Inland ship collision danger identification method and related equipment

Abstract

The invention discloses an inland ship collision danger identification method and related equipment, and the method comprises the steps: obtaining the point cloud data of a navigation hindering target encountered by a ship in the navigation process through installing a laser radar on the ship; performing clustering analysis and interpolation processing on the point cloud data to obtain spatial information of the navigation obstructing target; determining extreme value azimuth data of the ship and the navigation obstructing target in the navigation process according to the spatial information, and calculating relative speed data and relative distance data between the navigation obstructing target and the ship along an azimuth line direction according to the spatial information; and according to the extreme value azimuth data, the relative speed data and the relative distance data, judging whether the ship and the navigation hindering target have a collision risk or not. The collision danger can be identified only by depending on the original output of the shipborne laser radar without considering the ship motion posture of the navigation obstructing target, and the method is particularly suitable for application scenes of narrow navigation environment channels of inland rivers, relatively small safe meeting distance of ships and frequent maneuvering change of the ships.

Description

Technical Field

[0001] The present invention relates to the technical field of ship collision risk identification, and in particular to an inland river ship collision risk identification method and related equipment. Background Art

[0002] During the navigation of ships, accurate identification of collision risks is a key prerequisite for ensuring navigation safety. Compared with conventional marine ship collision avoidance, the navigation environment of inland ships has the characteristics of narrow waterways, dense ships, and many curved rivers. At the same time, inland ships usually have a small turning radius and high maneuverability, which makes the safety distance required for inland ship collision avoidance relatively small. In recent years, with the development of sensing technologies such as lidar, advanced sensing equipment including millimeter wave radar, phased array radar and lidar has gradually been applied to the field of ship navigation safety perception. However, since the working principle of lidar is limited by the laser wavelength and signal strength, its detection distance is limited, and it is difficult to meet the long-distance detection requirements of "early, large, wide, and clear" in marine ship collision avoidance. However, in the scenario of inland navigation, due to the small safety distance, lidar shows better applicability. Traditional collision risk identification methods usually need to combine the position information and speed data of the own ship and the target ship for calculation, while the ship-borne lidar mainly outputs the distance, azimuth and relative speed along the azimuth line of the detection target relative to the own ship. Traditional methods make it difficult to directly use the raw output data of the lidar to identify collision risks. Calculation of the raw output data of the lidar requires a lot of computing power resources, which also affects the efficiency of collision identification. Summary of the invention

[0003] Based on this, it is necessary to propose a method for identifying inland ship collision risks and related equipment to address the technical problems of low utilization of the original output data of the laser radar and low efficiency of collision identification in the prior art.

[0004] In a first aspect, a method for identifying collision risks of inland waterway vessels is provided, the method comprising:

[0005] The laser radar is installed on the ship to obtain point cloud data of the obstruction targets encountered by the ship during navigation;

[0006] Performing cluster analysis and interpolation processing on the point cloud data to obtain spatial information of the obstructing target;

[0007] Determine the extreme azimuth data of the own ship and the obstructing target during navigation according to the spatial information, and calculate the relative speed data and relative distance data between the obstructing target and the own ship along the azimuth line according to the spatial information;

[0008] Whether there is a collision risk between the own ship and the obstructing target is determined according to the extreme value azimuth data, the relative speed data and the relative distance data.

[0009] Optionally, the step of acquiring point cloud data of obstructing targets of the target ship during navigation by means of a laser radar installed on the target ship includes:

[0010] The 0° azimuth reference of the point cloud target output by the laser radar is overlapped with the bow line of the own ship, and four intervals to be identified with the same angle are divided around the own ship in a clockwise direction starting from the 0° direction;

[0011] The point cloud data of the obstructing target in the to-be-identified interval is obtained during the navigation of the own ship.

[0012] Optionally, the step of performing cluster analysis and interpolation processing on the point cloud data to obtain the spatial information of the obstructing target includes:

[0013] Performing point cloud clustering on the point cloud data according to a preset clustering method to obtain initial point cloud data belonging to the same navigation obstruction target;

[0014] The initial point cloud data is interpolated according to a preset interpolation method to obtain target point cloud data for fitting the outer contour of the obstruction target point cloud, and the spatial information of the obstruction target is obtained based on the target point cloud data.

[0015] Optionally, the step of determining the extreme azimuth data of the own ship and the obstructing target during navigation according to the spatial information, and calculating the relative speed data and relative distance data of the obstructing target and the own ship along the azimuth line direction according to the spatial information comprises:

[0016] The azimuth angle measured clockwise from the bow line of the own ship to the obstructing target is taken as the relative azimuth, and the maximum relative azimuth and the minimum relative azimuth of the own ship at the first moment during the navigation process are determined according to the spatial information, and the point cloud data corresponding to the maximum relative azimuth in the target point cloud data is taken as the first angle data in the extreme azimuth data, and the point cloud data corresponding to the minimum relative azimuth in the target point cloud data is taken as the second angle data in the extreme azimuth data;

[0017] Determine the maximum relative orientation and the minimum relative orientation of the ship at a second moment during navigation according to the spatial information, use the point cloud data corresponding to the maximum relative orientation in the target point cloud data as the third angle data in the extreme orientation data, and use the point cloud data corresponding to the minimum relative orientation in the target point cloud data as the fourth angle data in the extreme orientation data;

[0018] Calculating first relative speed data of the obstructing target along the azimuth line and the own ship at the first moment according to the spatial information, and calculating second relative speed data of the obstructing target along the azimuth line and the own ship at the second moment according to the spatial information;

[0019] The first relative distance data between the obstructing target and the own ship at the first moment is calculated according to the spatial information, and the second relative distance data between the obstructing target and the own ship at the second moment is calculated according to the spatial information, the relative speed data includes the first relative speed data and the second relative speed data, and the relative distance data includes the first relative distance data and the second relative distance data.

[0020] Optionally, the step of judging whether there is a collision risk between the own ship and the obstructing target according to the extreme value azimuth data, the relative speed data and the relative distance data comprises:

[0021] When the second moment is the next moment of the first moment, if the second relative distance data is less than or equal to the preset safety distance, the obstructing target is regarded as a dangerous obstruction and an alarm is generated;

[0022] If the second relative distance data is greater than the preset safety distance, determining whether the second relative speed data is less than or equal to 0;

[0023] When the second relative speed data is greater than 0, the interval to be identified where the obstructing target is located is determined based on the third angle data; if the third angle data is greater than or equal to the first angle data of the interval to be identified, and the fourth angle data is less than or equal to the second angle data, the obstructing target is regarded as a dangerous obstruction and an alarm is generated.

[0024] Optionally, the step of calculating, according to the spatial information, first relative speed data of the obstructing target and the own ship along the azimuth line at the first moment includes:

[0025] The average speed of the point cloud data is used as the moving speed of the obstructing target relative to the laser radar along the azimuth line at the first moment. The first relative speed data of the obstructing target along the azimuth line and the own ship at the first moment is calculated according to the spatial information through a preset formula. The mathematical representation of the preset formula is:

[0026]

[0027] Among them, m is the number of point cloud data, and t represents the first moment.

[0028] On the other hand, the present application provides a device for identifying collision risks of inland waterway vessels, the device comprising:

[0029] A data acquisition module, used for acquiring point cloud data of obstructing targets encountered by the ship during navigation by installing a laser radar on the ship;

[0030] A data processing module, used for performing cluster analysis and interpolation processing on the point cloud data to obtain spatial information of the obstructing target;

[0031] A calculation module, used to determine the extreme azimuth data of the own ship and the obstructing target during navigation according to the spatial information, and calculate the relative speed data and relative distance data between the obstructing target and the own ship along the azimuth line direction according to the spatial information;

[0032] The identification module is used to determine whether there is a collision risk between the own ship and the obstructing target according to the extreme value azimuth data, the relative speed data and the relative distance data.

[0033] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned inland vessel collision hazard identification method when executed by the processor.

[0034] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned inland vessel collision risk identification method are implemented.

[0035] This application obtains point cloud data of obstructing targets encountered by the ship during navigation by installing a laser radar on the ship; cluster analysis and interpolation processing are performed on the point cloud data to obtain spatial information of the obstructing target; the extreme azimuth data of the ship and the obstructing target during navigation are determined according to the spatial information, and the relative speed data and relative distance data between the obstructing target and the ship along the azimuth line are calculated according to the spatial information; and it is determined whether there is a collision risk between the ship and the obstructing target according to the extreme azimuth data, the relative speed data and the relative distance data. There is no need to consider the ship's motion posture of the obstructing target, and the collision risk can be identified only by relying on the original output of the ship-borne laser radar. It is particularly suitable for application scenarios where the waterway is narrow in inland navigation environment, the safe encounter distance of ships is small, and the ship maneuvers change frequently. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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 or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] in:

[0038] Figure 1 A flowchart of a method for identifying collision risks of inland waterway vessels in one embodiment;

[0039] Figure 2 A schematic diagram of the division of the space to be identified between the own ship and the obstructing target in one embodiment;

[0040] Figure 3 A flowchart of another method for identifying collision risks of inland vessels in one embodiment;

[0041] Figure 4 The module composition of an inland river vessel collision risk identification device in one embodiment;

[0042] Figure 5 is a structural block diagram of a computer device in one embodiment;

[0043] Figure 6 It is a structural block diagram of a computer device in another embodiment. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0045] The present invention is described in detail below through specific embodiments.

[0046] See also Figure 1 As shown, Figure 1 A flow chart of a method for identifying collision risk of inland waterway vessels provided by an embodiment of the present invention includes the following steps:

[0047] S101, obtaining point cloud data of obstructing targets encountered by the ship during navigation by installing a laser radar on the ship;

[0048] For example, obtain n point cloud data output by the laser radar at time t: (D1, θ1, v1), (D2, θ2, v2), ..., (Dn ,θ n ,v n ). Among them, D i Represents the distance between the target point corresponding to the i-th point cloud and the laser radar; θ i represents the relative position between the target point corresponding to the i-th point cloud and the laser radar; v i The velocity of the target point corresponding to the i-th point cloud relative to the laser radar, where v i >0 means the corresponding target point is gradually approaching the laser radar, v i <0 means that the corresponding target point is gradually moving away from the laser radar.

[0049] In a possible implementation, the step of acquiring point cloud data of obstructing targets encountered by the ship during navigation by installing a laser radar on the ship includes:

[0050] The 0° azimuth reference of the point cloud target output by the laser radar is overlapped with the bow line of the own ship, and four intervals to be identified with the same angle are divided around the own ship in a clockwise direction, starting from the 0° azimuth reference;

[0051] The point cloud data of the obstructing target in the to-be-identified interval is obtained during the navigation of the own ship.

[0052] Exemplarily, the 0° azimuth reference of the point cloud target output by the laser radar is overlapped with the bow line of the own ship, and four intervals to be identified with the same angle are divided around the own ship in a clockwise direction, starting from the 0° azimuth reference, such as Figure 2 As shown in the figure, θ∈[0, 360°). When θ∈[0, 180°), it means that the target ship is located on the starboard side of the own ship; when θ∈[180°, 360°), it means that the target ship is located on the port side of the own ship. According to the orientation of the target ship relative to the own ship, the meeting space between the own ship and the target ship is divided into Figure 2 There are four recognition intervals shown, among which θ∈[0,90°) for interval I, θ∈[90,180°) for interval II, θ∈[180°,270°) for interval III, and θ∈[270°,360°) for interval IV.

[0053] S102, performing cluster analysis and interpolation processing on the point cloud data to obtain spatial information of the obstructing target;

[0054] In a possible implementation, the step of performing cluster analysis and interpolation processing on the point cloud data to obtain the spatial information of the obstructing target includes:

[0055] Performing point cloud clustering on the point cloud data according to a preset clustering method to obtain initial point cloud data belonging to the same navigation obstruction target;

[0056] The initial point cloud data is interpolated according to a preset interpolation method to obtain target point cloud data for fitting the outer contour of the obstruction target point cloud, and the spatial information of the obstruction target is obtained based on the target point cloud data.

[0057] Exemplarily, a density-based clustering method, a graph-based clustering method, a k-means clustering method, etc. may be used to filter out point cloud data belonging to the same target ship through point cloud clustering;

[0058] Exemplarily, point cloud interpolation methods such as multivariate polynomial interpolation, spline interpolation, and triangulation interpolation are used to interpolate the clustered point cloud data, complete the target ship point cloud information, smoothly fit the outer contour of the ship point cloud, and obtain the spatial information of the obstructing target.

[0059] S103, determining the extreme azimuth data of the own ship and the obstructing target during navigation according to the spatial information, and calculating the relative speed data and relative distance data between the obstructing target and the own ship along the azimuth line according to the spatial information;

[0060] In a possible implementation, the step of determining the extreme azimuth data of the own ship and the obstructing target during navigation according to the spatial information, and calculating the relative speed data and relative distance data between the obstructing target and the own ship along the azimuth line according to the spatial information includes:

[0061] The azimuth angle measured clockwise from the bow line of the own ship to the obstructing target is taken as the relative azimuth, and the maximum relative azimuth and the minimum relative azimuth of the own ship at the first moment during the navigation process are determined according to the spatial information, and the point cloud data corresponding to the maximum relative azimuth in the target point cloud data is taken as the first angle data in the extreme azimuth data, and the point cloud data corresponding to the minimum relative azimuth in the target point cloud data is taken as the second angle data in the extreme azimuth data;

[0062] Determine the maximum relative orientation and the minimum relative orientation of the ship at a second moment during navigation according to the spatial information, use the point cloud data corresponding to the maximum relative orientation in the target point cloud data as the third angle data in the extreme orientation data, and use the point cloud data corresponding to the minimum relative orientation in the target point cloud data as the fourth angle data in the extreme orientation data;

[0063] Calculating first relative speed data of the obstructing target along the azimuth line and the own ship at the first moment according to the spatial information, and calculating second relative speed data of the obstructing target along the azimuth line and the own ship at the second moment according to the spatial information;

[0064] The first relative distance data between the obstructing target and the own ship at the first moment is calculated according to the spatial information, and the second relative distance data between the obstructing target and the own ship at the second moment is calculated according to the spatial information, the relative speed data includes the first relative speed data and the second relative speed data, and the relative distance data includes the first relative distance data and the second relative distance data.

[0065] For example, the speed, the closest distance and the extreme azimuth of the obstacle target relative to the laser radar at adjacent times (t1, t2) are obtained. Then, the point cloud and motion parameter information of the extreme azimuth point of the obstacle target at the corresponding time are extracted and calculated, which are respectively and

[0066]

[0067] S104: Determine whether there is a collision risk between the own ship and the obstructing target according to the extreme value azimuth data, the relative speed data and the relative distance data.

[0068] In a possible implementation, the step of judging whether there is a collision risk between the own ship and the obstructing target according to the extreme value azimuth data, the relative speed data and the relative distance data includes:

[0069] When the second moment is the next moment of the first moment, if the second relative distance data is less than or equal to the preset safety distance, the obstructing target is regarded as a dangerous obstruction and an alarm is generated;

[0070] If the second relative distance data is greater than the preset safety distance, determining whether the second relative speed data is less than or equal to 0;

[0071] When the second relative speed data is greater than 0, the interval to be identified where the obstructing target is located is determined based on the third angle data; if the third angle data is greater than or equal to the first angle data of the interval to be identified, and the fourth angle data is less than or equal to the second angle data, the obstructing target is regarded as a dangerous obstruction and an alarm is generated.

[0072] For example, if D t2 ≤D safe , it means that the obstacle target has entered the safe area of ​​the ship. Regardless of its speed and direction, it should be regarded as a dangerous ship and its dynamics should be paid attention to. t2 >D safe , it means that the obstacle target is outside the safe range of the ship; further, if v t2If ≤0, it means that the obstacle target is gradually moving away from the ship or is relatively stationary with the ship along the azimuth line, and there is no danger of collision.

[0073] Furthermore, if D t2 >D safe And v t2 >0, it means that the obstacle target has not yet invaded the safe area of ​​the ship, but is gradually approaching the ship. At this time, firstly according to the maximum value of the point cloud azimuth at the most recent moment Determine the direction of the obstruction target ship, Figure 2 The direction of the obstacle target shown is interval I. If and This means that there is a risk of collision between the obstacle and the ship. If reasonable avoidance measures are not taken, a collision accident will occur. and This means that the obstacle will pass by the starboard side of the ship. and This means that the obstacle target will pass by the bow of this ship.

[0074] The laser radar is installed on the ship to obtain the point cloud data of the obstruction target encountered by the ship during navigation; the point cloud data is clustered and interpolated to obtain the spatial information of the obstruction target; the extreme azimuth data of the ship and the obstruction target during navigation are determined according to the spatial information, and the relative speed data and relative distance data between the obstruction target and the ship along the azimuth line are calculated according to the spatial information; and the extreme azimuth data, the relative speed data and the relative distance data are judged whether there is a collision risk between the ship and the obstruction target. There is no need to consider the ship's motion posture of the obstruction target, and the collision risk can be identified only by relying on the original output of the ship-borne laser radar, which is particularly suitable for application scenarios where the waterway is narrow in inland navigation environment, the safe encounter distance of ships is small, and the ship maneuvers change frequently.

[0075] In a possible implementation, the step of calculating, according to the spatial information, first relative speed data of the obstructing target and the own ship along the azimuth line at the first moment includes:

[0076] The average speed of the point cloud data is used as the moving speed of the obstructing target relative to the laser radar along the azimuth line at the first moment. The first relative speed data of the obstructing target along the azimuth line and the own ship at the first moment is calculated according to the spatial information through a preset formula. The mathematical representation of the preset formula is:

[0077]

[0078] Among them, m is the number of point cloud data, and t represents the first moment.

[0079] For example, the average speed of point cloud data represents the target ship's velocity relative to the laser radar along the azimuth line at time t (v t ),Right now:

[0080]

[0081] The minimum distance in the point cloud data represents the actual distance between the target ship and the own ship at time t (D t ),Right now:

[0082] D t =min(D1,D2,…,D m )

[0083] Filter out the maximum value of the relative orientation of the target ship from the point cloud data and minimum value That is, the extreme value orientation in the point cloud data of the target ship, and extract the speed and distance information corresponding to the extreme value orientation point. The point cloud data corresponding to the extreme value orientation point is and Right now:

[0084]

[0085] In a possible implementation, the present application provides a method for identifying collision risks of inland vessels, such as Figure 3 As shown, including:

[0086] S1: Install a ship-borne laser radar on inland vessels to automatically acquire and output point cloud data of ships and obstacles in the navigation environment;

[0087] S2: Perform cluster analysis and interpolation processing on the acquired point cloud data to identify the spatial information of the target ship;

[0088] S3: By continuously collecting and tracking the spatial information of the obstructing target, the relative speed and distance parameters of the obstructing target along the azimuth line and the ship are calculated, and the extreme azimuth of the point cloud data at different times during the tracking process, that is, the maximum and minimum values ​​of the relative azimuth, are extracted;

[0089] S4: By analyzing the changing trend of the extreme value orientation, the risk of ship collision can be identified.

[0090] Specifically, it includes: radar data acquisition, clustering, target (obstruction target) identification, target tracking, multi-frame radar data extraction, and collision risk identification based on multi-frame radar data.

[0091] In a possible implementation, Figure 4 As shown, the present application provides a device for identifying collision risks of inland waterway vessels, the device comprising:

[0092] The data acquisition module 201 is used to obtain point cloud data of obstructions encountered by the ship during navigation by installing a laser radar on the ship;

[0093] The data processing module 202 performs cluster analysis and interpolation processing on the point cloud data to obtain the spatial information of the obstructing target;

[0094] The calculation module 203 determines the extreme azimuth data of the own ship and the obstructing target during navigation according to the spatial information, and calculates the relative speed data and relative distance data between the obstructing target and the own ship along the azimuth line according to the spatial information;

[0095] The identification module 204 determines whether there is a collision risk between the own ship and the obstructing target according to the extreme value azimuth data, the relative speed data and the relative distance data.

[0096] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the service side of a method for identifying collision hazards of inland ships.

[0097] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the client side of a method for identifying collision risks of inland ships.

[0098] In one embodiment, a computer device is proposed, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: acquiring point cloud data of obstructing targets encountered by the own ship during navigation by installing a laser radar on the own ship; performing cluster analysis and interpolation processing on the point cloud data to acquire spatial information of the obstructing targets; determining extreme azimuth data of the own ship and the obstructing targets during navigation according to the spatial information, and calculating relative speed data and relative distance data between the obstructing targets and the own ship along the azimuth line according to the spatial information; and judging whether there is a danger of collision between the own ship and the obstructing targets according to the extreme azimuth data, the relative speed data, and the relative distance data.

[0099] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented: obtaining point cloud data of obstructing targets encountered by the own ship during navigation by installing a laser radar on the own ship; performing cluster analysis and interpolation processing on the point cloud data to obtain spatial information of the obstructing targets; determining extreme azimuth data of the own ship and the obstructing targets during navigation according to the spatial information, and calculating relative speed data and relative distance data between the obstructing targets and the own ship along the azimuth line according to the spatial information; and judging whether there is a danger of collision between the own ship and the obstructing targets according to the extreme azimuth data, the relative speed data and the relative distance data.

[0100] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0101] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0102] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0103] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method and related equipment for identifying collision risk of inland vessels, characterized in that: The method comprises: The laser radar is installed on the ship to obtain point cloud data of the obstruction targets encountered by the ship during navigation; Performing cluster analysis and interpolation processing on the point cloud data to obtain spatial information of the obstructing target; Determine the extreme azimuth data of the own ship and the obstructing target during navigation according to the spatial information, and calculate the relative speed data and relative distance data between the obstructing target and the own ship along the azimuth line according to the spatial information; Whether there is a collision risk between the own ship and the obstructing target is determined according to the extreme value azimuth data, the relative speed data and the relative distance data.

2. The method for identifying the risk of inland river vessel collision according to claim 1, characterized in that: The step of acquiring point cloud data of obstructing targets encountered by the ship during navigation by installing a laser radar on the ship includes: The 0° azimuth reference of the point cloud target output by the laser radar is overlapped with the bow line of the own ship, and four intervals to be identified with the same angle are divided around the own ship in a clockwise direction, starting from the 0° azimuth reference; The point cloud data of the obstructing target in the to-be-identified interval is obtained during the navigation of the own ship.

3. The method for identifying the risk of inland waterway vessel collision according to claim 1, characterized in that: The step of performing cluster analysis and interpolation processing on the point cloud data to obtain the spatial information of the obstructing target includes: Performing point cloud clustering on the point cloud data according to a preset clustering method to obtain initial point cloud data belonging to the same navigation obstruction target; The initial point cloud data is interpolated according to a preset interpolation method to obtain target point cloud data for fitting the outer contour of the obstruction target point cloud, and the spatial information of the obstruction target is obtained based on the target point cloud data.

4. The method for identifying the risk of inland river vessel collision according to claim 3, characterized in that: The step of determining the extreme azimuth data of the own ship and the obstructing target during navigation according to the spatial information, and calculating the relative speed data and relative distance data of the obstructing target and the own ship along the azimuth line according to the spatial information, comprises: The azimuth angle measured clockwise from the bow line of the own ship to the obstructing target is taken as the relative azimuth, and the maximum relative azimuth and the minimum relative azimuth of the own ship at the first moment during the navigation process are determined according to the spatial information, and the point cloud data corresponding to the maximum relative azimuth in the target point cloud data is taken as the first angle data in the extreme azimuth data, and the point cloud data corresponding to the minimum relative azimuth in the target point cloud data is taken as the second angle data in the extreme azimuth data; Determine the maximum relative orientation and the minimum relative orientation of the ship at a second moment during navigation according to the spatial information, use the point cloud data corresponding to the maximum relative orientation in the target point cloud data as the third angle data in the extreme orientation data, and use the point cloud data corresponding to the minimum relative orientation in the target point cloud data as the fourth angle data in the extreme orientation data; Calculating first relative speed data of the obstructing target along the azimuth line and the own ship at the first moment according to the spatial information, and calculating second relative speed data of the obstructing target along the azimuth line and the own ship at the second moment according to the spatial information; The first relative distance data between the obstructing target and the own ship at the first moment is calculated according to the spatial information, and the second relative distance data between the obstructing target and the own ship at the second moment is calculated according to the spatial information, the relative speed data includes the first relative speed data and the second relative speed data, and the relative distance data includes the first relative distance data and the second relative distance data.

5. The method for identifying the risk of inland river vessel collision according to claim 4, characterized in that: The step of judging whether the own ship is in danger of collision with the obstructing target according to the extreme value azimuth data, the relative speed data and the relative distance data comprises: When the second moment is the next moment of the first moment, if the second relative distance data is less than or equal to the preset safety distance, the obstructing target is regarded as a dangerous obstruction and an alarm is generated; If the second relative distance data is greater than the preset safety distance, determining whether the second relative speed data is less than or equal to 0; When the second relative speed data is greater than 0, the interval to be identified where the obstructing target is located is determined based on the third angle data; if the third angle data is greater than or equal to the first angle data of the interval to be identified, and the fourth angle data is less than or equal to the second angle data, the obstructing target is regarded as a dangerous obstruction and an alarm is generated.

6. The method for identifying the risk of inland river vessel collision according to claim 4, characterized in that: The step of calculating the first relative speed data of the obstructing target and the own ship along the azimuth line at the first moment according to the spatial information comprises: The average speed of the point cloud data is used as the moving speed of the obstructing target relative to the laser radar along the azimuth line at the first moment. The first relative speed data of the obstructing target along the azimuth line and the own ship at the first moment is calculated according to the spatial information through a preset formula. The mathematical representation of the preset formula is: Among them, m is the number of point cloud data, and t represents the first moment.

7. An inland river vessel collision risk identification device, characterized in that: The device comprises: A data acquisition module, used for acquiring point cloud data of obstructing targets encountered by the ship during navigation by installing a laser radar on the ship; A data processing module, used for performing cluster analysis and interpolation processing on the point cloud data to obtain spatial information of the obstructing target; A calculation module, used to determine the extreme azimuth data of the own ship and the obstructing target during navigation according to the spatial information, and calculate the relative speed data and relative distance data between the obstructing target and the own ship along the azimuth line direction according to the spatial information; The identification module is used to determine whether there is a collision risk between the own ship and the obstructing target according to the extreme value azimuth data, the relative speed data and the relative distance data.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for identifying collision risks of inland vessels as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for identifying collision risks of inland vessels as described in any one of claims 1 to 6 are implemented.

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