Channel depth updating method and storage medium

Abstract

The invention provides a method for updating the depth of a channel and a storage medium, and the method comprises the steps: firstly carrying a depth finder on a ship, and collecting discrete new depth sounding points containing coordinates, a water depth value and a timestamp in real time; then generating a sounding surface by using a concave shell boundary extraction algorithm and a triangulated irregular network interpolation algorithm; uniformly converting the newly generated sounding surface and the old water depth surface into a point set form for projection registration to obtain a registration point pair corresponding to the space; the registration point pairs are processed through a Huber loss function and an iterative reweighted least square algorithm, and the real-time draught depth of the depth finder is obtained through inversion; and finally, superposing the draft obtained by inversion with the sounding surface, and outputting a new version of water depth surface. According to the method, traditional periodic off-line water depth survey is converted into on-ship instant on-line updating, the problem of water depth data lag caused by water level dynamic change and terrain deposition is effectively solved, and the current trend of data is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of inland waterway water depth updating method, and particularly relates to a method for updating water depth of waterway. BACKGROUND

[0002] The water depth data of waterway is mainly dependent on the periodic manual survey of waterway management department, and after processing, the water depth data layer in the electronic channel map is formed. However, the updating cycle of the electronic channel map is usually more than three months, for example, the water level of the inland waterway fluctuates dynamically due to the rainy season and dry season, and the rapid topographic change caused by seasonal sediment deposition and shoreline erosion, resulting in significant deviation between the marked water depth of the electronic channel map and the actual navigation environment.

[0003] Especially in the complex scenes such as narrow waterway and shallow water area of inland river, the traditional static monitoring draft method is difficult to real-time calibrate the actual navigation condition of waterway, which causes the ship to face the risk of grounding and bottom touching. Therefore, it is urgent to develop a water depth data updating mechanism based on the self-sensing of ship navigation process. SUMMARY

[0004] In view of the deficiencies in the prior art, the purpose of the present application is to provide a method for updating water depth of waterway to solve the problems mentioned in the background section.

[0005] The present application is realized by the following technical scheme: A method for updating water depth of waterway, the method comprising the following steps: S1, a depth sounder is carried on a ship, and discrete new sounding point data is obtained by the depth sounder, the new sounding point comprising: new sounding depth, time stamp, and detection point coordinates; S2, a concave shell boundary extraction algorithm and an irregular triangle net interpolation algorithm are called to generate a sounding surface for the new sounding point; S3, an old version water depth surface recorded in an electronic channel map is obtained, the sounding surface and the old version water depth surface are both extracted in the form of point set, projection processing is performed using a unified coordinate reference system to obtain a registration point pair; S4, the registration point pair is inverted to the draft depth of the depth sounder by a Huber loss function and an iterative reweighted least squares algorithm; S5, the inverted draft depth of the depth sounder is superimposed with the sounding surface to obtain a new version water depth surface.

[0006] Further, the depth sounder is arranged on the bottom surface of the ship, and the draft depth of the depth sounder is equal to the draft depth of the ship.

[0007] Further, the step S2 comprises the following substeps: S21, a concave shell boundary extraction algorithm is called for the new sounding point to form a preliminary sounding coverage surface; S22, obtain the land area geometric boundary recorded in the electronic channel chart, call the boundary difference set algorithm of the GDAL library, perform spatial difference set operation on the land area geometric boundary and the preliminary sounding coverage, remove the land area from the preliminary sounding coverage, and output an effective water depth area containing only water area; S23, convert the coordinates of the new sounding points in the effective water depth area into a two-dimensional array format, perform spatial interpolation on the discrete new sounding points in the effective water depth area by using the irregular triangle network interpolation algorithm, and generate the sounding surface covering the entire effective water depth area.

[0008] Further, the step S2 further includes the following sub-steps: S24, call the vector mask raster clipping algorithm of the GDAL library, clip the sounding surface with the effective water depth area as a mask, remove the residual land area in the grid, and output a new sounding surface.

[0009] Further, the step S3 includes the following sub-steps: S31, call the contour extraction algorithm of the GDAL library, scan the new sounding surface at a preset interval, extract a contour set, and generate a new sounding depth line; The new sounding surface is composed of a plurality of new sounding depth lines ; The first new sounding depth line is composed of a plurality of new sounding points ; Each new sounding point contains the following parameters, wherein, represents the spatial coordinates of the new sounding point ; represents the water depth value of the new sounding point ; represents the timestamp of the new sounding point ; The old version of the water depth surface is composed of a plurality of old version water depth lines ; The first old version water depth line is composed of a plurality of water depth points ; Each water depth point contains the following parameters, wherein, represents the spatial coordinates of the water depth point , represents the electronic channel chart annotated water depth value of the water depth point , representing the water depth point .

[0010] S32, calculating the directed distance from the new surveyed depth line to the old version water depth line by the directed Hausdorff distance algorithm: ; calculating the directed distance from the old version water depth line to the new surveyed depth line by the directed Hausdorff distance algorithm: ; wherein, represents the directed Hausdorff distance; represents the two-dimensional Euclidean distance from the new surveyed depth line to the old version water depth line ; represents the two-dimensional Euclidean distance from the old version water depth line to the new surveyed depth line ; S33, matching the most similar old version water depth line for each new surveyed depth line by the symmetric Hausdorff distance algorithm, specifically by the following formula: ; S34, for each new surveyed depth line , traversing all old version water depth lines , calculating the symmetric Hausdorff distance between the new surveyed depth line and each old version water depth line by the line-to-line matching formula, selecting the one with the smallest distance as the matching object, specifically by the following formula: ; S35, for each new surveyed point of a new surveyed depth line, traversing all water depth points on the corresponding old version water depth line to find the nearest water depth point , specifically by the following formula: ; S36, matching the new surveyed point found in step S35 with the water depth point to obtain the said registration point pair, and the expression of the registration point pair is: .

[0011] Furthermore, step S4 includes the following sub-steps: S41. Calculate the residuals for all the registration point pairs, using the following formula: ; in: Indicates the record number on the electronic navigation chart. The water depth at the point; This indicates the first measurement taken by the depth sounder. The water depth at the point; Indicates the total number of registration point pairs; S42. Construct the time-exponentially decaying weight, the formula of which is as follows: ; in, Indicates the first Initial time weights for each registration point pair; This represents the time decay factor, which controls the rate at which historical data is affected. The timestamp indicates the current moment of the sounding operation; Indicates the first The timestamp of the acquisition of each registration point pair; S43. The draft of the depth sounder is estimated using the following formula: ; in, This represents the initial draft estimate of the depth sounder; S44. The draft of the depth sounder is calculated iteratively using the following formula: ; in, Indicates the first During the nth iteration The draft error of the depth sounder at each point; For the first The draft estimate of the depth sounder in the next iteration; S45. The Huber threshold is adaptively calculated using the following formula. : Calculate the median of the residuals : ; Calculate the median MAD, which is the absolute deviation of the residuals relative to the median: ; Calculate Huber threshold : ; S46. Based on the draft error of the current depth sounder With Huber threshold Relationship calculation During the nth iteration Huber weight at each point Its formula is: ; S47. Combining the initial time weight and Huber weight, update the... During the nth iteration The overall weight of each point Its formula is: ; S48. Using the updated comprehensive weights, recalculate the first... The formula for the draft estimate of the depth sounder in the next iteration is: ; Convergence criteria: If satisfied or number of iterations If the result is positive, then output the final draft estimate D; otherwise, return to step S44 to continue iterating. Indicates the maximum number of iterations. This represents the iteration convergence threshold.

[0012] Furthermore, step S41 is followed by the following sub-steps: S411, Regarding the residual set Sort the data and calculate the first quartile. and the third and fourth quartiles The interquartile range is obtained. ; S412. Remove outlier registration point pairs using the following formula: like or If the registration point pair is found to be abnormal, it will be removed. It is a constant.

[0013] Furthermore, in step S411, the first quartile 25th percentile, third and fourth percentiles It is at the 75th percentile.

[0014] On the other hand, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method described in any of the preceding claims.

[0015] The method has the advantages that the method changes water depth updating from periodic offline surveying to online process in real time with the ship by using the data continuously collected by the depth sounder during ship navigation, overcomes the problem that the water depth marked on the electronic channel map does not match the actual hydrological environment due to water level fluctuation in rainy season and dry season, silt accumulation and the like, significantly improves the timeliness of water depth data, greatly reduces the economic cost and time cost of water depth data updating of the channel, makes large-scale and high-frequency channel maintenance possible, and provides an efficient technical means for intelligent channel and digital shipping construction. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A flowchart of the method for updating water depth of a channel.

[0017] Figure 2 A schematic diagram of the relationship among the draft of the depth sounder, the newly measured depth and the old version water depth. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings of the embodiments of the present application. It should be explained that the description of the embodiments is used to help understand the present application, but does not constitute limitation on the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as there is no conflict.

[0019] Referring to Figure 1 Fig. 1 shows a method for updating water depth of a channel, which comprises the following steps: S1, a depth sounder is carried on a ship, and discrete newly measured depth point data are obtained by the depth sounder, wherein the newly measured depth point comprises a newly measured depth, a time stamp and a detection point coordinate; S2, a concave shell boundary extraction algorithm and an irregular triangle net interpolation algorithm are called to generate a depth surface for the newly measured depth point; S3, an old version water depth surface recorded in an electronic channel map is obtained, the depth surface and the old version water depth surface are both extracted in the form of a point set, projection processing is performed by using a unified coordinate reference system, and a registration point pair is obtained; S4, the registration point pair is inversed to the draft of the depth sounder by using a Huber loss function and an iterative reweighted least square algorithm; S5, the draft of the depth sounder obtained by inversion is superimposed with the depth surface, and a new version water depth surface is obtained.

[0020] The ship continuously collects new sounding point data by using the depth finder during navigation, and the depth finder continuously generates discrete water depth information during daily navigation of the ship, and the new sounding points of different time periods and different positions are automatically accumulated, and then the new sounding points are regenerated as a sounding surface, the old water depth surface and the sounding surface are converted into a point set form, and each point is matched in coordinates to obtain a registration point pair, the draft of the depth finder is inversely deduced through the registration point pair, the sum of the draft of the depth finder and the height of the new sounding point is equal to the new water depth height, the updated water depth contour is reconstructed through the points, and a new channel water depth data file is formed, so that dynamic updating of water depth data in the electronic channel chart is realized.

[0021] The water depth data consistent with the real terrain can be obtained immediately with the ship without waiting for the off-line survey of the channel department once every three months, the problems of long measurement period and updating lag in the traditional measurement are solved, time and space continuous coverage and real-time updating of the channel water depth are realized, and the present situation of the water depth data and the navigation safety guarantee capability are significantly improved.

[0022] Referring to Figure 2 As a preferred embodiment, the depth finder is arranged on the bottom surface of the ship, and the draft of the depth finder is equal to the draft of the ship. Therefore, the real draft of the ship can be directly inversely deduced from the measured water depth of the depth finder without additionally installing a draft sensor or performing complicated draft calibration, the system structure is simplified, the hardware cost is reduced, the draft conversion error caused by the difference in installation positions is eliminated, and the real-time performance and accuracy of the estimation of the draft of the ship are improved.

[0023] The step S2 comprises the following sub-steps: S21, calling a concave shell boundary extraction algorithm on the new sounding point to form a preliminary sounding coverage surface; S22, acquiring a land area geometric boundary recorded in the electronic channel chart, calling a GDAL library boundary difference set algorithm, performing spatial difference set operation on the land area geometric boundary and the preliminary sounding coverage surface, eliminating the land area from the preliminary sounding coverage surface, and outputting an effective water depth area containing only water area; S23, converting the coordinates of the new sounding points in the effective water depth area into a two-dimensional array format, performing spatial interpolation on the discrete new sounding points in the effective water depth area by using an irregular triangular net interpolation algorithm, and generating the sounding surface covering the entire effective water depth area.

[0024] In the above embodiment, the concave shell boundary extraction algorithm is adopted, the boundary tightness is controlled by adjusting the maximum side length threshold of the triangular net, and the preliminary sounding coverage surface is generated. Then, the GDAL library boundary difference set algorithm is called to eliminate the land geometric object of the preliminary sounding coverage surface, and the effective water depth area containing only water area is generated.

[0025] Compared with the boundary generated by the convex hull algorithm, a large number of invalid regions are contained, which leads to an increase in the amount of subsequent interpolation calculation and a reduction in accuracy. The concave shell boundary extraction algorithm can better fit the actual water area form, and reduce about 30-50% of the invalid coverage area compared with the convex hull algorithm, thereby providing a more accurate basic geometric framework for subsequent land area elimination and reducing invalid interpolation operation in step S24.

[0026] In step S23, the irregular triangle network interpolation algorithm is used to perform spatial interpolation on the discrete new sounding points in the effective water depth area to generate a sounding surface with high-resolution water depth grid.

[0027] The sounding surface generated in step S23 may have a small amount of land area pixels at the boundary, which affects the final result quality. To solve the above problem, in the present application, the step S2 further includes the following substeps: S24, calling the GDAL library vector mask grid clipping algorithm to clip the sounding surface with the effective water depth area as a mask to remove the residual land area in the grid, and output a new sounding surface.

[0028] The gdalwarp function or Clip tool of the GDAL library is called to accurately clip the sounding surface grid data with the vector boundary of the effective water depth area obtained in step S22 as a mask. The residual land area in the grid data is completely removed, and the final water depth model is strictly limited within the water area, thereby improving the production standardization and practicality of the new sounding surface.

[0029] Further, the step S3 includes the following substeps: S31, calling the contour extraction algorithm of the GDAL library to scan the new sounding surface at a preset interval, extracting a contour set, and generating new sounding depth lines; The new sounding surface is composed of a plurality of new sounding depth lines ; The first new sounding depth line is composed of a plurality of new sounding points ; Each new sounding point contains the following parameters, wherein, represents the spatial coordinates of the new measuring point ; represents the water depth value of the new measuring point ; represents the timestamp of the new measuring point ; The old version of the water depth surface is composed of a plurality of old version of water depth lines ; No. The old version of the depth line mentioned above From several water depth points Composed of a collection; Each water depth point Includes the following parameters, ,in, Indicates water depth spatial coordinates, Indicates water depth The water depth value of the electronic navigation channel icon. Indicates water depth Timestamp.

[0030] S32. Calculate the new depth gauge using the directed Hausdorff distance algorithm. To the old version of the depth line Directed distance: ; The old depth line was calculated using the directed Hausdorff distance algorithm. To the new depth gauge Directed distance: ; in, Indicates the directed Hausdorff distance; Indicates the new depth gauge To the old version of the depth line The two-dimensional Euclidean distance; Indicates the old version of the depth line To the new depth gauge The two-dimensional Euclidean distance; S33. Use the symmetric Hausdorff distance algorithm for each new depth gauge line. Match the most similar old version depth line Specifically, it is done through the following formula: ; S34. Using the line-to-line matching formula, for each new depth measurement line... traverse all Old version of the depth line Calculate the new depth gauge With each old version of the depth line Choose the symmetric Hausdorff distance that minimizes the distance. The matching object is specifically determined by the following formula: ; S35. Using the nearest point search formula, for each new measuring point of a new depth measurement line... Traverse its corresponding old version depth line All water depth points This allows us to find the nearest water depth. Specifically, it is done through the following formula: ; S36. The new measurement point found in step S35 With water depth The matching process yields the registration point pairs, and the expression for each registration point pair is: .

[0031] In step S31, since the sounding surface generated in step S2 and the old version of the electronic navigation chart cannot be directly matched point-to-point, they need to be converted into discrete point sets to eliminate data format differences and provide support for subsequent registration.

[0032] The contour line extraction algorithm of the GDAL library is called to scan the new sounding surface and the old water depth surface at preset intervals (such as 0.5 meters) to extract a set of contour lines. Each contour line consists of a series of ordered points, and each point contains coordinates, water depth and timestamp attributes.

[0033] Since the new depth gauge line and the old depth gauge line may deviate in shape and position due to terrain changes or measurement errors, it is necessary to quantify their spatial differences to assess similarity. Therefore, in step S32, the present invention adopts the directed Hausdorff distance algorithm. The directed distance can capture the local feature differences of linear geometry. Furthermore, the present invention avoids single-direction deviation through bidirectional calculation, providing multi-dimensional quantitative basis for similarity matching.

[0034] Furthermore, a symmetric Hausdorff distance algorithm is used to comprehensively measure the overall similarity between the new depth sounding line and the old depth sounding line, avoiding the one-sidedness of the directed Hausdorff distance algorithm in step S32. The symmetric Hausdorff distance algorithm can eliminate direction bias and more robustly reflect the spatial fit between lines, making it particularly suitable for winding river channels or complex terrain scenarios.

[0035] Step S34 is used for each new depth gauge line. Quickly extract from a large number of old depth lines The most similar objects are selected from the samples, and new depth gauges are calculated. With each old version of the depth line The symmetrical Hausdorff distance is used to select the old depthline with the smallest distance. As the matching object.

[0036] After online line matching is completed, the newly matched depth measurement line is matched in step S35. Each new measuring point on the new surveying route , traverse all the water depth points on the old bathymetric line ; , calculate the Euclidean distance and find the nearest point, thereby realizing point-level spatial alignment and eliminating registration errors caused by differences in the shape of the contour line, and providing a high-precision data basis for the draft inversion.

[0037] Through step S36, each new measuring point obtained in step S35 is paired with the nearest water depth point to form a set of registration point pairs, and the new and old water depth data are directly associated through the registration point pairs, ensuring the spatial and temporal consistency of the data during the draft inversion and simplifying the subsequent calculation process.

[0038] The present application introduces curve-level matching through the Hausdorff distance algorithm, measures the global worst deviation through the directed and symmetric Hausdorff distance, ensures the overall optimal alignment of the shape, and effectively solves the problem of spatial deviation caused by ship positioning errors, electronic chart lag, and environmental changes. For example, due to seasonal changes in river morphology caused by sediment deposition or erosion in inland waterways, there are significant differences in the shape of the new and old bathymetric lines. By utilizing the overall shape matching characteristics of the Hausdorff distance, even if the river profile changes, accurate registration of the new bathymetric line and the old bathymetric line can still be achieved based on the overall shape similarity.

[0039] Further, the step S4 includes the following sub-steps: S41, calculate the residual error of all registration point pairs, and the specific formula is as follows: ; Wherein: represents the water depth value of the first point recorded in the electronic channel map; represents the water depth value of the first point measured by the depth sounder; represents the total number of registration point pairs; S42, construct a time index decay weight, and the formula is as follows: ; Wherein, represents the initial time weight of the first registration point pair; represents a time decay factor that controls the influence rate of historical data; represents the time stamp of the current depth surveying time; represents the collection time stamp of the first registration point pair; S43, estimate the draft depth of the depth sounder, and the formula is as follows: ; wherein, denotes the initial draft estimation of the sounder; S44, iteratively calculates the draft of the sounder by the following formula: ; wherein, denotes the draft error of the i-th point in the j-th iteration; ; is the draft estimation of the sounder in the j-th iteration; S45, adaptively calculates the Huber threshold by the following formula: : calculates the median of the residuals : ; calculates the median of the absolute deviation of the residuals from the median MAD: ; calculates the Huber threshold : ; ; S46, calculates the Huber weight of the i-th point in the j-th iteration according to the relationship between the current draft error of the sounder and the Huber threshold , and the formula is: ; ; S47, combines the initial time weight and the Huber weight to update the comprehensive weight of the i-th point in the j-th iteration, and the formula is: ; ; S48, recalculates the draft estimation of the sounder in the j-th iteration by using the updated comprehensive weight, and the formula is: ; ; Convergence judgment: if the following condition is met or the iteration number , the final draft estimation D is output, otherwise, return to step S44 for iteration, wherein, denotes the maximum iteration number, denotes the iteration convergence threshold.​​​​​

[0040] In the above embodiment, each registration point pair is assigned a time decay weight by step S42, the closer to the current registration point pair, the larger the time weight Avoiding early data from having a large disturbance on the current draft estimate, ensuring that recent measurement data contributes most to the draft estimate, quickly responding to water level changes, and moderately preserving historical data trends to avoid draft estimate deviations caused by short-term fluctuations.

[0041] By step S44, the draft error is iteratively calculated by the iteratively reweighted least squares algorithm, and this error result is passed to step S46 for calculating new Huber weights. By combining the Huber loss function with the iteratively reweighted least squares algorithm for robust estimation, the final draft estimate D is output, which helps to improve the anti-noise capability and precision of the draft depth calculation and improve robustness.

[0042] By step S45, the adaptive Huber threshold is calculated using the median , the median of absolute deviation MAD, avoiding the difficulty of relying on empirical preset threshold, enhancing the adaptive ability and practicality of the algorithm. According to the current residual distribution, the threshold is adaptively calculated, so that the algorithm achieves the best balance between processing normal data (using quadratic loss for statistical efficiency) and abnormal data (using linear loss for robustness). The time decay factor controls the rate of influence of historical data, which is usually set to 0.1-0.5 according to the data update frequency.

[0043] By step S46, by the size relationship between the draft error and the Huber threshold , the normal error points, i.e. are assigned high weights to pursue accuracy; the abnormal error points, i.e. are assigned low weights to reduce interference, effectively resisting "abnormal noise" in the measured data (such as instantaneous measurement error of the depth sounder caused by water flow impact, registration point pair matching deviation, etc.).

[0044] By step S47, the time exponential decay weight Huber weight is combined to update the comprehensive weight of the first iteration, which realizes that in the pairing process, the registration point pair not only considers the "time freshness" of the data (prefer recent data), but also considers the "error reliability" of the data (prefer normal error data), further optimizing the input data quality of the draft estimate and improving the robustness of the output result.

[0045] Through step S48, the convergence judgment mechanism avoids insufficient iteration leading to insufficient precision or excessive iteration wasting computing resources, and controls the computing cost while ensuring the accuracy of the result.

[0046] Further, the step S41 further includes the following sub-steps: S411, sorting the residual set to calculate the first quartile and the third quartile , and obtaining the interquartile range . S412, eliminating abnormal registration point pairs by the following formula: If or , it is determined that the registration point pair is abnormal and is eliminated, wherein is a constant.

[0047] Further, in the step S411, the first quartile is the 25% quartile, and the third quartile is the 75% quartile, which are used to define the distribution range of normal residuals. The factor is generally 1.5.

[0048] Through steps S411 and S412, abnormal registration point pairs are effectively identified and eliminated, reducing the interference of "extreme residuals" on the draft estimation. For example, if the abnormal point with an extremely large residual is retained, it will cause the calculation of the time decay weight in S42 to be biased towards invalid data, and then the initial draft estimation value in S43 will deviate from the true value, and the subsequent iterative optimization will also be difficult to converge to the accurate result due to the "starting point deviation". This step purifies the registration point pair set to ensure that the subsequent draft inversion is based on "high-quality, low-noise" data, directly improving the accuracy of the final draft estimation value.

[0049] In step 5, the depth sounder draft depth value D obtained through steps S41 to S48 is superimposed with the depth surface, and the isobaths are reconstructed by spatial interpolation and contour fitting, for example, by the method of steps S21 to S23, to obtain a new version of the water depth surface, thereby realizing the updating of the electronic navigation chart.

[0050] On the other hand, the application provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and when the computer execution instructions are executed by a processor, the computer execution instructions are used to implement the method according to any one of the above.

[0051] "Read only memory medium" of the embodiments of the invention refers to any medium that participates in providing the processor with program or instructions for execution. The medium can take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as a storage device. Volatile media include dynamic memory, such as a main memory. Transmission media include coaxial cables, copper wire, and fiber optics, including the wires that comprise a bus. Transmission media can also take the form of acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read. The program or instructions stored on the read only memory medium are executed by the processor to perform the registration method described above.

[0052] In the description of the present invention, it needs to be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present invention.

[0053] In the description of the present invention, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features.

[0054] The above only describes the preferred embodiments of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for updating channel depth, characterized in that, The method includes the following steps: S1. Elevation sounder is mounted on the ship to acquire discrete new sounding point data, wherein the new sounding point includes: new depth, timestamp, and coordinates of the detection point; S2. Use the concave shell boundary extraction algorithm and the irregular triangular mesh interpolation algorithm to generate a sounding surface for the new sounding point; S3. Obtain the old version of the water depth surface recorded in the electronic navigation chart. Both the sounding surface and the old version of the water depth surface are extracted into point sets. Projection processing is performed using a unified coordinate reference system to obtain registration point pairs. S4. The registration point pair is used to invert the draft of the depth sounder using the Huber loss function and the iterative reweighted least squares algorithm. S5. The depth of the sounder obtained by inversion is superimposed on the sounding surface to obtain a new version of the water depth surface.

2. The method for updating channel depth according to claim 1, characterized in that, The depth sounder is installed on the bottom of the ship, and the draft of the depth sounder is equal to the draft of the ship.

3. The method for updating channel depth according to claim 1, characterized in that, Step S2 includes the following sub-steps: S21. Apply the concave shell boundary extraction algorithm to the new sounding point to form a preliminary sounding coverage area; S22. Obtain the geometric boundary of the land area recorded in the electronic navigation chart, call the boundary difference algorithm of the GDAL library, perform spatial difference operation on the geometric boundary of the land area and the preliminary sounding coverage, remove the land area from the preliminary sounding coverage, and output the effective water depth area containing only the water area. S23. Convert the coordinates of the new sounding points within the effective water depth domain into a two-dimensional array format, and use the irregular triangular mesh interpolation algorithm to perform spatial interpolation on the discrete new sounding points within the effective water depth domain to generate the sounding surface covering the entire effective water depth domain.

4. The method for updating channel depth according to claim 3, characterized in that, Step S2 further includes the following sub-steps: S24. Call the GDAL library vector mask grid clipping algorithm to clip the sounding surface using the effective water depth region as a mask, remove the remaining land area in the grid, and output a new sounding surface.

5. The method for updating channel depth according to claim 1, characterized in that, Step S3 includes the following sub-steps: S31. Call the contour line extraction algorithm of GDAL library, scan the new sounding surface at preset intervals, extract the contour line set, and generate a new sounding line; The new depth sounding surface Consisting of several new depth gauges Composed of a collection; No. A new depth gauge line From several new depth sounding points Composed of a collection; Each new sounding point Includes the following parameters, ,in, Indicates new measuring point Spatial coordinates; Indicates new measuring point The water depth value; Indicates new measuring point timestamp; The old version of the water depth surface From several old versions of depth lines Composed of a collection; No. The old version of the depth line mentioned above From several water depth points Composed of a collection; Each water depth point Includes the following parameters, ,in, Indicates water depth spatial coordinates, Indicates water depth The water depth value of the electronic navigation channel icon. Indicates water depth Timestamp. S32. Calculate the new depth gauge using the directed Hausdorff distance algorithm. To the old version of the depth line Directed distance: ； The old depth line was calculated using the directed Hausdorff distance algorithm. To the new depth gauge Directed distance: ; in, Indicates the directed Hausdorff distance; Indicates the new depth gauge line To the old version of the depth line The two-dimensional Euclidean distance; Indicates the old version of the depth line To the new depth gauge The two-dimensional Euclidean distance; S33. Use the symmetric Hausdorff distance algorithm for each new depth gauge line. Match the most similar old version depth line Specifically, it is done through the following formula: ； S34. Using the line-to-line matching formula, for each new depth measurement line... traverse all Old version of the depth line Calculate the new depth gauge With each old version of the depth line Choose the symmetric Hausdorff distance that minimizes the distance. The matching object is specifically determined by the following formula: ； S35. Using the nearest point search formula, for each new measuring point of a new depth measurement line... Traverse its corresponding old version depth line All water depths on This allows us to find the nearest water depth. Specifically, it is done through the following formula: ； S36. The new measurement point found in step S35 With water depth The matching process yields the registration point pairs, and the expression for each registration point pair is: 。 6. The method for updating channel depth according to claim 5, characterized in that, Step S4 includes the following sub-steps: S41. Calculate the residuals for all the registration point pairs, using the following formula: ； in: Indicates the record number on the electronic navigation chart. The water depth at the point; This indicates the first measurement taken by the depth sounder. The water depth at the point; Indicates the total number of registration point pairs; S42. Construct the time-exponentially decaying weight, the formula of which is as follows: ； in, Indicates the first Initial time weights for each registration point pair; This represents the time decay factor, which controls the rate at which historical data is affected. The timestamp indicates the current moment of the sounding operation; Indicates the first The timestamp of the acquisition of each registration point pair; S43. The draft of the depth sounder is estimated using the following formula: ； in, This represents the initial draft estimate of the depth sounder; S44. The draft of the depth sounder is calculated iteratively using the following formula: ； in, Indicates the first During the nth iteration The draft error of the depth sounder at each point; For the first The draft estimate of the depth sounder in the next iteration; S45. The Huber threshold is adaptively calculated using the following formula. : Calculate the median of the residuals : ； Calculate the median MAD, which is the absolute deviation of the residuals relative to the median: ； Calculate Huber threshold : ； S46. Based on the draft error of the current depth sounder With Huber threshold Relationship calculation During the nth iteration Huber weight at each point Its formula is: ； S47. Combining the initial time weight and Huber weight, update the... During the nth iteration The overall weight of each point Its formula is: ； S48. Using the updated comprehensive weights, recalculate the first... The formula for the draft estimate of the depth sounder in the next iteration is: ； Convergence criteria: If satisfied or number of iterations If the result is positive, then output the final draft estimate D; otherwise, return to step S44 to continue iterating. Indicates the maximum number of iterations. This represents the iteration convergence threshold.

7. A method for updating channel depth according to claim 6, characterized in that, The following sub-steps are included after step S41: S411, Regarding the residual set Sort the data and calculate the first quartile. and the third and fourth quartiles The interquartile range is obtained. ; S412. Remove outlier registration point pairs using the following formula: like or If the registration point pair is found to be abnormal, it will be removed. It is a constant.

8. A method for updating channel depth according to claim 7, characterized in that: In step S411, the first quartile 25th percentile, third and fourth percentiles It is at the 75th percentile.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, are used to implement the method as described in any one of claims 1 to 8.

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