Route design method based on marine climate data and historical ship trajectory

Abstract

The invention discloses an airline design method based on marine climate data and a historical ship trajectory, relates to the technical field of airline design, and aims to solve the problem that an airline design scheme is difficult to adapt to a high-risk section due to neglect of environmental adaptability experience and risk evolution rules contained in a historical ship navigation trajectory. The method comprises the following steps: constructing a global port geographic information database and a shipping channel data set, obtaining historical ship trajectories and marine climate data of starting and target positions, generating an initial route, screening basic approach points with the minimum risk based on risk mutation coefficients and environmental stability of each approach point of a path, and connecting to form a basic route. The ship advances according to the ship route, attitude feedback data and a target position are collected, then an accumulated navigation efficiency difference value is calculated, the consistency of the return flight route is evaluated in combination with feedback, the ship route optimization under the complex marine environment is achieved, and the navigation safety and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of route design, and more particularly to a route design method based on ocean climate data and historical ship tracks. Background Art

[0002] With the continuous expansion of global shipping, long-distance, cross-regional voyages under complex ocean and climatic conditions have become the norm. Route design and optimization not only impacts vessel energy consumption and efficiency but also directly impacts navigation safety and risk management. In recent years, the advancement of global positioning systems, automatic identification systems, and remote sensing technologies has enabled the systematic collection and storage of massive amounts of historical ship trajectory data and multi-source meteorological and oceanographic data, laying the foundation for building data-driven route analysis models.

[0003] The existing technology has the following deficiencies: At present, most existing technologies rely on static shipping layers or path corrections based on real-time weather forecasts, ignoring the environmental adaptability experience and risk evolution laws contained in historical ship navigation trajectories. It is difficult to make dynamic, closed-loop route optimization decisions for uncertain marine climate environments, resulting in route design plans being difficult to adapt to high-risk sections. Therefore, a route design method based on marine climate data and historical ship trajectories is proposed.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a route design method based on ocean climate data and historical ship trajectories, which solves the problems raised in the above-mentioned background technology by applying path risk mutation coefficient modeling, environmental stability quantitative analysis and track similarity matching algorithm.

[0006] To achieve the above object, the present invention provides the following technical solution: a route design method based on ocean climate data and historical ship trajectories, comprising the following steps: 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. 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.

[0007] 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; 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.

[0008] 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; 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.

[0009] 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; 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.

[0010] In a preferred embodiment, in step S2, the risk mutation coefficient and the path environment stability are comprehensively analyzed using the entropy weight method 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.

[0011] In a preferred embodiment, in step S3, the latitude and longitude of each waypoint in the basic route and the initial route are integrated 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.

[0012] In a preferred embodiment, in step S3, a wave cycle is preset and divided into a plurality of 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 degree of the ship attitude at each approach point; The stability fluctuation of the hull attitude at each approach point is recorded and used as feedback data until the ship reaches the target position.

[0013] In a preferred embodiment, 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 value; 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.

[0014] In a preferred embodiment, in step S4, the ratio of the accumulated navigation efficiency difference to the feedback data characteristic value 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.

[0015] Technical effects and advantages of the present invention: The present invention constructs a global port geographic information database and a shipping channel data set, obtains historical ship navigation trajectory data and historical ocean climate data of the origin and target locations, and generates an initial route. Based on the risk mutation coefficient and environmental stability of each waypoint on the historical navigation path, the basic waypoints with the lowest risk are screened and connected to form a basic route. The selected route is determined by comparing the similarity between the initial route and the basic route. The ship navigates along the route and collects attitude feedback data. After arriving at the target location, the cumulative navigation efficiency difference is calculated. Combined with the feedback data of each waypoint, it is evaluated whether the return route is consistent with the selected route, thereby realizing intelligent optimization of route selection in complex marine environments, improving navigation safety and overall efficiency, and integrating historical trajectory data with dynamic attitude feedback to achieve closed-loop optimization and risk adaptive correction of route planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of a route design method based on ocean climate data and historical ship trajectories.

[0017] Figure 2 The figure is a schematic diagram of the steps of a route design method based on ocean climate data and historical ship trajectories of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] The present invention constructs a global port geographic information database and a shipping channel data set, obtains historical ship navigation trajectory data and historical ocean climate data of the origin and target locations, and generates an initial route. Based on the risk mutation coefficient and environmental stability of each waypoint on the historical navigation path, the basic waypoints with the lowest risk are screened and connected to form a basic route. The selected route is determined by comparing the similarity between the initial route and the basic route. The ship navigates along the route and collects attitude feedback data. After arriving at the target location, the cumulative navigation efficiency difference is calculated. Combined with the feedback data of each waypoint, it is evaluated whether the return route is consistent with the selected route, thereby realizing intelligent optimization of route selection in complex marine environments and improving navigation safety and overall efficiency.

[0020] Example 1 See also Figures 1 to 2 A route design method based on ocean climate data and historical ship trajectories includes the following steps: 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. 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 combined with the comprehensive value of the feedback data collected at each waypoint to evaluate whether the return route is consistent with the selected route; The specific implementation is as follows: In step S1, the global port geographic information database is accessed to obtain the geographic location data of standard ports that are publicly available and have shipping functions around the world. The geographic location data is the longitude and latitude coordinates of the standard ports. According to the departure and arrival requirements of the ship, any pair of longitude and latitude coordinates of the standard ports are selected as the departure location and the target location, and the actual shipping activities of the ships between the departure location and the target location are detected to construct a route dataset of the world shipping channel. The route dataset includes historical ship navigation trajectory data and historical ocean climate data.

[0021] For historical ship navigation trajectory data, continuous position records of each ship as it sails from its origin to its destination are collected from the Global Automatic Identification System. The position records contain timestamps and longitude and latitude coordinates, arranged in chronological order. The trajectory recognition model is used to eliminate anchorage points, reverse points, and abnormal sections that deviate from the main channel, retaining the valid trajectory segments of each ship in actual navigation status. The valid trajectory segments are counted as historical navigation paths from the starting position to the target position. A fixed number of waypoints are marked in each historical navigation path according to spatial continuity. Each historical navigation path consists of a fixed number of waypoints arranged in increasing order of time. The longitude and latitude coordinates of each waypoint are collected, and the longitude and latitude coordinates of the waypoints are used to construct a waypoint dataset according to the historical navigation path to which they belong. All waypoint datasets are used as historical ship navigation trajectory data.

[0022] Collect historical ocean climate data corresponding to the spatiotemporal locations of each waypoint in the global marine environment forecast system. That is, based on the timestamp and the longitude and latitude coordinates of the waypoint, obtain the sea surface pressure, wind speed, significant wave height, and ocean current direction deviation of the waypoint as historical ocean climate data; The static pressure of the gas on the sea level at the waypoint is collected to obtain the sea surface pressure at the waypoint.

[0023] The wind speed is defined as the composite modulus of the horizontal wind speed at the path point. The longitudinal wind speed and zonal wind speed of the horizontal wind speed are synthesized and calculated using the following formula: ; in, is the wind speed at the approach point, is the meridional wind speed at the path point, is the zonal wind speed at the path point.

[0024] Taking the timestamp of the waypoint as the center, the fixed time periods are moved forward and backward, the wave height monitoring period is preset, and the average wave height value of the waypoint within the wave height monitoring period is obtained, which is used as the effective wave height of the waypoint.

[0025] 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.

[0026] 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.

[0027] 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: ; 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.

[0028] To unify the dimensions, each standard deviation is normalized. The normalized calculation expression is as follows: ; in, is the standard deviation normalized result of each sequence, is the maximum value of the standard deviation of each series.

[0029] 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.

[0030] 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.

[0031] 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: ; 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.

[0032] 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.

[0033] 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: ; 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 approach 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; 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.

[0034] 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: 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.

[0035] 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: ; in, is the weight coefficient, is the entropy value of the j-th indicator.

[0036] Finally, the risk value of the i-th path point is calculated: ; 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.

[0037] 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.

[0038] 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.

[0039] 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; 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; 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; 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, is the ocean climate similarity of the i-th path point; The ocean climate similarity and distance similarity of each path point are summed and averaged to obtain the overall route similarity; The overall similarity of the route is compared with the preset similarity threshold to select a route. If the overall similarity of the route is greater than the similarity threshold, the initial route is selected as the navigation trajectory. Otherwise, the basic route is selected as the navigation trajectory.

[0040] The wave cycle is preset and divided into multiple sampling moments. When passing through the waypoint, the gyroscope is used to collect the roll angle of the ship at each sampling moment. The stability fluctuation of the ship's attitude at each approach point is calculated by the roll angle during navigation: ,in, is the average value of the roll angle within the wave period, is the roll angle at the kth sampling moment, is the total number of sampling moments, is the stability fluctuation of the ship attitude at the i-th approach point; The stability fluctuation of the hull attitude at each approach point is recorded and used as feedback data until the ship reaches the target position.

[0041] By accurately calculating and dynamically comparing the similarity between the basic route and the initial route, intelligent route selection based on multi-dimensional feature fusion is achieved, taking into account multiple factors such as historical navigation tracks and current ocean climate environment, and improving the scientificity and rationality of route selection.

[0042] It should be noted that the preset similarity threshold is a threshold parameter used to determine whether the overall similarity of different routes meets the standard during the route selection process. The specific numerical setting is set by professionals based on historical data and actual navigation needs; the gyroscope can output the rate of change of the angular displacement of the hull per unit time in real time during navigation. By integrating with time synchronization, the trajectory of the change of the ship's three-dimensional posture over time can be obtained to accurately calculate the roll angle of the hull; the spherical cosine theorem method can calculate the geographical distance between the longitude and latitude coordinates of any two points, which is suitable for great circle distance calculation under the spherical model, and can avoid the errors caused by the plane approximation method in long-distance path calculation.

[0043] In step S4, the ratio of the total 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 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; 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0053] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0054] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0055] If the functions 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 this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling 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 method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0056] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection 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. 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 3, characterized in that: 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 ocean current direction deviation of each path point, a fuzzy logic fusion function is constructed to calculate the path environment stability.

5. The route planning method based on ocean climate data and ship historical trajectories according to claim 4, 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.

6. The route planning method based on ocean climate data and ship historical trajectories according to claim 5, 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.

7. 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 degree of the ship attitude at each approach point; The stability fluctuation of the hull attitude at each approach point is recorded and used as feedback data until the ship reaches the target position.

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 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.

9. The route planning method based on ocean climate data and ship historical trajectories according to claim 8, 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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