A waterway scale automatic checking method, device, equipment and medium based on multi-benchmark surface partition and local refined section

Abstract

This invention discloses an automatic channel dimension verification method, apparatus, equipment, and medium based on multi-datum zoning and local refined cross-sections. The method includes: acquiring channel verification input data; determining the datum water level corresponding to each datum zoning zone; associating sounding points with the corresponding datum zoning zones and calculating the water depth value of each sounding point; generating a first-stage cross-section and performing water depth sampling and navigable width analysis on the first-stage cross-section to obtain initial cross-section analysis results; identifying suspected shallow areas based on the initial cross-section analysis results; generating a second-stage cross-section along the centerline at a second preset cross-section spacing and performing refined analysis based on the second-stage cross-section; calculating the minimum water depth, minimum navigable width, and obstruction zone length based on the refined analysis results, and outputting the verification results. This invention can automatically close the loop between shallow area identification, cross-section refinement, and indicator statistics, reducing inconsistencies in results caused by different personnel, different layers, and different datums.

Description

Technical Field

[0001] This invention relates to the fields of waterway engineering informatization, geospatial data processing, computer-aided design, and intelligent analysis of surveying and mapping data. Specifically, it relates to a method, device, equipment, and medium for automatic verification of waterway dimensions based on multi-datum zoning and local refined cross-sections. Background Technology

[0002] Channel dimension verification is typically used to confirm whether a channel meets the prescribed navigation conditions. Common indicators include minimum water depth, minimum navigation width, and obstruction zone length. In current engineering practice, this work usually relies on technicians manually performing tasks such as river section boundary identification, reference surface zoning interpretation, water level reading, water depth conversion, cross-sectional measurement, shallow area identification, result statistics, and drawing annotation in AutoCAD or other graphic environments.

[0003] For single river sections and small-scale data scenarios, the above method is still feasible; however, in scenarios involving multiple river sections, multiple reference datum zones, and hundreds of thousands to millions of sounding points, the above method has the following problems:

[0004] 1. The reference plane has many zones and the sources are complex. The water level of each zone often needs to be judged manually based on the layer information or adjacent annotations, which is easy to miss or misjudge.

[0005] 2. The amount of depth sounding point cloud data is large. If manual screening or simple scripts are used to process each point, the computational efficiency is low and it is difficult to meet the timeliness requirements of the project.

[0006] 3. If a fixed-interval cross-section is used for full-line analysis, a smaller cross-section interval is usually required to avoid missing shallow areas, which leads to a significant increase in the overall calculation workload.

[0007] 4. If a large cross-sectional spacing is used for rough analysis, it is easy to miss local shallow and narrow areas, thus affecting the accuracy of identifying the minimum navigation width and obstruction length;

[0008] 5. Existing general-purpose CAD / GIS tools typically only provide geometric drawing, local measurement, or single spatial analysis functions, lacking an automated closed-loop processing mechanism suitable for the business logic of waterway engineering;

[0009] 6. The final results still need to be manually written back to the CAD layer and labeled, which involves a lot of repetitive work and is not conducive to a consistent presentation of the results.

[0010] Therefore, there is an urgent need for an automatic channel scale verification technology that can automatically complete datum identification, point cloud water depth conversion, cross section generation and analysis, shallow area refinement identification, and result writing back, so as to balance processing efficiency, verification accuracy, and convenience of engineering application. Summary of the Invention

[0011] The purpose of this invention is to provide an automatic method, device, equipment, and medium for waterway scale verification based on multi-datum zoning and local refined cross-sections, so as to solve the problems of low efficiency in the conversion of depth of sounding point cloud water depth under multi-datum zoning, large calculation burden of cross-section analysis, easy omission of local shallow areas, and inconsistent expression of results in the prior art.

[0012] To achieve the above objectives, the present invention adopts the following technical solution:

[0013] In a first aspect, the present invention provides a randomized analysis method for ice thickness tolerance, comprising the following steps:

[0014] Step 1: Obtain the waterway verification input data and extract the polyline geometry from the waterway verification input data in a unified manner;

[0015] Step 2: Based on the semantic information of the layers and / or the text annotation information near the reference surface partition, determine the reference surface water level corresponding to each reference surface partition;

[0016] Step 3: Based on the spatial inclusion relationship between the sounding points and the datum zone, associate the sounding points with the corresponding datum zone and calculate the water depth value of each sounding point;

[0017] Step 4: Generate the first stage cross section along the centerline at the first preset cross section spacing, and perform water depth sampling and navigable width analysis on the first stage cross section to obtain the initial cross section analysis results;

[0018] Step 5: Identify the suspected shallow area range based on the initial cross-sectional analysis results;

[0019] Step 6: For the suspected shallow area, generate a second-stage cross-section along the centerline at the second preset cross-sectional spacing, and perform a refined analysis based on the second-stage cross-section;

[0020] Step 7: Calculate the minimum water depth, minimum navigation width, and obstruction zone length based on the refined analysis results, and output the verification results.

[0021] Furthermore, the waterway verification input data includes river section boundary data, datum zoning data, left and right sideline data of the navigation channel, centerline data, and bathymetric point cloud data;

[0022] When uniformly extracting the geometry of multi-segment lines from the input data of the waterway inspection in step 1, for multi-segment lines containing arc bulge information, a dual-threshold discretization mechanism is used for discretization processing, and the arc is discretized into a sequence of polyline vertices.

[0023] Furthermore, the process of determining the reference surface water level corresponding to each reference surface partition based on layer semantic information and / or text annotation information near the reference surface partition in step 2 includes:

[0024] Based on the semantic information of the layers, numerical water level information is extracted from the layer name of the reference plane partition;

[0025] If the text annotation is not successfully extracted from the layer name, the text annotation object will be searched within the preset search range near the partition boundary, and the water level information will be extracted from the text annotation information near the reference plane partition.

[0026] If both of the above methods fail, the partition will be marked as a partition awaiting manual confirmation or an invalid partition.

[0027] Furthermore, step 3, which describes associating sounding points with corresponding datum zones based on the spatial inclusion relationship between sounding points and datum zone partitions, and calculating the water depth value of each sounding point, includes:

[0028] Step 3.1: Using the centroid of the reference surface zone as the representative point, associate the centroid with the boundary of each river segment to establish the affiliation relationship between the river segment and the reference surface zone;

[0029] Step 3.2: Perform spatial correlation calculations on the sounding point cloud to determine the reference surface zone to which each sounding point belongs, and establish the spatial affiliation relationship between the river segment, the reference surface zone, and the sounding points;

[0030] Step 3.3: Calculate the water depth of the sounding points in the associated reference surface zone using the water depth conversion formula.

[0031] Furthermore, step 4, which involves generating a first-stage cross-section along the centerline at a first preset cross-sectional spacing, and performing water depth sampling and navigable width analysis on the first-stage cross-section to obtain the initial cross-sectional analysis results, includes:

[0032] Step 4.1: Construct the channel polygon based on the left and right channel edges;

[0033] Step 4.2: Generate the first stage cross section along the centerline at the first preset cross section spacing, and determine the effective cross section located inside the channel based on the channel polygon;

[0034] Step 4.3: For each effective cross section, extract multiple sampling points along the cross section direction according to the preset sampling step size, and estimate the water depth value of each sampling point based on the surrounding depth sounding data;

[0035] Step 4.4: For each effective cross section, identify continuous deep water sections based on the preset minimum navigable water depth threshold, determine the navigable width of each effective cross section, and mark the effective cross sections with water depth less than the minimum navigable water depth threshold to form the initial cross section analysis results.

[0036] Furthermore, step 5, which involves identifying the suspected shallow area based on the initial cross-sectional analysis results, includes:

[0037] Step 5.1: Based on the range of continuous cross-sections identified as having shallow zones in the initial cross-section analysis results, determine the suspected shallow zone intervals;

[0038] Step 5.2: Based on the station distance obtained by projecting the global shallow point or low water depth point along the centerline, expand the nearby interval to form a suspected shallow area supplementary range;

[0039] Step 5.3: Merge the above suspected shallow area intervals and suspected shallow area supplementary ranges to obtain the final suspected shallow area range.

[0040] Furthermore, step 6, which involves generating a second-stage cross-section along the centerline at a second preset cross-sectional spacing for the suspected shallow area, and performing a refined analysis based on the second-stage cross-section, includes:

[0041] Step 6.1: Based on the identified suspected shallow area range, generate a second stage cross section along the center line at a second preset cross section spacing, wherein the second preset cross section spacing is smaller than the first preset cross section spacing.

[0042] Step 6.2: Repeat the cross-section trimming, water depth sampling, continuous deep water zone identification, and navigable width calculation for the second-stage cross-section to obtain refined cross-section analysis results;

[0043] Step 6.3: Combine the cross-sectional analysis results that do not belong to the suspected shallow area in the initial cross-sectional analysis results with the refined cross-sectional analysis results to form the refined analysis results described in Step 7.

[0044] Secondly, the present invention provides an automatic channel dimension verification device based on multi-reference surface partitioning and local refined cross-sections, the device comprising:

[0045] The data acquisition module is used to acquire waterway verification input data and uniformly extract polyline geometry from the waterway verification input data;

[0046] The datum water level identification module is used to determine the datum water level corresponding to each datum partition based on the semantic information of the layer and / or the text annotation information near the datum partition.

[0047] The water depth conversion module is used to associate the sounding points with the corresponding reference surface zones based on the spatial inclusion relationship between the sounding points and the reference surface zones, and to calculate the water depth value of each sounding point.

[0048] The first-stage cross-section generation and initial analysis module is used to generate the first-stage cross-section along the centerline at the first preset cross-section spacing, and to perform water depth sampling and navigable width analysis on the first-stage cross-section to obtain the initial cross-section analysis results.

[0049] The suspected shallow area identification module is used to identify the range of suspected shallow areas based on the initial cross-section analysis results;

[0050] The second-stage local refinement section analysis module is used to generate a second-stage section along the centerline at a second preset section spacing for the suspected shallow area, and to perform refinement analysis based on the second-stage section.

[0051] The indicator calculation and result output module is used to calculate the minimum water depth, minimum navigation width, and obstruction zone length based on the refined analysis results, and output the verification results.

[0052] Thirdly, the present invention provides a computer device, the device including a processor, a memory, and a communication interface; the memory and the communication interface are coupled to the processor, the memory being used to store computer program code, the computer program code including computer instructions; wherein, when the processor executes the computer instructions, the device performs the method as described in the first aspect above.

[0053] Fourthly, the present invention provides a computer-readable storage medium comprising computer instructions that, when executed on a device, cause the device to perform the method as described in the first aspect.

[0054] In this invention, the multi-reference surface point cloud water depth conversion based on waterway verification input data, the two-stage refinement of suspected shallow areas, and the automatic verification of three waterway indicators are not isolated processing steps, but constitute a continuous technical chain for waterway-scale verification: First, the influence of different water level references on the elevation of sounding points is eliminated by the reference surface zoning, so that sounding points of different river sections and different zones are uniformly converted into comparable water depth data; then, the unified water depth data is used to perform coarse cross-section risk screening in the first stage, and suspected shallow areas are determined by combining the projection results of shallow points along the centerline; finally, the second stage of high-density cross-section analysis is performed only in the suspected shallow areas, and the minimum water depth, minimum navigation width, and obstruction zone length are calculated based on the merged cross-section results. Therefore, this invention forms a channel scale verification process that uses point cloud partitioning to determine the true water depth, identifies suspected shallow areas to determine the refined calculation range, and outputs engineering judgment results through three-index verification. This process can simultaneously solve the problems of water depth not being directly comparable under multiple reference water levels, excessive calculation volume for high-density cross sections along the entire line, easy omission of local shallow and narrow areas in low-density cross sections, and difficulty in automatically and uniformly expressing index results.

[0055] Compared with the prior art, the present invention has the following beneficial technical effects:

[0056] 1. This invention automates the conversion of water depth at sounding points by automatically identifying and spatially associating water levels in datum zones, reducing manual intervention, improving consistency, and adapting to multi-river sections and multi-zone scenarios.

[0057] 2. The present invention avoids performing indiscriminate high-density analysis on non-risk areas along the entire line by using a processing flow of first-stage coarse cross-section screening and second-stage local refined cross-section analysis, which significantly reduces the computational redundancy of high-density cross-section analysis along the entire line.

[0058] 3. The present invention is based on a continuous shallow area merging mechanism that combines the suspected shallow area interval and the suspected shallow area supplementary range to form the suspected shallow area range. This mechanism can more effectively capture local shallow areas, narrow areas and continuous obstacle areas, thereby improving the accuracy of minimum navigation width and obstacle length identification.

[0059] 4. This invention provides a unified and accurate water depth field for subsequent cross-sectional analysis by using multi-reference surface partitioning for water depth conversion, avoiding misjudgments caused by directly judging shallow areas based on measurement point elevations or a single water level reference; the first-stage coarse cross-sectional analysis and shallow point projection jointly limit the range of suspected shallow areas, avoiding local shallow point omissions caused by relying solely on cross-sectional sampling; the second stage performs high-density cross-sectional analysis only on suspected shallow areas, concentrating the identification accuracy of minimum navigation width and obstruction zone length on risk areas; finally, the calculation of the three indicators directly inherits the refined water depth and cross-sectional results, ensuring that the three types of results—minimum water depth, minimum navigation width, and obstruction zone length—have the same spatial reference and the same quality control caliber. Compared to schemes that only perform point cloud water depth conversion without cross-section refinement, this invention can output the minimum navigation width and obstruction zone length that can be used for channel scale determination; compared to schemes that only perform cross-section analysis at fixed intervals along the entire line, this invention can reduce redundant cross-sections in non-risk areas by utilizing shallow point projection and suspected area merging; compared to schemes that only perform local manual verification, this invention can automatically close the loop of shallow area identification, cross-section refinement, and index statistics, reducing inconsistencies in results caused by different personnel, different layers, and different reference surfaces.

[0060] 5. This invention supports scenarios involving multiple river sections, multiple zones, and massive depth sounding point clouds, and has good engineering adaptability.

[0061] 6. This invention is linked with the CAD graphics environment, enabling automatic extraction of input geometry and automatic writing back of results, improving project delivery efficiency and consistency, and facilitating the formation of an efficient delivery loop in actual engineering projects.

[0062] 7. In terms of engineering effectiveness, this invention can transform the uniform distribution of computing resources across the entire line into a concentrated distribution towards suspected shallow areas. When the total length of the centerline is L, the first-stage cross-sectional spacing is Δs1, the second-stage cross-sectional spacing is Δs2, and the total length of the merged suspected shallow areas is L... r In contrast, the number of cross-sections in a traditional high-density full-line scheme is approximately L / Δs2, while the number of cross-sections in this invention is approximately L / Δs1 + Lr / Δs2. Since Lr is usually significantly smaller than L, the number of cross-sections and interpolation sampling times can be significantly reduced while maintaining high-resolution analysis of suspected shallow areas. Attached Figure Description

[0063] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0064] Figure 1 This is a flowchart of the method described in this invention;

[0065] Figure 2 A schematic diagram showing the zoning of river sections and the reference surface, and the spatial allocation of sounding points;

[0066] Figure 3 This is a schematic diagram of the first-stage cross-sectional screening and localized refinement of suspected shallow areas;

[0067] Figure 4 This is a schematic block diagram of the device described in this invention;

[0068] Figure 5 This is a schematic block diagram of the device described in this invention. Detailed Implementation

[0069] The present invention will be further described below with reference to the embodiments and accompanying drawings.

[0070] Based on the background information of this application, it is clear that the prior art has at least the following drawbacks:

[0071] Firstly, in multi-reference-plane partitioning scenarios, it is impossible to efficiently and stably associate depth sounding points with corresponding partitions automatically, resulting in water depth conversion relying on manual intervention, which is inefficient and inconsistent.

[0072] Secondly, when faced with massive depth sounding point clouds, if a uniform high-density cross-section analysis is used for the entire line, it will result in a large number of redundant calculations for locations that are not suspected shallow areas, making it difficult to balance efficiency and accuracy.

[0073] Third, if only low-density cross-sectional analysis is used for the entire line, there may be problems with missing detection of local shallow areas, narrow areas and continuous obstacle sections, which is especially obvious in scenarios where the channel edge is tortuous and the reference plane changes frequently.

[0074] Fourth, existing solutions typically separate data reading, calculation and analysis, and result presentation, making it impossible to form an efficient linkage between the CAD graphics environment and the verification calculation environment.

[0075] Therefore, the technical problems solved by this invention are:

[0076] 1. How to automatically extract key geometric elements such as river section boundaries, datum zoning, channel edges, and centerlines from CAD drawing data;

[0077] 2. How to automatically identify the reference surface water level corresponding to each reference surface zone and associate the bathymetry point cloud with the corresponding zone;

[0078] 3. How to automatically calculate water depth based on the reference surface water level and the elevation of the sounding point;

[0079] 4. How to quickly identify suspected shallow areas through coarse cross-section analysis, and then perform local refined cross-section analysis to reduce the overall computational load;

[0080] 5. How to automatically output key waterway dimensional indicators such as minimum water depth, minimum navigation width, and obstruction zone length;

[0081] 6. How to export the verification results as a structured deliverable file and automatically write them back to the CAD layer to improve project delivery efficiency.

[0082] To address the aforementioned issues, the specific implementation process of this application is as follows:

[0083] Example 1:

[0084] like Figure 1 As shown in the figure, this embodiment provides an automatic verification method for waterway dimensions based on multi-reference plane partitioning and local refined cross-sections. The specific steps are as follows:

[0085] Step 1: Obtain the waterway verification input data and extract the polyline geometry from the waterway verification input data in a unified manner;

[0086] Step 2: Based on the semantic information of the layers and / or the text annotation information near the reference surface partition, determine the reference surface water level corresponding to each reference surface partition;

[0087] Step 3: Based on the spatial inclusion relationship between the sounding points and the datum zone, associate the sounding points with the corresponding datum zone and calculate the water depth value of each sounding point;

[0088] Step 4: Generate the first stage cross section along the centerline at the first preset cross section spacing, and perform water depth sampling and navigable width analysis on the first stage cross section to obtain the initial cross section analysis results;

[0089] Step 5: Identify the suspected shallow area range based on the initial cross-sectional analysis results;

[0090] Step 6: For the suspected shallow area, generate a second-stage cross-section along the centerline at the second preset cross-sectional spacing, and perform a refined analysis based on the second-stage cross-section;

[0091] Step 7: Calculate the minimum water depth, minimum navigation width, and obstruction zone length based on the refined analysis results, and output the verification results.

[0092] In practical implementation, the input data for waterway verification includes river section boundary data, datum zoning data, left and right sideline data of the navigation channel, centerline data, and bathymetric point cloud data. Specifically:

[0093] River segment boundary data is used to represent the spatial extent of different river segments, preferably polygonal boundaries corresponding to closed polylines;

[0094] Reference surface partition data is used to represent the boundary of a region with a uniform reference surface water level, preferably a polygonal boundary corresponding to a closed polyline;

[0095] The left and right edge data of the channel are used to represent the channel boundary, preferably two polyline lines on the left and right sides;

[0096] Centerline data is used to represent the direction of the channel center, and is preferably a multi-segment line;

[0097] Sounding point cloud data is used to represent the coordinates of the measurement point and the bottom elevation, including at least three-dimensional coordinates of X, Y, and Z, where Z is the absolute elevation of the riverbed.

[0098] To unify subsequent calculations, this invention transforms polyline objects in CAD into a unified two-dimensional vertex sequence representation. For polyline segments containing bulge arcs, a dual-threshold discretization mechanism is used for discretization, dividing the arc into several intermediate vertices to ensure the stability and accuracy of subsequent spatial calculations. The dual thresholds include at least a maximum chord height constraint and a maximum segment length constraint to balance curve approximation accuracy and computational complexity.

[0099] Specifically, for any bulge arc segment, first calculate the chord vector v = (d... x ,d y ) = (x1-x0, y1-y0), midpoint of chord M = ((x0+x1) / 2, (y0+y1) / 2), and unit normal vector n0 = (-d y ,d x ) / c. Where, d x d y Let be the coordinate increments of point P0 to point P1 along the x-axis and y-axis, respectively, and let c be the chord length from point P0 to point P1, where c = sqrt(d). x ^2+d y ^2), where b is the bulge value of the arc segment. The center O can be represented as O=M+sgn(b)·n0·Rcos(θ / 2), where R is the radius of the arc, θ is the central angle of the arc, and sgn(b) is the sign function of b.

[0100] Let φ0 = atan2(y0 - O) y ,x0-O x ), φ1=atan2(y1-O y ,x1-O x), and correct the angle difference Δφ according to the sign of b. Where φ0 is the polar angle of the starting point P0 relative to the center O, φ1 is the polar angle of the ending point P1 relative to the center O, atan2 is the arctangent function in the four quadrants, and O x O y Let be the x and y coordinates of the center O, respectively; b be the bulbe value of the arc segment; and Δφ be the directional angular difference from φ0 to φ1. After correction, Δφ is a positive counterclockwise angle when b > 0 and a negative clockwise angle when b < 0. The j-th intermediate discrete point Q... j The coordinates are Q j= (O x +Rcos(φ0+jΔφ / n),O y +Rsin(φ0+jΔφ / n)), j=1,2,...,n-1. Where j is the index of the intermediate discrete point, and n is the number of discrete segments of the circular arc.

[0101] In a preferred engineering parameter, the maximum chord height h max Take 0.1m as the maximum segment length l max Take 5.0m; the above parameters can also be adjusted according to the drawing scale, point cloud density, or verification accuracy requirements.

[0102] In a preferred embodiment, the process of determining the reference surface water level corresponding to each reference surface partition based on layer semantic information and / or text annotation information near the reference surface partition in step 2 includes:

[0103] Based on the semantic information of the layers, numerical water level information is extracted from the layer name of the reference plane partition;

[0104] If the text annotation object is not successfully extracted from the layer name, the text annotation object is searched within the preset search range near the partition boundary or the centroid of the partition, and the water level information is extracted from the text annotation information near the reference plane partition. Finally, the reference plane water level is assigned to each successfully identified partition.

[0105] If both of the above methods fail, the partition will be marked as a partition awaiting manual confirmation or an invalid partition.

[0106] Preferably, layer name parsing supports at least one of the following expressions: numerical expressions containing prefixes such as "DATUM", "WL", "datum", "H", or "EL". Text annotation parsing supports numerical expressions containing keywords such as "meter", "m", "datum", and "WL".

[0107] This multi-source fusion approach can improve the success rate of water level identification in different zones and reduce manual intervention caused by non-standard naming of CAD layers.

[0108] It should be noted that, in addition to layer names and adjacent text, the reference water level identification method can also come from attribute blocks, extended data, external configuration tables, or database mapping relationships.

[0109] In a preferred embodiment, the specific implementation process of step 3, which involves associating the sounding points with the corresponding reference surface partitions based on the spatial inclusion relationship between the sounding points and the reference surface partitions, and calculating the water depth value of each sounding point, is as follows:

[0110] Step 3.1: Using the centroid of the reference surface zone as the representative point, associate the centroid with the boundary of each river segment to establish the affiliation relationship between the river segment and the reference surface zone;

[0111] The attribution relationship between each reference surface zone and the river segment is determined through spatial inclusion relationships. Preferably, the centroid of the zone is used as a representative point, and a point-on-polygon test is performed between the centroid and the boundary of each river segment. When the centroid falls inside the boundary of a certain river segment, the zone is associated with the corresponding river segment.

[0112] The centroid of the partition is calculated using the polygon area weighting formula. Let the partition vertex be (x... i ,y i ), i=0,1,...,m-1, where m is the number of vertices of the polygon in this partition; when performing closure processing, let (x m ,y m ) = (x0, y0). Then we have , , Where Σ represents the summation over i=0 to m-1, A is the directed area of ​​the partitioned polygon, and C... x C y Let A and B be the x and y coordinates of the centroid C of the partition, respectively. When |A| approaches 0, it degenerates into the arithmetic mean of the coordinates of each vertex.

[0113] The preferred method for testing points on polygons is the ray method: draw a ray from the point P=(x,y) in the positive x-direction, traversing the edges E of the polygon. i =(x i ,y i )-(x i+1 ,y i+1 If (y) i >y) and (y i+1 >y) XOR holds true, and x <x i +(yy i ) (x i+1 -x i ) / (y i+1 -y i If the number of intersections is odd, then P is determined to be inside the polygon; if the number of intersections is even, then P is determined to be outside the polygon.

[0114] This processing method can quickly establish the affiliation between river segments and zones in most engineering scenarios, and facilitates subsequent statistics by river segment.

[0115] Step 3.2: Perform spatial correlation calculations on the sounding point cloud to determine the reference surface partition to which each sounding point belongs, and establish a system as follows: Figure 2 The spatial assignment of river sections, reference surface zones, and sounding points is shown.

[0116] After obtaining the reference surface water level, this invention performs spatial correlation calculations on the sounding point cloud to determine the reference surface partition to which each sounding point belongs. Preferably, a spatial index structure of the partition boundaries is first established, candidate partitions are pre-screened for the sounding points, and then the final partition is determined by judging the points' positions on polygons.

[0117] Specifically, the system first calculates the axial bounding box B of the polygon partition of each reference plane. z =[x min ,x max ]×[y min ,y max ], and axially surround the box with B z Construct an R-tree spatial index. For the sounding point P... i =(x i ,y i ,z i First, query the query that satisfies x. i ∈[x min ,x max And y i ∈[y min ,y max The candidate partition set C(P) i ), and then for set C(P) i The partitions in the test are executed one by one on the polygon to determine the uniquely belonging partition.

[0118] For sounding points falling near the partition boundary, a boundary tolerance ε can be set, for example, ε = 0.1m; when the shortest distance from the point to any line segment of the partition boundary is dist(P i When E)≤ε, the point is considered to be located within the partition to reduce the impact of CAD boundary discretization error on the assignment results.

[0119] Step 3.3: For any associated sounding point, calculate the water depth of the sounding points within the associated datum zone using the water depth conversion formula. The water depth of the sounding point is represented by the symbol d. i =Hz i -z i , where d i The converted water depth for the i-th sounding point is Hz. i The reference water level of the reference surface zone to which this sounding point belongs, zi Let be the absolute elevation of the riverbed at this sounding point. If the i-th sounding point cannot be matched with any zone, then its zone is... id This is marked as empty, meaning it is not included in the calculation of effective points for minimum water depth and cross-sectional interpolation, but it is included in coverage statistics. Among them:

[0120] When the water depth is greater than zero, it indicates that the measuring point is below the water surface;

[0121] When the water depth is zero, it indicates that the measuring point is located at the water surface.

[0122] When the water depth is less than zero, it indicates that the measuring point is located in an area where a shallow beach or an area above the water surface is exposed.

[0123] For sounding points that cannot be assigned to any reference surface zone, they can be recorded as unassigned points, and a coverage alarm can be output during the quality inspection process.

[0124] The obtained water depth results can be further calculated into point cloud quality statistics: total number of points N. total Number of allocated points N assigned =ΣI(zone id (Not empty), number of unallocated points N unassigned =N total -N assigned Coverage ρ=N assigned / N total Number of negative water depth points N negative =ΣI(d i <0), minimum water depth d min =min(d i Maximum water depth d max =max(d i and average water depth d avg =(1 / N assigned ) Σd i This information is used as a data quality indicator. The above statistics are used for subsequent coverage alerts, abnormal water depth verification, and result table output.

[0125] In practice, the above data quality indicators are applied in the following ways: First, before calculation, they are used to determine whether the reference plane partition covers the main area of ​​the point cloud. For example, when the coverage rate ρ is less than 95%, the user is prompted to check the partition boundary or point cloud coordinates. Second, during calculation, they are used to remove unassigned points to avoid incorrect reference planes from participating in minimum water depth and cross-sectional interpolation. Third, in the result output, they are used as quality fields in the result table for engineers to review abnormal negative water depth points and low coverage areas.

[0126] In some optional embodiments, the specific process of generating the first stage cross section along the centerline at a first preset cross section spacing in step 4, and performing water depth sampling and navigable width analysis on the first stage cross section to obtain the initial cross section analysis results is as follows:

[0127] Step 4.1: In order to limit the scope of verification of minimum water depth and minimum navigation width, construct a navigation channel polygon based on the left and right navigation channel side lines; preferably, the left side line vertex sequence and the right side line vertex sequence are spliced ​​together to form a closed boundary according to the principle of minimizing the cost of endpoint connection, thereby forming a navigation channel region polygon.

[0128] In this example, the principle of minimizing endpoint connection cost is specifically as follows: Let the sequence of vertices on the left side be L = {L1, L2, ..., L...} m The right-side vertex sequence is R = {R1, R2, ..., R}. n}, dist(·) represents the Euclidean distance. Calculate the reverse stitching cost C. rev =dist(L m ,R n )+dist(L1,R1), and the cost of splicing in the same direction C fwd =dist(L m ,R1)+dist(L1,R n Let m and n be the number of vertices in the left and right vertex sequences, respectively. If C rev ≤C fwd Then the vertex sequence of the channel polygon is {L1,...,L...} m ,R n Otherwise, take {L1, ..., L}; m ,R1,...,R n}

[0129] By comparing the costs mentioned above, the system can automatically determine whether the beginning and end directions of the left and right sidelines are consistent, reducing the risk of closed boundary intersections or misalignments caused by manually specifying directions. This allows global sounding points to be limited to the interior of the navigation channel for verification, avoiding the influence of points outside the navigation channel on the results.

[0130] Step 4.2: Construct the navigation channel area according to the left and right side lines of the navigation channel, and generate the first stage cross section along the center line according to the first preset cross section spacing. Then, arrange each cross section perpendicularly to the local direction of the center line, extend it to the left and right sides to a sufficient length, and then find the intersection with the navigation channel area to obtain the effective cross section located inside the navigation channel.

[0131] In this example, the cross-section generation process is as follows: The centerline is represented as a sequence of polyline vertices V0, V1, ..., V k Where k is the index of the vertex at the end of the centerline; the cumulative mileage S0 = 0 is defined. , j=1,2,...,k. Where, S j This represents the path from the starting point V0 of the center line along the broken line to the j-th vertex V. j The cumulative length, t is the summation index, and dist(·) represents the Euclidean distance between two points. For station distance s, find the value that satisfies S. j ≤s≤S j+1 line segment V j V j+1 Let λ=(sS) j ) / dist(V j V j+1 ), where λ is the station distance s on line segment V j V j+1 Given the normalized position parameters, the center point of the cross-section C(s) = V j +λ(V j+1 -V j The local tangent angle of the centerline is α = atan2(y). j+1 -y j ,x j+1 -x j The cross-sectional direction angle β = α + π / 2.

[0132] Let the half-width of the preset cross-section be W, then the two endpoints of the initial cross-section are P. L =C(s)+W(cosβ,sinβ), P R =C(s)-W(cosβ,sinβ). In a preferred implementation, W can be a value sufficient to cover the full width of the channel, such as 2000m, and then the actual effective cross-sectional segment is obtained by intersecting with the channel polygon.

[0133] When cutting the cross section, the cross section is written as Q(t) = P. L +t(P R -P L ), t∈[0,1], write the channel boundary edge as E(u)=A+u(BA), u∈[0,1]. If cross(P R -P L If ,BA) is not 0, then the intersection parameter t = cross(AP) can be obtained. L ,BA) / cross(P R -P L ,BA), u=cross(AP) L ,P R -P L ) / cross(P R -P L,BA). The intersection points satisfying t∈[0,1] and u∈[0,1] participate in the division of the inner and outer intervals of the cross section; the system prioritizes the selection of the inner interval containing the center point of the cross section, and if it does not exist, it selects the inner interval with the longest length.

[0134] Step 4.3: For each effective cross section, extract multiple sampling points along the cross section direction according to the preset sampling step size, and estimate the water depth value of each sampling point based on the surrounding depth sounding data;

[0135] The specific implementation process is as follows: For each valid cross section, the present invention extracts multiple sampling points along the cross section direction according to a preset sampling step size, and estimates the water depth value of each sampling point based on the surrounding depth sounding point data. Preferably, the estimation process adopts a local nearest neighbor interpolation mechanism, such as inverse distance weighted interpolation, k-nearest neighbor weighted interpolation, or other local interpolation methods suitable for engineering depth sounding scenarios.

[0136] When sampling a cross-section, let the effective cross-sectional width be B = dist(P). L ,P R The basic sampling step size is Δq0, and the maximum number of sampling points is M. max Then the actual sampling step size Δq = max(Δq0, B / (M) max -1)), the number of sampling points m=ceil(B / Δq)+1, and the j-th sampling point is Q. j =P L +(j / (m-1))(P R -P L ), j=0,1,...,m-1. In the first stage, Δq0=2m, M can be taken. max =160; In the second stage, Δq0 can be taken as 1m, M max =320.

[0137] The optimal method for estimating water depth at sampling points is k-nearest neighbor inverse distance weighting. Let Q... j Select k effective depth sounding points P in the vicinity i , let r i =dist(Q j ,P i If r exists i If the depth is less than 0.001m, then the water depth d(Q) is taken directly. j )=d i Otherwise, let w i =1 / (r i ^2), and according to the formula d(Q j )=Σ(w i ·d i ) / Σw i Calculate the water depth. In a preferred implementation, k=8.

[0138] To avoid interference from distant points on local cross-sections, the interpolation model can first localize the effective sounding points according to river segments; if there are no effective points in the current river segment, then the global effective points are used instead. For high-density point clouds, conservative thinning can be performed on a grid cell basis: the sounding point with the lowest water depth is retained as a representative point in each grid cell, thereby reducing the computational load while preserving the risk of shallow points.

[0139] Step 4.4: For each effective cross-section, identify continuous deep-water sections based on the preset minimum navigable depth threshold Dmin. Identify the continuous deep-water sections for each cross-section and select the width of the largest continuous deep-water section as the navigable width of that cross-section. This determines the navigable width of each effective cross-section. Specifically:

[0140] When identifying continuous deep-water sections, a state g is defined for each sampling point. j =1(d(Q j )≥Dmin). Adjacent and g j Sampling points with a value of 1 form a continuous deep-water segment, and adjacent points with g j Sampling points with a value of 0 form a shallow water continuous segment. If the start and end distances of a certain deep water continuous segment on the cross-section are a and b respectively, then the width of the segment is ba; the navigable width W of the cross-section is... nav =max(ba).

[0141] If at least one sampling point in a cross section has a water depth less than Dmin, then the effective cross section with a water depth less than the minimum navigable water depth threshold is marked as a shallow cross section, forming the initial cross section analysis results.

[0142] After obtaining the results of the first-stage cross-sectional analysis, the present invention does not immediately recalculate the entire line using a higher density cross-section, but instead prioritizes identifying suspected shallow areas.

[0143] In some preferred embodiments, the suspected shallow area range in step 5 can be determined by at least one of the following methods:

[0144] Step 5.1: Based on the range of continuous cross-sections identified as having shallow zones in the initial cross-section analysis results, determine the suspected shallow zone intervals;

[0145] Step 5.2: Based on the station distance obtained by projecting the global shallow point or low water depth point along the centerline, expand the nearby interval to form a suspected shallow area supplementary range;

[0146] Step 5.3: Merge the above suspected shallow area intervals and suspected shallow area supplementary ranges to obtain the final set of intervals to be refined, which is also the suspected shallow area range.

[0147] Please refer to the appendix for the specific implementation process. Figure 3 Let the first stage cross-sectional spacing be Δs1. If the continuous shallow cross-sections form a station spacing interval [s],a ,s b If ], then expand it to [max(0,s a -Δs1),min(L,s b +Δs1)], where L is the total length of the centerline. For the low-depth measuring point P i You can first put P i Projecting onto the centerline yields the station spacing sp. i Then, with [max(0,sp i -Δs1),min(L,sp i +Δs1)] forms a supplementary suspected interval.

[0148] When merging multiple suspected intervals, first sort them in ascending order by their starting points; if the starting point of the next interval is not greater than the ending point of the current interval, then merge the two into [current starting point, max(current ending point, next interval ending point)]; otherwise, start a new interval. This results in a set of non-overlapping intervals to be refined.

[0149] In a preferred implementation, the second-stage cross-sectional spacing Δs2 can be initially set to min(1.0m, Δs1); if the total length L of all intervals to be refined is... refine Satisfy L refine / Δs2 exceeds the preset maximum number of refined sections N max Then Δs2 is adjusted to min(L) refine / N max ,Δs1), to avoid local refinement leading to uncontrolled computational scale.

[0150] The key to this mechanism is that high-precision analysis resources are concentrated in suspected risk areas, rather than being evenly distributed across all areas of the entire line. This avoids performing indiscriminate high-density analysis on all non-risk areas of the entire line and significantly reduces the computational redundancy of high-density cross-sectional analysis across the entire line.

[0151] In a preferred embodiment, the specific implementation process of step 6, which involves generating a second-stage cross-section along the centerline at a second preset cross-sectional spacing for the suspected shallow area, and performing a refinement analysis based on the second-stage cross-section, is as follows:

[0152] Step 6.1: Based on the identified suspected shallow area range, generate a second-stage cross-section along the centerline at a second preset cross-sectional spacing, wherein the second preset cross-sectional spacing is smaller than the first preset cross-sectional spacing. If necessary, the sampling step size can be reduced simultaneously to improve local resolution;

[0153] Step 6.2: Repeat the cross-section trimming, water depth sampling, continuous deep water zone identification, and navigable width calculation for the second-stage cross-section to obtain refined cross-section analysis results;

[0154] Step 6.3: Combine the cross-sectional analysis results that do not belong to the suspected shallow area in the initial cross-sectional analysis results with the refined cross-sectional analysis results to form the refined analysis results described in Step 7.

[0155] This embodiment, through this combination of coarse and fine approaches, can improve the recognition accuracy of minimum flight width and obstruction zone length while keeping the overall computational scale under control.

[0156] In this embodiment, three key indicators are calculated based on the effective sounding points inside the channel and the final cross-sectional analysis results.

[0157] For the minimum water depth, from the effective sounding points that have completed the zonal water depth conversion and are located inside the navigation channel area, the sounding point with the minimum water depth and a positive value is selected as the minimum water depth point, and its plane coordinates, corresponding riverbed elevation, reference surface water level, and mileage obtained by projection along the centerline are recorded.

[0158] For the minimum navigation width, find the section with the smallest navigable width from all the cross-section analysis results, determine the width of its maximum continuous deep-water section as the minimum navigation width, and record the mileage and section number of the section.

[0159] For the length of the obstruction zone, all sections with shallow zones are sorted according to the centerline mileage, and continuous or adjacent shallow zone sections are merged to form several obstruction zone segments; the length of each obstruction zone segment is determined by the difference between its start and end mileages, and the sum of the lengths of all obstruction zone segments is the total length of the obstruction zone.

[0160] The three indicators can be expressed uniformly using the following formula:

[0161] Minimum water depth: Dmin result =min{d i |P i Located within the sluice polygon, zone id Not empty, d i >0};

[0162] Minimum flight width: Wmin result =min{W nav (s)|W nav (s)>0};

[0163] Total length of obstruction zone: ;

[0164] Where [s] sr ,s er [ ] represents the starting and ending mileages after the merging of the r-th consecutive shallow section cross-section.

[0165] The minimum water depth point is calculated by projecting the distance along the centerline onto a polyline. For the centerline segment AB, let τ = clip(((PA)·(BA)) / ||BA||^2,0,1), the projection point is A+τ(BA), and the distance corresponding to the segment with the minimum distance is S(A)+τ||BA||.

[0166] In practice, step 7 will also export the calculated minimum water depth point, obstruction section, and sub-section statistical results into structured output files, such as CSV, GeoJSON, or other engineering-acceptable formats.

[0167] Simultaneously, the system writes the minimum water depth point into the designated CAD layer and automatically generates text labels near it; it also writes the obstruction section into the designated CAD layer and labels its corresponding length information. This allows engineers to directly view and verify the results within the CAD graphics environment, reducing the need for secondary processing.

[0168] In this example, the method further includes a quality inspection step, which includes at least: reference surface zone overlap check; sounding point zone coverage check; reference surface water level rationality check; centerline and channel edge line consistency check; and low confidence section statistics.

[0169] The process for determining low-confidence sections is as follows: assign an interpolation confidence level c to each sampling point within the section. j When the water depth is obtained from the sampling points through a local triangulation or a direct high-confidence model, c j A value of 1.0 can be used; when the water depth is obtained by weighting the sampling points using the inverse k-nearest neighbor distance, c j A value of 0.7 can be used; when there are no available nearby sounding points at the sampling point and a reliable estimate cannot be made, c j Set the confidence level to 0. Cross-sectional confidence level C section =(1 / m)Σc j , where m is the number of sampling points for that cross section.

[0170] If Csection < 0.5, then the section is marked as a low-confidence section; if the average section confidence level of a river segment is C... avg =(Σ(C section ·m section )) / Σm section Below 0.6, or cross-sectional sampling coverage C overage =1-N nodata / N sample If the value is below 0.9, a corresponding alarm will be output in the quality inspection results.

[0171] After verification, this invention can output at least one of the following results: a three-index result table summarized by river section and globally; spatial result files such as minimum water depth point and obstruction section; and annotation results written back to CAD layers. Among them, the results written back to CAD can at least include the minimum water depth point layer, the obstruction section layer, and related text annotations.

[0172] It should be noted that, in addition to CAD write-back, the output format may also include Web visualization, GIS service publishing, or push to the engineering management system interface.

[0173] Specific numerical examples and index applications of the method described in this embodiment:

[0174] In a specific case, a river section A contains two reference surface zones, Z1 and Z2, with reference surface water levels of H1=186.50m and H2=185.80m, respectively. Sounding point P1=(1200.00,2300.00,183.72) spatially falls into Z1, so d1=186.50-183.72=2.78m; sounding point P2=(1260.00,2335.00,183.50) spatially falls into Z2, so d2=185.80-183.50=2.30m. If the minimum navigable depth Dmin=2.70m, then P1 is a valid deep-water point that meets the depth requirement, and P2 is a shallow point and participates in the identification of suspected shallow areas.

[0175] For a circular arc containing a radius of 0.4142, if the chord lengths at the start and end points are c = 30.00 m, then θ = 4arctan(0.4142) ≈ 90°, R = c / (2sin45°) ≈ 21.21 m, and h = R(1 - cos45°) ≈ 6.21 m. When h... max =0.1m, l max When the value is 5m, we can take n=max(2,ceil(6.21 / 0.1),ceil(30 / 5))=63, and generate 62 intermediate points accordingly, so that the arc can participate in the subsequent spatial inclusion and clipping calculations as a polyline sequence.

[0176] If, on an effective cross-section with a station spacing of 350m, the cross-sectional width B = 80m within the navigation channel, and the basic sampling step size for the first stage is 2m, then the number of sampling points is approximately ceil(80 / 2) + 1 = 41. After obtaining the cross-sectional water depth sequence through k-nearest neighbor inverse distance weighted interpolation, if the continuous deep-water sections satisfying d ≥ 2.70m are [0m, 32m] and [45m, 80m], then the navigable width of this cross-section is max(32, 35) = 35m; the intermediate section [32m, 45m] is determined to be a shallow-water section.

[0177] If the corresponding station spacings of continuous shallow sections are 300m, 350m, and 400m, then the obstruction section [300m, 400m] is obtained by merging them, with an obstruction length of 100m. If the first-stage section spacing is 50m, then the range to be refined can be expanded to [250m, 450m], and the second-stage sections can be generated within this range at 1m or adaptive spacing. The final output of minimum water depth, minimum navigation width, and obstruction length is used to determine whether the river section meets the waterway grade scale requirements, and is simultaneously written to the CSV / GeoJSON results and CAD annotation layers.

[0178] Compared to uniformly applying 1m high-density cross sections across the entire line, the above two-stage processing only refines the results in suspected risk areas such as [250m, 450m]; the results of the first-stage cross section analysis are retained in other non-risk areas, thereby reducing the number of redundant cross sections and the amount of interpolation calculations without sacrificing the accuracy of shallow area identification.

[0179] Case Study: Assume the total length of the centerline to be verified is L = 5000m, the average effective cross-sectional width within the channel is B = 80m, the first-stage cross-sectional spacing Δs1 = 50m, the second-stage cross-sectional spacing Δs2 = 1m, and the cross-sectional sampling step size is 2m for the first stage and 1m for the second stage. If a high-density cross-section of 1m is used along the entire line, approximately 5001 cross-sections need to be generated, with approximately 81 sampling points per cross-section, resulting in approximately 405081 interpolation sampling operations.

[0180] When using this invention, approximately 101 coarse cross-sections are generated in the first stage, with approximately 41 sampling points per cross-section. The number of interpolation sampling operations in the first stage is approximately 4141. If the first stage identifies and merges the suspected shallow region, the total length L is... r If the length is 600m, then the second stage will generate approximately 601 refined cross-sections, with approximately 81 sampling points per cross-section. The number of interpolation samplings in the second stage will be approximately 48,681. The total number of interpolation samplings for both stages is approximately 52,822, which is only about 13.0% of the total number of high-density schemes for the entire line.

[0181] If only a 50m low-density section is used, the number of sections is approximately 101. Although the computational load is small, when a local shallow and narrow area is located between two adjacent coarse sections, it may not be able to reflect the minimum navigation width position and the true length of the continuous obstruction zone. This invention forms a supplementary suspected interval by projecting the shallow point along the centerline. Even if the shallow point does not fall exactly on the coarse section, it can trigger local refinement, thereby reducing the risk of missed detection by low-density sections.

[0182] Therefore, compared with the high-density scheme across the entire line, the present invention achieves the technical effect of reducing the amount of computation; compared with the low-density scheme across the entire line, it achieves the technical effect of reducing the risk of missed detection and improving the recognition accuracy of minimum navigation width and obstruction zone length; and compared with the manual CAD verification scheme, it achieves the technical effect of consistency in multi-reference surface water depth conversion, shallow area positioning, and three-index output.

[0183] Example 2:

[0184] like Figure 4 As shown, this embodiment provides an automatic channel dimension verification device based on multi-reference surface partitioning and local refined cross-sections. The device includes:

[0185] The data acquisition module is used to acquire waterway verification input data and uniformly extract polyline geometry from the waterway verification input data;

[0186] The datum water level identification module is used to determine the datum water level corresponding to each datum partition based on the semantic information of the layer and / or the text annotation information near the datum partition.

[0187] The water depth conversion module is used to associate the sounding points with the corresponding reference surface zones based on the spatial inclusion relationship between the sounding points and the reference surface zones, and to calculate the water depth value of each sounding point.

[0188] The first-stage cross-section generation and initial analysis module is used to generate the first-stage cross-section along the centerline at the first preset cross-section spacing, and to perform water depth sampling and navigable width analysis on the first-stage cross-section to obtain the initial cross-section analysis results.

[0189] The suspected shallow area identification module is used to identify the range of suspected shallow areas based on the initial cross-section analysis results;

[0190] The second-stage local refinement section analysis module is used to generate a second-stage section along the centerline at a second preset section spacing for the suspected shallow area, and to perform refinement analysis based on the second-stage section.

[0191] The indicator calculation and result output module is used to calculate the minimum water depth, minimum navigation width, and obstruction zone length based on the refined analysis results, and output the verification results.

[0192] Example 3:

[0193] like Figure 5 As shown, this embodiment provides a device based on the above-described overall reliability assessment method, the device comprising:

[0194] One or more processors, one or more power supplies, one or more operating systems, one or more databases, memory, one or more network interfaces and one or more input / output interfaces; wherein the memory stores one or more programs;

[0195] When the one or more programs are executed by the one or more processors, the one or more processors perform the steps of the method as described in Embodiment 1.

[0196] It should be understood that, in the embodiments of the present invention, the processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0197] The general-purpose processor can be a microprocessor or any conventional processor. The memory can include read-only memory and random access memory, providing instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory can also store device type information. The power supply is sufficient to meet the power requirements for normal operation or overclocking of the computer equipment. The operating system, for example... When choosing an operating system, such as TM, MacOSX TM, Unix TM, Linux TM, etc., pay attention to the compatibility between the version of the code being run and the operating system.

[0198] Example 4:

[0199] This embodiment provides a computer-readable storage medium including computer instructions that, when executed on a device, cause the device to perform the steps of the method described in Embodiment 1.

[0200] The computer-readable storage medium can be an internal storage unit of the controller, such as the controller's hard drive or memory. The computer-readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the controller. The computer-readable storage medium may also include both internal storage units and external storage devices of the controller.

[0201] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0202] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus terminals (systems), and computer program products according to the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus terminal, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0203] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device terminal to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0204] These computer program instructions can also be loaded onto a computer or other programmable data processing device terminal, causing a series of operational steps to be executed on the computer or other programmable device terminal to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device terminal for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0205] In summary, this invention first eliminates the influence of different water level benchmarks on the elevation of sounding points by assigning benchmark zones, thus unifying the sounding points of different river sections and zones into comparable water depth data. Then, this unified water depth data is used for coarse cross-section risk screening in the first stage, and suspected shallow areas are identified by combining the projection results of shallow points along the centerline. Finally, a second stage of high-density cross-section analysis is performed only within the suspected shallow areas, and the minimum water depth, minimum navigation width, and obstruction zone length are calculated based on the merged cross-section results. Therefore, this invention forms a navigation scale verification process that uses point cloud zone conversion to determine the basis for true water depth, identifies suspected shallow areas to determine the refined calculation range, and outputs engineering judgment results through three-index verification. This process can simultaneously solve the problems of incomparable water depths under multiple benchmark water levels, excessive calculation volume for high-density cross-sections along the entire line, easy omission of local shallow and narrow areas in low-density cross-sections, and difficulty in automatically and uniformly expressing index results. Compared to schemes that only perform point cloud water depth conversion without cross-section refinement, this invention can output the minimum navigation width and obstruction zone length that can be used for channel scale determination; compared to schemes that only perform cross-section analysis at fixed intervals along the entire line, this invention can reduce redundant cross-sections in non-risk areas by utilizing shallow point projection and suspected area merging; compared to schemes that only perform local manual verification, this invention can automatically close the loop of shallow area identification, cross-section refinement, and index statistics, reducing inconsistencies in results caused by different personnel, different layers, and different reference surfaces.

[0206] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention. Those skilled in the art, under the guidance of the present invention, can make various similar representations without departing from the spirit and claims of the present invention, and such modifications all fall within the protection scope of the present invention.

Claims

1. A method for automatic verification of waterway dimensions based on multi-reference surface partitioning and local refined cross-sections, characterized in that, Includes the following steps: Step 1: Obtain the waterway verification input data and extract the polyline geometry from the waterway verification input data in a unified manner; Step 2: Based on the semantic information of the layers and / or the text annotation information near the reference surface partition, determine the reference surface water level corresponding to each reference surface partition; Step 3: Based on the spatial inclusion relationship between the sounding points and the datum zone, associate the sounding points with the corresponding datum zone and calculate the water depth value of each sounding point; Step 4: Generate the first stage cross section along the centerline at the first preset cross section spacing, and perform water depth sampling and navigable width analysis on the first stage cross section to obtain the initial cross section analysis results; Step 5: Identify the suspected shallow area range based on the initial cross-sectional analysis results; Step 6: For the suspected shallow area, generate a second-stage cross-section along the centerline at the second preset cross-sectional spacing, and perform a refined analysis based on the second-stage cross-section; Step 7: Calculate the minimum water depth, minimum navigation width, and obstruction zone length based on the refined analysis results, and output the verification results.

2. The automatic channel dimension verification method based on multi-reference surface partitioning and local refined cross-sections according to claim 1, characterized in that, The input data for waterway verification includes river section boundary data, datum zoning data, left and right sideline data of the channel, centerline data, and bathymetric point cloud data. When uniformly extracting the geometry of multi-segment lines from the input data of the waterway inspection in step 1, for multi-segment lines containing arc bulge information, a dual-threshold discretization mechanism is used for discretization processing, and the arc is discretized into a sequence of polyline vertices.

3. The automatic channel dimension verification method based on multi-reference surface partitioning and local refined cross-sections according to claim 1, characterized in that, Step 2, which describes the process of determining the reference surface water level corresponding to each reference surface partition based on layer semantic information and / or text annotation information near the reference surface partition, includes: Based on the semantic information of the layers, numerical water level information is extracted from the layer name of the reference plane partition; If the text annotation is not successfully extracted from the layer name, the text annotation object will be searched within the preset search range near the partition boundary, and the water level information will be extracted from the text annotation information near the reference plane partition. If both of the above methods fail, the partition will be marked as a partition awaiting manual confirmation or an invalid partition.

4. The automatic channel dimension verification method based on multi-reference surface partitioning and local refined cross-sections according to claim 1, characterized in that, Step 3, which describes associating sounding points with their corresponding datum zones based on the spatial inclusion relationship between the sounding points and the datum zone, and calculating the water depth value for each sounding point, includes: Step 3.1: Using the centroid of the reference surface zone as the representative point, associate the centroid with the boundary of each river segment to establish the affiliation relationship between the river segment and the reference surface zone; Step 3.2: Perform spatial correlation calculations on the sounding point cloud to determine the reference surface zone to which each sounding point belongs, and establish the spatial affiliation relationship between the river segment, the reference surface zone, and the sounding points; Step 3.3: Calculate the water depth of the sounding points in the associated reference surface zone using the water depth conversion formula.

5. The automatic channel dimension verification method based on multi-reference surface partitioning and local refined cross-sections according to claim 1, characterized in that, Step 4, which involves generating a first-stage cross-section along the centerline at a first preset cross-sectional spacing and performing water depth sampling and navigable width analysis on the first-stage cross-section to obtain the initial cross-sectional analysis results, includes: Step 4.1: Construct the channel polygon based on the left and right channel edges; Step 4.2: Generate the first stage cross section along the centerline at the first preset cross section spacing, and determine the effective cross section located inside the channel based on the channel polygon; Step 4.3: For each effective cross section, extract multiple sampling points along the cross section direction according to the preset sampling step size, and estimate the water depth value of each sampling point based on the surrounding depth sounding data; Step 4.4: For each effective cross section, identify continuous deep water sections based on the preset minimum navigable water depth threshold, determine the navigable width of each effective cross section, and mark the effective cross sections with water depth less than the minimum navigable water depth threshold to form the initial cross section analysis results.

6. The automatic channel dimension verification method based on multi-reference surface partitioning and local refined cross-sections according to claim 1, characterized in that, Step 5, which describes the process of identifying the suspected shallow area based on the initial cross-sectional analysis results, includes: Step 5.1: Based on the range of continuous cross-sections identified as having shallow zones in the initial cross-section analysis results, determine the suspected shallow zone intervals; Step 5.2: Based on the station distance obtained by projecting the global shallow point or low water depth point along the centerline, expand the nearby interval to form a suspected shallow area supplementary range; Step 5.3: Merge the above suspected shallow area intervals and suspected shallow area supplementary ranges to obtain the final suspected shallow area range.

7. The automatic channel dimension verification method based on multi-reference surface partitioning and local refined cross-sections according to claim 1, characterized in that, Step 6, which describes generating a second-stage cross-section along the centerline at a second preset cross-sectional spacing for the suspected shallow area, and performing a refined analysis based on the second-stage cross-section, includes: Step 6.1: Based on the identified suspected shallow area range, generate a second stage cross section along the center line at a second preset cross section spacing, wherein the second preset cross section spacing is smaller than the first preset cross section spacing. Step 6.2: Repeat the cross-section trimming, water depth sampling, continuous deep water zone identification, and navigable width calculation for the second-stage cross-section to obtain refined cross-section analysis results; Step 6.3: Combine the cross-sectional analysis results that do not belong to the suspected shallow area in the initial cross-sectional analysis results with the refined cross-sectional analysis results to form the refined analysis results described in Step 7.

8. An automatic channel dimension verification device based on multi-reference surface partitioning and local refined cross-sections, characterized in that, The device includes: The data acquisition module is used to acquire waterway verification input data and uniformly extract polyline geometry from the waterway verification input data; The datum water level identification module is used to determine the datum water level corresponding to each datum partition based on the semantic information of the layer and / or the text annotation information near the datum partition. The water depth conversion module is used to associate the sounding points with the corresponding reference surface zones based on the spatial inclusion relationship between the sounding points and the reference surface zones, and to calculate the water depth value of each sounding point. The first-stage cross-section generation and initial analysis module is used to generate the first-stage cross-section along the centerline at the first preset cross-section spacing, and to perform water depth sampling and navigable width analysis on the first-stage cross-section to obtain the initial cross-section analysis results. The suspected shallow area identification module is used to identify the range of suspected shallow areas based on the initial cross-section analysis results; The second-stage local refinement section analysis module is used to generate a second-stage section along the centerline at a second preset section spacing for the suspected shallow area, and to perform refinement analysis based on the second-stage section. The indicator calculation and result output module is used to calculate the minimum water depth, minimum navigation width, and obstruction zone length based on the refined analysis results, and output the verification results.

9. A computer device, characterized in that, The device includes a processor, a memory, and a communication interface; the memory and the communication interface are coupled to the processor, the memory being used to store computer program code, the computer program code including computer instructions; wherein, when the processor executes the computer instructions, the device performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer instructions that, when executed on the device, cause the device to perform the method as described in any one of claims 1-7.

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