MBSE-based complex relation decoupling and process method and system for complex equipment test

Abstract

The invention provides an MBSE-based complex relation decoupling and process method and system for a complex equipment test. The method comprises the following steps: S1, constructing an MBSE multi-view model and completing structured processing of core elements of a test system; s2, converting the MBSE multi-view model into a dynamic weighted directed graph and generating a coupling strength matrix; s3, a community discovery algorithm is adopted to divide coupling units and quantify coupling risk levels; s4, constructing a double-queue scheduling mechanism and determining scheduling logic of tasks and resources; s5, executing a decoupling optimization strategy for three types of coupling of the time sequence, the resources and the data; and S6, evaluating a decoupling effect through a three-level verification closed loop and iteratively updating model parameters.

Description

Technical Field

[0001] This invention proposes a method and system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE, which relates to the field of MBSE technology. Background Technology

[0002] As information warfare continues to evolve, complex equipment such as radars, missiles, and aero engines are gradually moving towards multi-task parallel execution, multi-device collaborative operation, and dynamic competition for multiple resources. Equipment testing and evaluation have also upgraded from traditional single-performance verification to a comprehensive performance assessment covering timing matching, resource scheduling, and data interaction. While traditional equipment testing and management models played a fundamental coordinating role in early small-to-medium-scale testing scenarios, they have gradually revealed significant limitations when facing increasingly complex testing mission systems and dynamically changing testing environments.

[0003] First, the document-driven management and control model suffers from blind spots in coupling identification and efficiency bottlenecks. This model relies on tables to record task sequences, documents to annotate equipment parameters, and reports to explain resource allocation schemes, dispersing the three core coupling relationships—sequence conflicts, resource competition, and data congestion—across different document carriers. Test personnel must manually compare and analyze these coupling relationships across documents, resulting in low accuracy in coupling identification and a long timeframe from conflict discovery to initial response, significantly lagging behind the pace of the testing process. For example, in some equipment type approval tests, the failure to promptly identify equipment occupancy conflicts between different test tasks has led to significant delays in single tests.

[0004] Secondly, single-algorithm optimization methods lack dynamic adaptability and full-process coverage. Existing local optimization schemes either use finite element analysis tools to focus on the physical coupling problems of individual equipment or use simple scheduling algorithms to optimize only resource load balancing, neither of which forms a dynamic linkage mechanism. Even if some schemes introduce MBSE technology, they only remain at the static SysML view modeling stage. When the test equipment experiences performance abnormalities, temporary addition of test tasks, or changes in the test environment, the model parameters and optimization strategies cannot be updated in real time, resulting in long response delays and difficulty in adapting to the dynamic changes in the test process.

[0005] Furthermore, the existing technology system suffers from two major drawbacks: incomplete verification loops and insufficient technological collaboration. Most optimization methods rely solely on simulation tools for verification, neglecting physical prototype testing and real-world scenario verification. This leads to significant discrepancies between simulation optimization results and actual experiments. For instance, in some equipment launch process tests, simulation-verified procedures often fail with high failure rates during physical prototype execution due to neglecting inter-device compatibility issues. Simultaneously, data silos exist between graph theory topology analysis and MBSE modeling. The coupled analysis results output by the algorithm cannot update the resource allocation parameters of the SysML internal block graph or the device switching timing of the state machine graph, making it difficult to implement optimization strategies and creating a technological gap between analysis, optimization, and implementation.

[0006] This deficiency may lead to frequent overlapping of test conflicts, low resource utilization, and insufficient process reliability during the testing of complex equipment. On the one hand, the intertwining of timing conflicts and resource competition results in a significant increase in the total test duration. Some equipment test projects have exceeded the planned test cycle due to multiple coupled conflicts. On the other hand, process schemes lacking practical verification are prone to sudden failures during equipment finalization tests, directly affecting the equipment deployment schedule. They may even cause irreversible technical losses due to the loss of key test data caused by data interaction congestion.

[0007] To address the aforementioned issues, model-based systems engineering (MBSE) has gradually become an important methodology for the design, analysis, and control of complex systems in recent years. MBSE utilizes standardized modeling languages ​​such as SysML to construct multi-view models, enabling structured definition and relational management of test elements. This breaks down the information fragmentation inherent in traditional document-driven models and provides a model foundation for the systematic identification of coupling relationships. However, current applications of MBSE in equipment testing remain limited to static modeling. It lacks a dynamic mapping mechanism between the model and graph topology, and it hasn't integrated dynamic scheduling algorithms to form a full-cycle control system. Therefore, it cannot effectively solve the core problems of coupling / decoupling and process optimization in complex equipment testing.

[0008] Therefore, how to provide a solution for decoupling and optimizing the coupling of complex equipment tests by integrating MBSE, graph theory topology analysis and dynamic scheduling technology, which can accurately identify multiple types of coupling relationships, quickly respond to dynamic changes in tests, and form a closed-loop verification system, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0009] In view of this, to fill the gaps and deficiencies in existing technologies, this invention proposes a method and system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE. This invention fully utilizes MBSE's multi-view modeling capabilities, the advantages of graph theory topology in coupling unit partitioning and strength quantification, and the dynamic scheduling dual-queue decision-making characteristics to construct a full-cycle management and control system. It solves the problems of blind spots and efficiency bottlenecks in coupling identification in traditional document-driven models, insufficient dynamic adaptation of single algorithms, and incomplete verification loops and technological collaboration gaps in existing technologies. Simultaneously, it breaks down data silos between MBSE and graph theory analysis, achieving real-time linkage between model parameters and optimization strategies. It possesses advantages such as high accuracy in coupling relationship identification, fast dynamic response speed in testing, reliable closed-loop verification of optimization schemes, and strong adaptability across equipment models, effectively improving the process stability and management efficiency of complex equipment testing.

[0010] This invention proposes a method and system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE, including the following:

[0011] This invention proposes a method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE, characterized by the following:

[0012] Step S1: Construct the MBSE multi-view model and complete the structuring of the core elements of the experimental system;

[0013] Step S2: Convert the MBSE multi-view model into a dynamically weighted directed graph and generate the coupling strength matrix;

[0014] Step S3: Use a community detection algorithm to divide the coupling units and quantify the coupling risk level;

[0015] Step S4: Construct a dual-queue scheduling mechanism and determine the scheduling logic for tasks and resources;

[0016] Step S5: Execute decoupling optimization strategies for the three types of coupling: timing, resources, and data.

[0017] Step S6: Evaluate the decoupling effect through a three-level verification closed loop and iteratively update the model parameters.

[0018] Further, step S1 includes the following:

[0019] Step S11: Classify the core elements of the test system into three categories: test tasks, test equipment, and auxiliary resources;

[0020] Step S12: Design the core view types of the MBSE multi-view model, including module definition diagram, internal block diagram, state machine diagram, and activity diagram;

[0021] Step S13: Determine the attribute parameters of various core elements, including the task element including Task_ID, priority P, time window [ts,te], the device element including Equip_ID, performance parameter set, resource consumption, and the resource element including Res_ID, rated capacity, current load, and splittable identifier.

[0022] Step S14: Conduct interface standardization design for the MBSE model, and design three types of interaction interfaces for the module definition diagram: requirements, supply, and data flow.

[0023] Step S15: Perform static consistency verification of the MBSE model to verify that the task timings do not overlap, the total consumption of equipment resources does not exceed the limit, and the equipment performance matches the task requirements.

[0024] Step S16: Store the verified MBSE model and establish a real-time data interface between the model and the topology transformation module.

[0025] Further, step S2 includes the following:

[0026] Step S21: Formulate bidirectional mapping rules between the MBSE model and the topology graph, and clarify the correspondence between elements and nodes, i.e., relationships and edges;

[0027] Step S22: Construct a topology graph node set, mapping test tasks to task nodes, devices to device nodes, and resources to resource nodes;

[0028] Step S23: Construct a topology graph edge set, mapping task time-series dependencies to time-series edges, device and resource requirements to resource edges, and device data interactions to data edges;

[0029] Step S24: Calculate edge weights and construct a weight matrix, with time-series weights arranged according to... Calculation, resource weight according to Calculation, data weights according to calculate;

[0030] Step S25: Generate a coupling strength matrix based on the weight matrix. Use multi-round feedback calibration by experts in the experimental field to determine the time-series weight coefficient α, resource weight coefficient β, and data weight coefficient γ. The α, β, and γ satisfy α+β+γ=1. After normalizing the three weights, calculate the coupling strength by weighted sum according to the determined weights.

[0031] Step S251: Experts independently score α, β, and γ based on the "degree of influence of temporal coupling, resource coupling, and data coupling on the stability of the experimental process", with the initial scoring range limited to [0,1].

[0032] Step S252: Calculate the standard deviation σ1 of the first round of scoring according to the sample standard deviation formula. If σ1≤0.05, proceed to sub-step S254; if σ1>0.05, proceed to sub-step S253.

[0033] Step S253: Analyze the weighted items with significant scoring differences (σ1>0.2), organize a technical seminar with experts, and report the difference data and the corresponding impact analysis basis; the experts adjust the scores based on the seminar results and submit the second scoring results.

[0034] Step S254: Repeat sub-steps 252-253 until σ≤0.05, confirming calibration convergence.

[0035] Step S26: Set the dynamic update cycle of the topology graph. Task nodes update their attributes every 10 seconds, and device and resource nodes update their attributes every 5 seconds. The weight matrix and coupling strength matrix are updated synchronously.

[0036] Further, step S3 includes the following:

[0037] Step S31: Select the Louvain algorithm as the community detection algorithm, and determine the algorithm iteration termination condition as the module degree Q no longer increases;

[0038] Step S32: Execute the local optimization phase of the algorithm, traverse all nodes in the topology graph, calculate the modularity increment ΔQ of moving a node to an adjacent community, and if ΔQ>0, execute the move until the local modularity is optimal;

[0039] Step S33: Execute the algorithm community aggregation phase, treat each locally optimized community as a "super node", calculate the edge weights between super nodes, and construct a simplified topology graph;

[0040] Step S34: Repeat the S32-S33 iteration process until the modularity Q is stable, output the final coupled unit partitioning result, and ensure that the node similarity threshold satisfies Q>0.3;

[0041] Step S35: Quantify the coupling risk level based on the coupling strength matrix: S>0.6 indicates severe coupling, 0.3≤S≤0.6 indicates moderate coupling, and S<0.3 indicates mild coupling.

[0042] Step S36: Locate the coupling propagation path and core nodes using the DFS algorithm, integrate information on coupling units, risk levels, and core nodes, and generate a coupling early warning list.

[0043] Further, step S4 includes the following:

[0044] Step S41: Construct a dual-queue scheduling mechanism framework, including a task priority queue and a resource allocation queue;

[0045] Step S42: Design the task priority queue structure as a binary heap, calculate the comprehensive priority value, the higher the value, the higher the scheduling priority;

[0046] Step S421: Read the base priority P (an integer from 1 to 10) of the target task from the MBSE activity diagram;

[0047] Step S422: Read the real-time original weights w_T(t) of the temporal edges corresponding to the task and the real-time original weights w_R(t) of the resource edges from the weighted directed topology graph;

[0048] Step S423: Substitute the values ​​of P, timing correction term, and resource correction term into the formula S_p=0.4P + 0.3 (1 - w_T (t)) + 0.3 (1 - w_R (t)) to calculate S_P;

[0049] Step S43: Design a resource allocation queue that is categorized by resource type, such as power supply, data bus, storage, etc., stores resource nodes and their current load, and sorts them in ascending order based on this value;

[0050] Step S44: Set the queue update cycle. The task priority queue reads coupling strength data and reorders every 10 seconds, and the resource allocation queue updates load data and adjusts the order every 5 seconds.

[0051] Step S45: Develop scheduling priority rules, prioritizing severely coupled tasks over moderately coupled tasks, and prioritizing high-priority tasks over low-priority tasks;

[0052] Step S46: Pre-detect scheduling conflicts, determine whether there are new conflicts between the task scheduling order and the resource allocation scheme, and if there are conflicts, return to the logic of adjusting the queue sorting.

[0053] Further, step S5 includes the following:

[0054] Step S51: For severe temporal coupling S_T>0.6, a task rearrangement strategy is adopted: extract related tasks within the coupling unit, sort them in descending order of comprehensive priority S_p, and shift the time window of low-priority tasks with S_p>0.5 backward, with the shift starting at [time value missing]. ,in This is the earliest finish time for other tasks within the unit. This is the safe interval time;

[0055] Step S52: For moderate timing coupling where 0.3 < S_T < 0.6, adopt a buffer insertion strategy: Insert a buffer duration between adjacent coupled tasks, which is the sum of the device switching time read from the MBSE state machine diagram and the task pre - preparation time, and directly update the timing parameters of the corresponding tasks in the MBSE activity diagram;

[0056] Step S53: For severe resource coupling where S_R > 1.0, adopt a resource splitting strategy: Determine whether the overloaded resource supports physical splitting. If it does, determine the number of splitting paths n, and allocate the total resource load to the split sub - resources according to the proportion of the resource consumption C_{E,i} of each device. The allocated load is , and add sub - resource nodes to the resource allocation queue;

[0057] Step S54: For moderate resource coupling where 0.8 < S_R < 1. , adopt a timing peak - shifting strategy: Screen the time periods with resource load and the high - consumption tasks that occupy this resource during this period, and migrate these tasks to the low - load period of resource load

[0058] . If a single low - load period cannot accommodate them, split the tasks;

[0059] Step S55: For severe data coupling where S_D > 0.8, adopt a path reconstruction strategy: Use the Dijkstra algorithm to find the congested paths with bandwidth utilization U > 0.8 in the current data transmission path; Search all alternative paths and screen out the paths with bandwidth utilization ; Calculate the comprehensive weight of each alternative path, where is the number of path hops, is the matching degree of the path and the data requirement; Select the path with the highest for reconstruction, and update the data interface configuration in the topology diagram and the MBSE module definition diagram;

[0060] Step S56: For device conflicts where multiple tasks preempt the same device, adopt a device replacement strategy: Calculate the performance matching degree of each alternative device and the conflicting device, select the alternative device with the highest matching degree and an available time window that can cover the task period for replacement, and verify that no new conflicts occur after replacement.

[0061] Furthermore, Step S6 includes the following:

[0062] Step S61: Perform simulation verification. Load the MBSE model and the topology diagram on the simulation platform, run a full - cycle test simulation, and record basic data such as coupling recognition accuracy and conflict resolution rate;

[0063] Step S62: Perform physical verification by deploying the test system in the physical prototype environment, collecting sensor data such as equipment load and data transmission rate, and verifying parameter compliance.

[0064] Step S63: Perform practical verification by injecting dynamic disturbances such as device failure and task insertion to test the robustness of the method in complex scenarios;

[0065] Step S64: Calculate the evaluation metrics, including the temporal coupling resolution rate, resource coupling control rate, and data coupling optimization rate;

[0066] Step S65: Determine the verification effect. If all indicators meet the first-level standard, the verification is passed; otherwise, return to S1 to adjust the MBSE model or S2 to optimize the weight parameters.

[0067] Step S66: After verifying and solidifying the MBSE model parameters and topology mapping rules, generate a decoupling optimization report to form a reusable test process template.

[0068] According to a second aspect of the present invention, a system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE includes an electronic device, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements a method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE as described in any one of the present invention.

[0069] According to a third aspect of the present invention, a system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE includes a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements a method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE as described in any one of the present invention.

[0070] The present invention has the following advantages:

[0071] The meta-model construction method for aerospace equipment based on SysML definitions proposed in this invention overcomes many problems in traditional aerospace equipment design methods, such as inconsistent information transmission, low modeling efficiency, and insufficient adaptability to mission environments. By leveraging standardized ontology description elements and meta-language mapping relationships, this invention can uniformly define the functional attributes, dynamic behaviors, and performance indicators of aerospace equipment, making the model expression clearer and more consistent, and effectively reducing the difficulty of collaboration between different teams and subsystems. By introducing mission environment stereotypes, this invention achieves accurate modeling of key environmental parameters such as temperature, pressure, composition, and gravity, enabling aerospace equipment to better adapt to various complex mission scenarios.

[0072] Furthermore, this invention constructs a modular aerospace equipment model library, covering subsystems such as propulsion, navigation, communication, survivability, detection, and intelligence, providing a solid foundation for the rapid combination, reuse, and expansion of systems. This modular design method not only reduces the workload of repetitive modeling but also significantly improves the efficiency and flexibility of equipment development. Simultaneously, through the verification and optimization of the meta-model, this invention enables the model to meet multi-mission requirements while possessing higher scalability and adaptability. Compared to traditional document-driven methods and non-standardized modeling approaches, the construction method of this invention can more efficiently address the complex requirements of aerospace missions, accelerating the iterative process of equipment design and verification.

[0073] Therefore, this invention achieves end-to-end optimization of aerospace equipment design, from conceptual modeling to performance verification. This improves design efficiency while ensuring model accuracy and mission adaptability, laying a technological foundation for the multi-scenario application and rapid deployment of future aerospace equipment. Through this innovative method, the systematic nature, operability, and mission success rate of aerospace equipment are significantly enhanced. Attached Figure Description

[0074] Figure 1 This is a flowchart of a method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE, as proposed in this invention.

[0075] Figure 2 This is a schematic diagram illustrating the verification and optimization process of a method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE proposed in this invention.

[0076] Figure 3 This is a flowchart of the steps proposed in this invention. Detailed Implementation

[0077] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0078] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0079] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0080] like Figures 1 to 3As shown, this invention proposes a method and system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE, including the following:

[0081] This invention proposes a method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE, characterized by the following:

[0082] Step S1: Construct the MBSE multi-view model and complete the structuring of the core elements of the experimental system;

[0083] Step S2: Convert the MBSE multi-view model into a dynamically weighted directed graph and generate the coupling strength matrix;

[0084] Step S3: Use a community detection algorithm to divide the coupling units and quantify the coupling risk level;

[0085] Step S4: Construct a dual-queue scheduling mechanism and determine the scheduling logic for tasks and resources;

[0086] Step S5: Execute decoupling optimization strategies for the three types of coupling: timing, resources, and data.

[0087] Step S6: Evaluate the decoupling effect through a three-level verification closed loop and iteratively update the model parameters.

[0088] Further, step S1 includes the following:

[0089] Step S11: Classify the core elements of the test system into three categories: test tasks, test equipment, and auxiliary resources;

[0090] Step S12: Design the core view types of the MBSE multi-view model, including module definition diagram, internal block diagram, state machine diagram, and activity diagram;

[0091] Step S13: Determine the attribute parameters of various core elements, including the task element including Task_ID, priority P, time window [ts,te], the device element including Equip_ID, performance parameter set, resource consumption, and the resource element including Res_ID, rated capacity, current load, and splittable identifier.

[0092] Step S14: Conduct interface standardization design for the MBSE model, and design three types of interaction interfaces for the module definition diagram: requirements, supply, and data flow.

[0093] Step S15: Perform static consistency verification of the MBSE model to verify that the task timings do not overlap, the total consumption of equipment resources does not exceed the limit, and the equipment performance matches the task requirements.

[0094] Step S16: Store the verified MBSE model and establish a real-time data interface between the model and the topology transformation module.

[0095] Further, step S2 includes the following:

[0096] Step S21: Formulate bidirectional mapping rules between the MBSE model and the topology graph, and clarify the correspondence between elements and nodes, i.e., relationships and edges;

[0097] Step S22: Construct a topology graph node set, mapping test tasks to task nodes, devices to device nodes, and resources to resource nodes;

[0098] Step S23: Construct a topology graph edge set, mapping task time-series dependencies to time-series edges, device and resource requirements to resource edges, and device data interactions to data edges;

[0099] Step S24: Calculate edge weights and construct a weight matrix, with time-series weights arranged according to... Calculation, resource weight according to Calculation, data weights according to calculate;

[0100] Step S25: Generate a coupling strength matrix based on the weight matrix. Use multi-round feedback calibration by experts in the experimental field to determine the time-series weight coefficient α, resource weight coefficient β, and data weight coefficient γ. The α, β, and γ satisfy α+β+γ=1. After normalizing the three weights, calculate the coupling strength by weighted sum according to the determined weights.

[0101] Step S251: Experts independently score α, β, and γ based on the "degree of influence of temporal coupling, resource coupling, and data coupling on the stability of the experimental process", with the initial scoring range limited to [0,1].

[0102] Step S252: Calculate the standard deviation σ1 of the first round of scoring according to the sample standard deviation formula. If σ1≤0.05, proceed to sub-step S254; if σ1>0.05, proceed to sub-step S253.

[0103] Step S253: Analyze the weighted items with significant scoring differences (σ1>0.2), organize a technical seminar with experts, and report the difference data and the corresponding impact analysis basis; the experts adjust the scores based on the seminar results and submit the second scoring results.

[0104] Step S254: Repeat sub-steps 252-253 until σ≤0.05, confirming calibration convergence.

[0105] Step S26: Set the dynamic update cycle of the topology graph. Task nodes update their attributes every 10 seconds, and device and resource nodes update their attributes every 5 seconds. The weight matrix and coupling strength matrix are updated synchronously.

[0106] Further, step S3 includes the following:

[0107] Step S31: Select the Louvain algorithm as the community detection algorithm, and determine the algorithm iteration termination condition as the module degree Q no longer increases;

[0108] Step S32: Execute the local optimization phase of the algorithm, traverse all nodes in the topology graph, calculate the modularity increment ΔQ of moving a node to an adjacent community, and if ΔQ>0, execute the move until the local modularity is optimal;

[0109] Step S33: Execute the algorithm community aggregation phase, treat each locally optimized community as a "super node", calculate the edge weights between super nodes, and construct a simplified topology graph;

[0110] Step S34: Repeat the S32-S33 iteration process until the modularity Q is stable, output the final coupled unit partitioning result, and ensure that the node similarity threshold satisfies Q>0.3;

[0111] Step S35: Quantify the coupling risk level based on the coupling strength matrix: S>0.6 indicates severe coupling, 0.3≤S≤0.6 indicates moderate coupling, and S<0.3 indicates mild coupling.

[0112] Step S36: Locate the coupling propagation path and core nodes using the DFS algorithm, integrate information on coupling units, risk levels, and core nodes, and generate a coupling early warning list.

[0113] Further, step S4 includes the following:

[0114] Step S41: Construct a dual-queue scheduling mechanism framework, including a task priority queue and a resource allocation queue;

[0115] Step S42: Design the task priority queue structure as a binary heap, calculate the comprehensive priority value, the higher the value, the higher the scheduling priority;

[0116] Step S421: Read the base priority P (an integer from 1 to 10) of the target task from the MBSE activity diagram;

[0117] Step S422: Read the real-time original weights w_T(t) of the temporal edges corresponding to the task and the real-time original weights w_R(t) of the resource edges from the weighted directed topology graph;

[0118] Step S423: Substitute the values ​​of P, timing correction term, and resource correction term into the formula S_p=0.4P + 0.3 (1 - w_T (t)) + 0.3 (1 - w_R (t)) to calculate S_P;

[0119] Step S43: The design resource allocation queue is classified by resource type, such as power supply, data bus, storage, etc., stores the resource nodes and the current load, and sorts them in ascending order according to this value;

[0120] Step S44: Set the queue update period. The task priority queue reads the coupling strength data and re-sorts every 10s, and the resource allocation queue updates the load data and adjusts the order every 5s;

[0121] Step S45: Formulate the scheduling priority rules. Severely coupled tasks take precedence over moderately coupled tasks, and high-priority tasks take precedence over low-priority tasks;

[0122] Step S46: Pre-detect scheduling conflicts, and determine whether there are new conflicts between the task scheduling order and the resource allocation plan. If there are conflicts, return to adjust the queue sorting logic.

[0123] Further, Step S5 includes the following contents:

[0124] Step S51: For severe timing coupling S_T>0.6, adopt the task rearrangement strategy: extract the associated tasks within the coupling unit, sort them in descending order according to the comprehensive priority S_p, and move the time window of the low-priority tasks with S_p>0.5 backward. The starting time of the movement is , where is the earliest end time of other tasks within the unit, is the safety interval time;

[0125] Step S52: For moderate timing coupling 0.3<S_T<0.6, adopt the buffer insertion strategy: insert a buffer duration between adjacent coupling tasks. This duration is the sum of the device switching time and the task pre-preparation time read from the MBSE state machine diagram, and directly update the timing parameters of the corresponding tasks in the MBSE activity diagram;

[0126] Step S53: For severe resource coupling S_R>1.0, adopt the resource splitting strategy: determine whether the overloaded resource supports physical splitting. If it supports, determine the number of splitting paths n, and allocate the total resource load to the split sub-resources according to the proportion of the resource consumption C_{E,i} of each device. The allocated load is , and add sub-resource nodes to the resource allocation queue;

[0127] Step S54: For moderate resource coupling 0.8<S_R<1.0, adopt the timing peak-shifting strategy: screen the periods with resource load and the high-consumption tasks occupying this resource during this period, and migrate these tasks to the low-load period of resource load . If a single low-load period cannot accommodate them, split the tasks;

[0128] Step S55: For severe data coupling S_D>0.8, a path reconstruction strategy is adopted: Dijkstra's algorithm is used to find congested paths in the current data transmission path with bandwidth utilization U>0.8; all alternative paths are searched, and the path with the highest bandwidth utilization is selected. The path; calculate the comprehensive weight of each alternative path. ,in This represents the number of hops in the path. To determine the degree of matching between the path and data requirements; select The highest path is reconstructed, and the data interface configuration in the topology graph and MBSE module definition graph is updated;

[0129] Step S56: For device conflicts where multiple tasks preempt the same device, adopt a device replacement strategy: calculate the performance matching degree between each alternative device and the conflicting device. Select the alternative device with the highest matching degree and whose available time window can cover the task period for replacement, and verify that no new conflicts are generated after replacement.

[0130] Further, step S6 includes the following:

[0131] Step S61: Perform simulation verification. Load the MBSE model and topology diagram into the simulation platform, run the full-cycle experimental simulation, and record basic data such as coupling recognition accuracy and conflict resolution rate.

[0132] Step S62: Perform physical verification by deploying the test system in the physical prototype environment, collecting sensor data such as equipment load and data transmission rate, and verifying parameter compliance.

[0133] Step S63: Perform practical verification by injecting dynamic disturbances such as device failure and task insertion to test the robustness of the method in complex scenarios;

[0134] Step S64: Calculate the evaluation metrics, including the temporal coupling resolution rate, resource coupling control rate, and data coupling optimization rate;

[0135] Step S65: Determine the verification effect. If all indicators meet the first-level standard, the verification is passed; otherwise, return to S1 to adjust the MBSE model or S2 to optimize the weight parameters.

[0136] Step S66: After verifying and solidifying the MBSE model parameters and topology mapping rules, generate a decoupling optimization report to form a reusable test process template.

[0137] According to a second aspect of the present invention, a system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE includes an electronic device, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements a method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE as described in any one of the present invention.

[0138] According to a third aspect of the present invention, a system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE includes a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements a method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE as described in any one of the present invention.

[0139] In addition to the above, the present invention also has related embodiments, including the following:

[0140] To verify the feasibility and effectiveness of this invention in practical applications, the "Complex Equipment Test Coupling Decoupling and Process Optimization Method Based on MBSE and Graph Theory" proposed in this invention was applied to the full-performance verification test research of a certain type of vehicle-mounted radar equipment. This research needs to simulate the scenario of radar performing multiple tasks in parallel, from basic detection to anti-interference and environmental adaptability. It focuses on solving three types of coupling problems during the test: timing conflicts, resource conflicts, and data conflicts. At the same time, it verifies the adaptability of the method under different task priorities and equipment parameter constraints.

[0141] To address this issue, the method proposed in this invention is introduced into the radar test simulation. By constructing an MBSE multi-view model covering 7 core test tasks (T01-T07) and 6 types of key equipment, a dynamic mapping relationship between elements and graph topology is established. Decoupling operations are performed in conjunction with a dual-queue scheduling strategy, and performance verification is completed through simulation and physical parameter verification. This achieves adaptability verification under multiple task priorities, multiple equipment parameters, and multiple conflict types, ensuring that the method can cover the full-process control requirements of radar tests.

[0142] In application, the MBSE multi-view model is first constructed. Based on the provided test tasks and equipment parameters, core element attributes are defined: at the test task level, key parameters of relevant equipment and test tasks are clarified. Based on the above information, four types of SysML views are drawn: a module definition diagram divides the test into three top-level modules: "Test Task Group," "Equipment Management Group," and "Resource Scheduling Group," clarifying the affiliation of relevant tasks; resource interaction thresholds for internal block icons; a state machine diagram defines state transition logic and associates the triggering conditions for fault injection into equipment; and an activity diagram sorts out the pre-dependencies of test and reserves task rescheduling interfaces to handle conflicts.

[0143] After the model is built, dynamic mapping between MBSE and graph topology is performed. According to element and node rules, T01-T07 are mapped as task nodes, transmitting the time window and priority of each task; devices are mapped as device nodes, transmitting core parameters such as operating frequency and target simulation quantity; auxiliary resources such as power supply modules and data transmission buses are mapped as resource nodes, transmitting bandwidth and load thresholds. According to staker rules, the time dependency between T01 and T02 is mapped as a time edge, indicating the connection between the end time of T01 and the start time of T02; the device requirement of T01 for AWR1843 is mapped as a resource edge, indicating the device occupancy time; the interaction between AWR1843 and the data logger is mapped as a data edge, indicating the data transmission frequency. The mapping interface achieves data synchronization, with task nodes updating their status every 10 seconds and device nodes updating their parameters every 5 seconds, ensuring that the topology graph reflects the experimental dynamics in real time.

[0144] Simulation results demonstrate that the method of this invention significantly improves the efficiency and accuracy of radar test environment adaptation verification. After decoupling, the total number of various coupling conflicts is greatly reduced, the stability of the test process is significantly improved, and it can adapt to different equipment parameter constraints. Within a parameter deviation range of ±10%, the decoupling strategy can still be effectively executed without new conflicts. Simultaneously, the method's dynamic response capability meets the test requirements. When a fault injection task is temporarily added in T07, the system completes model updates, topology remapping, and queue reordering within 8 seconds, ensuring uninterrupted test flow.

[0145] Furthermore, the closed-loop mechanism of this invention enables effective feedback between experimental data and optimization strategies, providing reusable parameter templates for subsequent similar radar tests. Simultaneously, matching rules formed during the decoupling process can be incorporated into the experimental knowledge base, improving the planning efficiency of subsequent tests.

[0146] Table 1. Equipment Parameters and Test Task Related Data

[0147] Equipment Name Key equipment parameters Test identification methods AWR1843 Radar Sensor Operating frequency: 76 GHz to 81 GHz; Phase noise: –95 dBc / Hz @ 1 MHz (76-77 GHz); Transmit power: 12 dBm As the core equipment under test, it verifies indicators such as detection range error, target resolution capability, and anti-interference performance. R&S®QAR Vehicle Radar Testing System Target simulation: Simulates up to 512 objects simultaneously; Distance: Closest to 0.4m, furthest to over 400m; Distance resolution: Up to 3.5cm. Simulate multi-target scenarios to verify the radar's multi-target resolution capability and target-free confusion performance. Rohde & Schwarz FSW Spectrum Analyzer Frequency range: 2 Hz to 44 GHz; Sensitivity (DANL): Typical -172 dBm (at 1 GHz) Analyze the radar signal spectrum characteristics and noise floor to verify the phase noise ≤ -95dBc / Hz@1MHz specification. Signal source SMW200A Frequency range: 100 kHz to 40 GHz; Output power: typical >18 dBm (at 4 GHz); Phase noise: -142 dBc / Hz Simulating an active jamming environment, the radar's target detection probability decreased by no more than 10% under jamming conditions. R&S® ATS1500 target simulator Frequency range: 75 GHz to 82 GHz; Analog distance: 0 m to 387 m; Analog speed: -400 km / h to +200 km / h Simulate dynamic targets (accelerating / decelerating) to verify that the radar position tracking error is <±0.5m and the velocity tracking error is <±1km / h. Keysight 34972A data logger Channels: 3-slot LXI data acquisition unit; Temperature measurement accuracy: ±0.15°C (thermocouple T-type) Continuously record radar performance parameters and ambient temperature and humidity data to verify environmental adaptability and testability indicators. Temperature and humidity test chamber Temperature range: -40°C to 85°C; Humidity range: 10%-95% RH Provide extreme environmental conditions to verify that the radar's performance does not exceed 150% of the T01 permissible error in temperatures ranging from -40°C to 85°C. Fault injection equipment Fault type: power fluctuation, signal distortion; injection accuracy: ±0.1V (voltage fault) In the event of an injection device malfunction, the radar fault detection rate was verified to be ≥95%, the fault isolation rate ≥85%, and the false alarm rate ≤5%.

[0148] Table 2 Comparison of Experimental Verification Results Indicators

[0149] Indicator Categories Specific indicators Before decoupling After decoupling Optimization rate Conflict Quantification Indicators Total number of conflicts 6.3 1.1 82.5% Timing conflict 2.3 0.7 69.6% Resource conflict volume 2.5 0.1 96% Data conflict volume 1.5 0.4 73.3% Process efficiency metrics Total test duration (minutes) 1500 1470 2.0% Resource overload count 8 0 100%

[0150] As shown in Table 1, under traditional methods, the matching of equipment parameters with test tasks relies on manual judgment, which easily leads to problems such as "high-parameter equipment matching low-demand tasks" or "equipment parameters not meeting task requirements," resulting in wasted test resources or failure of indicator verification. In contrast, the method proposed in this invention can automatically identify the matching degree between equipment parameters and task requirements through the weight calculation of the "equipment-task" attribute association and topology mapping in the MBSE model. At the same time, by calculating the weight of resource edge E_R(t), the high load risk of AWR1843 can be identified in advance, providing data support for subsequent resource conflict decoupling and providing strong support for equipment selection and resource scheduling in radar tests.

[0151] Table 2 shows the significant differences between the traditional method and the method of this invention in terms of conflict resolution and process efficiency. In the radar test propulsion system, the total number of integrated conflicts before decoupling reached 6.3, of which the resource conflict was the highest at 2.5, mainly due to the multi-task preemption of core equipment such as AWR1843 and R&S®QAR, resulting in 8 resource overloads; the timing conflict of 2.3 was concentrated in the overlapping time windows of T02 and T04, and T05 and T06; the data conflict of 1.5 was due to the data flow preemption of the bus in T03 and T06, ultimately resulting in a total test duration of 1500 minutes. The method of this invention significantly reduces various conflict rates through time-series rearrangement of dual-queue scheduling, time-sharing resource allocation, and data path reconstruction: resource conflict rate drops from 2.5 to 0.1, an optimization rate of 96%, and resource overload counts are reduced to zero; the optimization rate for time-series conflicts is 69.6%, for data conflicts 73.3%, and for the overall total conflict rate 82.5%; the total test duration is shortened by 30 minutes, with an optimization rate of 2.0%. These data demonstrate that, through the combination of MBSE's structured modeling, dynamic coupling identification of graph theory topology, and dual-queue decoupling strategy, the method of this invention exhibits superior performance in conflict resolution accuracy, resource utilization, and process stability, especially in load management of core equipment, achieving a shift from "passively responding to overload" to "actively preventing conflicts."

[0152] The above analysis clearly demonstrates the advantages of this invention in terms of coupling identification accuracy and process optimization efficiency in radar testing. Table 1 clearly shows that the matching accuracy of the method presented in this invention is significantly higher than that of traditional manual methods, avoiding test errors caused by equipment mismatch. Table 2 further proves the significant improvement in conflict resolution effectiveness and resource utilization efficiency of this invention: a 96% source conflict optimization rate and zero overload cycles mean that the service life of core equipment is extended, indirectly reducing test costs; the overall conflict optimization rate of 82.5% reduces the number of test interruptions, improving the continuity and reliability of test data. Furthermore, the dynamic response capability and parameter reusability of this invention not only provide a standardized template for the test design of radar equipment in the same series, reducing repetitive development time, but also greatly reduce the operational complexity for test personnel, shifting test management from "experience-driven" to "data-driven," laying the foundation for the intelligent upgrade of complex equipment testing.

[0153] In one embodiment of the present invention, the code table for calculating S_P in step S423 of step S4 is as follows:

[0154] Table 3 Code table for step S423

[0155] Step S423 Code Table <![CDATA[Input: MBSE_Model (contains task / equipment / resource params),UpdateCycle (time interval for graph update, s)Output:CouplingRiskSet (set of tasks with high coupling risk)1: / / Initialize core data from MBSE model2: Extract TaskSet T = {T1, T2,..., Tn} from MBSE_Model (each Tᵢ has Task_ID, [ts,te], priority P)3: Extract ResourceSet R = {R1, R2, ..., Rm} from MBSE_Model (each Rⱼhas Res_ID, capacity, L(t))4: Build initial topology graph G = (V(t), E(t), W(t)):5: V(t) = TaskNodes(T) ∪ ResourceNodes(R) / / Task&resource nodes6: E(t) = TimingEdges(T) ∪ ResourceEdges(T,R) / / Timing&resource dependency edges7: W(t) = CalculateWeights(T,R) / / Compute weights via Eqs.(1)-(3)8: / / Dynamic coupling detectionloop9: while Experiment is running do10: Wait for UpdateCycle11:Update real-time data: L(t) of R, [ts,te] of T12: Adjust W(t) of Gusing updated data13: Call Louvain Algorithm to cluster nodes intocommunities C = {C1, C2, ..., C k}14: For each community Cᵢ in C:15:Compute coupling strength CS = Σ(edge weights in Cᵢ) / |Cᵢ|16: IfCS ≥ RiskThreshold (corresponds to 3-5 level risk):17: Add alltasks in Cᵢ to CouplingRiskSet18: end while19: returnCouplingRiskSet]]>

[0156] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE, characterized in that, Includes the following: Step S1: Construct the MBSE multi-view model and complete the structuring of the core elements of the experimental system; Step S2: Convert the MBSE multi-view model into a dynamically weighted directed graph and generate the coupling strength matrix; Step S3: Use a community detection algorithm to divide the coupling units and quantify the coupling risk level; Step S4: Construct a dual-queue scheduling mechanism and determine the scheduling logic for tasks and resources; Step S5: Execute decoupling optimization strategies for the three types of coupling: timing, resources, and data. Step S6: Evaluate the decoupling effect through a three-level verification closed loop and iteratively update the model parameters.

2. The method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE as described in claim 1, characterized in that, Step S1 includes the following: Step S11: Classify the core elements of the test system into three categories: test tasks, test equipment, and auxiliary resources; Step S12: Design the core view types of the MBSE multi-view model, including module definition diagram, internal block diagram, state machine diagram, and activity diagram; Step S13: Determine the attribute parameters of various core elements, including the task element including Task_ID, priority P, time window [ts,te], the device element including Equip_ID, performance parameter set, resource consumption, and the resource element including Res_ID, rated capacity, current load, and splittable identifier. Step S14: Conduct interface standardization design for the MBSE model, and design three types of interaction interfaces for the module definition diagram: requirements, supply, and data flow. Step S15: Perform static consistency verification of the MBSE model to verify that the task timings do not overlap, the total consumption of equipment resources does not exceed the limit, and the equipment performance matches the task requirements. Step S16: Store the verified MBSE model and establish a real-time data interface between the model and the topology transformation module.

3. The method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE as described in claim 1, characterized in that, Step S2 includes the following: Step S21: Formulate bidirectional mapping rules between the MBSE model and the topology graph, and clarify the correspondence between elements and nodes, i.e., relationships and edges; Step S22: Construct a topology graph node set, mapping test tasks to task nodes, devices to device nodes, and resources to resource nodes; Step S23: Construct a topology graph edge set, mapping task time-series dependencies to time-series edges, device and resource requirements to resource edges, and device data interactions to data edges; Step S24: Calculate edge weights and construct a weight matrix, with time-series weights arranged according to... Calculation, resource weight according to Calculation, data weights according to calculate; Step S25: Generate a coupling strength matrix based on the weight matrix. Use multi-round feedback calibration by experts in the experimental field to determine the time-series weight coefficient α, resource weight coefficient β, and data weight coefficient γ. The α, β, and γ satisfy α+β+γ=1. After normalizing the three weights, calculate the coupling strength by weighted sum according to the determined weights. Step S251: Experts independently score α, β, and γ based on the "influence of temporal coupling, resource coupling, and data coupling on the stability of the experimental process", with the initial scoring range limited to [0,1]. Step S252: Calculate the standard deviation σ1 of the first round of scoring according to the sample standard deviation formula. If σ1≤0.05, proceed to sub-step S254; if σ1>0.05, proceed to sub-step S253. Step S253: Analyze the weighted items with significant scoring differences (σ1>0.2), organize a technical seminar with experts, and report the difference data and the corresponding impact analysis basis; the experts adjust the scores based on the seminar results and submit the second scoring results; Step S254: Repeat sub-steps 252-253 until σ≤0.05, confirming calibration convergence; Step S26: Set the dynamic update cycle of the topology graph. Task nodes update their attributes every 10 seconds, and device and resource nodes update their attributes every 5 seconds. The weight matrix and coupling strength matrix are updated synchronously.

4. The method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE as described in claim 1, characterized in that, Step S3 includes the following: Step S31: Select the Louvain algorithm as the community detection algorithm, and determine the algorithm iteration termination condition as the module degree Q no longer increases; Step S32: Execute the local optimization phase of the algorithm, traverse all nodes in the topology graph, calculate the modularity increment ΔQ of moving a node to an adjacent community, and if ΔQ>0, execute the move until the local modularity is optimal; Step S33: Execute the algorithm community aggregation phase, treat each locally optimized community as a "super node", calculate the edge weights between super nodes, and construct a simplified topology graph; Step S34: Repeat the S32-S33 iteration process until the modularity Q is stable, output the final coupled unit partitioning result, and ensure that the node similarity threshold satisfies Q>0.3; Step S35: Quantify the coupling risk level based on the coupling strength matrix: S>0.6 indicates severe coupling, 0.3≤S≤0.6 indicates moderate coupling, and S<0.3 indicates mild coupling. Step S36: Locate the coupling propagation path and core nodes using the DFS algorithm, integrate information on coupling units, risk levels, and core nodes, and generate a coupling early warning list.

5. The method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE according to claim 1, characterized in that, Step S4 includes the following: Step S41: Construct a dual-queue scheduling mechanism framework, including a task priority queue and a resource allocation queue; Step S42: Design the task priority queue structure as a binary heap, calculate the comprehensive priority value, the higher the value, the higher the scheduling priority; Step S421: Read the base priority P (an integer from 1 to 10) of the target task from the MBSE activity diagram; Step S422: Read the real-time original weights w_T(t) of the temporal edges corresponding to the task and the real-time original weights w_R(t) of the resource edges from the weighted directed topology graph; Step S423: Substitute the values ​​of P, timing correction term, and resource correction term into the formula S_p=0.4P + 0.3 (1 - w_T (t)) + 0.3 (1 - w_R (t)) to calculate S_P; Step S43: Design a resource allocation queue that is categorized by resource type, such as power supply, data bus, storage, etc., stores resource nodes and their current load, and sorts them in ascending order based on this value; Step S44: Set the queue update cycle. The task priority queue reads coupling strength data and reorders every 10 seconds, and the resource allocation queue updates load data and adjusts the order every 5 seconds. Step S45: Develop scheduling priority rules, prioritizing severely coupled tasks over moderately coupled tasks, and prioritizing high-priority tasks over low-priority tasks; Step S46: Pre-detect scheduling conflicts, and determine whether there are new conflicts between the task scheduling order and the resource allocation scheme. If there are conflicts, return to adjust the queue sorting logic.

6. The method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE as described in claim 1, characterized in that, Step S5 includes the following: Step S51: For severe temporal coupling S_T>0.6, a task rearrangement strategy is adopted: extract related tasks within the coupling unit, sort them in descending order of comprehensive priority S_p, and shift the time window of low-priority tasks with S_p>0.5 backward, with the shift starting at [time value missing]. ,in This is the earliest finish time for other tasks within the unit. This is the safe interval time; Step S52: For medium time-series coupling where 0.3 < S_T < 0.6, adopt a buffer insertion strategy: insert a buffer duration between adjacent coupled tasks, which is the sum of the device switching time and the task pre-preparation time read from the MBSE state machine diagram, and directly update the time-series parameters of the corresponding tasks in the MBSE activity diagram; Step S53: For severe resource coupling S_R>1.0, adopt a resource splitting strategy: determine whether the overloaded resource supports physical splitting; if it does, determine the number of splitting paths n, and reduce the total resource load. The resources are allocated to the split sub-resources according to the proportion of each device's resource consumption C_{E,i}, and the load is distributed as follows: And add a sub-resource node to the resource allocation queue; Step S54: For moderate resource coupling where 0.8 < S_R < 1.0, adopt a time-series peak-shifting strategy: Screen the time periods with resource load and the high-consumption tasks that occupy the resource during this time period, and migrate these tasks to the resource load ; During the low valley period, if a single low valley period cannot accommodate it, split the task; Step S55: For severe data coupling S_D>0.8, a path reconstruction strategy is adopted: Dijkstra's algorithm is used to find congested paths in the current data transmission path with bandwidth utilization U>0.8; all alternative paths are searched, and the path with the highest bandwidth utilization is selected. The path; calculate the comprehensive weight of each alternative path. ,in This represents the number of hops in the path. To determine the degree of matching between the path and data requirements; select The highest path is reconstructed, and the data interface configuration in the topology graph and MBSE module definition graph is updated; Step S56: For device conflicts where multiple tasks preempt the same device, adopt a device replacement strategy: calculate the performance matching degree between each alternative device and the conflicting device. Select the alternative device with the highest matching degree and whose available time window can cover the task period for replacement, and verify that no new conflicts are generated after replacement.

7. The method for decoupling and streamlining complex relationships in complex equipment testing based on MBSE according to claim 1, characterized in that, Step S6 includes the following: Step S61: Perform simulation verification. Load the MBSE model and topology diagram on the simulation platform, run the full-cycle test simulation, and record basic data such as coupling recognition accuracy and conflict resolution rate; Step S$62: Perform physical verification. Deploy the test system in the physical prototype environment, collect sensor data such as device load and data transmission rate, and verify parameter compliance; Step S63: Perform actual combat verification. Inject dynamic disturbances such as device failures and task insertions to test the robustness of the method in complex scenarios; Step S64: Calculate evaluation indicators, calculate the time-series coupling elimination rate, resource coupling control rate, and data coupling optimization rate; Step S65: Determine the verification effect. If all indicators meet the first-level standard, it passes; otherwise, return to S1 to adjust the MBSE model or S2 to optimize the weight parameters; Step S66: After passing the verification, solidify the MBSE model parameters and topology mapping rules, generate a decoupling optimization report, and form a reusable test process template.

8. A system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE, comprising an electronic device, wherein the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a method for decoupling complex relationships and processes in complex equipment tests based on MBSE as described in any one of claims 1 to 7.

9. A system for decoupling and streamlining complex relationships in complex equipment testing based on MBSE, comprising a computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for decoupling complex relationships and processes in complex equipment tests based on MBSE as described in any one of claims 1 to 7.

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