Simulation method and device for discrete event

Abstract

The invention relates to the technical field of discrete event simulation, and discloses a discrete event simulation method and device.According to the discrete event simulation method and device, scenario parameters, task types and simulation entity parameters in a target simulation scene are obtained, the target simulation scene is each simulation scene in multiple simulation scenes, and the target simulation scene is defined in advance; based on the scenario parameter, the task type and the simulation entity parameter, creating an event simulation model in the target simulation scene; receiving a target simulation instruction sent by the target intelligent agent, and generating an event sequence list based on the target simulation instruction; the target agent is each agent in the multiple agents, and based on the event sequence list, the target entity is controlled to execute a simulation action in the target simulation scene through the event simulation model. Finally, the target agent can execute simulation actions in multiple simulation scenes, the purpose of improving the simulation practicability is achieved, the simulation process can be simplified, and then the simulation efficiency is optimized.

Description

Technical Field

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

[0002] Discrete Event Simulation (DES) is a digital simulation technology that predicts system changes based on the laws of event changes at discrete time points. The implementation of each interactive process in DES corresponds to the creation, scheduling and processing of events.

[0003] The application of artificial intelligence technology in military confrontation simulation is becoming more and more mature. As data-driven algorithms represented by reinforcement learning become the mainstream technology for building decision-making intelligent agents, the low sampling efficiency of simulation engines has become the main bottleneck restricting the improvement of intelligent agent effects. In related technologies, complex algorithms are used to simulate discrete events for specific scenarios. Since this method requires the use of a large number of complex algorithms to complete refined simulations in pursuit of accuracy, the simulation speed is reduced and it is not suitable for diverse simulations in different scenarios. Summary of the invention

[0004] In view of this, the present invention provides a discrete event simulation method and device to solve the problems of low simulation speed and complex simulation.

[0005] According to a first aspect, an embodiment of the present disclosure provides a discrete event simulation method, the method comprising:

[0006] Acquire assumption parameters, task types, and simulation entity parameters under a target simulation scenario, wherein the target simulation scenario is each simulation scenario among a plurality of simulation scenarios, and the target simulation scenario is predefined;

[0007] Create an event simulation model in the target simulation scenario based on the assumption parameters, task type and simulation entity parameters;

[0008] receiving a target simulation instruction sent by a target agent, and generating an event sequence list based on the target simulation instruction; wherein the target agent is each of the multiple agents, and the target simulation instruction includes: an entity movement instruction, an entity attack instruction, and an entity state change instruction;

[0009] Based on the event sequence list, the target entity is controlled by the event simulation model to perform simulation actions in the target simulation scenario.

[0010] By executing the above-mentioned implementation methods, the disclosed embodiment can not only enable the target intelligent agent to perform simulation actions in multiple simulation scenarios to achieve the purpose of improving the practicality of simulation, but also simplify the simulation process, thereby optimizing the simulation efficiency.

[0011] In some optional embodiments, the hypothetical parameters include the hypothetical simulation start and end time and the following entity attribute information, entity type, entity to which the intelligent agent belongs, entity name, entity initial position, and entity initial orientation; the event sequence list includes task type, multiple states of event changes according to the time series, and the time point corresponding to each state. The task types include: mobile task type, attack task type, and detection task type; the simulation entity parameters include: simulation entity name, simulation entity category, simulation entity maximum speed, simulation entity detection range, simulation entity health value, simulation entity interference range, simulation entity attack range, simulation entity hit probability, simulation entity attack force value, and simulation entity maximum height; multiple simulation scenarios include: aircraft simulation scenario, ship simulation scenario, submarine simulation scenario, missile simulation scenario, sensor simulation scenario, and fixed facility simulation scenario.

[0012] The disclosed embodiment executes the above-mentioned implementation manner to create an event simulation model and then controls the target entity to perform simulation actions based on the target simulation instructions issued by the target agent.

[0013] In some optional implementations, based on the event sequence list, controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model includes:

[0014] If the target simulation instruction is an entity movement instruction, based on the event sequence list generated by the entity movement instruction, the target entity is controlled by the event simulation model to perform the simulation movement action in a uniform linear motion in the target simulation scene;

[0015] If the target simulation instruction is an entity state change instruction, based on the event sequence list generated by the entity state change instruction, the target entity is controlled to perform a simulated detection action on the enemy entity in the circular detection area under the target simulation scene through the event simulation model.

[0016] The disclosed embodiment implements the simulated motion action and simulated detection action of the target entity based on the specific instructions issued by the target intelligent agent by executing the above-mentioned implementation manner.

[0017] In some optional embodiments, based on the event sequence list, when the target entity is controlled by the event simulation model to perform simulated motion actions in a target simulation scenario according to uniform linear motion, when the target entity changes its motion direction, the current state information of the target entity is updated, and based on the updated current state information of the target entity, the target position corresponding to the target entity after the motion direction is changed is calculated.

[0018] The disclosed embodiment updates the current motion state of the target entity in real time by executing the above implementation mode, so as to timely obtain the target position after the change when the target entity changes the motion direction.

[0019] In some optional implementations, based on the target simulation instruction and the event sequence list, the target entity is controlled to perform simulation actions in the target simulation scenario through the event simulation model, including:

[0020] If the target simulation instruction is an entity attack instruction, based on the event sequence list generated by the entity attack instruction, the target entity is controlled by the event simulation model to perform simulated attack actions in the target simulation scenario according to the linear relationship between the missile power and the missile attack power, and the linear relationship between the missile power and the missile movement distance.

[0021] The disclosed embodiment simplifies the simulation attack process on the target entity based on the specific instructions issued by the target intelligent agent by executing the above-mentioned implementation mode.

[0022] In some optional implementations, during the execution of a simulated motion action in a target simulation scenario, if the remaining health value of the target entity is less than zero, the target entity is controlled to execute a simulated destruction action.

[0023] The disclosed embodiment simplifies the process of controlling the target entity to perform simulated destruction by executing the above-mentioned implementation methods.

[0024] In some optional implementations, during the process of controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model, the simulation performance index is monitored in real time.

[0025] The disclosed embodiment executes the above-mentioned implementation mode to analyze in real time the simulation efficiency, simulation speed and system operation speed corresponding to the simulation actions performed by the observed target entity, and is helpful in proposing improvement measures for bad analysis results.

[0026] In some optional implementations, it also includes: in the process of controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model, receiving observation instructions sent by the target intelligent agent in real time, and returning the current state information of the target entity to the target intelligent agent based on the observation instructions.

[0027] By executing the above-mentioned implementation methods, the disclosed embodiment is conducive to the target intelligent agent to obtain the current state information of the target entity during the simulation action in real time.

[0028] According to a second aspect, an embodiment of the present disclosure provides a discrete event simulation device, the device comprising:

[0029] A data acquisition module is used to acquire the assumed parameters, task types and simulation entity parameters in a target simulation scenario, wherein the target simulation scenario is each simulation scenario in a plurality of simulation scenarios, and the target simulation scenario is predefined;

[0030] A model creation module, used to create an event simulation model in a target simulation scenario based on entity assumption parameters, task type and simulation entity parameters;

[0031] The instruction receiving module is used to receive the target simulation instruction sent by the target intelligent agent, and generate an event sequence list based on the target simulation instruction; wherein the target intelligent agent is each intelligent agent among the multiple intelligent agents, and the target simulation instruction includes: entity movement instruction, entity attack instruction and entity state change instruction;

[0032] The simulation execution module is used to control the target entity to perform simulation actions in the target simulation scenario based on the event sequence list through the event simulation model.

[0033] According to the third aspect, an embodiment of the present disclosure provides a simulation system for discrete events, comprising: a server and a target intelligent agent, wherein the target intelligent agent is a plurality of mutually confronting intelligent agents, and the server comprises: a communication interaction unit, an instruction processing unit, a data maintenance unit and a simulation deduction unit which are sequentially interconnected, wherein the target intelligent agent is used to send a target simulation instruction to the server to control a target entity to perform a simulation action, and the server is used to execute the simulation method for discrete events in the first aspect or any one of the embodiments of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 is a schematic flow chart of a discrete event simulation method according to an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of an event sequence list according to an embodiment of the present invention;

[0037] Figure 3 is a schematic diagram for comparing the control of a target entity to perform simulated motion actions according to an embodiment of the present invention;

[0038] Figure 4 is a schematic diagram for comparing the simulated detection actions performed by the control target entity according to an embodiment of the present invention;

[0039] Figure 5 is a schematic diagram for comparing the control of a target entity to perform a simulated destruction action according to an embodiment of the present invention;

[0040] Figure 6is a structural block diagram of a discrete event simulation system according to an embodiment of the present invention;

[0041] Figure 7 is a schematic diagram of a movement corresponding to when a target entity changes direction according to an embodiment of the present invention;

[0042] Figure 8 is a structural block diagram of a discrete event simulation device according to an embodiment of the present invention;

[0043] Fig. 9 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0045] According to an embodiment of the present invention, an embodiment of a simulation method for discrete events is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0046] In this embodiment, a discrete event simulation method is provided, which can be used for computer devices such as mobile phones, tablet computers, desktop computers, portable notebooks, servers, etc. Figure 1 is a flow chart of a discrete event simulation method according to an embodiment of the present invention. The method is mainly applied in a server, such as Figure 1 As shown, the process includes the following steps:

[0047] Step S101, obtaining assumed parameters, task types and simulation entity parameters in a target simulation scenario, wherein the target simulation scenario is each simulation scenario among a plurality of simulation scenarios, and the target simulation scenario is predefined.

[0048] In some specific examples, the various simulation scenarios include: aircraft simulation scenarios, ship simulation scenarios, submarine simulation scenarios, missile simulation scenarios, sensor simulation scenarios, and fixed facility simulation scenarios.

[0049] Specifically, multiple simulation scenarios include: aircraft simulation scenarios, ship simulation scenarios, submarine simulation scenarios, missile simulation scenarios, sensor simulation scenarios, and fixed facility simulation scenarios, which constitute static scenarios. Among them, aircraft simulation scenarios include: fighter jets, early warning aircraft, bombers, electronic jammers, etc.; ship simulation scenarios include: destroyers, frigates, aircraft carriers, etc.; submarine simulation scenarios include: conventional submarines, nuclear submarines, etc.; missile simulation scenarios include: air-to-air missiles, surface missiles, underwater missiles, etc.; sensor simulation scenarios include: detection radars, jamming radars; fixed facility simulation scenarios include: airports, launch pads, bases, etc.

[0050] Furthermore, in each of the above simulation scenarios, multiple simulation entities are included, and the types of the multiple simulation entities may be the same or different.

[0051] In another specific example, the hypothetical parameters include the hypothetical simulation start and end time and the following entity attribute information: entity type, entity agent, entity name, entity initial position, and entity initial orientation.

[0052] For example, in an aircraft simulation scenario, the entity is an aircraft, and the hypothetical parameters include: hypothetical simulation start and end time, aircraft type, intelligent entity to which the aircraft belongs, aircraft name, aircraft initial position, aircraft initial speed, and aircraft initial orientation. Based on the above example, for an aircraft simulation scenario, the corresponding entity attribute information also includes aircraft initial speed and aircraft mounted equipment. Aircraft mounted equipment includes equipment type and equipment quantity. For example, aircraft mounted equipment can be radar or weapons.

[0053] For example, in a ship simulation scenario, the entity is a ship, and the assumption parameters include: the assumption simulation start and end time, the ship type, the intelligent agent to which the ship belongs, the ship name, the ship's initial position, and the ship's initial orientation. Based on the above example, for a boat simulation scenario, the corresponding entity attribute information also includes the boat's initial speed and the boat's mounted equipment. The boat's mounted equipment includes the equipment type and the equipment quantity. For example, the boat's mounted equipment can be a radar or a weapon.

[0054] For example, in a submarine simulation scenario, the entity is a submarine, and the assumption parameters include: the assumption simulation start and end time, the submarine type, the intelligent body to which the submarine belongs, the submarine name, the submarine initial position, and the submarine initial orientation. Based on the above example, for the submarine simulation scenario, the corresponding entity attribute information also includes the submarine initial speed and the submarine mounted equipment. The submarine mounted equipment includes the equipment type and the equipment quantity. For example, the submarine mounted equipment can be radar or weapons.

[0055] For example, in a missile simulation scenario, the entity is a missile, and the assumption parameters include: the assumption simulation start and end time, missile type, the intelligent agent to which the missile belongs, the missile name, and the entity to which the missile belongs. Based on the above example, for the missile simulation scenario, the corresponding entity attribute information also includes the number of missiles.

[0056] For example, in a sensor simulation scenario, if the entity is a sensor, the hypothetical parameters include: the start and end time of the hypothetical simulation, the sensor type, the intelligent agent to which the sensor belongs, the sensor name, the initial position of the sensor, and the initial orientation of the sensor.

[0057] For example, in a fixed facility simulation scenario, if the entity is a fixed facility, the assumption parameters include: the assumption simulation start and end time, the fixed facility type, the intelligent entity to which the fixed facility belongs, the fixed facility name, the fixed facility initial position, and the fixed facility initial orientation. Based on the above example, for the fixed facility simulation scenario, the corresponding entity attribute information also includes the fixed facility mounted equipment. The fixed mounted equipment includes the equipment type and the equipment quantity. For example, the fixed facility mounted equipment can be a radar or a weapon.

[0058] In another specific example, the task types include: a moving task type, an attack task type, and a detection task type.

[0059] In another specific example, the simulated entity parameters include: simulated entity name, simulated entity category, simulated entity maximum speed, simulated entity detection range, simulated entity health value, simulated entity interference range, simulated entity attack range, simulated entity hit probability, simulated entity attack force value, and simulated entity maximum height.

[0060] Similarly, these simulation entity parameters support aircraft simulation scenarios, ship simulation scenarios, submarine simulation scenarios, missile simulation scenarios, sensor simulation scenarios, and fixed facility simulation scenarios. For example, for an aircraft simulation scenario, the aircraft simulation scenario parameters include: aircraft name, aircraft category, aircraft maximum speed, aircraft detection range, aircraft health value, aircraft interference range, aircraft attack range, aircraft hit probability, aircraft attack force value, and aircraft maximum altitude. Other simulation scenarios are similar and will not be given here.

[0061] The various simulation scenarios mentioned above are predefined, for example, a plurality of predefined simulation scenarios are loaded into a server in advance.

[0062] Therefore, the discrete event simulation method in the embodiment of the present invention obtains hypothetical parameters, task types and simulation entity parameters composed of hypothetical simulation start and end times and various entity attribute information in a variety of different simulation scenarios, which is conducive to realizing the diversity of discrete event simulation, is not limited to simulation in specific scenarios, and thus improves the practicality of discrete event simulation.

[0063] Step S102, creating an event simulation model in a target simulation scenario based on assumption parameters, task types and simulation entity parameters.

[0064] In some optional implementations, creating an event simulation model in a target simulation scenario based on the assumption parameters, the task type, and the simulation entity parameters includes:

[0065] Step a1, based on the assumption parameters, task type and simulation entity parameters, determine the association topology diagram of events between nodes in different states.

[0066] Step a2: creating an event simulation model in a target simulation scenario based on the association topology diagram.

[0067] Specifically, for example, in a target simulation scenario (in an aircraft simulation scenario), based on the assumption parameters, task type, and simulation entity parameters related to the aircraft, the associated topological graph of the movement event between nodes in different states is determined, thereby forming an event simulation model. For example, in a target simulation scenario (in a ship simulation scenario), based on the assumption parameters, task type, and simulation entity parameters related to the ship, the associated topological graph of the attack event between nodes in different states is determined, thereby forming an event simulation model. For example, in a target simulation scenario (in a submarine simulation scenario), based on the assumption parameters, task type, and simulation entity parameters related to the submarine, the associated topological graph of the detection event between nodes in different states is determined, thereby forming an event simulation model.

[0068] Step S103, receiving the target simulation instruction sent by the target agent, and generating an event sequence list based on the target simulation instruction; wherein the target agent is each of the multiple agents, and the target simulation instruction includes: entity movement instruction, entity attack instruction and entity state change instruction.

[0069] Specifically, the target agent can be a red agent, a blue agent, or a white agent, wherein the red agent and the blue agent can confront each other, and the white agent serves as a neutral party. The target agent sends a target simulation instruction to the server, thereby realizing control of the target entity in the target simulation scenario. The target simulation instruction can be a simulation instruction issued by the target agent to control the target entity to execute a movement event, a simulation instruction to control the target entity to execute an attack event, or a simulation instruction to control the target entity to execute a detection event.

[0070] Step S104: Based on the event sequence list, the target entity is controlled by the event simulation model to perform simulation actions in the target simulation scenario.

[0071] In a specific example, the event sequence list includes a task type, multiple states of events changing in a time sequence, and a time point corresponding to each state.

[0072] Specifically, Figure 2 As shown in the figure, it is a schematic diagram of the event sequence list. Each time node corresponds to an event state. After the event sequence list is completed, the event sequence list needs to be updated in real time. For example, each time an event task is added, the event sequence list is updated in real time. For example, each time a task is added, the potential events in the task process are added to the event sequence list. In addition, the earliest time of the event is found from the event sequence list, and then the new state of the event after the preset time step Δt is calculated. The specific example is as follows: discrete event simulation, in, Among them, f(S t ,Δt) is the new state of the calculated state S after time Δt, g(St,e) calculates the minimum time corresponding to all entities in the current event sequence list, E t All events in the event sequence list at time t, for example, all event types include: reaching the target position, entering the detection range, entering the strike range, entering the interference range, and hitting the target. An entity can participate in multiple events at the same time.

[0073] Furthermore, since the task type refers to whether the event is a mobile task event, an attack task event, or a detection task event, when the event sequence list records a mobile task event, it includes receiving the task, setting off, and arriving at the target location; when the event sequence list records an attack task event, it includes receiving the task, setting off, launching a missile, and hitting / missing the target; when the event sequence list records a detection task event, it includes enemy target entering and enemy target leaving.

[0074] In the above, based on the event sequence list, the target entity is controlled by the event simulation model to perform simulation actions in the target simulation scenario. For example, when the target simulation instruction issued by the target agent is an attack event instruction, based on the event sequence list of the attack event instruction, the target entity (aircraft) is controlled by the event simulation model to perform the corresponding attack simulation action in the target simulation scenario (aircraft simulation scenario). The number of target entities in the embodiment of the present disclosure can be one or more.

[0075] The simulation method of discrete events in the disclosed embodiment obtains the assumed parameters, task type and simulation entity parameters in the target simulation scenario, wherein the target simulation scenario is each simulation scenario in a plurality of simulation scenarios, and the target simulation scenario is predefined; based on the assumed parameters, task type and simulation entity parameters, an event simulation model is created in the target simulation scenario; a target simulation instruction sent by a target intelligent agent is received, and an event sequence list is generated based on the target simulation instruction; wherein the target intelligent agent is each intelligent agent in a plurality of intelligent agents, and the target simulation instruction includes: entity movement instruction, entity attack instruction and entity state change instruction; based on the event sequence list, the target entity is controlled by the event simulation model to perform simulation actions in the target simulation scenario. Finally, the disclosed embodiment can not only realize that the target intelligent agent performs simulation actions in multiple simulation scenarios, thereby achieving the purpose of improving the practicality of simulation, but also simplify the simulation process, thereby optimizing the simulation efficiency.

[0076] In the embodiment of the present disclosure, the target agent is each agent among the multiple agents, so each agent sends a corresponding target simulation instruction to the server, thereby realizing the execution of simulation actions on the target entity in the target simulation scenario. If multiple agents all participate in the discrete event simulation in the target simulation scenario, the purpose of multiple agents confronting each other in the target simulation scenario can be finally achieved.

[0077] In some optional implementations, the above step S104, based on the event sequence list, controls the target entity to perform simulation actions in the target simulation scenario through the event simulation model, including:

[0078] Step b1, if the target simulation instruction is an entity movement instruction, based on the event sequence list generated by the entity movement instruction, the target entity is controlled by the event simulation model to perform a simulation movement action in a uniform linear motion in the target simulation scene.

[0079] Specifically, the entity movement instruction is a control instruction issued by the target intelligent agent, and the event sequence list is generated based on the entity movement instruction. Through the event sequence list, the target entity can be controlled to perform simulated motion actions. In the specific motion process, the event simulation model is used to control the target entity to perform simulated motion actions in a target simulation scenario according to uniform linear motion. Since the traditional method uses a "six-degree-of-freedom" model, it is necessary to consider parameters such as acceleration / deceleration, shape and size, rotation angle, and turning radius of the entity, resulting in a relatively complex simulation method. The embodiment of the present disclosure uses an event simulation model to control the target entity to perform simulated motion actions in a target simulation scenario according to uniform linear motion, which simplifies the simulation process by always moving at a uniform speed, thereby optimizing the simulation efficiency. Figure 3 As shown, Figure 3 The left side of the figure shows that the target entity moves at a uniform speed in the embodiment of the present disclosure. Figure 3 The right side of the target entity moves in a non-uniform parabola. The left side does not need to consider the acceleration and deceleration of the target entity, while the right side not only needs to consider the acceleration and deceleration of the target entity, but also needs to consider the arc turning.

[0080] Step b2, if the target simulation instruction is an entity state change instruction, based on the event sequence list generated by the entity state change instruction, the target entity is controlled to perform a simulated detection action on the enemy entity within the circular detection area under the target simulation scenario through the event simulation model.

[0081] Specifically, the entity state change instruction is a control instruction issued by the target entity. Based on the event sequence list generated by the entity state change instruction, the target entity can be controlled to perform simulated detection actions in a circular detection area under the target simulation scenario through the event sequence list. In the specific detection process, when the enemy entity enters the circular detection area, the enemy entity is about to be detected. The traditional method does not use a circular detection area to detect enemy entities in a target simulation scenario. For example, the traditional method uses fan-shaped, conical, elliptical and other detection areas as the detection range. The success or failure of the detection in this method is not binary, but a probability value obtained comprehensively based on radar type, radar wavelength, target distance, target type, target size, target speed, target angle, terrain, meteorological conditions, etc. Therefore, the detection method in the traditional method will increase the complexity of the simulation, while the embodiment of the present disclosure uses a circular detection area as the detection range, and there is no need to consider too many parameters affecting the detection results. Figure 4 As shown, the left figure shows that the target entity of the present disclosure adopts a circular detection area as the detection range, and the right figure shows that the target entity adopts a non-circular area as the detection range. As the detection distance increases, the detection capability will gradually decay. The above steps b1 to b2 are performed in no particular order.

[0082] In some optional embodiments, when the target entity is controlled by an event simulation model to perform simulated motion actions in a target simulation scenario according to uniform linear motion, when the target entity changes its motion direction, the current state information of the target entity is updated, and based on the updated current state information of the target entity, the target position corresponding to the target entity after the motion direction is changed is calculated.

[0083] Specifically, Figure 7 As shown, the initial position of the target entity is A, and the longitude and latitude corresponding to the initial position A are (λ A ,φ A ), at the initial time T0, it moves at a uniform speed in a straight line at a speed v, and changes its direction of motion when it reaches position B. The coordinate longitude and latitude corresponding to position B are (λ B ,φ B). The target entity moves from time T1 to position C, and the speed remains unchanged. The T1 time is calculated as follows:

[0084]

[0085]

[0086] d=R·c

[0087]

[0088] Among them, a is the first intermediate parameter, c is the second intermediate parameter, Δφ represents the difference in latitude between point A and point B, Δλ represents the difference in longitude between points AB, assuming that the earth is a perfect sphere with a radius R of approximately 6371 kilometers, and t is the time from position A to position B.

[0089] exist Figure 7 In the example, the azimuth of the target entity from time T0 to time T1 is φ A , the initial azimuth (forward direction) of the target entity from the starting position A to the position B is calculated by the following formula;

[0090]

[0091] When the target entity reaches position B and turns to the coordinates of position C (the target position corresponding to the target entity after changing the direction of movement), given the coordinates of position B, the travel distance d between position B and position C, and the forward direction (azimuth) φ B , the coordinates of the end position C (the target position corresponding to the target entity after the target entity changes its motion direction) can be calculated by the following formula. The coordinates of the end position C are (λ C ,φ C );

[0092] φ C =asin(sin(φ B )cos(d / R)+cos(φ B )sin(d / R)cos(θ B ))

[0093] Δλ=atan2(sin(θ B )sin(d / R)cos(φ A ),cos(d / R)-sin(φ B )sin(φ C ))

[0094] λ C =λ B +Δλ

[0095] Therefore, the updated current state information of the target entity includes the updated position, angle, time, travel distance, etc. of the target entity.

[0096] In some optional implementations, the above step S104, based on the event sequence list, controls the target entity to perform simulation actions in the target simulation scenario through the event simulation model, including:

[0097] If the target simulation instruction is an entity attack instruction, based on the event sequence list generated by the entity attack instruction, the target entity is controlled by the event simulation model to perform simulated attack actions in the target simulation scenario according to the linear relationship between the missile power and the missile attack power, and the linear relationship between the missile power and the missile movement distance.

[0098] Specifically, the power of a missile is proportional to its attack power. Therefore, when the power of a missile is greater, the missile's high attack power is greater, and at this time, the missile's attack power is enhanced; when the power of a missile is smaller, the missile's attack power is smaller, and at this time, the missile's attack power is weakened. Similarly, the power of a missile is inversely proportional to the distance it moves. Therefore, when the power of a missile is greater, the missile's movement distance is smaller, and at this time, the missile's attack power is weakened; when the power of a missile is smaller, the missile's movement distance is larger, and at this time, the missile's attack power is enhanced.

[0099] In a specific example, during the process of executing a simulated motion action in a target simulation scene, if the remaining life value of the target entity is less than zero, the target entity is controlled to execute a simulated destruction action.

[0100] Specifically, the remaining health value of the target entity = total health value - missile power value. Therefore, if the remaining health value of the entity is less than zero, the target entity is controlled to perform a simulated destruction action. When controlling an entity to perform a destruction action, the traditional method needs to comprehensively calculate the damage parameters of each component of the entity and consider the missile type, warhead type, distance, angle, speed and other parameters of the missile. Therefore, the destruction method in the traditional method will increase the complexity of the simulation. The embodiment of the present disclosure combines the remaining health value of the target entity to control the target entity to perform a simulated destruction action, which can simplify the simulation calculation process and achieve the purpose of optimizing the simulation speed. Figure 5 As shown, the left side figure is the simulation destruction method in the embodiment of the present disclosure, and the right side figure is the simulation destruction method in the traditional method.

[0101] In some optional implementations, during the process of controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model, the simulation performance index is monitored in real time.

[0102] Specifically, by real-time monitoring of simulation performance indicators, in order to analyze in real time the simulation efficiency, simulation speed and system operation speed corresponding to the simulation actions performed by the observed target entity, and to facilitate the proposal of improvement measures for poor analysis results.

[0103] In some optional embodiments, when controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model, observation instructions sent by the target agent are received in real time, and the current state information of the target entity is returned to the target agent based on the observation instructions.

[0104] Specifically, the observation instruction can be a local observation instruction or a global observation instruction. The local observation instruction means that if the target entity is a red agent, the local observation instruction is a red local observation instruction; if the local observation instruction means that if the target entity is a blue agent instruction, the local observation instruction is a blue agent local observation instruction. The global observation instruction can simultaneously observe the instructions of multiple agents participating in the confrontation. Since the target entity in the above can be one or more entities, the current state information returned can be the current state information of one or more entities.

[0105] According to an embodiment of the present disclosure, a discrete event simulation system is also provided. Figure 6 As shown, it includes: a server 61 and a target intelligent agent 62, the target intelligent agent 62 is a plurality of mutually confronting intelligent agents, the server 61 includes: a communication interaction unit 611, an instruction processing unit 612, a data maintenance unit 613 and a simulation deduction unit 614 which are sequentially connected to each other, wherein the target intelligent agent 62 is used to send a target simulation instruction to the server 61 to control the target entity to perform a simulation action, and the server 61 is used to execute the discrete event simulation method in the above-mentioned embodiment.

[0106] Specifically, the target agent can be a red agent, a blue agent and a white agent, wherein the white agent is a neutral party, and the red agent and the blue agent can confront each other. The target agent is deployed in the client, and a simulation diagram of the target agents confronting each other is displayed on the client through a visual interface. Among them, the communication interaction unit adopts the WebSocket transmission mode (full-duplex communication network protocol transmission mode) and the breakpoint-resume transmission mode to transmit data, and its transmission rate is high and the stability is strong. The instruction processing unit sets a mutex lock mode to process multi-process data to improve the efficiency of data interaction. For example, when multiple agents simultaneously send target simulation instructions to the server, multi-process data is processed in a mutex lock mode; the data maintenance unit uses a data queue storage mode to maintain entity and task information, and improves the efficiency of addition, deletion, query and modification, for example, processing data according to the data queue sorting result; the instruction processing unit also uses a cache mechanism to process data; the simulation method of discrete events in the embodiment of the present disclosure is specifically executed by the simulation deduction unit.

[0107] In this embodiment, a discrete event simulation device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0108] This embodiment provides a discrete event simulation device, such as Figure 8 As shown, the device includes:

[0109] A data acquisition module 81 is used to acquire the assumed parameters, task types and simulation entity parameters in a target simulation scenario, wherein the target simulation scenario is each simulation scenario in a plurality of simulation scenarios, and the target simulation scenario is predefined;

[0110] A model creation module 82, for creating an event simulation model in a target simulation scenario based on entity assumption parameters, task types and simulation entity parameters;

[0111] The instruction receiving module 83 is used to receive the target simulation instruction sent by the target agent, and generate an event sequence list based on the target simulation instruction; wherein the target agent is each of the multiple agents, and the target simulation instruction includes: entity movement instruction, entity attack instruction and entity state change instruction;

[0112] The simulation execution module 84 is used to control the target entity to execute simulation actions in the target simulation scenario based on the event sequence list through the event simulation model.

[0113] In some optional implementations, the assumption parameters include the assumption simulation start and end time and the following entity attribute information, entity type, entity-to-agent, entity name, entity initial position, entity initial orientation; the event sequence list includes task type, multiple states of event changes in time sequence, and time points corresponding to each state. Task types include: mobile task type, attack task type, and detection task type; simulation entity parameters include: simulation entity name, simulation entity category, simulation entity maximum speed, simulation entity detection range, simulation entity life value, simulation entity interference range, simulation entity attack range, simulation entity hit probability, simulation entity attack force value, and simulation entity maximum height; multiple simulation scenarios include: aircraft simulation scenario, ship simulation scenario, submarine simulation scenario, missile simulation scenario, sensor simulation scenario, and fixed facility simulation scenario. .

[0114] In some optional implementations, the simulation execution module 84 includes:

[0115] A first execution submodule is used for controlling the target entity to perform a simulation motion action in a target simulation scenario according to uniform linear motion based on an event sequence list generated by the entity movement instruction if the target simulation instruction is an entity movement instruction;

[0116] The second execution submodule is used to control the target entity to perform simulated detection actions on the enemy entity within the circular detection area under the target simulation scene through the event simulation model based on the event sequence list generated by the entity state transformation instruction if the target simulation instruction is an entity state transformation instruction.

[0117] In some optional embodiments, the first execution submodule is also used to control the target entity to perform simulated motion actions in a target simulation scenario in accordance with uniform linear motion based on an event sequence list through an event simulation model. When the target entity changes its motion direction, the current state information of the target entity is updated, and based on the updated current state information of the target entity, the target position corresponding to the target entity after the target entity changes its motion direction is calculated.

[0118] In some optional implementations, the simulation execution module 84 includes:

[0119] The third execution submodule is used to control the target entity to perform simulated attack actions in the target simulation scenario according to the linear relationship between the missile power and the missile attack power, and the linear relationship between the missile power and the missile movement distance, based on the event sequence list generated by the entity attack instruction if the target simulation instruction is an entity attack instruction through the event simulation model.

[0120] In some optional embodiments, the third execution submodule is further used to control the target entity to perform a simulated destruction action if the remaining health value of the target entity is less than zero during the execution of the simulated motion action in the target simulation scene.

[0121] In some optional implementations, it also includes: a simulation monitoring module, which is used to monitor simulation performance indicators in real time when the target entity is controlled by the event simulation model to perform simulation actions in the target simulation scenario.

[0122] In some optional embodiments, it also includes: a state receiving module, which is used to receive observation instructions sent by the target intelligent agent in real time during the process of controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model, and return the current state information of the target entity to the target intelligent agent based on the observation instructions.

[0123] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0124] The discrete event simulation device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0125] The embodiment of the present invention further provides a computer device having the above-mentioned discrete event simulation device.

[0126] See also Fig. 9 , Fig. 9 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig. 9 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig. 9 A processor 10 is taken as an example.

[0127] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0128] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0129] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0130] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0131] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0132] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0133] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0134] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A discrete event simulation method, characterized in that: The method comprises: Acquire assumption parameters, task types, and simulation entity parameters in a target simulation scenario, wherein the target simulation scenario is each simulation scenario in a plurality of simulation scenarios, and the target simulation scenario is predefined; Creating an event simulation model in the target simulation scenario based on the assumption parameters, the task type and simulation entity parameters; receiving a target simulation instruction sent by a target agent, and generating an event sequence list based on the target simulation instruction; wherein the target agent is each of the multiple agents, and the target simulation instruction includes: an entity movement instruction, an entity attack instruction, and an entity state change instruction; Based on the event sequence list, the target entity is controlled by the event simulation model to perform simulation actions in the target simulation scenario.

2. The method according to claim 1, characterized in that The hypothetical parameters include the start and end time of the hypothetical simulation and the following entity attribute information, entity type, intelligent agent to which the entity belongs, entity name, entity initial position, and entity initial orientation; the event sequence list includes task type, multiple states of event changes according to the time series, and the time point corresponding to each state. The task types include: mobile tasks, attack tasks, and detection tasks; the simulation entity parameters include: simulation entity name, simulation entity category, simulation entity maximum speed, simulation entity detection range, simulation entity health value, simulation entity interference range, simulation entity attack range, simulation entity hit probability, simulation entity attack force value, and simulation entity maximum height; the multiple simulation scenarios include: aircraft simulation scenario, ship simulation scenario, submarine simulation scenario, missile simulation scenario, sensor simulation scenario, and fixed facility simulation scenario.

3. The method according to claim 1 or 2, characterized in that: Based on the event sequence list, controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model includes: If the target simulation instruction is the entity movement instruction, based on the event sequence list generated by the entity movement instruction, the target entity is controlled by the event simulation model to perform a simulation movement action in a uniform linear motion in the target simulation scene; If the target simulation instruction is the entity state change instruction, based on the event sequence list generated by the entity state change instruction, the target entity is controlled to perform a simulated detection action on an enemy entity within a circular detection area under the target simulation scenario through the event simulation model.

4. The method according to claim 3, characterized in that Based on the event sequence list, when the target entity is controlled by the event simulation model to perform simulated motion actions in uniform linear motion in the target simulation scenario, when the target entity changes its motion direction, the current state information of the target entity is updated, and based on the updated current state information of the target entity, the target position corresponding to the target entity after the motion direction is changed is calculated.

5. The method according to claim 1 or 2, characterized in that: Based on the event sequence list, controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model includes: If the target simulation instruction is the entity attack instruction, based on the event sequence list generated by the entity attack instruction, the target entity is controlled by the event simulation model to perform a simulated attack action in the target simulation scenario according to the linear relationship between the missile power and the missile attack force, and the linear relationship between the missile power and the missile movement distance.

6. The method according to claim 1 or 2, characterized in that: During the process of executing the simulated motion action in the target simulation scene, if the remaining life value of the target entity is less than zero, the target entity is controlled to execute the simulated destruction action.

7. The method according to claim 1, characterized in that In the process of controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model, the simulation performance index is monitored in real time.

8. The method according to claim 1, characterized in that Also includes: In the process of controlling the target entity to perform simulation actions in the target simulation scenario through the event simulation model, the observation instructions sent by the target agent are received in real time, and the current state information of the target entity is returned to the target agent based on the observation instructions.

9. A discrete event simulation device, characterized in that: The device comprises: A data acquisition module, used to acquire the assumed parameters, task types and simulation entity parameters in a target simulation scenario, wherein the target simulation scenario is each simulation scenario in a plurality of simulation scenarios, and the target simulation scenario is predefined; A model creation module, used to create an event simulation model in the target simulation scenario based on the assumption parameters, the task type and the simulation entity parameters; An instruction receiving module is used to receive a target simulation instruction sent by a target intelligent agent, and generate an event sequence list based on the target simulation instruction; wherein the target intelligent agent is each of the multiple intelligent agents, and the target simulation instruction includes: an entity movement instruction, an entity attack instruction, and an entity state change instruction; A simulation execution module is used to control the target entity to execute simulation actions in the target simulation scenario based on the event sequence list through the event simulation model.

10. A discrete event simulation system, characterized in that: include: A server and a target intelligent agent, wherein the target intelligent agent is a plurality of mutually antagonistic intelligent agents, and the server comprises: a communication interaction unit, an instruction processing unit, a data maintenance unit and a simulation deduction unit which are sequentially connected to each other, wherein the target intelligent agent is used to send a target simulation instruction to the server to control a target entity to perform a simulation action, and the server is used to execute the discrete event simulation method described in any one of claims 1 to 8.

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