System and method for improving shooting accuracy and predicting shooting hit rate

1 . A system comprising:

a shooting system comprising at least one first processor configured to:

receive shooting ballistics-related data and shooting result data in real time, and

detect real-time surrounding data; and

an integrated computer comprising at least one second processor configured to:

receive the shooting ballistics-related data and the shooting result data from the shooting system,

implement a global neural network to derive learning result data based on the shooting ballistics-related data and the shooting result data, and

transmit the learning result data to the shooting system,

wherein the at least one first processor implements a local neural network configured to store and derive shooting control data and shooting prediction data based on the shooting ballistics-related data, the shooting result data, and the learning result data,

wherein the at least one second processor is further configured to implement the global neural network to update the learning result data based on the shooting ballistics-related data and the shooting result data received from the shooting system,

wherein the at least one second processor is further configured to transmit the updated learning result data to the shooting system,

wherein the at least one first processor is further configured to update the shooting ballistics-related data based on the updated learning result data received from the integrated computer, and

wherein the at least one first processor is further configured to update the shooting control data and the shooting prediction data of the local neural network based on the updated learning result data received from the integrated computer.

2 . The system of claim 1 , wherein the at least one second processor is further configured to implement learning based on the shooting ballistics-related data and the shooting result data received from the shooting system, and

wherein the learning implemented by the at least one second processor comprises shooting control learning for enhancing a shooting accuracy of the shooting system and shooting prediction learning for enhancing a real-time shooting hit rate of the shooting system.

3 . The system of claim 2 , wherein the shooting prediction learning of the shooting system comprises a ballistic state of the shooting system and a posture status, position status, situation status, deployment status, and ballistic correction angle status of the shooting system based on the real-time surrounding data.

4 . The system of claim 2 , wherein the at least one first processor is further configured to:

increase the shooting accuracy toward a target point and predict the real-time shooting hit rate by updating the shooting ballistics-related data and the shooting result data with the updated learning result data received from the integrated computer, and

transmit, to an operator, a prediction value data based on the updated shooting ballistics-related data and the updated shooting result data.

5 . The system of claim 4 , wherein the shooting system and the integrated computer both comprise a shooting control algorithm and a shooting prediction algorithm,

wherein the receiving, transmitting, and updating of the shooting ballistics-related data, the shooting result data, and the learning result data by the shooting system and the integrated computer is done in real time,

wherein the shooting control algorithm, of the shooting system and the integrated computer, is configured to perform the shooting control learning based on the receiving, transmitting, and updating the shooting ballistics-related data, the shooting result data, and the learning result data in real time, and

wherein the shooting prediction algorithm, of the shooting system and the integrated computer, is configured to perform the shooting prediction learning based on the receiving, transmitting, and updating the shooting ballistics-related data, the shooting result data, and the learning result data in real time.

6 . The system of claim 5 , wherein the shooting control algorithm comprises:

a shooting control local neural network part, which is provided in the local neural network of the shooting system and to which the shooting control data is input, and

a shooting control global neural network part, which is provided in the global neural network of the integrated computer, is configured to receive and learn from the shooting control data input to the shooting control local neural network part, and extract the learning result data, and

wherein the shooting control algorithm is configured to update the shooting control data input to the shooting control global neural network part based on the learning result data and reflect the updated shooting control data in real time.

7 . The system of claim 6 , wherein the shooting system comprises a driving part configured to adjust the shooting of the shooting system,

wherein the driving part is configured to be operated and controlled based on the updated shooting control data of the shooting control local neural network part and ballistic correction angle status of the shooting system, and

wherein the shooting system is adjusted toward the target point according to the operation of the driving part.

8 . The system of claim 6 , wherein the shooting control data input to the shooting control local neural network part comprises at least one of tracking image data, raw data from among the tracking image data, distance data, ballistic correction angle data, N-axis motor position data, navigational data, gyro data, or ground surface condition information,

wherein the shooting control local neural network part is configured to transmit the shooting control data,

wherein the shooting control global neural network part comprises:

a global input layer configured to receive the raw data from the shooting control local neural network part,

at least one global hidden layer, which is connected to the global input layer through a plurality of neural networks, and

a global output layer, which is connected to the at least one global hidden layer through the plurality of neural networks, configured to learn the learning result data, which is based on the raw data, through the at least one global hidden layer, and output the learning result data to the at least one first processor, and

wherein the at least one first processor is further configured to download the learning result data from the global output layer and update the learning result data as the raw data.

9 . The system of claim 5 , wherein the shooting prediction algorithm comprises:

a shooting prediction local neural network part, which is provided in the local neural network of the shooting system and to which the shooting prediction data is input; and

a shooting prediction global neural network part, which is provided in the global neural network of the integrated computer, is configured to receive and learn from the shooting prediction data input to the shooting prediction local neural network part, and extract the learning result data, and

wherein the shooting prediction algorithm is configured to update the shooting prediction data input to the shooting prediction global neural network part based on the learning result data and configured to reflect the updated shooting prediction data in real time.

10 . The system of claim 9 , wherein the shooting prediction data input to the shooting prediction local neural network part comprises at least one of tracking image data, raw data from among tracking image data, distance data, ballistic correction angle data, N-axis motor position data, navigational data, gyro data, or ground surface condition information,

wherein the shooting prediction local neural network part is configured to transmit the shooting prediction data,

wherein the shooting prediction global neural network part comprises:

a global input layer configured to receive the raw data from the shooting prediction local neural network part and store the raw data,

at least one global hidden layer, which is connected to the global input layer through a plurality of neural networks, and

a global output layer, which is connected to the at least one global hidden layer through the plurality of neural networks, configured to learn the learning result data, which is based on the raw data, through the at least one global hidden layer, and output the learning result data to the at least one first processor, and

wherein the at least one first processor is further configured to download the learning result data from the global output layer and update the learning result data as the raw data.

11 . The system of claim 1 , wherein the shooting system further comprises a Light Detection and Ranging (LiDAR) sensor and an environmental sensor, which both are configured to detect surrounding environment data of the shooting system.

12 . The system of claim 11 , wherein the at least one second processor is configured to implement:

a map data generation unit configured to receive the surrounding environment data from the LiDAR sensor and generate surrounding environment information that ranges up to a target point, and

a movement path analysis unit configured to generate and analyze at least one movement path of the shooting system to the target point based on the surrounding environment information generated by the map data generation unit.

13 . The system of claim 12 , wherein the at least one first processor is configured to transmit the surrounding environment data and position information about the shooting system to the integrated computer,

wherein the at least one second processor is further configured to generate at least one path for the map data generation unit for the shooting system and the surrounding environment of the target point, and

wherein the movement path analysis unit is configured to analyze the at least one movement path for the shooting system and analyze an optimal shooting position based on the learning result data received from the shooting system.

14 . The system of claim 11 , wherein the at least one second processor is further configured to implement a map data generation unit configured to generate at least one movement path between the shooting system and a target point, and

wherein the at least one movement path comprises at least one of shooting position information, terrain information, predicted shooting hit rate information, or estimated travel time to the target point based on whether the shooting system is moving or stationary.

15 . A method of improving a shooting accuracy and predicting a shooting hit rate in a system comprising a shooting system and an integrated computer, comprising:

receiving a target point;

receiving, in real time by the shooting system, data for shooting at the target point, the data comprising shooting ballistics-related data and shooting result data;

detecting, by the shooting system, real-time surrounding data;

transmitting the data for shooting at the target point received by the shooting system and the real-time surrounding data to the integrated computer;

deriving, by a global neural network implemented in the integrated computer, a learning result data for shooting control and shooting prediction by learning the data for shooting at the target point transmitted to the integrated computer;

transmitting, by the integrated computer, the learning result data from the integrated computer to the shooting system;

storing and deriving, by a local neural network implemented in the shooting system, shooting control data and shooting prediction data based on the shooting ballistics-related data, the shooting result data, and the learning result data;

updating, by the integrated computer, the learning result data based on the shooting ballistics-related data and the shooting result data received from the shooting system;

transmitting, by the integrated computer, the updated learning result data from the integrated computer to the shooting system;

updating, by the shooting system, the data for shooting at the target point transmitted by the shooting system based on the learning result data and reflecting the updated data in the shooting system in real time; and

updating, by the shooting system, the shooting control data and the shooting prediction data of the local neural network based on the updated learning result data received from the integrated computer.

16 . The method of claim 15 , further comprising, after the receiving the target point,

collecting, by a Light Detection and Ranging (LiDAR) sensor of the shooting system, surrounding environment data for a current position of the shooting system; and

generating, by the integrated computer and based on the surrounding environment data, at least one movement path for the shooting system and analyzing the at least one movement path to the target point.

17 . The method of claim 15 , further comprising, after the updating the data for shooting at the target point transmitted by the shooting system and the reflecting the updated data for shooting at the target point in the shooting system in real time, displaying, on the at least one movement path, at least one of shooting position information, terrain information, predicted shooting hit rate information, or estimated travel time to the target point based on whether the shooting system is moving or stationary.

18 . The method of claim 15 , further comprising:

reflecting the updated data in the shooting system in real time.

19 . The method of claim 18 ,

wherein the shooting ballistics-related data and the shooting result data comprise a ballistic state of the shooting system and a posture status, position status, situation status, deployment status, and ballistic correction angle status of the shooting system based on the real-time surrounding data.

20 . The method of claim 15 , wherein the learning result data for shooting control and shooting prediction comprises at least one of tracking image data, raw data from among the tracking image data, distance data, ballistic correction angle data, N-axis motor position data, navigational data, gyro data, or ground surface condition information.