Methods and devices for adjusting the distribution of inland waterway navigation marks based on waterway maintenance standards

Abstract

This application relates to a method and apparatus for adjusting the distribution of inland waterway navigation marks based on waterway maintenance scales. The method includes: selecting a set of points from water depth data based on a preset water depth threshold, and generating a water depth polygon based on the boundary; determining a navigation width polygon based on the waterway centerline and a preset waterway maintenance width; determining a navigation mark placement area polygon based on the water depth polygon and the navigation width polygon; if the navigation mark is within the navigation mark placement area polygon, it is determined that the navigation mark meets a preset location rationality condition; otherwise, the selection range of the navigation mark is adjusted to a preset selection range; normalizing distance grids, average distance grids, and weighted average bending radius value grids, and linearly weighting the processed grids to generate a score grid; and determining the navigation mark position that meets the preset optimal adjustment conditions based on the score grid. This solves the problem that related technologies limit the frequency and accuracy of navigation mark adjustments, making it difficult to ensure the scientific and accurate distribution of navigation marks.

Description

Technical Field

[0001] This application relates to the field of waterway maintenance and management technology, and in particular to a method and device for adjusting the distribution of inland waterway navigation marks based on waterway maintenance standards. Background Technology

[0002] Navigational aids, or navigational markers, play a crucial role in ship navigation. They provide positioning and navigation services by emitting light, sound, or radio signals, and also indicate channel boundaries and obstacles, ensuring safe navigation within the waterway. The scientific planning and layout of navigational aids are critical to ship navigation safety and waterway efficiency. Given the complex and varied topography of inland waterways and frequent riverbed changes, accurately and rationally distributing navigational aids to ensure they are always in optimal positions has become a pressing challenge for waterway management departments.

[0003] In related technologies, the placement of navigation aids relies heavily on the experience of the staff. This involves staff verifying the rationality of each aid's location and selecting the optimal position after considering various factors. First, staff use methods such as water depth measurement to determine whether the current location of each navigation aid provides accurate navigational information to ships. If the location is reasonable, the aid remains unchanged; otherwise, adjustment is necessary. Second, when adjusting the aid's position, staff need to consider multiple factors, including water depth, and rely on their experience to determine the appropriate adjustment location for each aid. Finally, they assess whether the adjusted location of each aid is reasonable in relation to other aids. If reasonable, the adjustment is complete; otherwise, further adjustments are required.

[0004] However, as the field of waterway maintenance and management continues to deepen its transformation towards digitalization and intelligence, this method still faces a series of challenges: (1) This method is highly dependent on the experience of the staff and is easily affected by human factors: Due to the differences in the experience level and judgment ability of different staff, there are differences in the adjustment methods and processes of navigation marks, and the adjustment results are also different, which will affect the accuracy of navigation mark distribution and adjustment; (2) This method is inefficient and requires staff to make judgments based on a variety of factors: Relying on manual judgment means that each adjustment of the mark requires staff to invest a lot of time and energy to analyze various factors such as the waterway environment, which not only increases the cost of manpower and material resources, but also limits the frequency and accuracy of navigation mark adjustment; (3) This method can only qualitatively describe the adjustment process and cannot quantify the process. It lacks a dynamic adjustment process and it is difficult to ensure the scientificity and accuracy of navigation mark distribution. Summary of the Invention

[0005] This application provides a method and apparatus for adjusting the distribution of inland waterway navigation marks based on waterway maintenance standards, in order to solve the problem that related technologies are highly dependent on the experience of personnel, are easily affected by human factors, limit the frequency and accuracy of navigation mark adjustments, and make it difficult to ensure the scientific nature and accuracy of navigation mark distribution.

[0006] The first aspect of this application provides a method for adjusting the distribution of inland waterway navigation marks based on waterway maintenance dimensions, comprising the following steps: selecting a set of points from water depth point data according to a preset water depth threshold, and extracting boundaries from the set of points to generate a water depth polygon based on the boundaries; calculating the waterway centerline of the navigation mark based on its position within the target area, and determining the navigation width polygon based on the waterway centerline and a preset waterway maintenance width; determining the navigation mark placement area polygon based on the water depth polygon and the navigation width polygon, and determining whether the navigation mark is within the navigation mark placement area polygon; if the navigation mark is within the navigation mark placement area polygon... Within the domain polygon, the navigation mark is determined to meet the preset position rationality conditions; otherwise, the selection range of the navigation mark is adjusted to the preset selection range. Within the preset selection range, the distance grid from the pixel center to the channel centerline, the average distance grid from the pixel center to the historical coordinate point, and the weighted average curvature radius value grid are calculated respectively. The distance grid, the average distance grid, and the weighted average curvature radius value grid are normalized to obtain the processed grid, and the processed grid is linearly weighted to generate a score grid. The navigation mark position that meets the preset optimal adjustment conditions is determined based on the score grid.

[0007] Optionally, in one embodiment of this application, before filtering the point set from the water depth point data according to a preset water depth threshold, the method further includes: performing spatial interpolation on the water depth measurement point data to obtain interpolated data, and establishing a grid surface based on the interpolated data; and performing point conversion processing on the grid surface to generate water depth point data that meets the preset uniform distribution conditions.

[0008] Optionally, in one embodiment of this application, the step of filtering a set of points from the water depth point data according to a preset water depth threshold and extracting boundaries from the set of points to generate a water depth polygon based on the boundaries includes: filtering a set of points from the water depth point data that does not exceed the preset water depth threshold and extracting boundaries from the set of points to determine a first boundary of the set of points; filtering a set of points from the water depth point data that exceeds the preset water depth threshold and is not within the first boundary, and determining a second boundary based on the set of points, i.e., determining the water depth polygon.

[0009] Optionally, in one embodiment of this application, adjusting the selection range of the navigation mark to a preset selection range includes: rasterizing the selection range of the navigation mark to obtain a processed selection range of the navigation mark; obtaining an instruction on the bending radius range of the navigation mark, and responding to the instruction to adjust the processed selection range of the navigation mark to the preset selection range.

[0010] A second aspect of this application provides an inland waterway navigation mark distribution adjustment device based on waterway maintenance scale, comprising: a screening module, configured to screen a set of points from water depth point data according to a preset water depth threshold, and extract boundaries from the set of points to generate a water depth polygon based on the boundaries; a determination module, configured to calculate the waterway centerline of the navigation mark based on the position of the navigation mark within a target area, and determine the navigation width polygon based on the waterway centerline and a preset waterway maintenance width; a judgment module, configured to determine the navigation mark placement area polygon based on the water depth polygon and the navigation width polygon, and determine whether the navigation mark is within the navigation mark placement area polygon; and a determination module, configured to determine whether the navigation mark is within the navigation mark placement area polygon. When the navigation mark is placed within the polygonal area, it is determined that the navigation mark meets the preset position rationality conditions; otherwise, the selection range of the navigation mark is adjusted to the preset selection range. The calculation module is used to calculate the distance grid from the pixel center to the channel centerline, the average distance grid from the pixel center to the historical coordinate point, and the weighted average curvature radius value grid within the preset selection range. The adjustment module is used to normalize the distance grid, the average distance grid, and the weighted average curvature radius value grid to obtain the processed grid, and to linearly weight the processed grid to generate a score grid, and to determine the navigation mark position that meets the preset optimal adjustment conditions based on the score grid.

[0011] Optionally, in one embodiment of this application, it further includes: an establishment module, used to perform spatial interpolation on the water depth measurement point data to obtain interpolated data before filtering the point set from the water depth point data according to a preset water depth threshold, and to establish a grid surface based on the interpolated data; and a generation module, used to process the grid surface by point conversion to generate water depth point data that meets a preset uniform distribution condition.

[0012] Optionally, in one embodiment of this application, the filtering module includes: a first filtering unit, configured to filter out a set of points from the water depth point data that do not exceed the preset water depth threshold, and extract a boundary from the set of points to determine a first boundary of the set of points; and a second filtering unit, configured to filter out a set of points from the water depth point data that exceed the preset water depth threshold and are not within the first boundary, and determine a second boundary based on the set of points, i.e., determine the water depth polygon.

[0013] Optionally, in one embodiment of this application, the determination module includes: a processing unit, configured to perform rasterization processing on the selection range of the navigation mark to obtain a processed selection range of the navigation mark; and an adjustment unit, configured to obtain an instruction on the bending radius range of the navigation mark, and in response to the instruction, adjust the processed selection range of the navigation mark to the preset selection range.

[0014] 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 inland waterway navigation mark distribution adjustment method based on waterway maintenance scale as described in the above embodiments.

[0015] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for adjusting the distribution of inland waterway navigation marks based on waterway maintenance standards.

[0016] This application's embodiments can simultaneously consider factors such as maintenance water depth, maintenance navigation width, channel curvature radius, and buoy spacing. It employs a multi-objective optimization algorithm to obtain a score grid, quantitatively describing the adjustment process. This reduces interference from human factors, significantly improves work efficiency, and enhances the accuracy and reliability of buoy placement adjustments, promoting the development of waterway management towards greater intelligence and automation. Therefore, it solves the problems of related technologies being highly dependent on the experience of personnel, easily affected by human factors, limiting the frequency and accuracy of buoy adjustments, and making it difficult to ensure the scientific nature and accuracy of buoy placement.

[0017] 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

[0018] 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:

[0019] Figure 1 This is a flowchart illustrating a method for adjusting the distribution of inland waterway navigation marks based on waterway maintenance standards, according to an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the positional relationship of navigation marks according to an embodiment of the present application, showing how to narrow down the selectable range of target navigation marks based on the bending radius range requirement;

[0021] Figure 3 This is a flowchart of an inland waterway navigation mark distribution adjustment method based on waterway maintenance scale according to an embodiment of this application;

[0022] Figure 4 This is a schematic diagram of an inland waterway navigation mark distribution adjustment device based on waterway maintenance scale, according to an embodiment of this application.

[0023] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0024] 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.

[0025] The following description, with reference to the accompanying drawings, describes an embodiment of an inland waterway navigation mark distribution adjustment method and apparatus based on waterway maintenance dimensions. Addressing the issues mentioned in the background art, such as the high reliance on worker experience and susceptibility to human factors limiting the frequency and accuracy of mark adjustments and hindering the scientific and accurate distribution of navigation marks, this application provides an inland waterway navigation mark distribution adjustment method based on waterway maintenance dimensions. This method simultaneously considers factors such as maintenance depth, maintenance width, waterway curvature radius, and mark spacing. A multi-objective optimization algorithm is used to obtain a score grid, quantitatively describing the adjustment process. This reduces human interference, significantly improves work efficiency, and enhances the accuracy and reliability of mark distribution adjustments, promoting the development of waterway management towards greater intelligence and automation. Therefore, this method solves the problems of high reliance on worker experience, susceptibility to human factors, limited frequency and accuracy of mark adjustments, and difficulty in ensuring the scientific and accurate distribution of navigation marks.

[0026] Specifically, Figure 1 This is a flowchart illustrating a method for adjusting the distribution of inland waterway navigation marks based on waterway maintenance standards, provided in an embodiment of this application.

[0027] like Figure 1 As shown, the method for adjusting the distribution of inland waterway navigation marks based on waterway maintenance standards includes the following steps:

[0028] In step S101, a set of points is selected from the water depth point data according to a preset water depth threshold, and the boundary is extracted from the set of points to generate a water depth polygon based on the boundary.

[0029] It is understood that the boundary extraction algorithm in the embodiments of this application can be the Alpha Shapes boundary extraction algorithm.

[0030] In actual implementation, the embodiments of this application can filter the water depth point data twice according to the water depth threshold, and use the Alpha Shapes boundary extraction algorithm to extract the boundary to obtain the water depth polygon, thereby providing support for obtaining the final buoy placement area polygon.

[0031] It should be noted that the preset water depth threshold can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0032] Optionally, in one embodiment of this application, before selecting a set of points from the water depth point data according to a preset water depth threshold, the method further includes: spatially interpolating the water depth measurement point data to obtain interpolated data, and establishing a grid surface based on the interpolated data; and converting the grid surface to generate water depth point data that meets the preset uniform distribution conditions.

[0033] Understandably, due to the uneven distribution of water depth measurement points, the natural nearest neighbor method is used for spatial interpolation during the preprocessing of water depth measurement points, followed by raster-to-point conversion to generate uniformly distributed water depth point data.

[0034] In this embodiment, the natural nearest neighbor method can be used to spatially interpolate the water depth measurement point data to obtain interpolated data, and a grid surface can be established based on the interpolated data. After the grid is converted to points, uniformly distributed water depth point data can be obtained.

[0035] The embodiments of this application can preprocess the water depth measurement point data to obtain uniformly distributed water depth point data, thereby providing a basis for subsequent screening of water depth point data to obtain water depth polygons.

[0036] It should be noted that the preset uniform distribution conditions can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0037] Optionally, in one embodiment of this application, selecting a set of points from the water depth point data according to a preset water depth threshold and extracting boundaries from the set of points to generate a water depth polygon based on the boundaries includes: selecting a set of points from the water depth point data that does not exceed the preset water depth threshold and extracting boundaries from the set of points to determine a first boundary of the set of points; selecting a set of points from the water depth point data that exceeds the preset water depth threshold and is not within the first boundary, and determining a second boundary based on the set of points, i.e., determining the water depth polygon.

[0038] In actual implementation, the embodiments of this application can calculate the water depth threshold based on the maintained water depth and water depth margin, and filter the water depth point data twice based on the water depth threshold. The first filtering is for point sets that do not meet the water depth threshold requirements, and the AlphaShapes algorithm is used for boundary extraction to determine the first boundary of the point set. The second filtering is for point sets that meet the water depth threshold requirements and are not within the boundary of the point sets that do not meet the water depth requirements, and the AlphaShapes algorithm is used for boundary extraction to obtain the water depth polygon.

[0039] The embodiments of this application can realize the distribution adjustment of navigation marks using a multi-objective optimization algorithm. It takes into account multiple factors such as maintenance water depth, maintenance navigation width, channel curvature radius and navigation mark spacing, thereby improving the accuracy and reliability of navigation mark distribution adjustment and providing a reference for staff.

[0040] It should be noted that the preset water depth threshold can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0041] In step S102, the channel centerline of the navigation mark is calculated based on the position of the navigation mark in the target area, and the navigation width polygon is determined based on the channel centerline and the preset channel maintenance width.

[0042] It is understood that the target area in this application embodiment can be the research area, and the preset channel maintenance width can be half of the channel maintenance width.

[0043] In actual implementation, the embodiments of this application can calculate the channel centerline based on the positions of all navigation marks within the study area, and perform buffer analysis with half the channel maintenance width as the radius to obtain the channel width polygon.

[0044] It should be noted that the preset channel maintenance width can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0045] In step S103, the buoy placement area polygon is determined based on the water depth polygon and the navigation width polygon, and it is determined whether the buoy is within the buoy placement area polygon.

[0046] As one possible implementation method, the embodiments of this application can obtain the final navigation mark placement area polygon by inverting the intersection of the water depth polygon and the navigation width polygon. The white buoy placement area polygon and the red buoy placement area polygon are distinguished according to the Y value of the geometric center of the polygon. If the Y value is large, it is the white buoy placement area polygon; otherwise, it is the red buoy placement area polygon. Each navigation mark is traversed to determine whether the navigation mark is within the navigation mark placement area polygon.

[0047] The embodiments of this application can determine the polygon of the navigation mark placement area and determine whether the navigation mark is within the polygon of the navigation mark placement area. Based on the determination result, the navigation mark can be checked and adjusted in a targeted manner, thereby realizing the generation of navigation mark distribution auxiliary information, reducing the reliance on human experience, and improving the accuracy and reliability of navigation mark distribution.

[0048] In step S104, if the navigation mark is within the polygon of the navigation mark placement area, it is determined that the navigation mark meets the preset position rationality condition; otherwise, the selection range of the navigation mark is adjusted to the preset selection range.

[0049] It is understood that in the embodiments of this application, satisfying the preset location rationality condition can be the condition for satisfying the rationality check; the preset selection range in the embodiments of this application can be the selectable range after the reduction of a single navigation mark.

[0050] In actual implementation, the embodiments of this application can determine that the navigation mark meets certain positional rationality conditions when it is located within the polygon of the navigation mark placement area, thereby realizing the rationality check of the navigation mark position; otherwise, the selection range of the navigation mark is adjusted to the selectable range.

[0051] It should be noted that the preset location rationality conditions and preset selection range can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0052] Optionally, in one embodiment of this application, adjusting the selection range of the navigation mark to a preset selection range includes: rasterizing the selection range of the navigation mark to obtain a processed selection range of the navigation mark; obtaining an instruction on the bending radius range of the navigation mark, and responding to the instruction to adjust the processed selection range of the navigation mark to the preset selection range.

[0053] In actual implementation, the embodiments of this application can be tailored to each target beacon that needs adjustment. Using the navigation marks on both sides as centers and the prescribed spacing between navigation marks as radii, a circle is drawn and its intersection with the corresponding polygonal placement area is taken, thus narrowing down the selectable range of a single navigation mark. Furthermore, embodiments of this application can further reduce the selectable range of a single navigation mark. The selectable range is rasterized, with the center coordinates of each cell ( Using the cell coordinates as the coordinates, calculate the position of each cell coordinate relative to the two navigation marks to the left of the target navigation mark. )and( The radius of the circle containing the target beacon and the positions of one beacon to the left and one beacon to the right of the target beacon. )and( The radius of the circle containing the target beacon and its position relative to the two beacons to the right of the target beacon. )and( The radius of the circle containing the target beacon is used as the bending radius. The selectable range is further narrowed based on the required bending radius. The positional relationship between the target beacon and surrounding beacons is as follows: Figure 2 As shown.

[0054] It should be noted that the preset selection range can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0055] In step S105, the distance grid from the pixel center to the channel centerline, the average distance grid from the pixel center to the historical coordinate point, and the weighted average curvature radius value grid are calculated within the preset selection range.

[0056] In actual implementation, the embodiments of this application can use the coordinates of the center of each pixel within a selectable range to represent the pixel. For the coordinates of each pixel within the selectable range ( The distance grid from the pixel center to the channel centerline, the average distance grid from the pixel center to the historical coordinate point, and the average curvature radius grid of the three circles are calculated.

[0057] In this embodiment of the application, the radius of the circle containing the three points can be used as the bending radius when calculating the bending radius, and the two navigation marks on the left and right sides of the target navigation mark are also taken into account.

[0058] It should be noted that the preset selection range can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0059] In step S106, the distance grid, average distance grid, and weighted average bending radius value grid are normalized to obtain the processed grid. The processed grid is then linearly weighted to generate a score grid. Based on the score grid, the position of the navigation mark that meets the preset optimal adjustment conditions is determined.

[0060] It is understood that the position of the navigation mark that meets the preset optimal adjustment conditions in the embodiments of this application can be the optimal position recommended for adjustment of the target navigation mark; the weights in the linear weighting in the embodiments of this application are determined by the entropy weight method.

[0061] In actual implementation, the embodiments of this application can normalize the distance grid, the average distance grid, and the weighted average bending radius value grid to obtain the processed grid, and use the entropy weight method to obtain the corresponding weights. The processed grid is then linearly weighted to obtain the score grid, where the center coordinates of the pixel with the highest score are the optimal position recommended for adjustment of the target beacon.

[0062] This application embodiment uses the coordinates of the center of the highest-scoring pixel as the optimal location for recommending the target beacon, thereby generating auxiliary information for beacon distribution, reducing reliance on human experience, and improving the accuracy and reliability of beacon distribution.

[0063] It should be noted that the preset optimal adjustment conditions can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0064] Specifically, it can be combined with Figure 3 As shown, a specific embodiment is used to elaborate in detail on the working principle of the inland waterway navigation mark distribution adjustment method based on waterway maintenance scale in this application.

[0065] like Figure 3 As shown, embodiments of this application may include:

[0066] Step S301: Water depth measurement data.

[0067] Step S302: Spatial interpolation and raster conversion.

[0068] Step S303: Maintain water depth.

[0069] Step S304: Boundary extraction to obtain the water depth polygon.

[0070] Step S305: Beacon status data.

[0071] Step S306: Buffer analysis to obtain the flight width polygon.

[0072] Step S307: Maintain navigation width.

[0073] Step S308: Obtain the area where the navigation beacon can be placed by inverting the intersection.

[0074] Step S309: Determine whether the navigation beacon is located within the placement area. If yes, proceed to step S310; otherwise, proceed to step S311.

[0075] Step S310: The beacon's position is reasonable and no adjustment is needed.

[0076] Step S311: The location of the navigation mark is unreasonable and needs to be adjusted.

[0077] Step S312: Beacon spacing.

[0078] Step S313: The target navigation mark and nearby navigation marks that need to be adjusted.

[0079] Step S314: Channel curvature radius range.

[0080] Step S315: Narrow down the range of target beacons.

[0081] Step S316: Calculate the distance grid to the centerline of the waterway.

[0082] Step S317: Calculate the average distance grid to historical coordinate points.

[0083] Step S318: Calculate the average bending radius grid.

[0084] Step S319: Obtain the score grid by linear weighting using the entropy weighting method.

[0085] Step S320: Output the center coordinates of the highest-scoring pixel as the recommended location.

[0086] The inland waterway buoy placement adjustment method based on waterway maintenance scale proposed in this application first determines the placement areas for red and white buoys by comprehensively considering maintenance water depth and maintenance navigation width. It then iterates through all buoys in the waterway, filtering out buoys requiring adjustment based on their actual positions and the topological relationships with their placement areas. For each buoy, the selection range is narrowed down based on buoy spacing and the buoys on either side. Finally, the score for each pixel and the recommended optimal adjustment position are obtained by combining three factors: distance to the waterway centerline, average distance to historical coordinate points, and weighted average curvature radius. This application considers factors such as maintenance water depth, maintenance navigation width, waterway curvature radius, and buoy spacing, and uses a multi-objective optimization algorithm to obtain a score grid, quantitatively describing the adjustment process. This reduces human interference, significantly improves work efficiency, and enhances the accuracy and reliability of buoy placement adjustments, promoting the development of waterway management towards greater intelligence and automation. This solves the problem that the relevant technologies are highly dependent on the experience of the staff, are easily affected by human factors, limit the frequency and accuracy of navigation mark adjustments, and make it difficult to ensure the scientific and accurate distribution of navigation marks.

[0087] Next, referring to the accompanying drawings, an inland waterway navigation mark distribution adjustment device based on waterway maintenance scale is described according to an embodiment of this application.

[0088] Figure 4 This is a schematic diagram of the structure of the inland waterway navigation mark distribution adjustment device based on waterway maintenance scale according to an embodiment of this application.

[0089] like Figure 4 As shown, the inland waterway navigation mark distribution adjustment device 10 based on waterway maintenance scale includes: a screening module 100, a determination module 200, a judgment module 300, a determination module 400, a calculation module 500, and an adjustment module 600.

[0090] Specifically, the filtering module 100 is used to filter out a set of points from the water depth point data according to a preset water depth threshold, and extract the boundary from the set of points to generate a water depth polygon based on the boundary.

[0091] The determination module 200 is used to calculate the channel centerline of the navigation mark based on the position of the navigation mark in the target area, and to determine the navigation width polygon based on the channel centerline and the preset channel maintenance width.

[0092] The judgment module 300 is used to determine the buoy placement area polygon based on the water depth polygon and the navigation width polygon, and to determine whether the buoy is within the buoy placement area polygon.

[0093] The determination module 400 is used to determine whether the navigation beacon meets the preset position rationality conditions when the navigation beacon is within the polygon of the navigation beacon placement area; otherwise, it adjusts the selection range of the navigation beacon to the preset selection range.

[0094] The calculation module 500 is used to calculate the distance grid from the pixel center to the channel centerline, the average distance grid from the pixel center to the historical coordinate point, and the weighted average curvature radius value grid within a preset selection range.

[0095] The adjustment module 600 is used to normalize the distance grid, the average distance grid, and the weighted average bending radius value grid to obtain the processed grid, and to linearly weight the processed grid to generate a score grid, and to determine the position of the navigation mark that meets the preset optimal adjustment conditions based on the score grid.

[0096] Optionally, in one embodiment of this application, the inland waterway navigation mark distribution adjustment device 10 based on waterway maintenance scale further includes: a setup module and a generation module.

[0097] The module is used to perform spatial interpolation on the water depth measurement point data before selecting the point set from the water depth point data according to the preset water depth threshold, so as to obtain interpolated data and establish a grid surface based on the interpolated data.

[0098] The generation module is used to process the raster surface by converting it into points to generate water depth point data that meets the preset uniform distribution conditions.

[0099] Optionally, in one embodiment of this application, the filtering module 100 includes: a first filtering unit and a second filtering unit.

[0100] The first filtering unit is used to filter out a set of points from the water depth data that do not exceed a preset water depth threshold, and extract the boundary from the set of points to determine the first boundary of the set of points.

[0101] The second filtering unit is used to filter out a set of points from the water depth data that exceed a preset water depth threshold and are not within the first boundary, and to determine the second boundary based on the set of points, that is, to determine the water depth polygon.

[0102] Optionally, in one embodiment of this application, the determination module 400 includes a processing unit and an adjustment unit.

[0103] The processing unit is used to rasterize the selection range of navigation marks to obtain the processed selection range of navigation marks.

[0104] The adjustment unit is used to obtain instructions on the range of the beacon's bending radius, and in response to the instructions, adjusts the selected range of the processed beacon to the preset selection range.

[0105] It should be noted that the foregoing explanation of the embodiment of the inland waterway navigation mark distribution adjustment method based on waterway maintenance scale also applies to the inland waterway navigation mark distribution adjustment device based on waterway maintenance scale in this embodiment, and will not be repeated here.

[0106] The inland waterway buoy distribution adjustment device based on waterway maintenance dimensions proposed in this application can simultaneously consider factors such as maintenance water depth, maintenance width, waterway curvature radius, and buoy spacing. It uses a multi-objective optimization algorithm to obtain a score grid, quantitatively describing the adjustment process. This reduces interference from human factors, significantly improves work efficiency, and enhances the accuracy and reliability of buoy distribution adjustments, promoting the development of waterway management towards greater intelligence and automation. Therefore, it solves the problem that related technologies are highly dependent on the experience of personnel, easily affected by human factors, limiting the frequency and accuracy of buoy adjustments, and making it difficult to ensure the scientific and accurate distribution of buoys.

[0107] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0108] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0109] When processor 502 executes the program, it implements the inland waterway navigation mark distribution adjustment method based on waterway maintenance scale provided in the above embodiments.

[0110] Furthermore, electronic devices also include:

[0111] Communication interface 503 is used for communication between memory 501 and processor 502.

[0112] The memory 501 is used to store computer programs that can run on the processor 502.

[0113] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0114] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 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 categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 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.

[0115] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0116] Processor 502 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.

[0117] 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 adjusting the distribution of inland waterway navigation marks based on waterway maintenance standards.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 adjusting the distribution of core navigation marks based on waterway maintenance standards, characterized in that, Includes the following steps: A point set is selected from the water depth point data according to a preset water depth threshold, and a boundary is extracted from the point set to generate a water depth polygon based on the boundary. The process includes: selecting a point set from the water depth point data that does not exceed the preset water depth threshold, and extracting a boundary from the point set to determine a first boundary of the point set; selecting a point set from the water depth point data that exceeds the preset water depth threshold and is not within the first boundary, and determining a second boundary based on the point set, i.e., determining the water depth polygon. Calculate the channel centerline of the navigation mark based on its location within the target area, and determine the navigation width polygon based on the channel centerline and the preset channel maintenance width. The navigation mark placement area polygon is determined based on the water depth polygon and the navigation width polygon, and it is determined whether the navigation mark is within the navigation mark placement area polygon. If the navigation mark is located within the polygon of the navigation mark placement area, the navigation mark is determined to meet the preset location rationality condition; otherwise, the selection range of the navigation mark is adjusted to the preset selection range. Within the preset selection range, calculate the distance grid from the pixel center to the channel centerline, the average distance grid from the pixel center to the historical coordinate point, and the weighted average curvature radius grid, respectively. The distance grid, the average distance grid, and the weighted average bending radius value grid are normalized to obtain a processed grid. The processed grid is then linearly weighted to generate a score grid. The position of a navigation beacon that meets the preset optimal adjustment conditions is determined based on the score grid.

2. The method according to claim 1, characterized in that, Before selecting a set of points from the water depth data based on a preset water depth threshold, the process also includes: Spatial interpolation is performed on the water depth measurement point data to obtain interpolated data, and a grid surface is established based on the interpolated data; The grid surface is processed to generate water depth point data that meets the preset uniform distribution conditions.

3. The method according to claim 1, characterized in that, Adjusting the selection range of the navigation mark to a preset selection range includes: The selection range of the navigation mark is rasterized to obtain the processed selection range of the navigation mark; The system obtains an instruction regarding the range of the beacon's bending radius and, in response to the instruction, adjusts the selected range of the processed beacon to the preset selection range.

4. A device for adjusting the distribution of core navigation marks based on waterway maintenance standards, characterized in that, include: A filtering module is used to filter a set of points from water depth point data according to a preset water depth threshold, and extract boundaries from the set of points to generate a water depth polygon based on the boundaries. The module includes: a first filtering unit, used to filter a set of points from the water depth point data that does not exceed the preset water depth threshold, and extract boundaries from the set of points to determine a first boundary of the set of points; and a second filtering unit, used to filter a set of points from the water depth point data that exceeds the preset water depth threshold and is not within the first boundary, and determine a second boundary based on the set of points, i.e., determine the water depth polygon. The determination module is used to calculate the channel centerline of the navigation mark based on the position of the navigation mark in the target area, and to determine the navigation width polygon based on the channel centerline and the preset channel maintenance width; The judgment module is used to determine the buoy placement area polygon based on the water depth polygon and the navigation width polygon, and to determine whether the buoy is within the buoy placement area polygon. The determination module is used to determine that the navigation mark meets the preset position rationality condition when the navigation mark is located within the polygon of the navigation mark placement area; otherwise, the selection range of the navigation mark is adjusted to the preset selection range. The calculation module is used to calculate the distance grid from the pixel center to the channel centerline, the average distance grid from the pixel center to the historical coordinate point, and the weighted average curvature radius value grid within the preset selection range, respectively. The adjustment module is used to normalize the distance grid, the average distance grid, and the weighted average bending radius value grid to obtain the processed grid, and to linearly weight the processed grid to generate a score grid, and to determine the position of the navigation mark that meets the preset optimal adjustment conditions based on the score grid.

5. The apparatus according to claim 4, characterized in that, Also includes: A module is established to perform spatial interpolation on the water depth measurement point data before selecting a point set from the water depth point data according to a preset water depth threshold, so as to obtain interpolated data, and to establish a grid surface based on the interpolated data. The generation module is used to process the grid surface to generate water depth point data that meets the preset uniform distribution conditions.

6. The apparatus according to claim 4, characterized in that, The determination module includes: The processing unit is used to perform rasterization processing on the selection range of the navigation mark to obtain the processed selection range of the navigation mark; An adjustment unit is used to obtain an instruction on the bending radius range of the navigation mark, and in response to the instruction, adjust the selection range of the processed navigation mark to the preset selection range.

7. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for adjusting the distribution of core navigation marks based on waterway maintenance scales as described in any one of claims 1-3.

8. 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 adjusting the distribution of core navigation marks based on the waterway maintenance scale as described in any one of claims 1-3.

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