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

Abstract

The present invention discloses a ship comprehensive information database management system and method based on big data analysis. The system includes: a marine environment perception and ship status monitoring module for real-time collection of marine environmental data and ship operating status data; a ship wave impact characteristic modeling module for constructing a ship wave impact characteristic model; a ship navigation optimization target setting module for modeling the ship navigation fuel consumption and energy conservation problem as a nonlinear constrained optimization problem; a variant mayfly optimization global path search module for determining a preliminary set of candidate ship optimal navigation paths within a dynamic search space; a water drop algorithm local path optimization module for obtaining locally optimal navigation paths; and a ship global path optimization and navigation control module for generating corresponding ship navigation control parameters. By combining the ship comprehensive information database, the present invention can predict the optimal energy consumption area under different sea conditions and fine-tune the path based on the ship's real-time position and marine environmental parameters.

Description

Technical Field

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

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

[0003] At present, most ship navigation systems mainly rely on traditional route planning methods, such as preset navigation plans based on fixed routes or simple shortest path algorithms. Traditional methods usually only consider sailing distance and sailing time, and do not fully consider the impact of the marine environment on ship fuel consumption. For example, navigation methods based on fixed routes cannot adapt to complex and changeable sea conditions in real time, causing ships to adopt suboptimal navigation strategies when facing adverse marine environments, thereby increasing fuel consumption. In addition, although traditional shortest path algorithms can shorten sailing time to a certain extent, they often ignore the impact of wind, waves, and ocean currents on ship energy consumption, resulting in large energy consumption deviations in navigation plans in actual applications.

[0004] In recent years, some intelligent optimization algorithms have been applied to the optimization of ship comprehensive information databases, but there are still some shortcomings: First, some algorithms are prone to falling into local optimality and cannot globally optimize the ship's navigation path, resulting in limited fuel consumption optimization effects; Second, existing optimization algorithms have a slow response speed when dealing with dynamic environmental changes, making it difficult to adjust the navigation path in real time to adapt to sudden changes in sea conditions; Third, some optimization methods fail to fully combine the propulsion characteristics and fuel consumption characteristics of the ship, and lack comprehensive modeling of the ship's power system and environmental factors, which affects the practical application value of the optimization results.

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

[0006] One purpose of the present invention is to propose a ship comprehensive information database management system and method based on big data analysis. By combining the ship comprehensive information database, the present invention can predict the optimal energy consumption area under different sea conditions and fine-tune the path according to the real-time position of the ship and the marine environmental parameters.

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

[0008] The marine environment perception and ship status monitoring module includes marine environment sensors and ship status monitoring equipment installed on the ship, which are used to collect marine environment data and ship operation status data in real time;

[0009] The ship wave impact characteristics modeling module builds a ship wave impact characteristics model based on marine environment data and ship operation status data, and generates a comprehensive ship information database;

[0010] The ship navigation optimization target setting module sets the ship navigation optimization target based on the ship wave impact characteristic model, and constructs the navigation optimization objective function in combination with the ship navigation constraints. It also models the ship navigation fuel consumption and energy saving problem as a nonlinear constrained optimization problem.

[0011] The mutation mayfly optimization global path search module uses the mutation mayfly optimization algorithm to perform a global search on the navigation optimization objective function and determine the optimal navigation path set of preliminary candidate ships in the dynamic search space;

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

[0013] The ship global path optimization and navigation control module comprehensively compares the local optimal navigation path with the preliminary candidate ship optimal navigation path set, screens out the global optimal navigation path that meets the navigation optimization goal, and generates the corresponding ship navigation control parameters.

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

[0015] S1. Real-time collection of marine environmental data and ship operation status data, and preprocessing and standardization of the collected marine environmental data and ship operation status data to obtain a preprocessed ship navigation data set;

[0016] S2. Based on the preprocessed ship navigation data set, a ship wave impact characteristic model is constructed, a mapping relationship between the ship's operating status and fuel consumption under different sea conditions is established, and a comprehensive ship information database is formed;

[0017] S3. Using the ship wave impact characteristic model, set the ship navigation optimization goal, and construct the navigation optimization objective function based on the set ship navigation optimization goal, modeling the ship navigation fuel consumption and energy saving problem as a nonlinear constrained optimization problem;

[0018] S4. Apply the mutational mayfly optimization algorithm to perform a global search for the navigation optimization objective function. Using the real-time feedback of the ship navigation dataset, dynamically adjust the candidate solutions in the search space to generate a preliminary set of candidate optimal ship navigation paths.

[0019] S5. Use the water drop algorithm to locally optimize the set of preliminary candidate ship optimal navigation paths. Based on the dynamic changes of the ship navigation data set, locally modify the set of preliminary candidate ship optimal navigation paths to obtain the local optimal navigation path.

[0020] S6. Comprehensively compare the local optimal navigation path with the set of preliminary candidate ship optimal navigation paths, screen out the global 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 global optimal navigation path, thereby achieving the goal of managing ship fuel consumption according to the ship comprehensive information database.

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

[0022] S11. Use marine environmental sensors installed on ships to obtain real-time marine environmental data, including wave direction, wind speed, current speed and current direction, and define the marine environmental data set :

[0023] ;

[0024] in, is the wave direction based on the bow direction, which indicates the angle of the wave propagation direction. is the wind speed on the ocean surface, which affects the ship's sailing resistance. is the ocean current velocity, The direction of the current based on the bow direction indicates the direction of the current relative to the ship;

[0025] S12. Use the ship status monitoring equipment installed on the ship to obtain real-time ship operation status data, including speed, fuel consumption rate, propulsion efficiency and hull posture, and define the ship operation status data set :

[0026] ;

[0027] in, is the speed of the ship relative to the water, is the fuel consumption rate, is the propulsion efficiency, which represents the energy conversion efficiency of the ship propulsion system. is the ship's attitude, including roll angle, pitch angle and bow angle, which respectively represent the angular changes of the ship around its own transverse axis, longitudinal axis and vertical axis;

[0028] S13. Marine environment dataset and ship operation status dataset Perform data preprocessing, filter out abnormal data and missing data, and normalize the data to construct a preprocessed marine environment dataset and a preprocessed ship operation status dataset;

[0029] S14. Based on the preprocessed marine environment dataset and preprocessed ship operation status dataset , build a ship navigation dataset :

[0030] .

[0031] Optionally, the S2 includes the following steps:

[0032] S21. Based on the preprocessed ship navigation dataset Integrate ship operation status data and marine environment data to construct joint feature vector :

[0033] ;

[0034] S22. Based on joint feature vector Establish fuel consumption mapping relationship and define mapping function :

[0035] ;

[0036] in, is the fuel consumption rate, mapping function Reflect the inherent relationship between ship operation status and fuel consumption under different sea conditions;

[0037] S23. According to the mapping function Establishing a model of ship wave impact characteristics :

[0038] ;

[0039] in, represents the set of joint eigenvectors, is the sample size;

[0040] S24. Model the ship wave impact characteristics The joint eigenvector contained in and the corresponding fuel consumption rate Store and build a comprehensive ship information database .

[0041] Optionally, S3 includes the following steps:

[0042] S31. Based on the ship wave impact characteristic model and the ship comprehensive information database, set ship navigation optimization goals, including optimal navigation path, minimum fuel consumption and maximum navigation stability;

[0043] S32. Set the ship's navigation path Discrete waypoints The navigation track consists of:

[0044] ;

[0045] in, Indicates the The geographical coordinates of the waypoints, is the number of waypoints in the navigation path;

[0046] S33. Based on the ship comprehensive information database Define total fuel consumption of a ship :

[0047] ;

[0048] in, For ships from waypoints Run to waypoint The time required, Indicates the Fuel consumption rate per waypoint;

[0049] S34. Setting navigation stability evaluation indicators , define the ship navigation stability objective function:

[0050] ;

[0051] in, are the weight coefficients of roll angle, pitch angle and yaw angle respectively, Respectively represent Roll angle, pitch angle and heading angle at each waypoint;

[0052] S35. Combine the total fuel consumption of the ship and the ship's navigation stability objective function to construct the navigation optimization objective function :

[0053] ;

[0054] in, is the weight coefficient;

[0055] S36. Based on the ship navigation constraints, construct the constraints of the optimization problem, including navigation safety constraints, navigation speed constraints, and navigation path constraints:

[0056] Navigation safety constraints:

[0057] ;

[0058] ;

[0059] ;

[0060] in, are the safety thresholds of roll angle, pitch angle and heading angle respectively;

[0061] Sailing speed constraints: ;

[0062] in, are the minimum and maximum speeds of the ship, respectively. is the current speed of the ship;

[0063] Navigation path constraints:

[0064] ;

[0065] in, A feasible navigation area;

[0066] S37. Optimize the navigation objective function by constructing and constraints, the ship navigation fuel consumption and energy saving problem is modeled as a nonlinear constrained optimization problem.

[0067] Optionally, the S4 includes the following steps:

[0068] S41. Optimize the objective function based on navigation and constraints, combined with real-time feedback of ship navigation data sets Constructing a dynamic and adaptive navigation path search space , Dynamic Adaptive Navigation Path Search Space Dynamically adjust over time t, and perform real-time adaptive updates based on historical ship navigation data in the ship comprehensive information database;

[0069] S42. In the dynamic adaptive navigation path search space In the definition of the number of individuals in the population of the mutation mayfly optimization algorithm , initialize the female mayfly population and male mayfly populations Initial navigation path and speed:

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] in, and Respectively represent The initial flight paths of male and female mayfly individuals, and Respectively represent The initial speed of male and female mayflies, represents uniform distribution;

[0075] S43. Based on the ship wave impact characteristic model Counting male mayflies With female mayflies The mating probability :

[0076] ;

[0077] in, Female mayfly Navigation stability evaluation index at Female mayfly Navigation stability evaluation index at Male mayfly Navigation stability evaluation index at Male mayfly Navigation stability evaluation index at is a mating regulatory factor, 、 、 、 is the fuel consumption influencing factor, Indicates the Fuel consumption rate per waypoint, Indicates the Fuel consumption rate per waypoint, Indicates the Fuel consumption rate per waypoint, Indicates the Fuel consumption rate per waypoint, is the base of natural logarithm;

[0078] Random matching is performed based on the mating probability to generate new mayfly individuals:

[0079] ;

[0080] in, is the mating weight;

[0081] S44. Combine the speed update strategy of wave disturbance to correct the speed of individual mayflies:

[0082] ;

[0083] ;

[0084] in, and Respectively represent The male and female mayflies are The speed of generation, Represents the dynamic inertia weight, which determines the degree to which the current individual inherits the speed of its previous generation during speed update. and Respectively represent The male and female mayflies are The speed of generation, represents the acceleration factor, Represents a random number between (0,1), Represents the global optimal path, which represents the optimal navigation path currently found by the entire population. Indicates the position of the best individual in the male mayfly group. Female mayflies tend to be close to this individual. represents the coefficient of variation, represents a standard normally distributed random variable, is the historical optimal position of the male mayfly individual, represents the wave disturbance weight coefficient, is the g-th female mayfly individual, The gth male mayfly individual, Indicates targeting male mayflies The wave disturbance field correction term at the location, represents the wave disturbance field correction term:

[0085] ;

[0086] in, Waypoint The wave height at the location affects the stability of the ship's path. Waypoint The wave direction at , which indicates the direction angle of wave propagation, The angle between the current sailing direction of the ship and the wave direction determines the impact of the waves on the ship's path. Waypoint The greater the current velocity, the more significant the impact of the fluid on the ship. are weight coefficients, which respectively control the influence of waves and currents on path optimization;

[0087] S45. Calculate fitness and perform individual screening, and calculate fitness value based on fuel consumption rate and navigation stability :

[0088] ;

[0089] in, is the fuel consumption constraint penalty factor, so that the optimization solution meets the fuel consumption requirements To optimize the objective function value;

[0090] S46. Select the best individual from the current population based on the fitness value to form a preliminary set of optimal navigation paths for candidate ships :

[0091] .

[0092] Optionally, the S5 includes the following steps:

[0093] S51. Set of optimal navigation paths for preliminary candidate ships Each candidate path in Each waypoint Define the local cost function , the local cost function reflects the fuel consumption, navigation stability and sea state adaptability at the waypoint:

[0094] ;

[0095] in, Indicates waypoints The actual fuel consumption rate at is the reference value of fuel consumption, Indicates waypoints The degree of fluctuation of the hull attitude, is the stability reference threshold, It represents the wave energy consumption impact value calculated based on the local wave height, wave direction and wave period. is the corresponding reference energy consumption value, The coefficient for adjusting the weight of each indicator;

[0096] S52. Based on the water drop algorithm, the waypoint Use gradient descent to calculate the local correction vector , the local correction vector simulates the process of water droplets flowing naturally along the negative direction of the slope, so that the waypoint position is adjusted in the direction of cost reduction:

[0097] ;

[0098] in, Waypoint The gradient of the cost function reflects the waypoint Local trends in fuel consumption and stability changes, is the local optimization step size factor, Small positive numbers to prevent division by zero errors;

[0099] S53. Use the local correction vector to update the position of each waypoint in the candidate path and iterate until local convergence:

[0100] ;

[0101] in, Indicates in Iteration time waypoint location, Indicates in Iteration time waypoint location, To preset the convergence threshold, ensure that when the position change is less than Stop iteration when

[0102] S54. For each candidate path after local optimization Constructing local navigation optimization objective function To comprehensively evaluate the overall performance:

[0103] ;

[0104] in, Waypoints updated by S53 The local cost of is a regularization term to make the generated track smooth and in line with the actual navigation requirements of the ship. is the regularization weight coefficient, Indicates the number of candidate path individuals selected from the set of preliminary candidate optimal navigation paths for ships;

[0105] S55. Select the local optimal navigation path that meets the requirements of minimum energy consumption and best navigation stability based on the local navigation optimization objective function value :

[0106] ;

[0107] The final selected navigation path achieves cost minimization at each local waypoint, which directly corresponds to the goal of reducing ship fuel consumption and improving navigation safety in practical applications.

[0108] The beneficial effects of the present invention are:

[0109] (1) The present invention introduces the mutation mayfly optimization algorithm, which enhances the individual mutation mechanism on the basis of the traditional mayfly optimization algorithm, so that the optimization population can explore the search space more widely and avoid falling into the local optimum. When dealing with nonlinear constrained optimization problems, the traditional optimization algorithm is easily affected by the initial population distribution and has premature convergence problems, resulting in unstable optimization results. The mutation mayfly optimization algorithm introduces dynamic mating probability calculation, so that the navigation path optimization not only considers fuel consumption, but also takes into account navigation stability, ensuring that the generated optimal path has better adaptability under complex sea conditions.

[0110] (2) The present invention further adopts the water drop algorithm to locally optimize the preliminary candidate path. The water drop algorithm simulates the characteristics of water droplets flowing along the terrain. In the navigation path planning, the path can be locally adjusted according to the ocean current, wind and wave environmental factors to reduce energy consumption and improve navigation stability. The water drop algorithm can better adapt to the dynamic changes in sea conditions, making the local adjustment of the optimized path more refined. During the path optimization process, the local cost function of the waypoint is dynamically calculated to ensure that the roll angle, pitch angle and bow angle of the hull are within a reasonable range during navigation, avoiding unstable factors caused by changes in sea conditions, thereby improving the overall navigation safety.

[0111] (3) The present invention dynamically adjusts the optimization search space based on the real-time feedback of the ship navigation data set, so that the optimization algorithm can perceive the environmental changes in real time and make adaptive corrections to the path. By combining the ship's comprehensive information database, it can predict the optimal energy consumption area under different sea conditions and fine-tune the path according to the ship's real-time position and ocean environment parameters. The dynamic adaptive search strategy can improve the accuracy of path optimization, so that the optimized path can achieve a better balance between energy saving and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0113] Figure 1This is a flow chart of a ship comprehensive information database management system based on big data analysis proposed by the present invention. DETAILED DESCRIPTION

[0114] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0115] refer to Figure 1 , a ship comprehensive information database management system based on big data analysis, including:

[0116] The marine environment perception and ship status monitoring module includes marine environment sensors and ship status monitoring equipment installed on the ship, which are used to collect marine environment data and ship operation status data in real time;

[0117] The ship wave impact characteristics modeling module builds a ship wave impact characteristics model based on marine environment data and ship operation status data, and generates a comprehensive ship information database;

[0118] The ship navigation optimization target setting module sets the ship navigation optimization target based on the ship wave impact characteristic model, and constructs the navigation optimization objective function in combination with the ship navigation constraints. It also models the ship navigation fuel consumption and energy saving problem as a nonlinear constrained optimization problem.

[0119] The mutation mayfly optimization global path search module uses the mutation mayfly optimization algorithm to perform a global search on the navigation optimization objective function and determine the optimal navigation path set of preliminary candidate ships in the dynamic search space;

[0120] The local path optimization module of the water drop algorithm uses the water drop algorithm to locally optimize the preliminary candidate ship optimal navigation path set based on the preliminary candidate ship optimal navigation path set to obtain the local optimal navigation path;

[0121] The ship global path optimization and navigation control module comprehensively compares the local optimal navigation path with the preliminary candidate ship optimal navigation path set, screens out the global optimal navigation path that meets the navigation optimization goal, and generates the corresponding ship navigation control parameters.

[0122] A ship comprehensive information database management method based on big data analysis is applied to a ship comprehensive information database management system based on big data analysis, comprising the following steps:

[0123] S1. Real-time collection of marine environmental data and ship operation status data, and preprocessing and standardization of the collected marine environmental data and ship operation status data to obtain a preprocessed ship navigation data set;

[0124] S2. Based on the preprocessed ship navigation data set, a ship wave impact characteristic model is constructed, a mapping relationship between the ship's operating status and fuel consumption under different sea conditions is established, and a comprehensive ship information database is formed;

[0125] S3. Using the ship wave impact characteristic model, set the ship navigation optimization goal, and construct the navigation optimization objective function based on the set ship navigation optimization goal, modeling the ship navigation fuel consumption and energy saving problem as a nonlinear constrained optimization problem;

[0126] S4. Apply the mutational mayfly optimization algorithm to perform a global search for the navigation optimization objective function. Using the real-time feedback of the ship navigation dataset, dynamically adjust the candidate solutions in the search space to generate a preliminary set of candidate optimal ship navigation paths.

[0127] S5. Use the water drop algorithm to locally optimize the set of preliminary candidate ship optimal navigation paths. Based on the dynamic changes of the ship navigation data set, locally modify the set of preliminary candidate ship optimal navigation paths to obtain the local optimal navigation path.

[0128] S6. Comprehensively compare the local optimal navigation path with the set of preliminary candidate ship optimal navigation paths, screen out the global 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 global optimal navigation path, thereby achieving the goal of managing ship fuel consumption according to the ship comprehensive information database.

[0129] In this embodiment, S1 includes the following steps:

[0130] S11. Use marine environmental sensors installed on ships to obtain real-time marine environmental data, including wave direction, wind speed, current speed and current direction, and define the marine environmental data set :

[0131] ;

[0132] in, is the wave direction based on the bow direction, which indicates the angle of the wave propagation direction. is the wind speed on the ocean surface, which affects the ship's sailing resistance. is the ocean current velocity, The direction of the current based on the bow direction indicates the direction of the current relative to the ship;

[0133] S12. Use the ship status monitoring equipment installed on the ship to obtain real-time ship operation status data, including speed, fuel consumption rate, propulsion efficiency and hull posture, and define the ship operation status data set :

[0134] ;

[0135] in, is the speed of the ship relative to the water, is the fuel consumption rate, is the propulsion efficiency, which represents the energy conversion efficiency of the ship propulsion system. is the ship's attitude, including roll angle, pitch angle and bow angle, which respectively represent the angular changes of the ship around its own transverse axis, longitudinal axis and vertical axis;

[0136] S13. Marine environment dataset and ship operation status dataset Perform data preprocessing, filter out abnormal data and missing data, and normalize the data to construct a preprocessed marine environment dataset and a preprocessed ship operation status dataset;

[0137] S14. Based on the preprocessed marine environment dataset and preprocessed ship operation status dataset , build a ship navigation dataset :

[0138] .

[0139] This implementation method can obtain more comprehensive information on the ocean environment and ship status, provide rich data input for the intelligent optimization algorithm, enhance the adaptability and calculation accuracy of the optimization algorithm, enable ships to more accurately perceive changes in sea conditions, ensure that the optimized path matches the actual navigation environment, and improve the accuracy and energy efficiency of ship navigation.

[0140] In this embodiment, S2 includes the following steps:

[0141] S21. Based on the preprocessed ship navigation dataset Integrate ship operation status data and marine environment data to construct joint feature vector :

[0142] ;

[0143] S22. Based on joint feature vector Establish fuel consumption mapping relationship and define mapping function :

[0144] ;

[0145] in, is the fuel consumption rate, mapping function Reflect the inherent relationship between ship operation status and fuel consumption under different sea conditions;

[0146] S23. According to the mapping function Establishing a model of ship wave impact characteristics :

[0147] ;

[0148] in, represents the set of joint eigenvectors, is the sample size;

[0149] S24. Model the ship wave impact characteristics The joint eigenvector contained in and the corresponding fuel consumption rate Store and build a comprehensive ship information database .

[0150] By establishing a fuel consumption mapping relationship, this implementation method can effectively quantify the impact of sea conditions on ship fuel consumption and provide data support. Traditional ship navigation often uses fixed empirical values or a single fuel consumption model, which is difficult to adapt to dynamic sea conditions. However, this method realizes a nonlinear mapping between the ship's operating status and fuel consumption by constructing a ship wave impact characteristic model, making the fuel consumption calculation more consistent with actual navigation conditions.

[0151] In this embodiment, S3 includes the following steps:

[0152] S31. Based on the ship wave impact characteristic model and the ship comprehensive information database, set ship navigation optimization goals, including optimal navigation path, minimum fuel consumption and maximum navigation stability;

[0153] S32. Set the ship's navigation path Discrete waypoints The navigation track consists of:

[0154] ;

[0155] in, Indicates the The geographical coordinates of the waypoints, is the number of waypoints in the navigation path;

[0156] S33. Based on the ship comprehensive information database Define total fuel consumption of a ship :

[0157] ;

[0158] in, For ships from waypoints Run to waypoint The time required, Indicates the Fuel consumption rate per waypoint;

[0159] S34. Setting navigation stability evaluation indicators , define the ship navigation stability objective function:

[0160] ;

[0161] in, are the weight coefficients of roll angle, pitch angle and yaw angle respectively, Respectively represent Roll angle, pitch angle and heading angle at each waypoint;

[0162] S35. Combine the total fuel consumption of the ship and the ship's navigation stability objective function to construct the navigation optimization objective function :

[0163] ;

[0164] in, is the weight coefficient;

[0165] S36. Based on the ship navigation constraints, construct the constraints of the optimization problem, including navigation safety constraints, navigation speed constraints, and navigation path constraints:

[0166] Navigation safety constraints:

[0167] ;

[0168] ;

[0169] ;

[0170] in, are the safety thresholds of roll angle, pitch angle and heading angle respectively;

[0171] Sailing speed constraints:

[0172] ;

[0173] in, are the minimum and maximum speeds of the ship, respectively. is the current speed of the ship;

[0174] Navigation path constraints:

[0175] ;

[0176] in, A feasible navigation area;

[0177] S37. Optimize the navigation objective function by constructing and constraints, the ship navigation fuel consumption and energy saving problem is modeled as a nonlinear constrained optimization problem.

[0178] This implementation method realizes multi-objective optimization modeling by combining the optimal navigation path, minimum fuel consumption and maximum navigation stability. Compared with the traditional path planning method, this method not only takes into account the shortest voyage, but also improves the economy and safety of navigation by constructing the navigation optimization objective function. This implementation method 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, thereby avoiding the local optimal problem caused by the optimization of a single factor.

[0179] In this embodiment, S4 includes the following steps:

[0180] S41. Based on navigation optimization objective function and constraints, combined with real-time feedback of ship navigation data sets Constructing a dynamic and adaptive navigation path search space , Dynamic Adaptive Navigation Path Search Space Dynamically adjust over time t, and perform real-time adaptive updates based on historical ship navigation data in the ship comprehensive information database;

[0181] S42. In the dynamic adaptive navigation path search space In the definition of the number of individuals in the population of the mutation mayfly optimization algorithm , initialize the female mayfly population and male mayfly populations Initial navigation path and speed:

[0182] ;

[0183] ;

[0184] ;

[0185] ;

[0186] in, and Respectively represent The initial flight paths of male and female mayfly individuals, and Respectively represent The initial speed of male and female mayflies, represents uniform distribution;

[0187] S43. Based on the ship wave impact characteristic model Counting male mayflies With female mayflies The mating probability :

[0188] ;

[0189] in, Female mayfly Navigation stability evaluation index at Female mayfly Navigation stability evaluation index at Male mayfly Navigation stability evaluation index at Male mayfly Navigation stability evaluation index at is a mating regulatory factor, 、 、 、 is the fuel consumption influencing factor, Indicates the Fuel consumption rate per waypoint, Indicates the Fuel consumption rate per waypoint, Indicates the Fuel consumption rate per waypoint, Indicates the Fuel consumption rate per waypoint, is the base of natural logarithm;

[0190] Random matching is performed based on the mating probability to generate new mayfly individuals:

[0191] ;

[0192] in, is the mating weight;

[0193] S44. Combine the speed update strategy of wave disturbance to correct the speed of individual mayflies:

[0194] ;

[0195] ;

[0196] in, and Respectively represent The male and female mayflies are The speed of generation, Represents the dynamic inertia weight, which determines the degree to which the current individual inherits the speed of its previous generation during speed update. and Respectively represent The male and female mayflies are The speed of generation, represents the acceleration factor, Represents a random number between (0,1), Represents the global optimal path, which represents the optimal navigation path currently found by the entire population. Indicates the position of the best individual in the male mayfly group. Female mayflies tend to be close to this individual. represents the coefficient of variation, represents a standard normally distributed random variable, is the historical optimal position of the male mayfly individual, represents the wave disturbance weight coefficient, is the g-th female mayfly individual, The gth male mayfly individual, Indicates targeting male mayflies The wave disturbance field correction term at the location, represents the wave disturbance field correction term:

[0197] ;

[0198] in, Waypoint The wave height at the location affects the stability of the ship's path. Waypoint The wave direction at , which indicates the direction angle of wave propagation, The angle between the current sailing direction of the ship and the wave direction determines the impact of the waves on the ship's path. Waypoint The greater the current velocity, the more significant the impact of the fluid on the ship. are weight coefficients, which respectively control the influence of waves and currents on path optimization;

[0199] S45. Calculate fitness and perform individual screening, and calculate fitness value based on fuel consumption rate and navigation stability :

[0200] ;

[0201] in, is the fuel consumption constraint penalty factor, so that the optimization solution meets the fuel consumption requirements To optimize the objective function value;

[0202] S46. Select the best individual from the current population based on the fitness value to form a preliminary set of optimal navigation paths for candidate ships :

[0203] .

[0204] This implementation method combines marine environmental data and ship operating status data to achieve global search and dynamic optimization path planning. By simulating the mating behavior and environmental adaptation mechanism of mayfly populations, the algorithm's global exploration capability is enhanced, enabling the optimization algorithm to quickly converge to the optimal solution. In addition, a dynamic mating strategy coupled with sea conditions is introduced to dynamically adjust the mating probability according to fuel consumption and navigation stability, so that the generated optimal path is more in line with the ship's energy-saving and safe navigation needs.

[0205] In this embodiment, S5 includes the following steps:

[0206] S51. Set of optimal navigation paths for preliminary candidate ships Each candidate path Each waypoint Define the local cost function , the local cost function reflects the fuel consumption, navigation stability and sea state adaptability at the waypoint:

[0207] ;

[0208] in, Indicates waypoints The actual fuel consumption rate at is the reference value of fuel consumption, Indicates waypoints The degree of fluctuation of the hull attitude, is the stability reference threshold, It represents the wave energy consumption impact value calculated based on the local wave height, wave direction and wave period. is the corresponding reference energy consumption value, The coefficient for adjusting the weight of each indicator;

[0209] S52. Based on the water drop algorithm, the waypoint Use gradient descent to calculate the local correction vector , the local correction vector simulates the process of water droplets flowing naturally along the negative direction of the slope, so that the waypoint position is adjusted in the direction of cost reduction:

[0210] ;

[0211] in, Waypoint The gradient of the cost function reflects the waypoint Local trends in fuel consumption and stability changes, is the local optimization step size factor, Small positive numbers to prevent division by zero errors;

[0212] S53. Use the local correction vector to update the position of each waypoint in the candidate path and iterate until local convergence:

[0213] ;

[0214] in, Indicates in Iteration time waypoint location, Indicates in Iteration time waypoint location, To preset the convergence threshold, ensure that when the position change is less than Stop iteration when

[0215] S54. For each candidate path after local optimization Constructing local navigation optimization objective function To comprehensively evaluate the overall performance:

[0216] ;

[0217] in, Waypoints updated by S53 The local cost of is a regularization term to make the generated track smooth and in line with the actual navigation requirements of the ship. is the regularization weight coefficient, Indicates the number of candidate path individuals selected from the set of preliminary candidate optimal navigation paths for ships;

[0218] S55. Filter out the local optimal navigation path that meets the requirements of minimum energy consumption and best navigation stability based on the local navigation optimization objective function value:

[0219] ;

[0220] The final selected navigation path achieves cost minimization at each local waypoint, which directly corresponds to the goal of reducing ship fuel consumption and improving navigation safety in practical applications.

[0221] This implementation simulates the natural flow characteristics of water droplets in the terrain to make the local optimization of the navigation path more consistent with the movement laws of the ship under actual sea conditions. Through the path adjustment mechanism of the water droplet algorithm, it can dynamically optimize the waypoint position, reduce path deviation, and improve navigation stability and fuel economy.

[0222] In this embodiment, S6 includes the following steps:

[0223] S61. Calculate the fuel consumption, navigation stability, and path smoothness of each path based on the set of local optimal navigation paths and the set of preliminary candidate ship optimal navigation paths. Construct a global fitness function. By comparing the fitness values of different paths, select the path with the lowest fuel consumption, highest navigation stability, and smoothest track as the global optimal navigation path.

[0224] S62. Based on the global optimal navigation path, the ship's optimal speed and heading angle are calculated at each waypoint. Optimization and adjustment are performed based on the relative relationships between speed and fuel consumption, and heading angle and wave direction. The calculation process comprehensively considers the ship's propulsion efficiency, fuel utilization, and the impact of waves on course, ensuring the ship operates with minimal energy consumption and maximum stability along the optimized navigation path.

[0225] S63. Based on the calculated optimal speed and heading angle, generate ship navigation control instructions and input them into the ship's comprehensive information database. During actual navigation, adjust navigation parameters in real time based on fuel consumption data, the ship's real-time heading, and wave environment data fed back by sensors to adapt to changing sea conditions and maintain optimal navigation status in the event of sudden environmental disturbances.

[0226] Example 1:

[0227] On December 15, 2024, a 50,000-ton cargo ship named "Ocean Pioneer" departed from Port A and headed for Port B. The ship, carrying a large amount of cargo, planned to cross the North Atlantic Ocean using the traditional shortest route navigation method. However, before departure, the meteorological data analysis system found that the sea area between 50° and 55° north latitude was affected by a low-pressure cyclone. It was expected that the wind speed would reach 90 kilometers per hour and the wave height would exceed 7 meters in the next 48 hours. If it sailed according to the traditional route, it was expected that the ship would experience a strong storm in the area for up to 12 hours, and both navigation stability and fuel consumption would be seriously affected.

[0228] After leaving Port A, the ship's marine environmental monitoring system collected real-time data on wave direction (60°), wind speed (55km / h), and current speed (3.5 knots) and uploaded them to the ship's data analysis platform. Simultaneously, the ship's propulsion system provided feedback on the current fuel consumption rate (7.2 tons / hour) and propulsion efficiency (82%). The optimization system then combined real-time environmental data with a historical sea condition database, using a variant mayfly optimization algorithm to perform the first round of optimization calculations on the navigation path.

[0229] The system generates three alternative paths:

[0230] 1. Traditional shortest route: A straight line through the North Atlantic storm zone, with an estimated fuel consumption of 1,280 tons, a sailing time of 185 hours, and a maximum roll angle of 18°;

[0231] 2. Southerly route (optimized by the present invention): avoids storm areas and sails along 47° north latitude. Estimated fuel consumption is 1,120 tons, sailing time is 182 hours, and the maximum roll angle is 9°.

[0232] 3. Long-distance detour route: avoids all bad weather areas, but the range is increased by 250 nautical miles, the estimated fuel consumption is 1,360 tons, and the sailing time is 192 hours.

[0233] After calculating the fitness, the mutant mayfly optimization algorithm selected the second route as the optimal path. After receiving the system's recommendation, the captain confirmed the optimized route plan and reported the adjustment plan to the shipping company.

[0234] At 2:30 a.m. on December 17, 2024, the ship entered the waters of 49° north latitude. The ship monitoring system detected that the wind speed increased to 75km / h and the wave height reached 5.5 meters, causing the roll angle to increase from 8° to 11°. At this time, the ship navigation optimization system automatically triggered the water drop algorithm to adjust the local navigation path and generate a new correction plan.

[0235] The system calculated that if the ship continued to sail along the current path, the roll angle might exceed 12° in the next three hours and the fuel consumption rate might rise to 7.5 tons / hour. The optimization system re-evaluated the environmental data and recommended that the ship adjust 2° to the south, offset by about 18 nautical miles, to avoid areas with wave heights above 6 meters. After accepting the suggestion, the captain adjusted the sailing path. The ship's roll angle dropped back to 8.5° within 20 minutes, and the fuel consumption rate stabilized at 6.9 tons / hour.

[0236] After arriving at Port B, the shipping company compared the actual data of this voyage with the traditional route plan:

[0237]

[0238] The fuel savings from the optimization approach were particularly significant during the most severe storm-affected sailing period (2:30 to 5:30 a.m. on December 17):

[0239]

[0240] Analysis results show that the method of the present invention can effectively reduce fuel consumption while maintaining navigation stability, especially during periods of strong winds and waves. The navigation experiments of this embodiment demonstrate that the optimized navigation method based on the variant mayfly optimization algorithm and the water droplet algorithm can intelligently adjust the ship's route, effectively reducing fuel consumption in complex sea conditions while improving navigation safety. Compared with traditional fixed routes, this method can achieve a fuel saving rate of 12.5%, reduce sailing time by 3 hours, and reduce roll angle by 40%, fully demonstrating the superiority of the present invention in practical applications.

[0241] The present invention introduces a variant mayfly optimization algorithm, which enhances the individual mutation mechanism on the basis of the traditional mayfly optimization algorithm, enabling the optimized population to explore the search space more widely and avoid falling into local optimality. Traditional optimization algorithms are easily affected by the initial population distribution when dealing with nonlinear constrained optimization problems, resulting in premature convergence problems and unstable optimization results. The variant mayfly optimization algorithm introduces dynamic mating probability calculation, allowing navigation path optimization to not only consider fuel consumption but also navigation stability, ensuring that the generated optimal path has better adaptability in complex sea conditions.

[0242] The present invention further adopts the water drop algorithm to locally optimize the preliminary candidate paths. The water drop algorithm simulates the characteristics of water droplets flowing along the terrain. In the navigation path planning, the path can be locally adjusted according to the ocean current and wind and wave environmental factors to reduce energy consumption and improve navigation stability. The water drop algorithm can better adapt to the dynamic changes in sea conditions, making the local adjustment of the optimized path more refined. During the path optimization process, the local cost function of the waypoint is dynamically calculated to ensure that the roll angle, pitch angle and bow angle of the hull are within a reasonable range during navigation, avoiding unstable factors caused by changes in sea conditions, thereby improving the overall navigation safety.

[0243] The present invention dynamically adjusts the optimized search space based on real-time feedback of ship navigation data sets, enabling the optimization algorithm to perceive environmental changes in real time and adaptively correct the path. By combining the ship's comprehensive information database, it can predict the optimal energy consumption area under different sea conditions and fine-tune the path according to the ship's real-time position and marine environmental parameters. The dynamic adaptive search strategy can improve the accuracy of path optimization, so that the optimized path achieves a better balance between energy saving and stability.

[0244] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for managing a ship comprehensive information database based on big data analysis, characterized in that: The following steps are involved: S1. Real-time collection of marine environmental data and ship operation status data, and preprocessing and standardization of the collected marine environmental data and ship operation status data to obtain a preprocessed ship navigation data set; S2. Based on the preprocessed ship navigation data set, a ship wave impact characteristic model is constructed, a mapping relationship between the ship's operating status and fuel consumption under different sea conditions is established, and a comprehensive ship information database is formed; S3. Using the ship wave impact characteristic model, set the ship navigation optimization goal, and construct the navigation optimization objective function based on the set ship navigation optimization goal, modeling the ship navigation fuel consumption and energy saving problem as a nonlinear constrained optimization problem; S4. Apply the mutational mayfly optimization algorithm to perform a global search for the navigation optimization objective function. Using the real-time feedback of the ship navigation dataset, dynamically adjust the candidate solutions in the search space to generate a preliminary set of candidate optimal ship navigation paths. S5. Use the water drop algorithm to locally optimize the set of preliminary candidate ship optimal navigation paths. Based on the dynamic changes of the ship navigation data set, locally modify the set of preliminary candidate ship optimal navigation paths to obtain the local optimal navigation path. S6. Comprehensively compare the local optimal navigation path with the preliminary candidate ship optimal navigation path set, screen out the global optimal navigation path that meets the navigation optimization goal, generate corresponding ship navigation control parameters, and control the ship to navigate according to the global optimal navigation path, achieving the goal of managing ship fuel consumption based on the ship comprehensive information database; The S3 includes the following steps: S31. Based on the ship wave impact characteristic model and the ship comprehensive information database, set ship navigation optimization goals, including optimal navigation path, minimum fuel consumption and maximum navigation stability; S32. Set the ship's navigation path Discrete waypoints The navigation trajectory of the composition; S33. Based on the ship comprehensive information database Define total fuel consumption of a ship ; S34. Setting navigation stability evaluation indicators ; S35. Combine the total fuel consumption of the ship and the ship's navigation stability objective function to construct the navigation optimization objective function ; ;in, is the weight coefficient; S36. Based on the ship navigation constraints, construct the constraints of the optimization problem, including navigation safety constraints, navigation speed constraints, and navigation path constraints; S37. Optimize the navigation objective function by constructing and constraints, the ship navigation fuel consumption and energy saving problem is modeled as a nonlinear constrained optimization problem; The S4 comprises the following steps: S41. Based on navigation optimization objective function and constraints, combined with real-time feedback of ship navigation data sets Constructing a dynamic and adaptive navigation path search space , Dynamic Adaptive Navigation Path Search Space Dynamically adjust over time t, and perform real-time adaptive updates based on historical ship navigation data in the ship comprehensive information database; S42. In the dynamic adaptive navigation path search space In the definition of the number of individuals in the population of the mutation mayfly optimization algorithm , initialize the female mayfly population and male mayfly populations Initial navigation path and speed; S43. Based on the ship wave impact characteristic model Counting male mayflies With female mayflies The mating probability ; Random matching is performed based on the mating probability to generate new mayfly individuals ; S44. Combine the velocity update strategy of wave disturbance to correct the velocity of individual mayflies; S45. Calculate fitness and perform individual screening, and calculate fitness value based on fuel consumption rate and navigation stability ; S46. Select the best individual from the current population based on the fitness value to form a preliminary set of optimal navigation paths for candidate ships ; The S5 comprises the following steps: S51. Set of optimal navigation paths for preliminary candidate ships Each candidate path in Each waypoint Define the local cost function , the local cost function reflects the fuel consumption, navigation stability and sea state adaptability at the waypoint; S52. Based on the water drop algorithm, the waypoint Use gradient descent to calculate the local correction vector ,The local correction vector simulates the natural flow of water droplets along the negative direction of the slope, so that the waypoint position is adjusted towards the direction of cost reduction; S53. Using the local correction vector to update the position of each waypoint in the candidate path, and iterating until local convergence; S54. For each candidate path after local optimization Constructing local navigation optimization objective function To comprehensively evaluate the overall performance; ; in, Waypoints updated by S53 The local cost of is a regularization term to make the generated track smooth and in line with the actual navigation requirements of the ship. is the regularization weight coefficient, Indicates the number of candidate path individuals selected from the set of preliminary candidate optimal navigation paths for ships; S55. Select the local optimal navigation path that meets the requirements of minimum energy consumption and best navigation stability based on the local navigation optimization objective function value The final selected navigation path achieves cost minimization at each local waypoint, which directly corresponds to the goal of reducing ship fuel consumption and improving navigation safety in practical applications.

2. A method for managing a ship comprehensive information database based on big data analysis according to claim 1, characterized in that: Said S1 comprises the following steps: S11. Use marine environmental sensors installed on ships to obtain real-time marine environmental data, including wave direction, wind speed, current speed and current direction, and define the marine environmental data set ; S12. Use the ship status monitoring equipment installed on the ship to obtain real-time ship operation status data, including speed, fuel consumption rate, propulsion efficiency and hull posture, and define the ship operation status data set ; S13. Marine environment dataset and ship operation status dataset Perform data preprocessing, filter out abnormal data and missing data, and normalize the data to construct a preprocessed marine environment dataset and a preprocessed ship operation status dataset; S14. Based on the preprocessed marine environment dataset and preprocessed ship operation status dataset , build a ship navigation dataset .

3. A ship comprehensive information database management method based on big data analysis according to claim 2, characterized in that: The S2 comprises the following steps: S21. Based on the preprocessed ship navigation dataset Integrate ship operation status data and marine environment data to construct joint feature vector ; S22. Based on joint feature vector Establish fuel consumption mapping relationship and define mapping function ; S23. According to the mapping function Establishing a model of ship wave impact characteristics ; S24. Model the ship wave impact characteristics The joint eigenvector contained in and the corresponding fuel consumption rate Store and build a comprehensive ship information database .

4. A ship comprehensive information database management system based on big data analysis, applied to a ship comprehensive information database management method based on big data analysis according to any one of claims 1 to 3, characterized in that: include: The marine environment perception and ship status monitoring module includes marine environment sensors and ship status monitoring equipment installed on the ship, which are used to collect marine environment data and ship operation status data in real time; The ship wave impact characteristics modeling module builds a ship wave impact characteristics model based on marine environment data and ship operation status data, and generates a comprehensive ship information database; The ship navigation optimization target setting module sets the ship navigation optimization target based on the ship wave impact characteristic model, and constructs the navigation optimization objective function in combination with the ship navigation constraints. It also models the ship navigation fuel consumption and energy saving problem as a nonlinear constrained optimization problem. The mutation mayfly optimization global path search module uses the mutation mayfly optimization algorithm to perform a global search on the navigation optimization objective function and determine the optimal navigation path set of preliminary candidate ships in the dynamic search space; The local path optimization module of the water drop algorithm uses the water drop algorithm to locally optimize the preliminary candidate ship optimal navigation path set based on the preliminary candidate ship optimal navigation path set to obtain the local optimal navigation path; The ship global path optimization and navigation control module comprehensively compares the local optimal navigation path with the preliminary candidate ship optimal navigation path set, screens out the global optimal navigation path that meets the navigation optimization goal, and generates the corresponding ship navigation control parameters.

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