System operation state machine simulation implementation method and device

Abstract

The application discloses a system combat state machine simulation implementation method and device, the method comprises the following steps: obtaining systematized cooperative combat task information; analyzing the systematized cooperative combat task information to obtain a systematized cooperative combat task flow; and processing the systematized cooperative combat task flow to obtain a system combat state machine. The application realizes the organic integration of different level combat behaviors, maintains the integrity of strategic command, takes into account the details of tactical execution, provides more reliable simulation support for the verification and evaluation of new combat concepts. At the same time, the visual state machine modeling tool greatly reduces the threshold of expert participation in simulation construction, significantly improves the response ability of the simulation system to actual combat requirements, and realizes efficient conversion of combat experience to simulation models.

Description

Technical Field

[0001] This invention relates to the field of system simulation and combat behavior modeling technology, and in particular to a method and apparatus for simulating system combat state machines. Background Technology

[0002] Modern warfare is rapidly evolving towards systematization and intelligence, with system-of-systems combat capabilities becoming a key factor in determining victory or defeat. In complex and ever-changing environments, a single combat platform is insufficient to cope with high-intensity multi-domain integrated confrontations; system advantages must be formed through the integration and coordinated cooperation of various combat units. This system-of-systems combat model emphasizes information sharing, effectiveness aggregation, and flexible reorganization, enabling optimal allocation of battlefield resources and a multiplier effect on combat capabilities.

[0003] The coordinated operation of manned and unmanned equipment, as an important manifestation of modern system warfare, significantly enhances the overall effectiveness of the combat system by organically combining the advantages of human decision-making with the tactical characteristics of unmanned equipment. In specific combat applications, this coordinated model manifests itself in three dimensions: In the dimension of situational awareness, relying on the collaborative mechanism of forward reconnaissance by unmanned equipment and information fusion with manned equipment, the battlefield surveillance range can be greatly expanded, effectively solving the blind spot problem of traditional reconnaissance methods; in the dimension of firepower strike, through the combat formation of "manned command + unmanned assault," precision strike capabilities can be maintained while reducing the risk of personnel casualties; in the dimension of system protection, unmanned equipment swarms construct a multi-layered defense system for manned combat platforms through electronic jamming, decoy deception, and other means, greatly improving the battlefield survivability of key nodes.

[0004] Employing simulation technology to study manned / unmanned collaborative combat has significant benefits and technical value. The "modeling-simulation-verification" research method not only greatly reduces the cost and risk of field testing but also provides technical support for the rapid iteration of new collaborative combat concepts. Finite state machines (FSMs), through formal descriptions of state sets, event triggering, and transition rules, can effectively characterize the dynamic behavioral features of various combat entities in collaborative operations. At the simulation level of complex combat systems, FSMs provide a standardized behavioral modeling framework, abstracting discretized actions such as reconnaissance, target tracking, and launch into state nodes and transforming decision conditions into state transition rules, thereby achieving accurate modeling of the collaborative combat process.

[0005] From a technical implementation perspective, finite state machines (FSMs) support modular descriptions of multi-level combat behaviors, capable of representing tactical action sequences of a single platform as well as constructing formation-level collaborative logical relationships. This modeling approach is particularly suitable for describing the interaction process between manned command and control and unmanned autonomous execution. Through clear definition of state transition conditions, it can accurately simulate the coupling relationship between human decision-making logic and the autonomous behavior of unmanned equipment. In the simulation verification phase, FSM-based models exhibit good verifiability and traceability. By monitoring and analyzing state nodes, researchers can intuitively assess the rationality of tactical rules and pinpoint bottlenecks in the collaborative process.

[0006] From an engineering application perspective, this method lowers the technical threshold for modeling complex systems, enabling experts to directly participate in rule-making and model verification. By transforming doctrine into state transition conditions, it achieves efficient conversion of combat experience into simulation models. This rule-based formal expression facilitates phased planning of system-wide operational behavior, and the design results of relevant phases can be used as application examples in other behavioral processes. It also provides a structured framework for the subsequent introduction of intelligent algorithms, serving as a crucial link between traditional rule-driven and emerging data-driven simulation methods. Summary of the Invention

[0007] The technical problem this invention aims to solve is to provide a method and apparatus for simulating system-of-systems combat state machines, addressing the formal modeling and dynamic simulation of multi-level combat behaviors in complex combat systems. This invention focuses on constructing multi-level, multi-entity combat behavior models in system-of-systems warfare, achieving cross-level behavioral modeling from strategic command to tactical execution through hierarchical state machine technology. Its key technologies include event-driven state transition mechanisms, multi-granularity combat behavior abstraction, and interactive modeling of command-execution coordination, effectively supporting dynamic behavioral simulation of multi-domain combat elements such as land, sea, air, and space.

[0008] From an application perspective, this invention, through the hierarchical design of state machines, decomposes the operational process into a sequence of executable tactical actions, achieving an organic unity between command and decision-making logic and platform autonomous behavior. At the technical implementation level, this invention integrates the formal specifications and rule-based knowledge representation of finite state machines, preserving the logical rigor of state transitions while lowering the technical barrier for personnel involved in modeling through visual modeling tools. Particularly in the dynamic evolution simulation of complex combat systems, it demonstrates a precise ability to characterize typical combat features such as multi-entity parallel interaction and cross-domain collaborative decision-making.

[0009] To address the aforementioned technical problems, a first aspect of this invention discloses a method for simulating a system combat state machine, the method comprising:

[0010] S1, acquire information on systematic collaborative combat missions;

[0011] S2, parse the system-wide collaborative combat mission information to obtain the system-wide collaborative combat mission flow;

[0012] S3, process the system-wide collaborative combat mission flow to obtain the system combat state machine.

[0013] As an optional implementation, in the first aspect of the present invention, processing the system-wide collaborative combat mission flow to obtain a system combat state machine includes:

[0014] S31, The system-wide collaborative combat mission process is processed to obtain the system combat state; the system combat state includes the initial preparation state (State1), the early warning aircraft guidance state (State2), the manned / unmanned aircraft command and coordination state (State3), and the return and withdrawal state (State4).

[0015] S32, the operational state of the system is divided into a set of sub-states; the set of sub-states includes the initial preparation sub-state, the early warning aircraft guidance sub-state, the manned / unmanned aircraft command and coordination sub-state, and the return and ion withdrawal sub-state.

[0016] S33, process the system's combat state and sub-state set to obtain state transition relationships and sub-state transition relationships;

[0017] S34, process the sub-state set to obtain the sub-state action information set;

[0018] S35, process the set of sub-state action information to obtain the system combat state machine.

[0019] As an optional implementation, in the first aspect of the present invention, the step of dividing the system's operational state to obtain a set of sub-states includes:

[0020] S321, the initial preparation state State1 is divided into taxiing and takeoff sub-state, maneuvering and standby sub-state and command authority transfer sub-state;

[0021] S322, the guidance state State2 of the early warning aircraft is divided into early warning patrol sub-state and flight guidance sub-state;

[0022] S323, the manned / unmanned aircraft command and coordination state 3 is divided into reconnaissance and detection sub-state, cooperative combat sub-state, electronic countermeasures sub-state and combat evaluation sub-state;

[0023] S324, the return and withdrawal state State4 is divided into the formation withdrawal state, the command handover sub-state and the individual return state.

[0024] As an optional implementation, in the first aspect of the present invention, the processing of the system's operational state and sub-state set to obtain state transition relationships and sub-state transition relationships includes:

[0025] S331, Process the operational state of the system to obtain the state transition relationship; the state transition relationship is:

[0026]

[0027] Where C = {C1, C2, C3} is the set of conditions for the first action;

[0028] S332, Process the set of sub-states to obtain sub-state transition relationships; the sub-state transition relationships include a first sub-state transition relationship, a second sub-state transition relationship, a third sub-state transition relationship, and a fourth sub-state transition relationship.

[0029] As an optional implementation, in the first aspect of the present invention, processing the sub-state set to obtain a sub-state action information set includes:

[0030] S341, Process the taxiing and takeoff sub-states to obtain runway taxiing and takeoff action information for manned and unmanned aircraft;

[0031] S342, process the said maneuvering flight and standby sub-state to obtain the maneuvering flight and standby action information of the combat unit toward the predetermined assembly area;

[0032] S343, process the command authority transfer sub-state to obtain command authority transfer action information from the ground control station to the manned aircraft operator;

[0033] S344, Process the aforementioned early warning patrol sub-state to obtain information on the airborne radar's wide-area scanning and target tracking actions;

[0034] S345, process the flight guidance substate to obtain the optimal trajectory action information for real-time situational analysis;

[0035] S346, Process the reconnaissance and detection sub-state to obtain multi-source information fusion processing action information;

[0036] S347, Process the cooperative combat sub-state to obtain dynamic task allocation action information;

[0037] S348, Process the state of the electronic countermeasure to obtain electronic countermeasure action information;

[0038] S349, Process the combat assessment sub-state to obtain real-time damage assessment action information;

[0039] S350, process the formation withdrawal ion state to obtain withdrawal action information;

[0040] S351, process the command handover sub-state to obtain the reverse transfer action information of UAV control authority from manned aircraft to ground station;

[0041] S352, process the respective return sub-states to obtain safe return to base action information;

[0042] S353 integrates the following information to obtain a set of sub-state action information: runway taxiing and takeoff actions of the manned and unmanned aircraft, maneuvering and standby actions of the combat unit towards the predetermined assembly area, transfer of command authority from the ground control station to the manned aircraft operator, wide-area scanning and target tracking actions of the airborne radar, real-time situational awareness calculation of the optimal trajectory, multi-source information fusion processing, dynamic task allocation, electronic countermeasures sub-actions, real-time damage assessment, evacuation, reverse transfer of UAV control authority from the manned aircraft to the ground station, and safe return to base.

[0043] As an optional implementation, in the first aspect of the present invention, processing the maneuvering flight and standby sub-state to obtain maneuvering flight and standby action information of the combat unit toward a predetermined assembly area includes:

[0044] S3421 processes the combat units and the predetermined assembly area to obtain the aircraft path planning cost function and constraints.

[0045] S3422, Set the initial information for the path planning model;

[0046] S3423, use Logistic chaotic mapping to generate a preset number of individuals for the path planning model, calculate the fitness value of the individuals, and select the individuals with better fitness values ​​as the initial individuals for the model.

[0047] S3424, in each iteration, updates the individual position according to the behavior of the path planning model and uses a nonlinear energy factor to update the strategy, making it move towards a lower fitness value;

[0048] S3425, In the local search phase, chaotic mapping is used to search the local solution space so that it can escape the local optimum.

[0049] S3426, repeat S3424~S3425 until the preset algorithm termination condition is met, output the optimal fitness value, and obtain the information on the combat unit's maneuvering flight and standby actions towards the predetermined assembly area.

[0050] As an optional implementation, in the first aspect of the present invention, processing the early warning patrol sub-state to obtain airborne radar wide-area scanning and target tracking action information includes:

[0051] S3441 uses airborne radar to perform wide-area scanning to obtain target data information;

[0052] S3442 processes the moving target in the target data information to obtain imaging parameters with N motion states.

[0053] S3443, Set the initial weights ω of the moving target. i =1, let t=1;

[0054] S3444, Process the moving target to obtain state transition information;

[0055] The state transition information expression is:

[0056]

[0057] Where A1 and A2 are the initial and final pixel values ​​for target detection in the horizontal direction, respectively, and B1 and B2 are the initial and final pixel values ​​for target detection in the vertical direction, respectively. Let be the number of state transitions at time t-1. Let be the x-coordinate value at time t. The ordinate value at time t. The x-coordinate value at time t-1 Horizontal coordinate value;

[0058] S3445, The state transition information is processed to obtain the airborne radar's wide-area scanning and target tracking action information;

[0059] The expression for the action information of the airborne radar performing wide-area scanning and target tracking is:

[0060]

[0061] In the formula, These are the x and y coordinates of the position obtained from tracking the moving target, respectively. The weights of the moving target at time t are determined experimentally.

[0062] S3446, let t = t + 1, execute S3444 to S3445 until the preset termination condition is met, and obtain the airborne radar's wide-area scanning and target tracking action information.

[0063] A second aspect of this invention discloses a system combat state machine simulation implementation device, the device comprising:

[0064] The information acquisition module is used to acquire information on systematic collaborative combat missions.

[0065] The information parsing module is used to parse the system-wide collaborative combat mission information to obtain the system-wide collaborative combat mission flow.

[0066] The system-of-systems combat state machine construction module is used to process the system-of-systems collaborative combat mission process to obtain the system-of-systems combat state machine.

[0067] As an optional implementation, in the second aspect of the present invention, processing the system-wide collaborative combat mission flow to obtain a system combat state machine includes:

[0068] S31, Process the system-wide collaborative combat mission flow to obtain the system combat state; The system combat state includes initial preparation state State1, early warning aircraft guidance state State2, manned / unmanned aircraft command and coordination state State3, and return and withdrawal state State4.

[0069] S32, the operational state of the system is divided into a set of sub-states; the set of sub-states includes the initial preparation sub-state, the early warning aircraft guidance sub-state, the manned / unmanned aircraft command and coordination sub-state, and the return and withdrawal ion state;

[0070] S33, process the system's combat state and sub-state set to obtain state transition relationships and sub-state transition relationships;

[0071] S34, process the sub-state set to obtain the sub-state action information set;

[0072] S35, process the set of sub-state action information to obtain the system combat state machine.

[0073] As an optional implementation, in the second aspect of the present invention, the step of dividing the system's operational state to obtain a set of sub-states includes:

[0074] S321, the initial preparation state State1 is divided into taxiing and takeoff sub-state, maneuvering and standby sub-state and command authority transfer sub-state;

[0075] S322, the guidance state State2 of the early warning aircraft is divided into early warning patrol sub-state and flight guidance sub-state;

[0076] S323, the manned / unmanned aircraft command and coordination state 3 is divided into reconnaissance and detection sub-state, cooperative combat sub-state, electronic countermeasures sub-state and combat evaluation sub-state;

[0077] S324, the return and withdrawal state State4 is divided into the formation withdrawal state, the command handover sub-state and the individual return state.

[0078] As an optional implementation, in the second aspect of the present invention, the processing of the system's operational state and sub-state set to obtain state transition relationships and sub-state transition relationships includes:

[0079] S331, Process the operational state of the system to obtain the state transition relationship; the state transition relationship is:

[0080]

[0081] Where C = {C1, C2, C3} is the set of conditions for the first action;

[0082] S332, Process the set of sub-states to obtain sub-state transition relationships; the sub-state transition relationships include a first sub-state transition relationship, a second sub-state transition relationship, a third sub-state transition relationship, and a fourth sub-state transition relationship.

[0083] As an optional implementation, in a second aspect of the present invention, processing the sub-state set to obtain a sub-state action information set includes:

[0084] S341, Process the taxiing and takeoff sub-states to obtain runway taxiing and takeoff action information for manned and unmanned aircraft;

[0085] S342, process the said maneuvering flight and standby sub-state to obtain the maneuvering flight and standby action information of the combat unit toward the predetermined assembly area;

[0086] S343, process the command authority transfer sub-state to obtain command authority transfer action information from the ground control station to the manned aircraft operator;

[0087] S344, Process the aforementioned early warning patrol sub-state to obtain information on the airborne radar's wide-area scanning and target tracking actions;

[0088] S345, process the flight guidance substate to obtain the optimal trajectory action information for real-time situational analysis;

[0089] S346, Process the reconnaissance and detection sub-state to obtain multi-source information fusion processing action information;

[0090] S347, Process the cooperative combat sub-state to obtain dynamic task allocation action information;

[0091] S348, Process the state of the electronic countermeasure to obtain electronic countermeasure action information;

[0092] S349, Process the combat assessment sub-state to obtain real-time damage assessment action information;

[0093] S350, process the formation withdrawal ion state to obtain withdrawal action information;

[0094] S351, process the command handover sub-state to obtain the reverse transfer action information of UAV control authority from manned aircraft to ground station;

[0095] S352, process the respective return sub-states to obtain safe return to base action information;

[0096] S353 integrates the following information to obtain a set of sub-state action information: runway taxiing and takeoff actions of the manned and unmanned aircraft, maneuvering and standby actions of the combat unit towards the predetermined assembly area, transfer of command authority from the ground control station to the manned aircraft operator, wide-area scanning and target tracking actions of the airborne radar, real-time situational awareness calculation of the optimal trajectory, multi-source information fusion processing, dynamic task allocation, electronic countermeasures sub-actions, real-time damage assessment, evacuation, reverse transfer of UAV control authority from the manned aircraft to the ground station, and safe return to base.

[0097] As an optional implementation, in the second aspect of the present invention, processing the maneuvering flight and standby sub-state to obtain the maneuvering flight and standby action information of the combat unit toward the predetermined assembly area includes:

[0098] S3421 processes the combat units and the predetermined assembly area to obtain the aircraft path planning cost function and constraints.

[0099] S3422, Set the initial information for the path planning model;

[0100] S3423, use Logistic chaotic mapping to generate a preset number of individuals for the path planning model, calculate the fitness value of the individuals, and select the individuals with better fitness values ​​as the initial individuals for the model.

[0101] S3424, in each iteration, updates the individual position according to the behavior of the path planning model and uses a nonlinear energy factor to update the strategy, making it move towards a lower fitness value;

[0102] S3425, In the local search phase, chaotic mapping is used to search the local solution space so that it can escape the local optimum.

[0103] S3426, repeat S3424~S3425 until the preset algorithm termination condition is met, output the optimal fitness value, and obtain the information on the combat unit's maneuvering flight and standby actions towards the predetermined assembly area.

[0104] As an optional implementation, in the second aspect of the present invention, processing the early warning patrol sub-state to obtain airborne radar wide-area scanning and target tracking action information includes:

[0105] S3441 uses airborne radar to perform wide-area scanning to obtain target data information;

[0106] S3442 processes the moving target in the target data information to obtain imaging parameters with N motion states.

[0107] S3443, Set the initial weights ω of the moving target. i =1, let t=1;

[0108] S3444, Process the moving target to obtain state transition information;

[0109] The state transition information expression is:

[0110]

[0111] Where A1 and A2 are the initial and final pixel values ​​for target detection in the horizontal direction, respectively, and B1 and B2 are the initial and final pixel values ​​for target detection in the vertical direction, respectively. Let be the number of state transitions at time t-1. Let be the x-coordinate value at time t. The ordinate value at time t. The x-coordinate value at time t-1 The ordinate value at time t-1;

[0112] S3445, The state transition information is processed to obtain the airborne radar's wide-area scanning and target tracking action information;

[0113] The expression for the action information of the airborne radar performing wide-area scanning and target tracking is:

[0114]

[0115] In the formula, These are the x and y coordinates of the position obtained from tracking the moving target, respectively. The weights of the moving target at time t are determined experimentally.

[0116] S3446, let t = t + 1, execute S3444 to S3445 until the preset termination condition is met, and obtain the airborne radar's wide-area scanning and target tracking action information.

[0117] A third aspect of this invention discloses another system combat state machine simulation implementation device, the device comprising:

[0118] Memory containing executable program code;

[0119] A processor coupled to the memory;

[0120] The processor calls the executable program code stored in the memory to execute some or all of the steps in the system combat state machine simulation implementation method disclosed in the first aspect of the present invention.

[0121] The fourth aspect of the present invention discloses a computer-storable medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the system combat state machine simulation implementation method disclosed in the first aspect of the present invention.

[0122] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0123] This invention significantly improves the accuracy and practicality of behavioral modeling for complex combat systems. Through a hierarchical state machine architecture, it achieves precise mapping from strategic intent to tactical actions, enabling the simulation of complex combat processes such as early warning aircraft command and guidance, and manned / unmanned aerial vehicle (UAV) coordination, with hierarchical display, event-driven processes, and controllable flow. The seamless integration of the state transition mechanism and rules described in this invention effectively solves the problem of discretized tactical decision-making logic in traditional simulations, ensuring the temporal consistency and causal correlation of combat process simulations. Particularly in cross-domain collaborative combat simulations, this invention achieves the organic integration of combat behaviors at different levels through modular design, maintaining the integrity of strategic command while also considering the detailed depiction of tactical execution, providing more reliable simulation support for the verification and evaluation of new combat concepts. Simultaneously, the visualized state machine modeling tool greatly lowers the barrier for experts to participate in simulation construction, significantly improving the simulation system's responsiveness to real-world needs. The application process is flexible and reusable, achieving efficient transformation of combat experience into simulation models. Attached Figure Description

[0124] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0125] Figure 1 This is a flowchart illustrating a system combat state machine simulation implementation method disclosed in an embodiment of the present invention;

[0126] Figure 2 This is a schematic diagram of the state machine editing process design disclosed in an embodiment of the present invention;

[0127] Figure 3 This is a schematic diagram of the creation state disclosed in an embodiment of the present invention;

[0128] Figure 4 This is a schematic diagram of the modified status name disclosed in an embodiment of the present invention;

[0129] Figure 5 This is a schematic diagram of the adding action disclosed in an embodiment of the present invention;

[0130] Figure 6 This is a schematic diagram of the addition conditions disclosed in the embodiments of the present invention;

[0131] Figure 7 This is a schematic diagram of condition parameter editing disclosed in an embodiment of the present invention;

[0132] Figure 8 This is a schematic diagram of the state machine disclosed in an embodiment of the present invention;

[0133] Figure 9 This is a schematic diagram of attribute interaction disclosed in an embodiment of the present invention;

[0134] Figure 10 This is a schematic diagram of the interaction between the conversion line and the blackboard attributes disclosed in an embodiment of the present invention;

[0135] Figure 11 This is a schematic diagram of a manned / unmanned aerial vehicle (UAV) collaborative system combat system disclosed in an embodiment of the present invention;

[0136] Figure 12 This is the process of an early warning aircraft guiding a fighter jet as disclosed in an embodiment of the present invention;

[0137] Figure 13 This is a typical combat flowchart of manned and unmanned aircraft formation coordination disclosed in the embodiments of the present invention;

[0138] Figure 14 This is a schematic diagram of typical system combat state transition disclosed in the embodiments of the present invention;

[0139] Figure 15 This is a schematic diagram of a typical system combat state machine configuration disclosed in an embodiment of the present invention;

[0140] Figure 16 This is a schematic diagram of the sub-state configuration within the initial preparation state disclosed in an embodiment of the present invention;

[0141] Figure 17 This is a schematic diagram of the sub-state configuration within the early warning guidance state disclosed in an embodiment of the present invention;

[0142] Figure 18 This is a schematic diagram of the sub-state configuration within the cooperative combat state disclosed in an embodiment of the present invention;

[0143] Figure 19 This is a schematic diagram of the sub-state configuration within the evacuation and return state disclosed in an embodiment of the present invention;

[0144] Figure 20 This is a schematic diagram of the state transition condition setting disclosed in an embodiment of the present invention.

[0145] Figure 21 This is a schematic diagram of setting multiple condition groups disclosed in an embodiment of the present invention.

[0146] Figure 22 This is a schematic diagram of configuring tasks in a sub-state as disclosed in an embodiment of the present invention;

[0147] Figure 23 This is a parameter configuration diagram of the early warning patrol mission model disclosed in an embodiment of the present invention;

[0148] Figure 24This is a schematic diagram illustrating the configuration of multiple simulation entities in a sub-state as disclosed in an embodiment of the present invention;

[0149] Figure 25 This is a parameter configuration diagram of the electronic warfare mission model disclosed in an embodiment of the present invention;

[0150] Figure 26 This is a diagram illustrating the operational state machine of the command post configuration system disclosed in an embodiment of the present invention;

[0151] Figure 27 This is a schematic diagram of the early warning aircraft physical configuration early warning patrol mission component model disclosed in an embodiment of the present invention;

[0152] Figure 28 This is a schematic diagram of the structure of a system combat state machine simulation implementation device disclosed in an embodiment of the present invention;

[0153] Figure 29 This is a schematic diagram of another system combat state machine simulation implementation device disclosed in an embodiment of the present invention. Detailed Implementation

[0154] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0155] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0156] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0157] This invention discloses a method and apparatus for simulating a system-wide combat state machine. The method includes: acquiring system-wide collaborative combat mission information; parsing the system-wide collaborative combat mission information to obtain a system-wide collaborative combat mission flow; and processing the system-wide collaborative combat mission flow to obtain a system-wide combat state machine. This invention achieves the organic integration of combat behaviors at different levels, maintaining the integrity of strategic command while also considering the detailed depiction of tactical execution, providing more reliable simulation support for the verification and evaluation of new combat concepts. Simultaneously, the visualized state machine modeling tool greatly lowers the threshold for experts to participate in simulation construction, significantly improves the simulation system's responsiveness to real-world needs, and offers flexible operation and convenient reuse, achieving efficient transformation of combat experience into simulation models. Detailed explanations follow.

[0158] Example 1

[0159] Please see Figure 1 , Figure 1 This is a flowchart illustrating a system-wide combat state machine simulation implementation method disclosed in an embodiment of the present invention. Figure 1 The described system-of-systems combat state machine simulation implementation method is applied to the field of system simulation and combat behavior modeling technology, and the embodiments of this invention are not limited thereto. Figure 1 As shown, the simulation implementation method of this system's combat state machine can include the following operations:

[0160] S1, acquire information on systematic collaborative combat missions;

[0161] S2, parse the system-wide collaborative combat mission information to obtain the system-wide collaborative combat mission flow;

[0162] S3, process the system-wide collaborative combat mission flow to obtain the system combat state machine.

[0163] Optionally, the process of processing the system-wide collaborative combat mission flow to obtain the system combat state machine includes:

[0164] S31, The system-wide collaborative combat mission process is processed to obtain the system combat state; the system combat state includes the initial preparation state (State1), the early warning aircraft guidance state (State2), the manned / unmanned aircraft command and coordination state (State3), and the return and withdrawal state (State4).

[0165] S32, the operational state of the system is divided into a set of sub-states; the set of sub-states includes the initial preparation sub-state, the early warning aircraft guidance sub-state, the manned / unmanned aircraft command and coordination sub-state, and the return and ion withdrawal sub-state.

[0166] S33, process the system's combat state and sub-state set to obtain state transition relationships and sub-state transition relationships;

[0167] S34, process the sub-state set to obtain the sub-state action information set;

[0168] S35, process the set of sub-state action information to obtain the system combat state machine.

[0169] Optionally, the division of the system's operational state into a set of sub-states includes:

[0170] S321, the initial preparation state State1 is divided into taxiing and takeoff sub-state, maneuvering and standby sub-state and command authority transfer sub-state;

[0171] S322, the guidance state State2 of the early warning aircraft is divided into early warning patrol sub-state and flight guidance sub-state;

[0172] S323, the manned / unmanned aircraft command and coordination state 3 is divided into reconnaissance and detection sub-state, cooperative combat sub-state, electronic countermeasures sub-state and combat evaluation sub-state;

[0173] S324, the return and withdrawal state State4 is divided into the formation withdrawal state, the command handover sub-state and the individual return state.

[0174] Optionally, the processing of the system's operational state and sub-state set to obtain state transition relationships and sub-state transition relationships includes:

[0175] S331, Process the operational state of the system to obtain the state transition relationship; the state transition relationship is:

[0176]

[0177] Where C = {C1, C2, C3} is the set of conditions for the first action;

[0178] S332, Process the set of sub-states to obtain sub-state transition relationships; the sub-state transition relationships include a first sub-state transition relationship, a second sub-state transition relationship, a third sub-state transition relationship, and a fourth sub-state transition relationship.

[0179] Optionally, processing the sub-state set to obtain the sub-state action information set includes:

[0180] S341, Process the taxiing and takeoff sub-states to obtain runway taxiing and takeoff action information for manned and unmanned aircraft;

[0181] S342, process the said maneuvering flight and standby sub-state to obtain the maneuvering flight and standby action information of the combat unit toward the predetermined assembly area;

[0182] S343, process the command authority transfer sub-state to obtain command authority transfer action information from the ground control station to the manned aircraft operator;

[0183] S344, Process the aforementioned early warning patrol sub-state to obtain information on the airborne radar's wide-area scanning and target tracking actions;

[0184] S345, process the flight guidance substate to obtain the optimal trajectory action information for real-time situational analysis;

[0185] S346, Process the reconnaissance and detection sub-state to obtain multi-source information fusion processing action information;

[0186] S347, Process the cooperative combat sub-state to obtain dynamic task allocation action information;

[0187] S348, Process the state of the electronic countermeasure to obtain electronic countermeasure action information;

[0188] S349, Process the combat assessment sub-state to obtain real-time damage assessment action information;

[0189] S350, process the formation withdrawal ion state to obtain withdrawal action information;

[0190] S351, process the command handover sub-state to obtain the reverse transfer action information of UAV control authority from manned aircraft to ground station;

[0191] S352, process the respective return sub-states to obtain safe return to base action information;

[0192] S353 integrates the following information to obtain a set of sub-state action information: runway taxiing and takeoff actions of the manned and unmanned aircraft, maneuvering and standby actions of the combat unit towards the predetermined assembly area, transfer of command authority from the ground control station to the manned aircraft operator, wide-area scanning and target tracking actions of the airborne radar, real-time situational awareness calculation of the optimal trajectory, multi-source information fusion processing, dynamic task allocation, electronic countermeasures sub-actions, real-time damage assessment, evacuation, reverse transfer of UAV control authority from the manned aircraft to the ground station, and safe return to base.

[0193] Optionally, the processing of the maneuvering flight and standby sub-state to obtain the maneuvering flight and standby action information of the combat unit toward the predetermined assembly area includes:

[0194] S3421 processes the combat units and the predetermined assembly area to obtain the aircraft path planning cost function and constraints.

[0195] (1) Maximum range constraint

[0196] Because drones carry a limited amount of fuel, their maximum flight distance is restricted to a finite range. Therefore, in order to improve the efficiency of drone flight... i For flight safety, a maximum range distance L needs to be set. i,max Its formula is:

[0197] L i <=L i,max

[0198] (2) Maximum turning angle constraint

[0199] When there are many obstacles in the planned flight path, the drone needs to avoid multiple obstacles, resulting in multiple turning angles. To ensure the drone's flight safety, the angle between planned waypoints must be less than the maximum turning angle, which depends on the drone's own performance. At the same time, a smaller turning angle allows for a smoother flight path. Therefore, a maximum turning angle θ must be set when planning the drone's flight path. max To ensure the flight safety of the drone. Assume the i-th segment of the track under projection is (x... i+1 -x i ,y i+1 -y i ) T Adjacent path segments should satisfy the following equation:

[0200] θ i ≤θ max

[0201] (3) Maximum climb angle constraint

[0202] The maximum climb angle refers to the maximum allowable climb angle for a drone during a mission; it is the angle between the drone's axis and the horizontal plane. An excessively large climb angle can adversely affect the drone's flight stability and safety. Therefore, a maximum climb angle needs to be set during path planning to ensure the drone's climb angle remains within a certain range during flight, thus guaranteeing flight safety. Let's assume the maximum climb angle is γ. max Assume the i-th segment's trajectory vector is:

[0203] Q i =(x i+1 -xi ,y i+1 -y i ,z i+1 -z i ) T

[0204] Adjacent path segments should satisfy the following:

[0205]

[0206] F tr =ω4f fu +ω5f hi +ω6f da

[0207] In the formula, ω4, ω5, and ω6 are the weighting factors for track length cost, track altitude cost, and risk cost, respectively, and ω4+ω5+ω6=1.

[0208] S3422, Set the initial information for the path planning model;

[0209] S3423, use Logistic chaotic mapping to generate a preset number of individuals for the path planning model, calculate the fitness value of the individuals, and select the individuals with better fitness values ​​as the initial individuals for the model.

[0210] S3424, in each iteration, updates the individual position according to the behavior of the path planning model and uses a nonlinear energy factor to update the strategy, making it move towards a lower fitness value;

[0211] S3425, In the local search phase, chaotic mapping is used to search the local solution space so that it can escape the local optimum.

[0212] S3426, repeat S3424~S3425 until the preset algorithm termination condition is met, output the optimal fitness value, and obtain the information on the combat unit's maneuvering flight and standby actions towards the predetermined assembly area.

[0213] Optionally, the processing of the early warning patrol sub-state to obtain the airborne radar's wide-area scanning and target tracking action information includes:

[0214] S3441 uses airborne radar to perform wide-area scanning to obtain target data information;

[0215] S3442 processes the moving target in the target data information to obtain imaging parameters with N motion states.

[0216] S3443, Set the initial weights ω of the moving target. i =1, let t=1;

[0217] S3444, Process the moving target to obtain state transition information;

[0218] The state transition information expression is:

[0219]

[0220] Where A1 and A2 are the initial and final pixel values ​​for target detection in the horizontal direction, respectively, and B1 and B2 are the initial and final pixel values ​​for target detection in the vertical direction, respectively. Let be the number of state transitions at time t-1. Let be the x-coordinate value at time t. The ordinate value at time t. The x-coordinate value at time t-1 Horizontal coordinate value;

[0221] S3445, The state transition information is processed to obtain the airborne radar's wide-area scanning and target tracking action information;

[0222] The expression for the action information of the airborne radar performing wide-area scanning and target tracking is:

[0223]

[0224] In the formula, These are the x and y coordinates of the position obtained from tracking the moving target, respectively. The weights of the moving target at time t are determined experimentally.

[0225] S3446, let t = t + 1, execute S3444 to S3445 until the preset termination condition is met, and obtain the airborne radar's wide-area scanning and target tracking action information.

[0226] The system-of-systems combat state machine simulation method of this invention is mainly used to support the dynamic simulation verification and extrapolation of complex combat systems under modern informationized conditions, providing technical support for decision-making and simulation of multi-domain collaborative operations. By modularly modeling multi-level combat behaviors through a hierarchical state machine architecture, it can accurately describe the temporal logic and interaction relationships of typical processes in system-of-systems combat.

[0227] In simulation experiments, the method of this invention simulates the dynamic behavioral evolution of combat units in a real environment through an event-driven state transition mechanism, supporting the simulation and deduction of the entire process from contingency plan verification to method innovation. Its core value lies in realizing the visual modeling of the control process, enabling discrete actions such as reconnaissance and target allocation to form an organic behavioral chain, providing a simulation basis for the quantitative analysis and optimization of system combat capabilities.

[0228] As can be seen, the method of this invention significantly improves the accuracy and practicality of behavioral modeling for complex combat systems. This invention achieves precise mapping from intent to action through a hierarchical state machine architecture, enabling the simulation and deduction of complex processes such as early warning aircraft command and guidance, and manned / unmanned aerial vehicle (UAV) collaboration, with hierarchical display, event-driven, and process-controllable technical means. The seamless integration of the state transition mechanism and rules described in this invention effectively solves the problem of discretized decision-making logic in traditional simulations, ensuring the temporal consistency and causal correlation of combat process simulations. Especially in cross-domain collaborative simulations, this invention achieves the organic integration of combat behaviors at different levels through modular design, maintaining the integrity of command while also considering the detailed characterization of execution, providing more reliable simulation support for verification and evaluation. At the same time, the visualized state machine modeling tool greatly lowers the threshold for experts to participate in simulation construction, significantly improves the responsiveness of the simulation system to actual needs, and is flexible and reusable in application, achieving efficient transformation of experience into simulation models.

[0229] Example 2

[0230] Operational System Modeling: This involves analyzing and abstracting various operational units participating in combat, classifying them into different types such as ground operational units, air operational units, and naval operational units. For each operational unit type, basic attributes are defined, including but not limited to the unit's number, name, performance parameters (such as speed, range, attack power, and defensive capabilities), and resource reserves (such as ammunition and fuel). Simultaneously, a connection model between operational units is established, clarifying information exchange and collaborative operations among them. For example, air operational units provide reconnaissance information and fire support to ground operational units, while ground operational units and naval operational units conduct logistical resupply and tactical coordination. By establishing a hierarchical operational system structure model, operational units are grouped and layered according to factors such as operational missions and operational areas, constructing a clear and complete operational system model.

[0231] State Machine Definition: A state machine is defined for each combat unit, containing multiple states such as standby, maneuver, attack, defense, damaged, and repair. Transition conditions between states are also defined, determined based on the combat unit's attributes, external environmental factors, and interactions with other combat units. For example, when a combat unit's ammunition reserves fall below a certain threshold and a nearby supply unit is available, it transitions from attack to supply; when a combat unit receives enemy attack information and its defenses are sufficient, it transitions from its current state to defense. Furthermore, corresponding actions are defined for each state transition process. These actions include specific operations performed by the combat unit, such as weapon firing, maneuvering, and information transmission, as well as operations updating the combat unit's attributes, such as consuming ammunition, reducing fuel, and changing position coordinates.

[0232] Simulation Initialization: Based on the specific combat scenario and mission requirements, the combat system model is initialized. Initial states and attribute values ​​are assigned to each combat unit, and external environmental parameters, such as weather conditions and terrain, are set. These environmental parameters affect the performance and combat operations of the combat units. Simultaneously, initial connections between combat units are established to ensure the combat system is in a reasonable initial state when the simulation begins.

[0233] Simulation Advancement: During the simulation, the state machine of each combat unit is updated according to a set time step. Based on the current state of the combat unit and the met transition conditions, corresponding state transitions and actions are triggered. During state transitions and action execution, the attribute values ​​of the combat units are updated in real time, and relevant information is transmitted promptly based on the connection relationships between combat units, achieving collaborative simulation between combat units. For example, when a ground combat unit launches an attack, the attack information is transmitted to relevant friendly and enemy combat units. Friendly combat units adjust their combat strategies based on the attack information, while enemy combat units respond with defensive or counterattack actions.

[0234] Interactive Processing: Establish an information exchange mechanism between combat units to achieve real-time, accurate transmission and sharing of information. Information exchange includes the transmission of tactical commands, sharing of battlefield intelligence, and requests for coordinated operations between combat units. Message queues and event-driven technologies are employed to ensure the efficiency and reliability of information exchange. When a combat unit receives information, it processes and responds accordingly based on the information content and its own status, such as adjusting combat strategies or changing its status.

[0235] Simulation Control and Management: Provides simulation control functions, including simulation start, pause, stop, and reset operations, allowing users to flexibly control the simulation process. Simultaneously, it monitors and records data in real time during the simulation, including changes in the state and attribute values ​​of combat units, and information interaction records. Through analysis and statistics of simulation data, it generates simulation reports, providing data support for combat plan evaluation and decision-making. Furthermore, based on simulation requirements and actual conditions, it dynamically adjusts and optimizes the combat system model and state machine, such as adding or deleting combat units, modifying state transition conditions and actions, to improve the adaptability and accuracy of the simulation.

[0236] Visualization: The simulation process and results are presented in a visual format, using a two-dimensional or three-dimensional graphical interface to intuitively display information such as the location, status, and movement trajectory of combat units. Animation effects are used to demonstrate interactions and combat actions between combat units, making the simulation results more vivid and engaging. Furthermore, multiple visualization views are provided, such as global views, local views, and detailed information views, allowing users to observe and analyze the simulation process and results from different perspectives.

[0237] The state machine editing process design in this embodiment is as follows: Figure 2 As shown; Figure 3 This is a schematic diagram of the creation state disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of the modified status name disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of the adding action disclosed in an embodiment of the present invention; Figure 6 This is a schematic diagram of the addition conditions disclosed in the embodiments of the present invention; Figure 7 This is a schematic diagram of condition parameter editing disclosed in an embodiment of the present invention; Figure 8 This is a schematic diagram of the state machine disclosed in an embodiment of the present invention; Figure 9 This is a schematic diagram of attribute interaction disclosed in an embodiment of the present invention; Figure 10 This is a schematic diagram of the interaction between the conversion line and the blackboard attributes disclosed in an embodiment of the present invention.

[0238] Example 3

[0239] Taking system-wide collaborative operations involving manned / unmanned aircraft formations as an example, the combat behavior of participating forces exhibits typical multi-layered characteristics. The overall command and control center, as a strategic node, is responsible for overall mission planning and constructs a comprehensive information channel through information support capabilities such as communication satellites; early warning aircraft serve as the battlefield awareness and command center, equipped with active phased array radar and collaborative combat systems; manned fighter jets act as tactical command nodes, possessing both air combat and unmanned aerial vehicle (UAV) control functions; and UAV swarms include various platforms such as reconnaissance, decoy, and attack aircraft, employing a modular payload design. This force configuration ensures both the integrity of system-wide operations and the functional complementarity of combat units at each level.

[0240] From the perspective of operational system hierarchy, it can be divided into a three-tiered architecture: strategic command, operational coordination, and tactical execution. The strategic command level consists of a general command and control center and unmanned aerial vehicle (UAV) ground command stations, responsible for generating operational missions and allocating resources globally. The operational coordination level, centered on early warning aircraft, integrates space-based and air-based information resources, implements battlefield situation fusion, and dynamically allocates missions. The tactical execution level consists of manned / unmanned aircraft formations, conducting specific reconnaissance, jamming, and strike operations based on the real-time battlefield situation. This hierarchical control structure effectively balances command efficiency and tactical flexibility, demonstrating good adaptability in operational practice. Figure 11 This is a schematic diagram of a manned / unmanned aerial vehicle (UAV) collaborative system combat system disclosed in an embodiment of the present invention.

[0241] In system-of-systems combat simulation research, the process of an AWACS aircraft commanding and guiding manned / unmanned aerial vehicle (UAV) coordinated operations can be divided into several key stages. First, the AWACS receives target intelligence information from the command center, or conducts a wide-area scan of the operational airspace using airborne radar to accurately calculate enemy target trajectories and establish an initial battlefield situation map. In the situation sharing stage, the AWACS uses a data link to achieve cross-platform information exchange, distributing the fused battlefield information to the manned / unmanned aircraft formation via a tactical data link. Upon entering the tactical guidance stage, the AWACS performs calculations based on guidance laws, determining the appropriate flight path for our fighter jets based on enemy target flight parameters and our own fighter jet's flight parameters. After receiving the flight command, the manned / unmanned aircraft formation adjusts its direction and speed to the guidance position. In the return-to-base stage, the AWACS notifies the manned / unmanned aircraft formation and issues a return-to-base command. In actual combat, the process of an AWACS aircraft guiding our manned / unmanned aircraft formation to intercept targets is as follows: Figure 12 As shown.

[0242] The operational execution phase primarily involves manned / unmanned aircraft formations completing combat missions. Upon reaching the designated guidance position, the manned / unmanned formations conduct combat operations. Manned aircraft provide command and control over the coordinated manned / unmanned aircraft, handling formation decisions, trajectory planning, tactical implementation, and command and control of the unmanned aircraft. Unmanned aircraft can also autonomously network, conduct situational awareness, and perform jamming and strike missions. Manned aircraft act as communication nodes within the system, embedding manned / unmanned aircraft into the entire adversarial system. This enables battlefield information sharing, unified scheduling of available resources, and comprehensive management of combat missions. Throughout the engagement, early warning aircraft continuously monitor the operational progress and implement tactical adjustments via two-way data links, forming a complete "observation-judgment-decision-action" operational cycle.

[0243] The mission phases of manned / unmanned aircraft formations include the following phases:

[0244] During the preparation phase, manned and unmanned aircraft take off separately and fly to the handover area. The unmanned aircraft ground control station transfers control of the unmanned aircraft to the manned aircraft, and under the command of the manned aircraft, they fly together to the mission area.

[0245] During the operational phase, based on current battlefield situation information, manned aircraft make decisions and assign tasks to each unmanned aerial vehicle (UAV), planning their flight paths and reconnaissance and jamming operations. Under the command of the manned aircraft, the UAVs follow the planned routes and, upon reaching the target area, perform reconnaissance, jamming, and strike missions, transmitting reconnaissance data back to the manned aircraft in real time. Through multi-source intelligence processing, new situational information is generated, providing a basis for further decision-making by the manned aircraft.

[0246] During the assessment phase, drones will be used to return to the attack area to perform battlefield awareness and assessment tasks, and the assessment results will be transmitted back to our side to determine whether to carry out a new round of combat operations.

[0247] In the final phase, if no new round of combat operations is required, the manned and unmanned aircraft will return to base after completing their respective missions. The manned aircraft will then direct the unmanned aircraft to the handover area and transfer control of the unmanned aircraft to the ground control station. Figure 13 This is a typical operational flowchart of manned-unmanned aircraft (UAV) swarm coordination disclosed in this invention. Manned-UAV combat operations can be flexibly conducted through specific manned / UAV coordinated tasks, leveraging the respective advantages of both types of aircraft. This differentiated capability configuration forms a key foundation for enhancing the overall system's operational effectiveness. The coordinated use of the two platforms generates significant tactical synergy. Manned aircraft, as tactical command nodes, can translate decision-making advantages into the operational effectiveness of the UAV swarm, while UAVs extend the operational radius of manned aircraft by expanding their perception range and undertaking high-risk missions. This diversity significantly improves the system's survivability. In the future, with the development of artificial intelligence technology, the capability boundaries of the two platforms will continue to evolve, but their inherent complementary characteristics will remain the core basis for constructing a collaborative combat system.

[0248] In the process of developing combat models, a series of key issues facing the development of behavioral modeling include how to effectively transform conceptual models into program design models, how to describe logically complex combat models in a simple and clear way, and how to provide personnel with an effective business model design specification, and how to enable the combat model framework to play an effective role in intelligent command and decision-making.

[0249] Finite state machine-based and rule-based behavior modeling methods are currently the two most successful behavior modeling approaches, widely used in the CGF combat simulation system and achieving significant research results. However, these two methods share similar problems: as the simulation scale expands, the number of participating simulation entities becomes excessively large, highlighting the issues of complex state rules and low reuse rate of behavior resources.

[0250] Event-driven hierarchical asynchronous finite state machines have been successfully applied in many game AI frameworks. This method combines the advantages of state machine design structure, such as clear structure, easy readability and understanding, distinct hierarchy, standardized design method, and easy tool support. It is one of the indispensable key technologies in intelligent object behavior modeling.

[0251] A finite state machine (FSM) is a mathematical model representing a finite number of states and the transitions and actions between them. Each state stores a set of information describing the model. When certain events occur and certain conditions are met, the system transitions from one state to another. An asynchronous finite state machine is a state machine without a fixed clock; its state transitions are not necessarily caused by clock jumps but may be triggered by other sources.

[0252] In practice, state machines have a fatal flaw: with a large number of states, their transitions become unmaintainable. For example, with n states, at most n*n transition links need to be maintained. In complex system modeling, the number of states is often directly proportional to the model's complexity. Hierarchical state machine structures aim to reduce these transitions. Using a hierarchical state machine, the relationships between each state are considered during modeling, all transition links are defined, and the modeled object's behavior is categorized. Several smaller states are grouped into one state, and transition links between higher-level states and their internal smaller states are defined. States within a state do not need to concern themselves with transitions to external states, thus isolating unrelated states and significantly reducing the complexity of the state machine. Furthermore, if a two-layer state machine still has too many states, more state layers can be defined to further reduce the number of transition links.

[0253] Taking the typical manned-unmanned aircraft formation combat process mentioned above as an example, in system-of-systems combat simulation research, by modeling the formation guided by the early warning aircraft and the manned-unmanned aircraft cooperative combat process using a hierarchical finite state machine, the multi-level dynamic process of system-of-systems combat can be accurately described. This model includes four main combat states, each consisting of several sub-states with clear meanings.

[0254] The preparation and deployment phase of combat forces is defined as the initial preparation state (S1), which specifically includes three key sub-states: the takeoff sub-state (S1-1), which completes runway taxiing and takeoff maneuvers for manned and unmanned aircraft; the arrival at designated airspace sub-state (S1-2), which enables combat units to maneuver and stand by to the designated assembly area; and the unmanned aircraft control handover sub-state (S1-3), which completes the transfer of command authority from the ground control station to the manned aircraft operator. The initial preparation state is an important prerequisite for subsequent coordinated operations.

[0255] The process of airborne command and control by an AWACS aircraft and guidance of combat aircraft is defined as the AWACS guidance state (S2). This state consists of two sub-states: AWACS patrol and combat aircraft guidance, reflecting the dominant role of AWACS airborne command in the battlefield situation. In the AWACS patrol sub-state (S2-1), the AWACS aircraft continuously maintains the airborne surveillance circle, conducting wide-area scanning and target tracking through its airborne radar. In the combat aircraft guidance sub-state (S2-2), the optimal interception trajectory is calculated in real time based on the battlefield situation, and navigation instructions are transmitted to the manned aircraft formation via data link, creating a favorable tactical situation for subsequent combat operations.

[0256] The core operational phase is defined as the manned / unmanned aircraft command and coordination state (S3), which includes four tactical sub-states. In the reconnaissance and detection sub-state (S3-1), manned aircraft organize unmanned aircraft swarms to conduct layered reconnaissance, with manned aircraft simultaneously performing multi-source information fusion processing. In the coordinated operations sub-state (S3-2), the formation dynamically allocates firepower tasks based on threat levels, coordinating manned and unmanned aircraft to conduct precision strikes. In the electronic countermeasures sub-state (S3-3), dedicated unmanned aircraft are deployed to perform tactical maneuvers such as electromagnetic suppression or decoy deception. The operational assessment sub-state (S3-4) conducts real-time damage assessment of the strike effects, providing a basis for subsequent decision-making.

[0257] The return-to-base and withdrawal status (S4) standardizes the operational end-of-combat process and includes three standardized sub-status. Formation withdrawal status (S4-1) coordinates the withdrawal of each combat unit from the combat airspace along a predetermined route; command handover sub-status (S4-2) completes the reverse transfer of UAV control authority from manned aircraft to ground stations; and individual return-to-base sub-status (S4-3) ensures the safe return of each combat platform to its base.

[0258] This state machine model achieves the organic integration of strategic-level operational processes and tactical-level actions through hierarchical design. The relevant state transition rules can be flexibly specified according to the operational situation and command rules, conforming to the command and control characteristics of modern system-of-systems warfare. The transitions of each state and sub-state are as follows: Figure 14 As shown.

[0259] Using the hierarchical asynchronous finite state machine software design function, the overall state configuration design of the above typical system-of-systems combat is shown in the figure below. The overall process includes four states: initial preparation, early warning guidance, coordinated combat, and withdrawal and return. Each state includes a series of sub-states, describing the detailed state process of each stage. Figure 15 This is a schematic diagram of a typical system combat state machine configuration disclosed in an embodiment of the present invention.

[0260] 1. Sub-state design

[0261] The initial preparation state (S1) includes three sub-states: taxiing and takeoff, maneuvering and standing by, and transfer of command authority. The configuration is as follows: Figure 16 As shown. The early warning guidance state (S2) includes two sub-states: early warning patrol and flight guidance. The configuration is as follows: Figure 17 As shown. Cooperative Operations State (S3) includes four sub-states: reconnaissance and detection, cooperative operations sub-state, electronic countermeasures, and operational assessment. The configuration is as follows: Figure 18 As shown. The evacuation and return status (S4) includes three sub-status: formation evacuation, handover of command, and individual return. The configuration is as follows: Figure 19 As shown.

[0262] 2. Design of conversion conditions

[0263] The state transition conditions between each state and sub-state can be set on the connection lines. When the judgment conditions are met, the state machine will implement the relevant state transition. Figure 20 This is a schematic diagram illustrating the state transition condition settings disclosed in an embodiment of the present invention. State transitions can be configured with condition groups, and multiple judgment conditions can be set under each condition group, allowing the combined judgment of multiple judgment conditions to serve as the basis for state transition determination. Figure 21 This is a schematic diagram of setting multiple condition groups disclosed in an embodiment of the present invention.

[0264] 3. Entity Task Configuration

[0265] The specific task content is configured in the sub-state, and the related actions are associated with the combat task model of the simulated entity. This task model is developed based on the specific task flow and task elements, and relevant parameters can be configured in the specific scenario. Figure 22 This is a schematic diagram of configuring tasks in a sub-state as disclosed in an embodiment of the present invention.

[0266] Figure 22 The model for the early warning patrol task component triggered by sub-states in the [model name] is described in [link to model details]. See [link to model details] for related parameters. Figure 23 .

[0267] When a substate consists of multiple simulated entities, multiple execution actions can be set within the substate to meet the requirements of multiple entities cooperating in the task. For example, in the electronic warfare substate in this example, task actions are set for manned and unmanned aircraft respectively, which can realize the simulation of coordinated combat between manned and unmanned aircraft in electronic warfare. Figure 24 This is a schematic diagram illustrating the configuration of multiple simulation entities in a sub-state as disclosed in an embodiment of the present invention. The parameter configuration content of the configured electronic warfare mission model is as follows: Figure 25 As shown in the example, the state machine defined in this example is a unified plan for the combat states of multiple simulated entities, similar to the planning of command capabilities in actual combat processes. Therefore, the configured system combat state machine can be configured in the command post capabilities and implemented in the manner of command post combat process planning. Figure 26 This is a diagram of the command post configuration system combat state machine disclosed in the embodiments of the present invention; the corresponding simulation task execution entity model is configured with the corresponding task component model, and the parameters can be configured in specific simulation scenarios. Figure 27 The diagram shows the configuration of the mission components related to the early warning aircraft.

[0268] Example 4

[0269] Please see Figure 28 , Figure 28 This is a schematic diagram of the structure of a system combat state machine simulation implementation device disclosed in an embodiment of the present invention. Figure 28 The described system-of-systems combat state machine simulation implementation device is applied in the field of system simulation and combat behavior modeling technology, and the embodiments of this invention are not limited thereto. Figure 28 As shown, the system's combat state machine simulation implementation device can include the following operations:

[0270] S301, Information Acquisition Module, is used to acquire information on systematic collaborative combat missions;

[0271] S302, Information parsing module, used to parse the system-wide collaborative combat mission information to obtain the system-wide collaborative combat mission flow;

[0272] S303, System Combat State Machine Construction Module, is used to process the system-wide collaborative combat mission process to obtain the system combat state machine.

[0273] Example 4

[0274] Please see Figure 29 , Figure 29 This is a schematic diagram of another system combat state machine simulation implementation device disclosed in an embodiment of the present invention. Figure 29The described system-of-systems combat state machine simulation implementation device is applied in the field of system simulation and combat behavior modeling technology, and the embodiments of this invention are not limited thereto. Figure 29 As shown, the system's combat state machine simulation implementation device can include the following operations:

[0275] Memory 401 storing executable program code;

[0276] Processor 402 coupled to memory 401;

[0277] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the system combat state machine simulation implementation method described in Embodiment 1, Embodiment 2 and Embodiment 3.

[0278] Example 5

[0279] This invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps in the system combat state machine simulation implementation method described in Embodiments 1, 2, and 3.

[0280] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0281] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0282] Finally, it should be noted that the system combat state machine simulation implementation method and apparatus disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating a system-wide combat state machine, characterized in that, The method includes: S1, acquire information on systematic collaborative combat missions; S2, parse the system-wide collaborative combat mission information to obtain the system-wide collaborative combat mission flow; S3, process the system-wide collaborative combat mission flow to obtain the system combat state machine, including: S31, Process the system-wide collaborative combat mission flow to obtain the system combat state; The system combat state includes initial preparation state State1, early warning aircraft guidance state State2, manned / unmanned aircraft command and coordination state State3, and return and withdrawal state State4. S32, the operational state of the system is divided into a set of sub-states; the set of sub-states includes the initial preparation sub-state, the early warning aircraft guidance sub-state, the manned / unmanned aircraft command and coordination sub-state, and the return and withdrawal ion state; S33, process the system's combat state and sub-state set to obtain state transition relationships and sub-state transition relationships; S34, process the sub-state set to obtain the sub-state action information set; The early warning patrol sub-state is processed to obtain information on the airborne radar's wide-area scanning and target tracking actions, including: S3441 uses airborne radar to perform wide-area scanning to obtain target data information; S3442 processes the moving target in the target data information to obtain imaging parameters with N motion states. i∈1,2,…,N; In the formula, For the i-th imaging parameter p i The x-coordinate value, For the i-th imaging parameter p i The ordinate value; S3443, Set the initial weights ω of the moving target. i =1, let t=1; S3444, Process the moving target to obtain state transition information; The state transition information expression is: Where A1 and A2 are the initial and final pixel values ​​for target detection in the horizontal direction, respectively, and B1 and B2 are the initial and final pixel values ​​for target detection in the vertical direction, respectively. Let t-1 be the number of state transitions. Let be the x-coordinate value at time t. The ordinate value at time t. The x-coordinate value at time t-1 Horizontal coordinate value; S3445, The state transition information is processed to obtain the airborne radar's wide-area scanning and target tracking action information; The expression for the action information of the airborne radar performing wide-area scanning and target tracking is: In the formula, These are the x and y coordinates of the position obtained from tracking the moving target, respectively. The weights of the moving target at time t are determined experimentally. S3446, let t = t + 1, execute S3444 ~ S3445 until the preset termination condition is reached, and obtain the airborne radar's wide-area scanning and target tracking action information; S35, process the set of sub-state action information to obtain the system combat state machine.

2. The system combat state machine simulation implementation method according to claim 1, characterized in that, The division of the system's operational state into sub-state sets includes: S321, the initial preparation state State1 is divided into taxiing and takeoff sub-state, maneuvering and standby sub-state and command authority transfer sub-state; S322, the guidance state State2 of the early warning aircraft is divided into early warning patrol sub-state and flight guidance sub-state; S323, the manned / unmanned aircraft command and coordination state 3 is divided into reconnaissance and detection sub-state, cooperative combat sub-state, electronic countermeasures sub-state and combat evaluation sub-state; S324, the return and withdrawal state State4 is divided into the formation withdrawal state, the command handover sub-state and the individual return state.

3. The system combat state machine simulation implementation method according to claim 1, characterized in that, The process of processing the system's operational state and sub-state set to obtain state transition relationships and sub-state transition relationships includes: S331, Process the operational state of the system to obtain the state transition relationship; the state transition relationship is: Where C = {C1, C2, C3} is the set of conditions for the first action; S332, Process the set of sub-states to obtain sub-state transition relationships; the sub-state transition relationships include a first sub-state transition relationship, a second sub-state transition relationship, a third sub-state transition relationship, and a fourth sub-state transition relationship.

4. The system combat state machine simulation implementation method according to claim 2, characterized in that, The process of processing the sub-state set to obtain the sub-state action information set includes: S341 processes the taxiing and takeoff sub-states to obtain runway taxiing and takeoff information for manned and unmanned aircraft. S342 processes the maneuvering and standby sub-state to obtain information on the maneuvering and standby actions of the combat unit toward the predetermined assembly area; S343, process the command authority transfer sub-state to obtain command authority transfer action information from the ground control station to the manned aircraft operator; S344 processes the early warning patrol sub-state to obtain information on the airborne radar's wide-area scanning and target tracking actions. S345 processes the flight guidance state to obtain the optimal trajectory and action information for real-time situational analysis; S346, Process the reconnaissance and detection sub-state to obtain multi-source information fusion processing action information; S347 processes the cooperative combat sub-state to obtain dynamic task allocation action information; S348, process the state of the electronic countermeasures to obtain the electronic countermeasures action information; S349 processes the combat assessment sub-state to obtain real-time damage assessment action information; S350 processes the formation's ion withdrawal status to obtain withdrawal action information; S351 processes the command handover sub-state to obtain the reverse transfer action information of UAV control authority from manned aircraft to ground station; S352, process the respective return sub-states to obtain safe return to base action information; S353 integrates the following information to obtain a set of sub-state action information: runway taxiing and takeoff actions of the manned and unmanned aircraft, maneuvering and standby actions of the combat unit towards the predetermined assembly area, transfer of command authority from the ground control station to the manned aircraft operator, wide-area scanning and target tracking actions of the airborne radar, real-time situational awareness calculation of the optimal trajectory, multi-source information fusion processing, dynamic task allocation, electronic countermeasures sub-actions, real-time damage assessment, evacuation, reverse transfer of UAV control authority from the manned aircraft to the ground station, and safe return to base.

5. The system combat state machine simulation implementation method according to claim 4, characterized in that, The process of processing the maneuvering and standby sub-state to obtain the maneuvering and standby action information of the combat unit toward the predetermined assembly area includes: S3421 processes the combat units and the predetermined assembly area to obtain the path planning cost function and constraints. S3422, Set the initial information for the path planning model; S3423, use Logistic chaotic mapping to generate a preset number of individuals for the path planning model, calculate the fitness value of the individuals, and select the individuals with better fitness values ​​as the initial individuals for the model. S3424, in each iteration, updates the individual position according to the behavior of the path planning model and uses a nonlinear energy factor to update the strategy, making it move towards a lower fitness value; S3425, In the local search phase, chaotic mapping is used to search the local solution space so that it can escape the local optimum. S3426, repeat S3424~S3425 until the preset algorithm termination condition is met, output the optimal fitness value, and obtain the information on the combat unit's maneuvering flight and standby actions towards the predetermined assembly area.

6. The system combat state machine simulation implementation method according to claim 1, characterized in that, include: The information acquisition module is used to acquire information on systematic collaborative combat missions. The information parsing module is used to parse the system-wide collaborative combat mission information to obtain the system-wide collaborative combat mission flow. The system-of-systems combat state machine construction module is used to process the system-of-systems collaborative combat mission process to obtain the system-of-systems combat state machine.

7. A system combat state machine simulation implementation device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the system combat state machine simulation implementation method as described in any one of claims 1-5.

8. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the system combat state machine simulation implementation method as described in any one of claims 1-5.

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