Simulation scenario system and method

Abstract

The invention discloses a simulation scenario system and method. The system comprises a scenario editing module, a scenario simulation module, a model assembly database and a scenario evaluation module. The scenario editing module is in data connection with the scenario simulation module and the model assembly database and is used for generating a simulation scenario file; the model assembly database is in data connection with the scenario editing module and is used for managing the simulation scenario model; the scenario simulation module is in data connection with the scenario editing module and the scenario evaluation module and is used for processing the simulation scenario file to obtain a simulation scenario result; and the scenario evaluation module is in data connection with the scenario simulation module and is used for evaluating the simulation scenario result. The method comprises the following steps: analyzing scenario demand information to obtain scenario key information; according to the scenario key information, processing a model assembly database to obtain a configuration database; the configuration database is processed through the scenario editing module, and simulation scenario is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation scenario, and in particular to a simulation scenario system and method. Background Art

[0002] In the fields of simulation exercises, air support planning, and air-to-ground support strategy formulation, simulation scenarios play a vital role. When developing simulation systems, scenarios are often ignored, or even considered to be just the initialization of simulation system data. The scenario generation system is too highly coupled with the simulation system, resulting in poor reusability and scalability of simulation scenarios and scenario generation systems. In various air-to-ground interactive tasks such as close air support, the existing simulation scenario generation methods have certain problems of manual intervention and low efficiency. Therefore, under the set mission background, under the constraints of meeting time requirements, survival requirements, and economic requirements, it is very necessary to study a simulation scenario system and method, and perform scenario simulation according to scenario requirements. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a simulation scenario system and method, the system includes a scenario editing module, a scenario simulation module, a model assembly database and a scenario evaluation module; the scenario editing module is connected with the scenario simulation module and the model assembly database data to generate a simulation scenario file; the model assembly database is connected with the scenario editing module data to manage the simulation scenario model; the scenario simulation module is connected with the scenario editing module and the scenario evaluation module data to process the simulation scenario file to obtain the simulation scenario result; the scenario evaluation module is connected with the scenario simulation module data to evaluate the simulation scenario result. The method of the present invention obtains the scenario key information by parsing the scenario requirement information; according to the scenario key information, the model assembly database is processed to obtain the configuration database; the configuration database is processed by the scenario editing module to realize the simulation scenario.

[0004] In order to solve the above technical problems, the first aspect of the embodiment of the present invention discloses a simulation scenario system, the system includes a scenario editing module, a scenario simulation module, a model assembly database and a scenario evaluation module;

[0005] The scenario editing module is connected with the scenario simulation module and the model assembly database data to generate a simulation scenario file;

[0006] The model assembly database is connected with the scenario editing module data to manage simulation scenario models;

[0007] The scenario simulation module is data-connected with the scenario editing module and the scenario evaluation module, and is used to process the simulation scenario file to obtain a simulation scenario result;

[0008] The scenario evaluation module is data-connected to the scenario simulation module and is used to evaluate the simulation scenario results.

[0009] As an optional implementation, in the first aspect of the embodiment of the present invention, the scenario editing module includes a data management unit, a scenario editing unit and a scenario editing auxiliary unit;

[0010] The data management unit is used to generate a scenario data file;

[0011] The scenario editing unit is used to process the scenario data file to generate a simulation scenario file;

[0012] The scenario editing auxiliary unit is used to provide auxiliary information for generating the simulation scenario file.

[0013] As an optional implementation, in the first aspect of the embodiment of the present invention, the assumption simulation module includes a parallel simulation module, a simulation operation control module, a memory sharing module, an information interaction module, a simulation management module, an auxiliary recording module and a system simulation object module;

[0014] The parallel simulation module is used to implement parallel simulation;

[0015] The simulation operation and control module is used to realize the control of the simulation;

[0016] The memory sharing module is used to realize data interaction between various models of the simulation scenario system;

[0017] The information interaction module is used to provide information interaction for the simulation object;

[0018] The simulation management module is used to manage and schedule memory data, simulation objects and interaction information;

[0019] The auxiliary recording module is used to record simulation operation information;

[0020] The system simulation object module is used to manage simulation entity units.

[0021] As an optional implementation, in the first aspect of the embodiment of the present invention, the model assembly database includes an attribute module, an equipment module, an environment module, a service module, a cognitive module, an interaction module and an agent module;

[0022] The property module is used to set properties of the simulation scenario;

[0023] The equipment module is used to set the simulation scenario equipment;

[0024] The environment module is used to set the simulation scenario environment;

[0025] The service module is used to perform task decomposition on the simulation scenario;

[0026] The cognitive module is used to perform cognitive simulation on the simulation scenario;

[0027] The interaction module is used for performing simulation scenario data interaction;

[0028] The proxy module is used for data interaction with an external system.

[0029] A second aspect of an embodiment of the present invention discloses a simulation scenario method, the method comprising:

[0030] S1, obtain the user's assumed demand information;

[0031] S2, parsing the assumption requirement information to obtain assumption key information;

[0032] S3, processing the model assembly database according to the assumed key information to obtain a configuration database;

[0033] S4, using the scenario editing module to process the configuration database to realize the simulation scenario.

[0034] As an optional implementation, in the second aspect of the embodiment of the present invention, the model assembly database is processed according to the assumed key information to obtain a configuration database, including:

[0035] S31, constructing a text graph for the model assembly database to obtain M model assembly text graphs, where M is a positive integer;

[0036] S32, constructing a text map of the scenario key information to obtain a scenario text map;

[0037] S33, performing node similarity calculation on the assumption text graph and the M model assembly text graphs to obtain a node similarity value;

[0038] S34, performing graph embedding similarity calculation on the assumption text graph and the M model assembly text graphs to obtain a graph embedding similarity value;

[0039] S35, processing the node similarity value and the graph embedding similarity value to obtain a text similarity value;

[0040] S36, processing the model assembly database according to the text similarity value to obtain a configuration database.

[0041] As an optional implementation, in the second aspect of the embodiment of the present invention, the using of the scenario editing module to process the configuration database to implement the simulation scenario includes:

[0042] S41, using the scenario editing module, set the participants of the air-to-ground confrontation;

[0043] S42, processing the configuration database to obtain the entity deployment plan of the participant;

[0044] S43, processing the entity deployment plan to obtain a simulation scenario file; the simulation scenario file includes simulation path information and simulation task allocation information;

[0045] S44, using a scenario simulation module, processing the simulation scenario file to obtain a simulation scenario result;

[0046] S45, using a scenario evaluation module to evaluate the simulation scenario results to implement the simulation scenario.

[0047] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the processing of the entity deployment scheme to obtain a simulation scenario file includes:

[0048] S431, demarcating an airspace according to the physical deployment plan to obtain a simulated airspace;

[0049] S432, performing route planning according to the physical deployment plan to obtain simulation path information;

[0050] S433: Perform task setting according to the simulation airspace and simulation path information to obtain simulation task allocation information.

[0051] As an optional implementation manner, in the second aspect of the embodiment of the present invention, performing route planning according to the entity deployment scheme to obtain simulation path information includes:

[0052] S4321, establishing a three-dimensional model according to the physical deployment plan and the simulated airspace, determining a cost function and constraint conditions for the UAV path planning, and initializing a particle swarm algorithm;

[0053] S4322, using Logistic chaotic mapping to generate the initialization individuals of the particle swarm and calculate the fitness value of the individuals;

[0054] S4323, using nonlinear energy factors to update individuals so that individuals move towards lower fitness values;

[0055] S4324, using chaotic mapping to search the local solution space so as to make it jump out of the local optimal value;

[0056] S4325, repeat S4323 and S4324, and when the algorithm termination condition is met, output the optimal fitness value; the optimal fitness value is the simulation path information.

[0057] As an optional implementation, in the second aspect of the embodiment of the present invention, the step of performing task setting according to the simulation airspace and simulation path information to obtain simulation task allocation information includes:

[0058] S4331, setting the task allocation cost-benefit function and constraint conditions according to the simulation airspace and simulation path information

[0059] S4332, setting initial parameters of the particle swarm algorithm and the simulated annealing algorithm;

[0060] S4333, randomly generating a particle swarm in the search space, and assigning a position and a velocity to each particle;

[0061] S4334, using the particle swarm algorithm to perform global search, and using the nonlinear inertia factor to update the position and velocity of the particles, while recording the overall optimal solution and the individual optimal solution;

[0062] The nonlinear inertia factor is:

[0063]

[0064] Where ω is the nonlinear inertia factor, ω max The value of is 0.95, ω min The value is 0.6, t represents the number of iterations, t max Indicates the maximum number of iterations;

[0065] S4335, using a simulated annealing algorithm to adjust the positions of all optimal solutions of the current iteration number to obtain a new solution;

[0066] S4336, comparing the new solution with the optimal solution obtained by the particle swarm algorithm, and when the new solution is better than the current optimal solution, taking the new solution as the optimal solution for the current number of iterations; when the current optimal solution is better than the new solution, accepting the new solution according to a preset probability;

[0067] S4337, when the termination condition is not met, execute S4334; when the termination condition is met, output the simulation task allocation information.

[0068] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0069] The invention discloses a simulation scenario system and method, which comprises a scenario editing module, a scenario simulation module, a model assembly database and a scenario evaluation module; the system has a hierarchical and modular structure, and the upper layer can call the services of the lower layer, so that the system has good maintainability and expansibility.

[0070] The method of the present invention obtains the key information of the scenario by parsing the scenario demand information; according to the key information of the scenario, the model assembly database is processed to obtain the configuration database; the configuration database is processed by the scenario editing module to realize the simulation scenario. The system provides a visual scenario editing environment with a good human-computer interaction interface. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0072] Figure 1 It is a structural schematic diagram of a simulation scenario system disclosed in an embodiment of the present invention;

[0073] Figure 2 It is a schematic diagram of the structure of a scenario simulation module disclosed in an embodiment of the present invention;

[0074] Figure 3 is a flow chart of the operation of the assumption simulation module disclosed in the embodiment of the present invention;

[0075] Figure 4 It is a structural diagram of a model assembly database disclosed in an embodiment of the present invention;

[0076] Figure 5 is a structural diagram of an equipment module disclosed in an embodiment of the present invention;

[0077] Figure 6 is a structural diagram of an environment module disclosed in an embodiment of the present invention;

[0078] Figure 7 It is a structural diagram of the attribute module disclosed in the embodiment of the present invention;

[0079] Figure 8 It is a schematic diagram of the input and output interface of the decision model component disclosed in the embodiment of the present invention;

[0080] Fig. 9 is a schematic diagram of a scenario editing module disclosed in an embodiment of the present invention;

[0081] Fig.10 is a schematic diagram of the workflow of the scenario editing module disclosed in the embodiment of the present invention;

[0082] Fig.11 It is a flowchart of a simulation scenario method disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0083] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0085] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0086] The present invention discloses a simulation scenario system and method, which includes a scenario editing module, a scenario simulation module, a model assembly database and a scenario evaluation module; the scenario editing module is connected with the scenario simulation module and the model assembly database data to generate a simulation scenario file; the model assembly database is connected with the scenario editing module data to manage the simulation scenario model; the scenario simulation module is connected with the scenario editing module and the scenario evaluation module data to process the simulation scenario file to obtain the simulation scenario result; the scenario evaluation module is connected with the scenario simulation module data to evaluate the simulation scenario result. The method of the present invention obtains the scenario key information by parsing the scenario requirement information; according to the scenario key information, the model assembly database is processed to obtain the configuration database; the configuration database is processed by the scenario editing module to realize the simulation scenario. The following are detailed descriptions.

[0087] Embodiment 1

[0088] See also Figure 1 , Figure 1 : is a structural diagram of a simulation scenario system disclosed in an embodiment of the present invention. Figure 1 The simulation scenario system described is applied to the field of simulation scenario technology, and the scenario scheme design is performed based on the scenario requirements, which is not limited in the embodiments of the present invention. Figure 1 As shown, the simulation scenario system includes a scenario editing module, a scenario simulation module, a model assembly database and a scenario evaluation module;

[0089] The scenario editing module is connected with the scenario simulation module and the model assembly database data to generate a simulation scenario file;

[0090] The model assembly database is connected with the scenario editing module data to manage simulation scenario models;

[0091] The scenario simulation module is data-connected with the scenario editing module and the scenario evaluation module, and is used to process the simulation scenario file to obtain a simulation scenario result;

[0092] The scenario evaluation module is data-connected to the scenario simulation module and is used to evaluate the simulation scenario results.

[0093] Optionally, the scenario editing module includes a data management unit, a scenario editing unit and a scenario editing auxiliary unit;

[0094] The data management unit is used to generate a scenario data file;

[0095] The scenario editing unit is used to process the scenario data file to generate a simulation scenario file;

[0096] The scenario editing auxiliary unit is used to provide auxiliary information for generating the simulation scenario file.

[0097] Optionally, the assumption simulation module includes a parallel simulation module, a simulation operation control module, a memory sharing module, an information interaction module, a simulation management module, an auxiliary recording module and a system simulation object module;

[0098] The parallel simulation module is used to implement parallel simulation;

[0099] The simulation operation and control module is used to realize the control of the simulation;

[0100] The memory sharing module is used to realize data interaction between various models of the simulation scenario system;

[0101] The information interaction module is used to provide information interaction for the simulation object;

[0102] The simulation management module is used to manage and schedule memory data, simulation objects and interaction information;

[0103] The auxiliary recording module is used to record simulation operation information;

[0104] The system simulation object module is used to manage simulation entity units.

[0105] The structure of the simulation module is as follows: Figure 2 As shown in the figure. The simulation module provides a unified distributed simulation running environment for the upper-layer application support tool set and simulation model. By encapsulating and shielding the underlying software / hardware, the upper-layer application development and the underlying software / hardware are decoupled, and the operation and management of the application support tool set and simulation model are realized through the background service software and tool software. The application support tool set and simulation model realize data communication and storage functions based on the software library provided by the simulation running engine.

[0106] The parallel simulation module uses an asynchronous drive mechanism to schedule and drive the simulation model system, simulation entity unit and information interaction center, and realizes the parallel simulation function based on information and event asynchronous drive;

[0107] The simulation operation control module receives external simulation control instructions and controls the simulation by scheduling the parallel engine and shared memory data modules, including simulation start, pause, continue, end and restart;

[0108] The shared memory module realizes data interaction between models in the simulation model system through a synchronous calling mechanism. The interactive data content mainly includes simulation information, model information, entity information, situation information, etc.

[0109] The simulation interaction module provides the interaction of events, messages, data and other information for simulation objects including simulation entity unit sets and simulation model systems. At the same time, if it is connected to the injected semi-physical simulation system, the module can interact with other test resources through the connection with the simulation middleware.

[0110] The simulation management module includes four sub-modules: shared data management, model registration management, assumption data management and information interaction management, which are used to manage and schedule memory data, simulation objects and interaction information.

[0111] The auxiliary recording module mainly records the engine operation status, model information, input instruction information, model registration and other key operation information in the form of logs, scripts and key events, and provides them to other modules.

[0112] The main operation process of the simulation module is as follows: Figure 3The operation process of the scenario simulation module is mainly divided into four stages: simulation initialization, scenario initialization, simulation operation, and simulation end. The following is the entire simulation operation process:

[0113] (1) Simulation initialization

[0114] Simulation initialization mainly includes two parts: engine initialization and model initialization:

[0115] The engine initialization mainly sets the engine parameters to prepare the simulation test environment conditions for the subsequent simulation test. The engine parameters include: simulation step length (distance accuracy, time accuracy), simulation multiple, clock resolution, single / multi-thread and other information;

[0116] Model initialization mainly involves the initialization of model prototype objects, public models, interaction models, environment models, and attribute models. Model prototype objects include 8 types of equipment models and cognitive models. Interaction models include the simulation object itself and the information between the model and the simulation object, interaction data between simulation object models, logs, key events, operation result data and other information. Environment models include feature, effect, atmosphere, airspace, terrain and other models. Attribute models include target characteristics, formations and other models. After all information is initialized, a prototype object is formed. According to the external input model file, a specific model simulation object model is generated and an entity object is generated.

[0117] (2) Scenario initialization

[0118] Load the external input scenario file and parse it to extract the basic information of the scenario (scenario start / end time, simulation step, simulation multiple) and deployment entity related information (entity name / quantity, assembly script, task script, structural relationship, communication network);

[0119] Combined with the data dictionary, script parsing is completed and entity objects are assembled and generated;

[0120] If the analysis is successful, the entity object is assembled according to the scenario-related information and a simulation object is generated. If the analysis is not successful, the scenario is returned and the analysis is repeated.

[0121] Generate simulation objects based on entity deployment information and prepare to start simulation.

[0122] (3) Simulation operation

[0123] The simulation operation is controlled based on the operation control instructions input from the external system, including start, pause, continue, end, restart, etc.

[0124] Based on the event-driven mechanism, it completes the driving of two levels of entity units and model components, and realizes asynchronous time calls and interactions between models and entities;

[0125] Based on shared data management, complete the synchronous data call and interaction between models and entities based on the shared memory pool;

[0126] Four types of data are generated during the simulation process: operation log, key events, process data and result data. The operation log, key events and process data can be used for front-end display, while process data and result data can be used for subsequent data analysis.

[0127] (4) End of simulation

[0128] If the simulation end time has been reached, the system simulation ends; if the simulation end time has not been reached, the simulation continues.

[0129] Optionally, the model assembly database includes an attribute module, an equipment module, an environment module, a service module, a cognitive module, an interaction module and an agent module;

[0130] The property module is used to set properties of the simulation scenario;

[0131] The equipment module is used to set the simulation scenario equipment;

[0132] The environment module is used to set the simulation scenario environment;

[0133] The service module is used to perform task decomposition on the simulation scenario;

[0134] The cognitive module is used to perform cognitive simulation on the simulation scenario;

[0135] The interaction module is used for performing simulation scenario data interaction;

[0136] The proxy module is used for data interaction with an external system.

[0137] Model assembly database such as Figure 4 shown.

[0138] Equipment modules are used to simulate the characteristics and behavior attributes of various types of equipment. These equipment model components can interact with the external environment or other entities that load these equipment model components through entities. If an entity does not have an attached equipment model, it cannot move, sense the environment or other entities, or destroy other entities. Equipment module components include communication equipment, data processors, etc. Figure 5 shown.

[0139] (1) Platform Model

[0140] It is used to describe equipment carrier objects with independent space and motion behaviors. It is mainly composed of navigation models, aircraft motion models and ground platform motion models. It completes the motion simulation of ground platforms, surface-to-air / air-to-air / air-to-ground, fixed-wing aircraft, UAVs and helicopters.

[0141] The navigation model simulates the navigation errors of the aircraft.

[0142] The aircraft motion model uses a 3-DOF model to simulate the aircraft platform, providing waypoints, emergency takeoff, aerial refueling, ultra-low altitude flight, return, avoidance / maneuvering, radio navigation and other flight modes. It can reflect the aircraft platform's endurance performance, flight envelope, fuel consumption rate, as well as conventional maneuverability such as climb rate, acceleration performance, deceleration performance, and overload range.

[0143] The ground platform motion model provides a ground motion path search function, which can simulate the ground platform's movement process according to plan or instruction, and can reflect the impact of vehicle type, soil conditions, terrain slope, etc. on the ground platform's movement speed.

[0144] (2) Sensor model

[0145] The sensor model describes a device that can respond to physical stimuli in the simulation scene and is used to determine the existence or location of another object. It specifically describes its detection process of other objects, and the data processing is described by the information processor model. It mainly includes satellite navigation device models, etc.

[0146] The sensor detection model simulates the detection process of radar, infrared, passive detection, image and other sensors through model and parameter configuration. It can reflect the performance characteristics of various sensors such as scanning cycle, detection target type, field of view, range, detection probability, track quality, and the impact of environmental factors such as interference and terrain on sensor detection.

[0147] (3) Communication equipment model

[0148] The communication equipment model describes the equipment objects for information transmission between different entities in the environment, mainly including: satellite communication equipment model, airborne data link equipment model, air-to-ground radio station model, etc.

[0149] The communication equipment model can simulate ground communication lines, simplex, duplex, one-to-many / many-to-many broadcast and other types of communication systems and satellite navigation information communications through model and parameter configuration. It can reflect the impact of network structure and protocol, transmitting / receiving antenna performance, relative position, and environmental factors such as interference and terrain on signal transmission.

[0150] (4) Electronic jamming equipment model

[0151] The electronic jamming equipment model describes the soft-kill equipment that interferes with or destroys the normal operation of the opponent's sensors and communication equipment, mainly including: GPS jamming system model, infrared jamming equipment model, etc.

[0152] (5) Data Processor Model

[0153] The data processor model describes the functions of the information processing device objects in the simulation scenario, and mainly includes: radar processor model, optoelectronic sensor processor model, information fusion processor model, etc.

[0154] (6) Subsystem model

[0155] A subsystem model is a model that is subordinate to this entity but actually includes other components.

[0156] The environment module is a general term for airspace, atmosphere, effects, routes, terrain, and earth models. It is an indispensable part of the simulation. The overall structure is as follows: Figure 6 As shown. Among them, the terrain model and the earth model are built-in models and do not support independent development. The terrain model can load elevation data in DTED format. The supported geographic range is inversely proportional to the resolution of the elevation data, and provides related functions such as line-of-sight query and height query. The earth model supports two earth-fixed coordinate systems, the ellipsoid and the sphere of WGS-84, and provides conversion functions for earth-fixed, local, and body coordinates.

[0157] Airspace model: used to describe the air volume or area, and the lines on the ground, such as transportation corridors, that are relevant to the model processing in the simulation scenario;

[0158] Atmospheric model: describes the atmospheric conditions of a given space, including pressure, density, temperature, humidity, wind speed, etc. It is an important component of the artificial synthetic environment;

[0159] Effect model: describes smoke, explosions, fire, etc. to produce practical effects.

[0160] Attribute modules such as Figure 7 The attribute model is a general term for models such as target characteristics, formations and feature models. This part is not a necessary model for system confrontation simulation and can be simplified in other ways or replaced by other models.

[0161] Target characteristic model: usually an optional parameter of the platform model component, used to describe the infrared and other characteristics of the platform as a detected target;

[0162] Formation model: It is also an optional parameter of the platform model component, used to describe the formation of entities such as aircraft when performing tasks;

[0163] Feature models: describe natural features such as forests, rivers, soil, and physical features such as buildings and roads.

[0164] Mounting scheme model: describes the aircraft's mounting scheme in the mission, including pods, auxiliary fuel tanks, etc., which can be internal or external.

[0165] Route model: describes the path planning during the flight.

[0166] The modeling method of cognitive module is consistent with the command decision-making process of personnel, which is easy to understand and acquire knowledge. It can realize flexible, powerful and user-defined decision-making behavior simulation and is the basis for simulating human behavior. The relationship between cognitive model and equipment module type and message module is as follows: Figure 8 shown.

[0167] The service modules include action plan model, experimental design model, etc. Taking the action plan model as an example, it can be used to automatically decompose the upper-level plan to generate an action plan for a single entity or formation in the simulation. The plan will control the movement of the entity or formation and is the basis for assigning tasks to entities in batches in the simulation.

[0168] The interactive modules include:

[0169] Message: Mainly used for communication interaction between entities (Unit);

[0170] Event: mainly used for natural interactions between entities (Units) that must be reached without considering the process, such as signals;

[0171] Control (control instructions): mainly used for mutual control between equipment or decision model components (Atomic) in entities (Unit, Network);

[0172] DecisionFunction (decision start instruction): mainly used for mutual invocation of decision functions under multiple decision model components (Atomic) in an entity (Unit, Network) or between entities;

[0173] MessageProcessFunction (message processing instruction): mainly used by the decision model component (Atomic) of the entity (Unit, Network) to process the messages received by the communication equipment component;

[0174] Alter (modification instruction): Mainly used to modify the properties of equipment or decision model components (Atomic) in entities (Unit, Network).

[0175] Agent module: The simulation module can support other external systems in distributed deployment to dynamically access the system by building agent modules. The agent module realizes real-time communication and interaction with external systems / models based on state distribution objects, thereby replacing the external systems / models of the system to access the system and realize joint simulation. The specific contents of this module include:

[0176] State distributed object operation mechanism: The state distributed object consists of an object interface and a published state. The object interface is location transparent, and the published state is a collection of all the properties of the object. Each state distributed object can be instantiated as an agent and a server in the model. The server is the owner of the state distributed object, which can modify the properties of the state distributed object and provide the implementation of the state distributed object's operations; the agent is a reference to the state distributed object, receives the latest data of the state distributed object's properties, and can access the object interface on the server to access the state distributed object's operations. At any time when the simulation program is running, each state distributed object can only belong to one server, but can have multiple agents.

[0177] Information interaction between external system / model and agent module: The external system / model of the simulation system does not communicate directly with the agent module, but interacts through the agent and server of a specific state distribution object. The data transmission mode in the system is divided into two mechanisms: "push" and "pull". "Push" means that the module updates the properties of the state distribution object through the server, and the agent passively receives new data and notifies the application where it is located. "Pull" means that the module actively calls the object interface of the state distribution object through the agent to access the implementation of the operation on the server. The server and agent of each state distribution object can exist in different models.

[0178] The function of the scenario editing module is to provide a human-computer interaction method for the experimenter to input the area, situation, equipment allocation of both sides, task plan, phase division and other information in the air-ground scenario into the system to generate the simulation scenario file required to drive the simulation. The functional composition of the scenario editing module is as follows: Fig. 9 shown.

[0179] Data management unit: Data management includes three modules: scenario data management, model system data management and adaptability check. Its main functions are to support the creation, opening, modification, saving and saving to a specified directory of scenario plans, support loading preset or specified model database data, and also support the compatibility check between the model database and scenario plans. The input is the scenario definition file (.sdef) and the output is the scenario data file (.sdata).

[0180] Scenario editing unit: Scenario editing includes five modules: scenario configuration display, scenario basic information setting, scenario unit plotting, unit attribute setting, and scenario task setting. Its main functions are to configure basic information such as the scenario environment, plot and create scenario units on a two-dimensional map, configure attribute information such as the deployment location, entity template, and affiliation / command relationship of the scenario units, record the relationship between the scenario units and the model entities, and plan the task plan, ultimately forming a complete scenario for subsequent simulation system deduction and analysis.

[0181] Scenario editing assistance unit: Scenario editing assistance includes four functional modules: line of sight calculation, map measurement, plotting management, and map operation control. It mainly provides basic analysis functions such as line of sight calculation and distance measurement for scenario planners, and provides decision-making information assistance for mission planning. The main workflow of the scenario editing module is as follows: Fig.10 shown.

[0182] It can be seen that the present invention discloses a simulation scenario system and method, which includes a scenario editing module, a scenario simulation module, a model assembly database and a scenario evaluation module; the system has a hierarchical and modular structure, and the upper layer can call the services of the lower layer, so that the system has good maintainability and extensibility.

[0183] Embodiment 2

[0184] See also Fig.11 , Fig.11 : is a flow chart of a simulation scenario method disclosed in an embodiment of the present invention. Fig.11 The simulation scenario method described is applied to the field of simulation scenario technology, and the scenario scheme design is performed based on the scenario requirements, which is not limited in the embodiments of the present invention. Fig.11 As shown, the simulation scenario method includes:

[0185] S1, obtain the user's assumed demand information;

[0186] S2, parsing the assumption requirement information to obtain assumption key information;

[0187] S3, processing the model assembly database according to the assumed key information to obtain a configuration database;

[0188] S4, using the scenario editing module to process the configuration database to realize the simulation scenario.

[0189] The user's assumption demand information is a text-based instruction, which contains a large amount of information, some of which is redundant, so it is parsed and extracted through keywords to obtain the assumption key information.

[0190] The method of keyword extraction is:

[0191] A lattice network is constructed for the assumed demand information. The lattice network construction is a prior art in this field. The set of nodes for constructing the lattice network is N = {n0, n1, ...}, the set of arcs is E = {e0, e1, ...}, and each node n i ∈N contains a time stamp t(n i ), arc e u→v represents the arc connecting node u to node v. Let the nodes in the keyword network be NS = {N0, N1, ...}, Represents the node N in the keyword network i Connect to node N j A set of arcs.

[0192] 1. Any n i ∈N i , n j ∈N i , by t(n i )<t(n j ) we can get i≤j.

[0193] 2. Any n i ∈N i , n j ∈N i , by t(n i )=t(n j ) we can get i=j.

[0194] 3. Any e u→v ∈E, if for u∈N i and v∈N j , e u→v Equivalent to a group Then i≤m≤n≤j. (Here n=m+1, e u→v The two endpoints belong to N m and N n . )

[0195] The word grid network is converted into a keyword network, and the generation algorithm is:

[0196] 1. Convert keywords into corresponding syllable strings: K1…K M (M is the number of syllables of the keyword). Traverse all nodes N in the Lattice network and find the node n that matches K1. k , set to

[0197] 2. As the end point, search for similar nodes n in reverse time order k-1 If n k-1 and There is no arc between them, then nk-1 Also belongs to Continue Search k-1 Otherwise, the search stops.

[0198] 3. As the starting point, search for similar nodes n backward in chronological order k+1 If n k+1 and There is no arc between them, then n k+1 belong Continue Search k+1 Otherwise, the search stops.

[0199] 4. For e k→k+1 belong n k+1 belong

[0200] 5. All included in The arc e k→k+1 Form a keyword confusion network.

[0201] 6. For each arc If u belongs to N s , n i Belong to N t When t=s+1, belong otherwise, belong (s+1≤n≤t).

[0202]

[0203]

[0204] Where w(l) and w(e) are the words corresponding to arcs l and e, sim(·,·) refers to the similarity between two words. It refers to the arc and e overlap in normalized time. In addition, each node n in the Lattice i ∈N contains the time stamp t(n i ) will be used as a constraint to determine the termination of the search.

[0205] The data normalization method is used to confirm the keywords. First, the linguistic probability likelihood score of each candidate is normalized:

[0206]

[0207] Among them, Value is the set of linguistic score values ​​in the keyword network; minValue and maxValue are the minimum and maximum values ​​in the set respectively; x is the score before normalization; y is the score after normalization. After that, the sum of the scores is calculated and marked as the final score of the arc in the keyword network.

[0208] In each keyword network, select the arc candidate with the highest score and determine whether it is a keyword. If it is, output the keyword and time tag; if not, skip and enter the next keyword network. In this way, the keyword detection is completed.

[0209] Optionally, the model assembly database is processed according to the assumed key information to obtain a configuration database, including:

[0210] S31, constructing a text graph for the model assembly database to obtain M model assembly text graphs, where M is a positive integer;

[0211] The text graph construction method is: use the Word2Vec method to convert sentences into word vectors. Based on the word vectors, the text is modeled into a text graph using the semantic similarity method.

[0212] S32, constructing a text map of the scenario key information to obtain a scenario text map;

[0213] S33, performing node similarity calculation on the assumption text graph and the M model assembly text graphs to obtain a node similarity value;

[0214] Node embedding is used to encode the text graph into a node vector, and node features are extracted to preserve the structure and attribute information of the graph. This embodiment uses a graph convolutional network (GCN) to implement node embedding.

[0215] Assume that graph G i YesN i nodes, graph G j YesN j nodes, there will be N i ×N j Interaction score for a node:

[0216]

[0217] Among them, U i , U j They are Figure G i and Figure G j Node embedding, σ is a nonlinear activation function, S node For U i , U j Node similarity value, T is the transpose.

[0218] S34, performing graph embedding similarity calculation on the assumption text graph and the M model assembly text graphs to obtain a graph embedding similarity value;

[0219]

[0220] Among them, S graph (G i ,G j ) is the graph embedding similarity value of graph Gi and graph Gj, is a three-dimensional weight tensor used to capture the relationship between graph embeddings, V is a weight tensor used to combine the features of two graph embeddings, b3 is the bias, f3 is the activation function, K is a hyperparameter, T is the transpose operation, and h i For Figure G i The graph embedding h j For Figure G j , controls the number of similarity scores the model generates for each pair of graph embeddings.

[0221] S35, processing the node similarity value and the graph embedding similarity value to obtain a text similarity value S;

[0222] S = concat(S node ,S graph )

[0223] Among them, S node is the node embedding similarity vector, S graph is the graph embedding similarity vector, and concat represents the text similarity operation.

[0224] S36, processing the model assembly database according to the text similarity value to obtain a configuration database.

[0225] Specifically, according to the text similarity value, the models required for simulation are extracted from the model assembly database to form a configuration database.

[0226] The model assembly database includes attribute module, equipment module, environment module, service module, cognitive module, interaction module and agent module. The attribute module includes target characteristic model, formation model, feature model, mounting scheme model and route model; the equipment module includes communication equipment model, data processor model, electronic jamming equipment model, subsystem model, sensor model and platform model; the environment module includes airspace model, atmosphere model, effect model, route model, terrain model, earth model, etc.; the service module includes action plan model and experimental design model; the interaction module includes Message, Event, Control, MessageProcessFunction, Alter, etc.; the agent module includes state distribution object operation mechanism, external system / model and agent model information interaction function, etc., and each module model contains many specific function models. The model assembly database is constructed by text graph to obtain M model assembly text graphs. When the user's assumption requirement information is simulated according to the assumption, the assumption key information is used to process the M model assembly text graphs to obtain a configuration database for this simulation assumption. According to the text similarity value, the model assembly database is processed to obtain a configuration database, that is, a model assembly text diagram suitable for this simulation scenario is found among the M model assembly text diagrams, and it is used as the configuration database for this simulation scenario.

[0227] Optionally, the utilizing a scenario editing module to process the configuration database to implement a simulation scenario includes:

[0228] S41, using the scenario editing module, set the participants of the air-to-ground confrontation;

[0229] The optional participants of the air-to-ground confrontation are set according to the specific simulation task, such as the commander, the rescuer, the rescued party, etc.

[0230] Use the data management unit to set up the participants in the air-to-ground confrontation;

[0231] S42, processing the configuration database to obtain the entity deployment plan of the participant;

[0232] Processing the configuration database using a scenario editing unit and a scenario editing auxiliary unit to obtain a physical deployment plan of the participant;

[0233] Entities are used to describe objects or things in the real world. Entities have clear attributes and behaviors, and they simulate behaviors and characteristics in the real world by defining attributes and methods. In software development, entities usually correspond to tables in the database, which are used to store and manage data. That is, a unit that implements a certain function. Each entity contains a time series of activities that change over time. Sequence diagrams can be used to describe the dynamic activity process of multiple entities. The activity process of each entity is regarded as a thread or an independent software unit. Generally, the activity of each entity is encapsulated in a separate program code. Every time the entity activity occurs, the activity code of the entity will be uniformly scheduled, so that the activity can be encapsulated into a function.

[0234] S43, processing the entity deployment plan to obtain a simulation scenario file; the simulation scenario file includes simulation path information and simulation task allocation information;

[0235] Processing the entity deployment scheme using a scenario editing unit and a scenario editing auxiliary unit to obtain a simulation scenario file;

[0236] S44, using a scenario simulation module, processing the simulation scenario file to obtain a simulation scenario result;

[0237] Use the parallel simulation module to set parameters and prepare the simulation test environment conditions for subsequent simulation tests, including simulation step length (distance accuracy, time accuracy), simulation multiple, clock resolution, single / multi-thread and other information;

[0238] Use the simulation operation and control module to set up the model, such as the environment model and interaction model required for this simulation, setting the effects, atmosphere, airspace, terrain, etc., setting the mounting scheme, target characteristics, formation, etc.;

[0239] Using the information interaction module to set the interaction information between the model and the simulation object, and the interaction data information between the simulation object models;

[0240] Use memory sharing modules to manage simulation memory information;

[0241] Use the simulation management module to manage logs, key events, and operation result data, etc., generate specific model simulation object models and generate entity objects. Load the simulation scenario file and parse it, extract the basic information of the scenario (scenario start / end time, simulation step, simulation multiple) and deployment entity related information (entity name / quantity, assembly script, task script, structural relationship, communication network); combine the data dictionary to complete the script parsing, assemble and generate entity objects;

[0242] If the analysis is successful, the entity object is assembled according to the assumption-related information and the simulation object is generated. If the analysis is not successful, it returns to re-analyze the assumption loading; the simulation object is generated for the entity deployment information, and the simulation is ready to start. The simulation operation is controlled based on the operation control instructions input by the external system, including start, pause, continue, end, restart, etc.; based on the event-driven mechanism, the two-level drive of the entity unit and the model component is completed, and the asynchronous time call and interaction between models and entities are realized; based on shared data management, the synchronous data call and interaction between models and entities based on the shared memory pool are completed; four types of data are generated during the simulation operation: operation log, key events, process data and result data, among which the operation log, key events and process data can be used for front-end display, and process data and result data can be used for subsequent data analysis. If the simulation end time has been reached, the system simulation ends; if the simulation end time has not been reached, the simulation continues.

[0243] Auxiliary management is performed using auxiliary recording modules and system simulation object modules.

[0244] S45, using a scenario evaluation module to evaluate the simulation scenario results to implement the simulation scenario.

[0245] Evaluation of simulation scenario results includes:

[0246] Collect operation data of the simulation scenario result operation process to obtain simulation operation data information;

[0247] The simulation operation data information includes the event execution time of each thread running in parallel, the event execution time of the same entity executed by the same thread in each sampling period, the execution time of the same type of entity executed by the same thread in each sampling period, and the execution time of the same event type of the same thread in each sampling period;

[0248] Analyzing the simulation operation data information to obtain a comprehensive data analysis result;

[0249] The comprehensive data analysis results are optimized and verified to obtain simulation efficiency analysis results. The above process is the process of evaluating the simulation assumption results and realizing the simulation assumptions.

[0250] Optionally, the processing of the entity deployment scheme to obtain a simulation scenario file includes:

[0251] S431, demarcating an airspace according to the physical deployment plan to obtain a simulated airspace;

[0252] S432, performing route planning according to the physical deployment plan to obtain simulation path information;

[0253] S433: Perform task setting according to the simulation airspace and simulation path information to obtain simulation task allocation information.

[0254] Optionally, performing route planning according to the entity deployment scheme to obtain simulation path information includes:

[0255] S4321, establishing a three-dimensional model according to the physical deployment plan and the simulated airspace, determining a cost function and constraint conditions for the UAV path planning, and initializing a particle swarm algorithm;

[0256] S4322, using Logistic chaotic mapping to generate the initialization individuals of the particle swarm and calculate the fitness value of the individuals;

[0257] S4323, using nonlinear energy factors to update individuals so that individuals move towards lower fitness values;

[0258] S4324, using chaotic mapping to search the local solution space so as to make it jump out of the local optimal value;

[0259] S4325, repeat S4323 and S4324, and when the algorithm termination condition is met, output the optimal fitness value; the optimal fitness value is the simulation path information.

[0260] Optionally, performing task setting according to the simulation airspace and simulation path information to obtain simulation task allocation information includes:

[0261] S4331, setting the task allocation cost-benefit function and constraint conditions according to the simulation airspace and simulation path information

[0262] S4332, setting initial parameters of the particle swarm algorithm and the simulated annealing algorithm;

[0263] S4333, randomly generating a particle swarm in the search space, and assigning a position and a velocity to each particle;

[0264] S4334, using the particle swarm algorithm to perform global search, and using the nonlinear inertia factor to update the position and velocity of the particles, while recording the overall optimal solution and the individual optimal solution;

[0265] The nonlinear inertia factor is:

[0266]

[0267] Where ω is the nonlinear inertia factor, ω max The value of is 0.95, ω min The value is 0.6, t represents the number of iterations, t max Indicates the maximum number of iterations;

[0268] S4335, using a simulated annealing algorithm to adjust the positions of all optimal solutions of the current iteration number to obtain a new solution;

[0269] S4336, comparing the new solution with the optimal solution obtained by the particle swarm algorithm, and when the new solution is better than the current optimal solution, taking the new solution as the optimal solution for the current number of iterations; when the current optimal solution is better than the new solution, accepting the new solution according to a preset probability;

[0270] S4337, when the termination condition is not met, execute S4334; when the termination condition is met, output the simulation task allocation information.

[0271] It can be seen that the method of the present invention obtains the key information of the scenario by parsing the scenario demand information; according to the key information of the scenario, the model assembly database is processed to obtain the configuration database; the configuration database is processed by the scenario editing module to realize the simulation scenario. The system provides a visual scenario editing environment with a good human-computer interaction interface.

[0272] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0273] Through the specific 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 a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0274] Finally, it should be noted that the simulation assumption system and method disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions recorded in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A simulation scenario system, characterized in that: The system includes a scenario editing module, a scenario simulation module, a model assembly database and a scenario evaluation module; The scenario editing module is connected with the scenario simulation module and the model assembly database data to generate a simulation scenario file; The model assembly database is connected with the scenario editing module data to manage simulation scenario models; The scenario simulation module is data-connected with the scenario editing module and the scenario evaluation module, and is used to process the simulation scenario file to obtain a simulation scenario result; The scenario evaluation module is data-connected to the scenario simulation module and is used to evaluate the simulation scenario results.

2. The simulation scenario system according to claim 1, characterized in that: The scenario editing module includes a data management unit, a scenario editing unit and a scenario editing auxiliary unit; The data management unit is used to generate a scenario data file; The scenario editing unit is used to process the scenario data file to obtain a simulation scenario file; The scenario editing auxiliary unit is used to provide auxiliary information for the simulation scenario file.

3. The simulation scenario system according to claim 1, characterized in that: The scenario simulation module includes a parallel simulation module, a simulation operation control module, a memory sharing module, an information interaction module, a simulation management module, an auxiliary recording module and a system simulation object module; The parallel simulation module is used to implement parallel simulation; The simulation operation and control module is used to realize the control of the simulation; The memory sharing module is used to realize data interaction between various models of the simulation scenario system; The information interaction module is used to provide information interaction for the simulation object; The simulation management module is used to manage and schedule memory data, simulation objects and interaction information; The auxiliary recording module is used to record simulation operation information; The system simulation object module is used to manage simulation entity units.

4. The simulation scenario system according to claim 1, characterized in that: The model assembly database includes an attribute module, an equipment module, an environment module, a service module, a cognitive module, an interaction module and an agent module; The property module is used to set properties of the simulation scenario; The equipment module is used to set the simulation scenario equipment; The environment module is used to set the simulation scenario environment; The service module is used to perform task decomposition on the simulation scenario; The cognitive module is used to perform cognitive simulation on the simulation scenario; The interaction module is used for performing simulation scenario data interaction; The proxy module is used for data interaction with an external system.

5. A simulation scenario method, characterized in that: Applicable to any simulation scenario system as claimed in claims 1 to 4, the method comprising: S1, obtain the user's assumed demand information; S2, parsing the assumption requirement information to obtain assumption key information; S3, processing the model assembly database according to the assumed key information to obtain a configuration database; S4, using the scenario editing module to process the configuration database to realize the simulation scenario.

6. The simulation scenario method according to claim 5, characterized in that: The model assembly database is processed according to the assumed key information to obtain a configuration database, including: S31, constructing a text graph for the model assembly database to obtain M model assembly text graphs, where M is a positive integer; S32, constructing a text graph for the scenario key information to obtain a scenario text graph; S33, performing node similarity calculation on the assumption text graph and the M model assembly text graphs to obtain a node similarity value; S34, performing graph embedding similarity calculation on the assumption text graph and the M model assembly text graphs to obtain a graph embedding similarity value; S35, processing the node similarity value and the graph embedding similarity value to obtain a text similarity value; S36, processing the model assembly database according to the text similarity value to obtain a configuration database.

7. The simulation scenario method according to claim 5, characterized in that: The use of the scenario editing module to process the configuration database to implement the simulation scenario includes: S41, using the scenario editing module, setting the participants of the simulation scenario; S42, processing the configuration database to obtain the entity deployment plan of the participant; S43, processing the entity deployment plan to obtain a simulation scenario file; the simulation scenario file includes simulation path information and simulation task allocation information; S44, using a scenario simulation module, processing the simulation scenario file to obtain a simulation scenario result; S45, using a scenario evaluation module to evaluate the simulation scenario results to implement the simulation scenario.

8. The simulation scenario method according to claim 7, characterized in that: The step of processing the entity deployment scheme to obtain a simulation scenario file includes: S431, demarcating an airspace according to the physical deployment plan to obtain a simulated airspace; S432, performing route planning according to the physical deployment plan to obtain simulation path information; S433: Perform task setting according to the simulation airspace and simulation path information to obtain simulation task allocation information.

9. The simulation scenario method according to claim 8, characterized in that: The step of performing route planning according to the entity deployment plan to obtain simulation path information includes: S4321, establishing a three-dimensional model according to the physical deployment plan and the simulated airspace, determining a cost function and constraint conditions for the UAV path planning, and initializing a particle swarm algorithm; S4322, using Logistic chaotic mapping to generate the initialization individuals of the particle swarm and calculate the fitness value of the individuals; S4323, using nonlinear energy factors to update individuals so that individuals move towards lower fitness values; S4324, using chaotic mapping to search the local solution space so as to make it jump out of the local optimal value; S4325, repeat S4323 and S4324, and when the algorithm termination condition is met, output the optimal fitness value; the optimal fitness value is the simulation path information.

10. The simulation scenario method according to claim 8, characterized in that: The step of setting tasks according to the simulation airspace and simulation path information to obtain simulation task allocation information includes: S4331, setting the task allocation cost-benefit function and constraint conditions according to the simulation airspace and simulation path information S4332, setting initial parameters of the particle swarm algorithm and the simulated annealing algorithm; S4333, randomly generating a particle swarm in the search space, and assigning a position and a velocity to each particle; S4334, using the particle swarm algorithm to perform global search, and using the nonlinear inertia factor to update the position and velocity of the particles, while recording the overall optimal solution and the individual optimal solution; The nonlinear inertia factor is: Where ω is the nonlinear inertia factor, ω max The value of is 0.95, ω min The value is 0.6, t represents the number of iterations, t max Indicates the maximum number of iterations; S4335, using a simulated annealing algorithm to adjust the positions of all optimal solutions of the current iteration number to obtain a new solution; S4336, comparing the new solution with the optimal solution obtained by the particle swarm algorithm, and when the new solution is better than the current optimal solution, taking the new solution as the optimal solution for the current number of iterations; when the current optimal solution is better than the new solution, accepting the new solution according to a preset probability; S4337, when the termination condition is not met, execute S4334; when the termination condition is met, output the simulation task allocation information.

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