Ship navigation speed optimization control method and device based on multi-objective optimization

Abstract

The invention discloses a ship navigation speed optimization control method and device based on multi-objective optimization, and belongs to the field of ship navigation control optimization. The method comprises the steps of firstly establishing a ship speed and power output simulation model, correcting a simulation result through weather forecast data, further performing calculation and analysis through the simulation model and a multi-objective optimization algorithm, minimizing time and energy consumption consumed in the ship navigation process, generating a power output control scheme, and controlling the ship speed and power output according to the power output control scheme. Therefore, the sailing speed of the ship is controlled by controlling power output. Compared with the prior art, by optimizing the control strategy of the power output and the sailing speed, the ship sailing efficiency is remarkably improved, and meanwhile the energy cost is reduced.

Description

Technical Field

[0001] This application relates to the field of ship navigation control optimization, and particularly to a ship navigation speed optimization control method and device based on multi-objective optimization. Background Art

[0002] Ship navigation is easily interfered by environmental factors such as wind waves, ocean currents, and weather. With the improvement of the accuracy of weather forecast data, by means of accurate weather forecast data, optimizing the speed control plan during navigation can effectively improve the reaction speed of the crew to emergencies, thereby improving the safety of navigation.

[0003] In the prior art, usually, an actual physical process simulation model is constructed, and the weather forecast model is embedded into the physical process simulation model to predict the speed under different environments and different power outputs. At the same time, a large number of sensors are set to obtain more accurate real-time weather data to ensure the accuracy of the physical process simulation model. However, the physical process simulation model used by this method has a large number of parameters, requires a large amount of observation data to adjust the parameters of the physical process simulation model, and has a high hardware cost, making it difficult to popularize for different ship types. At the same time, when the physical process simulation model estimates the influence of weather environment factors on the speed, it mainly focuses on the instantaneous change influence and fails to consider the influence of continuous weather factors on the speed, resulting in higher energy consumption and time consumption of the subsequent optimized solution.

[0004] Therefore, how to more accurately incorporate weather forecast data into the speed optimization problem while ensuring the computational complexity, so as to reduce the navigation time and navigation energy consumption of the speed control scheme is a technical problem that needs to be solved currently. Summary of the Invention

[0005] This application provides a ship navigation speed optimization control method based on multi-objective optimization, which can solve the technical problem in the prior art of how to more accurately incorporate weather forecast data into the speed optimization problem while ensuring the computational complexity, so as to reduce the navigation time and navigation energy consumption of the speed control scheme.

[0006] A ship navigation speed optimization control method based on multi-objective optimization provided by an embodiment of this application includes: Obtain the navigation route data to be planned and weather forecast data; the navigation route data includes several stopping points, the optimal navigation routes between each stopping point, and several preset speed change points corresponding to each of the optimal navigation routes; the preset speed change points are set according to the turning points of the corresponding optimal navigation routes; Take the arrival order of each of the stopping points as the first decision variable, and take the power output of each of the preset speed change points corresponding to each of the optimal navigation routes as the second decision variable; Based on the first decision variable, the second decision variable, and a preset dynamic simulation model, determine the speeds corresponding to a number of preset speed change points, and correct the speeds by combining the weather forecast data with a preset long short-term memory network model; the dynamic simulation model is a mapping relationship between power output and speed built under the condition of no environmental disturbance. Based on the first decision variable, the second decision variable, and the corrected speed, determine the first sailing time and the first sailing energy consumption respectively, and use the first sailing time and the first sailing energy consumption as the first optimization objective and the second optimization objective respectively. Based on the first optimization objective and the second optimization objective, construct a multi-objective optimization problem, and solve the multi-objective optimization problem through a multi-objective optimization algorithm to obtain an optimal power output scheme.

[0007] Compared with the prior art, the above embodiments have the following beneficial effects: Whenever the ship sails to the turning point of the optimal sailing route, it is easier for the influence of the weather environment on the speed to change. Therefore, by setting the turning points of each optimal sailing route as preset speed change points, the sensitivity of subsequent speed optimization to weather forecast data is improved; further, through a simplified dynamic simulation model, while solving the problem of low generality caused by the overly complex physical process model, a long short-term memory network model that can capture the change characteristics of time series data is introduced to further correct the speed. The process of estimating the speed through the weather environment not only reduces the computational complexity but also ensures the accuracy of speed estimation; finally, by establishing a multi-objective optimization problem and solving the optimal power output scheme, the final power output scheme can reduce both the sailing time and the sailing energy consumption during the sailing process.

[0008] Further, the determining the speeds corresponding to a number of preset speed change points based on the first decision variable, the second decision variable, and a preset dynamic simulation model includes: Based on the first decision variable, determine the arrival order of each of the docking points. Based on the arrival order of each of the docking points, determine a number of first optimal sailing routes. Use the preset speed change points corresponding to the first optimal sailing routes as the first preset speed change points. Through the simulation model, map the second decision variable corresponding to each of the first preset speed change points to the corresponding speed.

[0009] Compared with the prior art, the above embodiments have the following beneficial effects: In order to avoid the problem of excessive computational complexity in the optimization process caused by too many power output decisions corresponding to all preset speed change points in the entire solution space, the optimization space of the subsequent second decision variable is limited by the solution space defined by the first decision variable, thereby effectively improving the optimization speed; further, the second decision variable corresponding to each of the first preset speed change points is mapped through a simulation model, so that the sailing speed of the ship at each first preset speed change point can be preliminarily determined.

[0010] Further, the correcting the sailing speed by combining the preset long short-term memory network model with the weather forecast data includes: Obtaining first weather forecast data of each of the first optimal sailing routes within a preset time interval from the weather forecast data; Determining the sailing order of each of the first optimal sailing routes according to the first decision variable; According to the sailing order, sequentially correct the sailing speeds corresponding to each first preset speed change point by combining the preset long short-term memory network model with the first weather forecast data.

[0011] Compared with the prior art, the above embodiments have the following beneficial effects: Since the weather forecast has a time attribute, except for the initial departure time, the time when the ship reaches each subsequent first preset speed change point cannot be predicted in advance. Therefore, after determining several first optimal sailing routes, it is necessary to further consider the passing order of each first optimal sailing route. Starting from the first first preset speed change point (i.e., the initial starting point), and combining the first weather forecast data at the initial time, gradually iterate to determine the time period of the first weather forecast data required to correct the next first preset speed change point, thereby improving the accuracy of sailing speed correction. Further, the sequentially correcting the sailing speeds corresponding to each first preset speed change point by combining the preset long short-term memory network model with the first weather forecast data according to the sailing order includes: Taking the sailing speed to be currently corrected as the first sailing speed, and taking the time point when the ship reaches the first preset speed change point corresponding to the first sailing speed as the first time point, and determining whether the first preset speed change point corresponding to the first sailing speed is the first preset speed change point corresponding to any of the first optimal sailing routes; If so, correct the first sailing speed by a data-driven method in combination with the first weather forecast data at the first time point; Otherwise, taking the time point when the ship reaches the first preset speed change point corresponding to the previously corrected sailing speed as the second time point, and correcting the first sailing speed through the preset long short-term memory network model according to the first weather forecast data during the period from the first time point to the second time point.

[0012] Compared with the prior art, the above embodiments have the following beneficial effects: Since the ship starts from each docking point at the first first preset variable speed point of each first optimal navigation route, there are no accumulated weather environment factors affecting before the first first preset variable speed point. Therefore, the initial first ship speed needs to be corrected with the first weather forecast data in the form of time points; when the ship leaves the first first preset variable speed point, it starts to be affected by the accumulated weather environment factors. Therefore, according to the first weather forecast data experienced between the first preset variable speed points of the two vectors (at this time, the data is time series data within a time period), the feature information in the time series data is obtained through the preset long short-term memory network model steps to correct the first ship speed, thereby improving the correction accuracy.

[0013] Further, the hidden state recursion process of the preset long short-term memory network model includes: Wherein, the preset long short-term memory network model includes: spatio-temporal feature gating, environmental perturbation gating, short-term memory unit and long-term memory unit; Through the spatio-temporal feature gating, spatio-temporal features are extracted from the first weather forecast data of the current time step, and the short-term memory unit is updated through the spatio-temporal features; Fuse the feature information of the long-term memory unit of the previous time step and the feature information of the short-term memory unit of the current time step to obtain the feature information of the long-term memory unit of the current time step; Through the environmental perturbation gating, the perturbation weight is obtained by combining the historical mean of the weather forecast; According to the perturbation weight and the feature information of the short-term memory unit and the long-term memory unit of the current time step, the memory cell of the preset long short-term memory network model is updated; According to the updated memory cell, the hidden state output by the preset long short-term memory network model at the current time step is obtained.

[0014] Compared with the prior art, the above embodiments have the following beneficial effects: Since the weather forecast data includes not only wind speed, but also the influence of factors such as wave height and water flow velocity, these factors have not only instantaneous influence on the ship speed (such as local turbulence), but also long-term continuous influence, and the influence of both needs to be comprehensively considered. Therefore, through spatio-temporal feature gating, the time correlation of multi-source data in the first weather forecast data is fused to obtain spatio-temporal features, and this data is incorporated into the short-term memory unit, so that the long short-term memory network model can quickly respond to sudden perturbations; further through environmental perturbation gating, the degree of perturbation of different instantaneous environmental changes on the ship speed is identified from the historical mean of the weather forecast, and the fusion ratio of the feature information of the short-term memory unit and the long-term memory unit is adjusted according to the degree of perturbation, so as to ensure the accurate step of the influence degree of instantaneous change and continuous change on the ship speed, and improve the accuracy of correcting the first ship speed of the subsequent output hidden state.

[0015] Further, determining the first sailing time and the first sailing energy consumption according to the first decision variable, the second decision variable and the corrected ship speed respectively includes: Calculating the second sailing time required for the ship to pass through any two adjacent first preset variable speed points according to the corrected ship speed; Determining the first sailing time according to the sum of all the second sailing times; Determining the second sailing energy consumption during each of the second sailing times according to the second decision variable; Determining the first sailing energy consumption according to the sum of all the second sailing energy consumptions.

[0016] Compared with the prior art, the above embodiments have the following beneficial effects: Since the ship needs to adjust its speed after passing through each first preset variable speed point, the sailing time between every two adjacent first preset variable speed points needs to be calculated segment by segment, and the sailing energy consumption is calculated according to the sailing time of each segment combined with the power output of each segment, so as to ensure the accuracy of subsequent problem optimization.

[0017] Further, constructing a multi-objective optimization problem according to the first optimization target and the second optimization target includes: Taking the first optimization target and the second optimization target as the optimization targets of the multi-objective optimization problem; Constructing the constraint conditions of the multi-objective optimization problem according to the dynamic simulation model.

[0018] Further, the mathematical model of the multi-objective optimization problem includes:

[0019]

[0020]

[0021] Among them, represents the first voyage time; represents the first voyage energy consumption; represents the randomly generated docking sequence , represents deleting the docking point from the set , represents deleting the docking point from the set , is the total number of docking points; represents the set of variable-speed points on a certain optimal voyage route, is related to the docking point and the docking point ; represents the distance from the variable-speed point to the variable-speed point when corresponding to the optimal voyage route between the docking point and the docking point ; represents executing the optimal voyage route from the docking point to the docking point , represents not executing the optimal voyage route from the docking point to the docking point ; represents the power output of the ship when it is at the variable-speed point when corresponding to the optimal voyage route between the docking point and the docking point ; represents the weather forecast data when the ship is on the optimal voyage route between the docking point and the docking point ; is the speed correction operator; is the power simulation model operator; and represent the minimum power output limit and the maximum power output limit respectively; and represent the minimum speed limit and the maximum speed limit respectively.

[0022] ​​Compared with the prior art, the above embodiments have the following beneficial effects: the speed correction operator and the power simulation model operator are respectively embedded in the constructed first optimization objective and the second optimization objective to achieve an accurate mapping between the power output and the speed, so as to achieve synchronous and precise optimization of the sailing time and the sailing energy consumption; further, the speed correction operator and the power simulation model operator are embedded in the constraint conditions to limit the speed and ensure the effectiveness of the final power output scheme; finally, each docking point is further restricted to dock only once in the constraint conditions to optimize the solution space of the first decision variable, avoid the appearance of invalid solutions, and improve the optimization efficiency.

[0023] Another embodiment of the present application further provides a ship sailing speed optimization control device based on multi-objective optimization, including: a data acquisition module, a decision variable determination module, a speed correction module, an optimization objective determination module, and a mathematical model solving module; Among them, the data acquisition module is used to acquire the sailing route data to be planned and the weather forecast data; the sailing route data includes several docking points, the optimal sailing routes between the docking points, and several preset speed change points corresponding to each of the optimal sailing routes; the preset speed change points are set according to the turning points of the corresponding optimal sailing routes. The decision variable determination module is used to use the arrival order of each docking point as the first decision variable and the power output of each preset speed change point corresponding to each optimal sailing route as the second decision variable; The speed correction module is used to determine the speeds corresponding to several preset speed change points according to the first decision variable, the second decision variable, and a preset power simulation model, and correct the speeds by combining the weather forecast data through a preset long short-term memory network model; the power simulation model is a mapping relationship between the power output and the speed built under the condition of no environmental disturbance. The optimization objective determination module is used to determine the first sailing time and the first sailing energy consumption according to the first decision variable, the second decision variable, and the corrected speed, and use the first sailing time and the first sailing energy consumption as the first optimization objective and the second optimization objective respectively; The mathematical model solving module is used to construct a multi-objective optimization problem according to the first optimization objective and the second optimization objective, and solve the multi-objective optimization problem through a multi-objective optimization algorithm to obtain the optimal power output scheme.

[0024] Further, the step of determining the speeds corresponding to several preset speed change points according to the first decision variable, the second decision variable, and a preset power simulation model includes: Determine the arrival order of each docking point according to the first decision variable; Determine a number of first optimal sailing routes according to the arrival order of each of the said stopping points; Take the preset variable-speed points corresponding to the first optimal sailing routes as the first preset variable-speed points; Through the simulation model, map the second decision variables corresponding to each of the first preset variable-speed points to the corresponding sailing speeds. Description of the Drawings

[0025] To more clearly illustrate the technical solutions of the present application, the drawings required for the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a schematic flowchart of a ship sailing speed optimization control method based on multi-objective optimization provided in some embodiments of the present application; Figure 2 It is a schematic structural diagram of a ship sailing speed optimization control device based on multi-objective optimization provided in some embodiments of the present application. Detailed Embodiments

[0027] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above description of the drawings are intended to cover non-exclusive inclusion.

[0029] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order, or primary-secondary relationship of the indicated technical features. In the description of the embodiments of the present application, "a plurality" means two or more unless otherwise specifically defined.

[0030] Reference to "embodiment" in this document means that the specific features, structures, or characteristics described in connection with the embodiment may be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0031] In the description of the embodiments of this application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after.

[0032] In the description of the embodiments of this application, the term "plurality" refers to two or more (including two). Similarly, "multiple groups" refers to two or more groups (including two groups), and "multiple pieces" refers to two or more pieces (including two pieces).

[0033] In the description of the embodiments of this application, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "connection", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of this application can be understood according to specific situations.

[0034] In the prior art, usually by constructing an actual physical process simulation model, embedding the weather forecast model into the physical process simulation model to predict the ship speed under different environments and different power outputs, and at the same time setting a large number of sensors to obtain more accurate real-time weather data to ensure the accuracy of the physical process simulation model. However, the physical process simulation model used in this method has a large number of parameters, requires a large amount of observational data to adjust the parameters of the physical process simulation model, and has a high hardware cost, making it difficult to popularize for different ship types. At the same time, when the physical process simulation model estimates the influence of weather environment factors on the ship speed, it mainly focuses on the instantaneous change influence and fails to consider the influence of persistent weather factors on the ship speed, resulting in higher energy consumption and longer time for the subsequent optimized solutions.

[0035] Please refer to Figure 1, to solve the problem of how to more precisely integrate weather forecast data into the ship speed optimization problem while ensuring the computational complexity in the prior art, thereby reducing the sailing time and sailing energy consumption of the ship speed control scheme, an optimized ship sailing speed control method based on multi-objective optimization provided by an embodiment of the present application includes S101 to S105, specifically: S101: Obtain the sailing route data to be planned and weather forecast data; the sailing route data includes several stop points, the optimal sailing routes between each stop point, and several preset variable speed points corresponding to each of the optimal sailing routes; the preset variable speed points are set according to the turning points of the corresponding optimal sailing routes.

[0036] Preferably, in some embodiments of the present application, the preset variable speed points are set according to the turning points of the corresponding optimal sailing routes, specifically: read the longitude and latitude coordinates of each optimal sailing route, and further use the coordinate points where the change degree of the front and rear longitude and latitude coordinates exceeds a certain threshold as the variable speed points.

[0037] Preferably, in some embodiments of the present application, when the exact sailing route is known in advance, in addition to the preset variable speed points that can be set according to the turning points of the corresponding optimal sailing routes, weather forecast data can be further introduced to predict in advance the points where large weather fluctuations are likely to occur in the sailing route as the preset variable speed points.

[0038] Furthermore, in some embodiments of the present application, the weather forecast data is selected according to a preset interval, and this interval is determined according to the maximum duration allowed for the current sailing.

[0039] S102: Use the arrival order of each of the stop points as the first decision variable, and use the power output of each of the preset variable speed points corresponding to each of the optimal sailing routes as the second decision variable.

[0040] Furthermore, in some embodiments of the present application, when the exact sailing route is known in advance, that is, the arrival order of the exact stop points is known, at this time, there is no need to define the first decision variable, and the second decision variable will become the power output corresponding to each preset variable speed point under the exact sailing route. That is, the multi-objective optimization problem defined later in the present application can not only solve the optimization problem of the power output scheme for the incompletely planned sailing route, but also optimize the power output scheme based on the already planned sailing path, thereby improving the flexibility of the ship speed optimization control.

[0041] S103: Determine the speeds corresponding to a number of preset speed change points according to the first decision variable, the second decision variable, and a preset power simulation model, and correct the speeds by combining the weather forecast data with a preset long short-term memory network model; the power simulation model is a mapping relationship between power output and speed established under the condition of no environmental disturbance.

[0042] Further, in some embodiments of the present application, the determining the speeds corresponding to a number of preset speed change points according to the first decision variable, the second decision variable, and a preset power simulation model includes: Determine the arrival order of each of the docking points according to the first decision variable; Determine a number of first optimal navigation routes according to the arrival order of each of the docking points; Use the preset speed change points corresponding to the first optimal navigation routes as the first preset speed change points; Map the second decision variables corresponding to each of the first preset speed change points to the corresponding speeds through the simulation model.

[0043] To avoid the problem of excessive computational complexity in the optimization process caused by too many power output decisions for all preset speed change points within the entire solution space, the optimization space of the subsequent second decision variable is limited by the solution space defined by the first decision variable, thereby effectively improving the optimization speed; further, the second decision variables corresponding to each of the first preset speed change points are mapped through the simulation model, so that the navigation speed of the ship at each first preset speed change point can be preliminarily determined.

[0044] It can be understood that when the first optimal navigation route is determined, it is equivalent to fixing the first preset speed change points passed by the ship in sequence and the passing order of the first preset speed change points. At this time, only the power output of these first preset speed change points needs to be determined. Therefore, there is no need to perform an optimization operation on all preset speed change points of all optimal navigation routes. Therefore, in some embodiments of the present application, when the exact navigation route is known in advance, in the above process, it is only necessary to directly map the second decision variables corresponding to each of the first preset speed change points to the corresponding speeds through the simulation model.

[0045] Preferably, in some embodiments of the present application, the power simulation model is a mapping relationship between power output and speed established under the condition of no environmental disturbance, specifically a mapping relationship between power output and the first speed established based on Newton's fluid dynamics law and the hull resistance square law, that is, a simulation model equivalent to the ship sailing in a windless and wave-free environment.

[0046] Further, in some embodiments of the present application, the correcting the speeds by combining the weather forecast data with a preset long short-term memory network model includes: Obtain the first weather forecast data of each of the first optimal sailing routes within a preset time interval from the weather forecast data; Determine the sailing order of each of the first optimal sailing routes according to the first decision variable; In the sailing order, combine the first weather forecast data through a preset long short-term memory network model to sequentially correct the ship speeds corresponding to each first preset speed change point.

[0047] Since weather forecasts have a time attribute, except for the initial departure time, the time when the ship subsequently arrives at each first preset speed change point cannot be predicted in advance. Therefore, after determining several first optimal sailing routes, it is necessary to further consider the passing order of each first optimal sailing route. Starting from the first first preset speed change point (i.e., starting from the initial departure point), combined with the first weather forecast data at the initial time, gradually iterate to determine the time period of the first weather forecast data required to correct the next first preset speed change point, thereby improving the accuracy of ship speed correction.

[0048] Furthermore, in some embodiments of the present application, since weather forecast data not only has a time attribute but also a geographical attribute, when obtaining the first weather forecast data, it is not only necessary to ensure that the currently selected time interval can include all the weather forecast data used for subsequent ship speed correction, but also the data selected needs to be the weather forecast data corresponding to the area of the current first optimal sailing route. Therefore, the actual first weather forecast data is determined as it is continuously updated during the subsequent gradual iterative correction process. It can be understood that the first weather forecast data is a function related to time variables and geographical variables, and the time variables and geographical variables are related to the ship speed. By gradually correcting the ship speed, further gradually correct the accuracy of the first weather forecast data used subsequently to ensure the accuracy of the entire correction process.

[0049] Furthermore, in some embodiments of the present application, the step of combining the first weather forecast data through a preset long short-term memory network model to sequentially correct the ship speeds corresponding to each first preset speed change point in the sailing order includes: Take the ship speed to be currently corrected as the first ship speed, and take the time point when the ship arrives at the first preset speed change point corresponding to the first ship speed as the first time point, and judge whether the first preset speed change point corresponding to the first ship speed is the first preset speed change point corresponding to any of the first optimal sailing routes; If so, correct the first ship speed by a data-driven method in combination with the first weather forecast data at the first time point; Otherwise, take the time point when the ship reaches the first preset speed change point corresponding to the previous corrected speed as the second time point, and correct the first speed according to the first weather forecast data during the period from the first time point to the second time point through the preset long short-term memory network model.

[0050] Since the ship is at the first first preset speed change point of each first optimal navigation route when departing from each docking point, and there are no accumulated weather environment factors affecting before this first first preset speed change point, it is necessary to correct the initial first speed with the first weather forecast data in the form of time points; when the ship leaves the first first preset speed change point, it begins to be affected by the accumulated weather environment factors. Therefore, according to the first weather forecast data experienced between the first preset speed change points of the two vectors (at this time, the data is time series data within a period of time), the characteristic information in the time series data is passed through the preset long short-term memory network model to correct the first speed, thereby improving the correction accuracy.

[0051] Preferably, in some embodiments of the present application, correcting the first speed by a data-driven method in combination with the first weather forecast data at the first time point includes: wind speed correction, wave height correction, and ocean current correction; Among them, the formula for wind speed correction is:

[0052] The formula for wave height correction is:

[0053] The formula for ocean current correction is:

[0054] Then the finally corrected first speed is:

[0055] Among them, 、 and are the correction amounts of wind speed, wave height, and ocean current to the first speed respectively; and are the correction coefficients of wind speed and wave height respectively; is the wind speed; is the wind direction; is the ship form course; is the wave height; is the wave frequency; is the time; is the ocean current speed; is the ocean current direction.

[0056] Furthermore, in some embodiments of the present application, the hidden state recurrence process of the preset long short-term memory network model includes: wherein, the preset long short-term memory network model includes: a spatio-temporal feature gating, an environmental disturbance gating, a short-term memory unit, and a long-term memory unit; Through the spatio-temporal feature gating, spatio-temporal features are extracted from the first weather forecast data at the current time step, and the short-term memory unit is updated through the spatio-temporal features; Fusing the feature information of the long-term memory unit at the previous time step and the feature information of the short-term memory unit at the current time step to obtain the feature information of the long-term memory unit at the current time step; Through the environmental disturbance gating, combining the historical mean of the weather forecast to obtain a disturbance weight; According to the disturbance weight and the feature information of the short-term memory unit and the long-term memory unit at the current time step, update the memory cell of the preset long short-term memory network model; According to the updated memory cell, obtain the hidden state output by the preset long short-term memory network model at the current time step.

[0057] Specifically, in some embodiments of the present application, the hidden state recurrence formula of the preset long short-term memory network model includes:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] Among them, is the forgetting gate vector at the current time step; is the Sigmoid function; , , , , , and are the weight matrices for each update step; , , , , , and are the bias terms for each update step; is the input data at the current time step; and are the final output vectors of the previous time step and the current time step, respectively; is the input gate vector at the current time step; is the spatio-temporal feature gate vector at the current time step; is the environmental disturbance gate; is the candidate memory unit at the current time step; and are the short-term memory units of the previous time step and the current time step, respectively; and are the long-term memory units of the previous time step and the current time step, respectively; is the long and short-term memory fusion coefficient; is the memory unit at the current time step; is the short and long-term memory dynamic fusion weight; is the Softmax function; is the output gate vector at the current time step; is the hyperbolic tangent function; is the exponential decay function; is the element-wise multiplication; is the embedded feature extraction function; are the historical average data of wind speed, wave height, and ocean current velocity before the current time step, respectively; is the embedded feature at the current time step.

[0071] Furthermore, in some embodiments of the present application, the input data at each time step is:

[0072] Among them, Represents the wind speed data in the first weather forecast data at the current time step, which is vector data (i.e., it needs to include the direction of the wind); Is the maximum wind speed; Represents the wave height data in the first weather forecast data at the current time step, which is also vector data (i.e., it needs to include the direction of the waves, which is generally the same as the wind direction, but may change in special cases); Is the maximum wave height; Represents the ocean current velocity data in the first weather forecast data at the current time step, which is also vector data (i.e., it needs to include the direction of the ocean current); Is the maximum ocean current velocity. It should be noted that 、 And Should be the average value of the historical maximum statistical data in each current quarter on the first optimal navigation route where the current ship is located, to prevent the maximum value from being set too large resulting in insignificant changes in the characteristics of subsequent input data.

[0073] Furthermore, in some embodiments of the present application, when the hidden state recurrence formula of the long short-term memory network model is used for repeated iteration to obtain the hidden state at the final time step After that, it is further necessary to correct the first ship speed according to this hidden state The specific formula is:

[0074]

[0075] Wherein, Is the correction coefficient; Is the first ship speed, which is vector data and is mainly used to introduce the current ship's navigation direction in the correction coefficient calculation formula; Is the offset term; Is the corrected first ship speed.

[0076] As can be seen from the above embodiments, since the weather forecast data includes not only wind speed, but also the influence of factors such as wave height and water flow velocity, these factors have both instantaneous influence on the ship speed (such as local turbulence) and long-term continuous influence, and the influence of both needs to be considered comprehensively. Therefore, based on the traditional long short-term memory network model, this application additionally introduces spatio-temporal feature gating, environmental disturbance gating, short-term memory unit and long-term memory unit. In the improved long short-term memory network model, through spatio-temporal feature gating, the time correlation of multi-source data in the first weather forecast data is fused to obtain spatio-temporal features, and this data is incorporated into the short-term memory unit, so that the long short-term memory network model can quickly respond to sudden disturbances; further through environmental disturbance gating, different instantaneous environmental changes are identified from the historical mean of the weather forecast for the disturbance degree of the ship speed, and the fusion ratio of the feature information of the short-term memory unit and the long-term memory unit is adjusted according to the disturbance degree, so as to ensure the accurate steps of the influence degree of instantaneous change and continuous change on the ship speed, and improve the accuracy of the hidden state output later to correct the first ship speed.

[0077] S104: According to the first decision variable, the second decision variable and the corrected ship speed, respectively determine the first sailing time and the first sailing energy consumption, and use the first sailing time and the first sailing energy consumption as the first optimization target and the second optimization target respectively.

[0078] Further, in some embodiments of the present application, the step of respectively determining the first sailing time and the first sailing energy consumption according to the first decision variable, the second decision variable and the corrected ship speed includes: According to the corrected ship speed, calculate the second sailing time required for the ship to pass through any two adjacent first preset variable speed points; Determine the first sailing time according to the sum of all the second sailing times; According to the second decision variable, determine the second sailing energy consumption during each of the second sailing times; Determine the first sailing energy consumption according to the sum of all the second sailing energy consumptions.

[0079] Since the ship speed needs to be adjusted after passing through each first preset variable speed point, the sailing time between every two adjacent first preset variable speed points needs to be calculated segment by segment, and the sailing energy consumption is calculated according to the power output of each segment in combination with the sailing time of each segment, so as to ensure the accuracy of subsequent problem optimization.

[0080] It should be noted that the above process of determining the first sailing time and the first sailing energy consumption refers to establishing the functional relationship between the first decision variable and the second decision variable, and the first sailing time and the first sailing energy consumption, rather than directly calculating the actual first sailing time and the first sailing energy consumption.

[0081] S105: Construct a multi-objective optimization problem according to the first optimization objective and the second optimization objective, and solve the multi-objective optimization problem through a multi-objective optimization algorithm to obtain an optimal power output scheme.

[0082] Further, in some embodiments of the present application, the constructing a multi-objective optimization problem according to the first optimization objective and the second optimization objective includes: Taking the first optimization objective and the second optimization objective as the optimization objectives of the multi-objective optimization problem; Constructing the constraint conditions of the multi-objective optimization problem according to the power simulation model.

[0083] Further, in some embodiments of the present application, the mathematical model of the multi-objective optimization problem includes:

[0084]

[0085]

[0086] Wherein, represents the first sailing time; represents the first sailing energy consumption; represents a randomly generated docking sequence , represents deleting the docking point from the set ; , represents deleting the docking point from the set ; , is the total number of docking points; represents the set of variable-speed points on a certain optimal sailing route, is related to the docking point and the docking point ; represents when on the optimal sailing route from the docking point to the docking point , the distance from the variable-speed point to the variable-speed point ; represents executing on the optimal sailing route from the docking point to the docking point , represents not executing on the optimal sailing route from the docking point to the docking point ; represents when at the docking point to the docking point When corresponding to the optimal navigation route, the ship is located at the variable speed point and the power output at this time; represents that the ship is at the docking point to the docking point and the weather forecast data when corresponding to the optimal navigation route; is the speed correction operator; is the power simulation model operator; and respectively represent the minimum power output limit and the maximum power output limit; and respectively represent the minimum speed limit and the maximum speed limit.

[0087] Embed the speed correction operator and the power simulation model operator into the constructed first optimization objective and second optimization objective respectively to achieve an accurate mapping between the power output and the speed, so as to achieve the synchronous and precise optimization of the navigation time and the navigation energy consumption; further embed the speed correction operator and the power simulation model operator into the constraint conditions to limit the speed and ensure the effectiveness of the final power output scheme; finally, further limit that each docking point can only dock once in the constraint conditions to optimize the solution space of the first decision variable and avoid the appearance of invalid solutions, thereby improving the optimization efficiency.

[0088] Furthermore, in some embodiments of the present application, solving the multi-objective optimization problem through a multi-objective optimization algorithm to obtain the optimal power output scheme includes, but is not limited to, multi-objective optimization algorithms such as genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms.

[0089] Preferably, in some embodiments of the present application, in order to improve the optimization efficiency and the optimization effect, the mutation operator and the crossover operator in the genetic algorithm are next improved. Specifically, at the beginning of the iteration, the mutation probability of the first several genes preset for each chromosome is increased, and at the same time, the crossover operation is only performed on the first several genes preset; further, when the iteration result tends to be stable, the first several genes preset are fixed, and the crossover and mutation operations are only performed on the remaining genes.

[0090] Since in the ship speed optimization problem, the weather forecast data is the core key influencing factor, and at the same time, it is also known that the accuracy of the current speed correction is related to the accuracy of the current weather forecast data selection, and the accuracy of the current weather forecast data selection is related to the accuracy of the previously corrected speed, so in fact, it represents that on a navigation route, the power output decision corresponding to the preset variable speed point passed earlier will affect the power output decision corresponding to the preset variable speed point passed later to a greater extent.

[0091] Based on the above analysis results, it can be known that by focusing on the power output decision for searching the preset variable-speed points passed earlier in the early stage of iteration, a large number of ineffective searches can be carried out. For example, for a complete power output decision scheme, the output decision corresponding to the preset variable-speed point passed later reaches a better state. However, if the output decision corresponding to the preset variable-speed point passed earlier in this power output decision scheme is changed during the iteration process, the power output decision scheme obtained through a large number of iterations is likely to have its better state destroyed, thus affecting the optimization efficiency.

[0092] In summary, it can be seen that a ship navigation speed optimization control method based on multi-objective optimization provided by an embodiment of the present application has the following beneficial effects: Whenever the ship sails to the turning point of the optimal navigation route, it is easier for the influence of the weather environment on the sailing speed to change at this time. Therefore, by setting the turning point of each optimal navigation route as a preset variable-speed point, the sensitivity of subsequent sailing speed optimization to weather forecast data is improved; further, through a simplified power simulation model, while solving the problem of low versatility caused by the overly complex physical process model, a long short-term memory network model that can capture the change characteristics of time-series data is introduced to further correct the sailing speed. The above process of estimating the sailing speed through the weather environment not only reduces the computational complexity but also ensures the accuracy of the sailing speed estimation; finally, by establishing a multi-objective optimization problem and solving the optimal power output scheme, the final power output scheme can reduce the sailing time and sailing energy consumption during the sailing process at the same time.

[0093] As Figure 2 shown, based on the above method item embodiment, an embodiment of the present application provides a ship navigation speed optimization control device based on multi-objective optimization, including: a data acquisition module 201, a decision variable determination module 202, a sailing speed correction module 203, an optimization objective determination module 204, and a mathematical model solving module 205.

[0094] Further, in some embodiments of the present application, the data acquisition module 201 is configured to acquire the navigation route data to be planned and the weather forecast data; the navigation route data includes a plurality of stopping points, the optimal navigation routes between the stopping points, and a plurality of preset speed change points corresponding to each of the optimal navigation routes; the preset speed change points are set according to the turning points of the corresponding optimal navigation routes; the decision variable determination module 202 is configured to use the arrival order of each of the stopping points as the first decision variable and the power output of each of the preset speed change points corresponding to each of the optimal navigation routes as the second decision variable; the speed correction module 203 is configured to determine the speeds corresponding to a plurality of preset speed change points according to the first decision variable, the second decision variable, and a preset power simulation model, and correct the speeds by combining the weather forecast data through a preset long short-term memory network model; the power simulation model is a mapping relationship between power output and speed established under the condition of no environmental disturbance; the optimization objective determination module 204 is configured to respectively determine the first navigation time and the first navigation energy consumption according to the first decision variable, the second decision variable, and the corrected speed, and use the first navigation time and the first navigation energy consumption as the first optimization objective and the second optimization objective respectively; the mathematical model solving module 205 is configured to construct a multi-objective optimization problem according to the first optimization objective and the second optimization objective, and solve the multi-objective optimization problem through a multi-objective optimization algorithm to obtain an optimal power output scheme.

[0095] Further, in some embodiments of the present application, the step of determining the speeds corresponding to a plurality of preset speed change points according to the first decision variable, the second decision variable, and a preset power simulation model includes: determining the arrival order of each of the stopping points according to the first decision variable; determining a plurality of first optimal navigation routes according to the arrival order of each of the stopping points; using the preset speed change points corresponding to the first optimal navigation routes as the first preset speed change points; and mapping the second decision variables corresponding to each of the first preset speed change points to the corresponding speeds through the simulation model.

[0096] Further, in some embodiments of the present application, the step of correcting the speeds by combining the weather forecast data through a preset long short-term memory network model includes: acquiring the first weather forecast data of each of the first optimal navigation routes within a preset time interval from the weather forecast data; determining the navigation order of each of the first optimal navigation routes according to the first decision variable; and sequentially correcting the speeds corresponding to each of the first preset speed change points by combining the first weather forecast data through a preset long short-term memory network model in the navigation order.

[0097] Further, in some embodiments of the present application, the method of sequentially correcting the speeds corresponding to each first preset speed change point by combining the first weather forecast data through a preset long short-term memory network model according to the sailing order includes: taking the speed to be corrected currently as the first speed, taking the time point when the ship reaches the first preset speed change point corresponding to the first speed as the first time point, and determining whether the first preset speed change point corresponding to the first speed is the first preset speed change point corresponding to any of the first optimal sailing routes; if so, correcting the first speed by a data-driven method in combination with the first weather forecast data at the first time point; otherwise, taking the time point when the ship reaches the first preset speed change point corresponding to the speed corrected last time as the second time point, and correcting the first speed through the preset long short-term memory network model according to the first weather forecast data during the period from the first time point to the second time point.

[0098] Further, in some embodiments of the present application, the hidden state recursion process of the preset long short-term memory network model includes: wherein, the preset long short-term memory network model includes: a spatio-temporal feature gating, an environmental disturbance gating, a short-term memory unit, and a long-term memory unit; extracting spatio-temporal features from the first weather forecast data at the current time step through the spatio-temporal feature gating, and updating the short-term memory unit through the spatio-temporal features; fusing the feature information of the long-term memory unit at the previous time step and the feature information of the short-term memory unit at the current time step to obtain the feature information of the long-term memory unit at the current time step; obtaining a disturbance weight by combining the historical mean of the weather forecast through the environmental disturbance gating; updating the memory cell of the preset long short-term memory network model according to the disturbance weight and the feature information of the short-term memory unit and the long-term memory unit at the current time step; and obtaining the hidden state output by the preset long short-term memory network model at the current time step according to the updated memory cell.

[0099] Further, in some embodiments of the present application, the method of respectively determining the first sailing time and the first sailing energy consumption according to the first decision variable, the second decision variable, and the corrected speed includes: calculating the second sailing time required for the ship to pass through any two adjacent first preset speed change points according to the corrected speed; determining the first sailing time according to the sum of all the second sailing times; determining the second sailing energy consumption during each of the second sailing times according to the second decision variable; and determining the first sailing energy consumption according to the sum of all the second sailing energy consumptions.

[0100] Further, in some embodiments of the present application, constructing a multi-objective optimization problem according to the first optimization objective and the second optimization objective includes: using the first optimization objective and the second optimization objective as the optimization objectives of the multi-objective optimization problem; and constructing the constraint conditions of the multi-objective optimization problem according to the dynamic simulation model.

[0101] Further, in some embodiments of the present application, the mathematical model of the multi-objective optimization problem includes:

[0102]

[0103]

[0104] where, represents the first sailing time; represents the first sailing energy consumption; represents a randomly generated docking sequence , represents deleting the docking point from the set , represents deleting the docking point from the set , is the total number of docking points; represents the set of variable-speed points on a certain optimal sailing route, is related to the docking point and the docking point ; represents the distance from the variable-speed point to the variable-speed point on the optimal sailing route corresponding to the docking point to the docking point ; represents executing on the optimal sailing route from the docking point to the docking point , represents not executing on the optimal sailing route from the docking point to the docking point ; represents the power output of the ship when it is at the variable-speed point to the docking point on the optimal sailing route corresponding to the docking point ; represents the weather forecast data of the ship when it is at the docking point to the docking point on the optimal sailing route corresponding to the docking point is the speed correction operator; is an operator for the dynamic simulation model; and respectively represent the minimum power output limit and the maximum power output limit; and respectively represent the minimum speed limit and the maximum speed limit.

[0105] It can be understood that the above device item embodiments correspond to the method item embodiments of the present application, and can implement the method for optimizing the ship navigation speed based on multi-objective optimization provided by any one of the above method item embodiments of the present application.

[0106] It should be noted that the device embodiments described above are only illustrative, and some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided in the present application, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement without creative efforts.

[0107] In summary, it can be seen that a device for optimizing the ship navigation speed based on multi-objective optimization provided by the embodiments of the present application has the following beneficial effects: Whenever the ship sails to the turning point of the optimal navigation route, it is easier for the influence of the weather environment on the speed to change. Therefore, by setting the turning point of each optimal navigation route as a preset speed change point, the sensitivity of subsequent speed optimization to weather forecast data is improved; further, through a simplified dynamic simulation model, while solving the problem of low versatility caused by the overly complex physical process model, a long short-term memory network model that can capture the change characteristics of time series data is introduced to further correct the speed. The above process of estimating the speed through the weather environment not only reduces the computational complexity, but also ensures the accuracy of speed estimation; finally, by establishing a multi-objective optimization problem and solving the optimal power output scheme, the final power output scheme can reduce both the navigation time and navigation energy consumption during the navigation process.

[0108] Based on the above embodiments of the method for optimizing the ship navigation speed based on multi-objective optimization, another embodiment of the present application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for optimizing the ship navigation speed based on multi-objective optimization of any embodiment of the present application.

[0109] Exemplarily, in this embodiment, the computer program may be divided into one or more modules. The one or more modules are stored in the memory and executed by the processor to complete the present application. The one or more module elements may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the terminal device.

[0110] The terminal device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0111] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect all parts of the entire terminal device.

[0112] Based on the above method item embodiment, another embodiment of the present application provides a computer-readable storage medium, including a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for optimizing the ship navigation speed based on multi-objective optimization described in any one of the above method item embodiments of the present application.

[0113] Among them, if the modules / units integrated in the device / terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

Claims

1. A ship navigation speed optimization control method based on multi-objective optimization, characterized in that, Including: Obtain the navigation route data to be planned and the weather forecast data; The navigation route data includes several stop points, the optimal navigation routes between each stop point, and several preset speed change points corresponding to each of the optimal navigation routes; the preset speed change points are set according to the turning points of the corresponding optimal navigation routes; Take the arrival order of each of the stop points as the first decision variable, and take the power output of each of the preset speed change points corresponding to each of the optimal navigation routes as the second decision variable; According to the first decision variable, the second decision variable, and a preset power simulation model, determine the ship speeds corresponding to several preset speed change points, and correct the ship speeds by combining the weather forecast data through a preset long short-term memory network model; the power simulation model is a mapping relationship between power output and ship speed built under the condition of no environmental disturbance; According to the first decision variable, the second decision variable, and the corrected ship speeds, determine the first navigation time and the first navigation energy consumption respectively, and take the first navigation time and the first navigation energy consumption as the first optimization target and the second optimization target respectively; According to the first optimization target and the second optimization target, construct a multi-objective optimization problem, and solve the multi-objective optimization problem through a multi-objective optimization algorithm to obtain an optimal power output plan.

2. The method for optimizing and controlling the ship navigation speed based on multi-objective optimization according to claim 1, wherein The determining the ship speeds corresponding to several preset speed change points according to the first decision variable, the second decision variable, and a preset power simulation model includes: According to the first decision variable, determine the arrival order of each of the stop points; According to the arrival order of each of the stop points, determine several first optimal navigation routes; Take the preset speed change points corresponding to the first optimal navigation routes as the first preset speed change points; Through the simulation model, map the second decision variable corresponding to each of the first preset speed change points to the corresponding ship speed.

3. The optimal control method for ship navigation speed based on multi-objective optimization according to claim 2, characterized in that The correcting the ship speeds by combining the weather forecast data through a preset long short-term memory network model includes: Obtain the first weather forecast data of each of the first optimal navigation routes within a preset time interval from the weather forecast data; According to the first decision variable, determine the navigation order of each of the first optimal navigation routes; In the navigation order, correct the ship speeds corresponding to each of the first preset speed change points in turn by combining the first weather forecast data through a preset long short-term memory network model.

4. The optimization control method for ship navigation speed based on multi-objective optimization according to claim 3, characterized in that The correcting the ship speeds corresponding to each of the first preset speed change points in turn by combining the first weather forecast data through a preset long short-term memory network model in the navigation order includes: Take the ship speed to be corrected currently as the first ship speed, and take the time point when the ship arrives at the first preset speed change point corresponding to the first ship speed as the first time point, and judge whether the first preset speed change point corresponding to the first ship speed is the first preset speed change point corresponding to any of the first optimal navigation routes; If so, correct the first ship speed by combining the first weather forecast data at the first time point through a data-driven method; Otherwise, take the time point when the ship reaches the first preset speed change point corresponding to the previous corrected speed as the second time point, and correct the first speed according to the first weather forecast data during the period from the first time point to the second time point through the preset long short-term memory network model.

5. The optimization control method for ship navigation speed based on multi-objective optimization according to claim 4, characterized in that, The hidden state recurrence process of the preset long short-term memory network model includes: wherein, the preset long short-term memory network model includes: spatio-temporal feature gating, environmental disturbance gating, short-term memory unit and long-term memory unit; Extract spatio-temporal features from the first weather forecast data at the current time step through the spatio-temporal feature gating, and update the short-term memory unit through the spatio-temporal features; Fuse the feature information of the long-term memory unit at the previous time step and the feature information of the short-term memory unit at the current time step to obtain the feature information of the long-term memory unit at the current time step; Obtain the disturbance weight by combining the historical mean of the weather forecast through the environmental disturbance gating; Update the memory cell of the preset long short-term memory network model according to the disturbance weight and the feature information of the short-term memory unit and the long-term memory unit at the current time step; Obtain the hidden state output by the preset long short-term memory network model at the current time step according to the updated memory cell.

6. The optimization control method for ship navigation speed based on multi-objective optimization according to claim 3, characterized in that The determining the first sailing time and the first sailing energy consumption according to the first decision variable, the second decision variable and the corrected speed respectively includes: Calculate the second sailing time required for the ship to pass through any two adjacent first preset speed change points according to the corrected speed; Determine the first sailing time according to the sum of all the second sailing times; Determine the second sailing energy consumption during each of the second sailing times according to the second decision variable; Determine the first sailing energy consumption according to the sum of all the second sailing energy consumptions.

7. The optimization control method for ship navigation speed based on multi-objective optimization according to claim 1, characterized in that, The constructing a multi-objective optimization problem according to the first optimization target and the second optimization target includes: Take the first optimization target and the second optimization target as the optimization targets of the multi-objective optimization problem; Construct the constraint conditions of the multi-objective optimization problem according to the power simulation model.

8. The optimal control method for ship navigation speed based on multi-objective optimization according to claim 7, characterized in that, The mathematical model of the multi-objective optimization problem includes: ; ; ; Among them, represents the first sailing time; represents the first sailing energy consumption; represents the randomly generated docking sequence , represents deleting the docking point from the set , represents deleting the docking point from the set , is the total number of docking points; represents the set of variable speed points on a certain optimal sailing route, is related to the docking point and the docking point ; represents the distance from the variable speed point to the variable speed point on the optimal sailing route corresponding to the docking point to ; represents when executing the optimal sailing route from the docking point to the docking point , represents not executing the optimal sailing route from the docking point to the docking point ; represents the power output of the ship when it is at the variable speed point to the docking point on the optimal sailing route corresponding to the docking point ; represents the weather forecast data of the ship when it is at the docking point to the docking point on the optimal sailing route corresponding to the docking point is the speed correction operator; is the power simulation model operator; and represent the minimum power output limit and the maximum power output limit respectively; and represent the minimum speed limit and the maximum speed limit respectively.

9. An optimization control device for ship navigation speed based on multi-objective optimization, characterized in that, includes: data acquisition module, decision variable determination module, speed correction module, optimization target determination module and mathematical model solving module; wherein, the data acquisition module is used to acquire the sailing route data to be planned and the weather forecast data; the sailing route data includes several stop points, the optimal sailing routes between each stop point and several preset speed change points corresponding to each of the optimal sailing routes; the preset speed change points are set according to the turning points of the corresponding optimal sailing routes; The decision variable determination module is used to take the arrival order of each stop point as the first decision variable and the power output of each preset speed change point corresponding to each optimal sailing route as the second decision variable; The speed correction module is used to determine the speeds corresponding to a number of preset speed change points according to the first decision variable, the second decision variable, and a preset power simulation model, and correct the speeds by combining the weather forecast data with a preset long short-term memory network model; the power simulation model is a mapping relationship between power output and speed built under the condition of no environmental disturbance; The optimization objective determination module is used to determine the first sailing time and the first sailing energy consumption according to the first decision variable, the second decision variable, and the corrected speed, and use the first sailing time and the first sailing energy consumption as the first optimization objective and the second optimization objective respectively; The mathematical model solving module is used to construct a multi-objective optimization problem according to the first optimization objective and the second optimization objective, and solve the multi-objective optimization problem through a multi-objective optimization algorithm to obtain an optimal power output scheme.

10. The optimized control device for ship navigation speed based on multi-objective optimization according to claim 9, characterized in that, Determining the speeds corresponding to a number of preset speed change points according to the first decision variable, the second decision variable, and a preset power simulation model includes: Determining the arrival order of each of the docking points according to the first decision variable; Determining a number of first optimal sailing routes according to the arrival order of each of the docking points; Taking the preset speed change points corresponding to the first optimal sailing route as the first preset speed change points; Mapping the second decision variable corresponding to each of the first preset speed change points to the corresponding speed through the simulation model.

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