Multifunctional intelligent safety monitoring device for ship

Abstract

The invention relates to the technical field of ocean engineering, in particular to a multifunctional intelligent safety monitoring device for a ship, which comprises an intelligent monitoring system for monitoring the overall safety of the ship and a monitoring box, and is characterized in that the intelligent monitoring system comprises a basic identification module, an intelligent analysis module and an early warning decision module; the early warning decision module is used for receiving the power endurance time, the fuel endurance mileage, the current weather condition, the future weather condition, the environment where the ship is located, the ship moving speed and the ship moving angle which are analyzed and calculated by the intelligent analysis module, judging whether endurance hidden danger exists or not according to the power endurance time and the fuel endurance mileage, and sending the judgment result to the early warning decision module. Whether meteorological hidden dangers exist or not is judged according to the current weather condition and the future weather condition, and whether environmental safety hidden dangers and driving state hidden dangers exist or not is judged according to the environment where the ship is located, the ship moving speed and the ship moving angle; the method is complete in logic and easy to operate, and the safety performance of the ship can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of marine engineering technology, and in particular to a multifunctional intelligent safety monitoring device for ships. Background Art

[0002] Ships face many risks during navigation, such as bad weather, ship collisions and equipment failures, which may threaten the safety of ships and crew members. In order to solve these problems, governments and shipping companies have begun to seek an effective monitoring method to achieve real-time monitoring and early warning of ships. With the continuous development of computer technology, communication technology and sensor technology, multifunctional intelligent safety monitoring devices for ships have emerged.

[0003] In existing marine engineering technology, ships can identify the ship's own conditions and the conditions around the ship through multifunctional intelligent monitoring devices, and then analyze the safety hazards of the ship itself and the environment in which the ship is located through the observation and experience of the crew, so as to judge whether the ship is safe or what countermeasures need to be taken. This method can effectively monitor the ship's own conditions and the conditions around the ship, but it cannot provide a reasonable decision-making plan in the decision-making stage. It requires experienced crew members to analyze a reasonable response plan, which makes the ship's safety guarantee low in complex navigation conditions.

[0004] In summary, how to solve the problem that the existing monitoring devices in the prior art can effectively monitor the conditions of the ship itself and the conditions around the ship, but cannot provide reasonable decision-making solutions, resulting in low safety guarantees for ships in complex navigation conditions has become a difficult problem that needs to be solved urgently in the current field. Therefore, it is necessary to propose a multifunctional intelligent safety monitoring device for ships that can provide response solutions for crew members and improve navigation safety. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a multifunctional intelligent safety monitoring device for ships. Through the design of an early warning decision module, it can issue corresponding warnings when the ship faces various complex navigation situations, and provide operators with reasonable response plans based on the ship's own conditions and the environment in which it is located.

[0006] In order to achieve the above-mentioned purpose, the technical solution of the present invention is as follows: a multifunctional intelligent safety monitoring device for ships, including an intelligent monitoring system and a monitoring box for monitoring the overall safety of the ship, the intelligent monitoring system including a basic identification module, an intelligent analysis module and an early warning decision module.

[0007] The basic recognition module is used to collect image information, meteorological information and ship operation data of the ship and its surrounding environment, and transmit the image information, meteorological information and ship operation data to the intelligent analysis module; the basic recognition module is also used to identify the ship type based on the ship's image information, and transmit the ship type to the early warning decision module.

[0008] The intelligent analysis module is used to receive the image information, meteorological information and ship operation data collected by the basic recognition module, extract the ship's power reserve and fuel reserve according to the ship operation data, calculate the power endurance time and fuel endurance mileage according to the power reserve and fuel reserve, analyze the current weather conditions and future weather conditions according to the meteorological information, and analyze the current ship's environment, ship's moving speed and ship's moving angle according to the image information; the intelligent analysis module is also used to transmit the analyzed and calculated power endurance time, fuel endurance mileage, current weather conditions, future weather conditions, ship's environment, ship's moving speed and ship's moving angle to the early warning decision module.

[0009] The early warning decision module is used to receive the power endurance time, fuel endurance mileage, current weather conditions, future weather conditions, the ship's environment, ship's moving speed and ship's moving angle calculated by the intelligent analysis module, and filter the various types of information received.

[0010] The early warning decision module is also used to determine whether there are any endurance risks based on the power endurance time and fuel endurance mileage, determine whether there are any meteorological risks based on the current weather conditions and future weather conditions, and determine whether there are any environmental safety risks and driving status risks based on the ship's environment, ship movement speed and ship movement angle; the early warning decision module is also used to issue different early warnings based on different endurance risks, meteorological risks, environmental safety risks and driving status risks, and propose corresponding response plans.

[0011] Furthermore, the early warning decision module includes a fuzzy algorithm unit, a precise algorithm unit and a deep learning unit.

[0012] The fuzzy algorithm unit is constructed based on the fuzzy algorithm. Key information of various safety hazards is entered into the fuzzy algorithm unit. The fuzzy algorithm unit is used to filter information other than the key information and transmit the filtered information to the precise algorithm unit.

[0013] The precise algorithm unit is constructed based on the precise algorithm, and is used to identify various security risks based on the information filtered by the fuzzy algorithm unit.

[0014] The deep learning unit is built based on the deep learning model. The deep learning model is obtained through training on various safety hazards and their response plans. The deep learning unit uses the deep learning model to provide corresponding response plans for various safety hazards.

[0015] Furthermore, the image information includes ship images, water flow images, coastal images and obstacle images.

[0016] Furthermore, the meteorological information includes air temperature, air humidity, wind speed and wind direction.

[0017] Furthermore, the ship operation data includes the total operation mileage of the ship, the operation mileage of the current route, the power consumption, the fuel consumption, and the current operation speed, operation angle and navigation route.

[0018] Furthermore, in the early warning decision module, environmental safety hazards include bad weather conditions, bad water flow conditions, and many obstacles.

[0019] Furthermore, in the early warning decision module, driving status hazards include navigation speed deviation, navigation direction deviation and navigation route deviation.

[0020] The technical principles of the above scheme are as follows:

[0021] The basic recognition module collects the current ship image, air temperature, air humidity, wind speed and direction, as well as the total mileage of the ship, current power reserves and fuel reserves; and transmits these data to the intelligent analysis module. The basic recognition module also identifies the specific type of ship based on the ship image and transmits the ship type to the early warning decision module.

[0022] After the transmission is completed, the intelligent analysis module will analyze the current and future weather conditions based on the air temperature, air humidity, wind speed and wind direction; the intelligent analysis module will also analyze the service life of the ship based on the total mileage of the ship; the intelligent analysis module will also calculate the power endurance and fuel endurance based on the ship's power reserves and fuel reserves; at the same time, the intelligent analysis module will also analyze the ship's environment based on the current image information around the ship, and analyze the ship's moving speed and ship's moving angle based on the operating data; the intelligent analysis module will transmit the analyzed and calculated power endurance, fuel endurance, current weather conditions, future weather conditions, the ship's environment, the ship's moving speed and the ship's moving angle to the early warning decision module.

[0023] The early warning decision module will receive the power endurance time, fuel endurance mileage, current weather conditions, future weather conditions, the ship's environment, the ship's moving speed and the ship's moving angle calculated by the intelligent analysis module, and based on the type of ship, determine whether it can depart and whether there are any safety hazards at present, and issue different early warnings and propose corresponding response plans.

[0024] The above scheme has the following beneficial effects:

[0025] 1. Through the design of the basic recognition module, the present invention can comprehensively collect image information, meteorological information and ship operation data of the ship and its surrounding environment, realize multi-faceted monitoring of the ship's navigation status, and improve the comprehensiveness of ship safety monitoring. At the same time, the intelligent analysis module is used to deeply analyze and process various types of information collected by the basic recognition module, and key information is extracted, so as to analyze the type of ship, power endurance time, fuel endurance mileage, weather conditions and the environment in which the ship is located; and then the early warning decision module is used to monitor and control various safety hazards of the ship according to various types of information calculated and analyzed by the intelligent analysis module, and prompts and warnings are given to operators through a variety of different feedback methods, and reasonable response plans are provided to operators, thereby effectively improving the safety of ship operation.

[0026] 2. In the present invention, the early warning decision module can filter a large amount of non-critical information through the fuzzy algorithm unit and transmit the critical information to the precise algorithm unit, thereby greatly reducing the calculation amount of the precise algorithm unit and improving the processing efficiency of the precise algorithm unit for various types of information, so that the precise algorithm unit can quickly and accurately identify and analyze safety hazards, and the deep learning unit will propose corresponding response plans for various safety hazards.

[0027] 3. The present invention can be applied to different types of ships, whether cargo ships, passenger ships or small fishing boats, etc. The basic recognition module can identify the type of ship, conduct targeted monitoring and early warning according to its characteristics, and provide a more reasonable response plan for the ship.

[0028] Furthermore, the monitoring box is fixedly connected to a meteorological instrument, a radar, a wind vane, an alarm and several indicator lights of different colors; a rotating rod is rotatably connected to the monitoring box, and a camera is fixedly connected to the top of the rotating rod; the bottom of the rotating rod extends into the monitoring box and is provided with an adjustment component for adjusting the camera angle.

[0029] Furthermore, in the early warning decision module, different early warnings include different indicator light signals and voice alarm signals; the early warning decision module is also used to set the indicator light signal and voice alarm signal.

[0030] Furthermore, the adjustment component includes a driving member, which is fixedly connected to the inner wall of the monitoring box. The output shaft of the driving member is coaxially fixedly connected to the center rod, and a main bevel gear is fixedly sleeved on the center rod; a secondary bevel gear is fixedly connected to the bottom of the rotating rod; the main bevel gear and the secondary bevel gear are meshed; and the driving member is electrically connected to the controller.

[0031] Beneficial effects: The meteorological instrument can assist the basic identification module to collect air temperature and air humidity, providing a basis for the intelligent analysis module to analyze weather conditions. The radar can assist the basic identification module to collect the location information of the ship, making it easier for the intelligent analysis module to determine the navigation angle of the ship; the wind vane can assist the basic identification module to collect wind speed and wind direction; the alarm can send out different voice alarm signals according to different situations; and the indicator lights of different colors can send out indicator light signals according to different situations.

[0032] The driving member can adjust the rotation angle of the main bevel gear, thereby driving the secondary bevel gear to rotate, thereby driving the rotating rod to rotate, adjusting the monitoring range of the camera, so that the camera can perform all-round image detection.

[0033] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Flow chart of the intelligent monitoring system in an embodiment of the present invention.

[0035] Figure 2 This is an axonometric diagram of a multifunctional intelligent safety monitoring device for ships in an embodiment of the present invention.

[0036] Figure 3 It is a front cross-sectional view of the monitoring box in an embodiment of the present invention.

[0037] The figure marks in the drawings of the specification include: 1. monitoring box; 2. meteorological instrument; 3. camera; 4. rotating rod; 5. radar; 6. wind vane; 7. alarm; 8. indicator light; 9. secondary bevel gear; 10. servo motor; 11. center rod; 12. main bevel gear. DETAILED DESCRIPTION

[0038] The following is further described in detail through specific implementation methods:

[0039] Embodiment 1:

[0040] As attached Figure 1 As shown: a multifunctional intelligent safety monitoring device for ships, including an intelligent monitoring system and a monitoring box 1 for monitoring the overall safety of the ship, the intelligent monitoring system includes a basic recognition module for collecting various basic information of the ship, an intelligent analysis module for analyzing various basic information, and an early warning decision module for issuing early warnings and providing response plans; the basic recognition module, the intelligent analysis module and the early warning decision module are interconnected by signals.

[0041] The basic functions of each module are as follows:

[0042] The basic recognition module is used to collect image information, meteorological information and ship operation data of the ship and its surrounding environment, and transmit the image information, meteorological information and ship operation data to the intelligent analysis module; the basic recognition module is also used to identify the ship type based on the ship's image information, and transmit the ship type to the early warning decision module.

[0043] The image information includes ship images, water flow images, coastal images and obstacle images. The meteorological information includes air temperature, air humidity, wind speed and wind direction. The ship operation data includes the total operation mileage of the ship, the operation mileage of the current route, power consumption, fuel consumption, and the current operation speed, operation angle and navigation route.

[0044] The intelligent analysis module is used to receive the image information, meteorological information and ship operation data collected by the basic recognition module, extract the ship's power reserves and fuel reserves based on the ship operation data, calculate the power endurance time and fuel endurance mileage based on the power reserves and fuel reserves, analyze the current weather conditions and future weather conditions based on the meteorological information, and analyze the current ship's environment, ship's moving speed and ship's moving angle based on the image information; the intelligent analysis module is also used to transmit the analyzed and calculated power endurance time, fuel endurance mileage, current weather conditions, future weather conditions, ship's environment, ship's moving speed and ship's moving angle to the early warning decision module.

[0045] The early warning decision module is used to receive the power endurance time, fuel endurance mileage, current weather conditions, future weather conditions, the ship's environment, ship movement speed and ship movement angle calculated by the intelligent analysis module, and filter the various types of information received.

[0046] The early warning decision module is also used to determine whether there are any endurance risks based on the power endurance time and fuel endurance mileage, determine whether there are any meteorological risks based on the current weather conditions and future weather conditions, and determine whether there are any environmental safety risks and driving status risks based on the ship's environment, ship movement speed and ship movement angle; the early warning decision module is also used to issue different early warnings based on different endurance risks, meteorological risks, environmental safety risks and driving status risks, and propose corresponding response plans.

[0047] Among them, environmental safety hazards include bad weather conditions, bad water flow conditions, and many obstacles. For example, when a ship is sailing, if bad weather conditions or bad water flow conditions occur, the early warning decision module will issue a meteorological hazard warning and provide the operator with a response plan of "closing all doors, windows and hatches" to ensure the watertightness and wind and rain tightness of the ship and reduce the invasion of seawater and wind and rain; at the same time, it provides a further response plan of "reducing speed, adjusting routes, and avoiding bad weather areas" to reduce the impact of bad weather and waves on the ship and reduce the risk of damage to the hull.

[0048] Driving status hazards include deviation in speed, direction and route. For example, when a ship is impacted by large waves and its direction deviates, the warning decision module will issue a warning of driving status hazards and provide the operator with a response plan of "adjusting the navigation angle and correcting the route", so that the operator can adjust the route in time to avoid deviation.

[0049] The early warning decision module includes a fuzzy algorithm unit, a precise algorithm unit and a deep learning unit. The fuzzy algorithm unit is constructed based on the fuzzy algorithm. The key information of various safety hazards is recorded in the fuzzy algorithm unit. The fuzzy algorithm unit is used to filter information other than the key information and transmit the filtered information to the precise algorithm unit. The precise algorithm unit is constructed based on the precise algorithm. The precise algorithm unit is used to identify various safety hazards based on the information filtered by the fuzzy algorithm unit. The deep learning unit is constructed based on the deep learning model. The deep learning model is obtained through training on various safety hazards and their response plans. The deep learning unit uses the deep learning model to provide corresponding response plans for various safety hazards.

[0050] The specific implementation process is as follows:

[0051] In this embodiment, a cargo ship is used as an example. After the basic recognition module collects the ship image of the ship, it will analyze the ship type as a cargo ship based on the size of the ship in the current ship image and the cargo stacked on the ship. At the same time, the basic recognition module will collect the air temperature, air humidity, wind speed and wind direction in the environment where the ship is located, as well as the total mileage, current power reserves and fuel reserves of the ship; and transmit these data to the intelligent analysis module. The basic recognition module will also identify the specific type of the ship based on the ship image and transmit the ship type to the early warning decision module.

[0052] After the transmission is completed, the intelligent analysis module will analyze the current and future weather conditions based on the air temperature, air humidity, wind speed and wind direction data; the intelligent analysis module will also analyze the service life of the ship based on the total mileage of the ship; the intelligent analysis module will also calculate the power endurance and fuel endurance based on the ship's power reserves and fuel reserves; at the same time, the intelligent analysis module will also analyze the ship's environment based on the current image information around the ship, and analyze the ship's moving speed and ship's moving angle based on the operating data; the intelligent analysis module will transmit the analyzed and calculated power endurance, fuel endurance, current weather conditions, future weather conditions, the ship's environment, the ship's moving speed and the ship's moving angle to the early warning decision module.

[0053] The early warning decision module will receive the power endurance time, fuel endurance mileage, current weather conditions, future weather conditions, the ship's environment, the ship's moving speed and the ship's moving angle calculated by the intelligent analysis module, and based on the type of ship, determine whether it can depart and whether there are any safety hazards at present, and issue different early warnings and propose corresponding response plans. For example, the intelligent analysis module calculates that the remaining battery life is 8 hours. The early warning decision module will calculate the remaining driving time of the route based on the remaining mileage of the route and the current speed of the ship, and then compare the remaining driving time with the battery life. If the calculated remaining driving time is less than 8 hours, the current driving speed will be maintained; if the calculated remaining driving time is greater than 8 hours, a battery life hazard warning will be issued to the operator in the form of a voice broadcast, reminding the operator that the battery life is insufficient, and providing a response plan of "increasing the current driving speed and reducing unnecessary electricity consumption"; after the operator increases the driving speed according to the response plan and shuts down unnecessary electrical equipment, the early warning decision module will again calculate the adjusted remaining driving time based on the current speed of the ship and the remaining mileage, and again compare the current battery life with the adjusted remaining driving time to determine whether further adjustments are needed.

[0054] During the transmission of various types of data information, the early warning decision module will filter non-critical information through the fuzzy algorithm unit and transmit critical information to the precise algorithm unit, thereby greatly reducing the calculation amount of the precise algorithm unit and improving the processing efficiency of the precise algorithm unit for various types of information. The precise algorithm unit can then quickly and accurately identify and analyze safety hazards and provide operators with accurate response plans. For example, if the operator sets the power reserve less than 70%, the fuel reserve less than 80%, the power endurance less than 12 hours, the fuel endurance less than 12 hours, the wave height greater than 3m, the wind speed greater than 5m / s, and the water flow velocity greater than 3m / s as key information, the fuzzy algorithm will judge the power reserve information as non-key information and filter it when the power reserve is greater than 70%, and judge the fuel reserve information as non-key information and filter it when the fuel reserve is greater than 80%; when the basic recognition unit recognizes that the waves are greater than 3m, the wind speed is greater than 5m / s, or the water flow velocity is greater than 3m / s, the fuzzy calculation unit will immediately recognize the above key information and pass and transmit it to the precise calculation unit. At this time, the early warning decision module will analyze and calculate the safety hazards of the above key information through the precise calculation unit, so that the early warning decision module can process various aspects of information more efficiently and quickly, and then more accurately judge various safety hazards of the ship. The deep learning unit then provides corresponding response plans based on the current safety hazards.

[0055] Through the design of the basic recognition module, the present invention can comprehensively collect image information, meteorological information and ship operation data of the ship and its surrounding environment, realize multi-faceted monitoring of the ship's navigation status, and improve the comprehensiveness of ship safety monitoring. At the same time, the intelligent analysis module is used to deeply analyze and process various types of information collected by the basic recognition module, and key information is extracted, so as to analyze the type of ship, power endurance time, fuel endurance mileage, weather conditions, and the environment in which the ship is located; and then the early warning decision-making module is used to monitor and control various safety hazards of the ship according to various types of information calculated and analyzed by the intelligent analysis module, and prompts and warnings are given to operators through a variety of different feedback methods, and reasonable response plans are provided to operators, thereby effectively improving the safety of ship operation.

[0056] Embodiment 2:

[0057] As attached Figure 2-Figure 3As shown: Different from the above embodiment, the monitoring box 1 is bolted with a meteorological instrument 2, a radar 5, a wind vane 6, an alarm 7 and several indicator lights 8 of different colors; the monitoring box 1 is rotatably connected with a rotating rod 4, and the top of the rotating rod 4 is bolted with a camera 3; the bottom of the rotating rod 4 extends into the monitoring box 1 and is provided with an adjustment component for adjusting the angle of the camera 3. The adjustment component of this embodiment uses a monitoring bracket in the prior art. In the early warning decision module, different early warnings include different indicator light signals and voice alarm signals; the early warning decision module is also used to set indicator light signals and voice alarm signals.

[0058] The specific implementation process is as follows: the camera 3 can assist the basic recognition module to collect images around the ship, and the meteorological instrument 2 can assist the basic recognition module to collect air temperature and air humidity, providing a basis for the intelligent analysis module to analyze weather conditions and the environment. The radar 5 can assist the basic recognition module to collect the location information of the ship, so that the intelligent analysis module can judge the navigation angle of the ship; the wind vane 6 can assist the basic recognition module to collect wind speed and wind direction; the alarm 7 can send out different voice alarm signals according to different situations; the indicator lights 8 of different colors can send out indicator light signals according to different situations.

[0059] The operator can set the indicator light signal and voice alarm signal in different situations through the early warning decision module. For example, the operator sets the normal speed to 40km / h. When the speed is lower than the normal speed, the indicator light signal is set to blue; when the speed is equal to the normal speed, the indicator light signal is set to green; when the speed exceeds the normal speed, the indicator light signal is set to red; if the current speed of the ship is 30km / h, the blue indicator light 8 will light up at this time, indicating that the current speed is lower than the normal speed. Similarly, the indicator light signal can also be set to a voice alarm signal. For example, the current speed is 60km / h and the normal speed is 40km / h, and the alarm 7 sends a voice alarm signal: "The current speed is 60km / h, which is much higher than the normal speed. Please adjust the speed"; the operator can adjust the content of the voice alarm signal by himself.

[0060] Embodiment 3:

[0061] As attached Figure 3 As shown: Different from the above embodiment, the adjustment component includes a driving member. In this embodiment, the driving member is a servo motor 10, which is bolted and fixedly connected to the inner wall of the monitoring box 1. The output shaft of the servo motor 10 is coaxially bolted and fixedly connected to a center rod 11, and a main bevel gear 12 is bolted and fixedly provided on the center rod 11; the bottom of the rotating rod 4 is bolted and fixedly connected to a secondary bevel gear 9; the main bevel gear 12 and the secondary bevel gear 9 are meshed; the servo motor 10 is electrically connected to a controller.

[0062] The specific implementation process is as follows: the operator adjusts the rotation of the output shaft of the servo motor 10 through the controller, thereby driving the rotation of the center rod 11, and then adjusting the rotation angle of the main bevel gear 12 through the center rod 11; since the main bevel gear 12 and the secondary bevel gear 9 are meshed, the main bevel gear 12 will drive the secondary bevel gear 9 to rotate, thereby driving the rotating rod 4 to rotate, thereby adjusting the monitoring range of the camera 3, so that the camera 3 can perform all-round image detection.

[0063] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A multifunctional intelligent safety monitoring device for ships, characterized in that: It includes an intelligent monitoring system and a monitoring box (1) for monitoring the overall safety of a ship, wherein the intelligent monitoring system includes a basic identification module, an intelligent analysis module and an early warning decision module; The basic recognition module is used to collect image information, meteorological information and ship operation data of the ship and its surrounding environment, and transmit the image information, meteorological information and ship operation data to the intelligent analysis module; the basic recognition module is also used to identify the ship type based on the ship's image information, and transmit the ship type to the early warning decision module; Intelligent analysis module, used to receive image information, meteorological information and ship operation data collected by the basic recognition module; The power reserve and fuel reserve of the ship are extracted according to the ship operation data, and the power endurance and fuel endurance mileage are calculated according to the power reserve and fuel reserve; Analyze current and future weather conditions based on meteorological information, and analyze the current ship environment, ship movement speed, and ship movement angle based on image information; The intelligent analysis module is also used to transmit the analyzed and calculated power endurance time, fuel endurance mileage, current weather conditions, future weather conditions, the ship's environment, the ship's moving speed and the ship's moving angle to the early warning decision module; The early warning decision module is used to receive the power endurance time, fuel endurance mileage, current weather conditions, future weather conditions, the ship's environment, ship movement speed and ship movement angle calculated by the intelligent analysis module, and filter the received information; The early warning decision module is also used to determine whether there are any endurance risks based on the battery life and fuel endurance, whether there are any meteorological risks based on the current weather conditions and future weather conditions, and whether there are any environmental safety risks and driving status risks based on the ship's environment, ship movement speed and ship movement angle; The early warning decision module is also used to issue different warnings based on different endurance risks, meteorological risks, environmental safety risks and driving status risks, and propose corresponding response plans.

2. The multifunctional intelligent safety monitoring device for ships according to claim 1, characterized in that: The early warning decision module includes a fuzzy algorithm unit, a precise algorithm unit, and a deep learning unit; The fuzzy algorithm unit is constructed based on the fuzzy algorithm. The key information of various safety hazards is recorded in the fuzzy algorithm unit. The fuzzy algorithm unit is used to filter information other than the key information and transmit the filtered information to the precise algorithm unit. The precise algorithm unit is built based on the precise algorithm. The precise algorithm unit is used to identify various security risks based on the information filtered by the fuzzy algorithm unit. The deep learning unit is built based on the deep learning model. The deep learning model is obtained through training on various safety hazards and their response plans. The deep learning unit uses the deep learning model to provide corresponding response plans for various safety hazards.

3. The multifunctional intelligent safety monitoring device for ships according to claim 2, characterized in that: The image information includes ship images, water flow images, coastal images and obstacle images.

4. The multifunctional intelligent safety monitoring device for ships according to claim 3, characterized in that: Meteorological information includes air temperature, air humidity, wind speed and wind direction.

5. The multifunctional intelligent safety monitoring device for ships according to claim 4, characterized in that: The ship operation data includes the total operating mileage of the ship, the operating mileage of the current route, power consumption, fuel consumption, and the current operating speed, operating angle and navigation route.

6. The multifunctional intelligent safety monitoring device for ships according to claim 5, characterized in that: In the early warning decision-making module, environmental safety hazards include bad weather conditions, bad water flow conditions, and many obstacles.

7. The multifunctional intelligent safety monitoring device for ships according to claim 6, characterized in that: In the early warning decision module, driving status hazards include navigation speed deviation, navigation direction deviation and navigation route deviation.

8. The multifunctional intelligent safety monitoring device for ships according to claim 7, characterized in that: A meteorological instrument (2), a radar (5), a wind vane (6), an alarm (7) and a plurality of indicator lights (8) of different colors are fixedly connected to the monitoring box (1); a rotating rod (4) is rotatably connected to the monitoring box (1), and a camera (3) is fixedly connected to the top of the rotating rod (4); The bottom of the rotating rod (4) extends into the monitoring box (1) and is provided with an adjustment component for adjusting the angle of the camera (3).

9. The multifunctional intelligent safety monitoring device for ships according to claim 8, characterized in that: In the early warning decision module, different early warnings include different indicator light signals and voice alarm signals; the early warning decision module is also used to set the indicator light signals and voice alarm signals.

10. The multifunctional intelligent safety monitoring device for ships according to claim 9, characterized in that: The adjustment component comprises a driving member, the driving member is fixedly connected to the inner wall of the monitoring box (1), the output shaft of the driving member is coaxially fixedly connected to a center rod (11), a main bevel gear (12) is fixedly sleeved on the center rod (11); a secondary bevel gear (9) is fixedly connected to the bottom of the rotating rod (4); the main bevel gear (12) and the secondary bevel gear (9) are meshed; and the driving member is electrically connected to a controller.

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