CN-122018905-A - MBSE-based complex relation decoupling and flow method and system for complex equipment test

CN122018905ACN 122018905 ACN122018905 ACN 122018905ACN-122018905-A

Abstract

The invention provides a method and a system for decoupling complex relation and flow of a complex equipment test based on MBSE, which comprise the steps of S1, constructing MBSE a multi-view model and finishing structural processing of core elements of the test system, S2, converting the MBSE multi-view model into a dynamic weighted directed graph and generating a coupling strength matrix, S3, dividing a coupling unit by adopting a community discovery algorithm and quantifying coupling risk level, S4, constructing a double-queue scheduling mechanism and determining scheduling logic of tasks and resources, S5, respectively executing decoupling optimization strategies for three types of coupling of time sequence, resources and data, and S6, evaluating decoupling effects through three-level verification closed loops and iteratively updating model parameters.

Inventors

HUANG ZHEKANG
YUAN WENQIANG
WANG BING
NIU BIAO
LU JIANFENG

Assignees

杭州电子科技大学
杭州电子科技大学上虞科学与工程研究院有限公司

Dates

Publication Date: 20260512
Application Date: 20260131

Claims (9)

1. A method for decoupling complex relation and flow of complex equipment test based on MBSE is characterized by comprising the following steps: step S1, constructing MBSE multi-view models and finishing the structuring treatment of the core elements of the test system; s2, converting MBSE multi-view models into dynamic weighted directed graphs and generating a coupling strength matrix; Step S3, dividing the coupling units by adopting a community discovery algorithm and quantifying the coupling risk level; S4, constructing a double-queue scheduling mechanism and determining scheduling logic of tasks and resources; step S5, respectively executing decoupling optimization strategies aiming at three types of coupling of time sequence, resource and data; and S6, evaluating decoupling effect through three-stage verification closed loop and iteratively updating model parameters.
2. The method for decoupling complex relationships and flow of complex equipment testing based on MBSE of claim 1, wherein step S1 comprises the following steps: Step S11, dividing the core element types of the test system, wherein the clear elements are three types of test tasks, test equipment and auxiliary resources; Step S12, designing MBSE core view types of the multi-view model, wherein the core view types comprise a module definition diagram, an internal block diagram, a state machine diagram and an activity diagram; Step S13, determining attribute parameters of various core elements, wherein Task elements comprise task_ID, priority P, time window [ ts, te ], equipment elements comprise Equip _ID, performance parameter set and resource consumption, and resource elements comprise Res_ID, rated capacity, current load and splittability identification; s14, developing an interface standardized design of MBSE models, and defining three types of interaction interfaces of a diagram design requirement, supply and data flow for a module; Step S15, executing MBSE static consistency verification of the model, wherein the verification task time sequence is not overlapped, the total equipment resource consumption is not overrun, and the equipment performance is matched with the task requirement; And S16, storing MBSE models which pass verification, and establishing a real-time data docking interface of the models and the topology conversion module.
3. The method for decoupling complex relationships and flow of complex equipment testing based on MBSE of claim 1, wherein step S2 comprises the following steps: step S21, formulating MBSE a bidirectional mapping rule of the model and the topological graph, and determining the corresponding relation between elements and nodes, namely the relation and edges; S22, constructing a topological graph node set, mapping test tasks into task nodes, mapping equipment into equipment nodes and mapping resources into resource nodes; step S23, constructing a topological graph edge set, mapping task time sequence dependency to a time sequence edge, mapping equipment and resource requirements to a resource edge, and mapping equipment data interaction to a data edge; step S24, calculating edge weights and constructing a weight matrix, wherein the time sequence weights are according to Calculating, and weighting the resources according to Calculating, and pressing data weight Calculating; Step S25, generating a coupling strength matrix based on the weight matrix, adopting experimental field expert multi-round feedback calibration to determine a time sequence weight coefficient alpha, a resource weight coefficient beta and a data weight coefficient gamma, wherein the alpha, the beta and the gamma meet the conditions of alpha+beta+gamma=1, normalizing the three weights, and calculating the coupling strength according to the determined weights; Step S251, the expert independently scores alpha, beta and gamma based on the influence degree of time sequence coupling, resource coupling and data coupling on the stability of the test flow, and the initial scoring range is limited to be [0,1]; Step S252, calculating standard deviation sigma 1 of first round scoring according to a sample standard deviation formula, if sigma 1 is less than or equal to 0.05, entering a substep S254, and if sigma 1 is more than 0.05, entering a substep S253; Step 253, counting weight items with larger scoring difference (sigma 1 > 0.2), organizing expert to hold technical seminar, notifying the difference data and corresponding influence analysis basis, adjusting scoring by expert in combination with seminar result, and submitting secondary scoring result; step S254, repeating the substeps 252-253 until sigma is less than or equal to 0.05, and confirming calibration convergence; Step S26, setting a dynamic update period of the topological graph, updating the attribute of each 10S of the task node, updating the attribute of each 5S of the equipment and the resource node, and synchronously updating the weight matrix and the coupling strength matrix.
4. The method for decoupling complex relationships and flow of complex equipment testing based on MBSE of claim 1, wherein step S3 comprises the following steps: Step S31, selecting a community discovery algorithm as a Louvain algorithm, and determining an algorithm iteration termination condition that the module degree Q is not improved; Step S32, executing an algorithm local optimization stage, traversing all nodes of the topological graph, and executing movement when the calculated nodes move to the module degree increment delta Q of adjacent communities and delta Q >0 until the local module degree is optimal; S33, executing an algorithm community aggregation stage, regarding each community after local optimization as a super node, calculating edge weights among the super nodes, and constructing a simplified topological graph; s34, repeating the iterative process of S32-S33 until the modularity Q is stable, outputting a final coupling unit division result, and ensuring that the node similarity threshold meets Q >0.3; S35, quantifying a coupling risk level based on a coupling strength matrix, wherein S >0.6 is serious coupling, S is more than or equal to 0.3 and less than or equal to 0.6 is moderate coupling, and S <0.3 is mild coupling; And S36, positioning the coupling propagation path and the core node through a DFS algorithm, integrating the coupling unit, the risk level and the core node information, and generating a coupling early warning list.
5. The method for decoupling complex relationships and flow of complex equipment testing based on MBSE of claim 1, wherein step S4 comprises the following steps: S41, constructing a double-queue scheduling mechanism frame, wherein the frame comprises a task priority queue and a resource allocation queue; step S42, designing a task priority queue structure as a binary heap, and calculating a comprehensive priority value, wherein the higher the value is, the higher the scheduling priority is; step S421, the basic priority P (an integer of 1-10) of the target task is read from the MBSE activity diagrams; step S422, reading the real-time original weight w_T (T) of the corresponding time sequence edge of the task and the real-time original weight w_R (T) of the resource edge from the weighted directed topological graph; Step S423, substituting P, a time sequence correction term and a resource correction term value according to a formula S_p=0.4P+0.3 (1-w_T (T))+0.3 (1-w_R (T)), and calculating to obtain S_P; Step S43, designing a resource allocation queue, classifying according to resource types such as power supply, data bus, storage and the like, storing resource nodes and current loads, and sequencing according to the ascending order of the values; step S44, setting a queue update period, re-ordering the coupling strength data read every 10S by a task priority queue, and updating a load data adjustment sequence every 5S by a resource allocation queue; Step S45, a scheduling priority rule is formulated, a severe coupling task is prioritized over a moderate coupling task, and a high-priority task is prioritized over a low-priority task; Step S46, pre-detecting the dispatching conflict, judging whether the task dispatching sequence and the resource allocation scheme have new conflicts, and returning to the queue ordering adjustment logic if the conflicts exist.
6. The method for decoupling complex relationships and flow of complex equipment testing based on MBSE of claim 1, wherein step S5 includes the following: Step S51, aiming at severe time sequence coupling S_T >0.6, adopting a task rearrangement strategy, namely extracting related tasks in a coupling unit, sorting the tasks in descending order of comprehensive priority S_p, and moving a time window of a low-priority task with S_p >0.5 backwards, wherein the moving starting time is Wherein For the earliest end time of other tasks within the unit, Is a safe interval time; step S52, for the intermediate time sequence coupling of 0.3< S_T <0.6, adopting a buffer insertion strategy, namely inserting buffer time length between adjacent coupling tasks, wherein the time length is the sum of equipment switching time and task front preparation time read from a MBSE state machine diagram, and directly updating the time sequence parameters of corresponding tasks in a MBSE activity diagram; Step S53, for serious resource coupling S_R >1.0, adopting resource splitting strategy, judging whether overload resource supports physical splitting, if so, determining splitting path number n, and loading total resource load The split sub-resources are allocated according to the proportion of the resource consumption C_ { E, i } of each device, and the allocation load is as follows Adding sub resource nodes in a resource allocation queue; Step S54, aiming at medium resource coupling of 0.8< S_R <1.0, adopting a time sequence peak staggering strategy, namely screening resource load And high-consumption tasks occupying the resources during the period, and migrating the tasks to the resource load ; If a single valley period cannot be accommodated, splitting the task; Step S55, aiming at serious data coupling S_D >0.8, adopting a path reconstruction strategy, namely finding a congestion path with bandwidth utilization ratio U >0.8 in a current data transmission path through Dijkstra algorithm, searching all alternative paths, and screening out the bandwidth utilization ratio Calculating the comprehensive weight of each alternative path Wherein In order to provide a number of path hops, Selecting for matching degree of path and data requirement Reconstructing the highest path, and updating the data interface configuration in the topological graph and MBSE module definition graph; Step S56, aiming at the device conflict of the same device for multitasking, adopting a device replacement strategy, namely calculating the performance matching degree of each alternative device and the conflict device And selecting an alternative device which has the highest matching degree and can cover the task time period by using the available time window for replacement, and checking that no new conflict is generated after the replacement.
7. The method for decoupling complex relationships and flow of complex equipment testing based on MBSE of claim 1, wherein step S6 includes the following: Step S61, executing simulation verification, loading MBSE a model and a topological graph on a simulation platform, running full-period test simulation, and recording basic data such as coupling identification precision, conflict resolution and the like; Step S62, performing physical verification, deploying a test system in a physical prototype environment, collecting sensor data such as equipment load, data transmission rate and the like, and verifying parameter compliance; Step S63, performing actual combat verification, injecting dynamic disturbance such as equipment fault, task insertion and the like, and verifying the robustness of the method in a complex scene; Step S64, calculating an evaluation index, calculating a time sequence coupling digestion rate, a resource coupling control rate and a data coupling optimization rate; Step S65, judging the verification effect, and if all indexes meet the first-level standard, passing, otherwise, returning to the step S1 to adjust MBSE the model or the step S2 to optimize the weight parameters; And step S66, generating a decoupling optimization report by verifying the post-curing MBSE model parameters and the topology mapping rules to form a reusable test flow template.
8. A system for complex equipment trial complex relationship decoupling and flow based on MBSE comprising an electronic device, wherein the electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a method for complex equipment trial complex relationship decoupling and flow based on MBSE as claimed in any one of claims 1 to 7 when the computer program is executed by the processor.
9. A system for decoupling complex equipment trial relationship and flow based on MBSE, comprising a computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements a method for decoupling complex equipment trial relationship and flow based on MBSE as claimed in any one of claims 1 to 7.

Description

MBSE-based complex relation decoupling and flow method and system for complex equipment test Technical Field The invention provides a method and a system for decoupling complex relation and process of complex equipment test based on MBSE, and relates to the technical field of MBSE. Background Along with the continuous evolution of the informationized war form, complex equipment such as radars, missiles, aeroengines and the like gradually develop towards the directions of multi-task parallel execution, multi-equipment cooperative linkage and multi-resource dynamic competition, and equipment test identification is also upgraded from traditional single performance verification to full-dimensional efficiency assessment covering time sequence matching, resource scheduling and data interaction. Although the traditional equipment test control mode plays a basic overall role in early middle-small scale test scenes, the traditional equipment test control mode gradually shows obvious limitations when facing increasingly complex test task systems and dynamically changeable test execution environments. First, the document-driven management mode has a coupling recognition dead zone and an efficiency bottleneck. The mode relies on a table to record task time sequence, document labeling equipment parameters and report material to explain a resource allocation scheme, and three core coupling relations of time sequence conflict, resource competition and data congestion are dispersed in different document carriers. The experimenter needs to compare and analyze the coupling relation through manual cross-document, so that the coupling recognition accuracy is low, the time consumption from conflict discovery to preliminary response is long, the time is obviously delayed from the test progress rhythm, for example, in part of equipment sizing tests, the single test is obviously delayed because equipment occupation conflicts among different test tasks are not recognized in time manually. Second, the single algorithm optimization mode lacks dynamic adaptation capability and full flow coverage. The existing local optimization scheme adopts a finite element analysis tool to focus on the problem of single physical coupling of equipment, or only optimizes the resource load balancing through a simple scheduling algorithm, and a dynamic linkage mechanism is not formed. Even if part of schemes are introduced to MBSE technologies, the static SysML view modeling stage is only remained, when the performance of the test equipment is abnormal, a temporary new test task or a test environment is changed, model parameters and an optimization strategy cannot be updated in real time, response delay is long, and dynamic change requirements in the test process are difficult to adapt. In addition, the prior art system has the dual defects of verification of closed loop incomplete and insufficient technical cooperation. In the method, simulation verification is completed only through a simulation tool, physical prototype test and actual combat scene verification are not performed, so that obvious deviation exists between a simulation optimization result and an actual test, for example, in a part of equipment emission flow test, a flow scheme passing the simulation verification has higher test failure rate in the execution stage of the physical prototype due to neglecting of compatibility problems among devices, meanwhile, a data island phenomenon exists between graph theory topology analysis and MBSE modeling, a coupling analysis result output by an algorithm cannot reversely update resource allocation parameters of a block diagram in a SysML and equipment switching time sequence of a state machine diagram, so that an optimization strategy is difficult to execute in a landing mode, and technical faults of analysis, optimization and landing are formed. The defects can cause the problems of test conflict superposition, low resource utilization rate and insufficient flow reliability in the complex equipment test process, namely, on one hand, the time sequence conflict and the resource competition are interwoven with each other, so that the total test duration is obviously increased, the whole test period of part of equipment test projects exceeds the planned range due to multiple coupling conflicts, and on the other hand, the flow scheme lacking actual combat verification is easy to cause sudden faults in the equipment sizing test, directly affects the equipment assembly progress, and even can cause the loss of key test data due to the congestion of data interaction, so as to cause irreversible technical loss. In view of the above problems, in recent years, model-based system engineering is becoming an important methodology for complex system design, analysis and management. MBSE building a coverage multi-view model through standardized modeling languages such as SysML, realizing structural definition and association management of test elements, breaking