CN117612356A

CN117612356A - Bridge anti-collision early warning method and device, storage medium and electronic equipment

Info

Publication number: CN117612356A
Application number: CN202311576359.9A
Authority: CN
Inventors: 李健; 李可田; 李泽浩
Original assignee: Shenzhen Zhengjie Intelligent Engineering Co ltd
Current assignee: Shenzhen Zhengjie Intelligent Engineering Co ltd
Priority date: 2023-11-22
Filing date: 2023-11-22
Publication date: 2024-02-27

Abstract

The application provides a bridge anti-collision early warning method, a device, a storage medium and electronic equipment, and relates to the technical field of bridge anti-collision. The method comprises the following steps: acquiring first time and first course information when the ship reaches a first monitoring position, and acquiring a second monitoring position of the ship at a second time; determining second heading information of the ship from the first monitoring position to the second monitoring position; acquiring a plurality of pieces of image information of the ship between a first moment and a second moment, respectively extracting a plurality of pieces of ship height data in the plurality of pieces of image information, and arranging the plurality of pieces of ship height data in sequence from the first moment to the second moment; inputting the first heading information, the second heading information and a plurality of ship height data into a trained passing probability prediction model to obtain passing probability; and when the passing probability is lower than the set probability threshold, sending an early warning signal to the ship. The probability of the ship passing through the bridge can be accurately predicted, and accurate early warning is carried out on the ship when the collision risk is predicted to be large.

Description

Bridge anti-collision early warning method and device, storage medium and electronic equipment

Technical Field

The application relates to the technical field of bridge anti-collision, in particular to a bridge anti-collision early warning method, a device, a storage medium and electronic equipment.

Background

Along with the continuous increase of the traffic flow of ships and the construction quantity of bridges, the bridges serve as important transportation hubs, and accordingly, the accidents of the ships striking the bridges frequently occur, and even serious accidents such as bridge collapse, ship sinking and the like can be caused.

For the bridge anti-collision accident, the related technology mostly adopts sensors such as infrared sensors, laser radars and the like to monitor the profile of the ship driven to the bridge, monitors whether the driven ship can smoothly pass through the bridge without collision, and performs early warning when the profile of the ship cannot pass through a bridge hole. Reminding the ship to pay attention to safety. However, under severe weather conditions such as stormy waves and raining, the accuracy of the monitoring method in the related art is affected due to the influence of the water level change or the stormy waves on the bump of the ship body and the deviation of the route, so that accurate early warning cannot be performed.

Disclosure of Invention

The application provides a bridge anti-collision early warning method, a device, a storage medium and electronic equipment, which consider the influence of wind waves on the course of a ship in the horizontal direction and the influence of the ship bump and the water level change on the height in the vertical direction, calculate the passing probability by using a passing probability prediction model, and send an early warning signal to the ship when the communication probability is lower than a probability threshold value. The probability of the ship passing through the bridge can be accurately predicted, and then the ship is accurately pre-warned when the collision risk is predicted to be large.

In a first aspect, the present application provides a bridge anti-collision early warning method, where the method includes:

acquiring first moment and first course information when a ship arrives at a first monitoring position, and acquiring a second monitoring position of the ship at a second moment;

determining second heading information of the ship from the first monitoring position to the second monitoring position;

acquiring a plurality of pieces of image information of the ship between the first moment and the second moment, and respectively extracting a plurality of pieces of ship height data in the plurality of pieces of image information, wherein the plurality of pieces of ship height data are arranged in sequence from the first moment to the second moment;

inputting the first heading information, the second heading information and the plurality of ship height data into a trained passing probability prediction model to obtain passing probability;

and when the passing probability is lower than a set probability threshold, sending an early warning signal to the ship.

By adopting the technical scheme, the influence of stormy waves on the course of the ship in the horizontal direction and the influence of the ship bump and the water level change on the height of the ship in the vertical direction are considered, the passing probability is calculated by using the passing probability prediction model, and an early warning signal is sent to the ship when the communication probability is lower than the probability threshold value. The probability of the ship passing through the bridge can be accurately predicted, and then the ship is accurately pre-warned when the collision risk is predicted to be large.

Optionally, when the ship is monitored to enter the monitoring area at the first moment, an intelligent laser range finder is used for collecting a first monitoring position where the ship is located and first heading information of the ship, the two intelligent laser range finders are respectively arranged at two ends of a bridge, and laser beams emitted by the intelligent laser range finders respectively form an included angle with the bridge and are horizontally intersected in the monitoring area;

and acquiring a second monitoring position of the ship traveling to the monitoring area at a second moment.

By adopting the technical scheme, the intelligent laser range finder adopts the laser range finding principle, has higher measurement accuracy and resolution, can accurately measure the distance and the relative position between the ship and the bridge, and effectively avoids measurement errors caused by factors such as visual errors or meteorological conditions. Meanwhile, the intelligent laser range finder has strong anti-interference capability and adaptability. Under severe weather conditions such as stormy waves, raining and the like, the influence of factors such as visual shielding on the measurement result can be effectively avoided.

Optionally, determining second heading information of the ship from the first monitoring location to the second monitoring location includes:

and connecting the first monitoring position and the second monitoring position in a straight line to obtain second course information of the ship.

Through adopting above-mentioned technical scheme, can acquire the course skew of boats and ships fast under the adverse weather's circumstances to whether can in time predict the boats and ships and can pass through the bridge safely.

Optionally, the acquiring the plurality of image information of the ship between the first time and the second time includes:

determining a shooting position of an image acquisition device based on the first monitoring position and shooting parameters of the image acquisition device;

and shooting a plurality of image information of the ship between the first moment and the second moment by using the image acquisition equipment.

Through adopting above-mentioned technical scheme, when using image acquisition equipment to acquire the height of boats and ships, adjust image acquisition equipment, can improve the degree of accuracy of follow-up image information, the follow-up probability of passing that can accurately predict boats and ships of being convenient for.

Optionally, the extracting a plurality of ship height data in the plurality of image information respectively includes:

determining edge contours of the ship in the plurality of image information respectively by using an image processing technology;

and determining a plurality of ship height data from the edge profile of the ship according to the scaling of the image information.

By adopting the technical scheme, the height information of the ship can be accurately extracted, more accurate data support is provided for the traffic probability prediction model, and the prediction accuracy and reliability are improved.

Optionally, the inputting the first heading information, the second heading information and the plurality of ship height data into a trained traffic probability prediction model to obtain a traffic probability includes:

calculating to obtain route deviation data of the ship between a first moment and a second moment based on the first course information and the second course information;

fitting the plurality of ship height data by using a linear regression equation to obtain height change data;

and inputting the route deviation data and the altitude change data into a trained traffic probability prediction model to obtain traffic probability.

By adopting the technical scheme, the course and the height data of the ship can be acquired and analyzed, so that the passing probability of the ship can be predicted more accurately, and the ship can be early warned in time.

Optionally, the inputting the route deviation data and the altitude change data into a trained traffic probability prediction model to obtain a traffic probability includes:

using the course deviation data and the altitude change data to image the ship to obtain ship image data;

and comparing the ship portrait data with the bridge traffic data by using the trained traffic probability prediction model to obtain traffic probability.

By adopting the technical scheme, the technical effect of the method is to improve the safety and efficiency of ship passing. Through image technology and model comparison, the ship passing probability is predicted more accurately

In a second aspect, the present application provides a bridge anti-collision early warning device, the device comprising:

the first data acquisition module is used for acquiring first moment and first course information when the ship reaches a first monitoring position and acquiring a second monitoring position of the ship at a second moment;

the course information acquisition module is used for determining second course information of the ship from the first monitoring position to the second monitoring position;

the second data acquisition module is used for acquiring a plurality of pieces of image information of the ship between the first moment and the second moment, respectively extracting a plurality of pieces of ship height data in the plurality of pieces of image information, and arranging the plurality of pieces of ship height data in the sequence from the first moment to the second moment;

the probability prediction module is used for inputting the first heading information, the second heading information and the plurality of ship height data into a trained passing probability prediction model to obtain passing probability;

and the early warning module is used for sending an early warning signal to the ship when the passing probability is lower than a set probability threshold value.

In a third aspect, the present application provides a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform any of the methods described above.

In a fourth aspect, the present application provides an electronic device comprising a processor, a memory for storing instructions, and a transceiver for communicating with other devices, the processor for executing the instructions stored in the memory to cause the electronic device to perform a method as in any one of the above.

In summary, the beneficial effects brought by the technical scheme of the application include:

considering the influence of wind waves on the course of the ship in the horizontal direction and the influence of the ship bump and the height of the ship in the vertical direction caused by water level change, calculating the passing probability by using the passing probability prediction model, and sending an early warning signal to the ship when the communication probability is lower than a probability threshold value. The probability of the ship passing through the bridge can be accurately predicted, and then the ship is accurately pre-warned when the collision risk is predicted to be large.

Drawings

Fig. 1 is a schematic flow chart of a bridge anti-collision early warning method according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a bridge anti-collision early warning device according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Reference numerals illustrate: 201. a first data acquisition module; 202. the course information acquisition module; 203. a second data acquisition module; 204. a probability prediction module; 205. an early warning module; 300. an electronic device; 301. a processor; 302. a communication bus; 303. a user interface; 304. a network interface; 305. a memory.

Detailed Description

In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments.

In the description of embodiments of the present application, words such as "exemplary," "such as" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "illustrative," "such as" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "illustratively," "such as" or "for example," etc., is intended to present related concepts in a concrete fashion.

In the description of the embodiments of the present application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

First, a brief description will be given of a specific scenario of the present application. With the increase of the traffic flow of ships and the construction quantity of bridges, accidents caused by the collision of the ships with the bridges occur, and serious threat is brought to the life and property safety of people. In order to effectively prevent and reduce the occurrence of bridge crashproof accidents, sensors such as infrared sensors, laser radars and the like are generally adopted in the related art to monitor the profile of a ship driven to a bridge. The sensors can capture the profile information of the ship, so as to judge whether the ship can smoothly pass through the bridge without collision. When the ship profile is monitored to be unable to pass through the bridge opening, the system can send out an early warning signal to remind a ship driver of paying attention to safety and timely taking avoidance measures. However, in severe weather conditions such as stormy waves, rain, etc., the effects of water level changes and stormy waves on hull pitch and course shifting are not negligible. This may result in the accuracy of the monitoring method in the related art being affected, and no accurate early warning can be performed. Specifically, when the wind wave is large, the ship body is affected by wind force to generate swing and jolt, so that the course and speed of the ship body are changed, and whether the ship can pass through the bridge can not be accurately judged. Meanwhile, the wind waves can cause fluctuation of the water level, so that the water level changes, and the accuracy of the monitoring method is further affected. In rainy weather, fluctuation of water level and jolt of a ship body are caused due to falling and accumulation of rainwater, so that accuracy of a monitoring method is affected. In addition, the rain may cause errors and deviations of sensors such as a laser radar and the like, and the reliability of the monitoring method is reduced. Therefore, aiming at the problem of bridge collision prevention under severe weather conditions such as stormy waves, rainy days and the like, more reliable and accurate monitoring technology and method are required to be researched and developed.

Referring to fig. 1, a schematic flow chart of a bridge anti-collision early warning method provided in an embodiment of the present application may be implemented by a computer program, may be implemented by a single chip microcomputer, or may be run on a bridge anti-collision early warning device based on von neumann system. The computer program may be integrated in the application or may run as a stand-alone tool class application. Specific steps of the bridge anti-collision early warning method are described in detail below.

Step S101: and acquiring first time and first course information when the ship reaches the first monitoring position, and acquiring a second monitoring position of the ship at the second time.

The first monitoring position is the starting position where the present application starts to perform the ship monitoring, when the ship sails into the first monitoring position. The first monitoring location is a preset location, usually located at the entrance of a bridge, port or other critical channel, and records the first moment of entry. The first heading information refers to a heading direction of the vessel when the vessel enters the monitoring area from the first monitoring position. The first heading information may be obtained by obtaining a bow orientation of the vessel.

The second moment is the moment after the ship sails in the monitoring area for a period of time, the data of the ship sailing from the first moment to the second moment is taken as a sample, and the influence degree of bad weather on the ship is analyzed, so that whether the ship can safely pass through the bridge is judged. And at the second moment, the position of the ship in the monitoring area is the second monitoring position.

Specifically, the first monitoring position, the first heading information and the second monitoring position are obtained as follows: when the ship is monitored to enter a monitoring area at the first moment, an intelligent laser range finder is used for collecting a first monitoring position where the ship is located and first course information of the ship, the two intelligent laser range finders are respectively arranged at two ends of a bridge, and laser beams emitted by the intelligent laser range finders form included angles with the bridge respectively and are horizontally intersected in the monitoring area; and acquiring a second monitoring position of the ship sailing to the monitoring area at a second moment.

The laser beam emitted by the intelligent laser range finder forms an included angle with the bridge, and horizontally meets in the monitoring area. This arrangement ensures accurate monitoring and data acquisition of the vessel. The position and heading information of the ship in the monitoring area can be determined through the included angle and the intersection point of the laser beam and the bridge.

As the vessel sails within the monitored area, at a second moment, the intelligent laser rangefinder may acquire data of a second monitored location of the vessel sailing to the monitored area. This data reflects the course and heading change of the vessel over a period of time.

Additionally, when birds or other objects accidentally touch the laser light, false positives may result from the laser rangefinder. However, by setting two beams of laser beams emitted horizontally, each beam forms a certain included angle with the bridge, the false alarm can be effectively avoided. When a ship enters a monitoring area, it will touch the two lasers in sequence. Because the laser beams form an included angle with the bridge, and the two laser beams are horizontally intersected, the two laser beams can be simultaneously contacted only when the ship is in the monitoring area. The system can judge the position and heading information of the ship more accurately, and the accuracy and reliability of monitoring are improved. By acquiring the data of the two laser beams simultaneously, the data can be checked mutually, and the possibility of false alarm is further reduced. If the data measured by the two laser beams are inconsistent, the system can perform exception handling and judgment so as to avoid false alarm caused by inaccuracy of single data.

Step S102: second heading information of the vessel from the first monitoring location to the second monitoring location is determined.

The second heading information refers to information of an actual sailing direction and a track when the ship sails from the first monitoring position to the second monitoring position in the monitoring area. The second heading information can reflect a lane departure problem of the ship due to the influence of severe weather. By taking second course information from the first monitoring position to the second monitoring position as a sample, the deviation of the subsequent ship when passing through the bridge can be predicted, so that whether the bridge can be safely passed or not can be judged.

An alternative implementation manner is that the first monitoring position and the second monitoring position are connected in a straight line, so that second course information of the ship is obtained.

The accuracy of early warning is guaranteed under the condition of time in severe weather, and the linear path passing through the first monitoring position and the second monitoring position can be regarded as actual course information to a certain extent due to the smaller interval between the first moment and the second moment. Therefore, the heading sample is quickly obtained, and the prediction of the subsequent passing probability is facilitated.

Step S103: and acquiring a plurality of pieces of image information of the ship between the first moment and the second moment, respectively extracting a plurality of pieces of ship height data in the plurality of pieces of image information, and arranging the plurality of pieces of ship height data in the sequence from the first moment to the second moment.

When the height change of the ship caused by the influence of wind and wave bumping or the influence of water level fluctuation is required to be obtained in the vertical direction, under the condition of shorter time, the image of the ship in the monitoring area is obtained, and the extraction of the height data of a plurality of ships in a plurality of image information is a high-efficiency mode, so that the passing probability of the ship, which can safely pass through the bridge, can be timely judged.

In one embodiment, the recording position of the image recording device and the recording parameters of the image recording device are determined on the basis of the first monitoring position; the method comprises capturing a plurality of image information of the vessel between a first time and a second time using an image capturing device.

An optimal photographing position can be determined through the first monitoring position, and an image of the ship can be clearly photographed. Meanwhile, photographing parameters include, but are not limited to, exposure time, aperture size, ISO sensitivity, focal length, and the like. These parameters need to be determined from the shooting requirements and the information of the first monitoring location. For example, if the vessel's voyage speed is faster, it may be desirable to use a faster shutter speed to capture the vessel's dynamics; if the vessel is of a larger size or distance, it may be desirable to use a larger aperture or higher ISO sensitivity to obtain a clearer image.

And shooting the ship for multiple times by using the image acquisition equipment between the first moment and the second moment to acquire multiple image information. The object is to record the sailing state and the change of the ship between the first moment and the second moment. Through multiple shooting, the information of the position, the course, the speed and the like of the ship at different time points can be obtained, and further the information is analyzed and processed, such as track tracking, course calculation, course judgment and the like.

Specifically, an image processing technology is used for respectively determining the edge contours of the ships in the plurality of image information; a plurality of vessel height data is determined from the edge profile of the vessel based on the scaling of the image information.

The plurality of image information is analyzed using image processing techniques to determine an edge profile of the vessel in each image. Specific image processing techniques may include, but are not limited to, edge detection, binarization processing, morphological processing, and the like. By these processing techniques, the edges of the vessel can be extracted from the image, forming an edge profile of the vessel. The size of the ship in the image can be converted into an actual size by scaling of the image information. Then, the height data of the vessel can be extracted from the edge profile of the vessel.

Step S104: and inputting the first heading information, the second heading information and the plurality of ship height data into the trained passing probability prediction model to obtain the passing probability.

The pass probability prediction model is a pass probability prediction model which is completed by training, and the model is trained and optimized by using historical data as a training set and a test set of samples and by the actual passing condition of ships in the historical data and the pass probability of manual labeling. The passing probability is quantized data for evaluating the collision avoidance of the bridge, and whether collision avoidance early warning is needed for the ship passing through the bridge is judged through the passing probability.

Specifically, route deviation data of the ship between the first moment and the second moment is calculated based on the first course information and the second course information; fitting a plurality of ship height data by using a linear regression equation to obtain height change data; and inputting the route deviation data and the altitude change data into a trained traffic probability prediction model to obtain the traffic probability.

Converting the first heading information and the second heading information into angle values, subtracting the heading angles at two moments to obtain a course deviation angle, and converting the course deviation angle into corresponding course deviation data, wherein the course deviation data is the duration between the first moment and the second moment, and the course deviation angle of the ship.

The height change data of the vessel can be obtained by fitting a plurality of vessel height data using a linear regression equation. A plurality of ship height data are extracted from the plurality of image information. And calculating the actual height of the ship according to the image scaling ratio by the height of the edge contour of the ship in each image information. By fitting these height data using a linear regression equation, the height variation trend of the ship can be obtained. And according to the result of the linear regression equation, the height change data of the ship can be obtained. These data may be expressed as a height value at each instant, or as a rate of change of height over time. The maximum height reached by the ship when passing the bridge can be predicted through the height change data, so that the passing probability of the ship passing the bridge normally is judged.

And inputting the route deviation data and the altitude change data obtained by calculation into a trained passing probability prediction model, so that the probability of passing the ship can be obtained.

Specifically, the ship is imaged by using the course deviation data and the altitude change data, so as to obtain ship image data; and comparing the ship portrait data with the bridge traffic data by using the trained traffic probability prediction model to obtain traffic probability.

And carrying out feature description on the ship by using the course deviation data and the altitude change data to obtain ship image data. The representation may include various characteristics of the vessel's sailing conditions, sailing trajectories, altitude changes, etc. as it passes through the bridge opening. And converting the constructed ship portrait into a corresponding data format to obtain ship portrait data. The data can be used for subsequent pass probability prediction model comparison, namely, according to the state and the area of the ship when passing through the bridge, the ship portrait data and the bridge pass data are compared by using the trained pass probability prediction model, so that the probability of passing the ship is obtained.

Step S105: and when the passing probability is lower than the set probability threshold, sending an early warning signal to the ship.

And setting a threshold value of the passing probability according to the safety requirement of bridge collision prevention and the actual weather condition. The threshold may be a fixed value or may be a value that is dynamically adjusted based on a variety of factors.

If the calculated passing probability is lower than the set probability threshold, the ship is considered to have the passing risk, and an early warning signal is required to be sent to remind a ship driver to take corresponding avoidance measures. The early warning signal can be in various forms such as sound, lamplight, radio signal and the like, and is not particularly limited.

The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

Referring to fig. 2, a schematic structural diagram of a bridge anti-collision early warning device according to an exemplary embodiment of the present application is shown. The apparatus may be implemented as all or part of an apparatus by software, hardware, or a combination of both. The device comprises a first data acquisition module 201, a course information acquisition module 202, a second data acquisition module 203, a probability prediction module 204 and an early warning module 205.

The first data obtaining module 201 is configured to obtain a first moment when the ship arrives at the first monitoring position and first heading information, and obtain a second monitoring position where the ship is located at the second moment;

a heading information obtaining module 202, configured to determine second heading information of the ship from the first monitoring location to the second monitoring location;

a second data obtaining module 203, configured to obtain a plurality of image information of the ship between the first time and the second time, respectively extract a plurality of ship height data in the plurality of image information, and arrange the plurality of ship height data in order from the first time to the second time;

the probability prediction module 204 is configured to input the first heading information, the second heading information, and the plurality of ship height data into a trained traffic probability prediction model, so as to obtain a traffic probability;

and the early warning module 205 is used for sending an early warning signal to the ship when the passing probability is lower than the set probability threshold value.

Optionally, the first data acquisition module 201 further includes a laser ranging unit.

The system comprises a laser ranging unit, a monitoring area and a monitoring area, wherein the laser ranging unit is used for monitoring that a ship just enters the monitoring area at a first moment, an intelligent laser range finder is used for collecting a first monitoring position where the ship is located and first heading information of the ship, the two intelligent laser range finders are respectively arranged at two ends of a bridge, and laser beams emitted by the intelligent laser range finders form included angles with the bridge respectively and are horizontally intersected in the monitoring area; and acquiring a second monitoring position of the ship sailing to the monitoring area at a second moment.

Optionally, the heading information acquisition module 202 further includes a straight navigation unit.

And the linear navigation unit is used for connecting the first monitoring position and the second monitoring position in a linear manner to obtain second course information of the ship.

Optionally, the second data acquisition module 203 further includes a shooting adjustment unit and a height extraction unit.

The shooting adjustment unit is used for determining the shooting position of the image acquisition equipment and shooting parameters of the image acquisition equipment based on the first monitoring position; the method comprises capturing a plurality of image information of the vessel between a first time and a second time using an image capturing device.

A height extraction unit for determining edge profiles of the ship in the plurality of image information, respectively, using an image processing technique; a plurality of vessel height data is determined from the edge profile of the vessel based on the scaling of the image information.

Optionally, the probability prediction module 204 further includes a probability calculation unit and an image comparison unit.

The probability calculation unit is used for calculating route deviation data of the ship between the first moment and the second moment based on the first course information and the second course information; fitting a plurality of ship height data by using a linear regression equation to obtain height change data; and inputting the route deviation data and the altitude change data into a trained traffic probability prediction model to obtain the traffic probability.

The image comparison unit is used for performing image on the ship by using the heading deviation data and the altitude change data to obtain ship image data; and comparing the ship portrait data with the bridge traffic data by using the trained traffic probability prediction model to obtain traffic probability.

The embodiment of the present application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are adapted to be loaded by a processor and executed by the processor, where the specific execution process may be in the specific description of the embodiment shown in fig. 1, and is not described herein.

Referring to fig. 3, a schematic structural diagram of an electronic device is provided in an embodiment of the present application. As shown in fig. 3, the electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, at least one communication bus 302.

Wherein the communication bus 302 is used to enable connected communication between these components.

The user interface 303 may include a standard wired interface, a wireless interface, among others.

The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.

Wherein the processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various portions of the overall server, perform various functions of the server and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and invoking data stored in the memory 305. Alternatively, the processor 301 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 301 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 301 and may be implemented by a single chip.

The Memory 305 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 305 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 305 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. Memory 305 may also optionally be at least one storage device located remotely from the aforementioned processor 301. As shown in fig. 3, the memory 305, which is a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program of the bridge collision avoidance early warning method.

In the electronic device 300 shown in fig. 3, the user interface 303 is mainly used for providing an input interface for a user, and acquiring data input by the user; and processor 301 may be configured to invoke an application program in memory 305 that stores a bridge collision avoidance method, which when executed by one or more processors, causes the electronic device to perform the method as in one or more of the embodiments described above.

An electronic device readable storage medium storing instructions. The method of one or more of the above embodiments is performed by one or more processors, which when executed by an electronic device.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In the several embodiments provided herein, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.

The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains.

Claims

1. The bridge anti-collision early warning method is characterized by comprising the following steps of:

2. The method of claim 1, wherein the obtaining the first moment and the first heading information when the vessel arrives at the first monitoring location, and the obtaining the second monitoring location when the vessel is at the second moment, comprises:

when a ship is monitored to enter a monitoring area at a first moment, an intelligent laser range finder is used for collecting a first monitoring position where the ship is located and first heading information of the ship, the two intelligent laser range finders are respectively arranged at two ends of a bridge, and laser beams emitted by the intelligent laser range finders respectively form an included angle with the bridge and are horizontally intersected in the monitoring area;

3. The method of claim 1, wherein determining second heading information of the vessel from the first monitoring location to the second monitoring location comprises:

4. The method of claim 1, wherein the acquiring the plurality of image information of the vessel between the first time and the second time comprises:

5. The method according to claim 1, wherein the extracting the plurality of ship height data in the plurality of image information, respectively, comprises:

6. The method of claim 1, wherein the inputting the first heading information, the second heading information, and the plurality of vessel height data into the trained probability of passage prediction model results in a probability of passage, comprising:

7. The method of claim 6, wherein said inputting the course deviation data and the altitude change data into a trained probability of pass prediction model results in a probability of pass comprising:

8. A bridge anti-collision early warning device, the device comprising:

9. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method of any one of claims 1 to 7.

10. An electronic device comprising a processor, a memory and a transceiver, the memory configured to store instructions, the transceiver configured to communicate with other devices, the processor configured to execute the instructions stored in the memory, to cause the electronic device to perform the method of any one of claims 1-7.