Task model driven unmanned cluster command and control simulation method

Abstract

The application discloses a task model driven unmanned cluster command control simulation method, and belongs to the technical field of unmanned cluster command control, and comprises the following steps: establishing a universal task model, respectively establishing a formation level task model, a group level task model and a platform level task model based on the universal task model, respectively realizing the business process and function simulation of unmanned cluster multi-layer command control from formation cooperative decision, task group cooperative decision to unmanned platform autonomous control, and driving multi-level command business twist automation and command decision intelligence based on the formation level task model, the group level task model and the platform level task model, realizing manned or unmanned cooperative decision under a multi-domain task scene, inter-group and intra-group cooperative control of a UAV group, an unmanned ship group and an unmanned underwater vehicle group. The application meets the unmanned cluster task capability, command control capability verification and effect demonstration application requirement under a typical confrontation task scene.

Description

Technical Field

[0001] This invention belongs to the field of unmanned swarm command and control technology, and more specifically, relates to a task model-driven simulation method for unmanned swarm command and control. Background Technology

[0002] With the rapid development of the new military revolution, human warfare is evolving towards information warfare, and unmanned warfare has become a significant trend. This will expand the combat capabilities of manned platforms, becoming a crucial node in informationized and networked warfare, and changing traditional warfare models. Currently, after decades of development, unmanned systems have entered a period of rapid growth. Driven by advancements in key technologies such as information technology and control technology, the control methods of unmanned equipment are evolving towards autonomy, combat modes towards system collaboration, combat missions towards firepower strikes, and system architecture towards modularization and universalization. Their operational applications are expanding from reconnaissance and surveillance, and communication relay to all domains, ushering in the era of unmanned warfare.

[0003] For unmanned systems, their importance lies in two aspects: their ability to coordinate with manned systems and their ability to conduct swarm operations. Currently, facing a highly adversarial, highly uncertain, and highly dynamic battlefield environment, the operational style of unmanned systems has gradually evolved from single-platform operations to manned or unmanned coordinated and unmanned swarm operations.

[0004] In view of the current and future development trends of unmanned equipment and the application needs of swarm warfare, it is necessary and urgent to carry out research on the business processes and key technologies of unmanned swarm command and control. This will enable the organic integration of various unmanned platforms and their manned platforms, which will help to further enhance the overall effectiveness of system warfare.

[0005] Modeling and simulation are effective methods with inherent advantages for research on command and control processes and key technologies of unmanned swarms. By modeling and simulating unmanned swarm equipment and combat operations, the collaborative command and control processes and key technologies of swarms can be verified and improved in a virtual environment. Furthermore, through "virtual practice," the optimal combat application patterns of unmanned swarms for different missions can be explored, providing effective support for the development and exploration of unmanned swarm equipment and usage models.

[0006] However, an analysis of research on unmanned swarm warfare theory and application, as well as unmanned swarm warfare modeling and simulation, reveals several gaps compared to the needs of simulated unmanned swarm system confrontation in real-world application scenarios: 1) Modeling and simulation often focus on individual command and control functions or key technical issues, such as ad hoc networking, formation control, and task allocation, lacking simulation of the entire unmanned swarm system confrontation command and control process; 2) Unmanned swarm action models and simulations are overly simplified, failing to effectively simulate the autonomous capabilities of unmanned swarms at the inter-swarm, intra-swarm, and individual levels; 3) Research on system confrontation modeling and simulation under multi-level command and control of unmanned swarms is insufficient. Therefore, existing unmanned swarm command and control simulation schemes are insufficient to effectively support the verification and demonstration of unmanned swarm mission capabilities, command and control capabilities, and effects in typical confrontation mission scenarios. Summary of the Invention

[0007] To address the application needs of verifying and demonstrating the capabilities and effects of unmanned swarm missions and command and control in typical adversarial mission scenarios, this paper proposes a simulation method for unmanned swarm command and control. At the operational level, it has the capability to simulate the business processes and functions of multi-level command and control of unmanned swarms, from formation collaborative decision-making and task group collaborative decision-making to autonomous control of unmanned platforms, achieving the simulation of unmanned swarm collaborative combat effects of "unmanned platform, manned system". At the technical level, it has the capability of automating multi-level command and control business operations and intelligentizing command and decision-making, avoiding complex manual operations and facilitating multiple trials.

[0008] To achieve the above objectives, this invention provides a task model-driven simulation method for unmanned swarm command and control, comprising:

[0009] (1) Establish a generalized task model. Based on the generalized task model, establish a formation-level task model, a group-level task model, and a platform-level task model respectively.

[0010] (2) Realize the business process and function simulation of multi-level command and control of unmanned swarms from formation collaborative decision-making, task group collaborative decision-making to unmanned platform autonomous control, and drive multi-level command and control business to turn around automation and intelligent command decision-making based on formation-level task model, group-level task model and platform-level task model, realize manned or unmanned collaborative decision-making in multi-domain task scenarios, and achieve collaborative control between and within groups of unmanned swarms, unmanned boat swarms and unmanned underwater vehicles.

[0011] In some alternative implementations, step (1) includes:

[0012] (1.1) Based on the basic information of the model, the basic information of the task, the basic parameters of the task, the business parameters of the task, and the planning parameters of the task, a general task model is established;

[0013] (1.2) Instantiate general task models according to different task levels and task content to form corresponding formation-level task models, unmanned group-level task models or unmanned platform-level task models.

[0014] In some alternative implementations, step (2) includes:

[0015] (2.1) Implement the command and control business processes and function simulations of formation, unmanned group, and unmanned platform respectively, including functions such as task planning communication, intelligence processing, command decision-making, command and control and task management, generate unmanned platform task instructions or control instructions, and control the unmanned platform to execute tasks.

[0016] (2.2) Based on the formation-level task model, group-level task model and platform-level task model, drive the automation of multi-level command and control business, and realize intelligent command and decision-making to achieve manned or unmanned collaborative decision-making in multi-domain task scenarios, and collaborative control between and within groups of UAV swarms, unmanned surface vessels swarms and unmanned underwater vehicles swarms.

[0017] In some alternative implementations, step (2.2) includes:

[0018] (2.2.1) Receive and parse the task plan pushed by the command and control process and function simulation, and automatically set the task objectives, task start time, predecessor tasks and task end time in the basic task information according to the task plan and task planning model; set the task area and target list in the task business parameters to form an executable task at this level; or drive the command and control process and function simulation to guide and assist manual setting of relevant information through the human-computer interaction interface to form an executable task at this level.

[0019] (2.2.2) After an executable task is formed, based on the basic information of the task and the task business parameters, as well as the command and control process and function simulation push unmanned cluster and target situation information, the task planning and execution status is automatically managed and controlled, including: task pre-planning, task start control, task real-time planning, task end judgment, and push task execution status information to the task management function module of the command and control process and function simulation.

[0020] In some alternative implementations, step (2.2.2) includes:

[0021] (2.2.2.1) Task pre-planning: Based on the task business parameters and task planning parameter settings, determine whether task pre-planning needs to be performed. If so, according to the associated pre-planning method, based on the initial situation pushed by the command and control process and function simulation, the task pre-planning is automatically executed, and the results are pushed to the command decision function module of the command and control process and function simulation. It supports manual confirmation or modification of the pre-planning scheme and then issuing it as needed. If the pre-planning generates sub-tasks, it drives the planning of lower-level tasks after issuance, up to the unmanned platform task level.

[0022] (2.2.2.2) Task start control: Based on the task start conditions set in the basic task information, automatically monitor and determine whether the task has started. If it has started, push the task start information to the task control and task management function module of the command and control process and function simulation, issue instructions as needed to drive the unmanned platform to execute the task, and update the task status.

[0023] (2.2.2.3) Real-time task planning: Based on the task business parameters and task planning parameter settings, as well as the command and control process and function simulation push unmanned cluster and target situation information, the task real-time planning and task replanning are automatically triggered as needed, and the results are pushed to the command decision function module of the command and control process and function simulation. It supports manual confirmation or modification of the pre-planned scheme and then issues it as needed. If the real-time planning and task replanning generate sub-task assignments, the assignment will drive the command and control of the lower-level tasks, up to the unmanned platform task level.

[0024] (2.2.2.4) Task completion judgment: Based on the task completion conditions set in the basic task information, automatically monitor and judge whether the task has ended. If it has ended, automatically evaluate the task execution effect according to the task success criteria in the basic task information, and push it to the command decision and task management function module of the command and control process and function simulation to update the task status.

[0025] In some optional implementations, the general task model includes: basic model information, basic task information, task business parameters, and task planning parameters. The basic model information includes task domain, task type, task level, subtask set or sequence, and task description. The basic task information includes task objective, task start time, preceding task, task end time, task success criteria, task start condition, and task end condition. The task business parameters include task style set, task area, target list, and task resource requirements. The task planning parameters include task planning rules, process items, and associated required task planning methods or instruction sets.

[0026] In some optional implementations, the subtask set or sequence, the formation-level subtasks correspond to the group-level tasks, the group-level subtasks correspond to the unmanned platform-level tasks, the subtasks are triggered when the formation-level collaborative decision is made, the unmanned group tasks are automatically generated, and the group-level collaborative decision is driven; the subtasks are triggered when the group-level collaborative decision is made, the unmanned platform tasks are automatically generated, and the unmanned platform is driven to autonomously control, so as to realize the automation of multi-level command and control business reversal.

[0027] In some optional implementations, the task hierarchy includes formation level, group level and platform level, and the task resource requirements include task capability and index requirements, task platform type or model and quantity requirements and task payload type or model and quantity requirements. The task resource requirements should be set separately according to the task style and sub-tasks.

[0028] In some optional implementations, the mission planning parameters include mission planning rules, process items, and associated mission planning methods and instruction sets. The mission planning parameters set the logical flow and methods of mission planning. Mission planning includes two stages: pre-planning and real-time planning. The mission planning logical flow adopts behavior tree, state machine, or workflow methods. The required mission planning methods or instruction sets are associated with the planning logical flow as needed. The mission planning methods include: mission decomposition, mission allocation, target allocation, mission payload planning, command relationship planning, communication network planning, and mission route planning methods. Different mission levels (formation, group, platform) and different missions (reconnaissance, strike, support, etc.) will have different mission planning rules, processes, and associated mission planning methods set to support the intelligent decision-making needs of different missions.

[0029] In some optional implementations, the task grouping collaborative decision-making can realize one or more nodes, creating different nodes according to the task domain and unmanned cluster type, and the unmanned platform autonomous control can realize one or more nodes, creating different nodes according to the number of unmanned platforms.

[0030] In some optional implementations, the mission-driven command and decision simulation will create one-to-one mission-driven instances for the simulated formations, groups, and unmanned platform nodes based on the formation-level mission model, the unmanned group-level mission model, and the unmanned platform-level mission model, respectively.

[0031] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0032] This invention addresses the application needs of verifying and demonstrating the capabilities and effects of unmanned swarm missions and command and control in typical adversarial mission scenarios. It adopts a generalized mission model and operating mechanism design to drive the simulation of the entire command and control process, from formation collaborative decision-making and task group collaborative decision-making to autonomous control of unmanned platforms. It realizes manned or unmanned collaborative decision-making in multi-domain mission scenarios, and collaborative control between and within groups of UAV swarms, unmanned surface vessel swarms, and unmanned underwater vehicle swarms. It achieves the complementary advantages of "unmanned platform, manned system" unmanned swarm collaborative combat and simulates the swarm emergence effect. At the same time, it realizes the automation of multi-level command and control operations and the intelligentization of command and decision-making capabilities, avoiding complex manual operations and facilitating multiple sets of repeated tests. Attached Figure Description

[0033] Figure 1 This is a flowchart of a method provided in an embodiment of the present invention;

[0034] Figure 2 This is a general task model diagram provided in an embodiment of the present invention;

[0035] Figure 3 This is a grouping-level task model diagram provided in an embodiment of the present invention;

[0036] Figure 4 This is a schematic diagram of a task-driven group-level collaborative decision-making process provided by an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0038] This invention discloses a task model-driven unmanned swarm command and control simulation method, specifically comprising two major steps: task model construction and unmanned swarm command and control simulation. This invention employs a generalized task model and operational mechanism design to drive the entire process of command and control simulation, from formation collaborative decision-making and task group collaborative decision-making to autonomous control of unmanned platforms. It achieves manned or unmanned collaborative decision-making in multi-domain task scenarios, and simulates inter-swarm and intra-swarm collaborative control of UAV swarms, unmanned surface vessel swarms, and unmanned underwater vehicle swarms. This achieves the complementary advantages of "unmanned platforms, manned systems" in unmanned swarm collaborative operations and the simulation of swarm emergence effects. Simultaneously, it realizes automated multi-level command and control operations and intelligent command decision-making capabilities, avoiding complex manual operations and facilitating multiple trials.

[0039] The overall process is as follows Figure 1As shown, a business process and functional simulation environment for multi-layered command and control of unmanned swarms is constructed, encompassing formation collaborative decision-making, task group collaborative decision-making, remote command and control by manned platforms, and autonomous control by unmanned platforms. This environment features complete business processes, automated operation, and intelligent decision-making, and can support the verification and demonstration of unmanned swarm mission capabilities and command and control capabilities in typical adversarial mission scenarios. The task model construction includes two steps: general task model construction and task model construction at each level. The unmanned swarm command and control simulation includes two sub-steps: command and control process and function simulation, and task-driven command decision simulation.

[0040] Taking a formation-level unmanned swarm command and control simulation as an example (the formation contains three mission domains (formation-level), denoted as...), Mission domain 1 includes a drone swarm mission. Mission domain 2 includes one unmanned aerial vehicle (UAV) swarm mission (group level) and one unmanned surface vessel (USV) swarm mission (group level), denoted as follows: Mission domain 3 includes one unmanned aerial vehicle (UAV) swarm mission (group level), one unmanned surface vessel (USV) swarm mission (group level), and one unmanned underwater vehicle (UUV) swarm mission (group level), denoted as follows: Each UAV, unmanned surface vessel, and unmanned underwater vehicle contains one or more unmanned platform-level tasks. Specifically, in this example, the number and type of group-level and platform-level tasks are not pre-designed; group-level tasks are generated under the drive of formation tasks, and platform-level tasks are generated under the drive of group-level tasks. (The task and quantity settings in this example are merely illustrative.) The technical solution of this invention is described in detail below:

[0041] (1) Task Model Construction

[0042] Establish a generalized task model; based on the generalized task model, establish a formation-level task model, a group-level task model, and a platform-level task model respectively.

[0043] A. General Task Model Construction: Based on basic model information, basic task information, basic task parameters, task business parameters, and task planning parameters, a general task model is established. Specific details are as follows: Figure 2 As shown:

[0044] a) Basic model information: including task domain, task type, task level, subtask set or sequence, and task description item.

[0045] i. Task Domain: Identifies the business domain to which a task belongs. One or more task domains can be customized according to business type for selection when building task models at all levels.

[0046] ii. Task Type: Identifies the task type. This can be customized when building task models at each level. The task type must be unique.

[0047] iii. Task Level: Identifies the task level and its correspondence with the command and control level, including three types: formation level, group level, and platform level, for selection when constructing task models at each level.

[0048] iv. Subtask set or sequence: Identifies the lower-level task set or sequence, which must be the tasks supported by the lower level; if a task sequence is set, it must include the parallel or serial timing relationship.

[0049] v. Task Description: Brief description of the task information, using any text.

[0050] b) Basic Task Information: This includes the task objective, task start time, preceding tasks, task end time, task success criteria, task start conditions, and task end conditions. These are used for automatic task start / end determination and task effectiveness evaluation.

[0051] i. Task Objectives: Briefly describe the task objectives using any text.

[0052] ii. Task start time: The absolute time when the task starts.

[0053] iii. Preceding task: This task is set to begin after a certain task is completed.

[0054] iv. Task completion time: The absolute or relative time at which the task ends.

[0055] v. Task Success Criteria: Set the criteria for judging task completion. Set the criteria for judging task completion according to indicators and indicator values. Supports judging one or more indicators.

[0056] vi. Task start conditions: Set the conditions for starting a task, supporting triggering the task start based on time, previous tasks, target events, etc.

[0057] vii. Task termination conditions: Set the conditions for determining the termination of a task, supporting termination based on events, task-level functions, insufficient task resources, target events, and other conditions.

[0058] c) Task business parameters: including task style set, task area, target list, and task resource requirements, used for task planning and calculation. These parameters are set for use in task planning and calculation.

[0059] i. Task Style Set: Sets the supported task styles, allowing for custom settings when building task models at each level.

[0060] ii. Task Area: Sets the space area for task execution. Custom settings are available when instantiating tasks at each level.

[0061] iii. Target List: Sets the list of enemy targets when the task is executed. Custom settings are available when the task is instantiated at each level.

[0062] iv. Task Resource Requirements: Set the resource requirements for executing the task. Supports setting resource requirements in one or more of three modes: task capability and its index, task platform type or model and its quantity, and task payload type or model and its quantity. Task resource requirements should be set separately for task style and subtasks. After setting, an executable task planning program is generated.

[0063] d) Task planning parameters: These include task planning rules, process items, and the associated required task planning methods or instruction sets. When constructing candidate task models at different levels, different task levels (formation, grouping, platform) and different tasks (reconnaissance, strike, support, etc.) will have different task planning rules, processes, and associated task planning methods set to support the intelligent decision-making needs of different tasks.

[0064] i. Task planning rules and process items: The logical process of task planning can be set using behavior trees, state machines or workflows, and task planning can include two stages: pre-planning and real-time planning.

[0065] ii. Associated Required Task Planning Methods or Instruction Sets: Associate the required task planning methods or instruction sets as needed in the planning logic flow. Task planning methods include: task decomposition, task allocation, target allocation, task payload planning, command relationship planning, communication network planning, and task route planning methods.

[0066] B. Construction of Task Models at Each Level: Based on different task levels and content, instantiate general task models to form corresponding formation-level task models, unmanned swarm-level task models, or unmanned platform-level task models. In this embodiment, only one swarm-level task model, TGM(1, X), is used as an example (this task model generates unmanned swarm tasks after instantiation). For details, please see [link / details]. Figure 3 As shown, the construction of task models at each level is illustrated below:

[0067] a) Model basic information instantiation: Based on the current task, fill in the task domain, task type, task level, subtask set or sequence, and task description information. In this embodiment, the task domain of the group-level task model TGM(1, X) is set to 1, the task type is set to X, the task level is set to group-level, the subtask sequence is set to include three sequentially executed subtasks A1→A2→A3, and the task description is left blank.

[0068] b) Instantiation of basic task information: Based on the current task, fill in the task success criteria, task start conditions, and task end conditions. In this embodiment, the start condition of the group-level task model TGM(1, X) is set to be time-based, the end condition is set to task completion or completion, and the task success criteria is set to meet the requirements of task indicator P.

[0069] c) Instantiation of task business parameters: Based on the current task, fill in the task style set and task resource requirements; in this embodiment, the task style is set to two styles: independent execution and collaborative execution; set the task resource requirements for sub-tasks A1, A2 and A3 respectively. The resource requirement for A1 is to have the ability to execute task A1 and to be an aerial platform; the resource requirement for A2 is to have the ability to execute task A2 and to be equipped with payload L; the resource requirement for A3 is to have the ability to execute task A3.

[0070] d) Task planning parameter instantiation: Based on the current task, set the task planning rules and process, and configure the required task planning methods or instruction sets for the process. In this embodiment, the task planning process includes two stages: pre-planning and real-time planning. Pre-planning includes two sub-stages: task resource allocation and route planning, while real-time planning only includes task assignment. Based on the characteristics of the task planning process, a state machine is selected to drive the task process, and the transition conditions are set as follows: pre-planning is executed after receiving a superior task; real-time planning is triggered if a target is detected during task execution. Finally, the planning activity is associated with the planning model, including: a task resource allocation model, an initial route planning model, and a real-time task assignment model.

[0071] (2) Simulation of unmanned swarm command and control

[0072] This system simulates the business processes and functions of multi-level command and control for unmanned swarms, from formation collaborative decision-making and task group collaborative decision-making to autonomous control of unmanned platforms. Based on formation-level task models, unmanned group task models, and unmanned platform-level task models, it drives multi-level command and control operations towards automation and intelligent command decision-making. This enables manned or unmanned collaborative decision-making in multi-domain mission scenarios, and facilitates inter-swarm and intra-swarm collaborative control of UAV swarms, unmanned surface vessel swarms, and unmanned underwater vehicle swarms. The simulation achieves the complementary advantages and swarm emergence effects of "unmanned platforms, manned systems" in unmanned swarm collaborative operations. Specific processes are as follows: Figure 4 As shown.

[0073] A. Command and Control Process and Function Simulation: This section simulates the command and control business processes and functions at the formation, unmanned group, and unmanned platform levels, including functions such as mission planning communication, intelligence processing, command decision-making, command and control, and mission management. It generates unmanned platform mission instructions or control commands to control the unmanned platforms in executing missions. This embodiment includes a formation command and control simulation node. Command and control decisions for the formation across three task domains are implemented under the drive of three task instances; it includes six formation command and control simulation nodes. Command and decision-making for six group-level tasks are achieved under the drive of six task instances; based on the resource requirements of the six group tasks, one-to-one autonomous control simulation nodes of unmanned platforms are realized, and platform behavior control simulation is realized under the drive of task instances at each sub-platform level.

[0074] B. Task-Driven Command and Decision Simulation: Based on formation-level task models, unmanned formation task models, and unmanned platform-level task models, this simulation drives multi-level command and control operations to achieve automated turnaround and intelligent command and decision-making. It enables manned or unmanned collaborative decision-making in multi-domain task scenarios, and facilitates inter-swarm and intra-swarm collaborative control of UAV swarms, unmanned surface vessel swarms, and unmanned underwater vehicle swarms. In this embodiment, the task is instantiated using the formation-level task model TGM(1, X). For example, to illustrate the task The implementation scheme for driving the corresponding UAV swarm group-level command and decision-making is as follows:

[0075] a) Responding to superior tasks: Receiving and parsing task plans pushed by command and control processes and functional simulations. Automatically sets the task objective, start time, predecessor tasks, and end time in the basic task information according to the task plan and task planning model; sets the task area and target list in the task business parameters, thus forming an executable task at this level. Alternatively, it can drive command and control processes and function simulations through a human-computer interaction interface, guiding and assisting manual setting of relevant information to form executable tasks at this level. Therefore, in this embodiment, the number and type of group-level and platform-level tasks are not pre-designed. Group-level tasks are generated under the drive of formation tasks, and platform-level tasks are generated under the drive of group-level tasks.

[0076] b) Task execution control: After an executable task is formed, based on the basic task information and task business parameters, as well as the command and control process and function simulation push unmanned cluster and target situation information, the task execution status is automatically managed and controlled, including: task pre-planning, task start control, task real-time planning, task end judgment, and push task execution status information to the task management module of the command and control process and function simulation.

[0077] i. Task Pre-planning: Based on the task business parameters and task planning parameter settings, determine whether task pre-planning needs to be executed. If so, according to its associated pre-planning method, based on the initial situation pushed by the command and control process and function simulation, automatically execute the task pre-planning, and push the results to the command decision module of the command and control process and function simulation. Manual confirmation or modification of the pre-planning scheme is supported before it is issued as needed. If the pre-planning generates sub-tasks, it can drive the planning of lower-level tasks after issuance, up to the unmanned platform task level. In this example, according to the TGM(1, X) settings, task resource allocation and initial route planning are executed sequentially, and the planning results are sent to the command and control process and function simulation to drive the task at this level. implement.

[0078] ii. Task Start Control: Based on the task start conditions set in the basic task information, the system automatically monitors and determines whether the task has started. If it has started, it pushes task start information to the task control and task management modules of the command and control process and functional simulation, issues instructions as needed to drive the unmanned platform to execute the task, and updates the task status. In this example, based on the task's time-based start condition, according to the task... The task start time set during instantiation automatically controls the start of the task and updates the task status to the command and control process and function simulation.

[0079] iii. Real-time Task Planning: Based on the task business parameters and task planning parameter settings, as well as the unmanned cluster and target situation information pushed by the command and control process and function simulation, real-time task planning and task replanning are automatically triggered as needed, and the results are pushed to the command decision module of the command and control process and function simulation. Manual confirmation or modification of the pre-planned scheme is supported before it is issued as needed. If real-time planning and task replanning generate sub-task assignments, they can drive the command and control of lower-level tasks after issuance, up to the unmanned platform task level. In this embodiment, according to the TGM(1, X) settings, after receiving the target information pushed by the command and control process and function simulation, the real-time task assignment model is automatically called for planning. The planning results are sent to the command and control process and function simulation to drive the generation of platform-level tasks A2 and A3. Platform-level tasks A2 and A3 are also executed according to steps 2).B1)-2).B2) until completion.

[0080] iv. Task Completion Determination: Based on the task completion conditions set in the basic task information, the system automatically monitors and determines whether the task has ended. If completed, it automatically evaluates the task execution effectiveness according to the task completion standards in the basic task information and pushes the results to the command decision-making and task management modules of the command and control process and functional simulation to update the task status. In this example, based on the task's time-based completion condition, the system proceeds according to the task... The task end time set during instantiation automatically controls the task to end and updates the task status; based on the task success criteria set in TGM(1, X), the task effect is evaluated and sent to the command and control process and function simulation.

[0081] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0082] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A task model-driven simulation method for command and control of unmanned swarms, characterized in that, include: (1) Establish a generalized task model. Based on the generalized task model, establish a formation-level task model, a group-level task model, and a platform-level task model respectively. (2) Realize the business process and function simulation of multi-level command and control of unmanned swarms from formation collaborative decision-making, task group collaborative decision-making to unmanned platform autonomous control, and drive multi-level command and control business to automate and intelligentize command decision-making based on formation-level task model, group-level task model and platform-level task model, realize manned or unmanned collaborative decision-making in multi-domain task scenarios, and achieve collaborative control between and within groups of unmanned swarms, unmanned boat swarms and unmanned underwater vehicles; Step (2) includes: (2.1) Implement the command and control business process and function simulation of formation, unmanned group, and unmanned platform respectively, including task planning communication, intelligence processing, command decision-making, command and control and task management functions, generate unmanned platform task instructions or control instructions, and control the unmanned platform to execute tasks. (2.2) Based on the formation-level task model, group-level task model and platform-level task model, drive the automation of multi-level command and control business, and realize intelligent command and decision-making to achieve manned or unmanned collaborative decision-making in multi-domain task scenarios, and collaborative control between and within groups of UAV swarms, unmanned surface vessels swarms and unmanned underwater vehicles swarms. Step (2.2) includes: (2.2.1) Receive and parse the task plan pushed by the command and control process and function simulation, and automatically set the task objectives, task start time, predecessor tasks and task end time in the basic task information according to the task plan and task planning model; set the task area and target list in the task business parameters to form an executable task at this level; or drive the command and control process and function simulation to guide and assist manual setting of relevant information through the human-computer interaction interface to form an executable task at this level. (2.2.2) After an executable task is formed, based on the basic information of the task and the task business parameters, as well as the command and control process and function simulation push the unmanned cluster and target situation information, the task planning and execution status is automatically managed and controlled, including: task pre-planning, task start control, task real-time planning, task end judgment, and push task execution status information to the task management function module of the command and control process and function simulation. Step (2.2.2) includes: (2.2.2.1) Task pre-planning: Based on the task business parameters and task planning parameter settings, determine whether task pre-planning needs to be performed. If so, according to the associated pre-planning method, based on the initial situation pushed by the command and control process and function simulation, the task pre-planning is automatically executed, and the results are pushed to the command decision function module of the command and control process and function simulation. It supports manual confirmation or modification of the pre-planning scheme and then issuing it as needed. If the pre-planning generates sub-tasks, it drives the planning of lower-level tasks after issuance, up to the unmanned platform task level. (2.2.2.2) Task start control: Based on the task start conditions set in the basic task information, automatically monitor and determine whether the task has started. If it has started, push the task start information to the task control and task management function module of the command and control process and function simulation, issue instructions as needed to drive the unmanned platform to execute the task, and update the task status. (2.2.2.3) Real-time task planning: Based on the task business parameters and task planning parameter settings, as well as the command and control process and function simulation push unmanned cluster and target situation information, the task real-time planning and task replanning are automatically triggered as needed, and the results are pushed to the command decision function module of the command and control process and function simulation. It supports manual confirmation or modification of the pre-planned scheme and then issues it as needed. If the real-time planning and task replanning generate sub-task assignments, the assignment will drive the command and control of the lower-level tasks, up to the unmanned platform task level. (2.2.2.4) Task completion judgment: Based on the task completion conditions set in the basic task information, automatically monitor and judge whether the task has ended. If it has ended, automatically evaluate the task execution effect according to the task success criteria in the basic task information, and push it to the command decision and task management function module of the command and control process and function simulation to update the task status.

2. The method according to claim 1, characterized in that, Step (1) includes: (1.1) Based on the basic information of the model, the basic information of the task, the basic parameters of the task, the business parameters of the task, and the planning parameters of the task, a general task model is established; (1.2) Instantiate general task models according to different task levels and task content to form corresponding formation-level task models, unmanned group-level task models or unmanned platform-level task models.

3. The method according to claim 2, characterized in that, The general task model includes: basic model information, basic task information, task business parameters, and task planning parameters. The basic model information includes the task domain, task type, task level, subtask set or sequence, and task description. The basic task information includes the task objective, task start time, preceding task, task end time, task success criteria, task start condition, and task end condition. The task business parameters include the task style set, task area, target list, and task resource requirements. The task planning parameters include task planning rules, process items, and associated required task planning methods or instruction sets.

4. The method according to claim 3, characterized in that, The set or sequence of subtasks, the formation-level subtasks correspond to the group-level tasks, the group-level subtasks correspond to the unmanned platform-level tasks, the subtasks are triggered when the formation-level collaborative decision is made, the unmanned group tasks are automatically generated, and the group-level collaborative decision is driven. Group-level collaborative decision-making triggers sub-tasks, automatically generates unmanned platform tasks, drives autonomous control of unmanned platforms, and achieves automated multi-level command and control business reversal.

5. The method according to claim 4, characterized in that, The task hierarchy includes formation level, group level and platform level. The task resource requirements include task capability and index requirements, task platform type or model and quantity requirements, and task payload type or model and quantity requirements. Task resource requirements should be set separately according to task style and sub-task.

6. The method according to claim 5, characterized in that, The task planning parameters include task planning rules, process items, and associated task planning methods and instruction sets. These parameters define the logical flow and methods for task planning. Task planning comprises two stages: pre-planning and real-time planning. The task planning logical flow employs behavior trees, state machines, or workflows. Required task planning methods or instruction sets are associated with the planning logical flow as needed. Task planning methods include: task decomposition, task allocation, target allocation, task payload planning, command relationship planning, communication network planning, and task route planning. Different task levels and tasks will have different task planning rules, processes, and associated task planning methods to support the intelligent decision-making needs of different tasks.

7. The method according to claim 1, characterized in that, The task grouping collaborative decision-making can realize one or more nodes, creating different nodes according to the task domain and unmanned cluster type. The unmanned platform autonomous control can realize one or more nodes, creating different nodes according to the number of unmanned platforms. The task-driven command and decision simulation will create one-to-one corresponding task-driven instances for the simulated formation, group, and unmanned platform nodes based on the formation-level task model, the unmanned grouping-level task model, and the unmanned platform-level task model.

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