A route design method based on marine climate data and ship historical trajectory

Abstract

The application discloses a route design method based on marine climate data and ship historical track, relates to the technical field of route design, and is used for solving the problem that the environmental adaptability experience and risk evolution law contained in the historical ship navigation track are ignored, so that the route design scheme is difficult to adapt to the high-risk section, and the method comprises the following steps: constructing a global port geographic information database and a shipping channel data set, obtaining historical ship track and marine climate data of a starting position and a target position, generating an initial route, screening a basic route point with the minimum risk based on the risk mutation coefficient and the environmental stability of each route point of the path, connecting the basic route points to form a basic route, comparing the similarity to determine a selected route, and collecting attitude feedback data when the ship travels along the route, calculating a cumulative navigation efficiency difference after the target position, evaluating the consistency of the return route in combination with the feedback, and optimizing the route under the complex marine environment, so that the navigation safety and efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of route design, and more particularly, to a route design method based on marine climate data and ship historical trajectory. BACKGROUND

[0002] With the continuous expansion of global shipping business, it has become a common practice for ships to sail long distances across regions under complex marine climate conditions. The design and optimization of routes not only affect the energy consumption and efficiency of ship operation, but also directly relate to the safety and risk prevention and control capability of navigation. In recent years, with the development of global positioning system, automatic identification system and remote sensing observation technology, massive historical ship trajectory data and multi-source meteorological and ocean data have been systematically collected and stored, laying a foundation for building a data-driven route analysis model.

[0003] The prior art has the following disadvantages:

[0004] Currently, the prior art mostly relies on static shipping layers or path correction based on real-time weather forecasts, ignoring the environmental adaptability experience and risk evolution law contained in historical ship navigation trajectories, and it is difficult to make dynamic and closed-loop route optimization decisions for uncertain marine climate environments, resulting in that the route design scheme is difficult to adapt to high-risk sections. Therefore, a route design method based on marine climate data and ship historical trajectory is proposed.

[0005] The above information disclosed in the background section is only intended to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a route design method based on marine climate data and ship historical trajectory, which uses path risk mutation coefficient modeling, environmental stability quantitative analysis and trajectory similarity matching algorithm to solve the problems raised in the above background technology.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical scheme, a route design method based on marine climate data and ship historical trajectory, comprising the following steps:

[0008] Step S1: constructing a driving route data set of a world shipping channel to obtain historical ship navigation trajectory data and historical marine climate data of a starting position and a target position, and marking the way points of each historical navigation path;

[0009] Step S2: Obtaining an initial route based on historical ship navigation trajectory data and historical ocean climate data. Obtaining the risk mutation coefficient and path environmental stability corresponding to each waypoint of each historical navigation route based on the historical ocean climate data. Analyzing the risk value of each waypoint in turn. Selecting basic waypoints based on the risk value. Connecting the basic waypoints to obtain a basic route.

[0010] Step S3: Compare the basic route with the initial route for similarity, determine the selected route, and proceed along the selected route, collecting feedback data at each waypoint until the ship reaches the target location;

[0011] Step S4: After arriving at the target location, the cumulative navigation efficiency difference is collected, and the comprehensive value of the feedback data collected at each waypoint is combined to evaluate whether the return route is consistent with the selected route.

[0012] In a preferred embodiment, in step S1, any pair of latitude and longitude coordinates of a standard port is selected as the originating location and the destination location;

[0013] Count the effective trajectory segments of each ship in actual navigation state between the starting position and the target position as the historical navigation path;

[0014] Mark waypoints in each historical navigation route;

[0015] The latitude and longitude coordinates of all passing points are used as historical ship navigation trajectory data.

[0016] In a preferred embodiment, in step S1, the static pressure of the gas on the sea level at the waypoint is collected to obtain the sea surface pressure;

[0017] The composite modulus of horizontal wind speed at the approach point is defined as wind speed;

[0018] Obtain the average wave height value of the waypoint within the preset wave height monitoring period as the effective wave height;

[0019] Calculate the angle between the ship's sailing direction and the dominant direction of the ocean current to obtain the current direction deviation;

[0020] Sea surface pressure, wind speed, significant wave height and ocean current direction deviation are used as historical ocean climate data.

[0021] In a preferred embodiment, in step S2, a sea surface pressure sequence, a wind speed sequence, a significant wave height sequence, and an ocean current direction deviation sequence are constructed for each historical navigation path, and the standard deviation of each sequence is calculated respectively;

[0022] The standard deviations of each sequence are normalized and then added together to obtain the environmental stability score of each historical navigation route. The historical navigation route with the smallest environmental stability score is selected as the initial route.

[0023] Based on the sea surface pressure and wind speed of each approach point, a risk mutation coefficient is defined using a covariance analysis fusion function;

[0024] Based on the effective wave height and current direction offset of each approach point, a fuzzy logic fusion function is constructed to calculate the path environment stability.

[0025] In a preferred embodiment, in step S2, the risk mutation coefficient and the path environment stability are comprehensively analyzed using an entropy weight method to generate a risk value;

[0026] After calculating the risk value for all approach points, according to the time sequence of the approach points, from the multiple approach points corresponding to the same time position of all historical navigation paths, the approach point with the minimum risk value is selected as the basic approach point, and all basic approach points are connected in geographical spatial order to obtain a basic route.

[0027] In a preferred embodiment, in step S3, the latitude and longitude of each approach point in the basic route and the initial route are comprehensively analyzed, and the position distance of each approach point is calculated by the spherical cosine theorem method;

[0028] The distance similarity of each approach point is calculated based on the position distance of each approach point;

[0029] The historical marine climate data of each approach point in the basic route and the initial route are constructed into a basic route climate vector and an initial route climate vector, respectively;

[0030] The marine climate similarity of each approach point is calculated based on the basic route climate vector and the initial route climate vector;

[0031] The marine climate similarity and the distance similarity of each approach point are summed and averaged to obtain the overall similarity of the route;

[0032] If the overall similarity of the route is greater than a preset similarity threshold, the initial route is selected as the selected route, otherwise, the basic route is selected as the selected route.

[0033] In a preferred embodiment, in step S3, a fluctuation period is preset and divided into multiple sampling time points, and the roll angle of the ship body at each sampling time point is collected when passing through the approach point;

[0034] The standard deviation of the roll angle in the fluctuation period is taken as the ship body attitude stability fluctuation degree of each approach point;

[0035] The ship body attitude stability fluctuation degree of each approach point is recorded as feedback data until the ship reaches the target position.

[0036] In a preferred embodiment, in step S4, the ratio of the total distance of each historical navigation track to the corresponding navigation time is taken as the historical navigation efficiency value, and the average of the historical navigation efficiency values is taken as the historical navigation efficiency average value;

[0037] The ratio of the total distance of the selected route to the navigation time is taken as the selected navigation efficiency value;

[0038] The difference between the selected navigation efficiency value and the historical navigation efficiency average value is taken as the cumulative navigation efficiency difference value;

[0039] The maximum value of the feedback data of each waypoint is taken as the feedback data feature value.

[0040] In a preferred embodiment, in step S4, the ratio of the cumulative navigation efficiency difference value to the feedback data feature value is taken as the return route evaluation value;

[0041] The return route evaluation value is compared with the preset evaluation threshold value to evaluate whether the return route is consistent with the selected route;

[0042] If the return route evaluation value is greater than the evaluation threshold value, the return route is consistent with the selected route;

[0043] Otherwise, the return route is inconsistent with the selected route, and the return route is re-planned.

[0044] Technical effects and advantages of the present application:

[0045] The present application constructs a global port geographic information database and a shipping channel data set, obtains historical ship navigation track data and historical marine climate data of the starting position and the target position, and generates an initial route. Based on the risk mutation coefficient and the environmental stability of each waypoint of the historical navigation path, the basic waypoints with the smallest risk are selected, and the basic route is formed by connecting the waypoints. The similarity between the initial route and the basic route is compared to determine the selected route. The ship sails along the route and collects attitude feedback data. After reaching the target position, the cumulative navigation efficiency difference value is calculated, and the feedback data of each waypoint is combined to evaluate whether the return route is consistent with the selected route. The present application realizes intelligent optimization of route selection in complex marine environments, improves navigation safety and overall efficiency, and realizes closed-loop optimization and risk adaptive correction of route planning by combining historical track data and dynamic attitude feedback. BRIEF DESCRIPTION OF DRAWINGS

[0046] Fig. 1 The present application is a flowchart of the implementation process of a route design method based on marine climate data and ship historical track.

[0047] Fig. 2 The present application is a step schematic diagram of a route design method based on marine climate data and ship historical track. DETAILED DESCRIPTION

[0048] 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 only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0049] The present application constructs a global port geographic information database and a shipping channel data set, obtains historical ship voyage trajectory data and historical marine climate data of the starting position and the target position, and generates an initial route. Based on the risk mutation coefficient and the environmental stability of each way point of the historical voyage path, the basic way point with the minimum risk is screened, and the basic route is formed by connecting the basic way points. The similarity of the initial route and the basic route is compared to determine the selected route. The ship sails along the route and collects attitude feedback data. After reaching the target position, the cumulative navigation efficiency difference value is calculated, and the feedback data of each way point is combined to evaluate whether the return route is consistent with the selected route, so as to realize intelligent optimization of route selection in a complex marine environment and improve navigation safety and overall efficiency.

[0050] Embodiment 1

[0051] Please refer to Figs. 1-2 A route design method based on marine climate data and historical ship trajectory, comprising the following steps:

[0052] Step S1: constructing a driving route data set of a world shipping channel to obtain historical ship voyage trajectory data and historical marine climate data of a starting position and a target position, and marking way points of each historical voyage path;

[0053] Step S2: obtaining an initial route according to the historical ship voyage trajectory data and the historical marine climate data, obtaining the risk mutation coefficient and the path environmental stability corresponding to each way point of the historical voyage path according to the historical marine climate data, sequentially analyzing the risk values of each way point, and screening a basic way point based on the risk values to obtain a basic route by connecting the basic way points;

[0054] Step S3: comparing the similarity of the basic route and the initial route to determine the selected route, and the ship travels according to the selected route and collects feedback data at each way point until the ship reaches the target position;

[0055] Step S4: after reaching the target position, collecting the cumulative navigation efficiency difference value, combining the comprehensive value of the feedback data of each way point, and evaluating whether the return route is consistent with the selected route;

[0056] The specific implementation is as follows:

[0057] In step S1, access the global port geographic information database to obtain the geographic position data of the standard ports with shipping function in the world. The geographic position data is the latitude and longitude coordinates of the standard ports. According to the departure and arrival requirements of the ship, select the latitude and longitude coordinates of any pair of standard ports as the departure position and target position, and detect the actual shipping activities of the ship between the departure position and the target position to construct the driving route data set of the world shipping channel. The driving route data set includes historical ship track data and historical marine climate data.

[0058] For the historical ship track data, collect the continuous position records of each ship from the departure position to the target position from the global automatic identification system. The position records contain time stamps and position latitude and longitude coordinates, which are arranged in chronological order. The trajectory recognition model is used to eliminate the parking points, reverse points and abnormal sections deviating from the main channel, and to retain the effective trajectory segments of each ship in the actual sailing state.

[0059] The effective trajectory segments are counted as historical navigation paths from the departure position to the target position. In each historical navigation path, a fixed number of way points are marked according to spatial continuity. Each historical navigation path is composed of a fixed number of way points arranged in chronological order. The latitude and longitude coordinates of each way point are collected, and the latitude and longitude coordinates of the way points are constructed into a way point data set according to the historical navigation path to which they belong. All way point data sets are used as historical ship track data.

[0060] The historical marine climate data of the space-time position of each way point in the global marine environment prediction system is collected, i.e. based on the time stamp and the latitude and longitude coordinates of the way point, the sea surface pressure, wind speed, effective wave height and ocean current direction offset of the way point are obtained as historical marine climate data.

[0061] The static pressure of the gas on the sea surface of the way point is collected to obtain the sea surface pressure of the way point.

[0062] The wind speed is defined as the synthetic module length of the horizontal wind speed of the way point. The zonal wind speed and meridional wind speed of the horizontal wind speed are calculated by the following formula:

[0063] ;

[0064] Wherein, is the wind speed of the way point, is the meridional wind speed of the way point, is the zonal wind speed of the way point.

[0065] Take the time stamp of the way point as the center, move forward and backward by a fixed time period, preset the wave height monitoring period, and obtain the average wave height value of the way point within the wave height monitoring period as the effective wave height of the way point.

[0066] The current direction deviation is the angle between the ship's sailing direction and the dominant direction of the ocean current. The ship's heading angle and the ocean current direction angle are obtained, and the absolute difference between the ship's heading angle and the ocean current direction angle is calculated as the current direction deviation.

[0067] It should be noted that the Global Port Geographic Information Database is a spatial geographic information dataset that includes standard ports with shipping functions around the world; the Global Automatic Identification System is a ship-shore communication identification and tracking system based on high-frequency wireless communication links; the trajectory recognition model is a spatiotemporal behavior recognition and path pattern extraction algorithm system built based on historical trajectory datasets; the Global Ocean Environment Forecast System is a high-precision system for real-time monitoring of the physical environment of the ocean on a global scale. By integrating satellite remote sensing observation data, buoy measurement data, shipborne data and multi-source ocean reanalysis data, it achieves high-resolution modeling of global ocean environmental elements.

[0068] In step S2, based on the historical ship navigation track data and the historical ocean climate data, the standard deviation of the historical ocean climate data of each historical navigation path is calculated. Based on the historical ocean climate data of each waypoint of the historical navigation path, the sea surface pressure sequence, wind speed sequence, effective wave height sequence, and ocean current direction deviation sequence for each historical navigation path are constructed, and the standard deviation of each sequence is calculated respectively. The standard deviation calculation formula is as follows:

[0069] ;

[0070] in, For the The standard deviation of each sequence of historical navigation paths, is the index value of the historical navigation path, For a certain sequence, is the total number of waypoints in each sequence, is the ith value in each sequence, that is, the value corresponding to the ith waypoint, is the index value of the waypoint, is the mean of each series.

[0071] To unify the dimensions, each standard deviation is normalized. The normalized calculation expression is as follows:

[0072] ;

[0073] in, is the standard deviation normalized result of each sequence, is the maximum value of the standard deviation of each series.

[0074] The normalized standard deviation results of each sequence are added together to form the environmental stability score of each historical navigation route, and the historical navigation route with the smallest environmental stability score is selected as the initial route.

[0075] Based on the historical ocean climate data corresponding to each waypoint on each historical navigation route, the risk mutation coefficient and path environmental stability of each waypoint on the initial route are calculated to evaluate the risk value of each waypoint.

[0076] Based on the sea surface pressure and wind speed at each path point, the risk mutation coefficient is defined using the covariance analysis fusion function. The specific expression is:

[0077] ;

[0078] in, is the risk mutation coefficient of the i-th pathway point, is the covariance analysis fusion function, is the sea surface pressure at the i-th path point, is the wind speed at the i-th approach point, is the mean sea surface pressure, is the mean wind speed.

[0079] The covariance analysis fusion function is in the form of Euclidean distance transformation in covariance space. Its mathematical prototype is covariance analysis, which is used to evaluate the variability and discrete trend of sea surface pressure and wind speed. The more drastic the changes in sea surface pressure and wind speed, the greater the risk mutation coefficient.

[0080] Based on the effective wave height and current direction deviation of each waypoint, a fuzzy logic fusion function is constructed to calculate the path environment stability of the waypoint. The specific expression is as follows:

[0081] ;

[0082] in, is the path environment stability of the i-th waypoint; The fuzzy membership functions representing the effective wave height and the degree of deviation of the ocean current direction are modeled using the sigmoid function, such as: , a is the parameter that controls the slope of the curve, and its value range is greater than 0. The larger a is, the steeper the function changes. c is the center point of the function. is the effective wave height of the i-th path point; is the ocean current direction deviation of the i-th path point; and is the fuzzy importance weight coefficient, which is determined by historical regression fitting;

[0083] The fuzzy logic fusion function is based on the fuzzy comprehensive evaluation theory and is used to evaluate the impact of the deviation between the effective wave height and the ocean current direction on the path environmental stability. The greater the deviation between the effective wave height and the ocean current direction, the lower the path environmental stability.

[0084] The entropy weight method is used to perform unbiased weight calculation and comprehensive integration of the risk mutation coefficient and the path environment stability to generate a risk value to evaluate the comprehensive risk level of the path point. The specific calculation process is as follows:

[0085] Normalize the risk mutation coefficient and path environment stability of all pathway points to obtain a standardized matrix , where i is the index value of the waypoint, It is the index value of the indicator, and its value is 1 or 2, corresponding to the risk mutation coefficient and path environment stability respectively.

[0086] For the jth indicator of the i-th path point, calculate the proportion value, which is equal to its normalized value divided by the sum of the normalized values ​​of all path points under this indicator; multiply the proportion value by its natural logarithm, sum it and multiply it by the negative normalization factor to obtain the entropy value, and the normalization factor is , where n is the total number of waypoints; based on the entropy value, the weight coefficient is calculated, and the calculation expression of the weight coefficient is:

[0087] ;

[0088] in, is the weight coefficient, is the entropy value of the j-th indicator.

[0089] Finally, the risk value of the i-th path point is calculated:

[0090] ;

[0091] in, is the risk value of the i-th path point, 、 are the normalized risk mutation coefficient and path environment stability, 、 are the normalized risk mutation coefficient and the weight coefficient of path environment stability, respectively.

[0092] After calculating the risk values ​​of all waypoints, the risk values ​​are dynamically screened and the path is constructed based on the time sequence of the waypoints. That is, in the order of time series, the waypoint with the smallest risk value is selected from the multiple waypoints corresponding to the same time position of all historical navigation paths as the basic waypoint at that time position. All selected basic waypoints are connected in order according to their latitude and longitude coordinates in geographic space order to maintain the temporal progression and spatial continuity of the basic waypoints, and finally form a basic route.

[0093] It should be noted that the covariance analysis fusion function is a fusion method based on the mutual relationship of statistical features, which is used to integrate the degree of linear correlation between multiple source variables. The Euclidean distance transformation is a numerical standardization method based on the geometric distance measurement of Euclidean space. The fuzzy logic fusion function is a multi-indicator information fusion method based on fuzzy set theory, which is used to deal with environmental factors with uncertainty and ambiguity. The Sigmoid function is an S-shaped function commonly used for normalization and nonlinear mapping of continuous variables. The entropy weight method is an objective weighting method based on information entropy theory, which is used to determine the importance weight of each indicator in the comprehensive evaluation of multiple indicators.

[0094] In step S3, the latitude and longitude coordinates of each waypoint in the basic route are compared with those in the initial route, and the position distance based on the latitude and longitude coordinates is calculated using the spherical cosine theorem method: ,in, and is the latitude coordinate of the i-th waypoint of the basic route and the initial route, and is the longitude coordinate of the i-th waypoint of the basic route and the initial route, is the location distance of the i-th waypoint;

[0095] The distance similarity of each path point is calculated by the position distance of each path point: ,in, is the location distance of the i-th waypoint, is the distance similarity of the i-th waypoint;

[0096] The sea surface pressure, wind speed, significant wave height and ocean current direction deviation in the historical ocean climate data of each waypoint in the basic route and the initial route are respectively constructed into the basic route climate vector and the initial route climate vector;

[0097] Calculate the ocean climate similarity of the waypoints based on the basic route climate vector and the initial route climate vector: ,in, is the basic route climate vector of the i-th waypoint, is the initial route climate vector of the i-th waypoint, the marine climate similarity of the ith waypoint;

[0098] summing and averaging the marine climate similarity and the distance similarity of each waypoint to obtain the overall route similarity;

[0099] comparing the overall route similarity with a preset similarity threshold to select the route, if the overall route similarity is greater than the similarity threshold, the initial route is selected as the sailing trajectory, otherwise, the basic route is selected as the sailing trajectory.

[0100] a preset fluctuation period is divided into multiple sampling time points, and the roll angle of the ship body at each sampling time point is collected through the gyroscope when passing through the waypoint;

[0101] the ship body attitude stability fluctuation degree of each waypoint is calculated through the roll angle in the sailing process: wherein, is the average value of the roll angle in the fluctuation period, is the roll angle at the kth sampling time point, is the total number of sampling time points, is the ship body attitude stability fluctuation degree of the ith waypoint;

[0102] the ship body attitude stability fluctuation degree of each waypoint is recorded as feedback data until the ship reaches the target position.

[0103] Through accurate calculation and dynamic comparison of the similarity between the basic route and the initial route, intelligent route selection based on multi-dimensional feature fusion is realized, taking into account multiple factors of historical sailing trajectory and current marine climate environment, improving the scientificity and rationality of route selection.

[0104] It should be noted that the preset similarity threshold is a threshold parameter used to judge whether the overall similarity of different routes meets the standard in the route selection process, and the specific value is set by professional personnel based on historical data and actual sailing requirements; the gyroscope can output the change rate of the angular displacement of the ship body per unit time in the sailing process, and through synchronous integration with time, the three-dimensional attitude trajectory of the ship with time can be obtained, and the roll angle of the ship body can be accurately calculated; the spherical cosine theorem method can calculate the geographic distance between any two points, which is suitable for great circle distance calculation under the spherical model, and can avoid the error produced by the plane approximation method in long-distance path calculation.

[0105] In step S4, the ratio of the total distance of each historical sailing trajectory to the corresponding sailing time is taken as the historical sailing efficiency value, and the average of the historical sailing efficiency values is taken as the historical sailing efficiency average;

[0106] the ratio of the total distance of the selected route to the sailing time is taken as the selected sailing efficiency value;

[0107] The difference between the selected navigation efficiency value and the historical navigation efficiency mean is taken as the cumulative navigation efficiency difference;

[0108] The maximum value of the feedback data of each waypoint is used as the feedback data characteristic value, and the ratio of the cumulative navigation efficiency difference to the feedback data characteristic value is used as the return route evaluation value. The return route evaluation value is compared with the preset evaluation threshold to evaluate whether the return route is consistent with the selected route;

[0109] If the return route evaluation value is greater than the evaluation threshold, the return route is consistent with the selected route;

[0110] Otherwise, the return route is inconsistent with the selected route, and the return route is replanned.

[0111] When the return route evaluation value is large, it means that the selected route has achieved significant efficiency improvement while maintaining high stability, and is more suitable as a return route; when the return route evaluation value is small, it means that the current route has no obvious advantage in improving efficiency, and may even have severe fluctuations.

[0112] By constructing a feedback data feature value that includes the difference in navigation efficiency and the selected route, we can intelligently determine whether the currently selected route can be used as the return route, forming an evaluation indicator that emphasizes both efficiency and stability, improving the accuracy and adaptability of the return route judgment, and providing a scientific and reliable decision-making basis for the planning and execution of the return route.

[0113] It should be noted that the preset evaluation threshold is a parameter used to determine whether the return route evaluation value meets the requirements during the return route evaluation process, and is a standard for determining whether the currently selected route is consistent with the return route. The specific value is set by professionals and will not be elaborated here.

[0114] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0115] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0116] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0117] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0118] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0119] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. The division of the units is merely logical function division. There can be other division manners in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0120] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0121] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can be a physically independent unit, or two or more units can be integrated into one unit.

[0122] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0123] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A route planning method based on ocean climate data and historical ship trajectories, characterized by: The following steps are involved: Step S1: Construct a route dataset of the world's shipping channels, obtain historical ship navigation trajectory data and historical ocean climate data of the origin and destination locations, and mark the waypoints of each historical navigation path; Step S2: Obtaining an initial route based on historical ship navigation trajectory data and historical ocean climate data. Obtaining the risk mutation coefficient and path environmental stability corresponding to each waypoint of each historical navigation route based on the historical ocean climate data. Analyzing the risk value of each waypoint in turn. Selecting basic waypoints based on the risk value. Connecting the basic waypoints to obtain a basic route. In step S2, a sea surface pressure sequence, a wind speed sequence, a significant wave height sequence, and an ocean current direction deviation sequence are constructed for each historical navigation path, and the standard deviation of each sequence is calculated respectively; The standard deviations of each sequence are normalized and then added together to obtain the environmental stability score of each historical navigation route. The historical navigation route with the smallest environmental stability score is selected as the initial route. Based on the sea surface pressure and wind speed at each path point, the risk mutation coefficient is defined using the covariance analysis fusion function; Based on the effective wave height and current direction deviation of each path point, a fuzzy logic fusion function is constructed to calculate the path environment stability; Step S3: Compare the basic route with the initial route for similarity, determine the selected route, and proceed along the selected route, collecting feedback data at each waypoint until the ship reaches the target location; Step S4: After arriving at the target location, the cumulative navigation efficiency difference is collected, and the comprehensive value of the feedback data collected at each waypoint is combined to evaluate whether the return route is consistent with the selected route.

2. The route planning method based on ocean climate data and ship historical trajectories according to claim 1, characterized in that: In step S1, any pair of latitude and longitude coordinates of a standard port is selected as the origin and destination locations; Count the effective trajectory segments of each ship in actual navigation state between the starting position and the target position as the historical navigation path; Mark waypoints in each historical navigation route; The latitude and longitude coordinates of all passing points are used as historical ship navigation trajectory data.

3. The route planning method based on ocean climate data and ship historical trajectories according to claim 2, characterized in that: In step S1, the static pressure of the gas on the sea level at the waypoint is collected to obtain the sea surface pressure; The composite modulus of horizontal wind speed at the approach point is defined as wind speed; Obtain the average wave height value of the waypoint within the preset wave height monitoring period as the effective wave height; Calculate the angle between the ship's sailing direction and the dominant direction of the ocean current to obtain the current direction deviation; Sea surface pressure, wind speed, significant wave height and ocean current direction deviation are used as historical ocean climate data.

4. The route planning method based on ocean climate data and ship historical trajectories according to claim 1, characterized in that: In step S2, the entropy weight method is used to comprehensively analyze the risk mutation coefficient and the path environment stability to generate a risk value; After calculating the risk values ​​for all waypoints, the waypoint with the smallest risk value is selected from the multiple waypoints corresponding to the same time position in all historical navigation paths in chronological order. This point is used as the basic waypoint, and all basic waypoints are connected in geographic spatial order to obtain the basic route.

5. The route planning method based on ocean climate data and ship historical trajectories according to claim 1, characterized in that: In step S3, the latitude and longitude of each waypoint in the basic route and the initial route are combined to calculate the position distance of each waypoint using the spherical cosine theorem method; The distance similarity of each waypoint is calculated based on the position distance of each waypoint; The historical ocean climate data of each waypoint in the basic route and the initial route are constructed into the basic route climate vector and the initial route climate vector respectively; Calculate the ocean climate similarity of the waypoints based on the basic route climate vector and the initial route climate vector; The ocean climate similarity and distance similarity of each path point are summed and averaged to obtain the overall route similarity; If the overall similarity of the routes is greater than the preset similarity threshold, the initial route will be selected as the route; otherwise, the basic route will be selected as the route.

6. The route planning method based on ocean climate data and ship historical trajectories according to claim 1, characterized in that: In step S3, a wave cycle is preset and divided into multiple sampling moments, and the roll angle of the ship at each sampling moment is collected when passing through the waypoint; The standard deviation of the roll angle within the fluctuation period is taken as the stability fluctuation of the ship attitude at each approach point; The stability fluctuation of the ship's attitude at each approach point is recorded and used as feedback data until the ship reaches the target position.

7. The route planning method based on ocean climate data and ship historical trajectories according to claim 1, characterized in that: In step S4, the ratio of the total navigation distance of each historical navigation track to the corresponding navigation time is used as the historical navigation efficiency value, and the average of the historical navigation efficiency values ​​is taken as the mean historical navigation efficiency; The ratio of the total track distance of the selected route to the navigation time is taken as the selected navigation efficiency value; The difference between the selected navigation efficiency value and the historical navigation efficiency mean is taken as the cumulative navigation efficiency difference; The maximum value of the feedback data of each waypoint is taken as the characteristic value of the feedback data.

8. The route planning method based on ocean climate data and ship historical trajectories according to claim 7, characterized in that: In step S4, the ratio of the accumulated navigation efficiency difference to the characteristic value of the feedback data is used as the return route evaluation value; Compare the return route evaluation value with the preset evaluation threshold to evaluate whether the return route is consistent with the selected route; If the return route evaluation value is greater than the evaluation threshold, the return route is consistent with the selected route; Otherwise, the return route is inconsistent with the selected route, and the return route is replanned.

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