Ship Intelligent Heading Warning System

Abstract

The present invention relates to the field of heading warning technology and discloses an intelligent heading warning system for ships, including: The system uses a multidimensional data module to obtain navigation data of the ship at all time points, image data of the navigation area at all time points, and meteorological data of the navigation area at all time points, and classifies and forms data sets; an intelligent assessment module uses image processing technology to identify abnormal targets appearing in the monitoring data sets, comprehensively assesses the multidimensional data of the abnormal targets; if no abnormal targets exist in the monitoring data sets, analyzes the rate of change of seawater color, and generates a corresponding abnormal data set; the comprehensive assessment of abnormal targets is highly accurate; the intelligent assessment module analyzes monitoring indexes during the ship's navigation process in real time, ensuring high real-time monitoring safety; determines the degree of danger of abnormal targets within a close range of the ship, the degree of change of ocean currents in the navigation area, and the safety of the ship's navigation status; and generates corresponding warning signals and navigation suggestions, making real-time monitoring and warning 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's intelligent heading warning system is a system based on advanced information technology and artificial intelligence. First, sensors, radar, and other devices collect data such as the ship's position, speed, heading, and attitude. This data is then processed and analyzed, and intelligent algorithms are used to extract key information to determine whether the ship has safety hazards. Common intelligent algorithms include neural networks, genetic algorithms, and fuzzy logic. Intelligent algorithms analyze historical and real-time data to predict the ship's navigation status and provide warnings. Neural networks can learn and identify normal ship behavior patterns, thereby identifying abnormal situations. Genetic algorithms can optimize different parameters to improve navigation safety. Intelligent algorithms can determine whether the ship is in an abnormal situation and promptly send warnings to the crew and monitoring personnel through alarm systems and communication equipment. Based on the ship's navigation requirements, channel conditions, weather and sea conditions, and other factors, the intelligent algorithm automatically generates a safe, efficient, and economical route, assisting the ship in automatically avoiding collisions in complex environments such as open waters and narrow waterways, achieving autonomous navigation. The system not only monitors and warns of the ship's navigation status in real time, but also provides intelligent route planning and management services, providing 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 problems solved

[0005] In response to the shortcomings of the existing technology, 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 easily ignore abnormal phenomena within a close range and do not fully consider the priority of warning signals.

[0006] (2) 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 multidimensional data module consists of a navigation data unit, an observation data unit, and a meteorological data unit. The navigation data unit is connected to the AIS system via the network to collect navigation data sets. The navigation data sets include navigation data of the ship at all time points. The observation data unit is connected to the monitoring device via the network to collect monitoring data sets. 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 via the network to collect environmental data sets. The environmental data sets include meteorological data of the navigation area at all time points. The multidimensional data module transmits the navigation data sets, monitoring data sets, and meteorological data sets to the intelligent evaluation module via 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 sea water color based on the monitoring data set, generates a 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 based on 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. Combined with the abnormal data group Ycsj and the navigation data set, it judges the degree of danger 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. The emergency warning unit judges 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.

[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. The navigation data includes real-time positioning, speed, heading, destination location and planned route. d represents the specific time when the navigation data is obtained.

[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, including 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. The meteorological data include 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 of 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 time point c 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 the comparison of 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;

[0019] If UP c and DO c There are no abnormal targets in the dataset. The seawater colors at the c-2th time point, c-1th time point, and cth time point in the monitoring dataset 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 seawater at time point c-1, SY c Indicates 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, SY c -SY c-1 represents the change in seawater color between the cth time point and the c-1th time point, Indicates the rate 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 rate of change of seawater color between the c-1th time point and the c-2th time point, The difference in the rate of change of seawater color 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 dataset, mark the wave height at the zth time point as LGZ, and mark 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 under safe navigation conditions for ships, AHW max Indicates the highest sea temperature under safe navigation conditions for ships, 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 navigation of the ship. HX z Indicates the heading of the ship at the zth time point in the navigation data set, FX z ≠HX z It represents 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 abnormal target appearance color is consistent with the reference color, the abnormal target movement speed exceeds the speed threshold VY, or the distribution density exceeds the density threshold MY, it indicates 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.

[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 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.

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

[0030] Compared with the existing technology, the present invention provides an intelligent ship heading warning system with the following beneficial effects:

[0031] 1. The present invention uses a multidimensional data module to obtain navigation data of the ship at all time points, image data of the navigation area at all time points, and meteorological data of the navigation area at all time points, and classifies them into navigation data sets, monitoring data sets, and meteorological data sets. The intelligent evaluation module uses image processing technology to identify abnormal targets appearing in the monitoring data set, and comprehensively evaluates the multidimensional data of the abnormal targets, which helps to quickly determine 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, and the change rate of the seawater color is analyzed to generate a corresponding abnormal data group Ycsj. 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 to determine whether a detour is needed to reach the destination. The comprehensive evaluation of abnormal targets is highly accurate.

[0032] 2. The present invention uses an intelligent evaluation module to analyze the monitoring index Jczs during the ship's navigation in real time, and the real-time monitoring is highly safe. 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 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 generate corresponding first-level warning signals and navigation suggestions. The emergency warning unit judges the safety of the ship's navigation status based on 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 Schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0035] Because traditional ship heading warning systems tend to ignore minor anomalies within a short distance, such as floating objects on the sea surface, changes in sea water color, and changes in sea temperature, which can easily lead to maritime accidents, the priority of warning signals is not fully considered during risk response, and the timeliness and effectiveness of emergency measures need to be improved. Therefore, an intelligent ship 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 The navigation data of the ship from the first time point to the nth time point respectively include real-time positioning, speed, heading, destination location and planned route. d represents the specific time when the navigation data is obtained. Comprehensive collection of navigation information is helpful for subsequent rapid decision-making of navigation recommendations.

[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. The image data include sea surface images and radar images. m represents the specific time when the image data is obtained. This reduces the patrol pressure of the crew and allows for the timely detection of abnormal floating objects, people who have fallen into the water, cargo that has fallen into the water, or ice floes.

[0038] The meteorological data unit collects environmental data sets through the network connection to the big data platform. The environmental data sets include 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 The meteorological data for the navigation area from the first time point to the yth time point include wave height, sea temperature, wind speed and direction. t represents the specific time when the meteorological data is obtained. Comprehensive collection of real-time meteorological data along 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 via 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 speed of change of the seawater color in the ship's navigation area based on the monitoring data set, generates the corresponding abnormal data set Ycsj, and connects it to the emergency warning unit via the network. The calculation process is as follows:

[0040] Extract the image data of 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 time point c 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 the comparison of 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 dataset. The seawater colors at the c-2th time point, c-1th time point, and cth time point in the monitoring dataset 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 seawater at time point c-1, SY c Indicates 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, SY c -SY c-1 represents the change in seawater color between the cth time point and the c-1th time point, Indicates the rate 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 rate 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 an abnormal data group. The faster the seawater color changes, the more severe the ocean current changes. Special attention should be paid to the ship's course to determine whether a detour is needed to reach the destination. The comprehensive assessment of abnormal targets is highly accurate.

[0048] The meteorological monitoring unit analyzes the monitoring index JcZs during the ship's voyage 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 dataset, mark the wave height at the zth time point as LGZ, and mark 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 under safe navigation conditions for ships, AHW max Indicates the highest sea temperature under safe navigation conditions for ships, 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 navigation of the ship. HX z Indicates the heading of the ship at the zth time point in the navigation data set, FXz≠HX z It indicates the ratio of 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 secure.

[0052] The emergency warning unit is equipped with a fixed range of volume threshold TY, speed threshold VY, density threshold MY and change threshold BY. It 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 ship and the degree of change of the ocean current in the navigation area, and generates corresponding warning signals and navigation suggestions. If there is an abnormal target in the image data, and the target volume in the abnormal data group Ycsj exceeds the volume threshold TY, the abnormal target appearance color is consistent with the reference color, the abnormal target moving speed exceeds the speed threshold VY, or the distribution density exceeds the density threshold MY, it indicates 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. The recommended speed is then output in combination with the navigation data set. If there is no abnormal target 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 indicates that the ocean current in the navigation area changes violently, and a first-level warning signal is generated. The recommended speed is then 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 recommendations. If 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 ship's navigation status is unsafe and a secondary warning signal is generated. Combined with the navigation data set, it outputs a recommended course 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, there were 4 abnormal targets within the close range of the ship. 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 movement 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 can easily scratch the hull and has a high risk, so a level 1 warning signal is generated.

[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 was sailing in sea area B, the wave height was 5 meters, the sea temperature was -10°C, the wind speed was 400 km / h, the heading was due east, and the wind direction was due west. The maximum wave height under the condition of safe sailing of the ship was 8 meters, the sea temperature safety range was -5-35°C, and the maximum wind speed was 383 km / h. The monitoring index Jczs was calculated as follows:

[0059]

[0060] In the formula, Jczs represents the monitoring index, It represents the ratio of the wave height at the zth time point to the maximum wave height. -5≤-10≤35 means the sea temperature at the current time point is compared with the sea temperature safety range. Due west ≠ due east means 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. If the sea temperature exceeds the safe range and the ratio is greater than 1, the safety of the ship's navigation status is low and a level 2 warning signal is generated to remind the crew to adjust the planned route.

[0061] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The ship intelligent heading warning system is characterized by: Including multi-dimensional data module and intelligent evaluation module; The multidimensional data module consists of a navigation data unit, an observation data unit and a meteorological data unit. The navigation data unit collects a navigation data set 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 The navigation data of the ship from the first time point to the nth time point respectively include real-time positioning, speed, heading, destination location and planned route. d represents the specific time when the navigation data is obtained; The observation data unit collects monitoring data sets through a network connection monitoring device. The monitoring data sets include image data of all time points in the navigation area. The expression of the monitoring data sets is {J1 m 、J2 m 、J3 m 、...、Jk m }, J1 m To Jk m are the image data from the first time point to the kth time point of the navigation area, including sea surface images and radar images, and m represents the specific time when the image data is obtained; The meteorological data unit collects environmental data sets through a network connection to a big data platform. The environmental data sets include 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 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; The multidimensional data module transmits the navigation data set, the monitoring data set and the meteorological data set to the intelligent evaluation module via the network; 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 and the change speed of the sea water color in the ship's navigation area based on the monitoring data set, generates the corresponding abnormal data group Ycsj, and connects to the emergency warning unit through the network; The calculation process of the abnormal data group Ycsj is as follows: Extract the image data of 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 time point c 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, Ycjs 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 the comparison of 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, and 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; If UP c and DO c There are no abnormal targets in the dataset. The seawater colors at the c-2th time point, c-1th time point, and cth time point in the monitoring dataset 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 seawater at time point c-1, SY c Indicates 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, SY c -SY c-1 represents the change in seawater color between the cth time point and the c-1th time point, Indicates the rate 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 rate of change of seawater color between the c-1th time point and the c-2th time point, The difference in the rate of seawater color change between three adjacent time points is the abnormal data group; 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 of the monitoring index Jczs 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 , mark the sea temperature at time point z as HW z , mark the wind speed at the zth time point as FS z , mark the wind direction at time point z as FX z ; 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 under safe navigation conditions for ships, AHW max Indicates the highest sea temperature under safe navigation conditions for ships, 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 navigation of the ship. HX z Indicates the heading of the ship at the zth time point in the navigation data set, FX z ≠HX z It represents 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; 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 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 generate corresponding warning signals and navigation recommendations. The emergency warning unit judges 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 recommendations.

2. The intelligent ship heading warning system according to claim 1, characterized in that: 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 abnormal target appearance color is consistent with the reference color, the abnormal target movement speed 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. The recommended speed is then output in combination with the navigation data set.

3. The intelligent ship heading warning system according to claim 2, characterized in that: 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 drastically, and a first-level warning signal is generated. The recommended speed is then output in combination with the navigation data set.

4. The intelligent ship heading warning system according to claim 3, 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. Then, combined with the navigation data set, a recommended heading is output to remind the crew to adjust the planned route.

5. The intelligent ship heading warning system according to claim 4, 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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