Method and device for reconstructing drift trajectory of target object, electronic equipment and storage medium

Abstract

The invention relates to a drift trajectory reconstruction method and device of a target object, electronic equipment and a storage medium, and the method comprises the steps: converting a coordinate of a preset starting point of the target object into a geographic coordinate in a target coordinate system; determining the row number of the preset starting point in the sea ice drift data grid, determining the speed of the target object based on the row number, and determining the inference point location of the target object based on the speed of the target object and the preset starting point coordinate; updating a next inferred point location of the target object based on a preset time interval, and recording an accumulated inferred point location of the target object until a preset termination condition is reached; and obtaining a reconstruction trajectory coordinate of the target object based on the accumulated inference point positions updated each time, and drawing a trajectory result map based on the reconstruction trajectory coordinate. Therefore, the technical problems that the search range is large, the cost is high, consumed time is unstable, long-term research is not facilitated and the like due to the fact that the target object is replied in the mode of reporting outward expansion of the positioning point for the last time in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of marine sea ice motion technology, and in particular to a method, apparatus, electronic device and storage medium for reconstructing the drift trajectory of a target object. Background Technology

[0002] Sea ice is an important observation target for environmental factors in the North and South Poles. The movement of sea ice has always affected global climate change. In the polar environment, on the one hand, the movement of sea ice is observed in the field by setting up buoys. For example, thousands of buoys have been deployed in the North and South Poles to observe the movement of sea ice. However, the high cost of the buoys often leads to loss of signal after drifting with the ice for a period of time, and the recovery of the buoys has become a major problem.

[0003] In related technologies, buoy recovery often involves searching outwards from the last reported positioning point, which is costly, time-consuming, and not conducive to long-term research, and needs improvement. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for reconstructing the drift trajectory of a target object, in order to solve the technical problems in related technologies, such as the large search range, high cost, unstable time consumption, and unfavorable conditions for long-term research, in which the target object is recovered by expanding outward from the last reported positioning point.

[0005] The first aspect of this application provides a method for reconstructing the drift trajectory of a target object, comprising the following steps: converting the coordinates of a preset starting point of the target object into geographic coordinates in a target coordinate system; determining the row and column number of the preset starting point in a sea ice drift data grid, determining the velocity of the target object based on the row and column number, and determining the inferred position of the target object based on the velocity of the target object and the coordinates of the preset starting point; updating the next inferred position of the target object based on a preset time interval, and recording the cumulative inferred position of the target object until a preset termination condition is reached; obtaining the reconstructed trajectory coordinates of the target object based on the cumulative inferred position updated each time, and drawing a trajectory result map based on the reconstructed trajectory coordinates.

[0006] Optionally, in one embodiment of this application, determining the target velocity based on the row and column numbers includes: reading the sea ice drift vector of the target based on the row and column numbers; and obtaining the target velocity based on the sea ice drift vector.

[0007] Optionally, in one embodiment of this application, determining the target velocity based on the row and column numbers includes: obtaining the sea ice drift vectors of the four corner points of the row and column number where the preset starting point is located; and obtaining the target velocity based on the four corner point sea ice drift vectors.

[0008] Optionally, in one embodiment of this application, the step of updating the next inferred point of the target object based on a preset time interval and recording the cumulative inferred point of the target object until a preset termination condition is met includes: recording the cumulative duration of updating the inferred point of the target object; determining whether the cumulative duration meets a preset day-change condition; if the preset day-change condition is met, obtaining the seawater drift data for the new day and using the seawater drift data for the new day to update the inferred point until the preset termination condition is met.

[0009] Optionally, in one embodiment of this application, after acquiring the seawater drift data for a new day, the method further includes: processing the seawater drift data to obtain velocity component data that meets preset format conditions, and using the velocity component data to update the inferred point position.

[0010] A second aspect of this application provides a device for reconstructing the drift trajectory of a target object, comprising: a conversion module for converting the coordinates of a preset starting point of the target object into geographic coordinates in a target coordinate system; a determination module for determining the row and column number of the preset starting point in a sea ice drift data grid, determining the velocity of the target object based on the row and column number, and determining the inferred position of the target object based on the velocity of the target object and the coordinates of the preset starting point; an update module for updating the next inferred position of the target object based on a preset time interval, and recording the cumulative inferred position of the target object until a preset termination condition is reached; and a reconstruction module for obtaining the reconstructed trajectory coordinates of the target object based on the cumulative inferred position updated each time, and drawing a trajectory result map based on the reconstructed trajectory coordinates.

[0011] Optionally, in one embodiment of this application, the determining module includes: a reading unit, configured to read the sea ice drift vector of the target object based on the row and column numbers; and a first calculation unit, configured to obtain the velocity of the target object based on the sea ice drift vector.

[0012] Optionally, in one embodiment of this application, the determining module includes: an acquisition unit, configured to acquire the sea ice drift vectors of the four corner points of the row and column number of the preset starting point; and a second calculation unit, configured to obtain the velocity of the target object based on the four corner point sea ice drift vectors.

[0013] Optionally, in one embodiment of this application, the update module includes: an accumulation unit for recording the cumulative duration of the inferred location update of the target object; and a judgment unit for judging whether the cumulative duration meets the preset day-to-day condition.

[0014] The update unit is used to acquire the seawater drift data of the new day when the preset day-change conditions are met, and to update the inferred point position using the seawater drift data of the new day until the preset termination condition is reached.

[0015] Optionally, in one embodiment of this application, the update module further includes: a processing unit, configured to process the seawater drift data to obtain velocity component data that meets preset format conditions, and to update the inferred point using the velocity component data.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the target trajectory reconstruction method as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the drift trajectory reconstruction method for a target object as described in the above embodiments.

[0018] A fifth aspect of this application provides a computer program product, including a computer program, which, when executed, is used to implement the above-described method for reconstructing the drift trajectory of a target object.

[0019] This application's embodiments can determine the target object's velocity and inferred position based on the row and column number of the target object's preset starting point in the sea ice drift data grid. The next inferred position is updated at preset time intervals, and the cumulative inferred position is recorded. The reconstructed trajectory coordinates of the target object are obtained using each updated cumulative inferred position, and a trajectory result map is plotted. By directly utilizing long-term continuous observation data covering both poles, this method overcomes the limitations of traditional buoy trajectory inversion methods, improves trajectory inference accuracy, significantly enhances target tracing capabilities, and provides reliable technical support for polar environmental monitoring. This solves the technical problems in related technologies, such as the large search range, high cost, unstable time consumption, and unfavorable conditions for long-term research, which rely on expanding outwards from the last reported positioning point to retrieve the target object.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 This is a flowchart of a method for reconstructing the drift trajectory of a target object according to an embodiment of this application;

[0023] Figure 2This is a schematic diagram of sea ice drift data in CSV format provided according to an embodiment of this application;

[0024] Figure 3 This is a schematic diagram of a parameter selection interface provided according to an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of sea ice drift data when calculating the velocity of a target object using two different interpolation methods provided in one embodiment of this application;

[0026] Figure 5 This is a schematic diagram showing the comparison between the trajectory reconstruction result provided according to an embodiment of this application and the actual trajectory of the target object;

[0027] Figure 6a This is a schematic diagram showing the comparison between the endpoint of the trajectory reconstruction result and the actual endpoint of the target object according to an embodiment of this application;

[0028] Figure 6b This is a trajectory data diagram showing the endpoint of the trajectory reconstruction result and the actual endpoint of the target object according to an embodiment of this application;

[0029] Figure 7 This is a schematic diagram of a target trajectory reconstruction device according to an embodiment of this application;

[0030] Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0031] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0032] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for reconstructing the drift trajectory of a target object according to embodiments of this application. Addressing the technical problems mentioned in the background art, where the method of reconstructing a target object by expanding outwards from the last reported positioning point results in a large search range, high cost, unstable time consumption, and is unsuitable for long-term research, this application provides a method for reconstructing the drift trajectory of a target object. In this method, the target object's velocity is determined based on its preset starting point's row and column number in a sea ice drift data grid, and the inferred position of the target object is determined. The next inferred position of the target object is updated at preset time intervals, and the cumulative inferred position of the target object is recorded. The reconstructed trajectory coordinates of the target object are obtained using each updated cumulative inferred position, and a trajectory result diagram is drawn. By directly utilizing long-term continuous observation data covering both poles, this method overcomes the limitations of traditional buoy trajectory back-calculation methods, improves trajectory inference accuracy, significantly enhances target tracing capabilities, and provides reliable technical support for polar environment monitoring. Therefore, this method solves the technical problems in related technologies, such as the large search range, high cost, unstable time consumption, and unsuitability for long-term research, associated with reconstructing a target object by expanding outwards from the last reported positioning point.

[0033] Specifically, Figure 1 This is a flowchart illustrating a method for reconstructing the drift trajectory of a target object, as provided in an embodiment of this application.

[0034] like Figure 1 As shown, the method for reconstructing the drift trajectory of the target object includes the following steps:

[0035] In step S101, the coordinates of the preset starting point of the target object are converted into geographic coordinates in the target coordinate system.

[0036] Understandably, sea ice movement is one aspect of the study of the polar environments, but the trajectory of buoys and the origin of materials affected by sea ice movement have become major challenges.

[0037] To address the aforementioned issues, embodiments of this application can reconstruct the drift trajectory of a target object (such as a buoy or other material) based on sea ice drift data. First, embodiments of this application can select a starting point (latitude and longitude) and a starting time, and determine whether to infer the source trajectory forward or the tracking target backward, depending on the target object or the target being traced.

[0038] Furthermore, embodiments of this application can convert the starting point's latitude and longitude coordinates (Lon, Lat) into geographic coordinates in the target coordinate system, for example, into geographic coordinates (X,Y) in the EPSG:3408 (North Pole) or EPSG:3409 (South Pole) coordinate system.

[0039] When performing coordinate transformation, you can use the coordinate transformation encapsulation function in the open-source software package GDAL, which allows you to specify the EPSG number of the target coordinate system.

[0040] In step S102, the row and column numbers of the preset starting point in the sea ice drift data grid are determined, and the velocity (v) of the target object is determined based on the row and column numbers. x ,v y ), and based on the target object's velocity and the starting point coordinates (x k ,y k (At the starting point k=1) and the preset time interval t, the inferred location of the target object is determined according to the following formula:

[0041] Understandably, sea ice drift data grids are a core tool in polar research for systematically analyzing the movement patterns of sea ice. Their construction integrates numerical simulation and remote sensing observation technologies, achieving spatial resolution down to the kilometer level. In sea ice drift data grids, row and column numbers are key parameters used to uniquely identify the position of each grid cell in two-dimensional space.

[0042] Taking the first inferred point as an example, this embodiment of the application can determine the row and column number (Col, Row) of the starting point in the sea ice drift data grid, and select the corresponding interpolation method to obtain the velocity of the target object. Thus, the first inferred point of the target object is determined based on the velocity of the target object and the coordinates of the starting point, and the first inferred point is used as the new starting point coordinates in the subsequent inference point process.

[0043] Optionally, in one embodiment of this application, determining the velocity of a target object based on its row and column numbers includes: reading the sea ice drift vector of the target object based on its row and column numbers; and obtaining the velocity of the target object based on its sea ice drift vector.

[0044] The Nearest Neighbor Method (NNM) finds the nearest data point to a target point in a dataset and assigns its attribute value to the target point. In sea ice drift data grids, the NNM can be used for spatial interpolation and data missing completion. For example, in Arctic sea ice monitoring, sensor malfunctions or cloud cover may cause data loss in some grid cells. In this case, the NNM can be used to find known data grid cells adjacent to the missing data cell and assign their data values ​​to the missing data cell, thus achieving data missing completion.

[0045] When the nearest neighbor method is selected as the difference method, the embodiments of this application can directly read the sea ice drift vector as the target velocity (v) through the row and column numbers. x v y ).

[0046] Optionally, in one embodiment of this application, determining the velocity of the target object based on the row and column numbers includes: obtaining the sea ice drift vectors of the four corner points of the row and column number of the preset starting point; and obtaining the velocity of the target object based on the sea ice drift vectors of the four corner points.

[0047] Bilinear interpolation estimates the value of a target point by taking a linear weighted average of the values ​​of its four nearest neighbor data points. In sea ice drift data grids, bilinear interpolation can be used to interpolate discrete sea ice drift data into a continuous spatial field. For example, when estimating the sea ice drift vector at the center point of a grid cell, the four closest known sea ice drift data points to that center point can be found, and the estimated sea ice drift vector at that center point can be calculated using bilinear interpolation.

[0048] When bilinear interpolation is selected as the interpolation method, the embodiments of this application can treat sea ice drift data as points, read the sea ice drift vectors at the four corner points of the row and column numbers, and perform bilinear interpolation to obtain the target velocity (v). x v y ).

[0049] In the embodiments of this application, the accuracy of the two interpolation methods described above varies slightly in different regions and time periods, and can be selected according to actual needs.

[0050] In step S103, the next inferred point of the target object is updated based on a preset time interval, and the cumulative inferred point of the target object is recorded until a preset termination condition is met.

[0051] Furthermore, in this embodiment, the above steps can be repeated at certain time intervals to update the next inferred point until a certain termination condition is met, such as the forward or backward push duration reaching the cumulative update duration.

[0052] The preset time interval can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0053] Optionally, in one embodiment of this application, the next inferred point of the target object is updated based on a preset time interval, and the cumulative inferred point of the target object is recorded until a preset termination condition is met. This includes: recording the cumulative duration of the inferred point update of the target object; determining whether the cumulative duration meets a preset day-change condition; if the preset day-change condition is met, obtaining the seawater drift data for the new day, and using the seawater drift data for the new day to update the inferred point until the preset termination condition is met.

[0054] As one possible implementation method, this application embodiment can continuously accumulate time after setting the starting point to obtain the accumulated duration, and determine whether the time has entered the next day, i.e. whether the conditions for the next day are met, based on the accumulated duration. After entering the next day, the seawater drift data of the new day is obtained, and the inferred point position is updated using the seawater drift data of the new day.

[0055] Optionally, in one embodiment of this application, after acquiring the seawater drift data for a new day, the method further includes: processing the seawater drift data to obtain velocity component data that meets preset format conditions, and using the velocity component data to update the inferred point position.

[0056] It should be noted that the embodiments of this application can convert the NetCDF format sea ice drift data and save it as a CSV file, retaining only the two velocity components of sea ice drift (v). x v y ( ), making it easy to read.

[0057] The pre-converted sea ice drift data can come from the National Snow and Ice Data Center (NSIDC), which contains daily and weekly sea ice motion vectors, as well as browsing images representing weekly data. Input data sources include AVHRR, AMSRE, SMMR, SSMI, and SSMI / S sensors; IABP buoys; and NCEP / NCAR reanalysis predictions with a spatial resolution of 25 km × 25 km for generating daily and weekly sea ice motion estimates.

[0058] In step S104, the reconstructed trajectory coordinates of the target object are obtained based on the cumulative inferred points of each update, and the trajectory result map is drawn based on the reconstructed trajectory coordinates.

[0059] In actual implementation, the embodiments of this application can reconstruct the trajectory coordinates based on the inferred points updated each time, and use the reconstructed trajectory coordinates to draw the trajectory result map.

[0060] This application uses NSIDC sea ice drift data as the primary data source. Regardless of the availability of sea ice drift observation data, it can calculate the trajectory of a target object drifting with the sea ice. The NSIDC sea ice drift data covers both the North and South Poles, making this application highly applicable to various sea areas. The NSIDC sea ice drift data used in this application spans a long period and is still being updated. This invention can be applied to both searching for previously lost targets and tracing the origin of discovered targets. This application directly uses NSIDC sea ice drift data for calculations, which is more accurate and universally applicable compared to previous indirect methods based on buoy trajectories. Figure 2Figure 6 illustrates in detail the working principle of the target object drift trajectory reconstruction method of this application embodiment using a specific example.

[0061] For example, the trajectory of the Russian icebreaker "North Pole" during its unpowered drift in the Arctic Ocean from October 3, 2022 to December 31, 2022 was reconstructed. The starting point coordinates (Lon, Lat) were set as (82°36′21″N, 155°43′50″E) based on the location of the "North Pole" icebreaker on October 3, 2022. The starting time was set to October 3, 2022. The interpolation method was nearest neighbor difference (bilinear interpolation), the calculation direction was positive, and the forward time was 90 days. The files retrieved were the eastward and northward drift velocities of sea ice from NSIDC sea ice drift data (October 3, 2022 - December 31, 2022).

[0062] Step S1, as follows Figure 2 As shown, this embodiment of the application can convert NetCDF format sea ice drift data and save it as CSV format for easy reading. Based on the NSIDC sea ice drift data resolution (25km×25km) and the geographic coordinates of the corner points (taking the upper left corner as an example, the corner coordinates are (-4512154.5, 4512154.5)), the grid size of the CSV format data is confirmed to be 361×361. The grid only retains the eastward drift velocity and northward drift velocity "v" from the original sea ice drift data. x :v y "Open water areas are saved as empty data "--:--".

[0063] Step S2, as follows Figure 3 As shown, in this embodiment of the application, the starting point coordinates (Lon, Lat) can be selected as (82°36′21″N, 155°43′50″E), the starting time can be set as October 3, 2022, the interpolation method can be Nearest or Bilinear, the calculation direction can be positive, and the forward time length can be 90 days.

[0064] Step S3: Convert the starting point's latitude and longitude coordinates (Lon, Lat) to geographic coordinates (x, y) in the EPSG:3408 (North Pole) coordinate system. start ,y start ).

[0065] Step S4, as follows Figure 4As shown in (a) of this application embodiment, the row and column numbers (Col, Row) of the starting point in the sea ice drift data grid can be determined according to formula (1), where Floor() is a floor function. If the selected interpolation method is the nearest neighbor method, the sea ice drift vector is directly read as the initial velocity of the target object through the row and column numbers.

[0066]

[0067] like Figure 4 As shown in (b), if the selected interpolation method is bilinear interpolation: the sea ice drift data is treated as points, and the sea ice drift vectors of the four corner points of the row and column number of the starting point are read. The initial velocity (v) of the target object is obtained by bilinear interpolation according to the formulas in (2). x v y ):

[0068]

[0069] Where w1, w2, w3, and w4 represent the velocity weights of the four corner points during bilinear interpolation.

[0070] Step S5, in this embodiment of the application, the position (x) of the next point can be obtained by extrapolation according to the formula (3) at a preset time interval. k ,y k Repeat step S4 and record the cumulative duration t. k It determines whether the time has entered the next day, and if so, reads the sea ice drift data for the new day.

[0071]

[0072] Step S6: Repeat steps S4 and S5 until the cumulative duration t. k The forward push will stop when the preset forward or backward push time length of 90 days is reached.

[0073] Step S7, as shown in Table 1, the embodiments of this application can output the coordinates of the reconstructed trajectory points, wherein Table 1 is a table of coordinates of the reconstructed trajectory points.

[0074] Table 1

[0075] longitude latitude date 155.644 82.6104 2022-10-03 155.615 82.6061 2022-10-04 155.587 82.6018 2022-10-04 155.558 82.5976 2022-10-04 155.529 82.5933 2022-10-04 155.5 82.5891 2022-10-04 155.471 82.5848 2022-10-04 155.458 82.5848 2022-10-05 155.445 82.5847 2022-10-05

[0076] like Figure 5 The image shows the reconstructed trajectory, where the red line represents the reconstructed trajectory, the blue line represents the actual trajectory of the target object, and the right side shows the trajectory coordinates.

[0077] like Figure 6a and Figure 6bAs shown, in this embodiment of the application, the reconstructed trajectory endpoint of the target object from October 3, 2022 to December 31, 2022 differs from the actual trajectory endpoint of the target object by 8 kilometers. The target object moved a total of 910 kilometers from October 3, 2022 to December 31, 2022, with a relative error within 1%.

[0078] The target trajectory reconstruction method proposed in this application can determine the target's velocity and inferred position based on the row and column number of the target's preset starting point in the sea ice drift data grid. The method updates the next inferred position of the target at preset time intervals and records the cumulative inferred positions. The reconstructed trajectory coordinates of the target are obtained using each updated cumulative inferred position, and a trajectory result map is plotted. By directly utilizing long-term continuous observation data covering both poles, this method overcomes the limitations of traditional buoy trajectory inversion methods, improves trajectory inference accuracy, significantly enhances target tracing capabilities, and provides reliable technical support for polar environmental monitoring. This solves the technical problems in related technologies, such as the large search range, high cost, unstable time consumption, and unfavorable conditions for long-term research, caused by expanding outwards from the last reported positioning point to retrieve the target.

[0079] Next, the drift trajectory reconstruction apparatus for a target object according to an embodiment of this application is described with reference to the accompanying drawings.

[0080] Figure 7 This is a block diagram of a target object drift trajectory reconstruction device according to an embodiment of this application.

[0081] like Figure 7 As shown, the drift trajectory reconstruction device 10 for the target object includes: a conversion module 100, a determination module 200, an update module 300, and a reconstruction module 400.

[0082] Specifically, the conversion module 100 is used to convert the coordinates of the preset starting point of the target object into geographic coordinates in the target coordinate system.

[0083] The determination module 200 is used to determine the row and column number of the preset starting point in the sea ice drift data grid, determine the velocity of the target object based on the row and column number, and determine the inferred position of the target object based on the velocity of the target object and the coordinates of the preset starting point.

[0084] The update module 300 is used to update the next inference point of the target object based on a preset time interval and record the cumulative inference point of the target object until the preset termination condition is reached.

[0085] The reconstruction module 400 is used to obtain the reconstructed trajectory coordinates of the target object based on the cumulative inferred points of each update, and to draw the trajectory result map based on the reconstructed trajectory coordinates.

[0086] Optionally, in one embodiment of this application, the determining module 200 includes a reading unit and a first calculation unit.

[0087] The reading unit is used to read the sea ice drift vector of the target object based on the row and column numbers.

[0088] The first calculation unit is used to obtain the velocity of the target object based on the sea ice drift vector.

[0089] Optionally, in one embodiment of this application, the determining module 200 includes: an acquisition unit and a second calculation unit.

[0090] The acquisition unit is used to acquire the sea ice drift vectors of the four corner points of the preset starting point in the row and column number.

[0091] The second calculation unit is used to obtain the velocity of the target object based on the sea ice drift vectors at the four corner points.

[0092] Optionally, in one embodiment of this application, the update module 300 includes: an accumulation unit, a judgment unit, and an update unit.

[0093] The cumulative unit is used to record the cumulative duration of the inferred location update of the target object.

[0094] The judgment unit is used to determine whether the cumulative duration meets the preset conditions for the next day.

[0095] The update unit is used to acquire the seawater drift data of the new day when the preset conditions for the next day are met, and to update the inferred point position using the seawater drift data of the new day until the preset termination conditions are met.

[0096] Optionally, in one embodiment of this application, the update module 300 further includes a processing unit.

[0097] The processing unit is used to process the seawater drift data to obtain velocity component data that meets the preset format conditions, and to update the inferred point position using the velocity component data.

[0098] It should be noted that the explanation of the above-described method for reconstructing the drift trajectory of a target object also applies to the device for reconstructing the drift trajectory of the target object in this embodiment, and will not be repeated here.

[0099] The target drift trajectory reconstruction device proposed in this application can determine the target velocity and inferred position based on the row and column number of the target's preset starting point in the sea ice drift data grid. It updates the next inferred position of the target at preset time intervals and records the cumulative inferred positions. The reconstructed trajectory coordinates of the target are obtained using each updated cumulative inferred position, and a trajectory result map is plotted. By directly utilizing long-term continuous observation data covering both poles, it overcomes the limitations of traditional buoy trajectory back-calculation methods, improves trajectory inference accuracy, significantly enhances target tracing capabilities, and provides reliable technical support for polar environment monitoring. This solves the technical problems in related technologies, such as the large search range, high cost, unstable time consumption, and unfavorable conditions for long-term research, caused by expanding outwards from the last reported positioning point to recover the target.

[0100] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0101] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0102] When the processor 802 executes the program, it implements the method for reconstructing the drift trajectory of the target object provided in the above embodiments.

[0103] Furthermore, electronic devices also include:

[0104] Communication interface 803 is used for communication between memory 801 and processor 802.

[0105] The memory 801 is used to store computer programs that can run on the processor 802.

[0106] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0107] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0108] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0109] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0110] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for reconstructing the drift trajectory of a target object.

[0111] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method for reconstructing the drift trajectory of a target object provided in this embodiment of the invention.

[0112] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0113] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0114] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0115] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0116] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0117] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0118] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0119] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for reconstructing the drift trajectory of a target object, characterized in that, Includes the following steps: Convert the coordinates of the target object's preset starting point into geographic coordinates in the target coordinate system; Determine the row and column number of the preset starting point in the sea ice drift data grid, determine the velocity of the target object based on the row and column number, and determine the inferred position of the target object based on the velocity of the target object and the coordinates of the preset starting point; The next inferred point of the target object is updated based on a preset time interval, and the cumulative inferred point of the target object is recorded until a preset termination condition is met. The reconstructed trajectory coordinates of the target object are obtained based on the cumulative inferred points of each update, and the trajectory result map is drawn based on the reconstructed trajectory coordinates.

2. The method according to claim 1, characterized in that, Determining the target velocity based on the row and column numbers includes: Read the sea ice drift vector of the target object based on the row and column numbers; The velocity of the target object is obtained based on the sea ice drift vector.

3. The method according to claim 1, characterized in that, Determining the target velocity based on the row and column numbers includes: Obtain the sea ice drift vectors of the four corner points at the row and column numbers of the preset starting point; The velocity of the target object is obtained based on the sea ice drift vectors at the four corner points.

4. The method according to claim 1, characterized in that, The step of updating the next inferred point of the target object based on a preset time interval and recording the cumulative inferred point of the target object until a preset termination condition is met includes: Record the cumulative time for updating the inferred location of the target object; Determine whether the accumulated duration meets the preset day-to-day condition; If the preset conditions for the next day are met, the seawater drift data for the new day is obtained, and the inferred point is updated using the seawater drift data for the new day until the preset termination condition is reached.

5. The method according to claim 4, characterized in that, After obtaining the ocean drift data for the new day, it also includes: The seawater drift data is processed to obtain velocity component data that meets the preset format conditions, and the inferred point position is updated using the velocity component data.

6. A device for reconstructing the drift trajectory of a target object, characterized in that, include: The conversion module is used to convert the coordinates of the preset starting point of the target object into geographic coordinates in the target coordinate system; The determination module is used to determine the row and column number of the preset starting point in the sea ice drift data grid, determine the velocity of the target object based on the row and column number, and determine the inferred position of the target object based on the velocity of the target object and the coordinates of the preset starting point; The update module is used to update the next inferred point of the target object based on a preset time interval, and record the cumulative inferred point of the target object until a preset termination condition is reached. The reconstruction module is used to obtain the reconstructed trajectory coordinates of the target object based on the cumulative inferred points updated each time, and to draw the trajectory result map based on the reconstructed trajectory coordinates.

7. The apparatus according to claim 6, characterized in that, The determining module includes: The reading unit is used to read the sea ice drift vector of the target object based on the row and column numbers; A calculation unit is used to obtain the velocity of the target object based on the sea ice drift vector.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for reconstructing the drift trajectory of a target as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for reconstructing the drift trajectory of a target object as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the method for reconstructing the drift trajectory of a target object as described in any one of claims 1-5.

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