KR102266178B1 - Intelligent armed assignment control system and method of control thereof - Google Patents

Abstract

The present invention relates to an intelligent armament assignment control system and a control method thereof, and more particularly, to an intelligent armament assignment control system and a control method thereof in consideration of mission achievement at a specific point in time using a deep learning model. The intelligent armament assignment control system and the control method thereof according to the present invention generate an optimal armament assignment plan in consideration of a mission achievement rate at a current point in time and at a specific point in time thereafter with respect to a recommended armament assignment plan. In particular, the present invention estimates the mission achievement rate at a specific point in time by applying a plurality of armament allocation plans generated at the current time to a deep learning supervised learning model that implements various warfare aspects, and outputs an optimal random allocation plan by merging the mission achievement rate at the current point in time and the mission achievement rate at a specific point in time.

Description

Intelligent armed assignment control system and method of control thereof

The present invention relates to an intelligent armament assignment control system and a control method thereof, and more particularly, to an intelligent armament assignment control system and a control method thereof in which the degree of mission achievement at a specific point in time is considered using a deep learning model.

The content described in this section merely provides background information for the present embodiment and does not constitute the prior art.

Arming assignment is a technology controlled in the military operation environment to minimize the loss of the defense target of the friendly forces from the enemy attack or to maximize the destruction rate of the enemy target. In other words, arming assignment refers to a series of tactical information processing algorithms or actions that evaluate various threat forces existing in a given tactical situation according to set criteria, determine the threat priority, and allocate the optimal armament to each threat force. .

Korean Patent Publication No. 10-1217612 (Title of the Invention: Improved armament allocation method that minimizes armament dependence and a program medium recording the same) sets the priority assignment order for engageable armaments in advance, and the highest priority armament It discloses a configuration for selecting and assigning .

Such an arming assignment control configuration only considers the destruction rate of the currently identified target, and does not consider the mission achievement rate at a specific point in time after arming assignment. In addition, the weapon assignment control configuration of the above patent only considers the destruction rate when the weapon hits the target, and does not consider the probability of reaching the critical time of weakness when arming is assigned.

Korean Patent Publication No. 10-1217612 (Title of the invention: an improved method for allocating arms to minimize dependence on arms and a program medium recording the same)

Accordingly, it is an object of the present invention to provide an intelligent arming assignment control system and a control method thereof in consideration of the mission achievement rate at a specific point in time after the optimal arming assignment.

Another object of the present invention is to provide an intelligent arming assignment control system capable of predicting the degree of mission achievement at a specific point in time by using a deep learning model and applying an optimal arming assignment, and a control method thereof.

Another object of the present invention is to provide an intelligent arming assignment control system and a control method therefor in consideration of the probability of reaching the vulnerable time, which is the core of arming assignment.

Another object of the present invention is to support strategic decision-making in consideration of not only the currently identified target but also the target to appear in the future, and the availability of weapons at the present time as well as at a later time by using a deep learning model for predicting mission achievement at a specific time It is to provide an intelligent armament allocation control system and a control method therefor.

Another object of the present invention is to develop a model capable of evaluating various warfare aspects and arming assignment and result thereof, applying it to a deep learning model, and using it to provide an intelligent armament assignment control system and a control method thereof that can be utilized in the military field. is in

In order to solve the above technical problems, an intelligent armament allocation control system according to an embodiment of the present invention includes an identification target information receiving module for receiving enemy identification target information; a battlefield situation for receiving information on various conditions of the battlefield situation information receiving module; an armament recommendation module for generating a number of arming recommendations based on the priority in consideration of the arming arrival rate within the vulnerable time of the available armament of the friendly forces and the expected destruction rate during an attack in order to respond to the threat of the identified target; Evaluate the mission achievement for the arming recommendations at the present time, but apply the generated arming recommendations to the expected destruction rate when attacking, and the incidental damage evaluation result information based on the information collected through the battlefield situation information receiving module to reflect the mission achievement evaluation module to evaluate; Implement various war condition simulation models and estimate the mission achievement at a specific point in time by applying the generated arming recommendations, but the expected destruction rate when attacking the arming recommendations, the armed arrival rate within the vulnerable time, and weights for each mission at a specific point in time an estimation module for estimating the degree of mission achievement at a specific point in time; It is characterized in that it includes an arming assignment control module for generating an optimal arming assignment plan using the results of the estimation module and the evaluation module.

Preferably, the estimation module estimates the degree of mission achievement at a specific point in time based on deep learning, but provides guidance using a simple model capable of evaluating the results of an arming recommendation plan at a specific point in time for various war patterns and a deep learning dataset obtained through this. It is characterized by implementing a deep learning model as a learning method and estimating the task achievement at a specific point in time, weighted for a specific point in time, by applying the generated arming recommendation to the deep learning model.

Preferably, the intelligent armament allocation control system of the present invention includes a database unit for storing various types of information on available armaments of friendly forces and information on a plurality of programs, and various information on available armaments of friendly forces stored in the database unit. It is characterized by including the damage range for each weapon, effective range, speed, armament repellency, armament reliability, and air mission success rate.

Preferably, the arming arrival rate within the vulnerable time is the probability that the arming of the friendly forces arrives before a moving target, such as an enemy ballistic missile projectile, moves from the launch site, and the arming arrival rate within the vulnerable time is calculated by the following [Equation 2] do it with

[Equation 2]

Arming reach rate within vulnerable time = Pr( X > CT) = 1 - Pr( X ≤ CT),

Here, X = the time the Tel (enemy ballistic missile projectile) stays in the launch position (normal distribution), CT = C2 time + arming arrival time (carry time), and C2 time is the time required from the detection of the target to the command to fire. is set to

Preferably, the expected destruction rate when attacking is divided into the expected destruction rate when attacking with an air-to-ground weapon and the expected destruction rate when attacking with a ground-to-ground weapon, and the expected destruction rate when attacking with an air-to-ground weapon is calculated by the following [Equation 3] characterized.

[Equation 3]

When attacking with air-to-ground weapons, the expected destruction rate = 1-(1-P _k ) ^(n*v) ,

P _k = Armed arrival rate within vulnerable time * Calculated as air mission success rate,

Here, n is the armament repellency, v is the armament reliability, n and v are simulation results, and the air mission success rate is a value that changes according to the air superiority gain of the friendly force as a result of the neutralization of the enemy air defense network, which is the simulation result value.

Preferably, the expected destruction rate when attacking with ground-to-ground armament is characterized in that it is calculated as a value obtained by multiplying the arming reliability by the arming arrival rate within the vulnerable time.

Preferably, the armed arrival time (carry time) is divided into air-to-ground arming arrival time and ground-to-ground arming arrival time, and the air-to-ground arming arrival time is calculated by dividing the distance between the arming and the target by the aircraft speed.

Preferably, the ground-to-ground arming arrival time is calculated by dividing the distance between the arming and the target for each arming by the speed of each arming.

Preferably, the distance is the distance between the armament and the target, and it is characterized in that it is calculated by the following [Equation 1] using the coordinates for the identification target received from the identification target information receiving module.

[Equation 1]

Distance = ArcCos(Sin(Radians(Lat1))*Sin(Radians(Lat2))+Cos(Radians(Lat1)) *Cos(Radians(Lat2))*Cos(Radians(Lon1-Lon2)))*Radius of the Earth

Here, Lat1 and Lat2 are latitude information, and Lon1 and Lon2 are longitude information.

Preferably, the arming recommendation module checks the time-limited target for the identification target received from the identification target information receiving module, and sets the priority of the arming recommendation proposal in consideration of the expected destruction rate when attacking the time-limited target. do it with

Preferably, the incidental damage evaluation result information includes the value of evaluating the extent of damage based on the collected information by confirming the range of damage by armament, collecting information on facilities and people within the range of damage through the battlefield situation information receiving module, and It is characterized in that it includes a simulated value of the time taken for evaluation.

Preferably, the evaluation module determines whether there is an airspace collision based on the collected information of the battlefield situation information receiving module, and includes the aircraft introduction time in the total required time (CT) when an airspace collision occurs.

Preferably, the evaluation module calculates the total required time (CT) including the expected destruction rate during an attack, the time required to evaluate the degree of collateral damage, and the aircraft introduction time in the case of an airspace collision, and applies this to calculate the expected destruction rate and vulnerability in the case of an attack It is characterized by evaluating the mission achievement for the arming recommendation proposal at the current time by calculating the arming arrival rate within the time.

Preferably, the evaluation module confirms the time-limited target with respect to the identification target received from the identification target information receiving module, and sets the priority of the arming recommendation in consideration of the expected destruction rate when attacking the time-limited target. .

Preferably, the battlefield situation information receiving module receives the weather information, and provides a weight for the received weather information to the arming recommendation module, the evaluation module, and the estimation module.

Preferably, the battlefield situation information receiving module receives the air superiority security ratio information of the current time, and provides a weight for the received air superiority security ratio information to the evaluation module and the estimation module.

Preferably, in the estimation module, a weight for a specific time point of each mission is a preset value, a strategic mission goal to be achieved at a specific time point for each mission is given, and the weight is set based on the strategic mission goal do it with

Preferably, the arming assignment control module is characterized in that it assigns a weight to the current point and a specific point in time.

Preferably, the arming assignment control module is characterized in that weight assignment can be variably adjusted through an external input interface.

Preferably, the estimation module is characterized in estimating the task achievement at a specific point in time by the following [Equation 5].

[Equation 5]

Preferably, the estimation module is characterized in estimating the task achievement for at least two or more specific time points.

In order to solve the above technical problem, an intelligent armament allocation control method according to an embodiment of the present invention comprises the steps of: receiving identification target information of an enemy; Receiving information on various conditions of the battlefield situation; generating a plurality of arming recommendations based on priorities in consideration of the armed arrival rate within the vulnerable time for the available armament of the friendly forces and the expected destruction rate during an attack in order to respond to the threat of the identified target; Evaluate the mission achievement for the arming recommendations at the present time, but apply the generated arming recommendations to the expected destruction rate when attacking, and the incidental damage evaluation result information based on the information collected through the battlefield situation information receiving module to reflect the mission achievement an evaluation step to evaluate; Estimate the mission achievement at a specific point in time by applying the generated arming recommendation plan, but give weight to the expected destruction rate when attacking the arming recommendation, the arming arrival rate within the vulnerable time, and the specific point in time of each mission to estimate the mission achievement at a specific point in time. estimating step of estimating; It characterized in that it comprises the step of generating an optimal armament allocation plan using the results of the estimation step and the evaluation step.

Preferably, the estimation step estimates the degree of mission achievement at a specific point in time based on deep learning, but provides guidance using a simple model capable of evaluating the results of an armament recommendation plan at a specific point in time for various war patterns and a deep learning dataset obtained through this. It is characterized by implementing a deep learning module as a learning method and estimating the degree of task achievement at a specific point in time given a weight for a specific point in time by applying the generated arming recommendation to the deep learning module.

Preferably, the arming arrival rate within the vulnerable time is the probability that the arming of the friendly forces arrives before a moving target, such as an enemy ballistic missile projectile, moves from the launch site, and the arming arrival rate within the vulnerable time is calculated by the following [Equation 2] do it with

[Equation 2]

Arming reach rate within vulnerable time = Pr( X > CT) = 1 - Pr( X ≤ CT),

Here, X = the time the Tel (enemy ballistic missile projectile) stays in the launch position (normal distribution), CT = C2 time + arming arrival time (carry time), and C2 time is the time required from the detection of the target to the command to fire. is set to

Preferably, the expected destruction rate when attacking is divided into the expected destruction rate when attacking with an air-to-ground weapon and the expected destruction rate when attacking with a ground-to-ground weapon, and the expected destruction rate when attacking with an air-to-ground weapon is calculated by the following [Equation 3] characterized.

[Equation 3]

When attacking with air-to-ground weapons, the expected destruction rate = 1-(1-P _k ) ^(n*v) ,

P _k = Armed arrival rate within vulnerable time * Calculated as air mission success rate,

Here, n is the armament repellency, v is the armament reliability, n and v are simulation results, and the air mission success rate is a value that changes according to the air superiority gain of the friendly force as a result of the neutralization of the enemy air defense network, which is the simulation result value.

Preferably, the expected destruction rate when attacking with ground-to-ground armament is characterized in that it is calculated as a value obtained by multiplying the arming reliability by the arming arrival rate within the vulnerable time.

Preferably, the incidental damage evaluation result information includes the value of evaluating the extent of damage based on the collected information by confirming the range of damage by armament, collecting information on facilities and people within the range of damage through the battlefield situation information receiving module, and , it is characterized in that it includes a value that simulates the time taken for evaluation.

Preferably, the evaluation step is characterized in that it is determined whether there is an airspace collision, and when an airspace collision occurs, the aircraft evacuation time is included in the total required time (CT).

Preferably, the evaluation step calculates the total time required (CT) including the expected destruction rate during an attack, the time required to evaluate the degree of collateral damage, and the aircraft introduction time in the case of an airspace collision, and applies this to calculate the expected destruction rate and vulnerability during an attack It is characterized by evaluating the mission achievement for the arming recommendation proposal at the current time by calculating the arming arrival rate within the time.

Preferably, in the estimation step, the weight for a specific time point of each mission is a preset value, a strategic mission goal to be achieved at a specific time point for each mission is given, and the weight is set based on the strategic mission target value do it with

Preferably, the arming assignment control step is characterized in that weights are given to the current point and a specific point in time.

Preferably, the estimation step is characterized in that the task achievement at a specific point in time is estimated by the following [Equation 5].

[Equation 5]

The intelligent arming assignment control system and the control method thereof according to the present invention generate an optimal arming assignment plan in consideration of the mission achievement rate at the current point in time and at a specific point in time thereafter with respect to the recommended arming assignment plan. In particular, the present invention estimates the mission achievement rate at a specific point in time by applying a plurality of arming assignment plans generated at the current point in time to a deep learning supervised learning model that implements various warfare aspects, and the mission achievement rate at the current point in time and the mission at a specific point in time. Output the optimal random allocation plan by merging the achievement rates.

In addition, the present invention generates an armament assignment plan, and in evaluating and predicting the mission achievement rate, considers the arming arrival rate within the vulnerable time and outputs an optimal random assignment plan. Therefore, the present invention obtains the effect of supporting strategic decision-making in consideration of not only the currently identified target but also the target to appear later, and the availability of weapons at the present time as well as the future time.

In addition, the present invention utilizes a deep learning supervised learning model in various warfare aspects and the corresponding arming assignment and result evaluation, thereby presenting a new method using the deep learning model in the military field, and the effect of further advancing the military operation capability. get

1 shows an overall configuration diagram of an intelligent armament allocation control system according to an embodiment of the present invention.
Figure 2 shows the overall configuration of the intelligent armament allocation control system according to an embodiment of the present invention.
3 is a diagram showing the configuration of a decision-making model for arming assignment developed using a deep learning model in the intelligent arming assignment control system according to an embodiment of the present invention.
Figure 4 shows an overall control flow diagram of the intelligent armament allocation control system according to an embodiment of the present invention.
5 is a detailed control flowchart for arming recommendation in the intelligent arming assignment control system according to an embodiment of the present invention.
6 is an exemplary diagram showing the armed arrival rate within the vulnerable time in the intelligent armament allocation control system according to an embodiment of the present invention.
7 is a diagram illustrating a control flow for evaluating the degree of mission achievement at the current time in the intelligent weapon assignment control system according to an embodiment of the present invention.
8 is a control flow diagram for evaluating the mission achievement at the time of the main mission evaluation in the intelligent weapon assignment control system according to an embodiment of the present invention.
9 is a control flowchart for calculating an optimal arming assignment plan in the arming assignment control module in the intelligent arming assignment control system according to an embodiment of the present invention.
10 shows an exemplary diagram of a deep learning model according to an embodiment of the present invention.
11 shows an exemplary diagram of conditions available when estimating and evaluating task achievement by being applied to the deep learning model of the present invention.

Hereinafter, the embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but the same or similar components are assigned the same reference numerals regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "part" and "group", "module" and "part", "unit" and "part", "device" and "system", "terminal" and "node" for components used in the description below. and "digital walkie-talkie" are given or mixed in consideration of the ease of writing the specification, and do not have distinct meanings or roles by themselves.

In addition, in describing the embodiments disclosed in the present specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in the present specification, and the technical spirit disclosed herein is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present invention , should be understood to include equivalents or substitutes.

Terms including an ordinal number, such as first, second, etc., may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is referred to as being “connected” or “connected” to another component, it is understood that the other component may be directly connected or connected to the other component, but other components may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that no other element is present in the middle.

The singular expression includes the plural expression unless the context clearly dictates otherwise.

In the present application, terms such as "comprises" or "have" are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. It is apparent to those skilled in the art that the present invention may be embodied in other specific forms without departing from the spirit and essential characteristics of the present invention.

In the present invention, arming assignment is a control and act of performing optimal assignment of armaments of friendly forces in order to minimize the loss of the target of the friendly defense from the enemy's attack or to maximize the destruction rate of the enemy target. In particular, when a war occurs, how to configure armament allocation is very important in order to achieve the strategic mission objective at a specific point in time while maximizing the destruction rate of the enemy target.

A mission refers to a major operational unit of the military, such as a long-range artillery destruction mission and a strategic target strike mission, and each mission has a strategic mission objective that must be achieved at a specific point in time. The mission evaluation scale is evaluated by the destruction rate or the neutralization rate.

This arming assignment is a key function of the military command and control system, and in particular, in the case of a time-limited target with a limited strikeable time, the optimal armament assignment must be done quickly and accurately.

On the other hand, in the present invention, for optimal arming assignment, various variables and available armament such as distance between target and arming, arming arrival time, attack success rate, weather, collateral damage evaluation, airspace collision, etc. are considered, and in particular, the mission at the present time It is characterized by controlling the optimal armament allocation in consideration of the achievement rate and the mission achievement rate at a specific point in time.

The present invention implements a simplified model capable of simulating a mission (strategic target strike mission) in various warfare aspects in order to evaluate the mission achievement rate at a specific point in time for each mission. The simplified model is characterized by implementing it as a deep learning model for various warfare aspects based on deep learning iterative learning.

1 shows an overall configuration diagram of an intelligent armament allocation control system according to an embodiment of the present invention.

The radar 110 detects an enemy, tracks it when it is a threat target, and generates track information. The track information generated by the radar 110 is used as control information for arming assignment in the intelligent arming assignment control system 100 of the present invention. Here, the radar 110 is a means for detecting a plurality of targets corresponding to the enemy, and the configuration may be made in plurality. That is, it is a configuration for detecting enemy targets in various warfare aspects.

The intelligent arming assignment control system 100 acquires various information on the battlefield situation from the detector 120 made of various detection means in addition to the radar 110, and uses it as basic control information for arming assignment. For example, the detection unit 120 may obtain weather information of the battlefield and provide it to the intelligent armament allocation control system 100 . Through the configuration of another detection unit, incidental damage evaluation information of the battlefield may be acquired and provided to the intelligent armament allocation control system 100 . It is possible to obtain airspace collision information through the configuration of another detection unit and provide it to the intelligent armament allocation control system 100 . In the configuration of the detection unit 120 as described above, various well-known technical means for securing battlefield context information may be used.

The intelligent arming assignment control system 100 outputs an optimal arming assignment plan based on various information, but in the present invention, the mission achievement evaluation result at the present time and the deep learning supervised learning are used to estimate the mission achievement at a specific point in time, and Based on this, an optimal arming allocation plan is created. Therefore, based on the optimal arming assignment plan generated by the intelligent arming assignment control system 100, selection and response of specific armaments from among the available armaments 130 of the friendly forces are made.

Here, the available armaments 130 of the friendly army include armaments that can be launched toward the enemy in a war situation, such as a launch pad that can be launched on the ground or in the air, a guided missile, a fighter, a ballistic missile, and the like.

And the intelligent arming assignment control system 100 can use a system of general computer function that is equipped with arming assignment control algorithm. The intelligent arming assignment control system 100 includes a database unit 140 for storing information on a plurality of programs including various information on available armament of friendly forces and arming assignment control algorithm inside or outside.

The information on the available armaments of the friendly forces stored in the database unit 140 includes, for example, the damage range for each arming, effective range, speed, arm repellency, armament reliability, aviation mission success rate, etc., and in addition to controlling armament assignment In order to do this, all necessary control information for each weapon is stored.

Figure 2 shows the overall configuration of the intelligent armament allocation control system according to an embodiment of the present invention. And Figure 4 shows the overall control flow diagram of the intelligent arming allocation control system according to an embodiment of the present invention.

The intelligent arming assignment control system 100 of the present invention acquires tracking and detection information for a plurality of aircraft or targets through the radar 110, and receives and uses the tracking information for the target. Accordingly, the track information provided from the radar 110 is input to the intelligent armament allocation control system 100 through the identification target information receiving module 10 . The target information received from the identification target information receiving module 10 includes information such as coordinates, the type of the target, and the identification time (step 410).

When there are a plurality of targets detected by the radar 110 , various types of information may be received by dividing the targets for each target. For example, the target detected in the battlefield situation may be several or may be in several ranges. or a timed target. Therefore, by synthesizing all of these cases, an armament recommendation plan to respond to an arbitrary target is composed.

The intelligent armament allocation control system 100 of the present invention includes a battlefield situation information receiving module 20 for receiving various types of information on the battlefield situation, that is, weather information, incidental damage evaluation information, airspace collision information, and the like. The battlefield situation information receiving module 20 may acquire current weather information of the battlefield situation and meteorological information of a preset specific point in time. Of course, in this case, it is natural that a connection with a weather information providing device or a weather information providing app is made in order to acquire the weather information.

The battlefield situation information receiving module 20 may receive incidental damage evaluation information (step 420). The incidental damage assessment information is determined by collecting information on facilities or human life within the damage range other than the threat target, and evaluating the degree of damage based on the collected information. This value may be converted into time using a preset algorithm. This value is a value obtained by setting the range of damage that can be formed including the target when any armament reaches the target, and quantifying the degree of damage to facilities or people included in this damage range. In order to obtain incidental damage assessment information, GPS-based map information and satellite information can be used.

The intelligent arming allocation control system 100 of the present invention includes an arming recommendation module 30 . The arming recommendation module 30 derives an arming recommendation in consideration of target-specific information, arming arrival rate within the vulnerable time, air mission success rate, friendly armament, and the like. At this time, several recommended armament recommendations are recommended based on priorities. In particular, when there is a time-limited target, priority is set in consideration of the expected destruction rate of the time-limited target. That is, the arming recommendation module 30 generates a plurality of arming recommendations using available armaments of the friendly forces in order to respond to the threat of the identification target (step 430). Arming recommendation in step 430 is made based on various detection information about the battlefield situation, which will be described in detail with reference to FIG. 5 to be described later.

The intelligent arming assignment control system 100 of the present invention includes an evaluation module 40 for evaluating the mission achievement at the current point in time for the currently identified target. The evaluation module 40 evaluates the destruction rate of the target for each of a plurality of recommendations recommended by the arming recommendation module 30 for the identified target (step 440). In other words, there are missions to be accomplished at the present time, and the missions are divided into major military operational units such as long-range artillery destruction missions and strategic target strike missions. Each mission has a strategic mission objective that must be achieved at a specific point in time. And the mission evaluation scale is evaluated based on the destruction rate or the neutralization rate.

Therefore, the intelligent arming assignment control system 100 of the present invention evaluates the mission achievement when responding to the currently identified target for a plurality of arming recommendations using the evaluation module 40 . The mission achievement evaluation at the present time is calculated using the incidental damage evaluation result information, the airspace collision result information, and the expected destruction rate for the time-limited target.

Airspace collision result information is applied when it is judged that there is a risk of airspace collision. In other words, when the trajectory of a friendly surface-to-ground (or air-to-ground) missile and the air force's operational airspace collide with the armament recommendation proposal, it is a process for moving the aircraft in the relevant airspace out of the airspace. In general, it takes about 1 minute to introduce an aircraft due to the result of an airspace collision.

The evaluation module 40 evaluates the mission achievement rate by calculating the expected destruction rate when attacking the time-limited target when the time-limited target exists in the identified threat target. The task achievement evaluation in step 440 will be described in detail with reference to FIG. 7 to be described later.

In addition, the intelligent armament allocation control system 100 of the present invention includes an estimation module 50 for implementing various war condition simulation modules and estimating the mission achievement at a specific point in time by applying the generated arming recommendations. The estimation module 50 may estimate the mission achievement at a specific time of the mission (eg, H+5 time (5 hours after the war), D+4 time (4 days after the war)). The estimation module 50 estimates the mission achievement at the time of the main mission evaluation using the deep learning model (step 450). The estimation of the degree of mission achievement at the time of the main mission evaluation in step 450 will be described in detail with reference to FIG. 8, which will be described later.

The estimation module 50 implements and includes a simplified model capable of estimating the degree of mission achievement for the armed recommendation according to various warfare aspects for estimating the degree of mission achievement after the time of arming assignment. Then, the data set for deep learning obtained through this is created, and the deep learning model is implemented by applying the supervised learning method. By inputting the generated arming recommendation into the deep learning model created in this way, the mission achievement at a specific point in time (main mission evaluation time) is estimated.

The intelligent arming assignment control system 100 of the present invention derives an optimal arming assignment plan by comprehensively considering the mission achievement at the current point in time evaluated by the evaluation module 40 and the mission achievement at a specific point in time estimated by the estimation module 50 . It includes an arming assignment control module (60). The arming assignment control module 60 assigns weights to the mission achievement at the current time point and the mission achievement degree at a specific time point, and outputs an optimal arming assignment plan (step 460). The process of deriving an armament allocation plan in step 460 will be described in detail with reference to FIG. 9 .

In addition, the intelligent armament allocation control system 100 of the present invention may use various information about the available armaments of the friendly forces stored in the database unit 140 as shown in FIG. 1 .

3 is a diagram illustrating a control configuration of decision making for arming assignment developed by using a deep learning model in the intelligent arming assignment control system according to an embodiment of the present invention.

The present invention includes an evaluation or prediction module 210 to 230 for evaluating or estimating at least three task achievement levels.

The evaluation module 210 is a module for predicting and calculating the destruction rate/incapacity rate of the target currently appearing as a result of the arming assignment at the time of arming assignment. The prediction module 220 is a deep learning model for predicting the rate of destruction/incapacity at a specific time (eg, H+5) as a result of arming assignment at the time of arming assignment. The prediction module 230 is a deep learning model for predicting the rate of destruction/incapacity at a specific time (eg, D+4) as a result of arming assignment at the time of arming assignment.

Each deep learning model includes, through simulation, a simplified model capable of estimating the degree of mission achievement for arming recommendations according to various warfare aspects in order to estimate the degree of mission achievement after the time of arming assignment. Then, a dataset for deep learning obtained through this is created, and a supervised learning method is applied to implement a deep learning model. By inputting the generated arming recommendation into the deep learning model created in this way, the mission achievement at a specific point in time (main mission evaluation time) is estimated.

And the plurality of evaluation or prediction modules (210 ~ 230) calculated or predicted values include an output module (260 ~ 280) for outputting the standardized.

And a weighting module 250 for calculating the task achievement at the current point in time or at a specific point in time for the task by assigning weights to the output values of the output modules 260 to 280 for each task, and integrating and outputting them. The weight given to the weight module 250 can be variably adjusted through an external signal interface, and is variably adjusted according to a strategic target that a mission must achieve at a specific point in time. The output of the weight module 250 is again output through the final output module 290 .

Next, Figure 5 shows a detailed control flow chart for arming recommendation in the intelligent arming assignment control system according to an embodiment of the present invention.

The arming recommendation module 30 generates arming recommendations in consideration of the arrival rate within the vulnerable time, the air mission success rate, and the available armaments of the friendly forces, and the generated arming recommendations are generated in plurality based on the priority.

Armed recommendation module 30 calculates the distance between the arming and the target (step 510). In step 510, the distance between the current position of the available armament and the target is calculated using the coordinate information of the target obtained in step 410. Using the latitude information (Lat1, Lat2) and the longitude information (Lon1, Lon2) of the two points for which the distance is to be calculated, the distance is calculated based on the following [Equation 1].

[Equation 1]

Distance = ArcCos(Sin(Radians(Lat1))*Sin(Radians(Lat2))+Cos(Radians(Lat1)) *Cos(Radians(Lat2))*Cos(Radians(Lon1-Lon2)))*Radius of the Earth

(The Earth's radius is 6,371 km)

Armed recommendation module 30 calculates the arming arrival time (step 520). Step 520 calculates the air-to-ground arming arrival time and the ground-to-ground arming arrival time. The air-to-ground armament arrival time is obtained by dividing the distance calculated in step 510 by the aircraft speed. Here, the aircraft speed is a value obtained through the detector 120 . or a preset value.

The ground arming arrival time is determined by the time it takes for each armament to reach the distance calculated in step 510 from the current position. The current position of each armament is a preset stored value or a value obtained through the detection unit 120 . And the arrival time is calculated using the predetermined speed for each armament.

The arming arrival time calculation in step 520 is a process of calculating the time it takes to reach the target for the available armaments of the friendly forces. The available armament of the friendly forces may be composed of, for example, a ballistic missile, a cruise missile, etc. in the case of ground-to-ground armament. For the ballistic missile, the arrival time for each distance calculated in consideration of the specifications of the ballistic missile and the arrival time for each armament are calculated by referring to the calculated distance calculated in step 510. For cruise missiles, by dividing the calculated distance calculated in step 510 using the speed value of the missile, the time to reach the target for each armament is calculated. Similarly, the air-to-ground armament arrival time is calculated as the armament arrival time for the available armament launched from the aircraft. At this time, the earlier the arming arrival time, the higher the priority is given to the arming recommendation proposal.

The arming recommendation module 30 calculates the arrival rate within the vulnerable time (step 530). The reach rate within the vulnerable time is calculated by the following [Equation 2]. The arrival rate within the vulnerable time is the probability that the armament will arrive within the vulnerable time of the target. For example, it represents the probability that the arming of the friendly forces will arrive before a moving target such as an enemy ballistic missile projectile (TEL) moves from the launch position.

[Equation 2]

Arming reach rate within vulnerable time = Pr( X > CT) = 1 - Pr( X ≤ CT),

where, X = the time the Tel (enemy ballistic missile projectile) stays in the launch position (normal distribution),

CT = C2 time + arming arrival time (carry time),

Time C2 is set as the time required from the detection of the target to the command to fire.

That is, the arrival rate within the vulnerable time represents the probability that the enemy ballistic missile projectile (TEL) will stay in the launch position for a time greater than the time (CT) that is the sum of the C2 time and the carry time, and the white partial area of the normal distribution shown in FIG. same as In this way, in step 530, the arrival rate within the vulnerable time is calculated, and the higher the value of the arrival rate within the vulnerable time, the higher the arming priority is obtained.

Next, the arming recommendation module 30 calculates the arming priority (step 540). The arming priority calculation calculates the priority considering the expected rate of destruction of the time-limited target. For a time-limited target, the expected destruction rate when attacking with air-to-ground weapons and the expected destruction rate when attacking with ground-to-ground weapons are calculated by the following [Equation 3].

[Equation 3]

When attacking with air-to-ground weapons, the expected destruction rate is calculated as _{1-(1-P k} ) ^(n*v).

where P _k = armed arrival rate within vulnerable time * air mission success rate,

n is the armament repellency, v is the armament reliability, n and v are the simulation results, and the air mission success rate is a value that changes according to the air superiority of the friendly forces as a result of the neutralization of the enemy air defense network, which is a simulation result value. The air superiority acquisition rate is a value set by estimating the air superiority of the friendly forces, and is a value set variably within a certain range rather than a specific fixed value.

When attacking with ground-to-ground weapons, the expected destruction rate = the arming reach rate within the vulnerable time * it is determined by the armament reliability.

In step 540, for the air-to-ground armament, the arming priority is determined based on the expected destruction rate in the attack calculated in consideration of the armament arrival rate within the vulnerable time and the air mission success rate. For ground-to-ground armaments, the arming priority is determined based on the expected destruction rate in an attack calculated in consideration of the armament arrival rate and armament reliability within the vulnerable time.

In step 540, in particular, in order to attack a time-limited target, the expected destruction rate is calculated in an attack considering the armed arrival rate within the vulnerable time. This is because, in the case of a time-limited target, the attack effect is high, and if you do not respond quickly, it is difficult to move and hit, and it is a target that poses an immediate threat to allies. Therefore, in step 540, the expected destruction rate for air-to-ground and ground-to-ground arming is calculated by applying the arming arrival rate within the vulnerable time to the time-limited target, and based on this, the priority of arming is determined.

Through the above process, a number of arming recommendations are generated in consideration of the armed arrival rate during the vulnerable time, the air mission success rate, the available armament of the friendly forces, etc., and the process of evaluating the mission achievement of the generated multiple arming recommendations is carried out.

Next, Figure 7 shows a control flow chart for evaluating the mission achievement at the current time in the intelligent arming assignment control system according to an embodiment of the present invention.

The mission achievement evaluation module 40 evaluates the mission achievement by predicting the result of hitting the currently identified target (step 440).

First, an incidental damage evaluation result is input to evaluate the mission achievement at the present time (step 710). The collateral damage evaluation result is a value obtained by acquiring information on facilities or human life within the range of collateral damage for the battlefield situation from the detection unit 120, collecting them, and evaluating the degree of damage based on the collected information. It is a value that simulates time.

For example, when a missile reaches a target, a range of damage that may be caused by the explosion of the missile is predetermined according to the capacity, type, and the like of the missile. In this way, the predetermined damage range can be checked, and facilities and human lives including hospitals, schools, biochemical facilities, and institutions included within the damage range can be checked based on the detection information. And it is a value that evaluates the degree of damage to facilities or people included in the damage range, and a value that simulates the time required for evaluation. Therefore, step 710 is a configuration for evaluating the degree of such collateral damage and quantifying it using a predetermined algorithm or simulating the evaluation time.

Therefore, the collateral damage evaluation time confirmed in step 710 is checked, and if the collateral damage evaluation time is greater than the C2 time (the time required for the detection asset to fire after the target is found), the collateral damage evaluation time is applied as C2. Conversely, if the incidental damage evaluation time is less than C2 time, the original C2 time is used. In this way, when the incidental damage evaluation time is input in step 710, the total required time (CT) becomes the time C2 time plus the armed arrival time.

Next, an airspace collision result is input to evaluate the mission achievement at the current point in time (step 720). In step 720, if there is an airspace collision, the aircraft introduction time resulting from the airspace collision is added to the total required time (CT). Therefore, in step 720, the total required time (CT) is the sum of the C2 time, the aircraft introduction time, and the carry time. In the absence of airspace collisions, there is no additional time for aircraft evacuation.

Airspace collision is the time it takes to move the aircraft in the airspace out of the airspace when the trajectory of the friendly ground-to-ground or air-to-ground guided missile collides with the airspace of the air force, and generally takes about 1 minute.

Finally, an arming assignment result is calculated (step 730). In step 730, the total required time (CT) is calculated by including all incidental damage imprisonment time (C2), aircraft introduction time due to airspace collision, and armed movement time. Using this value, the expected destruction rate when attacking with air-to-ground weapons and the expected destruction rate when attacking with ground-to-ground weapons are calculated.

The expected destruction rate when attacking with an air-to-ground armed attack in step 730 and the expected destruction rate when attacking with a ground-to-ground weapon are calculated using the same process in step 530 shown in FIG. In addition, using the process of step 540 shown in FIG. 5, the expected destruction rate when attacking with air-to-ground arming and the expected rate of destruction when attacking with ground-to-ground arming using the armed arrival rate, armament reliability, armed water repellency, and air mission success rate within the vulnerable time to calculate That is, the expected destruction rate calculated at this time becomes the result of arming assignment.

In this way, the expected destruction rate during attack calculated in step 730 is the evaluation value of mission achievement for the recommended armament allocation plan at the present time. The value obtained in step 730 has a relatively high value in the mission achievement evaluation when there are several types of armament allocation plan.

That is, through the operation control of FIG. 5, the priority of the arming assignment for the current mission is proposed in various forms, and through the motion control of FIG. 7, it is possible to evaluate the mission achievement for a plurality of arming assignment plans at the current time point.

Next, Figure 8 shows a control flow chart for evaluating the mission achievement at the time of the main mission evaluation in the intelligent weapon assignment control system according to an embodiment of the present invention.

Conventionally, arming assignment considering only the enemy destruction rate at the time of arming assignment, can be defined as follows [Equation 4].

That is, in the prior art, the weapon assignment was made in the order of the highest destruction rate when attacking due to arbitrary armament.

However, in the present invention, it is applied when calculating the optimal armament allocation plan by estimating the mission achievement at a specific point in time (major point in time).

That is, the mission achievement estimation module 50 estimates the mission achievement at the time of the main mission evaluation. At this time, the mission achievement of the main mission evaluation point is estimated using the deep learning model, but in the present invention, the mission achievement degree of at least two specific points (H+5, D+4) is estimated as shown in FIG. . However, a module for estimating mission achievement at a specific point in time can be additionally implemented.

To this end, the present invention implements a simplified simulation model capable of evaluating the result of arming assignment of friendly forces at a specific point in time according to various battlefield situations. And using this, a dataset for deep learning is created. A deep learning model to which the supervised learning method is applied is implemented using the simplified model and dataset generated in this way (step 810).

In step 810, the deep learning model applies a Feed Forward Neural Network (FNN) model (one model per point in time) to predict task achievement at a specific point in time, and can be implemented as shown in FIG. 10 . In this case, the values exemplified in FIG. 11 may be described as an embodiment for the evaluation of the degree of achievement of the target.

And in step 810, using the supervised deep learning model, the degree of mission achievement at a specific point in time is evaluated for the recommended armament allocation plan. To this end, the deep learning model learned in step 810 is loaded (step 820), and the mission achievement at a specific point in time is estimated by applying the recommended arming assignment plan (step 830).

That is, the deep learning model of the present invention applies the following [Equation 5] to estimate the destruction rate for the target to appear after the current point, and evaluates the mission achievement at a specific point after the current point. Here, the value for a specific point in time can be defined as a time value determined according to the importance of the task.

[Equation 5]

The probability value that the armed i calculated in [Equation 5] will reach the vulnerable time of the target j is calculated by the same process as in step 530 of FIG. 5, and likewise, the destruction rate (expected at the time of attack) when the armed i reaches the target j destruction rate) is calculated by the same process as in step 540 of FIG. 5 .

Next, FIG. 9 shows a control flow chart for calculating an optimal arming assignment plan from the arming assignment control module in the intelligent arming assignment control system according to an embodiment of the present invention.

The arming assignment control module 60 derives an optimal arming assignment plan by synthesizing the mission achievement at the current time point calculated through the operation control of FIG. 7 and the mission achievement degree at a specific time point calculated through the motion control of FIG. 8 .

The arming assignment control module 60 assigns a preset weight to the task achievement degree at the current point in time, and gives a preset weight to the mission achievement degree at a specific point in time (step 910). The weight given at this time is a value set according to the degree of importance of the task, and may be variably adjusted from the outside through a signal input unit. In particular, a specific time point may be set as a case corresponding to a main task, and the weight given may be set higher as the importance of the task increases.

By synthesizing the value calculated by giving weight to the mission achievement at the current point in time given in step 910 and the value calculated by giving weight to the mission achievement at a specific point in time, the maximum arming assignment plan is derived as the optimal arming assignment plan ( 920 step).

The intelligent arming allocation control system of the present invention described above is implemented in a computing environment including a general computing device. The computing environment may include a computing device. The computing device may be any type of computing device that transmits/receives signals to and from other terminals.

The computing device includes at least one processor (or control module), a computer-readable storage medium, and a communication bus. The processor may control the computing device to operate according to the aforementioned embodiments. The processor may execute one or more programs stored in a computer-readable storage medium. The one or more programs may include one or more computer-executable instructions, which, when executed by a processor, may be configured to cause a computing device to perform operations in accordance with an exemplary embodiment.

The computer-readable storage medium is configured to store computer-executable instructions or computer-executable instructions to program code, program data and/or other suitable form of information. A program stored in a computer-readable storage medium includes a set of instructions executable by a processor. In one embodiment, the computer-readable storage medium includes memory (volatile memory, such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory device , other types of storage media that can be accessed by the computing device and can store desired information, or a suitable combination thereof.

A communication bus interconnects various other components of the computing device, including processors and computer-readable storage media.

The computing device may also include one or more input/output interfaces and one or more communication interfaces that provide interfaces for one or more input/output devices. The input/output interface and the communication interface are coupled to the communication bus. An input/output device (not shown) may be connected to other components of the computing device through an input/output interface. Exemplary input/output devices include input devices such as pointing devices (such as a mouse or trackpad), keyboards, touch input devices (such as touchpads or touchscreens), voice or sound input devices, various types of sensor devices and/or imaging devices; and/or output devices such as display devices, printers, speakers and/or network cards. The exemplary input/output device may be included in the computing device as a component constituting the computer device, or may be connected to the computing device as a separate device distinct from the computing device.

The operations according to the present embodiments may be implemented in the form of program instructions that can be performed through various computer means and recorded in a computer-readable medium. Computer-readable media refers to any medium that participates in providing instructions to a processor for execution. Computer-readable media may include program instructions, data files, data structures, or a combination thereof. For example, there may be a magnetic medium, an optical recording medium, a memory, and the like. A computer program may be distributed over a networked computer system so that computer readable code is stored and executed in a distributed manner. Functional programs, codes, and code segments for implementing this embodiment may be easily inferred by programmers in the technical field to which this embodiment belongs.

The above detailed description should not be construed as restrictive in all respects and should be considered as exemplary. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present invention are included in the scope of the present invention.

100: intelligent armament allocation control system
110: radar
120: multiple detection units
130: many armed forces
140: database unit
10: identification target information receiving module
20: battlefield situation information receiving module
30: armament recommendation module
40: Current mission achievement evaluation module
50: Mission achievement evaluation module at the time of main mission evaluation
60: arming assignment control module

Claims (32)

an identification target information receiving module for receiving identification target information of the enemy;
a battlefield situation information receiving module for receiving information on various conditions of the battlefield situation;
an armament recommendation module for generating a number of arming recommendations based on the priority in consideration of the arming arrival rate within the vulnerable time of the available armament of the friendly forces and the expected destruction rate during an attack in order to respond to the threat of the identified target;
Evaluate the mission achievement for the arming recommendations at the present time, but apply the generated arming recommendations to the expected destruction rate when attacking, and the incidental damage evaluation result information based on the information collected through the battlefield situation information receiving module to reflect the mission achievement evaluation module to evaluate;
Estimate the mission achievement at a specific point in time by applying the generated arming recommendation plan, but give weight to the expected destruction rate when attacking the arming recommendation, the arming arrival rate within the vulnerable time, and the specific point in time of each mission to estimate the mission achievement at a specific point in time. estimating module for estimating;
including an arming assignment control module that generates an optimal arming assignment plan using the results of the estimation module and the evaluation module,
The estimation module estimates the mission achievement at a specific point in time based on deep learning, but uses a simple model that can evaluate the results of the armament recommendation plan at a specific point in time for various war patterns, and a supervised learning method using the deep learning dataset obtained through this. Implement a deep learning model,
An intelligent armament allocation control system that estimates the mission achievement at a specific point in time, weighted for a specific point in time, by applying the generated arming recommendation proposal to a deep learning model. delete The method according to claim 1,
Includes a database unit for storing various information about the available armament of the friendly forces and information on a plurality of programs,
Various information about the available armament of the friendly forces stored in the database part is an intelligent armament allocation control system including the damage range, effective range, speed, armament repellency, armament reliability, and air mission success rate by armament. The method according to claim 1,
Armed arrival rate within vulnerable time is the probability that friendly armaments arrive before a moving target, such as an enemy ballistic missile projectile, moves from the launch site.
The armed arrival rate within the vulnerable time is an intelligent armament allocation control system that is calculated by the following [Equation 2].
[Equation 2]
Arming reach rate within vulnerable time = Pr( X > CT) = 1 - Pr( X ≤ CT),
Here, X = the time the Tel (enemy ballistic missile projectile) stays in the launch position (normal distribution), CT = C2 time + arming arrival time (carry time), and C2 time is the time required from the detection of the target to the command to fire. is set to The method according to claim 1,
The expected destruction rate when attacking is divided into the expected destruction rate when attacking with air-to-ground weapons and the expected destruction rate when attacking with ground-to-ground weapons,
When attacking with air-to-ground weapons, the expected destruction rate is an intelligent weapon allocation control system that is calculated by the following [Equation 3].
[Equation 3]
When attacking with air-to-ground weapons, the expected destruction rate = 1-(1-P _k ) ^(n*v) ,
P _k = Armed arrival rate within vulnerable time * Calculated as air mission success rate,
Here, n is the armament repellency, v is the armament reliability, n and v are simulation results, and the air mission success rate is a value that changes according to the air superiority gain of the friendly force as a result of the neutralization of the enemy air defense network, which is the simulation result value. 6. The method of claim 5,
The expected destruction rate when attacking with ground-to-ground weapons is an intelligent armament allocation control system that is calculated by multiplying the armament arrival rate within the vulnerable time by the armament reliability. 5. The method according to claim 4,
Armed arrival time (carry time) is divided into air-to-ground arming arrival time and ground-to-ground arming arrival time,
The air-to-ground armament arrival time is an intelligent armament assignment control system that is calculated by dividing the distance between armament and target by the aircraft speed. 8. The method of claim 7,
The intelligent armament allocation control system calculated by dividing the distance between the armament and the target by the speed of each armament for each armament. 9. The method according to claim 7 or 8,
The distance is the distance between the arming and the target, and using the coordinates for the identification target received from the identification target information receiving module, the intelligent arming allocation control system calculated by the following [Equation 1].
[Equation 1]
Distance = ArcCos(Sin(Radians(Lat1))*Sin(Radians(Lat2))+Cos(Radians(Lat1)) *Cos(Radians(Lat2))*Cos(Radians(Lon1-Lon2)))*Radius of the Earth
Here, Lat1 and Lat2 are latitude information, and Lon1 and Lon2 are longitude information. The method according to claim 1,
The arming recommendation module checks the time-limited target for the identification target received from the identification target information receiving module, and sets the priority of the arming recommendation plan in consideration of the expected destruction rate when attacking the time-limited target. . The method according to claim 1,
The incidental damage evaluation result information confirms the damage range for each armament, collects information on facilities and human life within the damage range through the battlefield situation information receiving module, and evaluates the degree of damage based on the collected information. Intelligent arming assignment control system including time spent. 12. The method of claim 11,
The evaluation module is an intelligent armament allocation control system that determines whether there is an airspace collision based on the information collected from the battlefield situation information receiving module, and includes the aircraft introduction time in the total required time (CT) when an airspace collision occurs. 13. The method of claim 12,
The evaluation module calculates the total time required (CT), including the expected destruction rate during an attack, the time required to evaluate the degree of collateral damage, and the aircraft introduction time in the case of an airspace collision.
By applying this, the expected destruction rate during an attack and the armed arrival rate within the vulnerable time are calculated, and the intelligent armament allocation control system evaluates the mission achievement for the armament recommendation plan at the present time. The method according to claim 1,
The evaluation module checks the time-limited target for the identification target received from the identification target information receiving module, and sets the priority of the armament recommendation in consideration of the expected destruction rate when attacking the time-limited target. The method according to claim 1,
The battlefield situation information receiving module receives the weather information, and an intelligent armament allocation control system that provides weights for the received weather information to the arming recommendation module, the evaluation module, and the estimation module. 16. The method of claim 15,
The battlefield situation information receiving module receives the air superiority acquisition rate information at the present time, and an intelligent armament allocation control system that provides weights for the received air superiority security ratio information to the evaluation module and the estimation module. The method according to claim 1,
In the estimation module, the weight of each mission at a specific point in time is a preset value, a strategic mission goal to be achieved at a specific point in time for each mission is given, and an intelligent armament assignment control system in which weights are set based on the strategic mission goal . The method according to claim 1,
The arming assignment control module is an intelligent arming assignment control system that assigns weights to the current point and a specific point in time. 19. The method of claim 18,
The arming assignment control module is an intelligent arming assignment control system that can variably control weight assignment through an external input interface. The method according to claim 1,
The estimation module is an intelligent armament allocation control system for estimating the mission achievement at a specific point in time by the following [Equation 5].
[Equation 5]

21. The method of claim 20,
The estimation module is an intelligent armament allocation control system for estimating mission achievement for at least two specific time points. receiving enemy identification target information;
Receiving information on various conditions of the battlefield situation;
generating a plurality of armament recommendations based on priorities in consideration of the armed arrival rate within the vulnerable time of the available armament of the friendly forces and the expected destruction rate during an attack to counter the threat of the identified target;
Evaluate the mission achievement for the arming recommendations at the present time, but apply the generated arming recommendations to the expected destruction rate when attacking, and the incidental damage evaluation result information based on the information collected through the battlefield situation information reception module to reflect the mission achievement an evaluation step to evaluate;
Estimate the mission achievement at a specific point in time by applying the generated arming recommendation plan, but assign weights to the expected destruction rate when attacking the arming recommendation, the arming arrival rate within the vulnerable time, and each mission at a specific point in time to estimate the mission achievement at a specific point in time. estimating step of estimating;
Including the step of generating an optimal armament allocation plan using the results of the estimation step and the evaluation step,
The estimation step estimates the mission achievement at a specific point in time based on deep learning, but is a supervised learning method using a simple model that can evaluate the results of the armament recommendation plan at a specific point in time for various war patterns, and the deep learning dataset obtained through this. Implement a deep learning model,
An intelligent armament allocation control method that estimates the mission achievement at a specific point in time, weighted for a specific point in time, by applying the generated arming recommendation proposal to a deep learning model. delete 23. The method of claim 22,
Armed arrival rate within vulnerable time is the probability that friendly armaments arrive before a moving target, such as an enemy ballistic missile projectile, moves from the launch site.
An intelligent armament allocation control method calculated by the following [Equation 2] for the armed arrival rate within the vulnerable time.
[Equation 2]
Arming reach rate within vulnerable time = Pr( X > CT) = 1 - Pr( X ≤ CT),
Here, X = the time the Tel (enemy ballistic missile projectile) stays in the launch position (normal distribution), CT = C2 time + arming arrival time (carry time), and C2 time is the time required from the detection of the target to the command to fire. is set to 23. The method of claim 22,
The expected destruction rate when attacking is divided into the expected destruction rate when attacking with air-to-ground weapons and the expected destruction rate when attacking with ground-to-ground weapons,
When attacking with air-to-ground weapons, the expected destruction rate is an intelligent weapon allocation control method that is calculated by the following [Equation 3].
[Equation 3]
When attacking with air-to-ground weapons, the expected destruction rate = 1-(1-P _k ) ^(n*v) ,
P _k = Armed arrival rate within vulnerable time * Calculated as air mission success rate,
Here, n is the armament repellency, v is the armament reliability, n and v are simulation results, and the air mission success rate is a value that changes according to the air superiority gain of the friendly force as a result of the neutralization of the enemy air defense network, which is the simulation result value. 26. The method of claim 25,
The expected destruction rate when attacking with ground-to-ground weapons is an intelligent weapon allocation control method that is calculated by multiplying the weapon reliability by the weapon arrival rate within the vulnerable time. 23. The method of claim 22,
The incidental damage evaluation result information confirms the damage range for each armament, collects information on facilities and human life within the damage range through the battlefield situation information receiving module, and evaluates the degree of damage based on the collected information. Intelligent weapon allocation control method including time spent. 28. The method of claim 27,
The evaluation step is an intelligent weapon allocation control method that determines whether there is an airspace collision and includes the aircraft introduction time in the total required time (CT) when an airspace collision occurs. 29. The method of claim 28,
In the evaluation stage, the total time required (CT) is calculated, including the expected destruction rate during an attack, the time required to evaluate the degree of collateral damage, and the aircraft introduction time in the case of an airspace collision.
By applying this, the expected destruction rate during an attack and the armed arrival rate within the vulnerable time are calculated, and the intelligent armament allocation control method evaluates the mission achievement for the arming recommendation proposal at the present time. 23. The method of claim 22,
In the estimation step, the weight for a specific time point of each mission is a preset value, a strategic mission goal to be achieved at a specific point in time for each mission is given, and a weight is set based on the strategic mission target value. . 23. The method of claim 22,
The arming assignment control step is an intelligent arming assignment control method that assigns weights to the current point and a specific point in time. 23. The method of claim 22,
The estimation step is an intelligent armament allocation control method for estimating the mission achievement at a specific point in time by the following [Equation 5].
[Equation 5]

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