Ship operation data backtracking method based on error correction algorithm

Abstract

The invention discloses a ship operation data backtracking method based on an error correction algorithm, which relates to the field of maritime engineering, solves the problem of high error rate of ship operation data abnormity backtracking, and comprises the following steps: setting a track monitoring area of a target ship in the navigation process, analyzing ship navigation data based on the track monitoring area, and determining the ship operation data according to the analyzed ship navigation data. Obtaining an optimized ship track of the target ship; the optimized ship track is analyzed, the target ship is classified according to the historical track of the historical ship, and the historical ship of the same category as the target ship is obtained; analyzing the ship operation data, judging an abnormal condition of the ship operation data corresponding to the target ship in combination with historical operation data, and generating a corresponding abnormal signal; after the abnormal signal is received, a ship operation function is constructed according to the historical operation data of the same type of historical ships, abnormity backtracking is conducted on the ship operation data of the target ship based on the ship operation function, and accurate backtracking of the ship operation data is achieved.

Description

Technical Field

[0001] This invention belongs to the field of marine engineering technology, specifically a method for backtracking ship operation data based on error correction algorithms. Background Technology

[0002] Error correction algorithms are a class of algorithms used to detect and correct errors caused by noise, interference, or human error during data transmission, storage, or processing. Their core objective is to enable the receiver to identify and correct errors by adding redundant information or utilizing the inherent structure of the data, thereby ensuring the integrity and accuracy of the data. Ship operation data backtracking methods refer to the methods of fully recording, storing, retrieving, and analyzing various types of data generated by ships during operations through technical means and management processes, in order to reproduce historical operation scenarios, locate problems, and trace responsibility.

[0003] However, at present, when backtracking ship operation data, only the historical operation data of the corresponding ship is referenced. Instead, historical ships of the same category are not selected based on the track, and the historical operation data of historical ships are not used to identify anomalies and backtrack data of target ships. This results in low accuracy of anomaly identification results, which in turn leads to a high anomaly rate in backtracking results. To address this, the present invention proposes a method for backtracking ship operation data based on error correction algorithms. Summary of the Invention

[0004] The purpose of this invention is to propose a method for backtracking ship operation data based on error correction algorithms, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: Ship operation data backtracking methods based on error correction algorithms include: Step S1: Set the track monitoring area for the target vessel during its navigation process, analyze the vessel's navigation data based on the track monitoring area, and obtain the optimized vessel track for the target vessel. Step S2: Analyze the optimized ship track, and classify the target ship into categories by combining the historical tracks of historical ships, and obtain historical ships of the same category as the target ship; among them, historical ships are ships of the same model as the target ship that perform the same shipping tasks. Step S3: Analyze the vessel operation data, combine it with the historical operation data of historical vessels of the same category as the target vessel to determine the abnormality of the vessel operation data corresponding to the target vessel and generate the corresponding abnormal signal. Step S4: After receiving the abnormal signal, a ship operation function is constructed based on the historical operation data of historical ships of the same category, and the abnormal ship operation data of the target ship is traced back based on the ship operation function.

[0006] Further, step S1 includes the following sub-steps: Step S101: Obtain the coordinates of the departure point of the target vessel and the coordinates of the destination of the target vessel. Step S102: Compare the departure coordinates with the destination coordinates. If the departure coordinates and the destination coordinates are equal only in the x-coordinate, connect the departure coordinates and the destination coordinates to obtain a vertical baseline. Set the track monitoring range on the left and right sides of the vertical baseline respectively. The track monitoring area is constructed from the track monitoring range on the left and right sides of the vertical baseline. Step S103: If the origin coordinates and destination coordinates are equal only in ordinate, then connect the origin coordinates and destination coordinates to obtain a horizontal baseline. Set track monitoring ranges on the upper and lower sides of the horizontal baseline respectively, and construct the track monitoring area from the track monitoring ranges on both sides of the upper and lower baselines.

[0007] Furthermore, step S1 also includes the following sub-steps: Step S104: If the x-coordinate and y-coordinate of the departure coordinates and the destination coordinates are different, draw a horizontal line and a vertical line at the departure coordinates and the destination coordinates respectively, and record the closed area constructed by the four lines as the track monitoring area. Step S105: Obtain the target vessel's navigation data, and obtain the target vessel's real-time speed, real-time course, and real-time coordinates at each time point; sort the real-time speed, course, and coordinates in sequence according to the time points to obtain the vessel speed sequence, course sequence, and coordinate sequence, respectively.

[0008] Furthermore, step S1 also includes the following sub-steps: Step S106: Identify the positional relationship between the real-time ship coordinates corresponding to each moment in the ship coordinate sequence and the track monitoring area, discard the real-time ship coordinates located outside the track monitoring area, record the corresponding moment as an abnormal moment, and retain the real-time ship coordinates located within the track monitoring area. Step S107: Discard the real-time ship heading at abnormal moments in the ship heading sequence, discard the real-time ship speed at abnormal moments in the ship speed sequence, and use blank characters to place the positions corresponding to the discarded positions in the ship speed sequence, ship heading sequence, and ship coordinate sequence. Step S108: Set the length of the moving window to n, control the moving window to move from the head to the tail of the ship speed sequence, and when the center of the moving window moves to the position of the blank character, add up the real-time ship speeds in the moving window, take the average, and replace the corresponding blank character.

[0009] Furthermore, step S1 also includes the following sub-steps: Step S109: Control the moving window to move from the head to the tail of the ship's heading sequence, and replace the blank characters in the abnormal time in the ship's heading sequence based on step S108; calculate the real-time ship coordinates corresponding to the abnormal time based on the real-time ship speed and real-time ship heading after the abnormal time is replaced, and replace the blank characters corresponding to the abnormal time in the ship coordinate sequence. Step S110: Mark all real-time ship coordinates in the ship coordinate sequence as the optimized ship track corresponding to the target ship.

[0010] Further, step S2 includes the following sub-steps: Step S21: Obtain the optimized ship track of the target ship and get the real-time ship coordinates of the target ship at each moment; Step S22: Calculate the shortest sailing distance using the starting coordinates and the destination coordinates, connect the starting coordinates and the destination coordinates to obtain a straight line, and define this straight line as the shortest sailing straight line for the target ship; Step S23: For the real-time ship coordinates of the target ship at any time, calculate the route offset distance of the target ship at the corresponding time using the distance formula between a point and a line. Step S24: Sum the route offset distances corresponding to all times to obtain the total route offset distance of the target vessel, and divide the total route offset distance by the shortest sailing distance to obtain the sailing offset index of the target vessel.

[0011] Further, step S2 includes the following sub-steps: Step S25: Obtain the historical track of the historical vessel, and calculate the historical offset index of the corresponding historical track based on steps S22-S24. Step S26: Compare the navigation deviation index of the target vessel with the deviation index threshold. If the navigation deviation index of the target vessel is less than the first deviation index threshold, the target vessel is classified as a Class I vessel. If the target vessel's navigation deviation index is greater than or equal to the first deviation index threshold and less than the second deviation index threshold, then the target vessel is classified as a second-class vessel. If the target vessel's navigation deviation index is greater than or equal to the second deviation index threshold, the target vessel will be classified as a third-class vessel. Step S27: Compare the historical offset index of the historical ships with the offset index threshold, and record the historical ships of the same category as the target ship.

[0012] Further, step S3 includes the following sub-steps: Step S31: Obtain the vessel operation data corresponding to the target vessel, and obtain the operation start time, operation end time, and loading / unloading volume of the target vessel; Step S32: Subtract the start time of the operation from the end time of the operation to obtain the operation duration corresponding to the target vessel, and divide the loading and unloading volume by the operation duration to obtain the vessel's operating speed. Step S33: Obtain historical operation data of historical vessels of the same category, and obtain the historical operation speed of the corresponding historical vessels based on step S32.

[0013] Furthermore, step S3 also includes the following sub-steps: Step S34: Add all historical operating speeds to the vessel operating speed, take the average to obtain the mean operating speed, and calculate the standard deviation of the operating speed based on the historical operating speed, the vessel operating speed, and the mean operating speed. Step S35: Calculate the outlier value of the target vessel's operating speed based on the vessel's operating speed, the mean operating speed, and the standard deviation of the operating speed. Step S36: Compare the abnormal operating speed value with the abnormal threshold. If the abnormal operating speed value is greater than or equal to the abnormal threshold, it is determined that there is an abnormality in the vessel operation data corresponding to the target vessel, and an abnormal signal is generated. If the abnormal operating speed value is less than the abnormal threshold, no operation is performed.

[0014] Further, step S4 includes the following sub-steps: Step S41: Using operation time as the independent variable and loading / unloading volume as the dependent variable, construct the initial fitting function corresponding to the target vessel: Loading / unloading volume = First generation × Operation time + Second generation; Step S42: Obtain historical operation data corresponding to historical vessels, and obtain the historical operation duration and historical loading and unloading volume for each historical vessel. Step S43: Combine the historical operation time and historical loading / unloading volume of each historical vessel, and calculate the actual values ​​of the first and second algebras based on the least squares method; substitute the first and second algebras into the initial fitting function to obtain the vessel operation function; Step S44: Obtain the operation duration corresponding to the target vessel, substitute the operation duration into the vessel operation function to obtain the expected loading and unloading volume of the target vessel; replace the loading and unloading volume of the target vessel with the expected loading and unloading volume to realize the backtracking of abnormal data.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention first sets up a tracking area for the target vessel during its voyage, analyzes the vessel's navigation data based on the tracking area, and obtains an optimized vessel track for the target vessel. Then, it analyzes the optimized vessel track and classifies the target vessel into categories by combining it with the historical tracks of historical vessels, thereby obtaining historical vessels of the same category as the target vessel and enabling the screening of historical vessels of the same category as the target vessel. 2. This invention analyzes ship operation data, combines historical operation data of historical ships of the same category as the target ship to determine the anomalies in the target ship's corresponding ship operation data and generates corresponding anomaly signals; finally, after receiving the anomaly signal, a ship operation function is constructed based on the historical operation data of historical ships of the same category, and the ship operation data of the target ship is backtracked for anomalies based on the ship operation function; by identifying the ship operation data of the target ship and ships of the same category and identifying anomalies in the target ship, accurate identification and precise backtracking of anomalies corresponding to ship operation data are achieved. Attached Figure Description

[0016] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the flight track monitoring area in this invention; Figure 3 This is a schematic diagram of the trajectory offset distance in this invention; Figure 4 This is a schematic diagram of the electronic device in this invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Please refer to Figures 1-3 As shown, the technical solution provided by the present invention is: a ship operation data backtracking method based on error correction algorithm, which sets a track monitoring area for the target ship during navigation and optimizes the track of the target ship; combines the optimized track with the historical track identification of historical ships of the same category as the target ship, identifies anomalies based on the ship operation data of the target ship and ships of the same category, and backtracks and optimizes the abnormal ship operation data corresponding to the target ship. It should be noted that the ship operation data specifically refers to the data on the target ship's operations in port, such as loading and unloading cargo.

[0020] In this invention, the method for backtracking ship operation data is specifically as follows: Step S1: Set the track monitoring area for the target vessel during its navigation process, analyze the vessel's navigation data based on the track monitoring area, and obtain the optimized vessel track for the target vessel. In this invention, step S1 includes the following sub-steps: Step S101: Obtain the coordinates of the departure point of the target vessel and the coordinates of the destination of the target vessel. Wherein, the x-coordinate of the departure point coordinates is the longitude of the departure point, and the y-coordinate of the departure point coordinates is the latitude of the departure point; the x-coordinate of the destination coordinates is the longitude of the destination, and the y-coordinate of the destination coordinates is the latitude of the destination. For step S102, please refer to... Figure 2 As shown, the origin coordinates are compared with the destination coordinates. If the origin coordinates and the destination coordinates are only equal in x-coordinate, then the origin coordinates and the destination coordinates are connected to obtain the vertical baseline. The track monitoring range is set on the left and right sides of the vertical baseline respectively. The track monitoring area is constructed by the track monitoring range on the left and right sides of the vertical baseline. Step S103: If the origin coordinates and destination coordinates are equal only in ordinate, then connect the origin coordinates and destination coordinates to obtain a horizontal baseline. Set track monitoring ranges on the upper and lower sides of the horizontal baseline respectively. The track monitoring area is constructed from the track monitoring ranges on both sides of the upper and lower baselines. Step S104: If the x-coordinate and y-coordinate of the departure coordinates and the destination coordinates are different, draw a horizontal line and a vertical line at the departure coordinates and the destination coordinates respectively, and record the closed area constructed by the four lines as the track monitoring area. Step S105: Obtain the target vessel's navigation data to get the target vessel's real-time speed, real-time course, and real-time coordinates at each time point; sort the real-time speed, course, and coordinates in order of time to obtain the vessel speed sequence, course sequence, and coordinate sequence, respectively. Among them, the real-time ship coordinates are the latitude and longitude coordinates of the target ship at the corresponding time; Step S106: Identify the positional relationship between the real-time ship coordinates corresponding to each moment in the ship coordinate sequence and the track monitoring area, discard the real-time ship coordinates located outside the track monitoring area, record the corresponding moment as an abnormal moment, and retain the real-time ship coordinates located within the track monitoring area. Among them, if the real-time ship coordinates are located within the track monitoring area, they are simultaneously located to the left of the right boundary, to the right of the left boundary, below the upper boundary, and above the lower boundary of the track monitoring area; otherwise, the real-time ship coordinates are considered to be outside the track monitoring area. In fact, the positional relationship between the real-time ship coordinates and the track monitoring area is determined by judging the positional relationship between the real-time ship coordinates and the points and lines corresponding to the four boundary lines of the track monitoring area. Step S107: Discard the real-time ship heading at abnormal moments in the ship heading sequence, discard the real-time ship speed at abnormal moments in the ship speed sequence, and use blank characters to place the positions corresponding to the discarded positions in the ship speed sequence, ship heading sequence, and ship coordinate sequence. Step S108: Set the length of the moving window to n, control the moving window to move from the head to the tail of the ship speed sequence, and when the center of the moving window moves to the position of the blank character, add up the real-time ship speeds in the moving window, take the average, and replace the corresponding blank character; where n is a constant. Step S109: Control the moving window to move from the head to the tail of the ship's heading sequence, and replace the blank characters in the abnormal time in the ship's heading sequence based on step S108; calculate the real-time ship coordinates corresponding to the abnormal time based on the real-time ship speed and real-time ship heading after the abnormal time is replaced, and replace the blank characters corresponding to the abnormal time in the ship coordinate sequence. Among them, real-time ship heading refers to the direction in which the bow of the ship points when it is sailing. Specifically, it refers to the angle between the ship and the geographic North Pole in a clockwise direction when it is sailing, and the unit is expressed in degrees (°). It should be noted that the calculation of the real-time ship coordinates corresponding to the abnormal moment can be directly obtained by solving the basic planar motion. That is, given the initial position (the real-time ship coordinates corresponding to the previous moment of the abnormal moment), the velocity (the real-time ship velocity corresponding to the abnormal moment), the duration (the duration between two moments), and the direction of motion (the real-time ship direction corresponding to the abnormal moment), the final position (the real-time ship coordinates corresponding to the abnormal moment) can be solved. Therefore, no further calculation explanation is required. Step S110: Mark all real-time ship coordinates in the ship coordinate sequence as the optimized ship track corresponding to the target ship.

[0021] Step S2: Analyze the optimized ship track and classify the target ship into categories based on the historical tracks of historical ships to obtain historical ships of the same category as the target ship; where historical ships refer to ships of the same model as the target ship that perform the same shipping tasks. In this invention, step S21 includes the following sub-steps: Step S21: Obtain the optimized ship track of the target ship and get the real-time ship coordinates of the target ship at each moment; Step S22: Calculate the shortest sailing distance using the starting coordinates and the destination coordinates, connect the starting coordinates and the destination coordinates to obtain a straight line, and define this straight line as the shortest sailing straight line for the target ship; Step S23: For the real-time ship coordinates of the target ship at any time, calculate the route offset distance of the target ship at the corresponding time using the distance formula between a point and a line. Step S24: Sum the route offset distances corresponding to all times to obtain the total route offset distance of the target vessel, and divide the total route offset distance by the shortest sailing distance to obtain the sailing offset index of the target vessel. Step S25: Obtain the historical track of the historical vessel, and calculate the historical offset index of the corresponding historical track based on steps S22-S24. Step S26: Compare the navigation deviation index of the target vessel with the deviation index threshold. If the navigation deviation index of the target vessel is less than the first deviation index threshold, the target vessel is classified as a Class I vessel. If the navigation deviation index of the target vessel is greater than or equal to the first deviation index threshold and less than the second deviation index threshold, the target vessel is classified as a Class II vessel. If the navigation deviation index of the target vessel is greater than or equal to the second deviation index threshold, the target vessel is classified as a Class III vessel. Among them, the offset index threshold is obtained by calculating the navigation offset index between the historical track and the shortest straight line of navigation for different historical ships; It should be noted that the categories of Class I, Class II, and Class III vessels are based on their deviation from the shortest straight line of navigation. Class I vessels are those with the smallest deviation from the shortest straight line of navigation, Class II vessels are those with the second smallest deviation from the shortest straight line of navigation, and Class III vessels are those with the largest deviation from the shortest straight line of navigation. The first category of vessels is considered to be vessels with no deviation during navigation; the second category of vessels is considered to be vessels with an acceptable deviation during navigation; and the third category of vessels is considered to be vessels with an unacceptable deviation during navigation. Generally speaking, an acceptable deviation will not affect the date of arrival at the end of the voyage, while an unacceptable deviation will affect the date of arrival at the end of the voyage, which may lead to a devaluation of the transported goods. Step S27: Compare the historical offset index of the historical ships with the offset index threshold, and record the historical ships of the same category as the target ship.

[0022] Step S3: Analyze the vessel operation data, combine it with the historical operation data of historical vessels of the same category as the target vessel to determine the abnormality of the vessel operation data corresponding to the target vessel and generate the corresponding abnormal signal. Among them, the ship operation data is loading and unloading data, specifically the start time of operation, end time of operation, and loading and unloading volume after the target ship arrives at the destination; In this invention, step S3 includes the following sub-steps: Step S31: Obtain the vessel operation data corresponding to the target vessel, and obtain the operation start time, operation end time, and loading / unloading volume of the target vessel; Step S32: Subtract the start time of the operation from the end time of the operation to obtain the operation duration corresponding to the target vessel, and divide the loading and unloading volume by the operation duration to obtain the vessel's operating speed. Step S33: Obtain historical operation data of historical vessels of the same category, and obtain the historical operation speed of the corresponding historical vessels based on step S32; Step S34: Add all historical operating speeds to the vessel operating speed, take the average to obtain the mean operating speed, and calculate the standard deviation of the operating speed based on the historical operating speed, the vessel operating speed, and the mean operating speed. Step S35: Calculate the outlier value of the target vessel's operating speed based on the vessel's operating speed, mean operating speed, and standard deviation of operating speed. The specific formula for calculating the outlier value of the operating speed is as follows: Outlier in operating speed = |Operating speed of vessel - Mean operating speed| / Standard deviation of operating speed; Step S36: Compare the abnormal operating speed value with the abnormal threshold. If the abnormal operating speed value is greater than or equal to the abnormal threshold, it is determined that there is an abnormality in the vessel operation data corresponding to the target vessel, and an abnormal signal is generated. If the abnormal operating speed value is less than the abnormal threshold, no operation is performed. Among them, the abnormal threshold is a boundary value obtained by the same calculation process as the abnormal value of the operating speed, which is obtained by calculating the abnormal value of the operating speed of historical vessels; Step S4: After receiving the abnormal signal, a ship operation function is constructed based on the historical operation data of the same type of historical ships, and the abnormal ship operation data of the target ship is traced back based on the ship operation function. In this invention, step S4 includes the following sub-steps: Step S41: Using operation time as the independent variable and loading / unloading volume as the dependent variable, construct the initial fitting function corresponding to the target vessel: Loading / unloading volume = First algebra × Operation time + Second algebra; where the first algebra and the second algebra are constants that need to be obtained by fitting and solving. It should be explained that when the loading and unloading equipment has a constant speed and the operation process is continuous and uninterrupted, the loading and unloading volume and the operation time are linearly related. Under other conditions, the loading and unloading volume and the operation time are approximately linearly related. Therefore, this invention selects a linear function as the fitting function. Step S42: Obtain historical operation data corresponding to historical vessels, and obtain the historical operation duration LSq and historical loading and unloading volume LZq for each historical vessel; where q is the number corresponding to the historical vessel, q=1,2,...,p, and p is a positive integer; Step S43: Combine the historical operation time and historical loading / unloading volume of each historical vessel, and calculate the actual values ​​of the first and second algebras based on the least squares method; substitute the first and second algebras into the initial fitting function to obtain the vessel operation function; The formula for calculating the first algebraic algebra A is: ; The formula for calculating the second algebraic number B is: ; For example, a group of historical vessels have historical operating times of 240 minutes, 60 minutes, 120 minutes, and 120 minutes, corresponding to historical loading and unloading volumes of 800 tons, 600 tons, 2000 tons, and 900 tons, respectively. The calculation based on the first algebraic formula yields A = (4 × 576000 - 540 × 4300) / (4 × 90000 - 540). 2 = (2304000-2322000) / (360000-291600) = -18000 / 68400 ≈ -0.2632; The formula calculated based on second algebra yields B = (4300 - 540 × -0.2632) / 4 = 1110.532; Therefore, the ship operation function is: Y = -0.2632X + 1110.532; Step S44: Obtain the operation duration corresponding to the target vessel, substitute the operation duration into the vessel operation function to obtain the expected loading and unloading volume of the target vessel; replace the loading and unloading volume of the target vessel with the expected loading and unloading volume to realize the backtracking of abnormal data.

[0023] Example 2: As Figure 4As shown, this embodiment provides an electronic device, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The processor can call logical instructions in the memory to execute a ship operation data backtracking method based on an error correction algorithm. This method includes: setting a tracking area for the target ship during its navigation; analyzing the ship's navigation data based on the tracking area to obtain an optimized ship track for the target ship; analyzing the optimized ship track and classifying the target ship into categories based on historical ship tracks to obtain historical ships of the same category as the target ship; analyzing the ship operation data and determining anomalies in the target ship's corresponding ship operation data based on historical operation data of historical ships of the same category as the target ship, and generating corresponding anomaly signals; upon receiving an anomaly signal, constructing a ship operation function based on historical operation data of historical ships of the same category, and performing anomaly backtracking on the target ship's ship operation data based on the ship operation function.

[0024] Furthermore, when the logical instructions in the aforementioned memory can be implemented as 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0025] On the other hand, this application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the ship operation data backtracking method based on the error correction algorithm provided by the above methods. The method includes: setting a track monitoring area for the target ship during navigation; analyzing the ship navigation data based on the track monitoring area to obtain an optimized ship track for the target ship; analyzing the optimized ship track and classifying the target ship into categories based on the historical tracks of historical ships to obtain historical ships of the same category as the target ship; analyzing the ship operation data and judging the abnormality of the ship operation data corresponding to the target ship based on the historical operation data of historical ships of the same category as the target ship and generating a corresponding abnormal signal; after receiving the abnormal signal, constructing a ship operation function based on the historical operation data of historical ships of the same category, and performing abnormal backtracking on the ship operation data of the target ship based on the ship operation function.

[0026] In another aspect, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned ship operation data backtracking methods based on error correction algorithms. The method includes: setting a track monitoring area for a target ship during its navigation process; analyzing ship navigation data based on the track monitoring area to obtain an optimized ship track for the target ship; analyzing the optimized ship track and classifying the target ship into categories based on historical ship tracks to obtain historical ships of the same category as the target ship; analyzing ship operation data and determining abnormalities in the target ship's corresponding ship operation data based on historical operation data of historical ships of the same category as the target ship, and generating corresponding abnormal signals; upon receiving an abnormal signal, constructing a ship operation function based on historical operation data of historical ships of the same category, and performing abnormal backtracking of the target ship's ship operation data based on the ship operation function.

[0027] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. 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 the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0028] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0029] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for backtracking ship operation data based on error correction algorithms, characterized in that, include: Step S1: Set the track monitoring area for the target vessel during its navigation process, analyze the vessel's navigation data based on the track monitoring area, and obtain the optimized vessel track for the target vessel. Step S2: Analyze the optimized ship track and classify the target ship into categories based on the historical tracks of historical ships to obtain historical ships of the same category as the target ship; among them, historical ships are ships of the same model as the target ship that perform the same shipping tasks. Step S3: Analyze the ship operation data, combine it with the historical operation data of historical ships of the same category as the target ship to determine the abnormality of the ship operation data corresponding to the target ship and generate the corresponding abnormal signal. Step S4: After receiving the abnormal signal, a ship operation function is constructed based on the historical operation data of historical ships of the same category, and the abnormal ship operation data of the target ship is traced back based on the ship operation function.

2. The ship operation data backtracking method based on error correction algorithm according to claim 1, characterized in that, Step S1 includes the following sub-steps: Step S101: Obtain the coordinates of the departure point of the target vessel and the coordinates of the destination of the target vessel. Step S102: Compare the departure coordinates with the destination coordinates. If the departure coordinates and the destination coordinates are equal only in the x-coordinate, then connect the departure coordinates and the destination coordinates to obtain a vertical baseline. Set the track monitoring range on the left and right sides of the vertical baseline respectively. The track monitoring area is constructed from the track monitoring range on the left and right sides of the vertical baseline. Step S103: If the origin coordinates and destination coordinates are equal only in ordinate, then connect the origin coordinates and destination coordinates to obtain a horizontal baseline. Set track monitoring ranges on the upper and lower sides of the horizontal baseline respectively, and construct the track monitoring area from the track monitoring ranges on both sides of the upper and lower baselines.

3. The ship operation data backtracking method based on error correction algorithm according to claim 2, characterized in that, Step S1 further includes the following sub-steps: Step S104: If the x-coordinate and y-coordinate of the departure coordinates and the destination coordinates are different, draw a horizontal line and a vertical line at the departure coordinates and the destination coordinates respectively, and record the closed area constructed by the four lines as the track monitoring area. Step S105: Obtain the target vessel's navigation data to obtain the target vessel's real-time speed, real-time course, and real-time coordinates at each time point; sort the real-time speed, course, and coordinates in sequence according to the time period to obtain the vessel speed sequence, course sequence, and coordinate sequence, respectively.

4. The ship operation data backtracking method based on error correction algorithm according to claim 3, characterized in that, Step S1 further includes the following sub-steps: Step S106: Identify the positional relationship between the real-time ship coordinates corresponding to each moment in the ship coordinate sequence and the track monitoring area, discard the real-time ship coordinates located outside the track monitoring area, record the corresponding moment as an abnormal moment, and retain the real-time ship coordinates located within the track monitoring area. Step S107: Discard the real-time ship heading at abnormal moments in the ship heading sequence, discard the real-time ship speed at abnormal moments in the ship speed sequence, and place the discarded positions in the ship speed sequence, ship heading sequence, and ship coordinate sequence using blank characters. Step S108: Set the length of the moving window to n, control the moving window to move from the head to the tail of the ship speed sequence, and when the center of the moving window moves to the position of the blank character, add up the real-time ship speeds in the moving window, take the average, and replace the corresponding blank character.

5. The ship operation data backtracking method based on error correction algorithm according to claim 4, characterized in that, Step S1 further includes the following sub-steps: Step S109: Control the moving window to move from the head to the tail of the ship's heading sequence, and replace the blank characters in the abnormal time in the ship's heading sequence based on step S108; calculate the real-time ship coordinates corresponding to the abnormal time based on the real-time ship speed and real-time ship heading after the abnormal time is replaced, and replace the blank characters corresponding to the abnormal time in the ship coordinate sequence. Step S110: Mark all real-time ship coordinates in the ship coordinate sequence as the optimized ship track corresponding to the target ship.

6. The ship operation data backtracking method based on error correction algorithm according to claim 1, characterized in that, Step S2 includes the following sub-steps: Step S21: Obtain the optimized ship track of the target ship and get the real-time ship coordinates of the target ship at each moment; Step S22: Calculate the shortest sailing distance using the starting coordinates and the destination coordinates, connect the starting coordinates and the destination coordinates to obtain a straight line, and define this straight line as the shortest sailing straight line for the target ship; Step S23: For the real-time ship coordinates of the target ship at any time, calculate the route offset distance of the target ship at the corresponding time using the distance formula between a point and a line. Step S24: Sum the route offset distances corresponding to all times to obtain the total route offset distance of the target vessel, and divide the total route offset distance by the shortest sailing distance to obtain the sailing offset index of the target vessel.

7. The ship operation data backtracking method based on error correction algorithm according to claim 6, characterized in that, Step S2 includes the following sub-steps: Step S25: Obtain the historical track of the historical vessel, and calculate the historical offset index of the corresponding historical track based on steps S22-S24. Step S26: Compare the navigation deviation index of the target vessel with the deviation index threshold. If the navigation deviation index of the target vessel is less than the first deviation index threshold, the target vessel is classified as a Class I vessel. If the target vessel's navigation deviation index is greater than or equal to the first deviation index threshold and less than the second deviation index threshold, then the target vessel is classified as a second-class vessel. If the target vessel's navigation deviation index is greater than or equal to the second deviation index threshold, the target vessel will be classified as a third-class vessel. Step S27: Compare the historical offset index of the historical ships with the offset index threshold, and record the historical ships of the same category as the target ship.

8. The ship operation data backtracking method based on error correction algorithm according to claim 7, characterized in that, Step S3 includes the following sub-steps: Step S31: Obtain the vessel operation data corresponding to the target vessel, and obtain the operation start time, operation end time, and loading / unloading volume of the target vessel; Step S32: Subtract the start time of the operation from the end time of the operation to obtain the operation duration corresponding to the target vessel, and divide the loading and unloading volume by the operation duration to obtain the vessel's operating speed. Step S33: Obtain historical operation data of historical vessels of the same category, and obtain the historical operation speed of the corresponding historical vessels based on step S32.

9. The ship operation data backtracking method based on error correction algorithm according to claim 8, characterized in that, Step S3 further includes the following sub-steps: Step S34: Add all historical operating speeds to the vessel operating speed, take the average to obtain the mean operating speed, and calculate the standard deviation of the operating speed based on the historical operating speed, the vessel operating speed, and the mean operating speed. Step S35: Calculate the outlier value of the target vessel's operating speed based on the vessel's operating speed, the mean operating speed, and the standard deviation of the operating speed. Step S36: Compare the abnormal operating speed value with the abnormal threshold. If the abnormal operating speed value is greater than or equal to the abnormal threshold, it is determined that there is an abnormality in the vessel operation data corresponding to the target vessel, and an abnormal signal is generated. If the abnormal operating speed value is less than the abnormal threshold, no operation is performed.

10. The ship operation data backtracking method based on error correction algorithm according to claim 9, characterized in that, Step S4 includes the following sub-steps: Step S41: Construct an initial fitting function for the target vessel with operation duration as the independent variable and loading / unloading volume as the dependent variable. Step S42: Obtain historical operation data corresponding to historical vessels, and obtain the historical operation duration and historical loading and unloading volume for each historical vessel. Step S43: Combine the historical operation time and historical loading / unloading volume of each historical vessel, and calculate the actual values ​​of the first and second algebras based on the least squares method; substitute the first and second algebras into the initial fitting function to obtain the vessel operation function; Step S44: Obtain the operation duration corresponding to the target vessel, substitute the operation duration into the vessel operation function to obtain the expected loading and unloading volume of the target vessel; replace the loading and unloading volume of the target vessel with the expected loading and unloading volume to realize the backtracking of abnormal data.

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