Intelligent course early warning system for ship

Abstract

The invention relates to the technical field of course early warning, and discloses an intelligent course early warning system for a ship. According to the system, navigation data of all time points of a ship, image data of all time points of a navigation area and meteorological data of all time points of the navigation area are obtained through a multi-dimensional data module and are classified to form a data set, and an intelligent evaluation module identifies and monitors abnormal targets in the data set through an image processing technology. The multi-dimensional data of the abnormal target is comprehensively evaluated, if the abnormal target does not exist in the monitoring data set, the change speed of the seawater color is analyzed, the corresponding abnormal data set is generated, the accuracy of comprehensively evaluating the abnormal target is high, the intelligent evaluation module analyzes the monitoring index in the ship navigation process in real time, and the real-time monitoring safety is high. According to the method, the danger degree of the abnormal target in the short-distance range of the ship body, the change degree of ocean current in the navigation area and the safety of the ship navigation state are judged, corresponding early warning signals and navigation suggestions are generated, and real-time monitoring and early warning are more timely and effective.

Description

Technical Field

[0001] The present invention relates to the technical field of heading warning, and in particular to an intelligent heading warning system for ships. Background Art

[0002] The ship intelligent heading warning system is a system based on advanced information technology and artificial intelligence technology. First, it is necessary to collect data such as the position, speed, heading, and attitude of the ship through sensors, radars, and other devices, and then process and analyze the collected data. The key information is extracted through intelligent algorithms to determine whether the ship has safety hazards. Common intelligent algorithms include neural networks, genetic algorithms, fuzzy logic, etc. Intelligent algorithms can predict the navigation status of ships and provide early warning information by analyzing historical data and real-time data. Among them, neural networks can learn and identify the normal behavior patterns of ships, thereby identifying abnormal situations. Genetic algorithms can improve the safety of ship navigation by optimizing different parameters. Intelligent algorithms can determine whether there are abnormal situations in ships, and send early warning information to crew members and monitoring personnel in a timely manner through alarm systems, communication equipment, etc., and then automatically generate safe, efficient, and economical routes based on the navigation requirements of ships, channel conditions, meteorological and sea conditions, etc., to assist ships in automatically avoiding collisions in complex environmental conditions such as open waters and narrow waterways, and realize autonomous navigation. It can not only monitor and warn the navigation status of ships in real time, but also provide intelligent route planning and management services to provide comprehensive support for ship navigation.

[0003] At present, traditional ship heading warning systems tend to ignore minor abnormal phenomena in close range, such as floating objects on the sea surface, changes in sea water color, changes in sea temperature, etc., which can easily cause maritime accidents. In the process of risk response, there is a lack of sufficient consideration of the priority of warning signals, and the timeliness and effectiveness of emergency measures need to be improved. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides an intelligent ship heading warning system, which has the advantages of high accuracy in comprehensive assessment of abnormal targets, more timely and effective real-time monitoring and warning, etc. It solves the problem that traditional ship heading warning systems tend to ignore abnormal phenomena within a close range and do not fully consider the priority of warning signals.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent ship heading warning system, comprising a multi-dimensional data module and an intelligent evaluation module;

[0008] The multi-dimensional data module is composed of a navigation data unit, a lookout data unit and a meteorological data unit. The navigation data unit is connected to the AIS system through the network to collect navigation data sets, and the navigation data sets include navigation data of the ship at all time points. The lookout data unit is connected to the monitoring device through the network to collect monitoring data sets, and the monitoring data sets include image data of the navigation area at all time points. The meteorological data unit is connected to the big data platform through the network to collect environmental data sets, and the environmental data sets include meteorological data of the navigation area at all time points. The multi-dimensional data module transmits the navigation data sets, monitoring data sets and meteorological data sets to the intelligent evaluation module through the network;

[0009] The intelligent assessment module consists of a lookout analysis unit, a meteorological monitoring unit and an emergency warning unit. The lookout analysis unit analyzes the abnormal targets in the ship's navigation area and the speed of change of the sea water color according to the monitoring data set, generates the corresponding abnormal data group Ycsj, and connects to the emergency warning unit through the network. The meteorological monitoring unit analyzes the monitoring index Jczs during the ship's navigation in real time according to the meteorological data set, and connects to the emergency warning unit through the network. The emergency warning unit is set with a fixed range of volume threshold TY, speed threshold VY, density threshold MY and change threshold BY, and then combines the abnormal data group Ycsj and the navigation data set to judge the danger level of abnormal targets within the close range of the hull and the degree of change of the ocean current in the navigation area, and generates corresponding warning signals and navigation suggestions. The emergency warning unit judges the safety of the ship's navigation status according to the monitoring index Jczs and the navigation data set, and generates corresponding warning signals and navigation suggestions.

[0010] Preferably, the expression of the navigation data set is {H1 d 、H2 d 、H3 d 、...、Hn d}, H1 d To Hn d They are the navigation data of the ship from the first time point to the nth time point, including real-time positioning, speed, heading, destination location and planned route. d represents the specific time of obtaining the navigation data.

[0011] Preferably, the expression of the monitoring data set is {J1 m 、J2 m 、J3 m ,...,Jk m}, J1 m To Jk m They are the image data from the first time point to the kth time point of the navigation area, the image data include sea surface images and radar images, and m represents the specific time when the image data is obtained.

[0012] Preferably, the expression of the meteorological data set is {Q1 t、Q2 t 、Q3 t 、...、Qy t}, Q1 t To Qy t They are the meteorological data from the first time point to the yth time point in the navigation area, including wave height, sea temperature, wind speed and wind direction. t represents the specific time when the meteorological data is obtained.

[0013] Preferably, the calculation process of the abnormal data group Ycsj is as follows:

[0014] Extract the image data at the cth time point in the monitoring data set, and mark the sea surface image at the cth time point as UP c , mark the radar image at the cth time point as DO c ;

[0015] Identify UP through image processing technology c and DO c Abnormal targets appearing in

[0016] If UP c and DO c There are abnormal targets in

[0017]

[0018] In the formula, Ycsj represents the abnormal data group, L×W×H represents the product of the length L, width W and height H of the abnormal target, that is, the target volume, YA represents the appearance color of the abnormal target, WY represents the reference color for measuring whether the target is abnormal, YA∩WY represents comparing the appearance color of the abnormal target with the reference color, YV represents the moving speed of the abnormal target, MS represents the total number of abnormal targets, QF represents the sea surface area of ​​the navigation area at the cth time point, It represents the total number of abnormal targets divided by the sea surface area to obtain the distribution density;

[0019] If UP c and DO c There are no abnormal targets in the data set. The seawater colors at the c-2th time point, c-1th time point, and cth time point in the monitoring data set are identified by image processing technology and marked as {SY c-2 , SY c-1 , SY c}, SY c-2 Indicates the color of seawater at time point c-2, SY c-1 Indicates the color of the seawater at the c-1th time point, SY c represents the color of seawater at the cth time point;

[0020]

[0021] In the formula, Ycsj represents the abnormal data group, Am represents the time difference between two time points, that is, unit time, and SY c -SY c-1 represents the change in seawater color between the cth time point and the c-1th time point, Indicates the speed of change of seawater color between the cth time point and the c-1th time point, SY c-1 -SY c-2 represents the change in seawater color between the c-1th time point and the c-2th time point, Indicates the speed of change of seawater color between the c-1th time point and the c-2th time point, The difference in the speed of seawater color change between three adjacent time points is the abnormal data group.

[0022] Preferably, the monitoring index JcZs calculation process is as follows:

[0023] Extract the meteorological data at the zth time point in the meteorological data set, and mark the wave height at the zth time point as LGZ, and the sea temperature at the zth time point as HW z , mark the wind speed at the zth time point as FS z , mark the wind direction at the zth time point as FXz;

[0024]

[0025] In the formula, JcZs represents the monitoring index, ALG represents the maximum wave height under the condition of safe navigation of the ship, Indicates the ratio of the wave height at the zth time point to the maximum wave height, AHW min Indicates the lowest sea temperature for safe navigation of ships, AHW max Indicates the highest sea temperature under safe navigation of the ship, AHW min ≤HW z ≤AHW max It means comparing the sea temperature at the zth time point with the sea temperature safety range. AFS means the maximum wind speed under the condition of safe sailing of the ship. HX z represents the heading of the ship at the zth time point in the navigation data set, FX z ≠HX z It indicates the ratio of the wind speed at the zth time point to the maximum wind speed when the ship's heading is opposite to the wind direction.

[0026] Preferably, when there are abnormal targets in the image data, and the target volume in the abnormal data group Ycsj exceeds the volume threshold TY, the appearance color of the abnormal target is consistent with the reference color, the moving speed of the abnormal target exceeds the speed threshold VY, or the distribution density exceeds the density threshold MY, it indicates that the abnormal targets within the close range of the ship are highly dangerous, and a first-level warning signal is generated, which is then combined with the navigation data set to output the recommended speed.

[0027] Preferably, there are no abnormal targets in the image data, but when the difference in the seawater color change rate in the abnormal data group Ycsj exceeds the change threshold BY, it indicates that the ocean current in the navigation area changes dramatically, and a first-level warning signal is generated, and the recommended speed is output in combination with the navigation data set.

[0028] Preferably, when the ratio of wave height to maximum wave height in the monitoring index Jczs is greater than 1, the sea temperature exceeds the sea temperature safety range, or the ratio of wind speed to maximum wind speed is greater than 1, it indicates that the safety of the ship's navigation state is low, and a secondary warning signal is generated. The recommended heading is output in combination with the navigation data set to remind the crew to adjust the planned route.

[0029] Preferably, the priority of the first-level warning signal is higher than that of the second-level warning signal.

[0030] Compared with the prior art, the present invention provides an intelligent ship heading warning system, which has the following beneficial effects:

[0031] 1. The present invention obtains the navigation data of the ship at all time points, the image data of the navigation area at all time points and the meteorological data of the navigation area at all time points through a multidimensional data module, and classifies them into a navigation data set, a monitoring data set and a meteorological data set. The intelligent evaluation module identifies the abnormal targets appearing in the monitoring data set through image processing technology, and comprehensively evaluates the multidimensional data of the abnormal targets, which helps to quickly judge the danger level of the abnormal targets. If there are no abnormal targets in the monitoring data set, the seawater color is identified through image processing technology, the changing speed of the seawater color is analyzed, and the corresponding abnormal data group Ycsj is generated. The faster the seawater color changes, the more drastic the ocean current changes, and special attention needs to be paid to the ship's heading and whether it is necessary to detour to reach the destination. The comprehensive evaluation of abnormal targets has high accuracy.

[0032] 2. The present invention uses an intelligent evaluation module to analyze the monitoring index Jczs during the navigation of the ship in real time, and the real-time monitoring has high safety. The emergency warning unit is provided with a fixed range of volume threshold TY, speed threshold VY, density threshold MY and change threshold BY, and then combined with the abnormal data group Ycsj and the navigation data set to judge the degree of danger of abnormal targets within the close range of the hull and the degree of change of the ocean current in the navigation area, and generate corresponding first-level warning signals and navigation suggestions. The emergency warning unit judges the safety of the ship's navigation status according to the monitoring index Jczs and the navigation data set, and generates corresponding second-level warning signals and navigation suggestions. The priority of the first-level warning signal is higher than that of the second-level warning signal, and real-time monitoring and warning are more timely and effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0034] 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 only 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.

[0035] Because traditional ship heading warning systems tend to ignore small abnormal phenomena in the close range, such as floating objects on the sea surface, changes in sea water color, changes in sea temperature, etc., which can easily cause maritime accidents, there is a lack of sufficient consideration of the priority of warning signals in the risk response process, and the timeliness and effectiveness of emergency measures need to be improved. Therefore, a ship intelligent heading warning system is provided. Please refer to Figure 1 ,The system includes a multi-dimensional data module and an intelligent evaluation module;

[0036] The multidimensional data module consists of a navigation data unit, an observation data unit, and a meteorological data unit. The navigation data unit collects navigation data sets through a network connection to the AIS system. The navigation data set includes the navigation data of the ship at all time points. The expression of the navigation data set is {H1 d 、H2 d 、H3 d 、...、Hn d}, H1 d To Hn d They are the navigation data of the ship from the first time point to the nth time point. The navigation data includes real-time positioning, speed, heading, destination location and planned route. d represents the specific time of obtaining the navigation data. Comprehensive collection of navigation information is helpful for subsequent rapid decision-making of navigation suggestions;

[0037] The observation data unit collects monitoring data sets through network connection monitoring devices. The monitoring data sets include image data of all time points in the navigation area. The expression of the monitoring data set is {J1 m 、J2 m 、J3 m ,...,Jk m}, J1 m To Jk m The image data are from the first time point to the kth time point in the navigation area, including sea surface images and radar images. m represents the specific time when the image data is obtained, which can reduce the patrol pressure of the crew and timely discover abnormal floating objects, people who fall into the water, cargoes that fall into the water or floating ice;

[0038] The meteorological data unit collects environmental data sets through the network connection to the big data platform. The environmental data sets include the meteorological data of all time points in the navigation area. The expression of the meteorological data set is {Q1 t 、Q2 t 、Q3 t 、...、Qy t}, Q1 t To Qy t They are the meteorological data from the first time point to the yth time point in the navigation area. The meteorological data include wave height, sea temperature, wind speed and wind direction. t represents the specific time of obtaining meteorological data. Comprehensive collection of real-time meteorological data on the planned route is key information to ensure navigation safety.

[0039] The multidimensional data module transmits the navigation data set, monitoring data set and meteorological data set to the intelligent evaluation module through the network; the intelligent evaluation module consists of a lookout analysis unit, a meteorological monitoring unit and an emergency warning unit. The lookout analysis unit analyzes the abnormal targets and the change speed of the sea water color in the ship's navigation area according to the monitoring data set, generates the corresponding abnormal data group Ycsj, and connects to the emergency warning unit through the network. The calculation process is as follows:

[0040] Extract the image data at the cth time point in the monitoring data set, and mark the sea surface image at the cth time point as UP c , mark the radar image at the cth time point as DO c ;

[0041] Identify UP through image processing technology c and DO c Abnormal targets appearing in

[0042] If UP c and DO c There are abnormal targets in

[0043]

[0044] In the formula, Ycsj represents the abnormal data group, L×W×H represents the product of the length L, width W and height H of the abnormal target, that is, the target volume, YA represents the appearance color of the abnormal target, WY represents the reference color for measuring whether the target is abnormal, YA∩WY represents comparing the appearance color of the abnormal target with the reference color, YV represents the moving speed of the abnormal target, MS represents the total number of abnormal targets, QF represents the sea surface area of ​​the navigation area at the cth time point, The distribution density is obtained by dividing the total number of abnormal targets by the sea surface area. Comprehensively evaluating the multi-dimensional data of abnormal targets helps to quickly determine the danger level of abnormal targets.

[0045] If UP c and DO c There are no abnormal targets in the data set. The seawater colors at the c-2th time point, c-1th time point, and cth time point in the monitoring data set are identified by image processing technology and marked as {SY c-2 , S Yc-1 , SY c}, SY c-2 Indicates the color of seawater at time point c-2, SY c-1 Indicates the color of the seawater at the c-1th time point, SY c represents the color of seawater at the cth time point;

[0046]

[0047] In the formula, Ycsj represents the abnormal data group, Δm represents the time difference between two time points, that is, unit time, and SY c -SY c-1 represents the change in seawater color between the cth time point and the c-1th time point, Indicates the speed of change of seawater color between the cth time point and the c-1th time point, SY c-1 -SY c-2 represents the change in seawater color between the c-1th time point and the c-2th time point, Indicates the speed of change of seawater color between the c-1th time point and the c-2th time point, The difference between the speed of seawater color change between three adjacent time points is the abnormal data group. The faster the seawater color changes, the more drastic the ocean current changes. It is necessary to pay special attention to the ship's heading and whether it is necessary to detour to reach the destination. The comprehensive assessment of abnormal targets has high accuracy.

[0048] The meteorological monitoring unit analyzes the monitoring index JcZs during the ship's navigation in real time based on the meteorological data set, and connects to the emergency warning unit through the network. The calculation process is as follows:

[0049] Extract the meteorological data at the zth time point in the meteorological data set, and mark the wave height at the zth time point as LGZ, and the sea temperature at the zth time point as HW z , mark the wind speed at the zth time point as FS z , mark the wind direction at the zth time point as FX2;

[0050]

[0051] In the formula, JCZs represents the monitoring index, ALG represents the maximum wave height under the condition of safe navigation of the ship, Indicates the ratio of the wave height at the zth time point to the maximum wave height, AHW min Indicates the lowest sea temperature for safe navigation of ships, AHW max Indicates the highest sea temperature under safe navigation of the ship, AHW min ≤HW z ≤AHW max It means comparing the sea temperature at the zth time point with the sea temperature safety range. AFS means the maximum wind speed under the condition of safe sailing of the ship. HX z represents the heading of the ship at the zth time point in the navigation data set, FXz≠HX z It indicates the ratio of the wind speed at the zth time point to the maximum wind speed when the ship's heading is opposite to the wind direction. Real-time monitoring is highly safe.

[0052] The emergency warning unit is provided with a fixed range of volume threshold TY, speed threshold VY, density threshold MY and change threshold BY, and then combined with the abnormal data group Ycsj and the navigation data set, judges the danger level of abnormal targets within the close range of the ship and the degree of change of the ocean current in the navigation area, and generates corresponding warning signals and navigation suggestions. If there are abnormal targets in the image data, and the target volume in the abnormal data group Ycsj exceeds the volume threshold TY, the appearance color of the abnormal target is consistent with the reference color, the moving speed of the abnormal target exceeds the speed threshold VY or the distribution density exceeds the density threshold MY, it means that the abnormal target within the close range of the ship has a high degree of danger, and a first-level warning signal is generated, and then the recommended speed is output in combination with the navigation data set. If there are no abnormal targets in the image data, but the difference in the speed of seawater color change in the abnormal data group Ycsj exceeds the change threshold BY, it means that the ocean current in the navigation area changes violently, and a first-level warning signal is generated, and then the recommended speed is output in combination with the navigation data set.

[0053] The emergency warning unit determines the safety of the ship's navigation status based on the monitoring index JcZs and the navigation data set, and generates corresponding warning signals and navigation suggestions. If the ratio of the wave height to the maximum wave height in the monitoring index JcZs is greater than 1, the sea temperature exceeds the sea temperature safety range, or the ratio of the wind speed to the maximum wind speed is greater than 1, it means that the safety of the ship's navigation status is low, and a secondary warning signal is generated. Combined with the navigation data set, the recommended heading is output to remind the crew to adjust the planned route;

[0054] The priority of the first-level warning signal is higher than that of the second-level warning signal, and real-time monitoring and warning are more timely and effective.

[0055] Example 1: In this experiment, a large cargo ship with a cargo capacity of 60,000 tons was selected as the experimental object. After detection, when the ship sailed to Sea Area A, the number of abnormal targets within the close range of the ship was 4. The length, width, height and color of the four abnormal targets were consistent, with a length of 6 meters, a width of 2 meters, a height of 3 meters, and an appearance color of orange. The reference colors for measuring whether the target is abnormal are black, orange and white. The moving speed is 0.5 meters per minute. The sea area of ​​Sea Area A is 10,000 square meters. The calculation formula of the abnormal data group Ycsj is as follows:

[0056]

[0057] In the formula, Ycsj represents the abnormal data group, 36 represents the volume of the abnormal target, the appearance color of the abnormal target is consistent with the reference color, 0.5 represents the moving speed of the abnormal target, and the distribution density of the abnormal target is 0.0004 per square meter. It is judged that the abnormal target may be a fallen container, which is easy to scratch the hull and has a high degree of danger, generating a first-level warning signal.

[0058] Example 2: In this experiment, a timber ship with a cargo capacity of 3,000 tons was selected as the experimental object. After testing, when the ship sailed to sea area B, the wave height was 5 meters, the sea temperature was -10°C, the wind speed was 400 kilometers per hour, the heading was due east, and the wind direction was due west. The maximum wave height under safe sailing conditions of the ship was 8 meters, the sea temperature safety range was -5-35°C, and the maximum wind speed was 383 kilometers per hour. The monitoring index Jczs calculation formula is as follows:

[0059]

[0060] In the formula, Jczs represents the monitoring index, It indicates the ratio of the wave height at the zth time point to the maximum wave height. -5≤-10≤35 indicates that the sea temperature at the current time point is compared with the sea temperature safety range. Due west ≠ due east indicates that the ship's heading is opposite to the wind direction. It indicates the ratio of the wind speed to the maximum wind speed at the current time point. It is judged that the sea temperature exceeds the sea temperature safety range, and the ratio of the wind speed to the maximum wind speed is greater than 1. The safety of the ship's navigation status is low, and a secondary warning signal is generated to remind the crew to adjust the planned route.

[0061] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Ship intelligent heading warning system, characterized by: Including multi-dimensional data module and intelligent evaluation module; The multidimensional data module is composed of a navigation data unit, a lookout data unit and a meteorological data unit. The navigation data unit is connected to the AIS system through a network to collect a navigation data set, and the navigation data set includes the navigation data of the ship at all time points. The lookout data unit is connected to the monitoring device through a network to collect a monitoring data set, and the monitoring data set includes the image data of the navigation area at all time points. The meteorological data unit is connected to the big data platform through a network to collect an environmental data set, and the environmental data set includes the meteorological data of the navigation area at all time points. The multidimensional data module transmits the navigation data set, the monitoring data set and the meteorological data set to the intelligent evaluation module through the network; The intelligent evaluation module is composed of a lookout analysis unit, a meteorological monitoring unit and an emergency warning unit. The lookout analysis unit analyzes the abnormal targets and the change speed of the sea water color in the ship's navigation area according to the monitoring data set, generates a corresponding abnormal data group Ycsj, and connects to the emergency warning unit through a network. The meteorological monitoring unit analyzes the monitoring index Jczs during the ship's navigation in real time according to the meteorological data set, and connects to the emergency warning unit through a network. The emergency warning unit is provided with a fixed range of volume threshold TY, speed threshold VY, density threshold MY and change threshold BY, and then combines the abnormal data group Ycsj and the navigation data set to judge the danger level of abnormal targets within the close range of the hull and the degree of change of the ocean current in the navigation area, and generates corresponding warning signals and navigation suggestions. The emergency warning unit judges the safety of the ship's navigation status according to the monitoring index Jczs and the navigation data set, and generates corresponding warning signals and navigation suggestions.

2. The ship intelligent heading warning system according to claim 1 is characterized in that: The expression of the navigation data set is {H1 d 、H2 d 、H3 d 、...、Hn d }, H1 d To Hn d They are the navigation data of the ship from the first time point to the nth time point, including real-time positioning, speed, heading, destination location and planned route. d represents the specific time of obtaining the navigation data.

3. The ship intelligent heading warning system according to claim 2 is characterized in that: The expression of the monitoring data set is {J1 m 、J2 m 、J3 m ,...,Jk m }, J1 m To Jk m They are the image data from the first time point to the kth time point of the navigation area, the image data include sea surface images and radar images, and m represents the specific time when the image data is obtained.

4. The intelligent ship heading warning system according to claim 3 is characterized in that: The expression of the meteorological data set is {Q1 t 、Q2 t 、Q3 t 、...、Qy t }, Q1 t To Qy t They are the meteorological data from the first time point to the yth time point in the navigation area, including wave height, sea temperature, wind speed and wind direction. t represents the specific time when the meteorological data is obtained.

5. The intelligent ship heading warning system according to claim 4 is characterized in that: The calculation process of the abnormal data group Ycsj is as follows: Extract the image data at the cth time point in the monitoring data set, and mark the sea surface image at the cth time point as UP c , mark the radar image at the cth time point as DO c ; Identify UP through image processing technology c and DO c Abnormal targets appearing in If UP c and DO c There are abnormal targets in In the formula, Ycsj represents the abnormal data group, L×W×H represents the product of the length L, width W and height H of the abnormal target, that is, the target volume, YA represents the appearance color of the abnormal target, WY represents the reference color for measuring whether the target is abnormal, YA∩WY represents comparing the appearance color of the abnormal target with the reference color, YV represents the moving speed of the abnormal target, MS represents the total number of abnormal targets, QF represents the sea surface area of ​​the navigation area at the cth time point, It represents the total number of abnormal targets divided by the sea surface area to obtain the distribution density; If UP c and DO c There are no abnormal targets in the data set. The seawater colors at the c-2th time point, c-1th time point, and cth time point in the monitoring data set are identified by image processing technology and marked as {SY c-2 , SY c-1 , SY c }, SY c-2 Indicates the color of seawater at time point c-2, SY c-1 Indicates the color of the seawater at the c-1th time point, SY c represents the color of seawater at the cth time point; In the formula, Ycsj represents the abnormal data group, Δm represents the time difference between two time points, that is, unit time, and SY c -SY c-1 represents the change in seawater color between the cth time point and the c-1th time point, Indicates the speed of change of seawater color between the cth time point and the c-1th time point, SY c-1 -SY c-2 represents the change in seawater color between the c-1th time point and the c-2th time point, Indicates the speed of change of seawater color between the c-1th time point and the c-2th time point, The difference in the speed of seawater color change between three adjacent time points is the abnormal data group.

6. The intelligent ship heading warning system according to claim 5 is characterized by: The monitoring index Jczs calculation process is as follows: Extract the meteorological data at the zth time point in the meteorological data set, and mark the wave height at the zth time point as LG z , the sea temperature at the zth time point is marked as HW z , mark the wind speed at the zth time point as FS z , mark the wind direction at the zth time point as FX2; In the formula, Jczs represents the monitoring index, ALG represents the maximum wave height under the condition of safe navigation of the ship, Indicates the ratio of the wave height at the zth time point to the maximum wave height, AHW min Indicates the lowest sea temperature for safe navigation of ships, AHW max Indicates the highest sea temperature under safe navigation of the ship, AHW min ≤HW z ≤AHW max It means comparing the sea temperature at the zth time point with the sea temperature safety range. AFS means the maximum wind speed under the condition of safe sailing of the ship. HX z represents the heading of the ship at the zth time point in the navigation data set, FX z ≠HX z It indicates the ratio of the wind speed at the zth time point to the maximum wind speed when the ship's heading is opposite to the wind direction.

7. The intelligent ship heading warning system according to claim 6 is characterized by: When there are abnormal targets in the image data, and the target volume in the abnormal data group Ycsj exceeds the volume threshold TY, the appearance color of the abnormal target is consistent with the reference color, the moving speed of the abnormal target exceeds the speed threshold VY, or the distribution density exceeds the density threshold MY, it means that the abnormal targets within the close range of the ship are highly dangerous, and a first-level warning signal is generated, and the recommended speed is output in combination with the navigation data set.

8. The intelligent ship heading warning system according to claim 7 is characterized in that: There are no abnormal targets in the image data, but when the difference in the speed of seawater color change in the abnormal data group Ycsj exceeds the change threshold BY, it means that the ocean current in the navigation area changes dramatically, and a first-level warning signal is generated, and the recommended speed is output in combination with the navigation data set.

9. The intelligent ship heading warning system according to claim 8, characterized in that: When the ratio of wave height to maximum wave height in the monitoring index Jczs is greater than 1, the sea temperature exceeds the sea temperature safety range, or the ratio of wind speed to maximum wind speed is greater than 1, it indicates that the safety of the ship's navigation status is low, and a secondary warning signal is generated. The recommended heading is output in combination with the navigation data set to remind the crew to adjust the planned route.

10. The ship intelligent heading warning system according to claim 9, characterized in that: The priority of the first-level warning signal is higher than that of the second-level warning signal.

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