MBSE-based path planning and design simulation integration method

Abstract

The invention discloses a path planning and design simulation integration method based on MBSE, and the method comprises the steps: constructing a system simulation behavior meta-model library, constructing a system simulation behavior model based on the meta-model library, and providing a model basis for the subsequent optimization and integration; converting the system simulation behavior model into a data format readable by an algorithm, wherein the data format comprises conversion from a path node object to a node and conversion from a relation to an edge; performing global path planning on path nodes in the system simulation behavior model by using an ant colony algorithm to obtain an optimal path node scheme; simulation integration is designed, a code generation technology is applied, information is extracted from a system simulation behavior model, a simulation configuration file is generated, and support is provided for system simulation.

Description

Technical Field

[0001] The present invention relates to the field of engineering, and in particular to an MBSE-based path planning and design simulation integration method. Background Art

[0002] With the rapid development of science and technology, the development cycle of complex systems is becoming increasingly shorter. Traditional systems engineering methods often rely on manual operations and phased document communication, which is not only inefficient but also prone to inconsistencies and errors in the design and implementation phases, thereby extending the R&D cycle. To address these challenges, the model-based systems engineering (MBSE) method has been widely used. MBSE improves the efficiency of design, analysis, verification, and implementation by using a unified system model throughout the system lifecycle. However, there is still room for improvement in certain aspects of the system design process. In existing technologies, although this method can automatically generate simulation models from design models, it still has the following major shortcomings in complex system design optimization and design simulation integration: it only supports the conversion from specific design models to specific simulation models, and the conversion from design models to simulation models is relatively limited. This limitation may require additional manual adjustments or customized system development for different types of design models and simulation tasks, resulting in poor flexibility and increased design and development workload. Existing technologies only address the challenges of integrated design and simulation, lack the ability to assist engineers in design, and lack intelligent optimization and decision-making support capabilities. Although they have improved the system development efficiency to a certain extent, there is still room for improvement in the system development efficiency.

[0003] It is difficult to provide decision support during the MBSE model design process on the design side, and the generation process of simulation configuration files on the simulation side still requires a lot of manual intervention, which limits the efficiency of system development. To solve these problems, the present invention proposes an MBSE-based path planning and design simulation integration method. First, an MBSE system simulation behavior model is constructed. Then, the system simulation behavior model is globally optimized by introducing an ant colony algorithm. Finally, the simulation configuration file is automatically generated using code generation technology. The present invention greatly reduces the workload and error rate of manual operations, ensures the accuracy of the design, and the consistency and seamless connection between the design and simulation ends. It not only improves work efficiency, but also significantly shortens the R&D cycle of complex systems. Summary of the Invention

[0004] The present invention provides an MBSE-based path planning and design simulation integration method. By constructing a system simulation behavior model, applying the ant colony algorithm for path planning, and using code generation technology to automatically generate simulation configuration file code to support the execution of simulation deduction; this method aims to help engineers quickly generate optimal path solutions based on system requirements and constraints, improve the efficiency of building simulation configuration files, and ensure data consistency and seamless connection between design and simulation.

[0005] The present invention first uses the multi-architecture modeling language KARMA to construct a system simulation behavior model meta-model and a model to obtain a preliminary system simulation behavior model; then, the system simulation behavior model is converted into a topological structure, which includes converting the path node objects and their importance attributes in the system simulation behavior model into nodes and their attributes, and converting the relationships and their threat attributes in the system simulation behavior model into edges and their attributes; then, with the topological structure as input, an ant colony algorithm is used to perform global path planning; after initialization, path generation, path evaluation, pheromone update, and iterative optimization of the ant colony algorithm are performed in sequence, an optimal path solution {topological structure} is generated, and the topological structure is mapped to the system simulation behavior model to obtain the final system simulation behavior model; finally, with the system simulation behavior model as input, code generation rules are written to specify the conversion process from the model to the target code, the code generation rules are executed, and a simulation configuration file is generated.

[0006] 1. Construction of system simulation behavior model

[0007] The purpose of building a system simulation behavior model is to provide optimization space for the subsequent optimization process and to provide input for design simulation integration. The construction of the system simulation behavior model mainly includes: the construction of the system simulation behavior model metamodel and the construction of the system simulation behavior model.

[0008] a) Metamodel Construction of System Simulation Behavior Model: Based on the multi-architecture modeling language KARMA, a metamodel library of system simulation behavior model is constructed. The metamodel library includes six metamodels {graph, object, relationship, attribute, role}. The graph metamodel includes: system behavior graph metamodel; object metamodel includes path node object metamodel {including name attribute, importance attribute, executor attribute, behavior description attribute, position X attribute, position Y attribute}, initial node object metamodel {including name attribute}, and end node object metamodel {including name attribute}; relationship metamodel includes:

[0009] The control flow relationship metamodel {start-end binding control flow start-end role metamodel, terminal binding control flow terminal role metamodel}, the control flow relationship includes a name attribute and a threat degree attribute.

[0010] b) System Simulation Behavior Model Construction: First, construct several path nodes in the system simulation behavior model {including the initial node, end node, path nodes and their attributes}; then, construct the topological relationship between the initial node, end node and several path nodes in the system simulation behavior model {including the control flow relationship and its attributes}

[0011] 2. Topology transformation

[0012] The purpose of topology transformation is to transform the system simulation behavior model into a data format that can be read by the algorithm. Topology transformation includes the transformation of path node objects to nodes and the transformation of relationships to edges.

[0013] a) Conversion from path node objects to nodes: First, for each path node object, convert it into a node in the adjacency matrix. Then, extract the importance attribute of each node and convert it into a node importance attribute table.

[0014] b) Relationship to edge conversion: First, determine the path node objects connected to the two ends of the relationship, extract the position X attribute and position Y attribute of the path node objects at both ends, calculate the length of the path and fill the length into the adjacency matrix;

[0015] Then, the threat attribute of each relationship is extracted and transformed into an edge threat attribute table.

[0016] 3. Global path planning

[0017] The purpose of global path planning is to plan paths for the path nodes in the system simulation behavior model, obtain the optimal path solution, and assist engineers in making design trade-offs and decisions during the design process. Global path planning includes: ant colony algorithm initialization, initial path generation, path selection, pheromone update, path evaluation, iterative optimization, optimal path solution generation, mapping the topology structure to the system simulation behavior model, and finally generating the system simulation behavior model.

[0018] a) Ant Colony Algorithm Initialization: Determine the number of ants m = 300, the initial pheromone concentration τ0 = 1, the pheromone volatility factor ρ = 0.5, the heuristic factor η as the inverse threat of the edge, the heuristic factor weight β = 2, and the pheromone importance factor α = 3. Initialize an N × N pheromone matrix τ based on the number of path nodes N in the system simulation behavior model. Initialize all elements to τ0 = 1, and set the maximum number of iterations to 500.

[0019] b) Path generation: Each ant starts from the initial node and chooses the next path according to the probability until it reaches the final node. The path selection probability formula is as follows {the probability of each ant choosing the next node j from the current node i}:

[0020]

[0021] Among them, τ ij is the pheromone concentration, η ij is the heuristic information {the inverse threat degree of the edge}, α and β are weight factors, and allowed is the set of nodes that the current ant can select.

[0022] c) Path evaluation: First, determine the fitness function. There are three influencing factors in the fitness calculation process: the total path length, the sum of the importance of the nodes in the path, and the sum of the threat levels of the edges in the path. The importance factor of the path length is set to ω1 = 10, the importance factor of the sum of the importance is set to ω2 = 5, and the importance factor of the sum of the threat levels is set to ω3 = -5. The evaluation formula is as follows:

[0023]

[0024] After each iteration, the current optimal path is updated and the global optimal path is recorded to ensure that the algorithm can find the global optimal solution.

[0025] d) Pheromone update: According to the fitness value of the path, the pheromone on the path is updated. The formula is as follows, where Δτ ij . is the path pheromone increment, and its value is the path fitness:

[0026]

[0027] In addition to increasing pheromones, it is also necessary to consider the natural volatilization of pheromones to prevent them from being overly concentrated on certain paths. The volatilization formula is:

[0028] τ ij (t+1)=(1-ρ)τ ij (t)

[0029] e) Iterative optimization: Repeat the path generation, path evaluation and pheromone update process until the maximum number of iterations 500 is met.

[0030] f) Generation of optimal path plan: After optimization by the ant colony algorithm, the optimal path plan is obtained, and a list of nodes and a list of edges in the topological structure are generated according to the optimal plan.

[0031] g) Map and generate the final system simulation behavior model: Using the node list and edge list under the optimal solution as input, the path node objects and relationships in the preliminary system simulation behavior model are mapped one by one. If the nodes or edges do not exist in the node list and edge list, they will be deleted to obtain the system simulation behavior model. Engineers can then perform detailed activity design on the system simulation behavior model after global path optimization to obtain the final system simulation behavior model.

[0032] 4. Design Simulation Integration

[0033] The purpose of design-simulation integration is to automatically extract information from the system simulation behavior model through code generation technology and generate simulation configuration files to support system simulation. Design-simulation integration includes: code generation rule formulation, code generation rule execution, and simulation configuration file generation.

[0034] a) Code generation rule development: Developing a rule involves three steps: First, determining the script template. Different simulation platforms require different formats for simulation configuration files. Therefore, it's necessary to first determine the script template for the simulation configuration file and set placeholders within the template to represent the design information that needs to be filled in. Second, declaring the source model refers to specifying which model (a user-created model, such as a system simulation behavior model) to source relevant data from. Declaring the model schema structure and constraints specifies which metamodel (primarily object metamodels and relational metamodels) to source relevant data from within that model. Third, determining the design information content to be filled in the template. For example, the metamodel values ​​for the "name" attribute and the "position X" attribute of the path node object metamodel correspond to each other. Multiple code generation rules can be developed based on the needs of the target simulation configuration file.

[0035] b) Code generation rule execution: Code generation rule execution includes two parts: query and generation. The query part mainly queries the instance of the path node object metamodel in the system simulation behavior model and obtains the query result {a path node object metamodel instance} based on the information source defined in each rule {for example, which object metamodel in which model, the path node object metamodel in the system simulation behavior model is taken as an example below}; the generation part mainly extracts the relevant information of the path node object metamodel instance in the query result {such as the "name" attribute, etc.} based on the defined script template and the information source of the placeholder in the template, fills it into the script template, and outputs the script to the pre-declared target code file. The query and generation are executed as many times as there are instances of the path node object metamodel in the system simulation behavior model, until the object metamodel in the model is completely queried.

[0036] c) According to the above code generation rule execution method, several pre-defined code generation rules are executed to obtain the target simulation configuration file, which can be directly used as input to the system simulation system to support the execution of system simulation, thereby realizing the integration and connection between the design end and the simulation end.

[0037] The present invention has the following advantages:

[0038] 1) Support path optimization of design models described in multiple modeling languages

[0039] The present invention is developed based on the multi-architecture modeling language KARMA, and can support the construction and path optimization of system simulation behavior models expressed in multiple different modeling languages, without being limited to models under a specific modeling language.

[0040] 2) Support integration of multiple system simulation platforms

[0041] The code generation technology in this invention is developed based on the KARMA language. It describes the code generation process by formulating text-based code generation rules. The templates in these rules can be customized based on specific requirements. Since different system simulation platforms have different simulation configuration file formats, the code generation technology proposed in this invention can flexibly formulate templates to meet the needs of different system design platforms, thereby supporting the generation of simulation configuration files required by different system simulation platforms. This enables the integration of multiple system simulation platforms.

[0042] 3) Support intelligent optimization and decision-making support in the system design process

[0043] This invention enhances intelligent optimization and decision-making capabilities during system design by using an ant colony algorithm. By simulating the foraging behavior of ants, the ant colony algorithm automatically finds the optimal path within a complex design space. By comprehensively considering the importance of nodes and the threat level of edges, it achieves global optimization of the system simulation behavior model, helping designers make decisions quickly. By utilizing intelligent optimization algorithms, this approach saves designers significant time and effort, improves design efficiency, and reduces human intervention and potential design errors, significantly enhancing the reliability and accuracy of system design.

[0044] 4) Improved consistency between system design and system simulation data

[0045] The code generation technology in this invention uses model queries to obtain information from the source model and then converts this information into a target simulation configuration file. Code generation enables automatic or semi-automatic conversion from the system model to other heterogeneous data files. When design information changes, re-execution of the code generation rules generates a new simulation configuration file, thereby improving the traceability and consistency of data between the system model and the simulation configuration file during the design process.

[0046] An integrated MBSE-based path planning and design simulation method can effectively assist engineers in system design, saving them time in weighing and making decisions. It also helps reduce errors and risks during the design process, and reduces the time spent on design adjustments. Through this method, users can use the multi-architecture modeling language KARMA to construct a preliminary system simulation behavior model. The system then uses an ant colony algorithm to perform trade-offs and global path optimization on the system simulation behavior model to obtain the final system simulation behavior model. Based on code generation technology, users write and run code generation rules to obtain a simulation configuration file that supports simulation execution, thereby optimizing system design and integrating design and simulation.

[0047] Digital twin: A technology that enables equipment monitoring, operating condition prediction, and control strategy optimization through information and data exchange between physical entities and virtual digital models. It includes three technical elements: physical entities, digital twin models, and information data flows.

[0048] Physical entity: In the context of digital twin technology, it refers to a collection of objectively existing materials in the real physical world, such as parts, components, systems, equipment, etc.

[0049] Digital twin model: refers to a digital model in a computer virtual space that contains information about a specific physical entity's three-dimensional structure, system architecture, functional composition, and operating mechanism. It has information equivalent to that of a real-world physical entity and is generally constructed by integrating and linking digital models such as the functional model, performance model, and physical model of a specific physical entity.

[0050] Functional model: A digital model that describes the internal modular composition, hierarchical relationships, functional allocation, integration interfaces, and runtime sequence of complex physical entities such as equipment and systems. It is also called an architecture model. It primarily takes the form of a collection of logical views and can be constructed using system engineering modeling tools.

[0051] Performance parameters: parameters that describe the state of a target entity such as a component, assembly, or equipment, or its interaction with other entities, such as stress, deformation, temperature distribution, radiation intensity, current, electromotive force, logical state, digital signal, etc.

[0052] Performance model: A digital model that describes the relationship between performance parameters associated with a specific object entity, including interpolation tables, analytical formulas, iterative analysis algorithms, and other forms;

[0053] Physical model: A model that describes the three-dimensional geometric structure information of target entities such as parts, components, and equipment. It is mainly constructed using dedicated computer design software and includes various formats such as prt, stp, step, and fbx.

[0054] Assembly relationship: describes the constraints of relative position, relative posture, and relative motion between two or more physical entities. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention has a drawing.

[0056] Figure 1 It is a path diagram of the integrated method of path planning and design simulation based on MBSE. DETAILED DESCRIPTION

[0057] 1. Construction of system simulation behavior model

[0058] The purpose of building a system simulation behavior model is to provide optimization space for the subsequent optimization process and to provide input for design simulation integration. The construction of the system simulation behavior model mainly includes: the construction of the system simulation behavior model metamodel and the construction of the system simulation behavior model.

[0059] 2. Topology transformation

[0060] The purpose of topology transformation is to transform the system simulation behavior model into a data format that can be read by the algorithm. Topology transformation includes the transformation of path node objects to nodes and the transformation of relationships to edges.

[0061] 3. Global path planning

[0062] The purpose of global path planning is to plan paths for the path nodes in the system simulation behavior model, obtain the optimal path solution, and assist engineers in making design trade-offs and decisions during the design process. Global path planning includes: ant colony algorithm initialization, initial path generation, path selection, pheromone update, path evaluation, iterative optimization, optimal path solution generation, mapping the topology structure to the system simulation behavior model, and finally generating the system simulation behavior model.

[0063] h) Ant Colony Algorithm Initialization: Determine the number of ants m = 300, the initial pheromone concentration τ0 = 1, the pheromone volatility factor ρ = 0.5, the heuristic factor η as the inverse threat of the edge, the heuristic factor weight β = 2, and the pheromone importance factor α = 3. Initialize an N × N pheromone matrix τ based on the number of path nodes N in the system simulation behavior model. Initialize all elements to τ0 = 1, and set the maximum number of iterations to 500.

[0064] i) Path Generation: Each ant starts from the initial node and chooses the next path according to the probability until it reaches the final node. The path selection probability formula is as follows {the probability of each ant choosing the next node j from the current node i}:

[0065]

[0066] Among them, τij is the pheromone concentration, η ij is the heuristic information {the inverse threat degree of the edge}, α and β are weight factors, and allowed is the set of nodes that the current ant can select.

[0067] j) Path evaluation: First, determine the fitness function. There are three influencing factors in the fitness calculation process: the total path length, the sum of the importance of the nodes in the path, and the sum of the threat of the edges in the path. The importance factor of the path length is set to ω1 = 10, the importance factor of the sum of the importance is set to ω2 = 5, and the importance factor of the sum of the threat is set to ω3 = -5. The evaluation formula is as follows:

[0068]

[0069] After each iteration, the current optimal path is updated and the global optimal path is recorded to ensure that the algorithm can find the global optimal solution.

[0070] k) Pheromone update: According to the fitness value of the path, the pheromone on the path is updated. The formula is as follows, where Δτ ij . is the path pheromone increment, and its value is the path fitness:

[0071]

[0072] In addition to increasing pheromones, it is also necessary to consider the natural volatilization of pheromones to prevent them from being overly concentrated on certain paths. The volatilization formula is:

[0073] τij(t+1)=(1-p)τij(t)

[0074] l) Iterative optimization: Repeat the path generation, path evaluation and pheromone update process until the maximum number of iterations 500 is met.

[0075] m) Optimal path solution generation: After optimization by the ant colony algorithm, the optimal path solution is obtained, and a list of nodes and a list of edges in the topological structure are generated according to the optimal solution.

[0076] n) Map and generate the final system simulation behavior model: Using the node list and edge list under the optimal solution as input, the path node objects and relationships in the preliminary system simulation behavior model are mapped one by one. If the nodes or edges do not exist in the node list and edge list, they will be deleted to obtain the system simulation behavior model. Engineers can then perform detailed activity design on the system simulation behavior model after global path optimization to obtain the final system simulation behavior model.

[0077] 4. Design Simulation Integration

[0078] The purpose of design-simulation integration is to automatically extract information from the system simulation behavior model through code generation technology and generate simulation configuration files to support system simulation. Design-simulation integration includes: code generation rule formulation, code generation rule execution, and simulation configuration file generation.

Claims

1. A path planning and design simulation integration method based on MBSE, characterized by: It includes using the multi-architecture modeling language KARMA to build a meta-model library and model of the system simulation behavior model, converting the system simulation behavior model into a topological structure, performing path planning on the path nodes in the system simulation behavior model to obtain the optimal path plan, and designing simulation integration.

2. The MBSE-based path planning and design simulation integration method according to claim 1, characterized in that: The multi-architecture modeling language KARMA supports the construction of simulation behavior models described in multiple modeling languages.

3. The MBSE-based path planning and design simulation integration method according to claim 1, characterized in that: The topology structure conversion supports converting the system simulation behavior model into a data format readable by the algorithm, including the conversion of path node objects to nodes and the conversion of relationships to edges.

4. The MBSE-based path planning and design simulation integration method according to claim 1, characterized in that: Global path planning is to plan the path nodes in the system simulation behavior model based on the ant colony algorithm to obtain the optimal path solution, which helps engineers to weigh and make decisions on design solutions during the design process.

5. The MBSE-based path planning and design simulation integration method according to claim 1, characterized in that: Design simulation integration automatically extracts information from system simulation behavior models and generates simulation configuration files through code generation technology to provide support for system simulation.

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