Towed shallow-profile positioning data correction method based on terrain

Abstract

The invention specifically relates to the technical field of data detection, and discloses a towed shallow profile positioning data correction method based on terrain, which comprises the following steps: S01, extracting time and position information of a measuring line in recorded shallow stratum profile data, and reading to obtain track line data (index, data, X and Y), where index is time, data is time, and X and Y are two-dimensional positions; s02, performing edge detection on the track line data based on a Canny edge detection algorithm to determine candidate edges; and S03, judging the candidate edge by using a lag threshold value, so as to obtain the index, the data, the X, the Y and the Z of the shallow profile bottom tracking data (index, data, X, Y and Z). According to the method, shallow profile positioning data is used as an initial value, sea / riverbed topographic data of shallow stratum profile data is obtained through a bottom tracking technology, the sea / riverbed topographic data is matched with a known topographic data result, and the actual position of the shallow profile data is found and corrected.

Description

Technical Field

[0001] The invention relates to the technical field of data detection, and in particular to a terrain-based towed shallow profile positioning data correction method. Background Art

[0002] There are two ways to mount a shallow-series profiler: side-mounted and towed. The towed installation has been widely used due to its advantages such as flexible adjustment of draft depth and less influence of ship posture during operation.

[0003] There are two main methods for position correction of towed shallow-sediment profilers: underwater acoustic positioning and Layback algorithm. The underwater acoustic positioning method generally uses an ultra-short baseline positioning system. Although it can accurately locate the towed body, it requires additional hardware equipment and has certain requirements for the working water depth, which has a certain threshold for use. Figure 1 As shown in the figure, it is assumed that the towfish is behind the ship (i.e., the track lines of the two are consistent), and the position of the towfish is estimated by the length and percentage of the towline. Due to the large differences in wind, waves, and current conditions experienced by the ship and the towfish in the water body, there are certain differences in the movement trajectories of the two, generally exceeding the meter level, resulting in large errors in the positioning data of the shallow layer profile results, which deviates from the actual situation and is difficult to meet the needs of actual production work. Summary of the invention

[0004] The purpose of the present invention is to provide a terrain-based towed shallow profile positioning data correction method for solving the problem of large errors in shallow stratum profile positioning data caused by the influence of wind, waves and water flow conditions.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a terrain-based towed shallow profile positioning data correction method, comprising the following steps:

[0006] S01, extracting the time and position information of the survey line in the recorded shallow layer profile data to obtain track line data (index, datetime, X, Y), where: index is time, datetime is time, and X, Y are two-dimensional positions;

[0007] S02, performing edge detection on the track data based on the Canny edge detection algorithm to determine candidate edges;

[0008] S03, using a hysteresis threshold to discriminate the candidate edge to obtain shallow bottom tracking data (index, datetime, X, Y, Z), where index is time, datetime is time, and X, Y, Z are three-dimensional positions;

[0009] S04, matching a specified range with the track data as an initial condition to obtain correction data, wherein the correction data is a curve with the greatest similarity;

[0010] S05, according to the start and end time of the track line data extraction, determine the nodes of the curve by isochronous interpolation method, and generate sequence data (index, datetime, X correction, Y correction, Z) from the data of the determined nodes in sequence;

[0011] S06. According to the time matching principle, the sequence data (X correction, Y correction) of the corresponding time is written back to the shallow layer profile data in step S03.

[0012] Preferably, the step of processing the track line data using the Canny edge detection algorithm in step 02 includes:

[0013] S21, use Gaussian smoothing filter convolution to reduce noise, the Gaussian kernel is N*N, then:

[0014]

[0015] S22, use a pair of convolutions to calculate the x and y directions respectively,

[0016]

[0017] S23, calculate the gradient amplitude and direction:

[0018]

[0019] Preferably, the hysteresis threshold in S03 includes a high threshold and a low threshold, and the candidate edge is identified based on the high threshold and the low threshold:

[0020] If the amplitude of a certain pixel position exceeds the high threshold, the pixel is retained as an edge pixel;

[0021] If the magnitude of a certain pixel position is less than the low threshold, the pixel is excluded;

[0022] If the magnitude of a pixel position is between two thresholds, the pixel is retained only if it is connected to a pixel above the upper threshold.

[0023] Preferably, the acquisition of the shallow bottom profile tracking data includes:

[0024] S31, obtaining the datetime in the track data of step S01 to form a time node;

[0025] S32, assigning geographic information coordinates to the sections of the identified candidate edges corresponding to the time nodes by means of equal time interpolation.

[0026] Preferably, in step S04, matching the specified range using the track data as the initial condition includes:

[0027] S41, extracting the first point (x1, y1) and the last point (x2, y2) of the track data in step S01, the known tow cable length is L, and the underwater terrain data near the track is determined according to 50% of the tow cable length, and the regional data is to be extracted;

[0028] S42. Calculate each corner point based on the acquired area data:

[0029]

[0030] α = arctan(k);

[0031]

[0032] The coordinates of the four fixed points are:

[0033] (x a ,y a )=(x2+Δx-Δy, y2+Δy+Δx);

[0034] (x b ,y b )=(x2+Δx+Δy, y2+Δy-Δx);

[0035] (x c ,y c )=(x1-Δx+Δy, y1-Δy-Δx);

[0036] (x d ,y d )=(x1-Δx-Δy, y1-Δy+Δx);

[0037] The water depth data within the range determined by the above four fixed points is used as the data source for matching with the cross section.

[0038] Preferably, the step S04 of obtaining the correction data includes:

[0039] S43, dividing the range determined by the four fixed points into isotropic areas A and B based on the first position of the track line data and randomly selecting N points as starting points;

[0040] S44. Let the coordinates of a point be Elevation is Then, the sum of α and α and the random number of -0.1 to 0.1 is taken as the direction, and the length of the shallow profile line is calculated. As stepping;

[0041] S45, then the coordinates of the next point are Elevation is Repeat steps S43 to S45 until the boundary is touched, and obtain the corresponding point set. The value can form a track line data;

[0042] S46, matching and comparing the N search track line data generated in step S43 with the shallow bottom tracking data using a cosine similarity algorithm, and calculating the similarity, the value range of the similarity is (0,1), then:

[0043]

[0044] Among them, A represents the search track line data, and B represents the shallow bottom tracking data;

[0045] S47. Sort the N similarities calculated from large to small and take the largest one. , as candidate survey lines;

[0046] S48, parallel to each candidate survey line, according to the length of the shallow profile survey line Width, duplicate the candidate line on both sides to form candidate regions;

[0047] S49, repeating steps S46-S47, and taking the curve with the greatest similarity as correction data.

[0048] Preferably, the number N of the N points in step S41 is not less than 20.

[0049] Preferably, the shallow stratum profile data in step S01 is in SEG-Y format.

[0050] In the above technical solution, the present invention provides a terrain-based towed shallow profile positioning data correction method, which has the following beneficial effects:

[0051] 1. Underwater terrain data is generally collected during shallow-sediment profile detection, and this algorithm can be used for correction without incurring additional costs.

[0052] 2. Bottom tracking is a necessary task for shallow profile data processing and analysis. As the input of the correction algorithm, it will not increase the workload; the matching work can be performed automatically by the algorithm.

[0053] 3. Multiple steps of this algorithm can be executed in parallel with high computational efficiency. Therefore, this correction algorithm does not require additional costs and manpower and material resources, and does not require the use of other hardware equipment. It can be completed efficiently and automatically while greatly improving positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0055] Figure 1 It is a schematic diagram of the layback algorithm in the prior art;

[0056] Figure 2 It is a schematic diagram of the original shallow section data image;

[0057] Figure 3 A schematic diagram of shallow profile data after bottom tracking is completed provided by an embodiment of the present invention;

[0058] Figure 4 A schematic diagram of a search range provided by an embodiment of the present invention;

[0059] Figure 5 A flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Embodiment 1

[0062] like Figure 5 As shown, a terrain-based towed shallow profile positioning data correction method includes the following steps:

[0063] S01, extracting the time and position information of the survey line in the recorded shallow layer profile data to obtain track line data (index, datetime, X, Y), where: index is time, datetime is time, and X, Y are two-dimensional positions;

[0064] S02, performing edge detection on the track data based on the Canny edge detection algorithm to determine candidate edges;

[0065] S03, using a hysteresis threshold to discriminate the candidate edge to obtain shallow bottom tracking data (index, datetime, X, Y, Z), where index is time, datetime is time, and X, Y, Z are three-dimensional positions;

[0066] S04, matching a specified range with the track data as an initial condition to obtain correction data, wherein the correction data is a curve with the greatest similarity;

[0067] S05, according to the start and end time of the track line data extraction, determine the nodes of the curve by isochronous interpolation method, and generate sequence data (index, datetime, X correction, Y correction, Z) from the data of the determined nodes in sequence;

[0068] S06. According to the time matching principle, the sequence data (X correction, Y correction) of the corresponding time is written back to the shallow layer profile data in step S03.

[0069] Specifically, the shallow layer profile data in step S01 is the data that has been detected and acquired, and the shallow layer profile data records the track line data. The shallow layer profile data in this embodiment is in SEG-Y format, and the SEG-Y format data consists of a file header and a data body, and the positioning data is recorded in the data body, and the data body consists of multiple data channels. Each data channel is divided into two parts: a header and sampling data. Time and positioning data are stored in the header. The header length is 240 bytes. Time data is stored in year (157-158), day (159-160), hour (161-162), minute (163-164) and second (165-166). The coordinate information stores the source coordinate X (73-76), the source coordinate Y (77-80), the detector group coordinate X (81-84) and the detector group coordinate Y (85-88). Since the source and receiver of the shallow subsurface profiler are in the same location, it is only necessary to read the information of one set of coordinates.

[0070] Furthermore, the shallow layer profile data in the embodiment exists in the form of an image. The Canny edge detection algorithm in step S02 is used to remove noise from the data, and then the gradient amplitude and direction are calculated to determine the four vertices of the range, and the four vertices are connected by straight lines. Because the interface between the water body and the bottom occurs in the vertical direction, it is only necessary to identify the edge with a gradient direction of 90° (such as Figure 2 as candidate edges.

[0071] Furthermore, in step S03 of the embodiment, the hysteresis threshold is judged, and the hysteresis threshold requires two thresholds, namely, a high threshold and a low threshold: if the amplitude of a certain pixel position exceeds the high threshold, the pixel is retained as an edge pixel; if the amplitude of a certain pixel position is less than the low threshold, the pixel is excluded; if the amplitude of a certain pixel position is between the two thresholds, the pixel is retained only when it is connected to a pixel higher than the high threshold. After completion, the bottom tracking data of the shallow profile data is realized, such as Figure 3The red line segment has time on its horizontal axis and the change in seabed height on its vertical axis. The time in the track data is extracted in step S01, and geographic information coordinates are assigned to each point of the section according to the equal time interpolation method. At this time, the bottom tracking data can be represented as serial number, date and three-dimensional position (index, datetime, X, Y, Z).

[0072] Furthermore, in step S04, the underwater terrain data near the shallow layer data track line is extracted based on the determined candidate edge, and then the track line data in step S01 is used as the initial condition to match the specified range. The detailed processing steps include:

[0073] S41, extracting the first point (x1, y1) and the last point (x2, y2) of the track data in step S01, the known tow cable length is L, and the underwater terrain data near the track is determined according to 50% of the tow cable length, and the regional data is to be extracted;

[0074] S42. Calculate each corner point based on the acquired area data:

[0075]

[0076] α = arctan(k);

[0077]

[0078] The coordinates of the four fixed points are:

[0079] (x a ,y a )=(x2+Δx-Δy, y2+Δy+Δx);

[0080] (x b ,y b )=(x2+Δx+Δy,y2+Δy-Δx);

[0081] (x c ,y c )=(x1-Δx+Δy,y1-Δy-Δx);

[0082] (x d ,y d )=(x1-Δx-Δy, y1-Δy+Δx);

[0083] The water depth data within the range determined by the above four fixed points is used as the data source for matching with the cross section.

[0084] S43, dividing the range determined by the four fixed points into isotropic areas A and B based on the first position of the track line data and randomly selecting N points as starting points;

[0085] S44. Let the coordinates of a point be Elevation is Then, the sum of α and α and the random number of -0.1 to 0.1 is taken as the direction, and the length of the shallow profile line is calculated. As stepping;

[0086] S45, then the coordinates of the next point are Elevation is Repeat steps S43 to S45 until the boundary is touched, and obtain the corresponding point set. The value can form a track line data;

[0087] S46, matching and comparing the N search track line data generated in step S43 with the shallow bottom tracking data using a cosine similarity algorithm, and calculating the similarity, the value range of the similarity is (0,1), then:

[0088]

[0089] Among them, A represents the search track line data, and B represents the shallow bottom tracking data;

[0090] S47. Sort the N similarities calculated from large to small and take the largest one. , as candidate survey lines;

[0091] S48, parallel to each candidate survey line, according to the length of the shallow profile survey line Width, duplicate the candidate line on both sides to form candidate regions;

[0092] S49, repeating steps S46-S47, and taking the curve with the greatest similarity as correction data.

[0093] In the above technology, the sea / riverbed topographic data of the shallow layer profile data is obtained through bottom tracking technology, and matched with the known topographic data results to find the actual position of the shallow profile data and make corrections

[0094] Embodiment 2

[0095] Combination Figure 3 It can be seen that the steps of processing the track line data by the Canny edge detection algorithm in step 02 recorded in the first embodiment include:

[0096] S21, use Gaussian smoothing filter convolution to reduce noise, the Gaussian kernel is N*N, then:

[0097]

[0098] S22, use a pair of convolutions to calculate the x and y directions respectively,

[0099]

[0100] S23, calculate the gradient amplitude and direction:

[0101]

[0102] The acquisition of the shallow bottom profile tracking data includes:

[0103] S31, obtaining the datetime in the track data of step S01 to form a time node;

[0104] S32, assigning geographic information coordinates to the cross-sections of the identified candidate edges corresponding to the time nodes by means of equal time interpolation.

[0105] Specifically, in the embodiment, the track data in step S01 is denoised by convolution denoising with a Gaussian smoothing filter, thereby obtaining the necessary data after denoising, and then using the Gaussian smoothing filter obtained from the necessary data to obtain the necessary data. x and G y , thereby calculating the gradient magnitude and direction, and thus determining the candidate edge.

[0106] In summary, underwater terrain data is generally collected during shallow-sediment profile detection, and there is no need to incur additional costs when using this algorithm for correction. Bottom tracking is a necessary task for shallow-sediment data processing and analysis, and as an input to the correction algorithm, it does not increase the workload; the matching work can be performed automatically by the algorithm. 3. Multiple steps of this algorithm can be executed in parallel, with high computational efficiency. Therefore, this correction algorithm does not require additional costs and manpower and material resources, and does not require the use of other hardware equipment. It can be completed efficiently and automatically on the basis of greatly improving positioning accuracy.

[0107] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0112] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the method in the above embodiments, and the electronic device specifically includes the following contents:

[0113] Processor, memory, communications interface and bus;

[0114] Wherein, the processor, memory, and communication interface communicate with each other via the bus;

[0115] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all the steps in the method in the above embodiment are implemented.

[0116] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps of the method in the above embodiments are implemented.

[0117] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Although the embodiment of this specification provides the method operation steps described in the embodiment or flow chart, more or less operation steps can be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way of executing the order of many steps, and does not represent the only execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. For the convenience of description, the above device is described by dividing it into various modules according to its functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The present invention is described with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the process and / or box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment. In the description of this specification, the description of the reference term "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification.

[0119] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradicting each other. The above is only an embodiment of the embodiment of this specification and is not intended to limit the embodiment of this specification. For those skilled in the art, the embodiment of this specification may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiment of this specification shall be included in the scope of the claims of the embodiment of this specification.

Claims

1. A terrain-based towing shallow profile positioning data correction method, characterized in that: The following steps are involved: S01, extracting the time and position information of the survey line in the recorded shallow layer profile data to obtain track line data (index, datetime, X, Y), where: index is time, datetime is time, and X, Y are two-dimensional positions; S02, performing edge detection on the track data based on the Canny edge detection algorithm to determine candidate edges; S03, using a hysteresis threshold to discriminate the candidate edge to obtain shallow bottom tracking data (index, datetime, X, Y, Z), where index is time, datetime is time, and X, Y, Z are three-dimensional positions; S04, matching a specified range with the track data as an initial condition to obtain correction data, wherein the correction data is a curve with the greatest similarity; S05, according to the start and end time of the track line data extraction, determine the nodes of the curve by isochronous interpolation method, and generate sequence data (index, datetime, X correction, Y correction, Z) from the data of the determined nodes in sequence; S06. According to the time matching principle, the sequence data (X correction, Y correction) of the corresponding time is written back to the shallow layer profile data in step S03.

2. A terrain-based towing shallow profile positioning data correction method according to claim 1, characterized in that: The step of processing the track data using the Canny edge detection algorithm in step 02 includes: S21, use Gaussian smoothing filter convolution to reduce noise, the Gaussian kernel is N*N, then: S22, use a pair of convolutions to calculate the x and y directions respectively, S23, calculate the gradient amplitude and direction:

3. The method for correcting the terrain-based towing shallow profile positioning data according to claim 1, characterized in that: The hysteresis threshold in S03 includes a high threshold and a low threshold, and the candidate edge is identified based on the high threshold and the low threshold: If the amplitude of a certain pixel position exceeds the high threshold, the pixel is retained as an edge pixel; If the magnitude of a certain pixel position is less than the low threshold, the pixel is excluded; If the magnitude of a pixel position is between two thresholds, the pixel is retained only if it is connected to a pixel above the upper threshold.

4. The terrain-based towing shallow profile positioning data correction method according to claim 1 is characterized in that: The acquisition of the shallow bottom profile tracking data includes: S31, obtaining the datetime in the track data of step S01 to form a time node; S32, assigning geographic information coordinates to the sections of the identified candidate edges corresponding to the time nodes by means of equal time interpolation.

5. The terrain-based towing shallow profile positioning data correction method according to claim 1, characterized in that: In step S04, matching the specified range using the track data as the initial condition includes: S41, extracting the first point (x1, y1) and the last point (x2, y2) of the track data in step S01, the known tow cable length is L, and the underwater terrain data near the track is determined according to 50% of the tow cable length, and the regional data is to be extracted; S42. Calculate each corner point based on the acquired area data: α = arctan(k); The coordinates of the four fixed points are: (x a ,y a )=(x2+Δx-Δy,y2+Δy+Δx); (x b ,y b )=(x2+Δx+Δy,y2+Δy-Δx); (x c ,y c )=(x1-Δx+Δy,y1-Δy-Δx); (x d ,y d )=(x1-Δx-Δy,y1-Δy+Δx); The water depth data within the range determined by the above four fixed points is used as the data source for matching with the cross section.

6. A terrain-based towing shallow profile positioning data correction method according to claim 5, characterized in that: The step S04 of obtaining the correction data includes: S43, dividing the range determined by the four fixed points into isotropic areas A and B based on the first position of the track line data and randomly selecting N points as starting points; S44. Let the coordinates of a point be Elevation is Then, the sum of α and α and the random number of -0.1 to 0.1 is taken as the direction, and the length of the shallow profile line is calculated. As stepping; S45, then the coordinates of the next point are Elevation is Repeat steps S43 to S45 until the boundary is touched, and obtain the corresponding point set. The value can form a track line data; S46, matching and comparing the N search track line data generated in step S43 with the shallow bottom tracking data using a cosine similarity algorithm, and calculating the similarity, the value range of the similarity is (0,1), then: Among them, A represents the search track line data, and B represents the shallow bottom tracking data; S47. Sort the N similarities calculated from large to small and take the largest one. , as candidate survey lines; S48, parallel to each candidate survey line, according to the length of the shallow profile survey line Width, duplicate the candidate line on both sides to form candidate regions; S49, repeating steps S46-S47, and taking the curve with the greatest similarity as correction data.

7. A terrain-based towing shallow profile positioning data correction method according to claim 6, characterized in that: In the step S41, the number N among the N points is not less than 20.

8. The terrain-based towing shallow profile positioning data correction method according to claim 1 is characterized in that: The shallow stratum profile data in step S01 is in SEG-Y format.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the visual positioning method based on local variance and posterior probability classifier according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the visual positioning method based on local variance and posterior probability classifier described in any one of claims 1 to 8 are implemented.

Images