real-time information exchange and rapid response in the data and reality system during test execution. The data fusion process is based on the weighted least squares state estimation method. Its core is to use an observation matrix that considers the observation weights to perform optimal estimation on the concatenated observation vector to obtain the fused state, and to calculate the L2 norm of the fusion residual to evaluate the fusion quality. , This indicates the concatenation of observation vectors. Represents the observation matrix. Represents the observation weight matrix (symmetric positive semi-definite). This represents the fusion state estimation. This represents the L2 norm of the fused residuals. When measured changes occur, model parameters are updated, and experimental procedures are adjusted as necessary. When discrepancies arise between numerical predictions and measurements, parameter identification and model correction are performed. After correction, the testing strategy is optimized to maintain the stability and effectiveness of data-real-world fusion. Through this two-way dynamic feedback mechanism, intelligent adjustment and collaborative optimization are achieved in the data-real-world fusion process, thereby improving the overall efficiency and reliability of the testing system.

[0067] The specific implementation of step S103 includes:

[0068] From a data perspective, a cross-system, cross-timescale data synchronization process model is established. By designing time synchronization, precision matching, and anomaly detection strategies among various data sources, the consistency of time sequence and dimensions of data collected by different test units is ensured during the fusion process. This achieves real-time alignment and dynamic updates of global data, thereby eliminating information drift and latency accumulation problems in data-real interaction.

[0069] At the model level, a consistency maintenance process model for digital and physical models is constructed. Due to the complex structure and variable operating conditions of aerospace equipment, in order to avoid parameter deviations during the use of digital models, a model update strategy based on difference analysis is designed. By comparing the deviations between simulation output and measured data, the key parameters or boundary conditions of the model are corrected in real time, so that it continuously reflects the real state of the physical system, thereby realizing the dynamic correspondence and collaborative evolution between digital and physical models.

[0070] From an execution perspective, a collaborative control process model for distributed testing tasks is constructed. Considering the characteristics of aerospace equipment testing involving multiple test sites, heterogeneous equipment, and multi-team collaboration, a unified control mechanism for task scheduling, instruction distribution, and status feedback is established to ensure consistency in time series, execution logic, and result aggregation across all testing phases. Potential asynchronous risks are detected at each stage of task initiation, execution, and feedback, and global synchronization is achieved through a consistency correction process. This ensures stable collaborative operation of data-real fusion testing in a large-scale distributed environment. The collaborative effect is quantified and evaluated by defining three types of indicators: time consistency, statistical consistency, and accuracy consistency.

[0071] Define time consistency: , To allow the maximum delay;

[0072] Statistical consistency: ,in , , For reference / current discrete distribution, It is an intermediate distribution. For Kullback-Leibler divergence, It is the natural logarithm;

[0073] Accuracy consistency: , where the root mean square error , Output sequences of numbers and actual measurements. The number of sample points. The acceptable maximum RMSE;

[0074] Furthermore, the three types of consistency mentioned above are combined into composite consistency using a weighted combination formula. , Given weights;

[0075] When the composite consistency index exceeds the preset alarm threshold, the system triggers an alarm and initiates a consistency correction process. The alarm triggering conditions are as follows: , As a trigger flag, This is the alarm threshold;

[0076] At the same time, the test stop recovery time from the issuance of the alarm to the system returning to normal was calculated. , For alarm time, The recovery time; and the convergence rate of the correction bias obtained by comparing the error quantization values ​​before and after the correction. , To quantify the errors before and after correction, the efficiency and effectiveness of the correction process are evaluated. Potential asynchronous risks are detected at each stage of task initiation, execution, and feedback. Global synchronization is achieved through this consistency correction process, ensuring stable and collaborative operation of data-real fusion testing in a large-scale distributed environment.

[0077] The specific implementation of step S104 includes:

[0078] The integration and management of the data-real fusion testing process model for aerospace equipment systematically integrates the data-real fusion testing process model. It designs modules for building the data-real fusion testing process model, data interaction process model, consistency monitoring and correction process model, and process integration and management. Through versioned process templates, parameter sets, and indicator definitions, it enables configuration migration and reuse across stages and scenarios. Through a unified scenario and object description system, it constructs cross-layer relationships between the entire machine, subsystems, and components, ultimately forming a scalable, reusable, and verifiable data-real fusion testing process management system, ensuring that data-real fusion testing maintains coverage, effectiveness, and stability throughout its entire lifecycle.

[0079] Secondly, this invention provides a data-real fusion testing process model construction system for the entire lifecycle of aerospace equipment, such as... Figure 2 As shown, the specific implementation includes:

[0080] A prototype system for data-real fusion testing of aerospace equipment. The system adopts a modular architecture, consisting of four core modules: a data-real fusion testing process model construction module, a data interaction process model construction module, a consistency monitoring and correction process model construction module, and a process integration and management module. These modules work together through a unified data interface and communication protocol to support the planning, scheduling, monitoring, data processing, and result evaluation of test tasks.

[0081] The data-real fusion test process model construction module is used to systematically organize the test tasks at each stage, form a clear overall process framework, as well as an error correction model and a status monitoring model; based on the input test task requirements, stage attributes and system hierarchy, it automatically constructs the process structure and defines the execution logic and control path of the test activities.

[0082] Data Interaction Process Model Construction Module: Used to build a data interaction process model that can be adapted to various data-real fusion modes, and to define the interaction mechanism and quality assurance measures between the digital domain and the physical domain.

[0083] Consistency Monitoring and Correction Process Model Building Module: This module is used to establish a consistency maintenance process model for data, models, and execution, and to use the state monitoring model to track the state, ensuring the stability and consistency of the testing process throughout its entire lifecycle and under the parallel operation of multiple systems.

[0084] Process Integration and Management Module: This module is used to systematically integrate the overall process framework, data interaction process model, and consistency maintenance process model to form a scalable and reusable management system.

[0085] In summary, this invention discloses a method and system for constructing a data-real fusion test process model for the entire lifecycle of aerospace equipment. The method and system for constructing the data-real fusion test process model for the entire lifecycle of aerospace equipment can, to some extent, solve the problems existing in current aerospace equipment testing and improve the consistency, coverage, efficiency, and traceability of aerospace equipment lifecycle testing.

[0086] Thirdly, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for constructing a data-real fusion test process model for the entire life cycle of aerospace equipment.

[0087] Fourthly, the present invention provides a computer-readable storage medium storing executable instructions thereon, which, when executed by a processor, enable the processor to implement the aforementioned method for constructing a data-real fusion test process model for the entire life cycle of aerospace equipment.

[0088] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a data-real fusion testing process model for the entire lifecycle of aerospace equipment, characterized in that, include: Step S101: Systematically sort out the test tasks at each stage to form a clear overall process framework, as well as an error correction model and a status monitoring model. Step S102: Construct a data interaction process model that can adapt to multiple data-physical fusion modes, and define the interaction mechanism and quality assurance measures between the digital domain and the physical domain. Step S103: Establish a data, model, and execution consistency maintenance process model, and use the state monitoring model to perform state tracking to ensure the stability and consistency of the testing process throughout the entire lifecycle and under multi-system parallel operation; Step S104: Systematically integrate the overall process framework, data interaction process model, and consistency maintenance process model to form a scalable and reusable management system.

2. The method for constructing a data-real fusion testing process model for the entire lifecycle of aerospace equipment according to claim 1, characterized in that, In step S101, forming a hierarchical overall process framework includes: systematically sorting out the test tasks of aerospace equipment in each stage of concept demonstration, scheme design, prototype integration, ground testing, joint testing before orbit insertion or first flight, on-orbit or in-service operation and maintenance, upgrade and modification, life assessment and disposal, and dividing the test tasks into whole-machine level, subsystem level and component level, and performing structured modeling of the logical relationships between stages; and constructing a unified process model framework driven by test task objectives and centered on information and decision-making collaboration.

3. The method for constructing a data-real fusion testing process model for the entire lifecycle of aerospace equipment according to claim 2, characterized in that, In step S101, constructing the error correction model includes: standardizing the input, output, interface relationship and feedback mechanism of each process link of the data-real fusion test to form a process system with clear hierarchy and traceable structure; by embedding feedback paths in each test link, ensuring dynamic adjustment and optimization of the test process, and realizing real-time correction of deviations through data-real fusion, accumulating verification experience and reverse correction of the design stage.

4. The method for constructing a data-real fusion testing process model for the entire lifecycle of aerospace equipment according to claim 1, characterized in that, In step S101, constructing the state monitoring model includes: Establish a unified indicator system and use the interval normalization method to transform the original indicators of different dimensions into a unified interval; Define a process phase readiness index, which is calculated based on multiple normalized phase indicators and their weights, and is used for the admission determination of process phases. Set the traceability rate from requirements to test cases, and set admission criteria including readiness threshold and coverage threshold to objectively determine whether to pass the current process stage.

5. The method for constructing a data-real fusion testing process model for the entire lifecycle of aerospace equipment according to claim 1, characterized in that, Step S102 includes: Construct a unified data interaction process model to clarify the dominant proportions, interaction directions, and decision-making methods of the digital and physical domains; A data interaction quality monitoring model is constructed to monitor the quality of data acquisition, transmission, processing, fusion, and feedback. The time delay is estimated by calculating the maximum value of the cross-correlation function between the digital and measured output sequences, and time alignment is performed to eliminate sampling misalignment; A real-time dynamic closed-loop feedback model is established, and data fusion is performed based on the state estimation method of weighted least squares. Parameter identification and model correction are then performed based on the fusion residuals to optimize the testing strategy.

6. The method for constructing a data-real fusion testing process model for the entire lifecycle of aerospace equipment according to claim 1, characterized in that, In step S103, establishing the data, model, and execution consistency maintenance process model includes: From a data perspective, a cross-system, cross-timescale data synchronization process model is established. Through time synchronization, precision matching, and anomaly detection strategies, the consistency of timing and dimensionality in the data fusion process is ensured. At the model level, a model update strategy based on the difference analysis between simulation output and measured data is constructed to correct model parameters or boundary conditions in real time. From the execution level, a collaborative control process model for distributed test tasks is constructed, and a unified control mechanism for task scheduling, instruction distribution, and status feedback is established.

7. The method of claim 6, wherein the method is characterized by: In step S103, a composite consistency index is used to quantitatively evaluate and alert on the consistency maintenance effect. The composite consistency index is a weighted combination of time consistency, statistical consistency and precision consistency. When the composite consistency index exceeds the preset alarm threshold, an alarm is triggered and the consistency correction process is initiated. The efficiency and effectiveness of the correction process are evaluated by calculating the test stop recovery time and the correction deviation convergence rate.

8. A digital-real fusion test process model construction system for the whole life cycle of aerospace equipment, characterized in that, include: The data-real fusion test process model building module is used to systematically sort out the test tasks at each stage, form a clear overall process framework, as well as an error correction model and a status monitoring model. The data interaction process model construction module is used to build a data interaction process model that can be adapted to various data-real fusion modes, and to define the interaction mechanism and quality assurance measures of data between the digital domain and the physical domain. The consistency monitoring and correction process model building module is used to establish a data, model, and execution consistency maintenance process model, and to use the state monitoring model to track the state, ensuring the stability and consistency of the test process throughout its entire lifecycle and under multi-system parallel operation. The process integration and management module is used to systematically integrate the overall process framework, data interaction process model, and consistency maintenance process model to form a scalable and reusable management system.

9. An electronic device, comprising: include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the method for constructing a data-real fusion test process model for the entire life cycle of aerospace equipment as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores executable instructions, which, when executed by a processor, enable the processor to implement the method for constructing a data-real fusion test process model for the entire life cycle of aerospace equipment as described in any one of claims 1-7.

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