Digital mission engineering development method based on meta-model library

Abstract

The invention discloses a digital mission engineering development method based on a meta-model library. The digital mission engineering development method is used for modeling of a maritime search and rescue task complex system. The method comprises the following steps: constructing a system model for currently input maritime search and rescue task data and modeling problems based on a pre-constructed meta-model library of a maritime search and rescue task system; according to the method, standardized processes of digital mission engineering are set, including activities including scene conception, mission definition, process analysis, resource generation, experimental design, simulation deduction and evaluation optimization, the activities are refined into action sequences, each action is clearly defined, and a standard method and a working template are provided for the digital mission engineering. According to the method, the problems of incomplete scene description, unclear mission definition, nonstandard architecture design, insufficient verification and evaluation and the like during modeling of the maritime search and rescue system are solved, the standardized process of modeling of the maritime search and rescue task system is realized, the modeling speed and precision are improved, and rapid generation and iteration of system modeling in different scenes are supported.

Description

Technical Field

[0001] This invention belongs to the field of digital twin technology for complex systems, and relates to digital twins of complex maritime search and rescue systems. Specifically, it is a digital mission engineering development method based on a meta-model library. Background Technology

[0002] Mission engineering is an interdisciplinary process that encompasses all the technical work involved in analyzing, designing, and integrating current and emerging operational needs and capabilities to achieve the intended mission objectives. The goal of mission engineering is to achieve the intended mission objectives by “identifying the right things” (i.e., technologies, systems, architectures, or processes), providing mission-based input to systems engineering processes to help users correctly build various systems. Mission engineering crosses the problem (business) domain and the solution (technology) domain, thus presenting numerous challenges during implementation, primarily in the following aspects:

[0003] ① Scope and complexity: It covers multiple systems, organizations and scenarios, involves multiple stakeholders, each with its own interests, motivations and perspectives, and has its own models and analytical tools at the system and mission levels;

[0004] ② Cross-organizational collaboration: The mission relies on the effective interaction between the systems owned, developed, managed and operated by different organizations. To effectively carry out cross-mission engineering work, it is necessary to achieve the same effective interaction at the engineering level as at the system level.

[0005] ③ Comprehensive analytical capability: Given the complexity and scope of a mission, effective mission engineering requires the ability to analyze a range of models and data to address mission-level options and impacts;

[0006] ④ Testing and Evaluation: The systems in Mission Engineering are all independent, at different stages of their lifecycle, and the development cycle is user-oriented, which limits the ability to synchronize and verify the impact of system changes on Mission.

[0007] ⑤ Common mission representation: A common view is needed to build a common view of mission operation concepts, system capabilities, and threats across the entire complex organizational system. Therefore, methods, processes, and tools are needed to establish a common view of key mission elements.

[0008] ⑥ Data: Common data shared across multiple models and analyses is key to the success of mission engineering. Often, each organization invests significant resources in developing data, which is often unknown or not shared within the mission.

[0009] Digital engineering is a systems engineering approach centered on digital models, data, and information technology. It uses authoritative sources of system data and models as an interdisciplinary continuum to support system lifecycle activities from concept to action. The development goals of digital engineering include five aspects:

[0010] ① The development, integration, and application of standardized models to guide decision-making in complex organizational structures and projects;

[0011] ② Provide a persistent and authoritative single data source;

[0012] ③ Introduce technological innovation to improve engineering practice;

[0013] ④ Establish supportive infrastructure and environment to carry out activities and promote collaboration and communication among stakeholders;

[0014] ⑤ Transform the culture and train employees to embrace and support digital engineering throughout the product lifecycle.

[0015] By leveraging collaborative infrastructure and current computing technologies to support the sharing and curation of models and data, digital engineering offers a practical and scalable set of tools and methodologies to address these challenges in mission engineering in innovative and cross-organizational ways. Therefore, the integration of digital engineering with mission engineering, and the use of digital technologies to solve mission engineering challenges, has become a current research hotspot.

[0016] Digital Mission Engineering (DME) uses digital engineering modeling, simulation, and analysis to incorporate the mission's operating environment and evaluate mission outcomes and effectiveness at every stage of its lifecycle. It integrates digital missions and system models to develop a true engineering analysis environment that measures mission effectiveness and defines outcomes at all fidelity levels. The key to DCE's success lies in reconnecting all critical decisions at each stage of the engineering lifecycle to the mission. Engineers can continuously validate mission effectiveness, avoiding the risk of perpetuating errors throughout the design lifecycle and confidently ensuring that whatever they are designing will function and respond as expected under every conceivable condition. At the heart of DCE are methodologies and model data, but neither of these aspects has been formalized into universally accepted standards and engineering guidelines. While the international mission engineering research community has provided general processes, including mission problem description, mission characteristic definition, mission architecture design, mission engineering analysis, and mission solution recommendation, it has not provided specific modeling and simulation methods. While the international digital engineering research field has put forward general principles, including the development, integration and application of standardized models, providing a persistent and authoritative single data source, introducing technological innovation to improve engineering practices, establishing supportive infrastructure and environments, and transforming culture and training employees, it has not provided specific, applicable model data specifications for particular fields.

[0017] Maritime rescue and search missions are highly collaborative multi-system, multi-platform operations. The systems involved are complex systems integrating satellite positioning, wireless communication, multi-platform collaboration, and command and dispatch. In this mission scenario, various equipment and functions from sea, land, and air platforms need to work together to complete the task. Using digital mission engineering to build a unified, quantifiable, and visual model framework for the maritime rescue and search system, achieving mission-level digital twins, allows for real-time evaluation of the system's contribution to mission objectives, helps identify deficiencies in mission scheduling, optimizes resource allocation, and improves the collaborative efficiency of system members. Therefore, researching the use of digital mission engineering to model such complex systems is highly meaningful. However, current applications of digital mission engineering to complex systems like maritime rescue and search suffer from problems such as incomplete scenario descriptions, unclear mission definitions, non-standardized architecture design, and insufficient verification and evaluation, leading to modeling difficulties and models with strong uncertainties. Summary of the Invention

[0018] To address the problems of incomplete scenario descriptions, unclear mission definitions, non-standardized architecture design, and insufficient verification and evaluation in digital mission engineering when modeling complex systems such as maritime search and rescue systems, this invention provides a digital mission engineering development method based on a meta-model library. This method enables the standardized construction of mission engineering models for complex systems such as maritime search and rescue, as well as standardized access to the meta-model library, thereby supporting the rapid generation and iteration of mission-oriented complex system architecture modeling solutions.

[0019] The present invention provides a digital mission engineering development method based on a meta-model library. For current maritime search and rescue missions, it loads a meta-model library of the maritime search and rescue mission system, which includes a scenario library, a mission library, and an element library. It receives the current input maritime search and rescue mission data and modeling questions; constructs a maritime search and rescue mission model for the current scenario; executes the digital mission engineering process; and builds the mission model. The digital mission engineering process based on the meta-model library of the present invention includes activities such as scenario conception, mission definition, process analysis, resource generation, experimental design, simulation deduction, and evaluation and optimization. Furthermore, each activity is further refined into an action sequence, defining the inputs, outputs, control items, and enable items for each action, thereby providing a standard method and working template for digital mission engineering.

[0020] ① Scenario Conception. The scenario conception activity involves describing the natural environment, participating entities, layout, operational intent, and key events within the operational scenario based on the input task data, chronologically. The input data is segmented into scenario vignettes, and the vignette to be studied is selected. For the current modeling problem, a maritime search and rescue mission template is selected from the mission library and inherited. For each scenario vignette to be studied, the mission layout, task conditions, environmental conditions, and duration are redefined, and the scenario vignette is output. The scenario conception activity includes the following sequence of actions: identifying stakeholders, describing the operational scenario, framing scenario vignettes, selecting mission templates, defining scenario vignettes, and managing scenario vignettes.

[0021] ② Mission Definition. A mission is a series of tasks required to achieve a specific effect for a problem object. Missions correspond to scenario fragments. For each scenario fragment output from the scenario conception, the mission definition activity selects a mission object from the scenario fragment, chooses a corresponding mission object template from the mission library, redefines the mission object's operational nodes, process activities, and metrics, and outputs the constructed mission object. In maritime search and rescue missions, the problem object is the search and rescue object. The mission definition activity includes the following actions: decomposing mission tasks, selecting a mission object template, defining the mission object structure, defining mission object behavior, and managing mission object information.

[0022] ③ Process Analysis. A mission thread is an end-to-end sequence of mission tasks, activities, and events, representing the process of achieving the mission. This activity, based on scenario fragments and mission object information, selects a corresponding mission thread template from the mission library to redefine the mission thread of the current task, selects a runtime node template from the mission thread library to redefine the runtime nodes of the mission thread, and establishes the runtime nodes, process activities, and node measurement indicators of the mission thread of the current task based on the selected templates. For the current maritime search and rescue mission, the mission thread template is the maritime search and rescue system template, and the mission thread of the current maritime search and rescue mission is established based on this template. Validation test cases that inherit the mission of the maritime search and rescue mission are established. By combining scenario fragments, mission objects, and mission threads, the parameters between runtime node performance measurement and mission success measurement are cascaded. The validation test cases are simulated to verify whether the currently established maritime search and rescue mission thread can complete the mission. The process analysis activity includes actions: selecting a mission thread template, defining the mission thread structure, defining mission thread behavior, validating the mission thread, and managing the mission thread.

[0023] ④ Resource Generation: The Mission Engineering Thread is a mission thread that includes detailed information on the capabilities, technologies, systems, and organization required to execute the mission. This activity, based on the mission thread's workflow, determines the possible resource systems for process activities according to the logical relationships of "process activities - functional services" and "functional services - resource systems"; assigns process activities to resource systems, determines the activities that resource systems need to implement, and establishes the mapping relationship between the mission thread's runtime nodes and resource systems; defines the functions of resource systems, describing behavioral logic through state machines; defines the interfaces of resource systems, describing interaction relationships through interface connections; and defines the constraint relationships between resource functions and runtime nodes, implementing parameter cascading between resource function performance measurements and runtime node performance measurements.

[0024] ⑤ Experimental Design: Based on mission object information and mission engineering clues, this activity establishes a simulation experiment scheme. The established simulation experiment scheme inherits the mission constructed in step 1 and is associated with the mission object of activity 2 and the mission engineering clues of activity 4. By combining scene fragments, mission objects, mission clues, and mission engineering clues, the parameters between resource function performance measurement, running node efficiency measurement, and mission success measurement are cascaded. The environmental block variables and resource factor variables of the simulation experiment scheme are determined, and the value range of the environmental block variables and resource factor variables is set. An orthogonal experimental design method is developed to generate the simulation experiment scheme of the mission system.

[0025] ⑥ Simulation and Deduction: This activity takes the simulation experiment scheme as input, sets the simulation configuration, including start time, end time, number of simulations, and simulation step size; develops a visualized spatiotemporal information model for simulation and deduction, establishes the semantic mapping relationship between the mission object, mission engineering clue model, and spatiotemporal information model; realizes the simulation and deduction of the mission system based on discrete event signals, performs sensitivity analysis on the simulation and deduction data, obtains key experimental factors, and thus supports system evaluation and optimization.

[0026] ⑦ Evaluation and Optimization: This activity defines an evaluation index system and establishes a mapping and calculation relationship between the evaluation index system and simulation data, realizing the aggregation of simulation data to the evaluation index system and calculating evaluation indicators; it supports the evaluation of the effects of the same mission engineering clues on different mission objects, and the evaluation methods include weight analysis and comprehensive evaluation methods; it supports the optimization of the effects of different mission engineering clues on the same mission objects, and the optimization methods include single index optimization and comprehensive optimization.

[0027] Compared with the prior art, the method of the present invention has the following advantages and positive effects:

[0028] (1) The method of this invention uses digital mission engineering to achieve digital twin modeling of complex maritime search and rescue systems, solving problems such as incomplete scenario description, unclear mission definition, non-standard architecture design, and insufficient verification and evaluation in existing modeling. Based on the study of mission engineering, system engineering, and other processes, this method integrates data-driven engineering practices in digital engineering, defining a well-structured data model and assigning precise semantics through a meta-model, thereby better supporting the transmission and management of various data in the mission engineering process. This method enables the standardized process construction of mission engineering models for complex systems such as maritime search and rescue, realizing seven activities including scenario conception, mission definition, process analysis, resource generation, experimental design, simulation, and evaluation and optimization.

[0029] (2) The complex system model constructed using the method of this invention for maritime search and rescue missions has advantages such as consistent data semantics, composable process activities, data-driven decision-making closed loop, cross-domain and cross-organizational collaboration, automated and intelligent verification, security and traceability, and decoupling of architectural and technological heterogeneity. This invention elevates the development paradigm of digital mission engineering to semantic data-driven by integrating meta-modeling and digital engineering technologies, solving the problem of data, tools, organization, and decision-making silos, and improving speed, accuracy, adaptability, and sustainability. Using this invention can improve the speed and accuracy of modeling the maritime search and rescue mission system, and supports the rapid generation and iteration of complex system modeling for maritime search and rescue missions under different mission scenarios. Attached Figure Description

[0030] Figure 1This is a schematic diagram of the metamodel library structure used in the digital mission engineering development method based on the metamodel library of the present invention.

[0031] Figure 2 This is a schematic diagram of the digital mission engineering process implemented in an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram illustrating the scenario conception activity of an embodiment of the present invention;

[0033] Figure 4 This is a schematic diagram illustrating the mission definition of the XX maritime search and rescue mission in an embodiment of the present invention;

[0034] Figure 5 This is a schematic diagram of the mission definition activity in an embodiment of the present invention;

[0035] Figure 6 This is a schematic diagram defining the structure of the XX search and rescue object in an embodiment of the present invention;

[0036] Figure 7 This is a schematic diagram illustrating the behavior definition of the XX search and rescue object in an embodiment of the present invention;

[0037] Figure 8 This is a schematic diagram of the process analysis activity in an embodiment of the present invention;

[0038] Figure 9 This is a schematic diagram defining the XX search and rescue process structure in an embodiment of the present invention;

[0039] Figure 10 This is a schematic diagram illustrating the definition of the XX search and rescue process behavior in an embodiment of the present invention;

[0040] Figure 11 This is a schematic diagram illustrating the verification process of the XX maritime search and rescue mission in an embodiment of the present invention;

[0041] Figure 12 This is a schematic diagram of resource generation activities in an embodiment of the present invention;

[0042] Figure 13 This is a schematic diagram illustrating the definition of the XX search and rescue resource structure in an embodiment of the present invention;

[0043] Figure 14 This is a schematic diagram illustrating the definition of XX search and rescue resource behavior in an embodiment of the present invention;

[0044] Figure 15 This is a schematic diagram illustrating the definition of the XX search and rescue resource function in an embodiment of the present invention;

[0045] Figure 16 This is a schematic diagram of the XX search and rescue resource interface definition in an embodiment of the present invention;

[0046] Figure 17This is a schematic diagram illustrating the constraint definition of the XX navigation node in an embodiment of the present invention;

[0047] Figure 18 This is a schematic diagram illustrating the constraint definition of the XX command node in an embodiment of the present invention;

[0048] Figure 19 This is a schematic diagram illustrating the constraint definition of the XX search node in an embodiment of the present invention;

[0049] Figure 20 This is a schematic diagram illustrating the constraint definition of the XX rescue node in an embodiment of the present invention;

[0050] Figure 21 This is a schematic diagram of the experimental design activities in an embodiment of the present invention;

[0051] Figure 22 This is a schematic diagram of the experimental scheme for the XX maritime search and rescue mission in an embodiment of the present invention;

[0052] Figure 23 This is a schematic diagram of the interface of the experimental scheme for the XX maritime search and rescue mission in this embodiment of the invention;

[0053] Figure 24 This is a schematic diagram of the simulation and deduction activity in an embodiment of the present invention;

[0054] Figure 25 This is a schematic diagram of the evaluation and preference activities in an embodiment of the present invention;

[0055] Figure 26 This is a schematic diagram of the case model structure for a maritime search and rescue mission constructed according to an embodiment of the present invention. Detailed Implementation

[0056] The technical solution of the present invention will be described below with reference to the accompanying drawings and embodiments.

[0057] This invention uses a complex maritime search and rescue system as an example to illustrate the implementation of the digital mission engineering development method based on a meta-model library. The implementation of this method is based on a meta-model library, the structure of which is as follows: Figure 1As shown. The meta-model library includes three types of models: ① Scenario library, based on spatiotemporal information theory, defines the time and space, operating environment, participating entities, layout and deployment, operating intent, key events, and other information of the top-level operational concept. ② Mission library includes mission templates and mission clue templates. Mission templates extract missions based on common operating intents in the scenario library, defining the constraint relationships between mission success measures, operating environment parameters, and the performance measures of the operating nodes participating in the mission. Mission clue templates define the process activities that the operating nodes participating in the mission need to execute, as well as the input and output, operating constraints, triggering states, and other information of each process activity, based on domain knowledge. ③ Element library is further subdivided into six elements: capability measurement, operating nodes, process activities, functional services, resource system, and constraint equations. The element library is the foundation for constructing the mission library. This invention pre-constructs a meta-model library for the maritime search and rescue mission system based on historical maritime search and rescue mission data. Operating nodes include participants in the mission scenario, such as navigation nodes, search nodes, command and control nodes, rescue nodes, and distressed vessels and personnel. The resource system includes resources used to provide functional services, such as navigation satellites, command and control centers, search and rescue helicopters, and maritime rescue vessels. Functional services include resource-provided functional services such as radar imaging search, satellite navigation, command and control, maritime mobility, air mobility, and rescue. Capability measures include mission success metrics, operational node effectiveness metrics, functional service performance metrics, and key parameters of the resource system. Process activities describe the operational activities performed at each operational node. Constraint equations solidify the constraint relationships between the attributes of elements such as mission, operational nodes, functional services, and resource systems, enabling rapid reuse through modular combination. The mission is described by combining capability measures, constraint equations, and other elements in the element library. Mission cues are described by combining capability measures, operational nodes, and process activities in the element library.

[0058] To address the standardization issue of methodologies and processes in digital mission engineering, this invention, based on practical experience in multiple complex system engineering projects, designs a digital mission engineering development process driven by a meta-model library and simulation data. The digital mission engineering process implemented in this embodiment mainly includes seven activities: scenario conception, mission definition, process analysis, resource generation, experimental design, simulation deduction, and evaluation and optimization. The input / output and control logic between these activities are as follows: Figure 2 As shown. The method of this invention further refines each activity into an action sequence, defining the inputs, outputs, control items, and enable items for each action, thereby providing a standard method and working template for mission engineering. In this embodiment, to standardize the use of model data, object-oriented techniques such as reuse, inheritance, cloning, and instantiation are used in process activities to implement the calling of the meta-model library. The implementation of each activity is explained in detail below using the "XX Maritime Search and Rescue Mission" case as a background.

[0059] Activity 1: Scenario Conception. A scenario describes the layout, operating environment, and duration of the problem that needs to be solved. A scenario can be further subdivided into numerous segments, describing the process of an event occurring within a specific time period, in a specific area, and targeting a specific type of objective. This activity defines the scenario segments of interest and selects appropriate mission templates from the mission library. It defines the mission's layout, operating environment, duration, and other parameters, providing a background environment for mission engineering.

[0060] The input to this activity is a problem description, and the output is a scenario fragment. In this embodiment of the invention, the input problem description can be: "Construct a mission task for the XX maritime search and rescue mission, including, as needed, six actions: identifying stakeholders, describing the operational scenario, defining scenario fragments, selecting a mission template, defining scenario fragments, and managing scenario fragments." Figure 3 As shown in the illustration. In the scenario design of this embodiment of the invention, the currently input maritime search and rescue mission data is divided into segments, the mission is defined, and the scene segments of interest are obtained.

[0061] Step 1.1, Identify Stakeholders: Identify key stakeholders by brainstorming, conducting focus groups, and other methods to obtain their needs to support the description of the operational scenario. For example, in the scenario of this embodiment, distressed vessels and personnel, search and rescue vessels and helicopters, and the command and control center all belong to the same stakeholder.

[0062] Step 1.2, describe the operational scenario: organize and synthesize the needs of stakeholders, and describe the natural environment, participating entities, layout and deployment, operational intent and key events in the operational scenario, with time and space as the main lines.

[0063] Step 1.3, Framing Scene Segments: In order to focus on the research object, the input task data needs to be segmented into scene segments. Each scene segment represents the interaction between participating entities and with the natural environment within a small time and space. Users select a scene segment for research based on the questions they are interested in.

[0064] Step 1.4, Select a Mission Template: Access the mission library, select a suitable mission template, and define a mission by inheriting from the mission template. For example... Figure 4 As shown, the maritime search and rescue mission template selected for mission inheritance in the XX maritime search and rescue mission is used. The mission template includes mission attributes such as hydrological conditions, meteorological conditions, and mission conditions. The mission metric is the success metric for the maritime search and rescue mission.

[0065] Step 1.5, Define Scene Fragments: Inherit the hydrological conditions, meteorological conditions, and mission conditions from the maritime search and rescue mission template. Based on the scene fragment data to be studied, redefine the XX hydrological conditions, XX meteorological conditions, XX mission conditions, and their environmental parameters in the scene, and then establish a connection with the XX maritime search and rescue mission through redefinition.

[0066] Step 1.6, Managing Scene Fragments: This activity will generate many scene fragments, and each scene fragment will be associated with different mission objects, mission clues, mission engineering clues, and simulation experiment schemes. Therefore, it is necessary to manage these data to support users' data retrieval and analysis calculations.

[0067] Activity 2: Mission Definition. A mission is a series of tasks that need to be completed to achieve a specific effect for a given problem object. Missions correspond to specific scenario segments. This activity selects the problem object of a mission based on the scenario segment of interest, chooses an appropriate mission object template from the mission library, and defines the mission object's execution nodes, process activities, and related metrics, providing input for activities such as process design, resource generation, and experiment design.

[0068] The input to this activity is a scene fragment, and the output is mission object information. It includes five actions: decomposing mission tasks, selecting a mission object template, defining the mission object structure, defining mission object behaviors, and managing mission object information. Figure 5 As shown.

[0069] Step 2.1, Decompose the mission task: Using scene fragments as input, analyze the entities within the scene fragments, prioritize them according to the level of concern of stakeholders, and select key problem entities as mission objects to define the mission. In this embodiment, the problem objects are search and rescue targets, namely ships and personnel in distress at sea. Steps 2.2 and 2.4 are then performed on the mission objects.

[0070] Step 2.2, Select Mission Object Template: Access the mission library to select a suitable mission object template for each mission object. Define mission objects by inheriting from the mission object template. The following will define the mission object structure and behavior based on the inherited mission object template. For example... Figure 6 As shown, the XX search and rescue object inherits the search and rescue object template, which defines the running nodes of the search and rescue object and the performance measurement, i.e., the measurement index, of each running node.

[0071] Step 2.3, Define the mission object structure: Based on the inherited mission object template, select the running node of the template from the element library and inherit it. In this embodiment, based on the inherited search and rescue object template, inherit the distressed personnel and distressed vessels from the search and rescue object template from the element library, redefine and establish XX distressed personnel, XX distressed vessels and their effectiveness measures, and then establish an association with XX search and rescue object through redefinition.

[0072] Step 2.4, Define Mission Object Behaviors: Clone the process activities in the inherited Mission Object template and make them the behaviors of the Mission Object. For example... Figure 7 As shown, the XX search and rescue object clones the process in the search and rescue object template, sets the swimlane to XX distressed personnel and XX distressed vessel, and can add corresponding running activities as needed to define the behavior of the XX search and rescue object.

[0073] Step 2.5, Managing Mission Object Information: This activity will generate a lot of mission object information, and mission objects will be associated with different mission clues, mission engineering clues and experimental plans. Therefore, it is necessary to manage this data to support users' data retrieval and analysis calculations.

[0074] Activity 3: Process Analysis. A Mission Thread is an end-to-end sequence of mission tasks, activities, and events, representing the process of achieving a mission. This activity selects a suitable Mission Thread template from the mission library based on scenario fragments and mission object information, defines the mission thread's execution nodes, process activities, and related metrics; establishes mission verification test cases; and achieves parameter cascading between execution node performance metrics and mission success metrics by combining scenario fragments, mission objects, and mission threads, supporting simulation verification of mission threads.

[0075] The input to this activity is mission object information, and the output is mission threads. It includes five actions: selecting a mission thread template, defining the mission thread structure, defining mission thread behaviors, validating mission threads, and managing mission threads. Figure 8 As shown.

[0076] Step 3.1, Select a Mission Clue Template: Access the mission library, select a suitable mission clue template based on the mission object and scene fragment, and define the mission clue by inheriting from the mission clue template. For example... Figure 9 As shown, the mission clue template for the XX maritime search and rescue mission process in this embodiment is the maritime search and rescue system template, and the XX search and rescue process in this embodiment inherits this maritime search and rescue system template.

[0077] Step 3.2, Define the mission clue structure: Access the element library, select and inherit the operational nodes in the maritime search and rescue system template, including navigation nodes, command and control nodes, search nodes and rescue nodes, and redefine them to form XX navigation nodes, XX command and control nodes, XX search nodes, XX rescue nodes and the effectiveness measurement of each node, i.e., the measurement index, and then establish the association with XX search and rescue process through redefinition.

[0078] Step 3.3, Define Mission Clue Behaviors: Clone the process activities in the mission clue template to make them mission clue behaviors. For example... Figure 10 As shown, the XX search and rescue process clones the process in the maritime search and rescue system template, setting the lanes as XX navigation node, XX command and control node, XX search node, and XX rescue node. It can also add corresponding extended operational activities to each operational node according to the needs of the current maritime search and rescue scenario. For example, based on the current XX rescue scenario information, the original search activity in the XX search node lane can be extended to a joint search activity at sea and in the air, thereby defining the behavior of the XX search and rescue process.

[0079] Step 3.4, Verify Mission Clues: To verify the correctness of the process design, create a verification use case for the XX maritime search and rescue mission process, inheriting the XX maritime search and rescue mission mission defined in Step 1.4, such as... Figure 11 As shown. The method of this invention redefines and associates the XX search and rescue object and XX search and rescue process with verification test cases, and then reuses mission constraints to conduct simulation verification of mission clues. This embodiment creates verification test cases that inherit the mission of the maritime search and rescue mission. By redefining and associating mission objects and mission clues, it establishes a parameter cascade between the performance measurement of the running node and the mission success measurement, and reuses mission constraints to conduct simulation verification in order to determine whether the established mission clues of the XX maritime search and rescue mission can successfully complete the XX maritime search and rescue mission.

[0080] Step 3.5, Managing Mission Clues: This activity will generate many different mission clues, and these mission clues will be associated with different mission engineering clues and experimental plans. Therefore, it is necessary to manage these data to support users' data retrieval and analysis calculations.

[0081] Activity 4: Resource Generation. The Mission Engineering Thread is a mission thread that includes detailed information on the capabilities, technologies, systems, and organization required to execute the mission. This activity, based on the mission thread's workflow, determines the possible resource systems for process activities according to the logical relationships of "process activities - functional services" and "functional services - resource systems"; assigns process activities to resource systems, determines the activities that resource systems need to implement, and establishes the mapping relationship between the mission thread's runtime nodes and resource systems; defines the functions of resource systems, describing behavioral logic through state machines; defines the interfaces of resource systems, describing interaction relationships through interface connections; and defines the constraint relationships between resource functions and runtime nodes, implementing parameter cascading between resource function performance measurements and runtime node performance measurements.

[0082] This activity takes a mission thread as input and a mission engineering thread as output. It includes nine actions: establishing the mission engineering thread, analyzing activity-function relationships, analyzing function-resource relationships, determining feasible resource sets, assigning activities to resources, defining resource system functions, defining resource system interfaces, defining resource system constraints, and managing the mission engineering thread. Figure 12 As shown.

[0083] Step 4.1, Establish Mission Engineering Clues: Establish mission engineering clues that inherit the mission clue, inheriting the structure and behavior of the mission clue. For example... Figure 13 As shown, XX search and rescue resources inherit XX search and rescue processes and clone its activities to ensure that the activities in XX search and rescue processes are fully transferred.

[0084] Step 4.2, Analyze Activity-Function Relationships: Access the "Process Activity-Function Service" logical relationship in the element library, and determine the functional services that implement the process activities based on the current mission engineering clue process. For example, for activities in the XX search and rescue process, determine the possible functions that implement the process activities. For instance, the available functional services for the "Execute Search Mission" activity include options such as "Infrared Imaging Search Function," "Electro-optical Imaging Search Function," and "Radar Imaging Search Function."

[0085] Step 4.3, Analyze Function-Resource Relationships: Access the "Function Service-Resource System" logical relationship in the element library. Based on the function service determined in the previous action, identify the possible resource systems that implement the function service. For example, the possible resources for "Radar Imaging Search Function" are resource systems such as maritime search vessels and search and rescue helicopters.

[0086] Step 4.4, determine the feasible resource set: By traversing the Cartesian product space formed by "process activities - functional services" and "functional services - resource systems", determine the set of feasible resource systems that can complete the mission. For example... Figure 13As shown, the resource system set that ultimately completes the XX maritime search and rescue mission includes XX navigation satellites, XX command and control systems, XX maritime search vessels, XX search and rescue helicopters, and XX maritime rescue vessels. These resource systems inherit the corresponding resource system templates, ensuring that the information in the element library is fully transferred.

[0087] Step 4.5, Assign Activities to Resources: After determining the set of feasible resource systems, assign the process activities of the mission engineering thread to the resource systems. For example... Figure 14 As shown, the process activities of XX search and rescue resources are assigned to the corresponding resource systems, and the process activities that each resource system needs to implement are determined. It can also be determined that the XX navigation node is implemented by XX navigation satellites, the XX command and control node is implemented by the XX command and control system, the XX search node is implemented by XX maritime search vessels and XX search and rescue helicopters, and the XX rescue node is implemented by XX maritime rescue vessels.

[0088] Step 4.6, Define Resource System Functions: Based on the process activities that the resource system needs to implement, define the functional behavioral logic of the resource system. For example... Figure 15 As shown, the following functions are defined by inheriting the functional service template: XX satellite navigation function, XX beyond-line-of-sight communication function, XX within-line-of-sight communication function, XX command and control function, XX maritime mobility function, XX air mobility function, XX radar imaging search function, XX infrared imaging search function, XX optoelectronic imaging search function, and XX rescue function. The behavioral logic of the functional service template is also cloned. By combining functional services through the resource system, the essential functional services that the resource system must possess are represented.

[0089] Step 4.7, Define Resource System Interfaces: Based on the information exchange between resource system swimlanes in the mission engineering thread, define the interface relationships between resource systems; based on the logical relationships of the process activities undertaken by resource systems, define the interface relationships between functional services within resource systems. For example... Figure 16 As shown, the interface relationships between resource systems and between internal functional services in the XX search and rescue resources are defined.

[0090] Step 4.8, Define Resource System Constraints: After defining the functions and interfaces of the resource system, it is also necessary to establish the constraint relationships between the resource system and the running nodes. For example... Figure 17-20 As shown, constraints were established between the performance measures of the XX navigation node, XX command and control node, XX search node, and XX rescue node and the performance measures of the functional services of the XX navigation satellite, XX command and control system, XX maritime search vessel, XX search and rescue helicopter, and XX maritime rescue vessel.

[0091] Step 4.9, Managing Mission Engineering Clues: This activity will generate many different mission engineering clues, and these clues will be associated with different experimental schemes. Therefore, it is necessary to manage these data to support users' data retrieval and analysis calculations.

[0092] Activity 5: Experimental Design. This activity, based on mission object information and mission engineering clues, establishes a simulation experiment scheme. By combining scene fragments, mission objects, mission clues, and mission engineering clues, it achieves parameter cascading between resource function performance measurement, operational node efficiency measurement, and mission success measurement; it determines the environmental block variables and resource factor variables of the simulation experiment scheme and sets their value ranges; and it develops an orthogonal experimental design method to generate a simulation experiment scheme for the mission system.

[0093] This activity takes mission object information and mission engineering clues as input and outputs a simulation experiment plan, including seven actions: creating a simulation experiment plan, defining mission object information, defining mission engineering clues, defining the experiment plan interface, setting experiment factors and methods, generating the simulation experiment plan, and managing the simulation experiment plan. Figure 21 As shown.

[0094] Step 5.1, Establish a simulation experiment plan: After resource generation, a mission solution is generated. Simulation experiments are needed to verify the correctness of the solution. Therefore, an experimental plan for the XX maritime search and rescue mission is created, inheriting the XX maritime search and rescue mission defined in Step 1.4, ensuring that all relevant information about the search and rescue mission is fully transferred, such as... Figure 22 As shown.

[0095] Step 5.2, Define Mission Object Information: The experimental plan needs to incorporate mission object information to ensure data consistency. Therefore, the mission object defined in Step 2.5 is redefined to establish a relationship with the XX maritime search and rescue mission experimental plan, such as... Figure 22 As shown.

[0096] Step 5.3, Define Mission Engineering Clues: Mission engineering clues need to be incorporated into the experimental design to ensure data consistency. Therefore, the relationship between the mission engineering clues defined in Step 4.9 and the XX maritime search and rescue mission experimental design is established by redefining them, such as... Figure 22 As shown.

[0097] Step 5.4, Define the Experimental Scheme Interface: After determining the mission object information and mission engineering clues of the simulation experimental scheme, it is also necessary to establish an interaction interface between the two to provide a signal interface for subsequent simulation deduction. For example... Figure 23As shown in the example, this example establishes an interaction interface between the XX search and rescue object and the XX search and rescue resource. The XX search and rescue resource has a signal receiving port and a rescue service port, while the XX search and rescue object has a service receiving port and a distress signal port.

[0098] Step 5.5, Set experimental factors and methods: Determine the environmental block variables and resource factor variables of the simulation experiment scheme, and set the value range of the environmental block variables and resource factor variables; develop orthogonal experimental design methods such as Monte Carlo and Latin hypercube to support the generation of simulation experiment schemes.

[0099] Step 5.6, Generate simulation experiment scheme: Incorporate environmental block variables and resource factor variables into the experimental design method, and generate an experimental scheme that meets the simulation requirements by combining completely randomized and stratified randomized methods, thereby reducing the number of experimental schemes while ensuring the accuracy and coverage of the experiment.

[0100] Step 5.7, Managing Simulation Experiment Schemes: This activity will generate many simulation experiment schemes that combine environmental block variables, resource factor variables, and experimental design methods. Therefore, it is necessary to manage these data to support users' data retrieval and analysis calculations.

[0101] Activity 6: Simulation and Deduction. This activity uses a simulation experiment scheme as input, sets the simulation configuration, including start time, end time, number of simulations, and simulation step size; develops a visualized spatiotemporal information model for simulation deduction, establishes the semantic mapping relationship between the mission object, mission engineering clue model, and spatiotemporal information model; realizes the simulation deduction of the mission system based on discrete event signals, performs sensitivity analysis on the simulation deduction data, obtains key experimental factors, and thus supports system evaluation and optimization.

[0102] This activity takes a simulation experiment plan as input and outputs simulation data as output. It includes six actions: selecting a simulation experiment plan, developing a simulation scenario, setting simulation parameters, conducting joint simulations, analyzing simulation data, and managing simulation data. Figure 24 As shown.

[0103] Step 6.1, Select a simulation experiment scheme: Select a set of experimental data from the generated simulation experiment scheme in a reuse manner as the input for simulation deduction to ensure the continuous transmission of experimental design data.

[0104] Step 6.2, Develop simulation scenarios: Develop a visualized spatiotemporal information model for simulation and incorporate experimental data into the model to provide simulation scenarios for joint simulation.

[0105] Step 6.3, Set simulation parameters: Set the start time, end time, number of simulations, and simulation step size of the simulation to establish the mapping relationship between the signal events between the simulation experiment scheme and the simulation scenario.

[0106] Step 6.4, Conduct joint simulation: Based on the principle of discrete event simulation, the simulation scenario generates various signal events through spatiotemporal simulation and transmits them to the simulation experiment scheme through mapping relationships; after receiving the signals, the simulation experiment scheme executes the corresponding control logic and generates control signals to control the behavior of objects in the simulation scenario. This process is repeated to achieve joint simulation.

[0107] Step 6.5, Analyze simulation data: By conducting simulations on a large amount of experimental data, sensitivity analysis is performed on the obtained simulation data to obtain key experimental variables, thereby supporting system optimization.

[0108] Step 6.6, Managing Simulation Data: This activity will select different simulation experiment schemes, thereby generating different simulation data. Therefore, it is necessary to manage this data to support users' data retrieval and analysis calculations.

[0109] Activity 7: Evaluation and Optimization. This activity defines an evaluation index system and establishes a mapping and calculation relationship between the evaluation index system and simulation data, realizing the aggregation of simulation data into the evaluation index system; it supports the evaluation of the effects of the same mission engineering clues on different mission objects, with evaluation methods including weighted analysis and comprehensive evaluation methods; it supports the optimization of the effects of different mission engineering clues on the same mission objects, with optimization methods including single index optimization and comprehensive optimization.

[0110] This activity takes simulation data as input and outputs evaluation and optimization results. It includes six actions: defining the evaluation index system, establishing data index mapping, calculating system evaluation indicators, defining multi-index evaluation methods, evaluating the system effectiveness, and managing the evaluation and optimization results. Figure 25 As shown.

[0111] Step 7.1, Define the evaluation index system: Propose an evaluation index system from five aspects: mission structure, behavior, effectiveness, cost, and agility. Define the data type, unit of measurement, and application area of ​​the indexes to determine their precise meaning.

[0112] Step 7.2, Establish data index mapping: Define the logical mapping relationship between the evaluation index system and the simulation data, and develop an aggregation algorithm between the evaluation index system and the simulation data to support the calculation of system evaluation indexes.

[0113] Step 7.3, Calculate system evaluation indicators: Collect the required data from the simulation data and inject it into the aggregation algorithm to calculate the values ​​of the system evaluation indicators, supporting multi-indicator evaluation of the system.

[0114] Step 7.4, define the multi-index evaluation method: use the normalization algorithm to preprocess the multiple indicators, and select evaluation methods such as weighted summation method, analytic hierarchy process, and fuzzy comprehensive method to conduct a comprehensive evaluation of the multiple indicators.

[0115] Step 7.5, Evaluate the effectiveness of the mission system: Support the evaluation of the effect of the same mission engineering clues on different mission objects, and support the optimization of the effect of different mission engineering clues on the same mission object.

[0116] Step 7.6, Managing Evaluation and Optimization Results: This activity will select different simulation data and evaluation and optimization methods to generate different evaluation and optimization results. Therefore, it is necessary to manage these data to support users' data retrieval and analysis calculations.

[0117] To address the standardization issue of model access in digital mission engineering, this invention, based on object-oriented technology, designs a meta-model library calling technology encompassing reuse, inheritance, cloning, and instantiation methods for the first five activities in digital mission engineering: scenario conception, mission definition, process analysis, resource generation, and experiment design. An example of the models obtained during the first five activities of the XX maritime search and rescue mission is shown below. Figure 26 As shown.

[0118] (1) Scene conception: This activity generates scene fragments. The calling technology is based on accessing the mission library through the XMI standard interface, selecting a suitable mission template, and defining the mission by inheriting the mission template; redefining the layout, operation environment, duration and other attributes in the mission, and defining specific background environment data for the mission.

[0119] (2) Mission definition: This activity generates mission objects. The calling technology is based on accessing the mission library through the XMI standard interface, selecting the corresponding mission object template, and defining the mission object by inheriting the mission object template; based on accessing the element library through the XMI standard interface, defining the mission object's running node by inheriting the running node template, and establishing the relationship with the mission object by redefining; cloning the process in the mission object template to establish the mission object's process.

[0120] (3) Process analysis: This activity generates mission clues. The calling technology is based on accessing the mission library through the XMI standard interface, selecting the corresponding mission clue template, and defining the mission clue by inheriting the mission clue template; based on accessing the element library through the XMI standard interface, defining the mission clue's running node by inheriting the running node template, and establishing the relationship with the mission clue by redefining; cloning the process in the mission clue template to establish the mission clue process; inheriting the mission from step (1) to generate mission verification test cases, and redefining the association between the mission object from step (2) and the mission clue in this step to establish parameter cascading between running node performance measurement and mission success measurement, and reusing mission constraints to carry out simulation verification.

[0121] (4) Resource generation: This activity generates mission engineering clues, inherits the mission clues from step (3) to establish mission engineering clues, and clones the mission clue process to generate mission engineering clue processes; access the element library based on the XMI standard interface, and determine the feasible set of resource systems based on the mission engineering clue process according to the logical relationship of "process activity-functional service" and "functional service-resource system", and define the resource system by inheriting the resource system template; allocate the process activities of the mission engineering clues to the resource system, determine the activities that the resource system needs to implement, and the mapping relationship between the mission clue running nodes and the resource system; define the functional services of each resource system and the interface relationship between these functional services according to the logical relationship of "process activity-functional service"; develop the constraint relationship between the mission clue running nodes and the resource system by reusing the constraint equation template, and realize the parameter cascading between resource function performance measurement and running node efficiency measurement.

[0122] (5) Experimental Design: This activity generates a simulation experiment scheme. The simulation experiment scheme inherits the mission of step (1). By redefining the mission object of step (2) and the mission engineering clue of step (4), the parameters between resource function performance measurement, running node efficiency measurement and mission success measurement are cascaded. By reading the attributes in mission, mission object and mission engineering clue, the environmental block variables and resource factor variables of the simulation experiment scheme are determined, and the value range of environmental block variables and resource factor variables is set. An orthogonal experimental design method is developed, and the simulation experiment scheme of mission system is generated by instantiation technology.

[0123] Except for the technical features described in the specification, all other technologies are known to those skilled in the art. Descriptions of well-known components and technologies are omitted in this invention to avoid redundancy and unnecessary limitation. The embodiments described above do not represent all embodiments consistent with this application. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this invention are still within the protection scope of this invention.

Claims

1. A digital mission engineering development method based on a meta-model library, comprising a pre-built meta-model library of a maritime search and rescue mission system, the meta-model library including a scenario library, a mission library, and an element library; for the currently input maritime search and rescue mission data and modeling problem, a digital mission engineering process is executed to construct a mission model; the modeling problem is to construct the current maritime search and rescue mission model; the digital mission engineering process includes the following steps: Step 1, performing a scenario conception activity on the input task data, describing the natural environment, participating entities, layout, running intention and key events in the running scenario by time, cutting the input data into scenario segments, and selecting a scenario segment to be studied; Select a maritime search and rescue mission template from the mission library and inherit the template to create a mission. For each scenario fragment to be studied, redefine the layout, mission conditions, environmental conditions and duration in the mission, and output the scenario fragment. Step 2: Perform the mission definition activity on each scene fragment output in Step 1. Select the problem object as the mission object from the scene fragment, select the corresponding mission object template from the mission library and inherit it, redefine the mission object's running nodes, process activities and metrics, and output the constructed mission object; the problem object for maritime search and rescue missions is the search and rescue object. Step 3: Perform process analysis activities. Select the corresponding mission clue template from the mission library and inherit it. Redefine the mission clue of the current task. Select the operation node template from the mission clue from the element library and inherit it. Redefine the operation node of the current task. Establish the mission clue of the current task, including operation nodes, process activities, and metrics. For the current maritime search and rescue mission, the mission clue template is the maritime search and rescue system template. Establish the mission clue of the current maritime search and rescue mission based on this template. Create verification test cases that inherit the mission of the maritime search and rescue mission. Simulate the verification test cases to verify whether the established maritime search and rescue mission clue can complete the mission. Step 4: Execute the resource generation activity to construct the mission engineering thread of the mission thread established in Step 3. This includes: based on the mission thread, determining the possible resource systems for the process activities according to the logical relationships between process activities, functional services, and resource systems; assigning process activities to resource systems, determining the activities that the resource systems need to implement, and the mapping relationship between the mission thread's running nodes and the resource systems; defining the functions of the resource systems and describing the behavioral logic through state machines; defining the interfaces of the resource systems and describing the interaction relationships through interface connections; defining the constraint relationships between resource functions and running nodes, and realizing the parameter cascading between resource function performance measurements and running node performance measurements. Step 5: Perform experimental design activities and establish a simulation experiment scheme based on the mission object and mission engineering clues. The established simulation experiment scheme inherits the mission constructed in Step 1 and is associated with the mission object in Step 2 and the mission engineering clues in Step 4 to realize the parameter cascading between resource function performance measurement, running node efficiency measurement and mission success measurement. Step 6: Perform simulation simulation activities. Using the simulation experiment scheme established in Step 5 as input, set the simulation configuration, develop a visualized spatiotemporal information model for simulation simulation, establish the semantic mapping relationship between the mission object, mission engineering clue model and spatiotemporal information model, and perform simulation simulation of the mission system based on discrete event signals; perform sensitivity analysis on the obtained simulation simulation data to obtain key experimental variables. Step 7, perform evaluation and optimization activities, including: defining the evaluation index system, establishing the mapping and calculation relationship between the evaluation index system and the simulation data, and calculating the evaluation index based on the simulation data.

2. The method of claim 1, wherein, In step 1, the scenario conception activity includes the following actions: identifying stakeholders, describing the operational scenario, framing scenario segments, selecting mission templates, defining scenario segments, and managing scenario segments. Among these, selecting mission templates refers to choosing a maritime search and rescue mission template from the mission library. The mission attributes included in the template are layout, mission conditions, environmental conditions, and duration.

3. The method of claim 1, wherein, In step 2, the mission definition activity includes the following actions: decomposing mission tasks, selecting mission object templates, defining mission object structure, defining mission object behavior, and managing mission object information. Selecting a mission object template means choosing the corresponding search and rescue object template from the mission library and inheriting that template to define the mission object. Defining the mission object structure means selecting and inheriting the running nodes of the inherited search and rescue object template from the element library, redefining the running nodes and measurement indicators, and establishing the relationship between the running nodes and the mission object through redefinition. The running nodes include people in distress and vessels in distress. Defining mission object behavior means cloning the process activities in the inherited search and rescue object template to establish the process of the search and rescue object.

4. The method of claim 1, wherein, In step 3, the process analysis activities include the following actions: selecting a mission clue template, defining the mission clue structure, defining mission clue behavior, validating mission clues, and managing mission clues. After generating mission clues that inherit from the maritime search and rescue system template, defining the mission clue structure means selecting and inheriting the operational node template from the element library, redefining the operational nodes and node metrics of the current mission clue, and establishing the relationship between operational nodes and mission clues through redefinition. Operational nodes include navigation nodes, command and control nodes, search nodes, and rescue nodes. Defining mission clue behavior means cloning the processes in the maritime search and rescue system template and adding extended operational activities in the current maritime search and rescue scenario to each operational node in the current mission template. Validating mission clues means creating validation use cases that inherit the maritime search and rescue mission mission, establishing parameter cascading between operational node metrics and mission success metrics by redefining the associated mission objects and mission clues, reusing mission constraints to conduct simulation verification, and determining whether the mission clues of the current maritime search and rescue mission can complete the maritime search and rescue mission.

5. The method of claim 1, wherein, In step 4, the resource generation activity includes the following actions: establishing a mission engineering thread, analyzing activity-function relationships, analyzing function-resource relationships, determining a feasible resource set, allocating activities to resources, defining resource system functions, defining resource system interfaces, defining resource system constraints, and managing the mission engineering thread. Specifically: establishing a mission engineering thread means inheriting the mission thread established in step 3, cloning the mission thread's process, and generating the mission engineering thread's process. Analyzing activity-function relationships means accessing the process activity-function service logical relationships in the element library and determining the functional services that can realize the process activities based on the mission engineering thread process. Analyzing function-resource relationships means accessing the function service-resource system logical relationships in the element library and determining the resource systems that can realize the function services based on the function services determined in the previous action. Determining a feasible resource set means traversing the process activity-function service and function service-resource system to form a set of feasible resources. The Cartesian product space identifies the set of feasible resource systems for completing the mission. Each resource system inherits the corresponding resource system template from the element library and redefines the resource system. Assigning activities to resources means allocating the process activities of the mission engineering thread to resource systems based on the set of feasible resource systems, determining the process activities that each resource system needs to implement. Defining resource system functions means defining the functional services of each resource system based on the process activities that each resource system needs to implement. These functional services are obtained by inheriting and redefining the corresponding functional service templates from the element library, and the behavioral logic of the functional service templates is cloned. Defining resource system interfaces means defining the interface relationships between resource systems based on the information interaction between resource systems in the mission engineering thread, and defining the interface relationships between functional services within a resource system based on the logical relationships of the process activities undertaken by the resource system. Defining resource system constraints means establishing the constraint relationships between resource systems and running nodes by reusing constraint equation templates.

6. The method of claim 1, wherein, In step 5, the experimental design activities include the following actions: establishing a simulation experiment scheme, defining mission object information, defining mission engineering clues, defining the experiment scheme interface, setting experiment factors and methods, generating the simulation experiment scheme, and managing the simulation experiment scheme. Establishing a simulation experiment scheme refers to creating the current maritime search and rescue mission experiment scheme, which inherits the mission mission constructed in step 1. Defining mission object information refers to establishing a relationship between the mission object constructed in step 2 and the current experiment scheme. Defining mission engineering clues refers to establishing a relationship between the mission engineering clues constructed in step 4 and the current experiment scheme. Defining the experiment scheme interface refers to establishing an interaction interface between the mission object and the mission engineering clue in the current experiment scheme. In setting experiment factors and methods, the environmental block variables and resource factor variables of the current experiment scheme are determined by the attributes in the established mission, mission object, and mission engineering clue, and the value range of the environmental block variables and resource factor variables is set.

7. The method of claim 1, wherein, In step 6, the simulation simulation activity includes the following actions: selecting a simulation experiment scheme, developing a simulation scenario, setting simulation simulation parameters, conducting joint simulation simulation, analyzing simulation simulation data, and managing simulation simulation data. Selecting a simulation experiment scheme means choosing a set of experimental data from the generated simulation experiment schemes as input for the simulation simulation through reuse. Developing a simulation simulation scenario means developing a visualized spatiotemporal information model for the simulation simulation and incorporating the experimental data into the model to provide a simulation simulation scenario for joint simulation. Setting simulation simulation parameters includes setting the start time, end time, number of simulations, and simulation step size, establishing a mapping relationship between the signal events of the simulation experiment scheme and the simulation simulation scenario. Conducting joint simulation simulation is based on the principle of discrete event simulation. The simulation simulation scenario generates various signal events through spatiotemporal simulation and transmits them to the simulation experiment scheme through the mapping relationship. After receiving the signals, the simulation experiment scheme executes the corresponding control logic and generates control signals to control the behavior of objects in the simulation simulation scenario. This process is repeated to achieve joint simulation simulation. Analyzing the simulation simulation data includes performing sensitivity analysis on the obtained simulation simulation data to obtain key experimental variables.

8. The method of claim 1, wherein, Step 7, evaluating the optimal results, includes defining an evaluation index system, establishing a data index mapping, calculating system evaluation indicators, defining a multi-index evaluation method, evaluating the effectiveness of the mission system, and managing the evaluation optimal results. Defining the evaluation index system involves proposing an evaluation index system from five aspects: mission structure, behavior, effectiveness, cost, and agility, defining the data type, unit of measurement, and application domain of the indicators. Establishing a data index mapping defines the logical mapping relationship between the evaluation index system and simulation data, and sets an aggregation algorithm between the evaluation index system and simulation data. Calculating system evaluation indicators involves collecting the necessary data from the simulation data and injecting it into the aggregation algorithm to calculate the evaluation indicators. Defining a multi-index evaluation method refers to using a normalization algorithm to preprocess multiple indicators before performing a comprehensive evaluation. Evaluating the effectiveness of the mission system means supporting the evaluation of the effects of the same mission engineering threads on different mission objects, and supporting the optimization of the effects of different mission engineering threads on the same mission object.

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