A combined fleet intelligent early warning method and system

Abstract

The present invention provides a combined fleet intelligent early warning method and system, which relates to the field of data processing technology. The method includes: obtaining fleet navigation data, route sea state data and cargo status data of the fleet, calculating the sway angle of the ship's hull when passing through a preset route according to the fleet navigation data and the route sea state data, analyzing the influence of the sway angle on the ship's center of gravity, calculating the hull stability of the ship, obtaining a route risk assessment result, determining the cargo center of gravity position according to the hull state data and the cargo state data, obtaining the center of gravity deviation between the cargo center of gravity and the ship center of gravity, calculating the cargo stability of the cargo, analyzing whether the cargo has a risk of leaving the storage position, obtaining a cargo risk assessment result, identifying high-risk sections on the preset route and cargo with potential safety hazards, generating early warning information, avoiding high-risk sections, adjusting the preset route, and generating a safe route. The present invention can perform intelligent early warning according to sea state data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent early warning method and system for a combined fleet. Background Art

[0002] The existing fleet early warning system mainly focuses on the impact of the external environment on fleet safety, especially the impact of weather, sea conditions and other factors on ship navigation. When the sea conditions are bad or there are strong winds, the traditional early warning system will analyze weather data to determine whether the fleet is facing navigation hazards and issue early warnings for potential risks. However, when dealing with severe shaking of the hull, existing technologies may not be able to effectively assess the interior of the ship, especially the safety risks that may be caused by dangerous goods.

[0003] When a fleet encounters strong winds, huge waves or other severe sea conditions, the shaking of the hull will not only affect the stability of the fleet's navigation, but may also pose a threat to the dangerous goods transported internally. For example, in waters with large fluctuations, the shaking of the hull may cause dangerous goods to become detached or leak, leading to safety accidents. Summary of the invention

[0004] The purpose of the present invention is to provide a combined fleet intelligent early warning method and system, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a combined fleet intelligent early warning method is provided, the method comprising:

[0007] Collect the fleet's navigation data and cargo data, and the sea condition data of the preset route. The navigation data includes the navigation speed, heading angle and hull information. The sea condition data includes the wave height and wave direction. The cargo data includes the cargo fixing method, cargo weight, storage location and cargo type.

[0008] By preprocessing the navigation data, sea condition data and cargo data, removing redundant data and unifying the dimensions, the fleet navigation data, route sea condition data and cargo status data are obtained;

[0009] According to the fleet navigation data and route sea condition data, the hull rolling angle of the ship when passing through the preset route is calculated to obtain the hull status data;

[0010] According to the hull status data, analyze the impact of the rolling angle on the ship's center of gravity, determine the deviation of the ship's center of gravity, and calculate the hull stability of the ship when passing through the preset route based on it;

[0011] According to the hull stability, analyze whether there is a risk of the fleet tilting when passing through the preset route, and obtain the route risk assessment result;

[0012] According to the hull status data and the cargo status data, the center of gravity of the cargo is determined, the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship is obtained, and the cargo stability when the hull is shaking is calculated based on the center of gravity deviation;

[0013] Based on cargo stability, analyze whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route, and obtain cargo risk assessment results;

[0014] Based on the route risk assessment results and cargo risk assessment results, identify high-risk segments on the preset routes and cargo with potential safety hazards, and generate early warning information;

[0015] Based on the early warning information, high-risk sections are avoided, preset routes are adjusted, safe routes are generated, and sea condition data is continuously collected to dynamically evaluate the risk assessment results of safe routes.

[0016] Furthermore, according to the fleet navigation data and the route sea condition data, the rolling angle of the ship when passing through the preset route is calculated to obtain the hull status data, including:

[0017] According to the sailing angle and the wave direction, the angle between the wave and the ship is determined to obtain the wave angle;

[0018] According to the hull information, the mass distribution and geometric shape of the ship are determined, and the inertia moment of the ship when rotating around the transverse axis and longitudinal axis of the ship is calculated based on the mass distribution and geometric shape of the ship, and the transverse moment of inertia and the longitudinal moment of inertia are obtained;

[0019] The ship's rolling angle is calculated based on the wave angle, transverse moment of inertia and longitudinal moment of inertia. The rolling angle calculation formula is:

[0020] ,

[0021] in, is the ship's rolling angle, For the high waves, is the wave angle, is the sailing speed, is the lateral moment of inertia, For the ship Unit mass, that is, one kilogram of mass is one unit mass. is the index of unit mass on the ship, is the total number of unit masses on the ship, For the ship The distance from the unit mass to the transverse axis of the ship, is the longitudinal moment of inertia, For the ship The distance from the unit mass to the longitudinal axis of the ship, and is the coefficient.

[0022] Furthermore, according to the hull status data, the influence of the rolling angle on the center of gravity of the ship is analyzed, the deviation of the center of gravity of the ship is determined, and the hull stability of the ship when passing through the preset route is calculated based on it, including:

[0023] According to the hull state data, the roll angle and pitch angle of the ship are extracted to obtain the roll angle and pitch angle;

[0024] According to the hull information, the draft of the ship is extracted, and the ship size is determined to obtain the wave-receiving area of ​​the hull;

[0025] Determine the center of gravity of the ship based on the hull information;

[0026] According to the roll angle, pitch angle and center of gravity position, the center of gravity offset amplitude caused by the hull shaking is calculated to obtain the deviation of the ship's center of gravity;

[0027] According to the rolling angle and deviation, the hull stability of the ship when passing through the preset route is calculated. The calculation formula of the hull stability is:

[0028] ,

[0029] in, For the stability of the ship's hull, is the ship's rolling angle, is the height of the ship's center of gravity, is the roll angle, , is the pitch angle,

[0030] , is the density of water at normal temperature and pressure, is the acceleration due to gravity, For the high waves, is the wave receiving area of ​​the hull, is the coefficient.

[0031] Furthermore, based on the hull stability, the risk of the ships tilting when the fleet passes through the preset route is analyzed to obtain the route risk assessment results, including:

[0032] Identify wave characteristics based on wave height and direction;

[0033] The preset route is divided according to the wave characteristics to obtain a sequence of flight segments;

[0034] According to the segment sequence, the sea condition data of each segment is obtained to obtain the segment data set;

[0035] According to the voyage segment data set, the predicted rolling angle of the ship passing through each voyage segment is calculated respectively to obtain the predicted rolling angle data set;

[0036] According to the predicted rolling angle data set, the ship stability corresponding to the ship is calculated to obtain a predicted ship stability data set;

[0037] Based on the predicted ship stability data set and the preset ship stability, it is determined whether there is a risk of ships tilting when the fleet passes through each section, and the route risk assessment result is obtained.

[0038] Further, according to the hull state data and the cargo state data, the center of gravity position of the cargo is determined, the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship is obtained, and the cargo stability of the cargo when the hull is shaking is calculated based on the center of gravity deviation, including:

[0039] According to the way the goods are fixed, the material strength of the fixing equipment is extracted and the friction coefficient between the fixing equipment and the goods is determined;

[0040] Determine the center of gravity of the cargo based on the storage location and calculate the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship;

[0041] Calculate cargo displacement caused by hull rolling based on center of gravity deviation;

[0042] According to the cargo offset, the cargo stability when the hull is shaking is calculated. The calculation formula of cargo stability is:

[0043] ,

[0044] in, For cargo stability of cargo, is the cargo offset, is the center of gravity deviation, is the ship's rolling angle, is the cargo weight, is the acceleration due to gravity, is the friction coefficient, is the coefficient.

[0045] Furthermore, based on the stability of the cargo, the risk of cargo leaving the storage location is analyzed when the fleet passes through the preset route, and the cargo risk assessment results are obtained, including:

[0046] According to the cargo type, the cargo status data is divided into safe cargo data set and dangerous cargo data set;

[0047] According to the dangerous goods data set and the predicted rolling angle data set, the cargo stability of different cargoes when the fleet passes through each section is calculated respectively to obtain the predicted cargo stability data set;

[0048] Based on the predicted cargo stability data set and the preset cargo stability, it is determined whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route, and the cargo risk assessment result is obtained.

[0049] Furthermore, according to the warning information, high-risk sections are avoided, preset routes are adjusted, safe routes are generated, and sea condition data is continuously collected to dynamically evaluate the risk assessment results of safe routes, including:

[0050] By acquiring the sea state data of each segment at different time points and arranging them in chronological order, a dynamic segment data set is obtained;

[0051] According to the dynamic flight segment data set and warning information, high-risk flight segments at different time points are removed to obtain a safe flight segment data set;

[0052] According to the safe segment data set and the sailing speed, the safe segments that the fleet arrives at at different time points are calculated and connected in chronological order to obtain the safe route.

[0053] In a second aspect, a combined fleet intelligent early warning system is provided, the system comprising:

[0054] The data collection module is used to collect the fleet's navigation data and cargo data, and the sea condition data of the preset route. The navigation data includes the navigation speed, heading angle and hull information, the sea condition data includes the wave height and wave direction, and the cargo data includes the cargo fixing method, cargo weight, storage location and cargo type;

[0055] The data preprocessing module is used to preprocess the navigation data, sea condition data and cargo data, remove redundant data and unify the dimensions, and obtain the fleet navigation data, route sea condition data and cargo status data;

[0056] The ship rolling module is used to calculate the rolling angle of the ship when it passes through the preset route according to the fleet navigation data and the route sea condition data, and obtain the hull status data;

[0057] The hull stability calculation module is used to analyze the influence of the rolling angle on the ship's center of gravity according to the hull status data, determine the deviation of the ship's center of gravity, and calculate the hull stability of the ship when passing through the preset route based on it;

[0058] The route assessment module is used to analyze whether there is a risk of the ship tilting when the fleet passes through the preset route based on the hull stability, and obtain the route risk assessment result;

[0059] The cargo stability calculation module is used to determine the cargo center of gravity according to the hull status data and the cargo status data, obtain the center of gravity deviation between the cargo center of gravity and the ship center of gravity, and calculate the cargo stability when the hull shakes based on the center of gravity deviation;

[0060] The cargo assessment module is used to analyze whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route based on cargo stability, and obtain cargo risk assessment results;

[0061] The risk warning module is used to identify high-risk segments and cargo with potential safety hazards on the preset routes based on the route risk assessment results and cargo risk assessment results, and generate warning information;

[0062] The route adjustment module is used to avoid high-risk sections according to early warning information, adjust the preset route, generate a safe route, continuously collect its sea condition data, and dynamically evaluate the risk assessment results of the safe route.

[0063] Furthermore, a computing device includes:

[0064] one or more processors;

[0065] A storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement a method as described in any one of the combined fleet intelligent early warning methods.

[0066] Furthermore, a computer-readable storage medium stores a program, and when the program is executed by a processor, a method as described in any one of the combined fleet intelligent early warning methods is implemented.

[0067] The above solution of the present invention includes at least the following beneficial effects:

[0068] The present invention generates hull status data by calculating the rolling angle of the ship when passing through a preset route. It can monitor the movement status of the ship in complex sea conditions in real time, providing a basis for evaluating the dynamic stability of the ship. Compared with the simple analysis of external weather conditions in the prior art, this function further combines multiple factors such as navigation speed, wave height, wave direction, etc. to accurately characterize the movement characteristics of the hull. This refined data analysis can help the fleet discover potential dangerous sections earlier, so as to take targeted risk avoidance measures and improve the safety of ship operation.

[0069] The present invention evaluates the stability of the hull during navigation by calculating the influence of the ship's rolling angle on the center of gravity deviation, which can effectively identify the risk of hull tilting or instability, and avoids the problem of only focusing on the overall external force and ignoring the internal moment balance. Especially in sea areas with strong waves, even a slight change in the center of gravity of the hull will lead to serious safety hazards. Therefore, quantitative analysis of the center of gravity deviation can provide a more scientific ship stability assessment result.

[0070] By calculating the position of the cargo's center of gravity and its deviation from the ship's center of gravity, the present invention can accurately evaluate the cargo's stability when the ship shakes, solving the problem of incomplete cargo status monitoring in traditional methods. Especially when transporting dangerous goods, this method can identify the risk that the cargo may be out of its fixed position, thereby significantly reducing the risk of secondary accidents caused by cargo movement or leakage. By combining with ship data, it can provide more comprehensive safety protection for the transportation of dangerous goods.

[0071] The present invention analyzes route risks through hull stability and can conduct accurate risk assessment for each voyage segment. Compared with the traditional early warning system that uses the entire voyage as the analysis unit, the precision of risk assessment is significantly improved. By dividing the voyage segments and calculating the predicted shaking angle segment by segment, the high-risk segments on the route can be identified, and specific risk avoidance plans can be provided for the fleet. This method makes risk assessment more accurate and provides a scientific basis for the fleet to optimize routes.

[0072] The present invention can effectively identify potential high-risk cargo and its corresponding safety hazards by performing separate analysis of dangerous goods through cargo status data. This refined assessment method enables the fleet to carry out targeted management of potential risks in the cargo transportation process, strengthen the fixing method of high-risk cargo or adjust the storage location to reduce the probability of cargo leaving the storage location, thereby ensuring the safety of cargo transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a flowchart of a combined fleet intelligent early warning method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0075] like Figure 1 As shown, an embodiment of the present invention proposes a combined fleet intelligent early warning method, the method comprising:

[0076] Collect the fleet's navigation data and cargo data, and the sea condition data of the preset route. The navigation data includes the navigation speed, heading angle and hull information. The sea condition data includes the wave height and wave direction. The cargo data includes the cargo fixing method, cargo weight, storage location and cargo type.

[0077] By preprocessing the navigation data, sea condition data and cargo data, removing redundant data and unifying the dimensions, the fleet navigation data, route sea condition data and cargo status data are obtained;

[0078] According to the fleet navigation data and route sea condition data, the hull rolling angle of the ship when passing through the preset route is calculated to obtain the hull status data;

[0079] According to the hull status data, analyze the impact of the rolling angle on the ship's center of gravity, determine the deviation of the ship's center of gravity, and calculate the hull stability of the ship when passing through the preset route based on it;

[0080] According to the hull stability, analyze whether there is a risk of the fleet tilting when passing through the preset route, and obtain the route risk assessment result;

[0081] According to the hull status data and the cargo status data, the center of gravity of the cargo is determined, the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship is obtained, and the cargo stability when the hull is shaking is calculated based on the center of gravity deviation;

[0082] Based on cargo stability, analyze whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route, and obtain cargo risk assessment results;

[0083] Based on the route risk assessment results and cargo risk assessment results, identify high-risk segments on the preset routes and cargo with potential safety hazards, and generate early warning information;

[0084] Based on the early warning information, high-risk sections are avoided, preset routes are adjusted, safe routes are generated, and sea condition data is continuously collected to dynamically evaluate the risk assessment results of safe routes.

[0085] In an embodiment of the present invention, the navigation data and cargo data of the fleet, and the sea condition data of the preset route are collected, and the comprehensiveness and real-time nature of the navigation data, sea condition data and cargo data are ensured through refined data collection; the navigation data, sea condition data and cargo data are preprocessed to remove redundant data and unify the dimensions to obtain the fleet navigation data, route sea condition data and cargo status data. Through data preprocessing, the risk of calculation error is reduced, laying a solid foundation for subsequent analysis; based on the fleet navigation data and route sea condition data, the hull roll angle of the ship when passing through the preset route is calculated to obtain the hull status data. By accurately calculating the roll angle, the dynamic balance state of the ship in different sections can be intuitively reflected, providing a basis for evaluating the stability of the hull.

[0086] According to the hull status data, the impact of the rolling angle on the ship's center of gravity is analyzed, the deviation of the ship's center of gravity is determined, and the hull stability of the ship when passing the preset route is calculated based on it. By comprehensively considering the dynamic analysis of the hull's center of gravity position and wave action, the ship's stability is evaluated more accurately, which makes up for the deficiency that traditional methods cannot reflect the changes in dynamic balance and provides a decision-making basis for route optimization. According to the hull stability, it is analyzed whether there is a risk of the fleet tilting when passing the preset route, and the route risk assessment results are obtained. The risk assessment method based on quantitative analysis can quickly identify potential dangerous sections, improve the accuracy of risk warnings, and reduce the operation risks of the fleet in complex sea conditions.

[0087] According to the hull status data and cargo status data, the center of gravity of the cargo is determined, and the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship is obtained. Based on this, the stability of the cargo when the hull is shaking is calculated. By accurately calculating the center of gravity position and offset of the cargo, the stability of the cargo during transportation can be effectively evaluated, especially providing important guarantees for the safe transportation of dangerous goods; according to the stability of the cargo, it is analyzed whether there is a risk of the cargo leaving the storage location when the fleet passes through the preset route, and the cargo risk assessment results are obtained. The separation risk assessment based on cargo stability can provide precise guidance and improve the protection of high-risk cargo during transportation.

[0088] According to the results of route risk assessment and cargo risk assessment, high-risk sections on the preset routes and cargo with safety hazards are identified, and early warning information is generated. By generating comprehensive early warning information, the safety status of the routes and cargoes is intuitively displayed, providing intelligent support for fleet safety. According to the early warning information, high-risk sections are avoided, preset routes are adjusted, safe routes are generated, and their sea condition data is continuously collected. The risk assessment results of safe routes are dynamically evaluated. By dynamically adjusting the routes, high-risk sections are effectively avoided to ensure the safety of the fleet's navigation. At the same time, transportation efficiency is improved and the risk of delays or accidents caused by emergencies is reduced. The continuously optimized data collection and evaluation process can continuously improve the accuracy and intelligence of the system, providing long-term technical support and safety guarantees for the fleet.

[0089] The collection includes the fleet’s navigation data, cargo data, and sea condition data for the preset route. The navigation data includes the navigation speed, heading angle, and hull information. The sea condition data includes the wave height and wave direction. The cargo data includes the cargo fixing method, cargo weight, storage location, and cargo type. Specifically, it includes:

[0090] Collection of navigation data:

[0091] Sailing speed: Use a ship speedometer to measure the real-time speed of the ship and record the speed change per unit time;

[0092] Heading angle: Obtain the ship's heading angle in real time through a gyrocompass or satellite navigation equipment to determine the sailing direction;

[0093] Hull information: read the basic parameters of the ship through the ship management system, such as hull size, draft, mass distribution, etc.

[0094] Collection of sea state data:

[0095] Sea condition data is obtained through meteorological monitoring stations or satellite data receiving terminals to obtain real-time wave height and wave direction data for preset routes;

[0096] The accuracy of parameters such as wave height and wave direction is further verified through hydrographic buoys to ensure that the data is consistent with actual sea conditions.

[0097] Collection of cargo data:

[0098] Fixing method: The type and connection method of the fixing equipment of the cargo are recorded through the cargo fixing monitoring system;

[0099] Cargo weight: Obtain cargo weight data in real time through cargo management system or weighing equipment;

[0100] Storage location: Combined with the ship's cargo hold layout, record the specific storage location of the cargo in the hold;

[0101] Cargo Type: Extract specific classification information from the cargo manifest, such as dangerous goods, liquid cargo, etc.

[0102] Among them, by preprocessing the navigation data, sea condition data and cargo data, removing redundant data and unifying the dimensions, the fleet navigation data, route sea condition data and cargo status data are obtained, including:

[0103] For duplicate records in navigation data, sea condition data and cargo data, data cleaning algorithms are used to remove invalid or redundant information. To avoid the impact of differences between different data units on calculations, the units of all parameters are standardized, such as wave heights in meters and weights in tons. Interpolation algorithms are used to complete missing data, and anomaly detection models are used to eliminate erroneous data, such as impossible speed values ​​or extreme cargo weight values.

[0104] Among them, according to the route risk assessment results and cargo risk assessment results, high-risk sections on the preset routes and cargo with safety hazards are identified, and early warning information is generated, including:

[0105] By integrating the risk classification data set, we can filter out the segments with high tilt risk or instability in the route, mark the start and end positions of each high-risk segment, and record the corresponding risk level;

[0106] Extract high-risk goods from the cargo risk classification data set and record the cargo number, storage location and specific hidden dangers;

[0107] Integrate high-risk identification data of routes and cargoes to generate detailed warning information forms, including:

[0108] List of high-risk flight segments, such as location, risk level;

[0109] List of high-risk goods, such as cargo number, location, and description of hidden dangers;

[0110] Visualization tools are used to dynamically display the risk information of routes and cargoes on the fleet management system interface, and the generated warning information is pushed to the terminal device via wireless communication.

[0111] In a preferred embodiment of the present invention, the hull rolling angle of the ship when passing through the preset route is calculated based on the fleet navigation data and the route sea condition data to obtain the hull state data, including:

[0112] According to the sailing angle and the wave direction, the angle between the wave and the ship is determined to obtain the wave angle;

[0113] According to the hull information, the mass distribution and geometric shape of the ship are determined, and the inertia moment of the ship when rotating around the transverse axis and longitudinal axis of the ship is calculated based on the mass distribution and geometric shape of the ship, and the transverse moment of inertia and the longitudinal moment of inertia are obtained;

[0114] The ship's rolling angle is calculated based on the wave angle, transverse moment of inertia and longitudinal moment of inertia. The rolling angle calculation formula is:

[0115] ,

[0116] in, is the ship's rolling angle, For the high waves, is the wave angle, is the sailing speed, is the lateral moment of inertia, For the ship Unit mass, that is, one kilogram of mass is one unit mass. is the index of unit mass on the ship, is the total number of unit masses on the ship, For the ship The distance from the unit mass to the transverse axis of the ship, is the longitudinal moment of inertia, For the ship The distance from the unit mass to the longitudinal axis of the ship, and is the coefficient.

[0117] In an embodiment of the present invention, the angle between the wave and the ship is determined according to the navigation angle and the wave direction, and the wave angle is obtained. By calculating the wave angle, the relationship between the wave force and the direction of the ship's movement can be intuitively reflected, laying the foundation for the subsequent dynamic analysis of the hull shaking; according to the hull information, the mass distribution and geometric shape of the ship are determined, and the moment of inertia of the ship when rotating around the transverse axis and longitudinal axis of the ship is calculated based on it, and the transverse moment of inertia and the longitudinal moment of inertia are obtained. The moment of inertia can quantify the resistance of the hull to the shaking caused by external forces. By distinguishing the transverse and longitudinal moments of inertia, the dynamic stability characteristics of the ship in different directions can be identified, providing a physical basis for the accurate prediction of the shaking angle; according to the wave angle, the transverse moment of inertia and the longitudinal moment of inertia, the rolling angle of the ship is calculated. The rolling angle directly reflects the impact of waves on the dynamic stability of the hull. Through quantitative analysis, the maximum shaking amplitude that the ship may encounter in different sections can be effectively identified, providing a scientific basis for the fleet to adjust the route.

[0118] Among them, according to the hull information, the mass distribution and geometric shape of the ship are determined, and the moment of inertia of the ship when rotating around the transverse axis and longitudinal axis of the ship is calculated based on the mass distribution and geometric shape of the ship, and the transverse moment of inertia and longitudinal moment of inertia are obtained, which specifically include:

[0119] Extracting the mass distribution and geometric parameters of the ship from the hull information, including the total mass of the ship, the length, width and height of the hull, and the internal mass distribution structure of the hull, such as uniform distribution, segmented distribution, etc.;

[0120] If there are multiple decks or compartments, the mass distribution and position coordinates of each layer or compartment need to be extracted. The hull is regarded as composed of multiple discrete small units. The mass and position coordinates of each unit are obtained through the discretization algorithm of the hull model. For the uniformly distributed part, the total mass can be directly distributed to each unit according to the volume ratio. For the segmented distribution part, it is necessary to combine the compartment load for calculation;

[0121] The transverse moment of inertia represents the inertia of a ship as it rotates about its transverse axis, which is the axis between the fore and aft centres of the hull;

[0122] The longitudinal moment of inertia represents the inertia of the ship when it rotates about its longitudinal axis, which is the axis from left to right of the hull;

[0123] In a preferred embodiment of the present invention, according to the hull state data, the influence of the rolling angle on the center of gravity of the ship is analyzed, the deviation of the center of gravity of the ship is determined, and the hull stability of the ship when passing through the preset route is calculated based on the deviation, including:

[0124] According to the hull state data, the roll angle and pitch angle of the ship are extracted to obtain the roll angle and pitch angle;

[0125] According to the hull information, the draft of the ship is extracted, and the ship size is determined to obtain the wave-receiving area of ​​the hull;

[0126] Determine the center of gravity of the ship based on the hull information;

[0127] According to the roll angle, pitch angle and center of gravity position, the center of gravity offset amplitude caused by the hull shaking is calculated to obtain the deviation of the ship's center of gravity;

[0128] According to the rolling angle and deviation, the hull stability of the ship when passing through the preset route is calculated. The calculation formula of the hull stability is:

[0129] ,

[0130] in, For the stability of the ship's hull, is the ship's rolling angle, is the height of the ship's center of gravity, is the roll angle, , is the pitch angle, , is the density of water at normal temperature and pressure, is the acceleration due to gravity, For the high waves, is the wave receiving area of ​​the hull, is the coefficient.

[0131] In an embodiment of the present invention, according to the hull state data, the roll angle and pitch angle of the ship are extracted to obtain the roll angle and pitch angle. The roll angle and pitch angle data can accurately capture the attitude changes of the ship in complex sea conditions; according to the hull information, the draft of the ship is extracted, and the ship size is determined to obtain the wave-affected area of ​​the hull. Through the accurate calculation of the draft and the ship size, the buoyancy distribution and wave influence range of the hull under different load conditions can be effectively evaluated; according to the hull information, the center of gravity position of the ship is determined, and determining the center of gravity position of the ship can provide a direct reference basis for evaluating the stability of the hull; according to the roll angle, the pitch angle and the center of gravity position, the center of gravity offset amplitude caused by the hull shaking is calculated to obtain the deviation of the center of gravity of the ship. By quantifying the center of gravity offset amplitude, the anti-capsulation ability of the hull can be intuitively evaluated; according to the rolling angle and the deviation, the hull stability of the ship when passing through the preset route is calculated. By scientifically quantifying the hull stability, the safety status of the fleet in complex sea conditions can be evaluated in real time, the risk of accidents caused by hull instability can be reduced, and the overall operation efficiency of the fleet can be improved.

[0132] In a preferred embodiment of the present invention, based on the hull stability, it is analyzed whether there is a risk of tilting of the ships when the fleet passes through the preset route, and the route risk assessment result is obtained, including:

[0133] Identify wave characteristics based on wave height and direction;

[0134] The preset route is divided according to the wave characteristics to obtain a sequence of flight segments;

[0135] According to the segment sequence, the sea condition data of each segment is obtained to obtain the segment data set;

[0136] According to the voyage segment data set, the predicted rolling angle of the ship passing through each voyage segment is calculated respectively to obtain the predicted rolling angle data set;

[0137] According to the predicted rolling angle data set, the ship stability corresponding to the ship is calculated to obtain a predicted ship stability data set;

[0138] Based on the predicted ship stability data set and the preset ship stability, it is determined whether there is a risk of ships tilting when the fleet passes through each section, and the route risk assessment result is obtained.

[0139] In the embodiment of the present invention, wave characteristics are identified according to wave height and wave direction, and detailed wave characteristic information in the sea area is obtained in real time, providing reliable environmental data support for subsequent segment division and risk assessment;

[0140] The preset route is divided according to the wave characteristics to obtain a sequence of segments, and the sea condition characteristics of different segments are evaluated by segmentation and refinement;

[0141] According to the segment sequence, the sea condition data of each segment is obtained to obtain the segment data set, avoiding the omission of details caused by the overall evaluation of the traditional method, and providing high-resolution data for the stability assessment of the ship in different segments;

[0142] According to the voyage segment data set, the predicted rolling angle of the ship in each voyage segment is calculated respectively to obtain the predicted rolling angle data set, which provides quantitative indicators for hull stability assessment and can identify in advance the rolling intensity that the ship may encounter in different voyage segments, laying the foundation for subsequent risk assessment;

[0143] According to the predicted rolling angle data set, the ship stability corresponding to the ship is calculated to obtain the predicted ship stability data set, which provides stability assessment information at the segment level, can fully reflect the dynamic balance ability of the ship under different sea conditions, and provide a scientific basis for judging the segment risk;

[0144] Based on the predicted ship stability data set and the preset ship stability, it is determined whether there is a risk of the ship tilting when the fleet passes through each section, and the route risk assessment result is obtained. By matching the stability data with the risk level, the location and characteristics of the high-risk sections can be intuitively identified, providing a clear decision-making basis, which helps to optimize the route to improve navigation safety.

[0145] Among them, according to the wave height and wave direction, the wave characteristics are identified, including:

[0146] Using onboard sensors and remote marine meteorological data services, the wave height and wave direction data of the current route area are obtained. The wave height data indicates the vertical height of the sea surface waves, and the wave direction data indicates the direction of wave propagation. Both are recorded in the form of time series.

[0147] Using the collected wave height time series, the wave spectrum is analyzed by fast Fourier transform, the main frequency components of the wave are extracted, and the wave period is calculated based on the frequency;

[0148] Calculate the wave energy density based on wave height and wave period, and generate a wave energy distribution map by counting the energy density of different wave bands;

[0149] Combined with wave direction data, the distribution of wave energy in different directions is statistically analyzed to generate a wave direction energy rose diagram for identifying the dominant wave direction;

[0150] Summarize and analyze the wave height, wave direction, wave period and wave energy distribution, and extract key characteristic parameters, including main wave direction, main period, significant wave height, wave slope, etc.

[0151] Among them, according to the predicted ship stability data set and the preset ship stability, it is judged whether there is a risk of the ship tilting when the fleet passes through each section, and the route risk assessment results are obtained, including:

[0152] Extract the stability parameters of each segment from the predicted ship stability data set, including the rolling angle, center of gravity offset and stability coefficient;

[0153] According to the design parameters and safety regulations of the ship, the lower limit of stability for each type of ship is preset, such as the critical value of the rolling angle, the lower limit of the stability coefficient, and other standardized methods to define the safety range of the ship under specific conditions;

[0154] For each voyage segment, the predicted ship stability parameters are compared with the preset standards to determine whether the actual rolling angle exceeds the critical value. If so, the segment is recorded as high risk;

[0155] According to the risk assessment results, each segment is assigned a risk level, such as low risk, medium risk, and high risk. The comprehensive assessment of the risk level can be based on the deviation of the rolling angle, the deviation of the stability coefficient, the segment length, and the wave duration.

[0156] Map the flight segment risk level to the entire route and combine it with geographic information to generate a risk assessment report for the route. The visual output includes a risk distribution map, the specific location of high-risk segments, and detailed parameters of the segments.

[0157] In a preferred embodiment of the present invention, the center of gravity of the cargo is determined according to the hull state data and the cargo state data, the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship is obtained, and the cargo stability of the cargo when the hull is shaking is calculated according to the center of gravity deviation, including:

[0158] According to the way the goods are fixed, the material strength of the fixing equipment is extracted and the friction coefficient between the fixing equipment and the goods is determined;

[0159] Determine the center of gravity of the cargo based on the storage location and calculate the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship;

[0160] Calculate cargo displacement caused by hull rolling based on center of gravity deviation;

[0161] According to the cargo offset, the cargo stability when the hull is shaking is calculated. The calculation formula of cargo stability is:

[0162] ,

[0163] in, For cargo stability of cargo, is the cargo offset, is the center of gravity deviation, is the ship's rolling angle, is the cargo weight, is the acceleration due to gravity, is the friction coefficient, is the coefficient.

[0164] In an embodiment of the present invention, according to the fixing method of the cargo, the material strength of the fixing equipment is extracted, and the friction coefficient between the fixing equipment and the cargo is determined. By accurately measuring and calculating the material strength and friction coefficient of the fixing equipment, basic data can be provided for subsequent cargo stability analysis; according to the storage position, the center of gravity position of the cargo is determined, and the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship is calculated, which intuitively reflects whether the placement of the cargo on the hull is balanced; according to the center of gravity deviation, the cargo displacement caused by the shaking of the hull is calculated, and it is analyzed whether the cargo will be out of the storage position due to the shaking, which provides a scientific basis for the formulation of cargo reinforcement measures and further improves the stability of cargo transportation; according to the cargo displacement, the cargo stability of the cargo when the hull is shaking is calculated. The quantitative analysis of cargo stability can comprehensively evaluate the storage safety of different cargoes in complex sea conditions, and provide technical support for cargo zoning management and transportation decisions.

[0165] Among them, according to the fixing method of the goods, the material strength of the fixing equipment is extracted, and the friction coefficient between the fixing equipment and the goods is determined, which specifically includes:

[0166] The fixing methods include rope fixing, clip fixing, bracket fixing or frame fixing, etc. According to the specific fixing method, identify the corresponding fixing device type, such as steel clip, rubber belt, nylon rope, etc.;

[0167] According to the type of fixed equipment, the mechanical properties parameters of the corresponding materials are extracted from the pre-established material database, including tensile strength, compressive strength, bending modulus, etc.;

[0168] The system extracts the friction coefficient of the material in contact between the fixed equipment and the cargo from the database. For example, the static friction coefficient between steel and wood is 0.4, and the dynamic friction coefficient is 0.3; the static friction coefficient between rubber and plastic is 0.6, and the dynamic friction coefficient is 0.5;

[0169] If the material combination of the contact surface between the fixed equipment and the cargo is not recorded in the database, a field test is carried out using a friction test device to measure the static slip critical force between the fixed equipment and the cargo under a known load.

[0170] In a preferred embodiment of the present invention, based on the cargo stability, it is analyzed whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route, and the cargo risk assessment result is obtained, including:

[0171] According to the cargo type, the cargo status data is divided into safe cargo data set and dangerous cargo data set;

[0172] According to the dangerous goods data set and the predicted rolling angle data set, the cargo stability of different cargoes when the fleet passes through each section is calculated respectively to obtain the predicted cargo stability data set;

[0173] Based on the predicted cargo stability data set and the preset cargo stability, it is determined whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route, and the cargo risk assessment result is obtained.

[0174] In an embodiment of the present invention, cargo status data is divided according to cargo type to obtain a safe cargo data set and a dangerous cargo data set. By refining the classification of cargo types, dangerous cargo that need to be focused on can be accurately identified, effectively improving data management and retrieval efficiency; based on the dangerous cargo data set and the predicted shaking angle data set, the cargo stability of different cargoes when the fleet passes through each segment is calculated respectively to obtain a predicted cargo stability data set, and the cargo status is accurately evaluated by quantifying the cargo stability; based on the predicted cargo stability data set and the preset cargo stability, whether there is a risk of the cargo leaving the storage location when the fleet passes through the preset route, a cargo risk assessment result is obtained, which provides a clear safety risk distribution map for the fleet, optimizes the expression of risk information, and enhances fleet safety assurance.

[0175] Among them, according to the predicted cargo stability data set and the preset cargo stability, whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route, the cargo risk assessment results are obtained, including:

[0176] According to the parameters such as cargo type, fixing method and storage location, the preset stability threshold of each cargo is set. The threshold can be determined by the following methods:

[0177] Experimental data: critical values ​​of cargo stability based on laboratory measurements;

[0178] Simulation calculation: Calculate the maximum stability critical value under different conditions through the cargo force model;

[0179] Empirical data: Combine historical data and actual transportation experience to set a reasonable safety margin;

[0180] The cargo stability threshold is divided into different levels:

[0181] Safety: The cargo is in a completely stable state;

[0182] Alert: The goods have certain risks and need to be paid attention to;

[0183] Danger: The goods may be out of storage and emergency measures are required;

[0184] According to the time series data in the predicted cargo stability data set, the environmental parameters of each section, such as wave height and wave direction, are matched one by one with the predicted stability value of the cargo;

[0185] For each flight segment, calculate the deviation between the actual stability value of the cargo and the preset stability threshold. When the stability value of the cargo is lower than the preset threshold, mark the cargo as high-risk cargo, and record the storage location, fixing method and corresponding high-risk flight segment information of the high-risk cargo;

[0186] Combining the cargo risk mark with the flight segment risk assessment results, a cargo risk index for each flight segment is generated. The risk index is composed of the following factors:

[0187] The volume percentage of high-risk goods;

[0188] Hazard level of high-risk cargo;

[0189] Wave characteristics of the route corresponding to high-risk cargo;

[0190] Based on the above information, the cargo risk assessment results are generated, including:

[0191] The serial number of high-risk goods and their specific storage location;

[0192] The number of the high-risk flight segment and its corresponding risk index;

[0193] A graphical representation of cargo risk at different flight stages.

[0194] In a preferred embodiment of the present invention, according to the early warning information, high-risk sections are avoided, the preset routes are adjusted, safe routes are generated, and the sea condition data is continuously collected to dynamically evaluate the risk assessment results of the safe routes, including:

[0195] By acquiring the sea state data of each segment at different time points and arranging them in chronological order, a dynamic segment data set is obtained;

[0196] According to the dynamic flight segment data set and warning information, high-risk flight segments at different time points are removed to obtain a safe flight segment data set;

[0197] According to the safe segment data set and the sailing speed, the safe segments that the fleet arrives at at different time points are calculated and connected in chronological order to obtain the safe route.

[0198] In an embodiment of the present invention, by acquiring the sea condition data of each segment at different time points and arranging them in chronological order, a dynamic segment data set is obtained, and the detailed sea conditions of each segment at different time points are mastered, providing accurate input data for the identification of high-risk segments; according to the dynamic segment data set and warning information, the high-risk segments at different time points are removed to obtain a safe segment data set, which effectively reduces the possibility of the fleet encountering severe sea conditions and ensures the navigation safety of the fleet; according to the safe segment data set and the sailing speed, the safe segments reached by the fleet at different time points are calculated, and they are connected in chronological order to obtain a safe route, which provides a highly reliable data basis for route planning, and dynamically adjusts according to the real-time updated sea condition data and the fleet position to ensure that the fleet is always in a safe and controllable navigation state.

[0199] Among them, according to the safe segment data set and navigation speed, the safe segments that the fleet arrives at at different time points are calculated and connected in chronological order to obtain the safe route, which includes:

[0200] For each safe segment, the system calculates the time range for the fleet to arrive at the segment based on the geographical distance between its starting point and end point and the current sailing speed of the fleet. If the expected sea condition stabilization time of the segment is shorter than the time it takes for the fleet to pass through the segment, the system will dynamically increase the risk factor of the segment and give priority to recommending other safe segments. For the segment status at different time periods, the system will dynamically update the sea conditions based on time to ensure that each calculated arrival time point matches the actual situation.

[0201] The system sorts all calculated safe segments in chronological order to ensure that the starting points and end points between segments can be seamlessly connected. If there is an uncovered area between two consecutive segments, the system will fill the gap with the data of the surrounding segments and calculate the best transition path. During the sorting and connection process, the system will re-check the risk of each segment to ensure that the connected path meets the overall safety requirements. According to the risk factors of different segments and the duration of sea conditions, the system will dynamically adjust the speed of the fleet.

[0202] In sections with stable and safe sea conditions, the fleet is advised to sail at the optimal economic speed to save energy and shorten sailing time. When approaching high-risk sections, the fleet is advised to slow down to ensure that the fleet can pass safely before the sea conditions change. By simulating the impact of ship speed adjustment on the overall route, the optimal speed distribution plan is selected and the final safe route schedule is generated.

[0203] The sorted and connected safety segments are integrated in time order to generate a complete safe route from the current fleet position to the destination. The output route data includes:

[0204] The geographical path coordinates of the route;

[0205] Estimated time of arrival and transit for each flight segment;

[0206] Risk assessment information for the corresponding flight segment.

[0207] An embodiment of the present invention further provides a combined fleet intelligent early warning system, the system comprising:

[0208] The data collection module is used to collect the fleet's navigation data and cargo data, and the sea condition data of the preset route. The navigation data includes the navigation speed, heading angle and hull information, the sea condition data includes the wave height and wave direction, and the cargo data includes the cargo fixing method, cargo weight, storage location and cargo type;

[0209] The data preprocessing module is used to preprocess the navigation data, sea condition data and cargo data, remove redundant data and unify the dimensions, and obtain the fleet navigation data, route sea condition data and cargo status data;

[0210] The ship rolling module is used to calculate the rolling angle of the ship when it passes through the preset route according to the fleet navigation data and the route sea condition data, and obtain the hull status data;

[0211] The hull stability calculation module is used to analyze the influence of the rolling angle on the ship's center of gravity according to the hull status data, determine the deviation of the ship's center of gravity, and calculate the hull stability of the ship when passing through the preset route based on it;

[0212] The route assessment module is used to analyze whether there is a risk of the ship tilting when the fleet passes through the preset route based on the hull stability, and obtain the route risk assessment result;

[0213] The cargo stability calculation module is used to determine the cargo center of gravity according to the hull status data and the cargo status data, obtain the center of gravity deviation between the cargo center of gravity and the ship center of gravity, and calculate the cargo stability when the hull shakes based on the center of gravity deviation;

[0214] The cargo assessment module is used to analyze whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route based on cargo stability, and obtain cargo risk assessment results;

[0215] The risk warning module is used to identify high-risk segments and cargo with potential safety hazards on the preset routes based on the route risk assessment results and cargo risk assessment results, and generate warning information;

[0216] The route adjustment module is used to avoid high-risk sections according to early warning information, adjust the preset route, generate a safe route, continuously collect its sea condition data, and dynamically evaluate the risk assessment results of the safe route.

[0217] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0218] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0219] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above method. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0220] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A combined fleet intelligent early warning method, characterized in that: The method comprises: Collect the fleet's navigation data and cargo data, and the sea condition data of the preset route. The navigation data includes the navigation speed, heading angle and hull information. The sea condition data includes the wave height and wave direction. The cargo data includes the cargo fixing method, cargo weight, storage location and cargo type. By preprocessing the navigation data, sea condition data and cargo data, removing redundant data and unifying the dimensions, the fleet navigation data, route sea condition data and cargo status data are obtained; According to the fleet navigation data and route sea condition data, the hull rolling angle of the ship when passing through the preset route is calculated to obtain the hull status data; According to the hull status data, analyze the impact of the rolling angle on the ship's center of gravity, determine the deviation of the ship's center of gravity, and calculate the hull stability of the ship when passing through the preset route based on it; According to the hull stability, analyze whether there is a risk of the fleet tilting when passing through the preset route, and obtain the route risk assessment result; According to the hull status data and the cargo status data, the center of gravity of the cargo is determined, the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship is obtained, and the cargo stability when the hull is shaking is calculated based on the center of gravity deviation; Based on cargo stability, analyze whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route, and obtain cargo risk assessment results; Based on the route risk assessment results and cargo risk assessment results, identify high-risk segments on the preset routes and cargo with potential safety hazards, and generate early warning information; Based on the early warning information, high-risk sections are avoided, preset routes are adjusted, safe routes are generated, and sea condition data is continuously collected to dynamically evaluate the risk assessment results of safe routes.

2. The combined fleet intelligent early warning method according to claim 1 is characterized in that: According to the fleet navigation data and route sea condition data, the hull rolling angle of the ship when passing through the preset route is calculated to obtain the hull status data, including: According to the sailing angle and the wave direction, the angle between the wave and the ship is determined to obtain the wave angle; According to the hull information, the mass distribution and geometric shape of the ship are determined, and the inertia moment of the ship when rotating around the transverse axis and longitudinal axis of the ship is calculated based on the mass distribution and geometric shape of the ship, and the transverse moment of inertia and the longitudinal moment of inertia are obtained; The ship's rolling angle is calculated based on the wave angle, transverse moment of inertia and longitudinal moment of inertia. The rolling angle calculation formula is: ， in, is the ship's rolling angle, For the high waves, is the wave angle, is the sailing speed, is the lateral moment of inertia, For the ship Unit mass, that is, one kilogram of mass is one unit mass. is the index of unit mass on the ship, is the total number of unit masses on the ship, For the ship The distance from the unit mass to the transverse axis of the ship, is the longitudinal moment of inertia, For the ship The distance from the unit mass to the longitudinal axis of the ship, and is the coefficient.

3. The combined fleet intelligent early warning method according to claim 2 is characterized in that: According to the hull status data, analyze the impact of the rolling angle on the ship's center of gravity, determine the deviation of the ship's center of gravity, and calculate the hull stability of the ship when passing through the preset route, including: According to the hull state data, the roll angle and pitch angle of the ship are extracted to obtain the roll angle and pitch angle; According to the hull information, the draft of the ship is extracted, and the ship size is determined to obtain the wave-receiving area of ​​the hull; Determine the center of gravity of the ship based on the hull information; According to the roll angle, pitch angle and center of gravity position, the center of gravity offset amplitude caused by the hull shaking is calculated to obtain the deviation of the ship's center of gravity; According to the rolling angle and deviation, the hull stability of the ship when passing through the preset route is calculated. The calculation formula of the hull stability is: ， in, For the stability of the ship's hull, is the ship's rolling angle, is the height of the center of gravity of the ship, is the roll angle, , is the pitch angle, , is the density of water at normal temperature and pressure, is the acceleration due to gravity, For the high waves, is the wave receiving area of ​​the hull, is the coefficient.

4. The combined fleet intelligent early warning method according to claim 3 is characterized in that: According to the hull stability, analyze whether there is a risk of the ship tilting when the fleet passes through the preset route, and obtain the route risk assessment results, including: Identify wave characteristics based on wave height and direction; The preset route is divided according to the wave characteristics to obtain a sequence of flight segments; According to the segment sequence, the sea condition data of each segment is obtained to obtain the segment data set; According to the voyage segment data set, the predicted rolling angle of the ship passing through each voyage segment is calculated respectively to obtain the predicted rolling angle data set; According to the predicted rolling angle data set, the ship stability corresponding to the ship is calculated to obtain a predicted ship stability data set; Based on the predicted ship stability data set and the preset ship stability, it is determined whether there is a risk of ships tilting when the fleet passes through each section, and the route risk assessment result is obtained.

5. The combined fleet intelligent early warning method according to claim 4 is characterized in that: According to the hull status data and cargo status data, the cargo center of gravity position is determined, and the center of gravity deviation between the cargo center of gravity and the ship center of gravity is obtained. Based on this, the cargo stability when the hull is shaking is calculated, including: According to the way the goods are fixed, the material strength of the fixing equipment is extracted and the friction coefficient between the fixing equipment and the goods is determined; Determine the center of gravity of the cargo based on the storage location and calculate the center of gravity deviation between the center of gravity of the cargo and the center of gravity of the ship; Calculate cargo displacement caused by hull rolling based on center of gravity deviation; According to the cargo offset, the cargo stability when the hull is shaking is calculated. The calculation formula of cargo stability is: ， in, For cargo stability of cargo, is the cargo offset, is the center of gravity deviation, is the ship's rolling angle, is the cargo weight, is the acceleration due to gravity, is the friction coefficient, is the coefficient.

6. The combined fleet intelligent early warning method according to claim 5 is characterized in that: Based on cargo stability, we analyze whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route, and obtain cargo risk assessment results, including: According to the cargo type, the cargo status data is divided into safe cargo data set and dangerous cargo data set; According to the dangerous goods data set and the predicted rolling angle data set, the cargo stability of different cargoes when the fleet passes through each section is calculated respectively to obtain the predicted cargo stability data set; Based on the predicted cargo stability data set and the preset cargo stability, it is determined whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route, and the cargo risk assessment result is obtained.

7. The combined fleet intelligent early warning method according to claim 6, characterized in that: According to the early warning information, avoid high-risk sections, adjust the preset routes, generate safe routes, and continuously collect sea condition data to dynamically evaluate the risk assessment results of safe routes, including: By acquiring the sea state data of each segment at different time points and arranging them in chronological order, a dynamic segment data set is obtained; According to the dynamic flight segment data set and warning information, high-risk flight segments at different time points are removed to obtain a safe flight segment data set; According to the safe segment data set and the sailing speed, the safe segments that the fleet arrives at at different time points are calculated and connected in chronological order to obtain the safe route.

8. A combined fleet intelligent early warning system, characterized in that: The system comprises: The data collection module is used to collect the fleet's navigation data and cargo data, and the sea condition data of the preset route. The navigation data includes the navigation speed, heading angle and hull information, the sea condition data includes the wave height and wave direction, and the cargo data includes the cargo fixing method, cargo weight, storage location and cargo type; The data preprocessing module is used to preprocess the navigation data, sea condition data and cargo data, remove redundant data and unify the dimensions, and obtain the fleet navigation data, route sea condition data and cargo status data; The ship rolling module is used to calculate the rolling angle of the ship when it passes through the preset route according to the fleet navigation data and the route sea condition data, and obtain the hull status data; The hull stability calculation module is used to analyze the influence of the rolling angle on the ship's center of gravity according to the hull status data, determine the deviation of the ship's center of gravity, and calculate the hull stability of the ship when passing through the preset route based on it; The route assessment module is used to analyze whether there is a risk of the ship tilting when the fleet passes through the preset route based on the hull stability, and obtain the route risk assessment result; The cargo stability calculation module is used to determine the cargo center of gravity according to the hull status data and the cargo status data, obtain the center of gravity deviation between the cargo center of gravity and the ship center of gravity, and calculate the cargo stability when the hull shakes based on the center of gravity deviation; The cargo assessment module is used to analyze whether there is a risk of cargo leaving the storage location when the fleet passes through the preset route based on cargo stability, and obtain cargo risk assessment results; The risk warning module is used to identify high-risk segments and cargo with potential safety hazards on the preset routes based on the route risk assessment results and cargo risk assessment results, and generate warning information; The route adjustment module is used to avoid high-risk sections according to early warning information, adjust the preset route, generate a safe route, continuously collect its sea condition data, and dynamically evaluate the risk assessment results of the safe route.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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