Ship comprehensive information database management system and method based on big data analysis

Abstract

The invention discloses a ship comprehensive information database management system and method based on big data analysis, and the system comprises a marine environment sensing and ship state monitoring module which is used for collecting marine environment data and ship operation state data in real time; the ship wave influence characteristic modeling module is used for constructing a ship wave influence characteristic model; the ship navigation optimization target setting module is used for modeling a ship navigation oil consumption and energy saving problem into a nonlinear constraint optimization problem; the mayfly variants naiad optimization global path search module is used for determining a preliminary candidate ship optimal navigation path set in a dynamic search space; the water drop algorithm local path optimization module is used for obtaining a local optimal navigation path; and the ship global path optimization and navigation control module generates corresponding ship navigation control parameters. The optimal energy consumption area under different sea conditions can be predicted by combining the ship comprehensive information database, and path fine adjustment is performed according to the real-time position of the ship and the marine environment parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of ship integrated information databases, and particularly to a ship integrated information database management system and method based on big data analysis. Background Art

[0002] With the development of the global shipping industry, the issues of fuel cost and carbon emissions have increasingly become the focus of attention. The operation of ships in the marine environment is affected by complex factors such as wind waves and ocean currents. Environmental factors not only affect the navigation stability but also have a significant impact on fuel consumption. Therefore, how to improve the fuel economy and navigation safety of ship navigation through the indirect management of ship integrated information databases and optimize the ship navigation path to reduce energy consumption has become an important research direction in current ship intelligent navigation technology.

[0003] Currently, most ship navigation systems mainly rely on traditional route planning methods, such as preset navigation schemes based on fixed sea routes or simple shortest path algorithms. Traditional methods usually only consider the navigation distance and navigation time and do not fully consider the impact of the marine environment on ship fuel consumption. For example, the navigation method based on a fixed route cannot adapt to complex and changeable sea conditions in real time, resulting in the ship adopting a suboptimal navigation strategy when facing adverse marine environments, thus increasing fuel consumption. In addition, although the traditional shortest path algorithm can shorten the navigation time to a certain extent, it often ignores the impact of wind waves and ocean currents on ship energy consumption, making the navigation scheme have a large energy consumption deviation in practical applications.

[0004] In recent years, some intelligent optimization algorithms have been applied to the optimization of ship integrated information databases, but there are still some deficiencies: First, some algorithms are prone to falling into local optima and cannot globally optimize the ship navigation path, resulting in limited optimization effects on fuel consumption; Second, the existing optimization algorithms have a slow response speed when dealing with dynamic environmental changes and are difficult to adjust the navigation path in real time to adapt to sudden sea conditions; Third, some optimization methods do not fully combine the propulsion characteristics and fuel consumption characteristics of ships, lacking a comprehensive modeling of the ship power system and environmental factors, thus affecting the practical application value of the optimization results.

[0005] In summary, the existing ship navigation optimization technologies have obvious defects in terms of fuel economy, path optimization accuracy, and dynamic adaptability, and cannot effectively meet the requirements of modern ships for efficient, energy-saving, and safe navigation. Therefore, there is an urgent need for a new method to overcome the limitations of the existing technology and achieve better navigation path optimization and fuel energy-saving control. Summary of the Invention

[0006] An object of the present invention is to provide a ship integrated information database management system and method based on big data analysis. The present invention can predict the optimal energy consumption area under different sea conditions by combining the ship integrated information database, and perform path fine-tuning according to the real-time position of the ship and the marine environmental parameters.

[0007] A ship integrated information database management system based on big data analysis according to an embodiment of the present invention includes:

[0008] A marine environment perception and ship state monitoring module, including marine environment sensors and ship state monitoring devices installed on the ship, for real-time collecting marine environment data and ship operation state data;

[0009] A ship wave influence characteristic modeling module, based on the marine environment data and ship operation state data, constructs a ship wave influence characteristic model, and generates a ship integrated information database;

[0010] A ship navigation optimization target setting module, based on the ship wave influence characteristic model, sets the ship navigation optimization target, constructs a navigation optimization target function in combination with the ship navigation constraint conditions, and models the ship navigation fuel consumption energy-saving problem as a non-linear constraint optimization problem;

[0011] A mutated mayfly optimization global path search module, uses the mutated mayfly optimization algorithm to globally search the navigation optimization target function, and determines a preliminary candidate set of the optimal ship navigation paths in the dynamic search space;

[0012] A water droplet algorithm local path optimization module, based on the preliminary candidate set of the optimal ship navigation paths, uses the water droplet algorithm to locally optimize the preliminary candidate set of the optimal ship navigation paths to obtain the locally optimal navigation path;

[0013] A ship global path optimization and navigation control module, comprehensively compares the locally optimal navigation path with the preliminary candidate set of the optimal ship navigation paths, screens out the globally optimal navigation path that meets the navigation optimization target, and generates the corresponding ship navigation control parameters.

[0014] A ship integrated information database management method based on big data analysis, applied to a ship integrated information database management system based on big data analysis, includes the following steps:

[0015] S1. Real-time collect marine environment data and ship operation state data, and preprocess and standardize the collected marine environment data and ship operation state data to obtain a preprocessed ship navigation data set;

[0016] S2. Based on the preprocessed ship navigation dataset, construct a ship wave influence characteristic model, establish the mapping relationship between the operating state and fuel consumption of the ship under different sea conditions, and form a ship comprehensive information database;

[0017] S3. Use the ship wave influence characteristic model to set the ship navigation optimization goal, and construct a navigation optimization objective function according to the set ship navigation optimization goal. Model the ship navigation fuel consumption energy-saving problem as a non-linear constrained optimization problem;

[0018] S4. Apply the variant mayfly optimization algorithm to globally search the navigation optimization objective function, dynamically adjust the candidate solutions in the search space with the help of the real-time feedback ship navigation dataset, and generate a preliminary candidate set of the ship's optimal navigation paths;

[0019] S5. Use the water droplet algorithm to locally optimize the preliminary candidate set of the ship's optimal navigation paths, and locally correct the preliminary candidate set of the ship's optimal navigation paths according to the dynamic changes of the ship navigation dataset to obtain the locally optimal navigation path;

[0020] S6. Comprehensively compare the locally optimal navigation path with the preliminary candidate set of the ship's optimal navigation paths, screen out the globally optimal navigation path that meets the navigation optimization goal, generate the corresponding ship navigation control parameters, and control the ship to navigate according to the globally optimal navigation path to complete the goal of managing the ship's fuel consumption based on the ship comprehensive information database.

[0021] Optionally, the S1 includes the following steps:

[0022] S11. Use the marine environment sensors installed on the ship to obtain marine environment data in real time, including wave direction, wind speed, sea current velocity and sea current direction, and define the marine environment dataset : ;

[0023] Among them, is the wave direction based on the bow direction, representing the angle of the wave propagation direction, is the wind speed on the ocean surface, which affects the ship navigation resistance, is the sea current velocity, is the sea current direction based on the bow direction, representing the movement direction of the sea current relative to the ship;

[0024] S12. Use the ship condition monitoring equipment installed on the ship to obtain ship operation state data in real time, including ship speed, fuel consumption rate, propulsion efficiency and hull attitude, and define the ship operation state dataset : ;

[0025] Among them, is the ship's speed relative to the water body, is the fuel consumption rate, is the propulsion efficiency, representing the energy conversion efficiency of the ship's propulsion system, is the hull attitude, including the roll angle, pitch angle and yaw angle, which respectively represent the angular changes of the ship around its own transverse axis, longitudinal axis and vertical axis;

[0026] S13. For the marine environment dataset and the ship operation status dataset perform data preprocessing, screen abnormal data and missing data, and perform data normalization to construct the preprocessed marine environment dataset and the preprocessed ship operation status dataset;

[0027] S14. Based on the preprocessed marine environment dataset and the preprocessed ship operation status dataset , construct the ship navigation dataset : .

[0028] Optionally, the S2 includes the following steps:

[0029] S21. Integrate the ship operation status data and the marine environment data according to the preprocessed ship navigation dataset to construct a joint feature vector : ;

[0030] S22. Based on the joint feature vector establish a fuel consumption mapping relationship and define a mapping function : ;

[0031] Among them, is the fuel consumption rate, and the mapping function reflects the internal relationship between the ship operation status and the fuel consumption under different sea conditions;

[0032] S23. According to the mapping function establish a ship wave influence characteristic model : ;

[0033] Among them, represents the joint feature vector set, is the number of samples;

[0034] S24. Store the combined feature vectors contained in the ship wave influence characteristic model and the corresponding fuel consumption rate to build a ship comprehensive information database .

[0035] Optionally, the S3 includes the following steps:

[0036] S31. Based on the ship wave influence characteristic model and the ship comprehensive information database, set the ship navigation optimization goals, including the optimal navigation path, the lowest fuel consumption, and the highest navigation stability;

[0037] S32. Set the ship navigation path as a navigation trajectory composed of discrete waypoints : ;

[0038] where represents the geographical coordinates of the th waypoint, is the number of waypoints of the navigation path;

[0039] S33. Define the total ship fuel consumption according to the ship comprehensive information database : ;

[0040] where is the time required for the ship to run from waypoint to waypoint , represents the fuel consumption rate of the th waypoint;

[0041] S34. Set the navigation stability evaluation index , and define the ship navigation stability objective function: ;

[0042] where are the weight coefficients of the roll angle, pitch angle, and yaw angle respectively, represent the roll angle, pitch angle, and yaw angle at the th waypoint respectively;

[0043] S35. Combine the total ship fuel consumption and the ship navigation stability objective function to build a navigation optimization objective function : ;

[0044] where is the weight coefficient;

[0045] S36. According to the ship navigation constraints, construct the constraint conditions of the optimization problem, including navigation safety constraints, navigation speed constraints and navigation path constraints:

[0046] Navigation safety constraints: ; ; ;

[0047] Among them, are the safety thresholds of the roll angle, pitch angle and yaw angle respectively;

[0048] Navigation speed constraints: ;

[0049] Among them, are the minimum and maximum speeds of the ship respectively, is the current speed of the ship;

[0050] Navigation path constraints: ;

[0051] Among them, is the feasible navigation area;

[0052] S37. Through the constructed navigation optimization objective function and the constraint conditions, model the ship navigation fuel consumption energy-saving problem as a non-linear constrained optimization problem.

[0053] Optionally, the S4 includes the following steps:

[0054] S41. According to the navigation optimization objective function and the constraint conditions, combined with the real-time feedback ship navigation data set construct a dynamic adaptive navigation path search space , the dynamic adaptive navigation path search space is dynamically adjusted with time t and is updated in real time adaptively according to the historical ship navigation data in the ship comprehensive information database;

[0055] S42. In the dynamic adaptive navigation path search space define the population individual number of the mutated mayfly optimization algorithm, and initialize the initial navigation paths and speeds of the female mayfly population and the male mayfly population : ; ; ; ;

[0056] Among them, and respectively represent the initial sailing paths of the th female and male mayfly individuals, and respectively represent the initial sailing speeds of the th female and male mayflies, represents a uniform distribution;

[0057] S43. Calculate the mating probability of male mayfly individuals and female mayfly individuals : : ;

[0058] Among them, is the sailing stability evaluation index at the female mayfly individual , is the sailing stability evaluation index at the female mayfly individual , is the sailing stability evaluation index at the male mayfly individual , is the sailing stability evaluation index at the male mayfly individual , is the mating regulation factor, , , , are the fuel consumption impact factors, represents the fuel consumption rate at the th waypoint, represents the fuel consumption rate at the th waypoint, represents the fuel consumption rate at the th waypoint, represents the fuel consumption rate at the th waypoint, is the base of the natural logarithm;

[0059] Randomly match according to the mating probability to generate new mayfly individuals: ;

[0060] Among them, is the mating weight;

[0061] S44. Update the velocity of mayfly individuals by combining the velocity update strategy with wave perturbation: ; ;

[0062] where and respectively represent the velocities of the th male mayfly and female mayfly in the th generation, represents the dynamic inertia weight, which determines the inheritance degree of the individual's velocity from its previous generation in velocity update, and respectively represent the velocities of the th male mayfly and female mayfly in the th generation, represents the acceleration coefficient, represents a random number between (0, 1), represents the global optimal path, which is the optimal navigation path found by the entire population currently, represents the position of the optimal individual in the male mayfly group, and female mayflies tend to approach this individual, represents the mutation coefficient, represents a standard normal distribution random variable, is the historical optimal position of the male mayfly individual, represents the wave perturbation weight coefficient, is the th female mayfly individual, th male mayfly individual, represents the wave perturbation field correction term for the position of the male mayfly individual represents the wave perturbation field correction term: ;

[0063] where is the wave height at the waypoint , which affects the stability of the ship's path, is the wave direction at the waypoint , representing the direction angle of wave propagation, is the current navigation direction of the ship, and the angle between it and the wave direction determines the influence of the wave on the ship's path, is the sea current velocity at the waypoint , and the greater the velocity, the more significant the influence of the fluid on the ship, is the weight coefficient, which respectively controls the influence degrees of waves and sea currents on path optimization;

[0064] S45. Calculate the fitness and perform individual screening, and calculate the fitness value based on the fuel consumption rate and navigation stability : ;

[0065] Among them, is the fuel consumption constraint penalty factor, which makes the optimization solution meet the fuel consumption requirements is the value of the optimization objective function;

[0066] S46. Select the optimal individual from the current population according to the fitness value to form a preliminary candidate set of the optimal navigation paths of the ship : .

[0067] Optionally, the S5 includes the following steps:

[0068] S51. Define a local cost function for each waypoint in each candidate path in the preliminary candidate set of the optimal navigation paths of the ship. The local cost function reflects the fuel consumption, navigation stability and sea condition adaptability at the waypoint: ;

[0069] Among them, represents the actual fuel consumption rate at waypoint , is the fuel consumption reference value, represents the fluctuation degree of the hull attitude at waypoint , is the stability reference threshold, represents the wave energy consumption influence value calculated according to the local wave height, wave direction and wave period, is the corresponding reference energy consumption value, is the coefficient for adjusting the weights of various indicators;

[0070] S52. Calculate the local correction vector using the gradient descent idea according to the water droplet algorithm for the waypoint. The local correction vector simulates the process of the water droplet flowing naturally along the negative direction of the slope, so that the waypoint position is adjusted towards the direction of cost reduction: ;

[0071] Among them, is the gradient of the cost function at waypoint , which reflects the local trend of fuel consumption and stability change at waypoint , is the local optimization step factor, is a small positive number to prevent division-by-zero error;

[0072] S53. Use the local correction vector to update the positions of each waypoint in the candidate path and iterate until local convergence: ;

[0073] wherein, represents the position of waypoint at the -th iteration, represents the position of waypoint at the -th iteration, is the preset convergence threshold to ensure that the iteration stops when the position change is less than ;

[0074] S54. For each candidate path after local optimization construct a local navigation optimization objective function to comprehensively evaluate the overall performance: ;

[0075] wherein, is the local cost at waypoint after being updated by S53, is the regularization term to make the generated track smooth and meet the actual navigation requirements of the ship, is the regularization weight coefficient, represents the number of candidate path individuals selected from the set of preliminary candidate optimal ship navigation paths;

[0076] S55. Select the locally optimal navigation path that meets the requirements of the lowest energy consumption and the best navigation stability according to the local navigation optimization objective function value : ;

[0077] The finally selected navigation path minimizes the cost at each local waypoint, directly corresponding to the goal of reducing ship fuel consumption and improving navigation safety in practical applications.

[0078] The beneficial effects of the present invention are:

[0079] (1) The present invention enhances the individual mutation mechanism on the basis of the traditional mayfly optimization algorithm by introducing the mutant mayfly optimization algorithm, enabling the optimized population to explore the search space more extensively and avoid falling into local optima. Traditional optimization algorithms are prone to premature convergence problems affected by the initial population distribution when dealing with non-linear constrained optimization problems, resulting in unstable optimization effects. The mutant mayfly optimization algorithm makes the voyage path optimization consider not only fuel consumption but also voyage stability by introducing the calculation of dynamic mating probability, ensuring that the generated optimal path has better adaptability under complex sea conditions.

[0080] (2) The present invention further uses the water droplet algorithm to locally optimize the preliminary candidate paths. The water droplet algorithm simulates the characteristics of water droplets flowing along the terrain and can locally adjust the path according to environmental factors such as ocean currents, wind waves, and tides in voyage path planning to reduce energy consumption and improve voyage stability. The water droplet algorithm can better adapt to dynamic sea condition changes, making the local adjustment of the optimized path more refined. During the path optimization process, the local cost function of waypoints is dynamically calculated to ensure that the roll angle, pitch angle, and yaw angle of the hull are within a reasonable range during the voyage, avoiding unstable factors caused by sea condition changes, thereby enhancing the overall voyage safety.

[0081] (3) Based on the real-time feedback of the ship voyage data set, the present invention dynamically adjusts the optimized search space, enabling the optimization algorithm to perceive environmental changes in real time and adaptively correct the path. By combining the ship comprehensive information database, the optimal energy consumption area under different sea conditions can be predicted, and path fine-tuning can be performed according to the real-time position of the ship and ocean environment parameters. The dynamic adaptation search strategy can improve the accuracy of path optimization, making the optimized path achieve a better balance between energy conservation and stability. Brief Description of the Drawings

[0082] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0083] Figure 1 is a flowchart of a ship comprehensive information database management system based on big data analysis proposed by the present invention. Detailed Embodiment

[0084] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only showing the basic structure of the present invention in a schematic way, so they only show the components related to the present invention.

[0085] Reference Figure 1 , a ship comprehensive information database management system based on big data analysis, includes:

[0086] The marine environment perception and ship status monitoring module includes marine environment sensors and ship status monitoring equipment installed on the ship, and is used to collect marine environment data and ship operation status data in real time;

[0087] The ship wave influence characteristic modeling module constructs a ship wave influence characteristic model based on the marine environment data and ship operation status data, and generates a ship comprehensive information database;

[0088] The ship navigation optimization target setting module sets the ship navigation optimization target based on the ship wave influence characteristic model, constructs a navigation optimization target function in combination with the ship navigation constraint conditions, and models the ship navigation fuel consumption energy-saving problem as a non-linear constraint optimization problem;

[0089] The mutated mayfly optimization global path search module uses the mutated mayfly optimization algorithm to globally search the navigation optimization target function, and determines a preliminary candidate set of optimal ship navigation paths in the dynamic search space;

[0090] The water droplet algorithm local path optimization module locally optimizes the preliminary candidate set of optimal ship navigation paths based on the water droplet algorithm to obtain a locally optimal navigation path;

[0091] The ship global path optimization and navigation control module comprehensively compares the locally optimal navigation path with the preliminary candidate set of optimal ship navigation paths, screens out the globally optimal navigation path that meets the navigation optimization target, and generates corresponding ship navigation control parameters.

[0092] A method for managing a ship comprehensive information database based on big data analysis is applied to a ship comprehensive information database management system based on big data analysis, and includes the following steps:

[0093] S1. Collect marine environment data and ship operation status data in real time, and preprocess and standardize the collected marine environment data and ship operation status data to obtain a preprocessed ship navigation data set;

[0094] S2. Based on the preprocessed ship navigation data set, construct a ship wave influence characteristic model, establish a mapping relationship between the ship operation status and fuel consumption under different sea conditions, and form a ship comprehensive information database;

[0095] S3. Use the ship wave influence characteristic model to set the ship navigation optimization target, construct a navigation optimization target function according to the set ship navigation optimization target, and model the ship navigation fuel consumption energy-saving problem as a non-linear constraint optimization problem;

[0096] S4. Apply the mutated mayfly optimization algorithm to globally search the navigation optimization objective function, and dynamically adjust the candidate solutions in the search space with the help of the real-time feedback ship navigation data set to generate a preliminary candidate set of the optimal ship navigation paths;

[0097] S5. Use the water droplet algorithm to locally optimize the preliminary candidate set of the optimal ship navigation paths, and locally correct the preliminary candidate set of the optimal ship navigation paths according to the dynamic changes of the ship navigation data set to obtain the locally optimal navigation paths;

[0098] S6. Comprehensively compare the locally optimal navigation paths with the preliminary candidate set of the optimal ship navigation paths, screen out the globally optimal navigation paths that meet the navigation optimization objectives, generate the corresponding ship navigation control parameters, and control the ship to navigate according to the globally optimal navigation paths to complete the goal of managing ship fuel consumption based on the ship comprehensive information database.

[0099] In this embodiment, S1 includes the following steps:

[0100] S11. Use the marine environment sensors installed on the ship to obtain the marine environment data in real time, including wave direction, wind speed, sea current velocity and sea current direction, and define the marine environment data set : ;

[0101] Among them, is the wave direction based on the bow direction, representing the angle of the wave propagation direction, is the wind speed on the sea surface, which affects the ship navigation resistance, is the sea current velocity, is the sea current direction based on the bow direction, representing the movement direction of the sea current relative to the ship;

[0102] S12. Use the ship condition monitoring equipment installed on the ship to obtain the ship operation status data in real time, including ship speed, fuel consumption rate, propulsion efficiency and hull attitude, and define the ship operation status data set : ;

[0103] Among them, is the ship speed relative to the water body, is the fuel consumption rate, is the propulsion efficiency, representing the energy conversion efficiency of the ship propulsion system, is the hull attitude, including roll angle, pitch angle and yaw angle, respectively representing the angle changes of the ship around its own transverse axis, longitudinal axis and vertical axis;

[0104] S13. For the marine environment data set and the ship operation status data set Perform data preprocessing on the ocean environment data set and the ship operation status data set, screen out abnormal data and missing data, perform data normalization, and construct the preprocessed ocean environment data set and the preprocessed ship operation status data set;

[0105] S14. Based on the preprocessed ocean environment data set and the preprocessed ship operation status data set , construct the ship navigation data set : .

[0106] This embodiment can obtain more comprehensive ocean environment and ship status information, provide rich data input for the intelligent optimization algorithm, enhance the adaptability and calculation accuracy of the optimization algorithm, enable the ship to more accurately perceive the changes in sea conditions, ensure the matching of the optimized path with the actual navigation environment, and improve the accuracy and energy efficiency level of ship navigation.

[0107] In this embodiment, the S2 includes the following steps:

[0108] S21. Integrate the ship operation status data and the ocean environment data according to the preprocessed ship navigation data set to construct a joint feature vector : ;

[0109] S22. Based on the joint feature vector establish a fuel consumption mapping relationship and define a mapping function : ;

[0110] wherein, is the fuel consumption rate, and the mapping function reflects the internal relationship between the ship operation status and the fuel consumption under different sea conditions;

[0111] S23. Establish a ship wave influence characteristic model according to the mapping function : : ;

[0112] wherein, represents the joint feature vector set, is the number of samples;

[0113] S24. The joint feature vectors contained in the ship wave influence characteristic model and the corresponding fuel consumption rate Store them to build a comprehensive ship information database 。

[0114] In this embodiment, by establishing the fuel consumption mapping relationship, the impact of sea conditions on ship fuel consumption can be effectively quantified and data support can be provided. Traditional ship navigation mostly uses fixed empirical values or single fuel consumption models, which are difficult to adapt to dynamic sea condition changes. However, this method realizes the non-linear mapping between the ship operation state and fuel consumption by constructing a ship wave influence characteristic model, making the fuel consumption calculation more in line with the actual navigation conditions.

[0115] In this embodiment, S3 includes the following steps:

[0116] S31. Based on the ship wave influence characteristic model and the comprehensive ship information database, set the ship navigation optimization objectives, including the optimal navigation path, the lowest fuel consumption, and the highest navigation stability;

[0117] S32. Set the ship navigation path as a navigation track composed of discrete waypoints : ;

[0118] Among them, represents the geographical coordinates of the th waypoint, and

[0119] is the number of waypoints of the navigation path; S33. Define the total ship fuel consumption according to the comprehensive ship information database ;

[0120] Among them, is the time required for the ship to travel from waypoint to waypoint , represents the fuel consumption rate of the th waypoint;

[0121] S34. Set the navigation stability evaluation index , and define the ship navigation stability objective function: ;

[0122] Among them, are the weight coefficients of the roll angle, pitch angle, and yaw angle respectively, respectively represent the roll angle, pitch angle, and yaw angle at the th waypoint;

[0123] S35. Combine the total ship fuel consumption and the ship navigation stability objective function to construct a navigation optimization objective function : ;

[0124] Among them, is the weight coefficient;

[0125] S36. According to the ship navigation constraints, construct the constraint conditions of the optimization problem, including navigation safety constraints, navigation speed constraints and navigation path constraints:

[0126] Navigation safety constraints: ; ; ;

[0127] Among them, are the safety thresholds of the roll angle, pitch angle and yaw angle respectively;

[0128] Navigation speed constraints: ;

[0129] Among them, are the minimum and maximum ship speeds respectively, is the current ship speed;

[0130] Navigation path constraints: ;

[0131] Among them, is the feasible navigation area;

[0132] S37. Through the constructed navigation optimization objective function and the constraint conditions, model the ship navigation fuel consumption and energy saving problem as a non-linear constrained optimization problem.

[0133] This embodiment realizes multi-objective optimization modeling by combining the optimal navigation path, the lowest fuel consumption and the highest navigation stability. Compared with the traditional path planning method, this method not only considers the shortest voyage, but also improves the economy and safety of navigation through the construction of the navigation optimization objective function. This embodiment combines the key factors of fuel consumption, ship attitude stability and path smoothness, so that the optimization model can comprehensively evaluate the adaptability of different navigation paths, thus avoiding the local optimum problem caused by the optimization of a single factor.

[0134] In this embodiment, the S4 includes the following steps:

[0135] S41. According to the navigation optimization objective function and constraint conditions, combined with the real-time feedback of the ship navigation data set Construct a dynamic adaptive navigation path search space The dynamic adaptive navigation path search space is dynamically adjusted with time t and is updated in real time adaptively according to the historical ship navigation data in the ship comprehensive information database;

[0136] S42. Define the number of population individuals of the mutant mayfly optimization algorithm within the dynamic adaptive navigation path search space and initialize the initial navigation paths and speeds of the female mayfly population and the male mayfly population : ; ; ; ;

[0137] Among them, and respectively represent the initial navigation paths of the th female and male mayfly individuals, and respectively represent the initial speeds of the th female and male mayflies, represents a uniform distribution;

[0138] S43. Calculate the mating probability of the male mayfly individual and the female mayfly individual according to the ship wave influence characteristic model : ;

[0139] Among them, is the navigation stability evaluation index at the female mayfly individual , is the navigation stability evaluation index at the female mayfly individual , is the navigation stability evaluation index at the male mayfly individual , is the navigation stability evaluation index at the male mayfly individual , is the mating adjustment factor, , , , are the fuel consumption influence factors, Indicates the fuel consumption rate of the th waypoint, Indicates the fuel consumption rate of the th waypoint, Indicates the fuel consumption rate of the th waypoint, Indicates the fuel consumption rate of the th waypoint, is the base of the natural logarithm;

[0140] Randomly match according to the mating probability to generate new mayfly individuals: ;

[0141] Among them, is the mating weight;

[0142] S44. Combine the speed update strategy with wave disturbance to correct the speed of mayfly individuals: ; ;

[0143] Among them, and respectively represent the speeds of the th male mayfly and female mayfly in the th generation, represents the dynamic inertia weight, which determines the degree of inheritance of the previous generation's speed by the current individual in speed update, and respectively represent the speeds of the th male mayfly and female mayfly in the th generation, represents the acceleration coefficient, represents a random number between (0, 1), represents the global optimal path, indicating the optimal navigation path currently found by the entire population, represents the position of the optimal individual in the male mayfly group, and female mayflies tend to approach this individual, represents the mutation coefficient, represents a standard normal distribution random variable, is the historical optimal position of the male mayfly individual, represents the wave disturbance weight coefficient, is the th male mayfly individual, represents the wave disturbance field correction term for the position of the th male mayfly individual, represents the wave disturbance field correction term: ;

[0144] Among them, is the wave height at the waypoint, which affects the stability of the ship's path. is the wave height at the waypoint, which affects the stability of the ship's path. is the wave direction at the waypoint, indicating the direction angle of wave propagation. is the wave direction at the waypoint, indicating the direction angle of wave propagation. is the current sailing direction of the ship. The angle between it and the wave direction determines the influence of the wave on the ship's path. is the sea current velocity at the waypoint. The greater the velocity, the more significant the influence of the fluid on the ship. is the sea current velocity at the waypoint. The greater the velocity, the more significant the influence of the fluid on the ship. is the weight coefficient, which respectively controls the influence degree of waves and sea currents on path optimization.

[0145] S45. Calculate the fitness and conduct individual screening. Calculate the fitness value based on the fuel consumption rate and sailing stability : ;

[0146] Among them, is the fuel consumption constraint penalty factor, which makes the optimization solution meet the fuel consumption requirements. is the optimization objective function value.

[0147] S46. Select the optimal individual from the current population according to the fitness value to form a preliminary candidate set of the optimal sailing paths of the ship : .

[0148] In this embodiment, by combining ocean environment data and ship operation state data, global search and dynamic optimization path planning are realized. By simulating the mating behavior and environmental adaptation mechanism of the mayfly population, the global exploration ability of the algorithm is enhanced, enabling the optimization algorithm to quickly converge to the optimal solution. In addition, a sea condition coupling dynamic mating strategy is introduced, and the mating probability is dynamically adjusted according to fuel consumption and sailing stability, making the generated optimal path more in line with the energy-saving and safe sailing requirements of the ship.

[0149] In this embodiment, S5 includes the following steps:

[0150] S51. Define a local cost function for each waypoint of each candidate path in the preliminary candidate set of the optimal sailing paths of the ship. The local cost function reflects the fuel consumption, sailing stability, and sea condition adaptability at the waypoint: ;

[0151]

[0151] Among them, represents the waypoint The actual fuel consumption rate at is the fuel consumption reference value, indicating the waypoint where the fluctuation degree of the hull attitude is, is the stability reference threshold, indicating the wave energy consumption influence value calculated based on the local wave height, wave direction and wave period, is the corresponding reference energy consumption value, is the coefficient for adjusting the weights of various indicators;

[0152] S52. According to the water droplet algorithm, for the waypoint adopt the gradient descent idea to calculate the local correction vector , the local correction vector simulates the process of the water droplet flowing naturally along the negative direction of the slope, so that the waypoint position is adjusted towards the direction of cost reduction: ;

[0153] Among them, is the gradient of the cost function at the waypoint , reflecting the local trend of fuel consumption and stability change at the waypoint , is the local optimization step size factor, is a small positive number to prevent division by zero error;

[0154] S53. Use the local correction vector to update the positions of each waypoint in the candidate path and perform iteration until local convergence: ;

[0155] Among them, represents the position of the waypoint at the th iteration, represents the position of the waypoint at the th iteration, is the preset convergence threshold to ensure that the iteration stops when the position change is less than ;

[0156] S54. For each candidate path after local optimization construct the local navigation optimization objective function to comprehensively evaluate the overall performance: ;

[0157] Among them, is the local cost at the waypoint after being updated by S53, ​​​is a regularization term, which makes the generated track smooth and meet the actual navigation requirements of the ship. is the regularization weight coefficient. represents the number of candidate path individuals selected from the set of preliminary candidate optimal navigation paths of the ship.

[0158] S55. Screen out the locally optimal navigation paths that meet the requirements of the lowest energy consumption and the best navigation stability according to the values of the local navigation optimization objective function: ;

[0159] The finally selected navigation path minimizes the cost at each local waypoint, directly corresponding to the goal of reducing the ship's fuel consumption and improving navigation safety in practical applications.

[0160] In this embodiment, by simulating the natural flow characteristics of water droplets in the terrain, the local optimization of the navigation path is made more in line with the movement law of the ship under actual sea conditions. Through the path adjustment mechanism of the water droplet algorithm, the position of the waypoint can be dynamically optimized, the path deviation can be reduced, and the navigation smoothness and fuel economy can be improved.

[0161] In this embodiment, the S6 includes the following steps:

[0162] S61. Calculate the fuel consumption, navigation stability and path smoothness of each path according to the set of locally optimal navigation paths and the set of preliminary candidate optimal navigation paths of the ship, and construct a global fitness function. By comparing the fitness values of different paths, screen out the path with the lowest fuel consumption, the highest navigation stability and the smoothest track as the global optimal navigation path;

[0163] S62. According to the global optimal navigation path, calculate the optimal speed and heading angle of the ship at each waypoint, and optimize and adjust based on the relative relationship between the speed and fuel consumption, and the heading angle and wave direction. During the calculation process, comprehensively consider the propulsion efficiency, fuel utilization rate of the ship and the influence of waves on the heading to ensure that the ship operates with the lowest energy consumption and the highest stability on the optimized navigation path;

[0164] S63. Generate ship navigation control instructions according to the calculated optimal speed and heading angle, and input them into the ship comprehensive information database. During the actual navigation process, adjust the navigation parameters in real time based on the fuel consumption data, ship real-time heading and wave environment data fed back by the sensor to adapt to the sea condition changes and maintain the optimal navigation state under sudden environmental disturbances.

[0165] Example 1:

[0166] On December 15, 2024, a 50,000-ton cargo ship named "Ocean Pioneer" departed from Port A with a destination of Port B. The ship carried a large amount of goods and planned to cross the North Atlantic according to the traditional shortest path navigation method. However, before departure, the meteorological data analysis system found that the sea area between 50°N and 55°N was affected by a low-pressure cyclone, and it was predicted that the wind speed would reach 90 kilometers per hour and the wave height would exceed 7 meters within the next 48 hours. If the ship sailed along the traditional route, it was expected to experience a strong storm in this area for up to 12 hours, and both the navigation stability and fuel consumption would be severely affected.

[0167] After the ship left berth at Port A, the ship's marine environment monitoring system real-time collected the wave direction (60°), wind speed (55 km / h), and sea current speed (3.5 knots), and uploaded them to the ship data analysis platform. At the same time, the ship propulsion system fed back the current fuel consumption rate (7.2 tons per hour) and propulsion efficiency (82%). The optimization system combined the real-time environmental data and the historical sea condition database, and used the mutated mayfly optimization algorithm to perform the first-round optimization calculation on the navigation path.

[0168] The system generated three alternative paths:

[0169] 1. Traditional shortest path route: Directly cross the North Atlantic storm area, with an estimated fuel consumption of 1,280 tons, a navigation time of 185 hours, and a maximum roll angle of 18°;

[0170] 2. Southern deviation route (optimized by the method of the present invention): Avoid the storm area and sail along 47°N, with an estimated fuel consumption of 1,120 tons, a navigation time of 182 hours, and a maximum roll angle of 9°;

[0171] 3. Long-distance detour route: Avoid all severe weather areas, but the voyage increases by 250 nautical miles, with an estimated fuel consumption of 1,360 tons and a navigation time of 192 hours.

[0172] After calculating the fitness by the mutated mayfly optimization algorithm, the second route was selected as the optimal path. After receiving the system recommendation, the captain confirmed to adopt the optimized route plan and reported the adjustment plan to the shipping company.

[0173] At 2:30 am on December 17, 2024, when the ship entered the sea area of 49°N, the ship monitoring system detected that the wind speed increased to 75 km / h and the wave height reached 5.5 meters, resulting in the roll angle rising from 8° to 11°. At this time, the ship navigation optimization system automatically triggered the water droplet algorithm to adjust the local navigation path and generated a new correction plan.

[0174] The system calculation found that if the ship continues to sail along the current path, the roll angle may exceed 12° within the next 3 hours, and the fuel consumption rate may rise to 7.5 tons per hour. The optimization system re-evaluated the environmental data and recommended that the ship adjust 2° southward, with an offset of approximately 18 nautical miles, to avoid areas with wave heights above 6 meters. After the captain accepted the recommendation and adjusted the sailing path, the ship's roll angle dropped to 8.5° within 20 minutes, and the fuel consumption rate stabilized at 6.9 tons per hour.

[0175] After arriving at Port B, the shipping company compared the actual data of this voyage with the traditional route plan:

[0176] At the same time, during the most severely storm-affected sailing stage (from 2:30 to 5:30 am on December 17), the fuel-saving effect of the optimization method was particularly significant:

[0177] The analysis results show that especially during periods with strong winds and waves, the method of the present invention can effectively reduce fuel consumption while maintaining sailing stability. The sailing experiment of this embodiment shows that the optimized navigation method based on the mutated mayfly optimization algorithm and the water droplet algorithm can intelligently adjust the ship's route, effectively reduce fuel consumption under complex sea conditions, and improve sailing safety at the same time. Compared with the traditional fixed route, this method can achieve a fuel saving rate of 12.5%, reduce the sailing time by 3 hours, and reduce the roll angle by 40%, fully demonstrating the superiority of the present invention in practical applications.

[0178] The present invention enhances the individual mutation mechanism on the basis of the traditional mayfly optimization algorithm by introducing the mutated mayfly optimization algorithm, enabling the optimized population to explore the search space more widely and avoid falling into local optima. Traditional optimization algorithms are prone to premature convergence problems affected by the initial population distribution when dealing with non-linear constrained optimization problems, resulting in unstable optimization effects. The mutated mayfly optimization algorithm makes the sailing path optimization consider not only fuel consumption but also sailing stability by introducing dynamic mating probability calculation, ensuring that the generated optimal path has better adaptability under complex sea conditions.

[0179] The present invention further uses the water droplet algorithm to perform local optimization on the preliminary candidate paths. The water droplet algorithm simulates the characteristics of water droplets flowing along the terrain and can make local adjustments to the path according to ocean current, wind and wave environmental factors during sailing path planning to reduce energy consumption and improve sailing stability. The water droplet algorithm can better adapt to dynamic sea condition changes, making the local adjustment of the optimized path more refined, dynamically calculating the local cost function of waypoints during the path optimization process, ensuring that the roll angle, pitch angle and yaw angle of the hull are within a reasonable range during sailing, and avoiding unstable factors caused by sea condition changes, thereby enhancing the overall sailing safety.

[0180] Based on the real-time feedback of the ship navigation data set, the present invention dynamically adjusts and optimizes the search space, enabling the optimization algorithm to perceive environmental changes in real time and adaptively correct the path. By combining the ship comprehensive information database, it can predict the optimal energy consumption area under different sea conditions and perform path fine-tuning according to the real-time position of the ship and ocean environmental parameters. The dynamic adaptation search strategy can improve the accuracy of path optimization, making the optimized path achieve a better balance between energy conservation and stability.

[0181] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A ship integrated information database management system based on big data analysis, characterized in that, Comprising: An ocean environment perception and ship status monitoring module, including ocean environment sensors and ship status monitoring devices installed on the ship, for real-time collection of ocean environment data and ship operation status data; A ship wave influence characteristic modeling module, based on the ocean environment data and ship operation status data, constructs a ship wave influence characteristic model, and generates a ship comprehensive information database; A ship navigation optimization goal setting module, based on the ship wave influence characteristic model, sets the ship navigation optimization goal, combines the ship navigation constraint conditions to construct a navigation optimization objective function, and models the ship navigation fuel consumption energy-saving problem as a non-linear constraint optimization problem; A mutated mayfly optimization global path search module, uses the mutated mayfly optimization algorithm to globally search the navigation optimization objective function, and determines a preliminary candidate set of optimal ship navigation paths in the dynamic search space; A water droplet algorithm local path optimization module, based on the preliminary candidate set of optimal ship navigation paths, uses the water droplet algorithm to locally optimize the preliminary candidate set of optimal ship navigation paths to obtain a locally optimal navigation path; A ship global path optimization and navigation control module, comprehensively compares the locally optimal navigation path with the preliminary candidate set of optimal ship navigation paths, screens out the globally optimal navigation path that meets the navigation optimization goal, and generates corresponding ship navigation control parameters.

2. A management method for a ship integrated information database based on big data analysis, which is applied to the ship integrated information database management system according to claim 1, and is characterized in that, Including the following steps: S1. Real-time collect ocean environment data and ship operation status data, and preprocess and standardize the collected ocean environment data and ship operation status data to obtain a preprocessed ship navigation data set; S2. Based on the preprocessed ship navigation data set, construct a ship wave influence characteristic model, establish a mapping relationship between the ship operation status and fuel consumption under different sea conditions, and form a ship comprehensive information database; S3. Use the ship wave influence characteristic model to set the ship navigation optimization goal, and construct a navigation optimization objective function according to the set ship navigation optimization goal, and model the ship navigation fuel consumption energy-saving problem as a non-linear constraint optimization problem; S4. Apply the mutated mayfly optimization algorithm to globally search the navigation optimization objective function, dynamically adjust the candidate solutions in the search space with the help of the real-time feedback ship navigation data set, and generate a preliminary candidate set of optimal ship navigation paths; S5. Use the water droplet algorithm to locally optimize the preliminary candidate set of optimal ship navigation paths, and locally correct the preliminary candidate set of optimal ship navigation paths according to the dynamic changes of the ship navigation data set to obtain a locally optimal navigation path; S6. Comprehensively compare the locally optimal navigation path with the preliminary candidate set of optimal ship navigation paths, screen out the globally optimal navigation path that meets the navigation optimization goal, generate corresponding ship navigation control parameters, and control the ship to navigate according to the globally optimal navigation path to complete the goal of managing the ship fuel consumption according to the ship comprehensive information database.

3. The method for managing a ship comprehensive information database based on big data analysis according to claim 2, wherein The said S1 includes the following steps: S11. Real-time obtain ocean environment data by using ocean environment sensors installed on a ship, including wave direction, wind speed, sea current velocity, and sea current direction, and define an ocean environment data set ; S12. Real-time obtain the ship operation status data by using the ship status monitoring equipment installed on the ship, including the ship speed, fuel consumption rate, propulsion efficiency and hull attitude, and define the ship operation status data set ; S13. Preprocess the marine environment dataset and the ship operation status dataset Perform data preprocessing, screen out abnormal data and missing data, and perform data normalization to construct the preprocessed marine environment dataset and the preprocessed ship operation status dataset; S14. Based on the pre-processed marine environment dataset and the pre-processed ship operation status dataset , construct a ship navigation dataset .

4. A method for managing a ship integrated information database based on big data analysis according to claim 3, characterized in that, The said S2 includes the following steps: S21. According to the preprocessed ship navigation data set Integrate the ship operation state data and the marine environment data to construct a joint feature vector ; S22. Based on the combined feature vector Establish a fuel consumption mapping relationship and define a mapping function ; S23. Establish a ship wave influence characteristic model according to the mapping function Establish a ship wave influence characteristic model ; S24. Store the combined feature vectors included in the ship wave influence characteristic model and the corresponding fuel consumption rate to build a ship comprehensive information database .

5. A method for managing a ship comprehensive information database based on big data analysis according to claim 4, characterized in that The said S3 includes the following steps: S31. Based on the ship wave influence characteristic model and the ship comprehensive information database, set the ship navigation optimization objectives, including the optimal navigation path, the lowest fuel consumption, and the highest navigation stability; S32. Set the ship's navigation path which is a navigation track composed of discrete waypoints S33. Based on the ship integrated information database Define the total ship fuel consumption ; S34. Set the evaluation index of sailing stability ; S35. Construct a navigation optimization objective function by integrating the total ship fuel consumption and the ship navigation stability objective function ; S36. According to the ship navigation constraints, construct the constraint conditions of the optimization problem, including navigation safety constraints, navigation speed constraints, and navigation path constraints; S37. By constructing the voyage optimization objective function and constraint conditions, the problem of fuel consumption energy saving in ship navigation is modeled as a non-linear constrained optimization problem.

6. A management method for a ship integrated information database based on big data analysis according to claim 5, characterized in that, The said S4 includes the following steps: S41. According to the navigation optimization objective function and constraint conditions, combined with the real-time feedback of the ship navigation data set Construct a dynamic adaptive navigation path search space , the dynamic adaptive navigation path search space is dynamically adjusted with time t and is updated in real time adaptively according to the historical ship navigation data in the ship comprehensive information database; S42. In the dynamic adaptive navigation path search space Define the number of population individuals of the mutated mayfly optimization algorithm and initialize the initial navigation paths and speeds of the female mayfly population and the male mayfly population ; S43. According to the ship wave influence characteristic model Calculate the mating probability of male mayfly individuals and female mayfly individuals ; ; Randomly match according to the mating probability to generate new mayfly individuals ; S44. Combine the speed update strategy of wave perturbation to correct the speed of the mayfly individuals; S45. Calculate the fitness and perform individual screening, and calculate the fitness value based on the fuel consumption rate and the sailing stability ; S46. Select the optimal individual from the current population according to the fitness value to form a preliminary candidate set of the optimal navigation paths of the ships .

7. A management method for a ship integrated information database based on big data analysis according to claim 6, characterized in that The said S5 includes the following steps: S51. For each candidate path in the set of optimal navigation paths of the preliminary candidate vessels and for each waypoint define a local cost function , where the local cost function reflects the fuel consumption, navigation stability and sea condition adaptability at the waypoint; S52. Calculate the waypoint according to the water drop algorithm Use the gradient descent idea to calculate the local correction vector The local correction vector simulates the natural flow process of the water drop along the negative direction of the slope, so that the waypoint position is adjusted towards the direction of cost reduction; S53. Use the local correction vector to update the positions of the waypoints in the candidate path and perform iteration until local convergence; For each candidate path after local optimization Construct a local navigation optimization objective function To comprehensively evaluate the overall performance; S55. Select the locally optimal navigation path that meets the requirements of minimum energy consumption and best navigation stability based on the locally optimal navigation objective function value , and the finally selected navigation path minimizes the cost at each local waypoint, directly corresponding to the goal of reducing ship fuel consumption and improving navigation safety in practical applications.

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