Active and passive combined type flexible bridge anti-collision system

Abstract

The invention discloses an active and passive combined flexible bridge anti-collision system, and the system comprises a ship trajectory prediction subsystem which is used for collecting the navigation image data of a marine target ship through a monitoring anti-collision platform, and achieving the detection and tracking of the navigation target ship based on a constructed cascade neural network; the active early warning subsystem is used for confirming the predicted channel number of the current ship according to the estimated ship motion state output by the target tracking network, and obtaining the first collision probability of the target ship and the bridge pier corresponding to the predicted channel number; based on the first collision probability and a collision probability threshold, determining a position number of a bridge pier with a collision risk; an early warning signal containing the position number is sent to the target ship through the monitoring anti-collision platform; the track movement positioning auxiliary subsystem is used for driving the monitoring anti-collision platform to a warning positioning position according to the position number, obtaining a second collision probability of the target ship and the corresponding collision pier through the ship track prediction subsystem and the active early warning subsystem when the monitoring anti-collision platform is located at the warning positioning position, and sending the second collision probability to the active early warning subsystem; and when the second collision probability exceeds a collision probability threshold value, an anti-collision starting signal is sent to the passive flexible anti-collision subsystem. The problems that an existing single anti-collision mode is prone to errors, dangerous accidents are caused, and the universality of early warning cannot be reflected are solved.

Description

Technical Field

[0001] The present invention relates to the field of flexible bridge anti-collision technology based on artificial intelligence, and in particular to an active-passive combined flexible bridge anti-collision system. Background Art

[0002] Bridges play an important role in land transportation, and the increasing number of navigable ships each year has increased the probability of ship-bridge collisions, seriously affecting the safety of bridges. Existing bridge-ship anti-collision devices are mainly divided into passive anti-collision devices and active anti-collision devices. Passive anti-collision devices use various flexible energy-consuming devices and are installed on the outside of the bridge. When a collision occurs, the force generated by the collision is reduced and the loss is reduced; active anti-collision devices install various sensors or cameras and other electronic equipment around the bridge area to establish an anti-collision warning system, monitor passing ships through artificial intelligence technology, predict routes to guide ship navigation, and reduce or avoid collision accidents. These solutions mainly use sensors to monitor the movement trajectory of ships and issue warnings when dangerous ships are found.

[0003] However, the existing bridge-ship anti-collision device is greatly affected by the environment and cannot distinguish the type and size of the ship, which leads to corresponding difficulties in the protection level and cost of ships and bridges under different situations. A single anti-collision method is also prone to errors and dangerous accidents, and cannot reflect the universality of early warning. Therefore, how to use the same set of anti-collision devices to effectively complete the collision avoidance function for different bridge types and different situations is a current difficulty. Summary of the invention

[0004] The present invention provides an active-passive combined flexible bridge anti-collision system to overcome the above technical problems.

[0005] In order to achieve the above object, the technical solution of the present invention is:

[0006] An active and passive combined flexible bridge anti-collision system, comprising:

[0007] Ship trajectory prediction subsystem, active warning subsystem, orbital mobile positioning auxiliary subsystem and passive flexible collision avoidance subsystem;

[0008] The ship trajectory prediction subsystem is used to continuously collect navigation image data of target ships at sea through a monitoring and collision avoidance platform equipped with a CCD monitoring camera, and to detect and track the navigation target ships based on the constructed cascade neural network;

[0009] The cascade neural network includes a pre-trained target detection and recognition network for obtaining the ship category and confidence and a target tracking network for estimating the ship motion state according to the output of the target detection and recognition network;

[0010] The active warning subsystem is used to confirm the predicted channel number of the current ship according to the estimated ship motion state output by the target tracking network, and obtain the first collision probability between the target ship and the bridge pier corresponding to the predicted channel number;

[0011] Based on the first collision probability and the collision probability threshold, the position number of the bridge pier with collision risk is determined; and a warning signal containing the position number is sent to the target ship through the monitoring anti-collision platform;

[0012] The track movement positioning auxiliary subsystem is used to drive the monitoring and anti-collision platform to the warning positioning position according to the position number, and obtain the second collision probability between the target ship and the corresponding collision pier when the monitoring and anti-collision platform is located at the warning positioning position through the ship trajectory prediction subsystem and the active early warning subsystem, and send a start anti-collision signal to the passive flexible anti-collision subsystem when the second collision probability exceeds the collision probability threshold.

[0013] Further, the track movement positioning auxiliary subsystem is communicatively connected with the active early warning subsystem and the passive flexible anti-collision subsystem respectively;

[0014] It includes a laser positioning system, a control module, a linear track structure, and a servo motor for driving the monitoring and anti-collision platform to reciprocate along the linear track structure;

[0015] And the linear track structure is fixedly installed at the bottom end of the bridge flange;

[0016] The laser positioning system includes a laser transmitter installed on the monitoring and anti-collision platform, and a laser receiver installed at the bottom end of the bridge flange and parallel to the central axis of the corresponding collision pier;

[0017] The control module is used to confirm the location of the collision pier with a number corresponding to the collision probability threshold value according to the warning signal sent by the active warning subsystem, and generate a positioning control instruction for driving the servo motor to move;

[0018] The servo motor drives the monitoring and anti-collision platform to move along the linear track structure through the received positioning control instruction, and performs position feedback through the laser positioning system to move the driven monitoring and anti-collision platform to the warning positioning position.

[0019] Furthermore, the passive flexible anti-collision subsystem includes an anti-collision mechanism device and an anti-collision system encapsulated inside the monitoring anti-collision platform;

[0020] And the anti-collision system includes a power module for providing electrical energy and a flexible airbag control module;

[0021] The anti-collision mechanism device includes a gas generator connected by an air guide tube and a flexible compressed air bag provided with a one-way inflation valve. After inflation, the flexible compressed air bag can fall to the water surface under its own weight to achieve anti-collision protection for the bridge pier.

[0022] The flexible airbag control module is used to receive the start-up anti-collision signal sent by the active warning subsystem to control the gas generator to inflate the flexible compression airbag;

[0023] A fragile plate structure is provided on one side of the monitoring and anti-collision platform and is arranged opposite to the flexible compressed airbag, so as to release the flexible compressed airbag when the flexible compressed airbag is squeezed and broken.

[0024] Furthermore, the method for realizing detection and tracking of a navigation target ship based on the constructed cascade neural network specifically comprises the following steps:

[0025] S01: Based on the pre-trained target detection and recognition network, the ship category and confidence are obtained according to the pre-processed navigation image data; and the target detection and recognition network is the feature extraction neural network Darknet and the target recognition module;

[0026] The preprocessing is to annotate the ship detection targets in the navigation image data with bounding boxes and ship category features, and segment the annotated navigation image data into S*S grid images to obtain an optimized navigation map;

[0027] The feature extraction neural network Darknet is used to extract the annotation features in the optimized navigation map to obtain the target feature map; the target recognition module is used to obtain the target recognition feature map based on the target feature map through the YOLO5 algorithm, and the target recognition feature map includes the coordinates of the bounding box, the ship category and the confidence level;

[0028] S02: Estimate the motion state of the ship according to the output of the target detection and recognition network through the target tracking network; and the target tracking network includes an optical flow algorithm module and a Kalman filter module;

[0029] Based on the optical flow algorithm module, the motion optical flow vector of the key pixel point in the two-dimensional pixel feature map is obtained according to the optical flow constraint equation, so as to obtain the heading speed and navigation direction of the current position of the target ship according to the motion optical flow vector of the key pixel point;

[0030] The two-dimensional pixel feature map is a feature map including water flow texture obtained by graying and denoising the target recognition feature map;

[0031] Through the Kalman filter module, based on the heading speed and sailing direction, the heading trajectory of the target ship is predicted according to the observation data of the current position of the target ship, so as to estimate the ship's motion state and thus realize the detection and tracking of the sailing target ship.

[0032] Furthermore, in S02, the heading trajectory of the target ship is predicted based on the heading speed and the sailing direction according to the observation data of the current position of the target ship through the Kalman filter module to estimate the motion state of the ship, which specifically includes the following steps:

[0033] S021: According to the prediction output of the optical flow algorithm module, the state vector of the target ship at the current moment is represented as X=[x, y, vx, vy]; wherein x represents the abscissa of the target ship position at the current moment; y represents the ordinate of the target ship position at the current moment; vx represents the lateral velocity component of the target ship position; vy represents the longitudinal velocity component of the target ship position;

[0034] S022: According to the state vector, the state prediction equation and the position observation equation are defined as follows:

[0035] Xk+1|k=AXk|k+Bkuk

[0036] Zk=HXk+vk,Zk=[zx,zy]

[0037] Where: Xk+1|k represents the predicted state vector at time k+1; Xk|k represents the estimated state vector at time k; A represents the state transfer matrix determined by the motion optical flow vector in the optical flow algorithm module; Bk represents the control input matrix; uk represents the control input vector; Zk represents the position observation vector; H represents the observation matrix; vk represents the observation noise vector; zx represents the horizontal coordinate of the position observation; zy represents the vertical coordinate of the position observation;

[0038] S023: Based on the state prediction equation and the position observation equation, the target state estimation equation and the initial covariance matrix are obtained, and the expression is:

[0039] Xk+1|k+1=Xk+1|k+Kk+1(Zk-HXk+1|k)

[0040] Pk+1|k=APk|kRk+Qk

[0041] Where: Kk+1 represents the Kalman gain used to determine the weights of the observed data and the predicted state in the updated state estimation; Pk|k represents the estimated covariance matrix at time k; Pk+1|k represents the predicted covariance matrix at time k+1; Qk represents the process noise covariance matrix; Rk represents the observation noise covariance matrix;

[0042] The output of the target state estimation equation is used as the motion state estimation of the target ship in the current iteration;

[0043] S024: Obtain an updated covariance matrix according to the initial covariance matrix;

[0044] Pk+1|k+1=(1-Kk+1H)Pk+1|k

[0045] Where: Pk+1|k+1 represents the updated covariance matrix at time k+1;

[0046] S025: Obtain the target state estimation at the next moment according to the covariance error matrix and the target state estimation equation;

[0047] S026: Use the target state estimation and updated covariance matrix at the next moment as input to the Kalman filter module again, and repeat steps S023 to S025.

[0048] Further, in S02, the motion optical flow vector of the key pixel point in the two-dimensional pixel feature map is obtained based on the optical flow constraint equation, so as to obtain the heading speed and navigation direction of the current position of the target ship according to the motion optical flow vector of the key pixel point, which specifically includes the following steps:

[0049] S001: Obtain the change relationship between the motion optical flow vector of the key pixel point in the two-dimensional pixel feature map and the image grayscale through the optical flow constraint equation, and the method for obtaining the optical flow constraint equation is:

[0050] The grayscale image expression for obtaining the two-dimensional pixel feature map is:

[0051] I(x,y,t)=I(x+uΔt,y+vΔt,t+Δt)

[0052] Where: I(x,y,t) represents the grayscale value of the image at time t and position (x,y); u and v represent the components of the motion optical flow vector in the x and y directions respectively; Δt represents the time interval between two frames of images;

[0053] Perform Taylor expansion on the grayscale image expression and ignore the higher-order terms to obtain the Taylor expansion formula:

[0054]

[0055] Where: Represents the partial derivative of the grayscale image in the x direction; Represents the partial derivative of the grayscale image in the y direction; Represents the partial derivative of the grayscale image at time t;

[0056] According to the grayscale image expression and Taylor expansion formula, the optical flow constraint equation is obtained as follows:

[0057]

[0058] S002: Set a sliding window to estimate the motion optical flow vector by minimizing the sum of squares of pixel intensity errors within the window, and the error function of the pixel intensity error is

[0059] E=Σ[(I(x+uΔt,y+vΔt,t+Δt)-I(x,y,t))^2]

[0060] Where: Σ represents the sum of all pixels in the sliding window; E represents the pixel intensity error;

[0061] Taylor expansion is performed on the error function and its first-order derivative is set to zero to obtain a linear equation system for solving the motion optical flow vector, which is expressed as follows:

[0062] A*[u;v]=-b

[0063] Wherein: A represents a 2n×2 matrix, and n is the number of pixels in the window; b represents a 2n×1 vector; [u; v] represents the motion optical flow vector to be solved, and then the heading speed and navigation direction of the current position of the target ship are confirmed according to the solved motion optical flow vector.

[0064] Furthermore, the method for selecting key pixels in the two-dimensional pixel feature map in S001 is as follows:

[0065] S0010: Calculate the autocorrelation matrix of each pixel in the two-dimensional pixel feature map, and its expression is

[0066]

[0067] Where: C(x,y) represents the autocorrelation matrix of the pixel point (x,y); Ix and Iy represent the partial derivatives of the image grayscale value in the x direction and y direction obtained by the Sobel operator or edge detection operator respectively;

[0068] S0011: Construct pixel feature equation based on autocorrelation matrix:

[0069] |C(x,y)-λI|=0

[0070] Where: I represents the unit matrix; λ represents the eigenvalue of the pixel;

[0071] Based on the corner point response function, the corner point response value is obtained according to the pixel point feature value, and the pixel point corresponding to the pixel point feature value whose corner point response value exceeds the preset response threshold is taken as the key pixel point;

[0072] And the corner point response function is R=min(λ1,λ2).

[0073] Furthermore, the method for obtaining the first collision probability or the second collision probability is:

[0074] S100: determining a predicted trajectory of a target ship through an estimated ship motion state obtained by a ship trajectory prediction subsystem;

[0075] S200: confirming whether a collision prediction is satisfied based on the coordinates of the track points in the predicted track of the ship in the first set target area and the predicted ship width;

[0076] The rule for confirming whether a collision prediction is satisfied is:

[0077] Determine whether the current predicted collision distance of the ship meets the preset distance threshold;

[0078] If the current predicted collision distance of the ship meets the preset distance threshold, the position number of the corresponding bridge pier where the ship may collide with the predicted channel number is confirmed, and the first accuracy of obtaining the predicted trajectory of the ship is calculated;

[0079] The calculation formula for the first accuracy of the ship's predicted trajectory is:

[0080] P1=1-ζ1

[0081] Where: P1 represents the first accuracy of the ship's predicted trajectory, that is, the first collision probability; ζ1 represents the corresponding element in the error covariance matrix of the Kalman filter, that is, the error of the current ship's predicted track;

[0082] Otherwise, the ship trajectory prediction subsystem re-detects other target ships sailing at sea and repeats S100;

[0083] S300: determining whether the first accuracy of the predicted trajectory of the ship meets a preset accuracy threshold, i.e., a collision probability threshold; if so, sending a warning signal containing the position number to the target ship through the monitoring anti-collision platform; otherwise, repeating S100;

[0084] S400: driving the monitoring and anti-collision platform to the warning positioning position according to the position number through the track movement positioning auxiliary subsystem, and obtaining the current target ship prediction trajectory confirmed according to the current estimated ship motion state through the ship trajectory prediction subsystem;

[0085] S500: confirming whether the secondary collision prediction is satisfied according to the coordinates of the track points in the predicted track of the ship in the second set target area and the predicted ship width;

[0086] The rule for confirming whether the secondary collision prediction is satisfied is:

[0087] Determine whether the current predicted collision distance of the ship meets the preset distance threshold;

[0088] If the current predicted collision distance of the ship meets the preset distance threshold, the position number of the corresponding bridge pier where the ship may collide with the predicted channel number is confirmed, and the second accuracy of obtaining the predicted trajectory of the ship is calculated;

[0089] The calculation formula for the second accuracy of the ship's predicted trajectory is:

[0090] P2=1-ζ2

[0091] Where: P2 represents the second accuracy of the ship's predicted trajectory, that is, the second collision probability; ζ2 represents the corresponding element in the error covariance matrix of the Kalman filter, that is, the error of the current ship's predicted track;

[0092] Otherwise, the ship trajectory prediction subsystem re-detects other target ships sailing at sea and repeats S100;

[0093] S600: Determine whether the second accuracy of the ship's predicted trajectory meets a preset accuracy threshold, i.e., a collision probability threshold. If so, send a start-up collision avoidance signal to the passive flexible collision avoidance subsystem through the collision avoidance monitoring platform; otherwise, repeat S100.

[0094] Beneficial effect: The present invention provides an active-passive combined flexible bridge anti-collision system, which can issue an early warning prompt before a collision occurs by combining an active early warning subsystem, a track movement positioning auxiliary subsystem and a passive flexible anti-collision subsystem, thereby reducing the risk of collision. That is, the active early warning subsystem is based on the ship's channel number, confirms the predicted channel number of the current ship according to the ship's motion state, obtains the collision pier with the number corresponding to the first collision probability exceeding the collision probability threshold, and sends an early warning signal to the target ship through the monitoring anti-collision platform, thereby realizing active early warning of the ship, facilitating the target ship to respond in time, so that the target ship can be in an early warning state when the anti-collision program is not triggered, thereby greatly improving the navigation capacity of the bridge; the monitoring anti-collision platform is moved to the warning positioning position at the bottom end of the bridge flange through the track movement positioning auxiliary subsystem; and the ship trajectory prediction subsystem is called The system obtains the navigation image data of the target ship again. When the second collision probability between the target ship and the corresponding collision pier exceeds the collision probability threshold, a start-up anti-collision signal is sent to the passive flexible anti-collision subsystem. By monitoring the anti-collision mechanism device carried by the anti-collision platform, the damage to the bridge pier by the target ship under unavoidable circumstances is realized. That is, when the collision is unavoidable, the flexible anti-collision device is deployed, which can not only effectively protect the bridge structure but also maximize the protection of the ship's safety. The combination of active and passive methods solves the problem that a single anti-collision method is prone to errors and causes dangerous accidents, and cannot reflect the versatility of early warning. In addition, by monitoring the anti-collision mechanism device carried by the anti-collision platform, the shortcomings of traditional anti-collision devices, such as high price, impact on navigation, and insufficient protection of ships, are avoided, which greatly improves the accuracy of collision avoidance prediction and early warning between different ships and different bridge types. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0096] Figure 1 A schematic diagram of the active and passive combined flexible bridge anti-collision system of the present invention;

[0097] Figure 2 This is a schematic diagram of the process of the active and passive combined flexible bridge anti-collision system in this embodiment;

[0098] Figure 3 This is a schematic diagram of the trajectory prediction and collision system principle in this embodiment;

[0099] Figure 4 Schematic diagram of the principle of the laser positioning system in this embodiment;

[0100] Figure 5 This is a schematic diagram of the principle of the track movement positioning auxiliary subsystem in this embodiment;

[0101] Figure 6 This is a schematic diagram of the flexible compression airbag excitation control principle in this embodiment;

[0102] Figure 7 Schematic diagram of the internal structure of the flexible anti-collision device in this embodiment;

[0103] Figure 8 This is a schematic diagram of the deployment of the compressed air bag in this embodiment;

[0104] Fig. 9 This is a simplified diagram of the interior of the flexible compression airbag in this embodiment. DETAILED DESCRIPTION

[0105] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0106] This embodiment provides an active and passive combined flexible bridge anti-collision system, such as Figure 1 As shown, it includes a ship trajectory prediction subsystem, an active warning subsystem, a track movement positioning auxiliary subsystem, and a passive flexible anti-collision subsystem;

[0107] The ship trajectory prediction subsystem is used to continuously collect navigation image data of target ships at sea through a monitoring and anti-collision platform equipped with a CCD monitoring camera, and detect and track the target ships based on the constructed cascade neural network; wherein the initial position of the monitoring and anti-collision platform is set to the center position of the bottom end of the flange of the bridge, and continuously collects and obtains image data in a set sea area at the initial position;

[0108] The cascade neural network includes a pre-trained target detection and recognition network for obtaining the ship category and confidence and a target tracking network for estimating the ship motion state according to the output of the target detection and recognition network;

[0109] Specifically, Figure 2 As shown, the method for detecting and tracking a navigation target ship based on the constructed cascade neural network specifically includes the following steps:

[0110] S01: Based on the pre-trained target detection and recognition network, the ship category and confidence are obtained according to the pre-processed navigation image data; and the target detection and recognition network is the feature extraction neural network Darknet and the target recognition module;

[0111] The preprocessing is to annotate the ship detection targets in the navigation image data with bounding boxes and ship category features, and segment the annotated navigation image data into S*S grid images to obtain an optimized navigation map;

[0112] The feature extraction neural network Darknet is used to extract the annotation features in the optimized navigation map to obtain the target feature map; the target recognition module is used to obtain the target recognition feature map based on the target feature map through the YOLO5 algorithm, and the target recognition feature map includes the coordinates of the bounding box, the ship category and the confidence; wherein, the confidence of each bounding box is calculated, which represents whether the bounding box contains an object and the probability of containing the object; the confidence is defined as Pr(Object)IoUpredtruth, where Pr(Object) represents the probability of containing an object in the bounding box, and IoUpredtruth represents the intersection over union (IoOverUnion) between the predicted bounding box and the true bounding box. The confidence calculation formula is: Confidence = Pr(Object)×IoUpredtruth; if the actual position of the object falls within a certain grid, then Pr(Object) takes the value of 1, otherwise it is 0; IoUpredtruth is the intersection over union (IoOverUnion) between the predicted bounding box and the true bounding box. The calculation formula for the probability of containing an object in the bounding box is:

[0113] Pr(Classi|Object)×Pr(Object)×IoUpredtruth=Pr(Classi)×IoUpredtruth; where Pr(Classi|Object) represents the probability that a grid cell belongs to a certain category under the condition that it contains an object; Pr(Object) represents the probability that a grid cell contains an object;

[0114] The pre-trained target detection and recognition network in this embodiment is to classify ships using the neural network in the YOLO5 algorithm, use the reverse ship training function to train the network, use the input vector and the corresponding target vector to train the neural network until the input vector can be classified in a defined appropriate manner. Through the pre-trained target detection and recognition network, ships can be divided into three categories: large, medium and small, corresponding to less than 5,000 tons, 5,000-10,000 tons and more than 10,000 tons, respectively. In addition, the tonnage (DWT) of the ship can be predicted by inputting the ship size into the network; the identified ship information is compared with the ship type in the database by the volov5 algorithm training method to obtain the type and size information of the ship; then, based on the target tracking network, the optical flow algorithm is used to calculate the actual position, actual navigation speed and direction of the ship from the obtained ship information and pixel position, and the saved data sets of various types of ship navigation in the past, including movement trajectory, speed, etc., are used for the purpose of subsequent prediction of ship trajectory, channel number and collision probability;

[0115] S02: Estimate the motion state of the ship according to the output of the target detection and recognition network through the target tracking network; and the target tracking network includes an optical flow algorithm module and a Kalman filter module;

[0116] Based on the optical flow algorithm module, the motion optical flow vector of the key pixel point in the two-dimensional pixel feature map is obtained according to the optical flow constraint equation, so as to obtain the heading speed and navigation direction of the current position of the target ship according to the motion optical flow vector of the key pixel point;

[0117] The two-dimensional pixel feature map is a feature map including water flow texture obtained by graying and denoising the target recognition feature map;

[0118] In a specific embodiment, S02 obtains the motion optical flow vector of the key pixel point in the two-dimensional pixel feature map based on the optical flow constraint equation, so as to obtain the heading speed and navigation direction of the current position of the target ship according to the motion optical flow vector of the key pixel point, which specifically includes the following steps:

[0119] S001: Obtain the change relationship between the motion optical flow vector of the key pixel point in the two-dimensional pixel feature map and the image grayscale through the optical flow constraint equation;

[0120] Specifically, the method for selecting key pixels in the two-dimensional pixel feature map in S001 is as follows:

[0121] S0010: Calculate the autocorrelation matrix of each pixel in the two-dimensional pixel feature map, and its expression is

[0122]

[0123] Where: C(x,y) represents the autocorrelation matrix of the pixel point (x,y); Ix and Iy represent the partial derivatives of the image grayscale value in the x direction and y direction obtained by the Sobel operator or edge detection operator respectively;

[0124] S0011: Construct pixel feature equation based on autocorrelation matrix:

[0125] |C(x,y)-λI|=0

[0126] Where: I represents the unit matrix; λ represents the eigenvalue of the pixel;

[0127] Based on the corner point response function, the corner point response value is obtained according to the pixel point feature value, and the pixel point corresponding to the pixel point feature value whose corner point response value exceeds the preset response threshold is taken as the key pixel point;

[0128] And the corner point response function is R=min(λ1,λ2);

[0129] The method for obtaining the optical flow constraint equation is:

[0130] The grayscale image expression for obtaining the two-dimensional pixel feature map is:

[0131] I(x,y,t)=I(x+uΔt,y+vΔt,t+Δt)

[0132] Where: I(x,y,t) represents the grayscale value of the image at time t and position (x,y); u and v represent the components of the motion optical flow vector in the x and y directions respectively; Δt represents the time interval between two frames of images;

[0133] Perform Taylor expansion on the grayscale image expression and ignore the higher-order terms to obtain the Taylor expansion formula:

[0134]

[0135] Where: Represents the partial derivative of the grayscale image in the x direction; Represents the partial derivative of the grayscale image in the y direction; Represents the partial derivative of the grayscale image at time t;

[0136] According to the grayscale image expression and Taylor expansion formula, the optical flow constraint equation is obtained as follows:

[0137]

[0138] S002: Set a sliding window to estimate the motion optical flow vector by minimizing the sum of squares of pixel intensity errors within the window, and the error function of the pixel intensity error is

[0139] E=Σ[(I(x+uΔt,y+vΔt,t+Δt)-I(x,y,t))^2]

[0140] Where: Σ represents the sum of all pixels in the sliding window; E represents the pixel intensity error;

[0141] Taylor expansion is performed on the error function and its first-order derivative is set to zero to obtain a linear equation system for solving the motion optical flow vector, which is expressed as follows:

[0142] A*[u;v]=-b

[0143] Where: A represents a 2n×2 matrix, and n is the number of pixels in the window; b represents a 2n×1 vector; [u; v] represents the motion optical flow vector to be solved, and then the heading speed and navigation direction of the current position of the target ship are confirmed according to the solved motion optical flow vector;

[0144] Through the Kalman filter module, based on the heading speed and sailing direction, the heading trajectory of the target ship is predicted according to the observation data of the current position of the target ship, so as to estimate the ship's motion state and thus realize the detection and tracking of the sailing target ship.

[0145] In a specific embodiment, in S02, the Kalman filter module is used to predict the heading trajectory of the target ship based on the heading speed and the sailing direction according to the observation data of the current position of the target ship to estimate the motion state of the ship, which specifically includes the following steps:

[0146] S021: According to the prediction output of the optical flow algorithm module, the state vector of the target ship at the current moment is represented as X=[x, y, vx, vy]; wherein x represents the abscissa of the target ship position at the current moment; y represents the ordinate of the target ship position at the current moment; vx represents the lateral velocity component of the target ship position; vy represents the longitudinal velocity component of the target ship position;

[0147] S022: According to the state vector, the state prediction equation and the position observation equation are defined as follows:

[0148] Xk+1|k=AXk|k+Bkuk

[0149] Zk=HXk+vk,Zk=[zx,zy]

[0150] Where: Xk+1|k represents the predicted state vector at time k+1; Xk|k represents the estimated state vector at time k; A represents the state transfer matrix determined by the motion optical flow vector in the optical flow algorithm module; Bk represents the control input matrix; uk represents the control input vector; Zk represents the position observation vector; H represents the observation matrix; vk represents the observation noise vector; zx represents the horizontal coordinate of the position observation; zy represents the vertical coordinate of the position observation;

[0151] S023: Based on the state prediction equation and the position observation equation, the target state estimation equation and the initial covariance matrix are obtained, and the expression is:

[0152] Xk+1|k+1=Xk+1|k+Kk+1(Zk-HXk+1|k)

[0153] Pk+1|k=APk|kRk+Qk

[0154] Where: Kk+1 represents the Kalman gain used to determine the weights of the observed data and the predicted state in the updated state estimation; Pk|k represents the estimated covariance matrix at time k; Pk+1|k represents the predicted covariance matrix at time k+1; Qk represents the process noise covariance matrix; Rk represents the observation noise covariance matrix;

[0155] The output of the target state estimation equation is used as the motion state estimation of the target ship in the current iteration;

[0156] S024: Obtain an updated covariance matrix according to the initial covariance matrix;

[0157] Pk+1|k+1=(1-Kk+1H)Pk+1|k

[0158] Where: Pk+1|k+1 represents the updated covariance matrix at time k+1;

[0159] S025: Obtain the target state estimation at the next moment according to the covariance error matrix and the target state estimation equation;

[0160] S026: Use the target state estimation and updated covariance matrix at the next moment as input to the Kalman filter module again, and repeat steps S023 to S025.

[0161] The active warning subsystem is used to confirm the predicted channel number of the current ship according to the estimated ship motion state output by the target tracking network, and obtain the first collision probability between the target ship and the bridge pier corresponding to the predicted channel number; if the predicted trajectory coincides with the bridge pier position, the following operation procedure is performed, i.e., obtaining the first collision probability between the ship and the corresponding bridge pier; otherwise, the initial procedure is returned, i.e., returning to the ship trajectory prediction subsystem to detect and track other ships sailing at sea;

[0162] Based on the first collision probability and the collision probability threshold, the position number of the bridge pier with collision risk is confirmed, that is, the position number of the collision pier corresponding to the first collision probability exceeding the collision probability threshold is obtained; and a warning signal is sent to the target ship through the monitoring anti-collision platform;

[0163] In this embodiment, the active anti-collision device uses real-time camera monitoring as the main data source for judging ship navigation information and warnings, and uses water flow information as an auxiliary means to achieve accurate prediction and warning capabilities. In this embodiment, dual (multi) eye cameras are used as image acquisition devices to record the video of each passing ship, and the video is input into the processing center of the system based on the ship trajectory prediction subsystem, that is, it is analyzed by artificial intelligence to obtain the predicted ship trajectory, and then the track mobile positioning auxiliary subsystem is used to accurately move the monitoring-anti-collision platform on the track. The principle of the trajectory prediction and collision system is as follows Figure 3 shown.

[0164] The track movement positioning auxiliary subsystem is used to drive the monitoring and anti-collision platform to the warning positioning position according to the position number, that is, the track movement positioning auxiliary subsystem is used to move the monitoring and anti-collision platform to the bottom end of the bridge flange and the warning positioning position parallel to the central axis of the corresponding collision pier according to the corresponding number of the collision pier;

[0165] Specifically, the track movement positioning auxiliary subsystem is respectively connected to the active warning subsystem and the passive flexible collision avoidance subsystem in communication; it includes a laser positioning system, a control module, a linear track structure, and a servo motor for driving the monitoring and collision avoidance platform to reciprocate along the linear track structure; and the linear track structure is fixedly installed at the bottom end of the bridge flange;

[0166] The laser positioning system includes a laser transmitter installed on the monitoring and anti-collision platform, and a laser receiver installed at the bottom end of the bridge flange and parallel to the central axis of the corresponding collision pier;

[0167] The control module is used to confirm the location of the collision pier with the number corresponding to the collision probability threshold value according to the warning signal sent by the active warning subsystem, and generate a positioning control instruction for driving the servo motor to move; the servo motor drives the monitoring and anti-collision platform to move along the linear track structure through the received positioning control instruction, and performs position feedback through the laser positioning system to move the driving monitoring and anti-collision platform to the warning positioning position;

[0168] This embodiment receives commands from the ship trajectory prediction subsystem through the track movement positioning auxiliary subsystem, and uses the servo motor as a power source to accurately and quickly move the monitoring-avoidance collision platform to the preset position. In order to eliminate the influence of accidental factors, this embodiment also adds a fine-tuning program for the servo motor, and combines the laser positioning system to achieve precise positioning of the monitoring-avoidance collision platform. The principle of the fine-tuning program is as follows: The laser receiver is arranged at the positioning position corresponding to each waterway. When the laser emitted by the laser generator of the monitoring-avoidance collision platform is received by the laser receiver, the laser receiver will transmit a signal to the platform that it has reached the predetermined position. At this time, the servo motor stops working, and the programming method of the fine-tuning program is an existing well-known technical means, which will not be elaborated here; the principle of the track movement positioning system is as follows Figures 4 to 5 As shown. Its working process is: after receiving the moving instruction from the active warning subsystem, the track movement positioning auxiliary subsystem starts to work. The track movement positioning auxiliary subsystem processes the received predicted channel number into the mobilized monitoring-avoidance platform number (each channel signal corresponds to a corresponding platform number, and the signal conversion is realized through the corresponding relationship) and the moving distance through the microprocessor and transmits the instruction to the servo motor controller. Then, the servo motor runs according to the instruction, and the laser positioning system starts to work. When the predetermined position is reached, the laser positioning system is used for inspection. If the laser receiver receives the laser signal, it means that the monitoring-avoidance platform is in the correct position; if the laser receiver does not receive the signal, it means that there is a deviation in the position. At this time, the fine-tuning program is used to drive the servo motor for fine-tuning to achieve the precise position;

[0169] The ship trajectory prediction subsystem and the active early warning subsystem obtain the second collision probability between the target ship and the corresponding collision pier when the monitoring anti-collision platform is located at the warning positioning position, and when the second collision probability exceeds the collision probability threshold, the passive flexible anti-collision subsystem sends a start-up anti-collision signal, that is, the navigation image data of the target ship is obtained again by calling the ship trajectory prediction subsystem, and the navigation target ship is detected and tracked again based on the cascade neural network.

[0170] By calling the active early warning subsystem to obtain the second collision probability between the target ship and the corresponding collision pier when the monitoring anti-collision platform is located at the warning positioning position, and when the second collision probability exceeds the collision probability threshold, a start-up anti-collision signal is sent to the passive flexible anti-collision subsystem, and the damage to the bridge pier by the target ship is realized in unavoidable circumstances by monitoring the anti-collision mechanism device carried inside the anti-collision platform;

[0171] Among them, if it is confirmed that the second collision probability does not exceed the collision probability threshold, the active warning subsystem sends a reset signal of the monitoring and anti-collision platform to the track movement positioning auxiliary subsystem. After the reset signal resets the command, the program controls the motor to move the monitoring and anti-collision platform to the initial position and waits for the next instruction; the principle of the track movement positioning auxiliary subsystem is as follows Figure 5 shown.

[0172] In a specific embodiment, the method for obtaining the first collision probability or the second collision probability is:

[0173] S100: determining a predicted trajectory of a target ship through an estimated ship motion state obtained by a ship trajectory prediction subsystem;

[0174] S200: confirming whether a collision prediction is satisfied based on the coordinates of the track points in the predicted track of the ship in the first set target area and the predicted ship width;

[0175] The rule for confirming whether a collision prediction is satisfied is:

[0176] Determine whether the current predicted collision distance of the ship meets the preset distance threshold;

[0177] The preset distance threshold is determined according to the horizontal coordinate of the position of the axis of the bridge pier corresponding to the ship width and the ship channel; the expression of the current ship predicted collision distance A is A=B+C, where B represents the ship width, that is, the maximum distance from the initial position point of the ship center to the edge of the ship; C represents the distance between the edge of the ship at the maximum distance from the ship center and the axis of the bridge pier;

[0178] If the current predicted collision distance of the ship meets the preset distance threshold, the position number of the corresponding bridge pier where the ship may collide with the predicted channel number is confirmed, and the first accuracy of obtaining the predicted trajectory of the ship is calculated;

[0179] The calculation formula for the first accuracy of the ship's predicted trajectory is:

[0180] P1=1-ζ1

[0181] Where: P1 represents the first accuracy of the ship's predicted trajectory, that is, the first collision probability; ζ1 represents the corresponding element in the error covariance matrix of the Kalman filter, that is, the error of the current ship's predicted track;

[0182] Otherwise, the ship trajectory prediction subsystem re-detects other target ships sailing at sea and repeats S100;

[0183] S300: determining whether the first accuracy of the predicted trajectory of the ship meets a preset accuracy threshold, i.e., a collision probability threshold; if so, sending a warning signal containing the position number to the target ship through the monitoring anti-collision platform; otherwise, repeating S100;

[0184] S400: driving the monitoring and anti-collision platform to the warning positioning position according to the position number through the track movement positioning auxiliary subsystem, and obtaining the current target ship prediction trajectory confirmed according to the current estimated ship motion state through the ship trajectory prediction subsystem;

[0185] S500: confirming whether the secondary collision prediction is satisfied according to the coordinates of the track points in the predicted track of the ship in the second set target area and the predicted ship width;

[0186] The rule for confirming whether the secondary collision prediction is satisfied is:

[0187] Determine whether the current predicted collision distance of the ship meets the preset distance threshold;

[0188] If the current predicted collision distance of the ship meets the preset distance threshold, the position number of the corresponding bridge pier where the ship may collide with the predicted channel number is confirmed, and the second accuracy of obtaining the predicted trajectory of the ship is calculated;

[0189] The calculation formula for the second accuracy of the ship's predicted trajectory is:

[0190] P2=1-ζ2

[0191] Where: P2 represents the second accuracy of the ship's predicted trajectory, that is, the second collision probability; ζ2 represents the corresponding element in the error covariance matrix of the Kalman filter, that is, the error of the current ship's predicted track;

[0192] Otherwise, the ship trajectory prediction subsystem re-detects other target ships sailing at sea and repeats S100;

[0193] S600: Determine whether the second accuracy of the ship's predicted trajectory meets a preset accuracy threshold, i.e., a collision probability threshold. If so, send a start-up collision avoidance signal to the passive flexible collision avoidance subsystem through the collision avoidance monitoring platform; otherwise, repeat S100.

[0194] Specifically, the passive flexible anti-collision subsystem includes an anti-collision mechanism device and an anti-collision system encapsulated inside the monitoring anti-collision platform;

[0195] And the anti-collision system includes a power module for providing electrical energy and a flexible airbag control module;

[0196] The anti-collision mechanism device comprises a gas generator and a flexible compressed air bag provided with a one-way inflation valve connected through an air guide tube;

[0197] The flexible airbag control module is used to receive the start-up anti-collision signal sent by the active warning subsystem to control the gas generator to inflate the flexible compression airbag;

[0198] A fragile plate structure is provided on one side of the monitoring and anti-collision platform, and is arranged opposite to the flexible compression airbag. The flexible compression airbag is used to increase in volume during the inflation process, thereby squeezing and destroying the fragile plate structure and expanding around the corresponding collision pier. After the inflation of the flexible compression airbag is completed, the flexible compression airbag falls to the water surface under its own weight to achieve anti-collision protection for the pier.

[0199] The passive flexible anti-collision subsystem in this embodiment uses a flexible airbag control module (i.e., a reference automobile airbag control system ACU) as the control center to communicate with the active warning subsystem; the connection is used to exchange information. When the anti-collision program is started, the activation of the compressed airbag is controlled by the gas generator, i.e., the igniter and the sodium azide inflator. The flexible compressed airbag and the ignition device are located at the front end of the flexible anti-collision device, such as Figure 6 The excitation process is as follows: the control system controls the power supply to be energized, the ignition coil is energized to generate high temperature, the ignition agent is ignited, and then the inflator burns and generates nitrogen which is sent into the flexible compressed airbag to expand the flexible compressed airbag. The flexible airbag excitation control principle is as follows Figure 3 As shown. The overall structure of the passive flexible anti-collision subsystem is rigidly connected in parallel with the ship trajectory prediction subsystem and the active warning subsystem to ensure that the displacement modes of the two subsystems remain consistent. The internal structure of the passive flexible anti-collision subsystem is mainly composed of flexible compressed airbags, control circuits, igniters, power supply systems, backup power supplies, and load blocks. The flexible airbag control module includes a control circuit, a wireless signal transmission module, and a microprocessor, which can receive instructions and start the gas generator. The large amount of nitrogen generated by the gas generator in a short time is input into the compressed airbag through the air duct to achieve rapid inflation. A fragile plate structure is provided on one side of the monitoring and anti-collision platform, and is arranged opposite to the flexible compressed airbag. When the flexible compressed airbag is squeezed and broken, the flexible compressed airbag is released, and the flexible compressed airbag is quickly unfolded to wrap the bridge pier structure. The flexible compressed airbag can fall to the water surface by its own weight after inflation is completed, so as to achieve anti-collision protection for the bridge pier. The flexible airbag deployment schematic diagram is shown in Figures 8 to 9 As shown; the internal structure of the flexible anti-collision system is as follows Figure 7 shown.

[0200] This embodiment, by combining an active warning subsystem, a track movement positioning auxiliary subsystem, and a passive flexible anti-collision subsystem, can issue a warning prompt before a collision occurs, reduce the risk of a collision, and deploy a flexible anti-collision device when a collision is unavoidable, which can not only effectively protect the bridge structure but also protect the safety of the ship to the maximum extent. Compared with the traditional rigid anti-collision device, its flexible anti-collision airbag has the characteristics of good energy absorption effect, long buffering time, small peak impact force, reusability, and low cost. The addition of the active warning system enables it to be in a folded state when the anti-collision program is not triggered, greatly improving the navigation capacity of the bridge and making it quite feasible. Its subsystem ship trajectory prediction and collision warning system has information processing capabilities, and with the support of artificial intelligence, it can independently process and make decisions. At the same time, it has a communication module inside, which can be upgraded and controlled at any time, increasing the controllability and transparency of the system. The equipment used in this system is convenient and easy to obtain, and has the possibility of large-scale promotion. According to simple statistics, the cost of a single bridge collision warning system is about 700,000 yuan, and the cost of a rigid anti-collision device using composite materials as energy-absorbing materials reaches millions of yuan. This system uses high-speed cameras and flexible anti-collision devices, which greatly reduces costs. The cost of a single bridge (2500+1500+3500+5000+800)*10 is about 130,000 yuan, which significantly increases the possibility of widespread installation. This embodiment integrates multiple emerging technologies such as active warning, mobile platform and artificial intelligence, avoiding the shortcomings of traditional anti-collision devices such as high prices, impact on navigation, and insufficient protection for ships, promoting the widespread use of bridge anti-collision devices and providing a new research direction for future research on bridge anti-collision devices.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An active and passive combined flexible bridge anti-collision system, characterized in that: include Ship trajectory prediction subsystem, active warning subsystem, orbital mobile positioning auxiliary subsystem and passive flexible collision avoidance subsystem; The ship trajectory prediction subsystem is used to continuously collect navigation image data of target ships at sea by monitoring the anti-collision platform, and realize detection and tracking of navigation target ships based on the constructed cascade neural network; The cascade neural network includes a pre-trained target detection and recognition network for obtaining the ship category and confidence and a target tracking network for estimating the ship motion state according to the output of the target detection and recognition network; The active warning subsystem is used to confirm the predicted channel number of the current ship according to the estimated ship motion state output by the target tracking network, and obtain the first collision probability between the target ship and the bridge pier corresponding to the predicted channel number; Based on the first collision probability and the collision probability threshold, the position number of the bridge pier with collision risk is determined; and a warning signal containing the position number is sent to the target ship through the monitoring anti-collision platform; The track movement positioning auxiliary subsystem is used to drive the monitoring and anti-collision platform to the warning positioning position according to the position number, and obtain the second collision probability between the target ship and the corresponding collision pier when the monitoring and anti-collision platform is located at the warning positioning position through the ship trajectory prediction subsystem and the active early warning subsystem, and send a start anti-collision signal to the passive flexible anti-collision subsystem when the second collision probability exceeds the collision probability threshold.

2. The active-passive combined flexible bridge anti-collision system according to claim 1 is characterized in that: The track movement positioning auxiliary subsystem is respectively connected to the active early warning subsystem and the passive flexible collision avoidance subsystem in communication; It includes a laser positioning system, a control module, a linear track structure, and a servo motor for driving the monitoring and anti-collision platform to reciprocate along the linear track structure; And the linear track structure is fixedly installed at the bottom end of the bridge flange; The laser positioning system includes a laser transmitter installed on the monitoring and anti-collision platform, and a laser receiver installed at the bottom end of the bridge flange and parallel to the central axis of the corresponding collision pier; The control module is used to confirm the location of the collision pier with a number corresponding to the collision probability threshold value according to the warning signal sent by the active warning subsystem, and generate a positioning control instruction for driving the servo motor to move; The servo motor drives the monitoring and anti-collision platform to move along the linear track structure through the received positioning control instruction, and performs position feedback through the laser positioning system to move the driven monitoring and anti-collision platform to the warning positioning position.

3. The active-passive combined flexible bridge anti-collision system according to claim 2 is characterized in that: The passive flexible anti-collision subsystem includes an anti-collision mechanism device and an anti-collision system encapsulated inside the monitoring anti-collision platform; And the anti-collision system includes a power module for providing electrical energy and a flexible airbag control module; The anti-collision mechanism device includes a gas generator connected by an air guide tube and a flexible compressed air bag provided with a one-way inflation valve. After inflation, the flexible compressed air bag can fall to the water surface under its own weight to achieve anti-collision protection for the bridge pier. The flexible airbag control module is used to receive the start-up anti-collision signal sent by the active warning subsystem to control the gas generator to inflate the flexible compression airbag; A fragile plate structure is provided on one side of the monitoring and anti-collision platform and is arranged opposite to the flexible compressed airbag, so as to release the flexible compressed airbag when the flexible compressed airbag is squeezed and broken.

4. The active-passive combined flexible bridge anti-collision system according to claim 3 is characterized in that: The method for realizing detection and tracking of a navigation target ship based on the constructed cascade neural network specifically comprises the following steps: S01: Based on the pre-trained target detection and recognition network, the ship category and confidence are obtained according to the pre-processed navigation image data; and the target detection and recognition network is the feature extraction neural network Darknet and the target recognition module; The preprocessing is to annotate the ship detection targets in the navigation image data with bounding boxes and ship category features, and segment the annotated navigation image data into S*S grid images to obtain an optimized navigation map; The feature extraction neural network Darknet is used to extract the annotation features in the optimized navigation map to obtain the target feature map; the target recognition module is used to obtain the target recognition feature map based on the target feature map through the YOLO5 algorithm, and the target recognition feature map includes the coordinates of the bounding box, the ship category and the confidence level; S02: Estimate the motion state of the ship according to the output of the target detection and recognition network through the target tracking network; and the target tracking network includes an optical flow algorithm module and a Kalman filter module; Based on the optical flow algorithm module, the motion optical flow vector of the key pixel point in the two-dimensional pixel feature map is obtained according to the optical flow constraint equation, so as to obtain the heading speed and navigation direction of the current position of the target ship according to the motion optical flow vector of the key pixel point; The two-dimensional pixel feature map is a feature map including water flow texture obtained by graying and denoising the target recognition feature map; Through the Kalman filter module, based on the heading speed and sailing direction, the heading trajectory of the target ship is predicted according to the observation data of the current position of the target ship, so as to estimate the ship's motion state and thus realize the detection and tracking of the sailing target ship.

5. The active-passive combined flexible bridge anti-collision system according to claim 4 is characterized in that: In S02, the Kalman filter module is used to predict the heading trajectory of the target ship based on the heading speed and the sailing direction according to the observation data of the current position of the target ship to estimate the motion state of the ship, which specifically includes the following steps: S021: According to the prediction output of the optical flow algorithm module, the state vector of the target ship at the current moment is represented as X=[x, y, vx, vy]; wherein x represents the abscissa of the target ship position at the current moment; y represents the ordinate of the target ship position at the current moment; vx represents the lateral velocity component of the target ship position; vy represents the longitudinal velocity component of the target ship position; S022: According to the state vector, the state prediction equation and the position observation equation are defined as follows: Xk+1|k=AXk|k+Bkuk Zk=HXk+vk,Zk=[zx,zy] Where: Xk+1|k represents the predicted state vector at time k+1; Xk|k represents the estimated state vector at time k; A represents the state transfer matrix determined by the motion optical flow vector in the optical flow algorithm module; Bk represents the control input matrix; uk represents the control input vector; Zk represents the position observation vector; H represents the observation matrix; vk represents the observation noise vector; zx represents the horizontal coordinate of the position observation; zy represents the vertical coordinate of the position observation; S023: Based on the state prediction equation and the position observation equation, the target state estimation equation and the initial covariance matrix are obtained, and the expression is: Xk+1|k+1=Xk+1|k+Kk+1(Zk-HXk+1|k) Pk+1|k=APk|kRk+Qk Where: Kk+1 represents the Kalman gain used to determine the weights of the observed data and the predicted state in the updated state estimation; Pk|k represents the estimated covariance matrix at time k; Pk+1|k represents the predicted covariance matrix at time k+1; Qk represents the process noise covariance matrix; Rk represents the observation noise covariance matrix; The output of the target state estimation equation is used as the motion state estimation of the target ship in the current iteration; S024: Obtain an updated covariance matrix according to the initial covariance matrix; Pk+1|k+1=(1-Kk+1H)Pk+1|k Where: Pk+1|k+1 represents the updated covariance matrix at time k+1; S025: Obtain the target state estimation at the next moment according to the covariance error matrix and the target state estimation equation; S026: Use the target state estimation and updated covariance matrix at the next moment as input to the Kalman filter module again, and repeat steps S023 to S025.

6. The active-passive combined flexible bridge anti-collision system according to claim 4 is characterized in that: In S02, the motion optical flow vector of the key pixel point in the two-dimensional pixel feature map is obtained based on the optical flow constraint equation, so as to obtain the heading speed and navigation direction of the current position of the target ship according to the motion optical flow vector of the key pixel point, which specifically includes the following steps: S001: Obtain the change relationship between the motion optical flow vector of the key pixel point in the two-dimensional pixel feature map and the image grayscale through the optical flow constraint equation, and the method for obtaining the optical flow constraint equation is: The grayscale image expression for obtaining the two-dimensional pixel feature map is: I(x,y,t)=I(x+uΔt,y+vΔt,t+Δt) Where: I(x,y,t) represents the grayscale value of the image at time t and position (x,y); u and v represent the components of the motion optical flow vector in the x and y directions respectively; Δt represents the time interval between two frames of images; Perform Taylor expansion on the grayscale image expression and ignore the higher-order terms to obtain the Taylor expansion formula: Where: Represents the partial derivative of the grayscale image in the x direction; Represents the partial derivative of the grayscale image in the y direction; Represents the partial derivative of the grayscale image at time t; According to the grayscale image expression and Taylor expansion formula, the optical flow constraint equation is obtained as follows: S002: Set a sliding window to estimate the motion optical flow vector by minimizing the sum of squares of pixel intensity errors within the window, and the error function of the pixel intensity error is E=Σ[(I(x+uΔt,y+vΔt,t+Δt)-I(x,y,t))^2] Where: Σ represents the sum of all pixels in the sliding window; E represents the pixel intensity error; Taylor expansion is performed on the error function and its first-order derivative is set to zero to obtain a linear equation system for solving the motion optical flow vector, which is expressed as follows: A*[u;v]=-b Wherein: A represents a 2n×2 matrix, and n is the number of pixels in the window; b represents a 2n×1 vector; [u; v] represents the motion optical flow vector to be solved, and then the heading speed and navigation direction of the current position of the target ship are confirmed according to the solved motion optical flow vector.

7. The active-passive combined flexible bridge anti-collision system according to claim 6 is characterized in that: The method for selecting key pixels in the two-dimensional pixel feature map in S001 is: S0010: Calculate the autocorrelation matrix of each pixel in the two-dimensional pixel feature map, and its expression is Where: C(x,y) represents the autocorrelation matrix of the pixel point (x,y); Ix and Iy represent the partial derivatives of the image grayscale value in the x direction and y direction obtained by the Sobel operator or edge detection operator respectively; S0011: Construct pixel feature equation based on autocorrelation matrix as |C(x,y)-λI|=0 Where: I represents the unit matrix; λ represents the eigenvalue of the pixel; Based on the corner point response function, the corner point response value is obtained according to the pixel point feature value, and the pixel point corresponding to the pixel point feature value whose corner point response value exceeds the preset response threshold is taken as the key pixel point; And the corner point response function is R=min(λ1,λ2).

8. The active-passive combined flexible bridge anti-collision system according to claim 1, characterized in that: The method for obtaining the first collision probability or the second collision probability is as follows: S100: determining a predicted trajectory of a target ship through an estimated ship motion state obtained by a ship trajectory prediction subsystem; S200: confirming whether a collision prediction is satisfied based on the coordinates of the track points in the predicted track of the ship in the first set target area and the predicted ship width; The rule for confirming whether a collision prediction is satisfied is: Determine whether the current predicted collision distance of the ship meets the preset distance threshold; If the current predicted collision distance of the ship meets the preset distance threshold, the position number of the corresponding bridge pier where the ship may collide with the predicted channel number is confirmed, and the first accuracy of obtaining the predicted trajectory of the ship is calculated; The calculation formula for the first accuracy of the ship's predicted trajectory is: P1=1-ζ1 Where: P1 represents the first accuracy of the ship's predicted trajectory, that is, the first collision probability; ζ1 represents the corresponding element in the error covariance matrix of the Kalman filter, that is, the error of the current ship's predicted track; Otherwise, the ship trajectory prediction subsystem re-detects other target ships sailing at sea and repeats S100; S300: determining whether the first accuracy of the predicted trajectory of the ship meets a preset accuracy threshold, i.e., a collision probability threshold; if so, sending a warning signal containing the position number to the target ship through the monitoring anti-collision platform; Otherwise, repeat S100; S400: driving the monitoring and anti-collision platform to the warning positioning position according to the position number through the track movement positioning auxiliary subsystem, and obtaining the current target ship prediction trajectory confirmed according to the current estimated ship motion state through the ship trajectory prediction subsystem; S500: confirming whether the secondary collision prediction is satisfied according to the coordinates of the track points in the predicted track of the ship in the second set target area and the predicted ship width; The rule for confirming whether the secondary collision prediction is satisfied is: Determine whether the current predicted collision distance of the ship meets the preset distance threshold; If the current predicted collision distance of the ship meets the preset distance threshold, the position number of the corresponding bridge pier where the ship may collide with the predicted channel number is confirmed, and the second accuracy of obtaining the predicted trajectory of the ship is calculated; The calculation formula for the second accuracy of the ship's predicted trajectory is: P2=1-ζ2 Where: P2 represents the second accuracy of the ship's predicted trajectory, that is, the second collision probability; ζ2 represents the corresponding element in the error covariance matrix of the Kalman filter, that is, the error of the current ship's predicted track; Otherwise, the ship trajectory prediction subsystem re-detects other target ships sailing at sea and repeats S100; S600: Determine whether the second accuracy of the ship's predicted trajectory meets a preset accuracy threshold, i.e., a collision probability threshold. If so, send a start-up collision avoidance signal to the passive flexible collision avoidance subsystem through the collision avoidance monitoring platform; otherwise, repeat S100.

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