Intelligent aiming shooting method and system

Abstract

The invention discloses an intelligent aiming shooting method and system, and relates to the technical field of intelligent aiming shooting, which comprises the following steps: through a software updating module, regularly examining and optimizing software codes, repairing known vulnerabilities and defects, continuously improving algorithms such as target identification, tracking and trajectory calculation, and improving the target tracking accuracy. A large amount of actual measurement data and simulation data are used for algorithm training and verification, the accuracy and stability of an algorithm are improved, through a sensor fusion module, in the dense fog weather, imaging of an optical sensor is limited, but a radar and an infrared thermal imager may still work effectively, and after data of the sensors are fused, the accuracy and stability of the algorithm are improved. A target can be detected and identified more accurately; through the central processing module, error correction can be automatically carried out or safety measures can be taken, for example, an automatic aiming function is temporarily stopped; through an environment sensing module, target data in different environments are collected and expanded, and the target data comprise target images, motion tracks and other information under different weather, terrain and illumination conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent aiming and shooting, and in particular to an intelligent aiming and shooting method and system. Background Art

[0002] The existing public document CN118657933B discloses an intelligent aiming and shooting method and system, including obtaining a video and image of a shooting scene through an image acquisition device, identifying target attributes and relative position information using a target recognition algorithm, wherein the target attributes include target type recognition, posture recognition and action recognition, and the relative position information includes the relative distance and orientation from the shooting position, and calculating the actual position coordinates; specifically including: performing target detection and classification; performing target type recognition through color features and texture features; extracting target key points, inferring target posture and performing action recognition through a long short-term memory network; predicting the next position and motion trajectory of the target through a Kalman filter, calculating the shooting timing and trajectory according to the real-time position of the target, shooting through a laser transmitter according to the calculated trajectory and timing, and providing feedback and correction for each shooting, and dynamically adjusting the shooting parameters; the application can achieve precise strikes on targets and can be applied to military training, simulated confrontation, and actual combat;

[0003] However, the existing intelligent aiming and shooting methods and systems still have the following disadvantages in actual use:

[0004] The process is greatly affected by environmental factors. Under complex meteorological conditions, such as heavy rain, heavy snow, and dense fog, the detection accuracy of the sensor will decrease, and the image recognition algorithm may not be able to accurately identify the target. In complex terrain environments, there are many background objects, which can easily lead to misidentification or target loss. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent aiming and shooting method and system to solve the problems existing in the prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solution: an intelligent aiming and shooting method and system, comprising:

[0007] S1: Establish a sound software update mechanism, regularly review and optimize software codes, fix known vulnerabilities and defects, and obtain software update modules;

[0008] S2: Acquire multiple different types of sensor data, including optical sensors, radars, and infrared thermal imagers, and integrate the multiple different sensor data through sensor fusion technology to obtain a sensor fusion module;

[0009] S3: Set up a powerful exception handling procedure. When encountering abnormal situations such as erroneous data input and electromagnetic interference, it can automatically correct the error or take safety measures, such as temporarily stopping the automatic aiming function and prompting the user to intervene manually.

[0010] S4: Establish an environmental perception module to collect surrounding environmental data. The environmental data is transmitted to the environmental analysis engine. Based on the deep learning model, the engine can quickly determine the type and complexity of the environment and identify and aim at the target.

[0011] Furthermore, the establishment of a sound software update mechanism, regular review and optimization of software codes, repair of known vulnerabilities and defects, and acquisition of software update modules specifically include:

[0012] Utilize a large amount of measured data and simulated data to train and verify algorithms, continuously improve algorithms such as target recognition, tracking, and trajectory calculation, and push software updates in a timely manner through the network or external storage devices. For example, when a new software vulnerability is discovered or a better algorithm version is available, the system can automatically prompt the user to update to ensure that the software is always in the best condition.

[0013] Furthermore, the acquisition of multiple different types of sensor data, such as optical sensors, radars, infrared thermal imagers, etc., and integration of multiple different sensor data through sensor fusion technology to obtain a sensor fusion module specifically includes:

[0014] Based on the sensor fusion module, when in dense fog, the imaging of optical sensors is limited, but radar and infrared thermal imagers can still work effectively to detect and identify targets.

[0015] Furthermore, the above-mentioned powerful exception handling program can automatically correct the error or take safety measures when encountering abnormal situations such as erroneous data input and electromagnetic interference, such as temporarily stopping the automatic aiming function and prompting the user to intervene manually; specifically, it includes:

[0016] Based on erroneous data input detection and correction algorithms;

[0017] Assume that the input data is a series of data vectors X = [x1, x2…, x n ], first, for each data point x i Set a reasonable value range, [min i ,max i ], which can be determined through historical data statistics or equipment specifications;

[0018] When inputting data, determine whether each data point satisfies the min i ≤x i ≤maxi , if it is not satisfied, the data point is judged to be erroneous data; for example, for the target distance data d, if the normal range is [0,1000] meters (assuming that this is the effective range of the weapon), when the input d = -50 meters or d = 1500 meters, it is judged to be abnormal;

[0019] If erroneous data is detected, for a single abnormal data point, there are multiple data points related to it (such as other sensor data at the same time), and the average or weighted average can be used to replace the abnormal data. Let the normal data point be x j1 ,x j2 ,…,x jk , the corrected x i (assuming an exception) can be (average method), or assign different weights w according to the reliability of the data l , (weighted average method);

[0020] Electromagnetic interference detection and response algorithms;

[0021] Assume that the sensor signal strength is S during normal operation, and its amplitude, frequency and other characteristics have a normal range. For example, for a radio frequency sensor, the normal operating frequency is f0, and the amplitude range is [A min ,A max ], the signal strength S(t), its frequency f(t) and amplitude A(t) can be collected regularly (every T time interval);

[0022] When |A(t)-A0|>ΔA (ΔA is the set amplitude deviation threshold) or |f(t)-f0|>Δf, (Δf is the set frequency deviation threshold), it is determined that it may be subject to electromagnetic interference. At the same time, it can also be judged by the noise level of the signal. Let the normal noise power be N0. If the detected noise power N(t) satisfies N(t)>N0+ΔN (ΔN is the noise deviation threshold), it is also determined that it may be subject to interference;

[0023] Once it is determined that there is electromagnetic interference, safety measures are automatically triggered. If the intensity of the electromagnetic interference exceeds a certain severity threshold, th , the automatic aiming function can be directly suspended, and a warning signal can be sent to the user at the same time. Assume that the electromagnetic interference intensity I can be obtained by a comprehensive evaluation of the degree of signal abnormality (such as I = w1ΔA + w2Δf + w3ΔN, w1, w2, w3 are weight coefficients), when I>I th , executes the operation of pausing automatic aiming and prompting the user to intervene manually.

[0024] Furthermore, the environment perception module is established to collect surrounding environment data, and the environment data is transmitted to the environment analysis engine. The engine is based on a deep learning model to quickly determine the type and complexity of the environment and identify and aim at the target, specifically including:

[0025] The target recognition algorithm formula is based on the convolutional neural network of deep learning. The calculation process mainly involves the operations of convolution, pooling and fully connected layers. Assume that the input image is I, and its size is H×W×C (height H, width W, number of channels C);

[0026] The convolution kernel is K and the size is k h ×k w ×C(Height k h , width k w ), the step size is s, the padding is p, and the size calculation formula of the convolutional layer output feature map O is: Each element o of the output feature map O i,j The calculation of is obtained through convolution operation:

[0027] Taking the maximum pooling as an example, the pooling kernel size is p h ×p w , the step length is s, and the size calculation formula of the output feature map P after pooling is: Each element p of the output feature map P i,j It is to take the maximum value within the pooling window:

[0028] After multiple convolution and pooling layers, the feature map is flattened into a vector x, and the output y of the fully connected layer is passed through the weight matrix W f and the bias vector b f Calculation yields: y = f(W f x+b f ), where f is the activation function. In target recognition, the output of the last fully connected layer is usually the category probability distribution, and the target category is determined by comparing the probability size;

[0029] The aiming algorithm formula is based on the basic principles of exterior ballistics. It is assumed that the distance to the target is d, the initial velocity of the bullet is v0, and the gravitational acceleration of the bullet is g (usually 9.8 m / s 2 ), ignoring the influence of other factors such as wind, the trajectory equations in the horizontal direction x and vertical direction y are:

[0030] Horizontal direction: x = v 0x t, where v ox is the horizontal component of the bullet's initial velocity, and t is the flight time;

[0031] Vertical direction: where v 0y is the vertical component of the bullet's initial velocity. When considering wind direction and wind speed (assuming wind speed is v w , the angle between the wind direction and the shooting direction is θ), the speed in the horizontal direction needs to be corrected: v′ 0x =v 0x -v w cosθ;

[0032] Finally, the intelligent aiming system also needs to combine the initial posture of the gun (pitch angle α and yaw angle β). After obtaining the gun posture data through the sensor, the aiming point is corrected accordingly. The correction amounts Δx and Δy of the aiming point in the horizontal and vertical directions can be calculated based on the above ballistic equation and the gun posture, thereby determining the final aiming point position.

[0033] An intelligent aiming and shooting system includes a software update module, a sensor fusion module, a central processing module and an environment perception module;

[0034] The software update module is connected to a sensor fusion module, the sensor fusion module is connected to a central processing module, and the central processing module is connected to an environment perception module.

[0035] Compared with the prior art, the intelligent aiming and shooting method and system provided by the present invention has the following beneficial effects:

[0036] The intelligent aiming and shooting method and system can regularly review and optimize the software code, fix known vulnerabilities and defects, and continuously improve algorithms such as target recognition, tracking and trajectory calculation through a software update module. A large amount of measured data and simulated data are used to train and verify the algorithms to improve the accuracy and stability of the algorithms. Through the sensor fusion module, in foggy weather, optical sensor imaging is limited, but radar and infrared thermal imagers may still be able to work effectively. After fusing the data of these sensors, targets can be detected and identified more accurately. Through the central processing module, errors can be automatically corrected or safety measures can be taken, such as temporarily stopping the automatic aiming function and prompting the user to intervene manually to avoid misidentification or target loss. Through the environmental perception module, target data in different environments are collected and expanded, including target images, motion trajectories and other information under different weather, terrain and lighting conditions, for training and optimizing the system's recognition and aiming capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0038] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0039] Figure 2 It is a schematic diagram of the system structure of the present invention;

[0040] Figure 3 It is a schematic structural diagram of the sight of the present invention. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0042] See also Figure 1-3 , an intelligent aiming and shooting method and system, comprising:

[0043] S1: Establish a sound software update mechanism, regularly review and optimize software codes, fix known vulnerabilities and defects, and obtain software update modules;

[0044] S2: Acquire various types of sensor data, including optical sensors, radars, infrared thermal imagers, etc., and integrate various sensor data through sensor fusion technology to obtain a sensor fusion module;

[0045] S3: Set up a powerful exception handling procedure. When encountering abnormal situations such as erroneous data input and electromagnetic interference, it can automatically correct the error or take safety measures, such as temporarily stopping the automatic aiming function and prompting the user to intervene manually.

[0046] S4: Establish an environmental perception module to collect surrounding environmental data. The environmental data is transmitted to the environmental analysis engine. Based on the deep learning model, the engine can quickly determine the type and complexity of the environment and identify and aim at the target.

[0047] The above mentioned establishment of a sound software update mechanism, regular review and optimization of software codes, repair of known vulnerabilities and defects, and acquisition of software update modules specifically include:

[0048] Utilize a large amount of measured data and simulated data to train and verify algorithms, continuously improve algorithms such as target recognition, tracking, and trajectory calculation, and push software updates in a timely manner through the network or external storage devices. For example, when a new software vulnerability is discovered or a better algorithm version is available, the system can automatically prompt the user to update to ensure that the software is always in the best condition.

[0049] The method of acquiring various types of sensor data, such as optical sensors, radars, infrared thermal imagers, etc., and integrating various different sensor data through sensor fusion technology to obtain a sensor fusion module specifically includes:

[0050] Based on the sensor fusion module, when in dense fog, the imaging of optical sensors is limited, but radar and infrared thermal imagers can still work effectively to detect and identify targets.

[0051] The above-mentioned powerful exception handling program can automatically correct the error or take safety measures when encountering abnormal situations such as erroneous data input and electromagnetic interference, such as temporarily stopping the automatic aiming function and prompting the user to intervene manually; specifically, it includes:

[0052] Based on erroneous data input detection and correction algorithms;

[0053] Assume that the input data is a series of data vectors X = [x1, x2…, x n ], first, for each data point x i Set a reasonable value range, [min i ,max i ], which can be determined through historical data statistics or equipment specifications;

[0054] When inputting data, determine whether each data point satisfies the min i ≤x i ≤max i , if it is not satisfied, the data point is judged to be erroneous data; for example, for the target distance data d, if the normal range is [0,1000] meters (assuming that this is the effective range of the weapon), when the input d = -50 meters or d = 1500 meters, it is judged to be abnormal;

[0055] If erroneous data is detected, for a single abnormal data point, there are multiple data points related to it (such as other sensor data at the same time), and the average or weighted average can be used to replace the abnormal data. Let the normal data point be x j1 ,x j2 ,…,x jk , the corrected x i (assuming an exception) can be (average method), or assign different weights w according to the reliability of the data l , (weighted average method);

[0056] Electromagnetic interference detection and response algorithms;

[0057] Assume that the sensor signal strength is S during normal operation, and its amplitude, frequency and other characteristics have a normal range. For example, for a radio frequency sensor, the normal operating frequency is f0, and the amplitude range is [A min ,A max ], the signal strength S(t), its frequency f(t) and amplitude A(t) can be collected regularly (every T time interval);

[0058] When |A(t)-A0|>ΔA (ΔA is the set amplitude deviation threshold) or |f(t)-f0|>Δf, (Δf is the set frequency deviation threshold), it is determined that it may be subject to electromagnetic interference. At the same time, it can also be judged by the noise level of the signal. Let the normal noise power be N0. If the detected noise power N(t) satisfies N(t)>N0+ΔN (ΔN is the noise deviation threshold), it is also determined that it may be subject to interference;

[0059] Once it is determined that there is electromagnetic interference, safety measures are automatically triggered. If the intensity of the electromagnetic interference exceeds a certain severity threshold, th , the automatic aiming function can be directly suspended, and a warning signal can be sent to the user at the same time. Assume that the electromagnetic interference intensity I can be obtained by a comprehensive evaluation of the degree of signal abnormality (such as I = w1ΔA + w2Δf + w3ΔN, w1, w2, w3 are weight coefficients), when I>I th , executes the operation of pausing automatic aiming and prompting the user to intervene manually.

[0060] The environment perception module is established to collect surrounding environment data, and the environmental data is transmitted to the environment analysis engine. The engine is based on a deep learning model to quickly determine the type and complexity of the environment and identify and aim at the target, specifically including:

[0061] The target recognition algorithm formula is based on the convolutional neural network of deep learning. The calculation process mainly involves the operations of convolution, pooling and fully connected layers. Assume that the input image is I, and its size is H×W×C (height H, width W, number of channels C);

[0062] The convolution kernel is K and the size is k h ×k w ×C(Height k h , width k w ), the step size is s, the padding is p, and the size calculation formula of the convolutional layer output feature map O is: Each element o of the output feature map O i,j The calculation of is obtained through convolution operation:

[0063] Taking the maximum pooling as an example, the pooling kernel size is p h ×p w , the step length is s, and the size calculation formula of the output feature map P after pooling is: Each element p of the output feature map P i,j It is to take the maximum value within the pooling window:

[0064] After multiple convolution and pooling layers, the feature map is flattened into a vector x, and the output y of the fully connected layer is passed through the weight matrix W f and the bias vector b f Calculation yields: y = f(W f x+b f ), where f is the activation function. In target recognition, the output of the last fully connected layer is usually the category probability distribution, and the target category is determined by comparing the probability size;

[0065] The aiming algorithm formula is based on the basic principles of exterior ballistics. It is assumed that the distance to the target is d, the initial velocity of the bullet is v0, and the gravitational acceleration of the bullet is g (usually 9.8 m / s 2 ), ignoring the influence of other factors such as wind, the trajectory equations in the horizontal direction x and vertical direction y are:

[0066] Horizontal direction: x = v 0x t, where v ox is the horizontal component of the bullet's initial velocity, and t is the flight time;

[0067] Vertical direction: where v 0y is the vertical component of the bullet's initial velocity. When considering wind direction and wind speed (assuming wind speed is v w , the angle between the wind direction and the shooting direction is θ), the speed in the horizontal direction needs to be corrected: v′ 0x =v 0x -v w cosθ;

[0068] Finally, the intelligent aiming system also needs to combine the initial posture of the gun (pitch angle α and yaw angle β). After obtaining the gun posture data through the sensor, the aiming point is corrected accordingly. The correction amounts Δx and Δy of the aiming point in the horizontal and vertical directions can be calculated based on the above ballistic equation and the gun posture, thereby determining the final aiming point position.

[0069] An intelligent aiming and shooting system includes a software update module, a sensor fusion module, a central processing module and an environment perception module;

[0070] The software update module is connected to a sensor fusion module, the sensor fusion module is connected to a central processing module, and the central processing module is connected to an environment perception module.

[0071] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An intelligent aiming and shooting method, characterized in that: include: S1: Establish a sound software update mechanism, regularly review and optimize software codes, fix known vulnerabilities and defects, and obtain software update modules; S2: Acquire multiple different types of sensor data, including optical sensors, radars, and infrared thermal imagers, and integrate the multiple different sensor data through sensor fusion technology to obtain a sensor fusion module; S3: Set up a powerful exception handling procedure. When encountering abnormal situations such as erroneous data input and electromagnetic interference, it can automatically correct the error or take safety measures, such as temporarily stopping the automatic aiming function and prompting the user to intervene manually. S4: Establish an environmental perception module to collect surrounding environmental data. The environmental data is transmitted to the environmental analysis engine. Based on the deep learning model, the engine can quickly determine the type and complexity of the environment and identify and aim at the target.

2. The intelligent aiming and shooting method according to claim 1, characterized in that: The above mentioned establishment of a sound software update mechanism, regular review and optimization of software codes, repair of known vulnerabilities and defects, and acquisition of software update modules specifically include: Utilize a large amount of measured data and simulated data to train and verify algorithms, continuously improve algorithms such as target recognition, tracking, and trajectory calculation, and push software updates in a timely manner through the network or external storage devices. For example, when a new software vulnerability is discovered or a better algorithm version is available, the system can automatically prompt the user to update to ensure that the software is always in the best condition.

3. The intelligent aiming and shooting method according to claim 1, characterized in that: The method of acquiring various types of sensor data, such as optical sensors, radars, infrared thermal imagers, etc., and integrating various different sensor data through sensor fusion technology to obtain a sensor fusion module specifically includes: Based on the sensor fusion module, when in dense fog, the imaging of optical sensors is limited, but radar and infrared thermal imagers can still work effectively to detect and identify targets.

4. The intelligent aiming and shooting method according to claim 1, characterized in that: The above-mentioned powerful exception handling program can automatically correct the error or take safety measures when encountering abnormal situations such as erroneous data input and electromagnetic interference, such as temporarily stopping the automatic aiming function and prompting the user to intervene manually; specifically, it includes: Based on erroneous data input detection and correction algorithms; Assume that the input data is a series of data vectors X = [x1, x2…, x n ], first, for each data point x i Set a reasonable value range, [min i ,max i ], which can be determined through historical data statistics or equipment specifications; When inputting data, determine whether each data point satisfies the min i ≤x i ≤max i , if it is not satisfied, the data point is judged to be erroneous data; for example, for the target distance data d, if the normal range is [0,1000] meters (assuming that this is the effective range of the weapon), when the input d = -50 meters or d = 1500 meters, it is judged to be abnormal; If erroneous data is detected, for a single abnormal data point, there are multiple data points related to it (such as other sensor data at the same time), and the average or weighted average can be used to replace the abnormal data. Let the normal data point be x j1 ,x j2 ,…,x jk , the corrected x i (assuming an exception) can be (average method), or assign different weights w according to the reliability of the data l , (weighted average method); Electromagnetic interference detection and response algorithms; Assume that the sensor signal strength is S during normal operation, and its amplitude, frequency and other characteristics have a normal range. For example, for a radio frequency sensor, the normal operating frequency is f0, and the amplitude range is [A min ,A max ], the signal strength S(t), its frequency f(t) and amplitude A(t) can be collected regularly (every T time interval); When |A(t)-A0|>ΔA (ΔA is the set amplitude deviation threshold) or |f(t)-f0|>Δf, (Δf is the set frequency deviation threshold), it is determined that it may be subject to electromagnetic interference. At the same time, it can also be judged by the noise level of the signal. Let the normal noise power be N0. If the detected noise power N(t) satisfies N(t)>N0+ΔN (ΔN is the noise deviation threshold), it is also determined that it may be subject to interference; Once it is determined that there is electromagnetic interference, safety measures are automatically triggered. If the intensity of the electromagnetic interference exceeds a certain severity threshold, th , the automatic aiming function can be directly suspended, and a warning signal can be sent to the user at the same time. Assume that the electromagnetic interference intensity I can be obtained by a comprehensive evaluation of the degree of signal abnormality (such as I = w1ΔA + w2Δf + w3ΔN, w1, w2, w3 are weight coefficients), when I>I th , executes the operation of pausing automatic aiming and prompting the user to intervene manually.

5. The intelligent aiming and shooting method according to claim 1, characterized in that: The environment perception module is established to collect surrounding environment data, and the environmental data is transmitted to the environment analysis engine. The engine is based on a deep learning model to quickly determine the type and complexity of the environment and identify and aim at the target, specifically including: The target recognition algorithm formula is based on the convolutional neural network of deep learning. The calculation process mainly involves the operations of convolution, pooling and fully connected layers. Assume that the input image is I, and its size is H×W×C (height H, width W, number of channels C); The convolution kernel is K and the size is k h ×k w ×C(Height k h , width k w ), the step size is s, the padding is p, and the size calculation formula of the convolutional layer output feature map O is: Each element o of the output feature map O i,j The calculation of is obtained through convolution operation: Taking the maximum pooling as an example, the pooling kernel size is p h ×p w , the step length is s, and the size calculation formula of the output feature map P after pooling is: Each element p of the output feature map P i,j It is to take the maximum value within the pooling window: After multiple convolution and pooling layers, the feature map is flattened into a vector x, and the output y of the fully connected layer is passed through the weight matrix W f and the bias vector b f Calculation yields: y = f(W f x+b f ), where f is the activation function. In target recognition, the output of the last fully connected layer is usually the category probability distribution, and the target category is determined by comparing the probability size; The aiming algorithm formula is based on the basic principles of exterior ballistics. It is assumed that the distance to the target is d, the initial velocity of the bullet is v0, and the gravitational acceleration of the bullet is g (usually 9.8 m / s 2 ), ignoring the influence of other factors such as wind, the trajectory equations in the horizontal direction x and vertical direction y are: Horizontal direction: x = v 0x t, where v ox is the horizontal component of the bullet's initial velocity, and t is the flight time; Vertical direction: where v 0y is the vertical component of the bullet's initial velocity. When considering wind direction and wind speed (assuming wind speed is v w , the angle between the wind direction and the shooting direction is θ), the speed in the horizontal direction needs to be corrected: v′ 0x =v 0x -v w cosθ; Finally, the intelligent aiming system also needs to combine the initial posture of the gun (pitch angle α and yaw angle β). After obtaining the gun posture data through the sensor, the aiming point is corrected accordingly. The correction amounts Δx and Δy of the aiming point in the horizontal and vertical directions can be calculated based on the above ballistic equation and the gun posture, thereby determining the final aiming point position.

6. An intelligent aiming and shooting system, characterized in that: Includes software update module, sensor fusion module, central processing module and environmental perception module; The software update module is connected to a sensor fusion module, the sensor fusion module is connected to a central processing module, and the central processing module is connected to an environment perception module.

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