CN117545135A - Self-adaptive control method and system for navigation mark lamp - Google Patents
Self-adaptive control method and system for navigation mark lamp Download PDFInfo
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/105—Controlling the light source in response to determined parameters
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/105—Controlling the light source in response to determined parameters
- H05B47/115—Controlling the light source in response to determined parameters by determining the presence or movement of objects or living beings
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/105—Controlling the light source in response to determined parameters
- H05B47/115—Controlling the light source in response to determined parameters by determining the presence or movement of objects or living beings
- H05B47/125—Controlling the light source in response to determined parameters by determining the presence or movement of objects or living beings by using cameras
Abstract
The invention discloses a self-adaptive control method and a self-adaptive control system for a navigation mark lamp, and belongs to the technical field of navigation mark lamps. Acquiring water environment information and underwater environment information around a ship, and generating navigation information according to the water environment information and the underwater environment information; acquiring a real-time video image around the navigation mark lamp, and acquiring weather information and target information according to the real-time video image; acquiring environmental parameters, and generating an optimal navigation path of the ship according to weather information, the environmental parameters, target information and navigation information; and determining the navigation mark lamp parameters to be adjusted according to the optimal navigation path so as to carry out self-adaptive control on the navigation mark lamp. The navigation device can adapt to various navigation requirements in real time, improves the navigation safety and efficiency, and solves the problem that the traditional navigation lamp usually adopts statically-set brightness, color and flickering modes and cannot adapt to continuously-changing environmental conditions.
Description
Technical Field
The invention relates to the technical field of navigation lights, in particular to a self-adaptive control method and a self-adaptive control system for a navigation light.
Background
The statements in this section merely relate to the background of the present disclosure and may not necessarily constitute prior art.
Navigation lights are vital navigational aids in navigation, and they are located on coastlines, ports, waterways, and in hazardous waters to provide critical navigational information to the vessel. Conventional beacon lights typically employ a static setting of brightness, color, and flashing patterns, however, such static settings cannot accommodate changing environmental conditions such as bad weather, reduced visibility, increased waterway traffic, and different types of vessels sailing in the same water area.
That is, the conventional navigation mark light control method generally controls the brightness, the operating time, the color, the blinking pattern, etc. of the light based on a fixed time table or a light intensity threshold, which may result in energy waste and unnecessary environmental pollution.
In addition, the navigation mark lamp needs to meet specific visibility requirements under different weather conditions, for example, the navigation mark lamp can effectively guide ships in severe weather such as fog, rain, strong wind and the like, and the navigation mark lamp control method with static setting cannot meet specific visibility requirements under different weather conditions. And traditional navigation mark lamp control method probably need regular inspection and manual adjustment light parameter, has increased artifical and maintenance cost.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a navigation mark lamp self-adaptive control method, a navigation mark lamp self-adaptive control system, electronic equipment and a computer readable storage medium, which can adapt to various navigation requirements in real time and improve navigation safety and efficiency.
In a first aspect, the invention provides a navigation mark lamp self-adaptive control method;
a self-adaptive control method of a navigation mark lamp comprises the following steps:
acquiring water environment information and underwater environment information around the navigation mark lamp, and generating navigation information according to the water environment information and the underwater environment information;
acquiring a real-time video image around the navigation mark lamp, and acquiring weather information and target information according to the real-time video image; acquiring environmental parameters, and generating an optimal navigation path of the ship according to weather information, the environmental parameters, target information and navigation information;
and determining the navigation mark lamp parameters to be adjusted according to the optimal navigation path so as to carry out self-adaptive control on the navigation mark lamp.
Further, the generating navigation information according to the above-water environment information and the underwater environment information specifically includes:
and establishing and updating the navigation map in real time through SLAM technology according to the water environment information and the underwater environment information.
Preferably, the generating the optimal navigation path of the ship according to the weather information, the environmental parameter, the target information and the navigation information specifically includes:
based on the navigation map, generating an optimal navigation path through a path planning algorithm according to the environmental parameters, the weather information and the target information.
Further, the determining, according to the optimal navigation path, the navigation mark lamp parameter to be adjusted includes:
determining navigation safety according to the optimal navigation path;
and determining the navigation mark lamp parameters to be adjusted according to the navigation safety and the optimal navigation path.
Further, the obtaining weather information and target information according to the real-time video image includes:
processing the real-time video image through an image classification algorithm to obtain the type of the ship and/or the type of the navigation mark;
and detecting whether severe weather conditions exist on the real-time video image through an image processing algorithm according to the changes of pixel colors, textures and contrast.
Further, the acquiring the water environmental information around the ship specifically includes:
acquiring the positions of the water objects around the navigation mark lamp and the distance, direction and/or moving speed between the corresponding water objects and the navigation mark lamp;
wherein, the radar processes the radio wave signals reflected by the water environment around the navigation mark lamp to generate the position of the water object and the distance, direction and moving speed between the water object and the ship.
Further, acquiring underwater environmental information around the ship includes:
acquiring an acoustic wave signal reflected by an underwater environment, and detecting an underwater object or underwater topography according to the time delay, amplitude and frequency characteristics of the acoustic wave signal;
and acquiring the distance between the ship and the underwater object according to the round trip time of the sound wave signal.
In a second aspect, the invention provides a navigation mark lamp self-adaptive control system;
a navigation light adaptive control system, comprising:
a navigation information generation module configured to: acquiring water environment information and underwater environment information around the navigation mark lamp, and generating navigation information according to the water environment information and the underwater environment information;
an optimal navigation path generation module configured to: acquiring a real-time video image around the navigation mark lamp, and acquiring weather information and target information according to the real-time video image; acquiring environmental parameters, and generating an optimal navigation path of the ship according to weather information, the environmental parameters, target information and navigation information;
an adaptive control module configured to: and determining the navigation mark lamp parameters to be adjusted according to the optimal navigation path so as to carry out self-adaptive control on the navigation mark lamp.
In a third aspect, the present invention provides an electronic device;
an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of the above-described adaptive control method for a beacon light.
In a fourth aspect, the present invention provides a computer-readable storage medium;
a computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the above-described beacon light adaptive control method.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the technical scheme provided by the invention, the navigation mark lamp system can be dynamically adjusted according to the actual environment condition, so that the visibility of the ship is improved, and the navigation safety is improved; the navigation device can adjust the setting of the navigation mark lamp according to the factors such as the water channel flow and the weather, provides more effective navigation guidance, is beneficial to the rapid and accurate identification of the safe navigation channel of the ship, and improves the navigation efficiency.
2. The technical scheme provided by the invention can realize intelligent self-adaptive adjustment of the navigation mark lamp, reduce energy consumption and avoid unnecessary energy waste, thereby realizing the effects of energy conservation and environmental protection; will play an important role in the field of marine security, bringing more opportunities and challenges to the development of the marine industry.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a schematic flow chart provided in an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms also are intended to include the plural forms, and furthermore, it is to be understood that the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, such as, for example, processes, methods, systems, products or devices that comprise a series of steps or units, are not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or inherent to such processes, methods, products or devices.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1
The navigation mark lamp control method in the prior art cannot be suitable for continuously changing maritime traffic environments and different navigation requirements; therefore, the invention provides a self-adaptive control method for the navigation mark lamp.
Next, a method for adaptively controlling a beacon light disclosed in this embodiment will be described in detail with reference to fig. 1. The self-adaptive control method of the navigation mark lamp comprises the following steps:
s1, acquiring the water environment information and the underwater environment information around the navigation mark lamp, and generating navigation information according to the water environment information and the underwater environment information.
Further, the step S1 specifically includes:
s101, acquiring the positions of the water objects around the navigation mark lamp and the distance, the direction and/or the moving speed between the corresponding water objects and the navigation mark lamp.
The water object comprises a ship, a water obstacle and the like, and the position of the water object, the distance, the direction and the moving speed between the corresponding water object and the ship are generated by radar processing radio wave signals reflected by the water environment around the navigation mark lamp.
Specifically, radar emits radio waves that propagate through the sky and reflect back. When waves interact with objects (such as ships, rocks, or other obstructions), they are reflected back to the radar system. The radar receives and processes the reflected signals and analyzes them to determine the distance, direction and speed of the object.
Target detection is achieved by analyzing the amplitude, frequency and phase characteristics of the signal, distance measurement is achieved by measuring the time difference of the signal from transmission to reception, and direction measurement is achieved by analyzing the phase difference of the signal. The velocity of the target is calculated by analyzing the Doppler shift of the signal. When the target object moves relative to the radar, the frequency of the signal changes, and by measuring this frequency shift, the speed of the target can be calculated.
The method comprises the steps of acquiring an acoustic wave signal reflected by an underwater environment, and detecting an underwater object or underwater topography according to time delay, amplitude and frequency characteristics of the acoustic wave signal; and acquiring the distance between the ship and the underwater object according to the round trip time of the sound wave signal.
Specifically, underwater obstacles, targets or underwater topography are detected by analyzing the time delay, amplitude and frequency characteristics of the echo signals; the distance of the underwater object is measured by calculating the round trip time of the acoustic signal. The sound wave signal is sent out from the water surface, returns to the water surface after being propagated underwater, and the water depth is calculated by measuring the round trip time; by continuously monitoring the echo of the acoustic wave signal and comparing it with the previous measurement results, target tracking can be achieved, and the position and motion state of the target can be determined.
The specific flow is as follows:
1. echo signal analysis:
echo signals from underwater obstacles are received, and the time delay of the echo signals, namely the time difference between the arrival time of the echo and the transmitting signal, is analyzed. The distance of the underwater obstacle or target can be determined by the time delay. A shorter time delay indicates that the object is closer to the sensor. The amplitude of the echo signal, i.e. the intensity of the echo signal, is analyzed.
The amplitude may provide information about the underwater target, such as the size, reflectivity, etc. of the target. A higher amplitude indicates a larger target or a higher reflectivity.
The frequency characteristics of the echo signal, i.e. the frequency components in the echo signal, are analyzed. Different underwater targets have different frequency response characteristics, and different targets or terrains can be distinguished by analyzing the frequency characteristics.
2. Distance and water depth were measured:
and transmitting the sound wave signal and recording the transmitting time. After a period of underwater propagation, an echo signal is received and the reception time is recorded. By calculating the time difference between the transmit and receive instants, i.e. the round trip time, the time of propagation of the acoustic signal in the water can be obtained.
Depending on the propagation velocity of the sound wave in the water, the round trip time can be converted into the distance of the underwater object. Distance = speed x round trip time.
If the signal propagates through the water surface, the water depth can be calculated by measuring the round trip time. Water depth = speed x round trip time/2.
3. Target tracking:
the echo of the acoustic wave signal is continuously monitored and compared with previous measurements. By comparing the time delay and the amplitude variation of the echo signal, the direction and speed of movement of the object can be determined. In combination with the measurements at a plurality of time points, the trajectory and motion state of the target can be deduced. Various tracking algorithms (e.g., kalman filtering or particle filtering) may be used to estimate and predict the position and motion state of the target.
Through the analysis process, the detection, the measurement of the distance and the target tracking of the underwater obstacle can be realized by utilizing the acoustic wave signals
S102, based on the above-water environment information and the underwater environment information, establishing and updating a navigation map S2 in real time through SLAM technology, acquiring a real-time video image around a navigation mark lamp, and acquiring weather information and target information according to the real-time video image, wherein the real-time video image is acquired by a camera.
Further, the step S2 specifically includes:
s201, processing the real-time video image through an image classification algorithm to acquire the ship type and/or the navigation mark type.
In this embodiment, the image classification algorithm is a convolutional neural network (Convolutional Neural Network, CNN) comprising a multi-layer convolution and pooling layer and a fully connected layer. The structure and parameters of these layers are learned by training data so that they can automatically extract and identify features in the image and map them to different classification categories.
The convolution layer generates a feature map by applying a series of convolution kernels (feature detectors) to extract local features in the real-time video image. The pooling layer is used to reduce the dimensionality of the feature map and extract more robust features, common pooling operations include max-pooling and average pooling. The fully connected layer connects the output of the pooling layer to one or more neurons for classification.
Training and optimizing: CNNs are trained by optimization algorithms such as back propagation and gradient descent to minimize the gap between the predicted output and the real labels.
The beacon light brightness to be adjusted is determined according to the type of the acquired ship and/or the navigation mark type, because the larger the ship is, the larger the area to be irradiated is.
S202, detecting whether severe weather conditions exist for the real-time video image through an image processing algorithm according to the changes of pixel colors, textures and contrast ratios. The method specifically comprises the following steps:
(1) And judging whether severe weather exists or not through texture analysis.
In severe weather conditions, the texture of the image may change, for example, the texture of the cloud layer becomes coarser.
Specifically, the texture information of the image is captured by a local binary pattern (Local Binary Patterns) technique, and whether severe weather exists is judged according to the change of the texture information.
(2) In severe weather conditions, there may be moving objects such as raindrops and snowflakes, and thus, the moving objects such as raindrops and snowflakes in the image are identified and weather conditions are inferred through a motion detection algorithm.
(3) Optical flow is a vector field that describes the temporal change of pixels in an image, which may change due to the presence of raindrops or snowflakes in severe weather conditions. Thus, the light can be analyzed by the Lucas-Kanade method to detect changes in moving objects and weather conditions.
And S3, acquiring environmental parameters, and generating an optimal navigation path suggestion of the ship according to the weather information, the environmental parameters, the target information and the navigation map.
Specifically, based on the navigation map, an optimal navigation path is generated through a path planning algorithm according to environmental parameters, weather information and target information.
The environmental parameters comprise temperature, humidity, wind speed and wind direction, and are acquired through meteorological sensors. The data (wind power, temperature, humidity and wind direction) from different sensors are combined and integrated by using a multi-sensor data fusion algorithm so as to acquire more accurate and comprehensive information; the path planning algorithm is an a-algorithm.
In this embodiment, the temperature, the humidity, the wind speed and the wind direction are fused by adopting a kalman filtering algorithm, so as to obtain comprehensive and accurate environmental parameters.
Wind power, temperature and humidity may affect turbulence and humidity in the air, which may affect light propagation and visibility. In severe weather conditions, it is desirable to increase the brightness of the beacon light to improve visibility.
The algorithm requires the use of the following valuation functions:
F(n)＝G(n)+H(n)
wherein F (n) is an estimated value moving from the starting point to the nth node; g (n) is a cost value that moves from the starting point to the nth node along the generated path; h (n) is a heuristic function for estimating the cost value of moving from the nth node to the target point.
The a algorithm also requires the use of Open and Closed tables: the Open table is used to store nodes that have been generated but not accessed, and the Closed table is used to store nodes that have been accessed.
The algorithm execution is as follows:
(A) Adding the starting point to an Open table;
(B) The following was repeated:
(a) Searching a node with the lowest F value in the Open table, and setting the current node as the node with the lowest F value;
(b) The current node is moved out of the Open table and put into a Closed table;
(c) All nodes adjacent to the current node are judged in sequence:
(1) If the node is already in the Closed table, the node is not considered;
(2) If the node is not in the OpenTable, adding the node to the OpenTable, taking the current node as a father node of the node and recording F, G and H values of the node; the method comprises the steps of carrying out a first treatment on the surface of the
(3) If the node is already in the Open table, when the G value reaching the node through the current node is smaller than the G value reaching the node without the current node, taking the current node as a father node of the node and recalculating F, G and H values of the node, otherwise, keeping F, G and H values of the node unchanged;
(d) Stopping searching: if the target point is added to the Closed table, saving the path; if the target point is not added to the Closed table, but the Open table is already empty, then the path does not exist.
Whether the algorithm can ensure that the optimal solution can be found or not is critical to the selection of the heuristic function H (n). In this embodiment, the heuristic function uses the euclidean distance, and its heuristic function H (n) is expressed as:
wherein d x ＝|x n -x goal |，d y ＝|y n -y goal |，x n For the coordinate value of the node n in the horizontal direction, y n For the coordinate value of the node n in the vertical direction, x goal Is the coordinate value of the target point in the horizontal direction, y goal Is the coordinate value of the target point in the vertical direction.
In this embodiment, each node includes the following information:
geographic location information (longitude and latitude): accurately represents the position of the vessel on the earth.
Chart data: including water depth, seafloor terrain, etc., have a significant impact on navigation.
Ship status: including current speed, heading, vessel type, etc.
Navigation information: including heading angle, speed, etc., may be used to calculate the next node.
Weather information: wind speed, wind direction, visibility, etc., to the real-time impact of the vessel voyage.
Historical path information: the past path information of the ship can be used to learn and adapt to future path planning.
The actual cost (G value) is calculated taking into account the sailing performance of the ship under different environmental conditions. The performance curve of the ship can be used taking into account fuel consumption and voyage time at different speeds. The actual cost can be calculated as follows:
g (n) = { actual path length } +w1{ environmental cost } (n) +w2{ weather cost } (n)
Where w1, w2 are weights.
In practical applications, environmental parameters and weather information may change. Thus, it is necessary to acquire such information in real time and update the state of the node. Thus, in this embodiment, sensors are used to monitor environmental conditions in real time and to obtain such information in real time.
The ship path planning needs to have a certain adaptation, if adverse weather changes or other sudden conditions are found in the sailing process, the path can be timely adjusted by considering environmental parameters, weather conditions or target obstacle information and the like, so that the path can be re-planned, a wind shelter port can be searched, the ship speed can be adjusted and the like.
And S4, determining the parameter of the navigation mark lamp to be adjusted according to the optimal navigation path so as to carry out self-adaptive control on the navigation mark lamp.
Specifically, the relevant regulations of the route points are screened according to the optimal navigation path, and the flickering frequency and mode of the navigation mark lamp are adjusted according to the relevant regulations of the optimal navigation path and the route points so as to provide specific navigation indication and warning information.
Example two
The embodiment discloses navigation mark lamp self-adaptation control system, includes:
a navigation information generation module configured to: acquiring water environment information and underwater environment information around the navigation mark lamp, and generating navigation information according to the water environment information and the underwater environment information;
an optimal navigation path generation module configured to: acquiring a real-time video image around the navigation mark lamp, and acquiring weather information and target information according to the real-time video image; acquiring environmental parameters, and generating an optimal navigation path of the ship according to weather information, the environmental parameters, target information and navigation information;
an adaptive control module configured to: and determining the navigation mark lamp parameters to be adjusted according to the optimal navigation path so as to carry out self-adaptive control on the navigation mark lamp.
It should be noted that, the navigation information generating module, the optimal navigation path generating module and the adaptive control module correspond to the steps in the first embodiment, and the modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.
Example III
The third embodiment of the invention provides an electronic device, which comprises a memory, a processor and computer instructions stored on the memory and running on the processor, wherein the steps of the self-adaptive control method of the navigation mark lamp are completed when the computer instructions are run by the processor.
Example IV
The fourth embodiment of the present invention provides a computer readable storage medium, configured to store computer instructions, where the computer instructions, when executed by a processor, complete the steps of the adaptive control method for a beacon light.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing embodiments are directed to various embodiments, and details of one embodiment may be found in the related description of another embodiment.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The self-adaptive control method for the navigation mark lamp is characterized by comprising the following steps of:
acquiring water environment information and underwater environment information around the navigation mark lamp, and generating navigation information according to the water environment information and the underwater environment information;
acquiring a real-time video image around the navigation mark lamp, and acquiring weather information and target information according to the real-time video image; acquiring environmental parameters, and generating an optimal navigation path of the ship according to weather information, the environmental parameters, target information and navigation information;
and determining the navigation mark lamp parameters to be adjusted according to the optimal navigation path so as to carry out self-adaptive control on the navigation mark lamp.
2. The adaptive control method of navigation lights according to claim 1, wherein generating navigation information based on the above-water environmental information and the underwater environmental information comprises:
and establishing and updating the navigation map in real time through SLAM technology according to the water environment information and the underwater environment information.
3. The adaptive control method of navigation lights according to claim 2, wherein the generating the optimal navigation path of the ship according to the weather information, the environmental parameter, the target information and the navigation information is specifically:
based on the navigation map, generating an optimal navigation path through a path planning algorithm according to the environmental parameters, the weather information and the target information.
4. The adaptive control method of a beacon light as claimed in claim 1, wherein the determining the beacon light parameter to be adjusted according to the optimal navigation path includes:
determining navigation safety according to the optimal navigation path;
and determining the navigation mark lamp parameters to be adjusted according to the navigation safety and the optimal navigation path.
5. The adaptive control method of navigation lights of claim 1, wherein the acquiring weather information and target information from the real-time video image comprises:
processing the real-time video image through an image classification algorithm to obtain the type of the ship and/or the type of the navigation mark;
and detecting whether severe weather conditions exist on the real-time video image through an image processing algorithm according to the changes of pixel colors, textures and contrast.
6. The adaptive control method of navigation lights according to claim 1, wherein the acquiring the environmental information on water around the ship is specifically:
acquiring the positions of the water objects around the navigation mark lamp and the distance, direction and/or moving speed between the corresponding water objects and the navigation mark lamp;
wherein, the radar processes the radio wave signals reflected by the water environment around the navigation mark lamp to generate the position of the water object and the distance, direction and moving speed between the water object and the ship.
7. The navigation light adaptive control method of claim 1, wherein acquiring underwater environmental information around the ship comprises:
acquiring an acoustic wave signal reflected by an underwater environment, and detecting an underwater object or underwater topography according to the time delay, amplitude and frequency characteristics of the acoustic wave signal;
and acquiring the distance between the ship and the underwater object according to the round trip time of the sound wave signal.
8. A beacon light adaptive control system, comprising:
a navigation information generation module configured to: acquiring water environment information and underwater environment information around the navigation mark lamp, and generating navigation information according to the water environment information and the underwater environment information;
a navigation advice generation module configured to: acquiring a real-time video image around the navigation mark lamp, and acquiring weather information and target information according to the real-time video image; acquiring environmental parameters, and generating an optimal navigation path of the ship according to weather information, the environmental parameters, target information and navigation information;
an adaptive control module configured to: and determining the navigation mark lamp parameters to be adjusted according to the optimal navigation path so as to carry out self-adaptive control on the navigation mark lamp.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of the method for adaptive control of a beacon light as claimed in any one of claims 1-7.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of adaptive control of a navigation light according to any one of claims 1-7.
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