Multi-beam sounding point cloud data precision evaluation method and device, and medium

Abstract

The invention discloses a multi-beam sounding point cloud data precision evaluation method and device and a medium, and the method comprises the steps: carrying out the partitioning storage of data in the range of all data according to the maximum distance value of a coincident point and a plurality of multiples of the distance value, and enabling each partition to correspond to a row ID and a column ID; when a water depth non-conformity value is calculated, searching for coincidence points is carried out zone by zone, data of all point clouds of a contact measuring line of a certain zone and data of all point clouds of a zone corresponding to a main measuring line are extracted point by point, the distance between a measuring point of the main measuring line and a comparison point of the measuring point of the contact measuring line in the zone is calculated, and the point with the minimum distance value is selected; and if the distance between the point and the comparison point meets the requirement of the coincident point, calculating a depth inconsistent value of the coincident point and storing the depth inconsistent value, and finally counting and calculating precision information according to all comparison statistical results. According to the method, the precision evaluation calculation can be quickly performed on the multi-beam water depth data, the precision of the multi-beam measurement result data can be accurately evaluated, and the method can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device and medium for evaluating the accuracy of multi-beam bathymetric point cloud data. Background Art

[0002] Multi-beam bathymetry has become one of the main means of bathymetry due to its high efficiency and full coverage. Multi-beam measurement data is collected dynamically and is affected by sea conditions, tides, human factors, etc., resulting in various errors. To evaluate the data quality of multi-beam measurement results, it is necessary to conduct an accuracy assessment of the data.

[0003] To evaluate the accuracy of multi-beam water depth measurement data, the mainstream method in the industry is to grid the main survey line and the contact survey line respectively, and then calculate the depth discrepancy value of the coincidence point and the multi-beam measurement accuracy of the grid data. Although the multi-beam water depth accuracy assessment using gridded water depth grid data has the characteristics of small calculation amount and fast speed, it is limited by the gridding method and parameters, and there is a phenomenon of water depth accuracy loss. Summary of the invention

[0004] The present invention aims to solve the problem of related technical limitations at least to a certain extent. To this end, the present invention proposes a multi-beam bathymetric point cloud data accuracy assessment method, device and medium, which can accurately assess the accuracy of multi-beam water depth measurement data.

[0005] On the one hand, an embodiment of the present invention provides a method for evaluating the accuracy of multi-beam bathymetric point cloud data, comprising:

[0006] The multi-beam bathymetric point cloud data to be evaluated is obtained as the first point cloud data; the first point cloud data includes the second point cloud data of the main survey line and the third point cloud data of the contact survey line; each point in the first point cloud data includes the data plane coordinates of the corresponding survey point and the depth value of the water depth;

[0007] Determine the coordinate range according to the maximum values ​​of the data plane coordinates of all points in the first point cloud data;

[0008] The coordinate range is partitioned based on the preset block side length to obtain a plurality of partition blocks; each partition block is marked with a partition number, which includes a row number and a column number;

[0009] Based on the data plane coordinates of each point in the first point cloud data, the partition number is matched, and the first point cloud data is stored point by point in the corresponding partition block;

[0010] Traversing the partition blocks based on a preset order, searching for the coincidence points of the second point cloud data and the third point cloud data in each partition block in turn, and then determining the water depth discrepancy value of the coincidence point;

[0011] The water depth discrepancies corresponding to all coincident points are statistically analyzed, and the accuracy evaluation results of the multi-beam bathymetric point cloud data are obtained based on the statistical results.

[0012] Optionally, the maximum values ​​include a first maximum value and a first minimum value corresponding to the plane horizontal coordinate and a second maximum value and a second minimum value corresponding to the plane vertical coordinate; determining the coordinate range according to the maximum values ​​of the data plane coordinates of all points in the first point cloud data includes the following steps:

[0013] Determine the plane abscissa range according to the first maximum value and the first minimum value;

[0014] Determine the plane ordinate range according to the second maximum value and the second minimum value;

[0015] The coordinate range is determined based on the plane abscissa range and the plane ordinate range.

[0016] Optionally, the coordinate range includes a plane horizontal coordinate range and a plane vertical coordinate range; partitioning the coordinate range based on a preset block side length to obtain a plurality of partitioned blocks includes the following steps:

[0017] Obtaining the block side length based on a preset multiple value of the preset maximum distance value of the coincidence points;

[0018] The ratio of the first range value of the plane vertical coordinate range to the side length of the block is rounded down, and then the result of the rounding down operation is added by 1 to obtain the number of partition rows;

[0019] The ratio of the second range value of the plane horizontal coordinate range to the block side length is rounded down, and then 1 is added to the result of the rounding down operation to obtain the number of partition columns;

[0020] Partition the coordinate range based on the number of partition rows and the number of partition columns to obtain multiple partition blocks;

[0021] The partition number is determined based on the row and column of the partition block; the total number of partition blocks is the product of the number of partition rows and the number of partition columns.

[0022] Optionally, based on matching the partition number with the data plane coordinates of each point in the first point cloud data, storing the first point cloud data point by point in the corresponding partition block includes the following steps:

[0023] Traverse the second point cloud data of all main survey lines, match the partition number based on the data plane coordinates of each point, and store the second point cloud data point by point in the corresponding partition block;

[0024] The third point cloud data of all contact survey lines are traversed, the partition number is matched based on the data plane coordinates of each point, and the third point cloud data is stored in the corresponding partition block point by point.

[0025] Optionally, matching the partition number based on the data plane coordinates of each point includes the following steps:

[0026] The traversed point is taken as the target point, and the data plane coordinates of the target point are obtained; the data plane coordinates include abscissa and ordinate;

[0027] Determine a first interval value according to the ordinate of the target point and the minimum ordinate value of the coordinate range;

[0028] The ratio of the first interval value to the block side length is rounded down, and then 1 is added to the result of the rounding down operation to obtain the row number of the partition block where the target point will be stored;

[0029] Determine the second interval value according to the abscissa of the target point and the minimum abscissa value of the coordinate range;

[0030] The ratio of the second interval value to the block side length is rounded down, and then 1 is added to the result of the rounding down operation to obtain the column number of the partition block where the target point will be stored.

[0031] Optionally, traversing the partition blocks based on a preset order, searching for coincident points of the second point cloud data and the third point cloud data in each partition block in turn, and then determining the water depth discrepancy value of the coincident point, comprises the following steps:

[0032] The partition block of the first row and the first column is taken as the target block;

[0033] Acquire the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block;

[0034] The first point in the fifth point cloud data is used as the first comparison point;

[0035] Searching the fourth point cloud data for a point closest to the first comparison point as the second comparison point;

[0036] When the distance between the first comparison point and the second comparison point is less than the preset maximum distance between the coincidence points, the first comparison point and the second comparison point are determined to be coincidence points;

[0037] Determine the difference between the depth value of the second comparison point and the depth value of the first comparison point as the water depth discrepancy value of the corresponding coincident point;

[0038] The next point of the first comparison point in the fifth point cloud data is used as the first comparison point, and the step of searching the fourth point cloud data for the point closest to the first comparison point as the second comparison point is returned to be executed until all the fifth point cloud data are traversed;

[0039] Based on the preset row and column traversal order, the next partition block of the target block is taken as the target block, and the step of obtaining the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block is returned to execute until all partition blocks are traversed.

[0040] Optionally, when no coincidence point is found after traversing all fifth point cloud data, the method further comprises the following steps:

[0041] The block coordinate range of the target block is obtained based on the partition number of the target block and the block side length; the block coordinate range includes the minimum horizontal coordinate value of the block, the maximum horizontal coordinate value of the block, the minimum vertical coordinate value of the block, and the maximum vertical coordinate value of the block;

[0042] The first point in the fifth point cloud data is used as the third comparison point;

[0043] Based on the data plane coordinates of the third comparison point and the block coordinate range, conditional comparison is performed on each adjacent block of the target block;

[0044] Among them, the expression of conditional comparison is:

[0045]

[0046] Where, (X1, Y1) represents the data plane coordinates of the third comparison point; Xmin_n represents the minimum horizontal coordinate value of the block; Xmax_n represents the maximum horizontal coordinate value of the block; Ymin_n represents the minimum vertical coordinate value of the block; Ymax_n represents the maximum vertical coordinate value of the block; d represents the maximum distance value of the coincident point; (n row ,n col ) represents the row number and column number corresponding to the partition number of the target block; (n′ row ,n′ col ) represents the row and column numbers corresponding to the partition numbers of adjacent blocks in sequence;

[0047] When there is an adjacent block that meets the preset conditions, obtain the sixth point cloud data of the main survey line in the adjacent block that meets the preset conditions;

[0048] Searching the sixth point cloud data for a point closest to the third comparison point as a fourth comparison point;

[0049] When the distance between the third comparison point and the fourth comparison point is less than the preset maximum distance between the coincidence points, the third comparison point and the fourth comparison point are determined to be coincidence points;

[0050] Determine the difference between the depth value of the fourth comparison point and the depth value of the third comparison point as the water depth discrepancy value of the corresponding coincident point;

[0051] The next point of the third comparison point in the fifth point cloud data is taken as the third comparison point, and the step of performing conditional comparison on each adjacent block of the target block based on the data plane coordinates and the block coordinate range of the third comparison point is returned until all the fifth point cloud data are traversed.

[0052] On the other hand, an embodiment of the present invention provides a multi-beam bathymetric point cloud data accuracy assessment device, comprising:

[0053] The first module is used to obtain the multi-beam bathymetric point cloud data to be evaluated as the first point cloud data; the first point cloud data includes the second point cloud data of the main survey line and the third point cloud data of the contact survey line; each point in the first point cloud data includes the data plane coordinates of the corresponding survey point and the depth value of the water depth;

[0054] The second module is used to determine the coordinate range according to the maximum value of the data plane coordinates of all points in the first point cloud data;

[0055] The third module is used to partition the coordinate range based on the preset block side length to obtain multiple partition blocks; each partition block is marked with a partition number, and the partition number includes a row number and a column number;

[0056] A fourth module is used to match the partition number based on the data plane coordinates of each point in the first point cloud data, and store the first point cloud data point by point into the corresponding partition block;

[0057] The fifth module is used to traverse the partition blocks based on a preset order, and sequentially search for the coincidence points of the second point cloud data and the third point cloud data in each partition block, and then determine the water depth discrepancy value of the coincidence point;

[0058] The sixth module is used to count the water depth discrepancies corresponding to all overlapping points and obtain the accuracy evaluation results of the multi-beam bathymetric point cloud data based on the statistical results.

[0059] Optionally, the fifth module is specifically used for:

[0060] The partition block of the first row and the first column is taken as the target block;

[0061] Acquire the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block;

[0062] The first point in the fifth point cloud data is used as the first comparison point;

[0063] Searching the fourth point cloud data for a point closest to the first comparison point as the second comparison point;

[0064] When the distance between the first comparison point and the second comparison point is less than the preset maximum distance between the coincidence points, the first comparison point and the second comparison point are determined to be coincidence points;

[0065] Determine the difference between the depth value of the second comparison point and the depth value of the first comparison point as the water depth discrepancy value of the corresponding coincident point;

[0066] The next point of the first comparison point in the fifth point cloud data is used as the first comparison point, and the step of searching the fourth point cloud data for the point closest to the first comparison point as the second comparison point is returned to be executed until all the fifth point cloud data are traversed;

[0067] Based on the preset row and column traversal order, the next partition block of the target block is taken as the target block, and the step of obtaining the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block is returned to execute until all partition blocks are traversed.

[0068] Optionally, when no coincidence point is found after traversing all fifth point cloud data, the device further includes:

[0069] The seventh module is used to obtain the block coordinate range of the target block based on the partition number of the target block combined with the block side length processing; the block coordinate range includes the minimum horizontal coordinate value of the block, the maximum horizontal coordinate value of the block, the minimum vertical coordinate value of the block and the maximum vertical coordinate value of the block;

[0070] An eighth module is used to use the first point in the fifth point cloud data as a third comparison point;

[0071] A ninth module, configured to perform conditional comparison on each adjacent block of the target block based on the data plane coordinates of the third comparison point and the block coordinate range;

[0072] Among them, the expression of conditional comparison is:

[0073]

[0074] Where, (X1, Y1) represents the data plane coordinates of the third comparison point; Xmin_n represents the minimum horizontal coordinate value of the block; Xmax_n represents the maximum horizontal coordinate value of the block; Ymin_n represents the minimum vertical coordinate value of the block; Ymax_n represents the maximum vertical coordinate value of the block; d represents the maximum distance value of the coincident point; (n row ,n col ) represents the row number and column number corresponding to the partition number of the target block; (n′ row ,n′ col ) represents the row and column numbers corresponding to the partition numbers of adjacent blocks in sequence;

[0075] The tenth module is used to obtain the sixth point cloud data of the main survey line in the adjacent block that meets the preset conditions when there is an adjacent block that meets the preset conditions;

[0076] An eleventh module is used to search the sixth point cloud data for a point closest to the third comparison point as a fourth comparison point;

[0077] A twelfth module is used to determine that the third comparison point and the fourth comparison point are coincident points when the distance value between the third comparison point and the fourth comparison point is less than a preset maximum distance value of coincident points;

[0078] A thirteenth module is used to determine the difference between the depth value of the fourth comparison point and the depth value of the third comparison point as the water depth discrepancy value of the corresponding coincidence point;

[0079] The fourteenth module is used to take the next point of the third comparison point in the fifth point cloud data as the third comparison point, and return to execute the ninth module until all the fifth point cloud data are traversed.

[0080] On the other hand, an embodiment of the present invention provides an electronic device, including: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the above-mentioned multi-beam bathymetric point cloud data accuracy assessment method.

[0081] On the other hand, an embodiment of the present invention provides a computer storage medium, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the above-mentioned multi-beam bathymetric point cloud data accuracy assessment method.

[0082] The embodiment of the present invention obtains multi-beam bathymetric point cloud data to be evaluated as the first point cloud data; the first point cloud data includes the second point cloud data of the main survey line and the third point cloud data of the connecting survey line; each point in the first point cloud data includes the data plane coordinates of the corresponding survey point and the depth value of the water depth; the coordinate range is determined according to the maximum value of the data plane coordinates of all points in the first point cloud data; the coordinate range is partitioned based on the preset block side length to obtain a plurality of partition blocks; each partition block is marked with a partition number, and the partition number includes a row number and a column number; the partition number is matched based on the data plane coordinates of each point in the first point cloud data, and the first point cloud data is stored point by point in the corresponding partition block; the partition blocks are traversed based on a preset order, and the overlapping points of the second point cloud data and the third point cloud data in each partition block are searched in turn, and then the water depth discrepancy value of the overlapping point is determined; the water depth discrepancy values ​​corresponding to all overlapping points are counted, and the accuracy evaluation result of the multi-beam bathymetric point cloud data is obtained according to the statistical result. The present invention includes the following beneficial effects: when the embodiment of the present invention calculates the non-conformity value of the point cloud of the main survey line and the connecting survey line, the overlapping points are searched for partition by partition, and the accuracy evaluation calculation of the multi-beam water depth data can be quickly performed. Because the data used for evaluation is the measured point cloud data, the disadvantage of inaccurate accuracy calculation caused by inaccurate water depth in the gridding method in the mainstream gridding accuracy evaluation method is avoided, and the accuracy of the multi-beam measurement result data can be accurately evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation on the technical solution of the present invention.

[0084] Figure 1 A schematic diagram of an implementation environment for performing multi-beam bathymetric point cloud data accuracy assessment provided by an embodiment of the present invention;

[0085] Figure 2 A schematic diagram of a flow chart of a method for evaluating the accuracy of multi-beam bathymetric point cloud data provided by an embodiment of the present invention;

[0086] Figure 3 A schematic diagram of the expanded flow of step S500 provided in an embodiment of the present invention;

[0087] Figure 4 An example diagram of an extended process based on step S500 provided in an embodiment of the present invention;

[0088] Figure 5 A schematic diagram of the overall process of a multi-beam bathymetric point cloud data accuracy assessment method provided by an embodiment of the present invention;

[0089] Figure 6 An example diagram of the distribution of some measuring points of a single measuring line provided by an embodiment of the present invention;

[0090] Figure 7 A schematic diagram of an example of a main survey line and a contact survey line provided in an embodiment of the present invention;

[0091] Figure 8 A schematic diagram of an example of water depth discrepancy value distribution provided by an embodiment of the present invention;

[0092] Fig. 9 A schematic diagram of the structure of a multi-beam bathymetric point cloud data accuracy assessment device provided by an embodiment of the present invention;

[0093] Fig.10 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0095] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100", "second / S200", etc. in the specification, claims and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0096] Reference to "embodiments" in the present invention means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in the present invention may be combined with other embodiments.

[0097] It is understandable that the multi-beam bathymetric point cloud data accuracy assessment method provided in the embodiment of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, tablet computer, laptop computer, desktop computer, etc., but is not limited to this.

[0098] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the invention. Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to a network wirelessly or wired to complete data transmission and exchange.

[0099] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0100] In addition, the server 101 can also be a node server in the blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0101] The terminal 102 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present invention.

[0102] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a method for evaluating the accuracy of multi-beam bathymetric point cloud data. The following is explained using the example of applying the method for evaluating the accuracy of multi-beam bathymetric point cloud data in the server 101. It can be understood that the method for evaluating the accuracy of multi-beam bathymetric point cloud data can also be applied to the terminal 102.

[0103] Reference Figure 2 , Figure 2 The flowchart of the multi-beam bathymetric point cloud data accuracy assessment method applied to the server provided in the embodiment of the present invention, the execution subject of the multi-beam bathymetric point cloud data accuracy assessment method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps:

[0104] S100, obtaining multi-beam bathymetric point cloud data to be evaluated as first point cloud data;

[0105] The first point cloud data includes the second point cloud data of the main survey line and the third point cloud data of the contact survey line; each point in the first point cloud data includes the data plane coordinates of the corresponding survey point and the depth value of the water depth;

[0106] For example, in some specific implementations, the multi-beam measurement main line point cloud data and the contact line point cloud data are read separately. While reading the data, the maximum and minimum values ​​of all data plane coordinates can be recorded, so that after all data are read, the coordinate range of the point cloud data can be obtained through subsequent process steps.

[0107] S200, determining a coordinate range according to the maximum values ​​of the data plane coordinates of all points in the first point cloud data;

[0108] It should be noted that the maximum values ​​include the first maximum value and the first minimum value corresponding to the plane horizontal coordinate and the second maximum value and the second minimum value corresponding to the plane vertical coordinate; in some embodiments, step S200 may include the following steps: determining the plane horizontal coordinate range according to the first maximum value and the first minimum value; determining the plane vertical coordinate range according to the second maximum value and the second minimum value; determining the coordinate range based on the plane horizontal coordinate range and the plane vertical coordinate range.

[0109] S300, partitioning the coordinate range based on a preset block side length to obtain a plurality of partitioned blocks;

[0110] Each partition block is marked with a partition number, which includes a row number and a column number;

[0111] It should be noted that the coordinate range includes a plane horizontal coordinate range and a plane vertical coordinate range; in some embodiments, step S300 may include the following steps: obtaining the block side length based on a preset multiple value of the preset maximum distance value of the overlapping points; rounding down the ratio of the first range value of the plane vertical coordinate range to the block side length, and then adding 1 to the result of the rounding down operation to obtain the number of partition rows; rounding down the ratio of the second range value of the plane horizontal coordinate range to the block side length, and then adding 1 to the result of the rounding down operation to obtain the number of partition columns; partitioning the coordinate range based on the number of partition rows and the number of partition columns to obtain multiple partition blocks; wherein the partition number is determined based on the row and column of the partition block; the total number of partition blocks is the product of the number of partition rows and the number of partition columns.

[0112] Exemplarily, in some specific implementations, the coordinate range of the point cloud data is partitioned.

[0113] Let the minimum value of the plane horizontal coordinate X in the point cloud be Xmin, and the maximum value be Xmax; let the minimum value of the plane vertical coordinate Y be Ymin, and the maximum value be Ymax. The maximum distance value of the coincident point is d. The partition is square in size, and the side length is several times of d (the specific multiple can be adjusted according to the actual application requirements), and let the side length be A;

[0114] Then the point cloud partition has a total of row rows:

[0115]

[0116] There are col columns:

[0117]

[0118] The floor is a round-down operation.

[0119] The number of partitioned blocks within the coordinate range is n, then n=row*col.

[0120] S400, matching the partition number based on the data plane coordinates of each point in the first point cloud data, and storing the first point cloud data point by point in the corresponding partition block;

[0121] It should be noted that, in some embodiments, step S500 may include the following steps: traversing the second point cloud data of all main survey lines, matching the partition number based on the data plane coordinates of each point, and storing the second point cloud data point by point in the corresponding partition block; traversing the third point cloud data of all connecting survey lines, matching the partition number based on the data plane coordinates of each point, and storing the third point cloud data point by point in the corresponding partition block.

[0122] In some preferred embodiments, matching the partition number based on the data plane coordinates of each point may include the following steps: taking the traversed point as the target point, and obtaining the data plane coordinates of the target point; the data plane coordinates include a horizontal coordinate and a vertical coordinate; determining a first interval value based on the vertical coordinate of the target point and the minimum vertical coordinate value of the coordinate range; rounding down the ratio of the first interval value to the block side length, and then adding 1 to the result of the rounding down operation to obtain the row number of the partition block where the target point will be stored; determining a second interval value based on the horizontal coordinate of the target point and the minimum horizontal coordinate value of the coordinate range; rounding down the ratio of the second interval value to the block side length, and then adding 1 to the result of the rounding down operation to obtain the column number of the partition block where the target point will be stored.

[0123] For example, in some specific implementations, the multi-beam measurement contact line point cloud data is first stored in the corresponding block. The point cloud data of all contact lines are traversed, and the data is stored point by point in the corresponding block, that is, the block stores all the contact line point data whose coordinates are within the block range.

[0124] If the plane coordinates of the measuring point are X and Y, and the depth value is H, then it is necessary to calculate which block the plane coordinates are in. According to the Y coordinate, calculate the row number of the point and set it as n row .

[0125] Calculated based on the minimum ordinate value of the coordinate range of the data and the block side length value:

[0126]

[0127] According to the X coordinate, calculate the column number of the point, set it to n col .

[0128] Calculated based on the minimum horizontal coordinate value of the coordinate range where the data is located and the block side length value:

[0129]

[0130] Then, the point cloud data of the main survey line of the multi-beam measurement is stored in the corresponding block. The point cloud data of all the main survey lines are traversed and the data are stored point by point in the corresponding block, that is, the point cloud data of all the main survey lines whose coordinates are within the range of the block are stored in the block.

[0131] If the plane coordinates of the measuring point are X and Y, and the depth value is H, then it is necessary to calculate which block the plane coordinates are in. According to the Y coordinate, calculate the row number of the point and set it as n row .

[0132] Calculated based on the minimum ordinate value of the coordinate range of the data and the block side length value:

[0133]

[0134] According to the X coordinate, calculate the column where the point is located, set it to n col .

[0135] Calculated based on the minimum horizontal coordinate value of the coordinate range where the data is located and the block side length value:

[0136]

[0137] S500, traversing the partition blocks based on a preset order, searching for the coincidence points of the second point cloud data and the third point cloud data in each partition block in turn, and then determining the water depth discrepancy value of the coincidence point;

[0138] It should be noted that in some embodiments, Figure 3 As shown, step S500 may include the following steps:

[0139] S501, taking the partition block in the first row and the first column as the target block;

[0140] S502, obtaining the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block;

[0141] S503, taking the first point in the fifth point cloud data as the first comparison point;

[0142] S504, searching the fourth point cloud data for a point closest to the first comparison point as a second comparison point;

[0143] S505: When the distance between the first comparison point and the second comparison point is less than the preset maximum distance between the coincidence points, determine that the first comparison point and the second comparison point are coincidence points;

[0144] S506, determining the difference between the depth value of the second comparison point and the depth value of the first comparison point as the water depth discrepancy value of the corresponding coincident point;

[0145] S507, taking the next point of the first comparison point in the fifth point cloud data as the first comparison point, and returning to the step of searching the fourth point cloud data for the point closest to the first comparison point as the second comparison point, until all the fifth point cloud data are traversed;

[0146] S508. Based on the preset row and column traversal order, the next partition block of the target block is taken as the target block, and the step of obtaining the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block is returned to execute until all partition blocks are traversed.

[0147] For example, in some specific implementations, taking Example A as an example, the process of steps S501 to S508 can be implemented as follows:

[0148] Search for the overlapping points between the water depth point cloud data of each survey line and the point cloud data of the main survey line and calculate the water depth discrepancy value.

[0149] Since all the measurement point data have been stored in the corresponding partition blocks, calculations are performed block by block, and the overlapping points in the blocks are searched and their water depth discrepancies are calculated, which can save a lot of computer calculations.

[0150] Traverse the blocks row by row and column by column, from row 1 to row, and from column 1 to col. Calculate the coincidence points of each block. The steps to calculate a single block are as follows:

[0151] 1) Read the main survey line point cloud data in the block:

[0152] Assume that the row and column number of the block is row and column number n row With n col , read the measuring point data into the memory from the file storing the main measuring line point cloud data.

[0153] 2) Read the contact line point cloud data in the block:

[0154] The row and column number of the slave block is row and column number n row With n col Read all the contact line measurement points from the storage file.

[0155] 3) Search for coincidence points in the block data:

[0156] Traverse the read contact line points one by one, and search for all corresponding coincidence points from the main line points.

[0157] Assume that the plane coordinates of the measuring points of the connecting survey line used for comparison are (X1, Y1) and the water depth is H1.

[0158] According to the main survey line measuring points in the obtained block, search for the measuring point closest to the comparison point of the contact survey line.

[0159] Assume the plane coordinates of the measuring points of the main survey line are (X2, Y2), the water depth is H2, and d is the maximum distance value of the coincident points.

[0160] If X2-X1>d or Y2-Y1>d, skip it, and the point cannot be a coincidence point. Otherwise, the measuring point may be the closest coincidence point. Record the information of the point and the distance between the measuring point and (X1, Y1). Assume that the distance value is dist. Then:

[0161]

[0162] Compare all the measuring points in the block one by one. If the distance between the measuring point and the comparison point of the contact measuring line meets the requirement of the maximum distance value of the coincidence point, obtain the measuring point with the closest distance. And calculate its water depth non-conformity value δ:

[0163] δ = H2 - H1;

[0164] In some preferred embodiments, when all fifth point cloud data are traversed and no coincidence point is found, such as Figure 4 As shown, the method may further include the following steps:

[0165] T100, based on the partition number of the target block and the block side length, the block coordinate range of the target block is obtained; the block coordinate range includes the minimum horizontal coordinate value of the block, the maximum horizontal coordinate value of the block, the minimum vertical coordinate value of the block, and the maximum vertical coordinate value of the block;

[0166] T200, taking the first point in the fifth point cloud data as the third comparison point;

[0167] T300, based on the data plane coordinates of the third comparison point and the block coordinate range, perform conditional comparison on each adjacent block of the target block;

[0168] Among them, the expression of conditional comparison is:

[0169]

[0170] Where, (X1, Y1) represents the data plane coordinates of the third comparison point; Xmin_n represents the minimum horizontal coordinate value of the block; Xmax_n represents the maximum horizontal coordinate value of the block; Ymin_n represents the minimum vertical coordinate value of the block; Ymax_n represents the maximum vertical coordinate value of the block; d represents the maximum distance value of the coincident point; (n row ,n col ) represents the row number and column number corresponding to the partition number of the target block; (n′ row ,n′ col ) represents the row and column numbers corresponding to the partition numbers of adjacent blocks in sequence;

[0171] T400, when there is an adjacent block that meets the preset conditions, obtain the sixth point cloud data of the main survey line in the adjacent block that meets the preset conditions;

[0172] T500, searching the sixth point cloud data for a point closest to the third comparison point as a fourth comparison point;

[0173] T600, when the distance between the third comparison point and the fourth comparison point is less than the preset maximum distance between the coincidence points, determining that the third comparison point and the fourth comparison point are coincidence points;

[0174] T700, determining the difference between the depth value of the fourth comparison point and the depth value of the third comparison point as the water depth discrepancy value of the corresponding coincident point;

[0175] T800, taking the next point of the third comparison point in the fifth point cloud data as the third comparison point, returning to execute the step of conditionally comparing each adjacent block of the target block based on the data plane coordinates and the block coordinate range of the third comparison point, until all the fifth point cloud data are traversed.

[0176] For example, in some specific implementations, taking the application scenario of Example A as an example:

[0177] If there is no overlapping point in the current block being traversed, search for data in the adjacent blocks.

[0178] Calculate the row and column number as n row 、n col The coordinate range of the block is Xmin_n, Xmax_n, Ymin_n, Ymax_n, where:

[0179] Xmin_n=(n col -1)*A+Xmin;

[0180] Xmax_n=n col *A+Xmin;

[0181] Ymin_n=(n row -1)*A+Ymin;

[0182] Ymax_n=n row *A+Ymin;

[0183] Then, judge in the following way:

[0184]

[0185] When there is no overlap point, directly compare with the next measuring point of the connecting measuring line. When there may be overlap points in the adjacent blocks, use the newly calculated row and column number n′ row , n′ colAs the row and column numbers of the storage file to be read, return to step 2), search and calculate the overlapped points again to see whether there are overlapped points in the new block.

[0186] S600, statistics are collected on the water depth discrepancies corresponding to all the coincident points, and an accuracy evaluation result of the multi-beam bathymetric point cloud data is obtained according to the statistical results.

[0187] For example, in some specific implementations, the accuracy of the multi-beam bathymetric data is finally counted and evaluated. Specifically, according to the water depth of the measuring point and the relevant standard specifications, the water depth non-conformity value of the point cloud data of the main survey line and the contact survey line can be calculated and evaluated. If the water depth is below 100 meters, the number and percentage of the measuring points in each interval of the water depth non-conformity value of the overlapping point can be counted, and the situation of the water depth non-conformity value exceeding the limit can be counted.

[0188] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and cannot be regarded as a limitation of the present invention.

[0189] First of all, it should be noted that in multi-beam data collection, the main survey line and the contact survey line are usually laid out. The contact survey line is generally perpendicular to the main survey line and evenly distributed. The accuracy is evaluated by comparing the water depth discrepancy value at the intersection of the main survey line and the contact survey line. There are two methods for accuracy evaluation: 1. One is to grid the main survey line and the contact survey line separately, compare and calculate the grid data of the main survey line with the grid data of the contact survey line, calculate the water depth discrepancy value of the coincidence point of each grid node, and finally count and calculate the accuracy information. 2. One is to search and match all the point cloud data of the main survey line belt and all the point cloud data of the contact survey line point by point, and then calculate the discrepancy value of the coincidence point and the accuracy statistics. Due to the large amount of measurement point data of multi-beam water depth measurement, the shallow water area can reach tens of millions, even hundreds of millions, billions of multi-beam measurement points, the calculation is complex, the workload is large, and the efficiency is low. The industry basically does not use measured point cloud data to evaluate the depth measurement accuracy. All public papers use grid data for accuracy evaluation, that is, the first method is used. However, the first method, that is, the gridding accuracy calculation method, will affect the coincidence point mismatch value and the accuracy of the accuracy calculation result due to the gridding method, gridding parameters, etc.

[0190] In order to evaluate the accuracy of multi-beam water depth measurement data, the mainstream method in the industry is to grid the main survey line and the contact survey line respectively, and then calculate the depth discrepancy value of the overlapped point and the multi-beam measurement accuracy for the gridded data. The prior art uses data from different multi-beam measurement systems to generate grid data with a grid size of 2m×2m, calculates the water depth difference at the same grid position as the depth discrepancy value of the overlapped point, and calculates the comparison accuracy; the prior art also optimizes the grid data search algorithm to achieve a rapid evaluation of the accuracy of multi-beam water depth data. Although the use of gridded water depth grid data for multi-beam water depth accuracy evaluation has the characteristics of small calculation amount and fast speed, it is limited by the gridding method and parameters, and there is a phenomenon of water depth accuracy loss.

[0191] In view of this, the present invention proposes a method for directly calculating the multi-beam water depth accuracy using the multi-beam water depth data point cloud. Since the amount of point cloud data is extremely large, in order to quickly calculate the water depth accuracy information, the water depth data point cloud is partitioned and stored, and the method of quickly positioning and reading the data is adopted. This can realize the search for the coincidence points of the multi-beam water depth data with a large amount of data and the calculation of the discrepancy value, thereby completing the water depth accuracy evaluation, which has a strong engineering practical significance. In some specific implementations, such as Figure 5 As shown, the technology of the present invention can be implemented through the following process steps:

[0192] A. Set the parameters of the coincidence point, that is, the maximum distance value of the coincidence point. That is to say, when the distance between the point of the main survey line and the point of the connecting survey line is greater than this value, it cannot be considered as a coincidence point.

[0193] B. Read the point cloud data of the main survey line and the contact survey line of the multi-beam measurement respectively. While reading the data, record the maximum and minimum values ​​of the plane coordinates of all data. After all the data are read, the coordinate range of the point cloud data can be obtained.

[0194] C. Partition the coordinate range of the point cloud data:

[0195] Let the minimum value of the plane horizontal coordinate X in the point cloud be Xmin, and the maximum value be Xmax; let the minimum value of the plane vertical coordinate Y be Ymin, and the maximum value be Ymax. The maximum distance value of the coincident point is d. The partition is square in size, and the side length is several times of d, let the side length be A;

[0196] Then the point cloud partition has a total of row rows:

[0197]

[0198] There are col columns:

[0199]

[0200] The floor is a round-down operation.

[0201] The number of partitioned blocks within the coordinate range is n, then n=row*col.

[0202] D. Store the multi-beam measurement contact line point cloud data in the corresponding block. Traverse the point cloud data of all contact lines and store the data point by point in the corresponding block. That is to say, the block stores all the contact line point data whose coordinates are within the block range.

[0203] If the plane coordinates of the measuring point are X and Y, and the depth value is H, then it is necessary to calculate which block the plane coordinates are in. According to the Y coordinate, calculate the row number of the point and set it as n row .

[0204] Calculated based on the minimum ordinate value of the coordinate range of the data and the block side length value:

[0205]

[0206] According to the X coordinate, calculate the column number of the point, set it to n col .

[0207] Calculated based on the minimum horizontal coordinate value of the coordinate range where the data is located and the block side length value:

[0208]

[0209] E. Store the point cloud data of the main survey line of multi-beam measurement in the corresponding block. Traverse the point cloud data of all main survey lines and store the data point by point in the corresponding block. In other words, the point cloud data of all main survey lines whose coordinates are within the range of the block are stored in the block.

[0210] If the plane coordinates of the measuring point are X and Y, and the depth value is H, then it is necessary to calculate which block the plane coordinates are in. According to the Y coordinate, calculate the row number of the point and set it as n row .

[0211] Calculated based on the minimum ordinate value of the coordinate range of the data and the block side length value:

[0212]

[0213] According to the X coordinate, calculate the column where the point is located, set it to n col .

[0214] Calculated based on the minimum horizontal coordinate value of the coordinate range where the data is located and the block side length value:

[0215]

[0216] F. Search for the overlapping points between the water depth point cloud data of each survey line and the point cloud data of the main survey line and calculate the water depth discrepancy value.

[0217] Since all the measurement point data have been stored in the corresponding partition blocks, calculations are performed block by block, and the overlapping points in the blocks are searched and their water depth discrepancies are calculated, which can save a lot of computer calculations.

[0218] Traverse the blocks row by row and column by column, from row 1 to row, and from column 1 to col. Calculate the coincidence points of each block. The steps to calculate a single block are as follows:

[0219] 1) Read the main survey line point cloud data in the block:

[0220] Assume that the row and column number of the block is row and column number n row With n col , read the measuring point data into the memory from the file storing the main measuring line point cloud data.

[0221] 2) Read the contact line point cloud data in the block:

[0222] The row and column number of the slave block is row and column number n row With n col Read all the contact line measurement points from the storage file.

[0223] 3) Search for coincidence points in the block data:

[0224] Traverse the read contact line points one by one, and search for all corresponding coincidence points from the main line points.

[0225] Assume that the plane coordinates of the measuring points of the connecting survey line used for comparison are (X1, Y1) and the water depth is H1.

[0226] According to the main survey line measuring points in the obtained block, search for the measuring point closest to the comparison point of the contact survey line.

[0227] Assume the plane coordinates of the measuring points of the main survey line are (X2, Y2), the water depth is H2, and d is the maximum distance value of the coincident points.

[0228] If X2-X1>d or Y2-Y1>d, skip it, and the point cannot be a coincidence point. Otherwise, the measuring point may be the closest coincidence point. Record the information of the point and the distance between the measuring point and (X1, Y1). Assume that the distance value is dist. Then:

[0229]

[0230] Compare all the measuring points in the block one by one. If the distance between the measuring point and the comparison point of the contact measuring line meets the requirement of the maximum distance value of the coincidence point, obtain the measuring point with the closest distance. And calculate its water depth non-conformity value δ:

[0231] δ = H2 - H1;

[0232] 4) When there is no overlap in the current block, search for data in the adjacent blocks.

[0233] Calculate the row and column number as n row 、n col The coordinate range of the block is Xmin_n, Xmax_n, Ymin_n, Ymax_n, where:

[0234] Xmin_n=(n col -1)*A+Xmin;

[0235] Xmax_n=n col *A+Xmin;

[0236] Ymin_n=(n row -1)*A+Ymin;

[0237] Ymax_n=n row *A+Ymin;

[0238] Then, judge in the following way:

[0239]

[0240] When there is no overlap point, directly compare with the next measuring point of the connecting measuring line. When there may be overlap points in the adjacent blocks, use the newly calculated row and column number n′ row , n′ col As the row and column numbers of the storage file to be read, return to step 2), search and calculate the overlapped points again to see whether there are overlapped points in the new block.

[0241] G. Conduct accuracy statistics and evaluation on multi-beam bathymetric data.

[0242] According to the water depth of the measuring point and the requirements of relevant standards and specifications, the water depth non-conformity values ​​of the points where the point cloud data of the main survey line and the contact survey line coincide with each other are calculated and evaluated. If the water depth is below 100 meters, the number and percentage of measuring points in each interval where the water depth of the coincidence point does not conform to the limit can be counted, and the water depth non-conformity values ​​exceeding the limit can be counted.

[0243] In some specific application scenarios, taking the point cloud data of multi-beam water depth measurement results in a certain work area as an example, the algorithm is verified through the technical solution of the present invention, and the specific implementation is as follows:

[0244] There are 123 main survey lines in this operation area, the data file size is 40.3GB, there are 1,391,034,861 (about 1.39 billion) water depth measurement points, and the average number of measurement points per survey line is 11,309,226. Figure 6As shown in the figure, it is an example diagram of the distribution of some measuring points of a single survey line; there are 20 connecting survey lines, the data file size is 8.10GB, there are 280,707,609 (about 280 million) water depth measuring points, and the average number of measuring points per survey line is 14,035,380. According to the distance information between the measuring point and its surrounding measuring points, the maximum distance of the overlapping points is set to 0.7m during calculation, and the side length of a block is set to 50 times the maximum distance of the overlapping points when partitioning the data, that is, the side length of the partition block is set to 35m. According to the read data, the range of plane coordinate X is 1158268~1182094, and the range of plane coordinate Y is 475951~500951. Figure 7 Shown is a schematic diagram of an example of a main survey line (green) and a tie survey line (red).

[0245] The point cloud partition has a total of row rows:

[0246]

[0247] There are col columns:

[0248]

[0249] The floor is a round-down operation.

[0250] According to the calculated number of rows and columns, the survey area data is divided into 715*618=441,870 areas. Each area of ​​the main survey line and the contact survey line corresponds to a data file. The partition number is calculated for all the contact survey line point cloud data, and the corresponding point cloud data is stored in the corresponding file of the partition. Then, the same method is used to store the main survey line measurement point data in the partition file corresponding to the measurement point.

[0251] After the data is partitioned and stored, the point cloud data of the main survey line and the connecting survey line in the corresponding partition are extracted one by one. The point coordinates of the overlapping points and their water depth non-conformity values ​​are calculated according to the rules of the overlapping points. After the calculation of the overlapping points of all partitions is completed, the water depth non-conformity values ​​of the overlapping points are counted. In this case, the method of the present invention is used to search for the overlapping points in the point cloud of the main survey line and the connecting survey line, and a total of 2841214 overlapping points are obtained, such as Figure 8 As shown in (Schematic diagram of an example of water depth non-conforming value distribution), the distribution of non-conforming values ​​is as follows:

[0252] The number of coincidence points with a water depth of 20-30 meters: 2450396;

[0253] The number of points where the overlap depth is greater than 0.6m: 0;

[0254] The number of coincidence points with a water depth of 30-50 meters: 390818;

[0255] The number of points where the overlap depth discrepancy value is greater than 0.7m: 2;

[0256] The average water depth of all coincident points is less than 100m.

[0257] The number of points with a depth discrepancy value of less than -0.7m: 2, accounting for 7.03924e-005%;

[0258] The number of points with inconsistent depth values ​​between -0.6 and -0.7m: 3, accounting for 0.000105589%;

[0259] The number of points with inconsistent depth values ​​between -0.5 and -0.6m: 3, accounting for 0.000105589%;

[0260] The number of points with inconsistent depth values ​​between -0.4 and -0.5m: 39, accounting for 0.00137265%;

[0261] The number of points with inconsistent depth values ​​between -0.3 and -0.4m: 150, accounting for 0.00527943%;

[0262] The number of points with inconsistent depth values ​​between -0.2 and -0.3m is 3656, accounting for 0.128677%;

[0263] The number of points with inconsistent depth values ​​between -0.1 and -0.2m is 104008, accounting for 3.66069%;

[0264] The number of points with inconsistent depth values ​​between -0.1 and 0m is 1,183,572, accounting for 41.6573%;

[0265] The number of points with inconsistent depth values ​​between 0 and 0.1m is 1,348,827, accounting for 47.4736%;

[0266] The number of points with inconsistent depth values ​​of 0.1 to 0.2 m: 194,853, accounting for 6.85809%;

[0267] The number of points with inconsistent depth values ​​of 0.2 to 0.3 m: 5974, accounting for 0.210262%;

[0268] The number of points with inconsistent depth values ​​between 0.3 and 0.4 m: 127, accounting for 0.00446992%;

[0269] The number of points with inconsistent depth values ​​between 0.4 and 0.5 m: 0, accounting for 0%;

[0270] The number of points with inconsistent depth values ​​between 0.5 and 0.6 m: 0, accounting for 0%;

[0271] The number of points with inconsistent depth values ​​between 0.6 and 0.7 m: 0, accounting for 0%;

[0272] The number of points with overlapped depth discrepancies greater than 0.7m: 0, accounting for 0%;

[0273] The calculation took 20262 seconds, or about 5.63 hours.

[0274] From the distribution of the calculated water depth discrepancies of the coincident points and the time taken for the program to calculate, it can be seen that the method of the present invention can be used to search and calculate coincident points for large amounts of multi-beam bathymetric point cloud data, and to evaluate the accuracy of the data. The coincident points calculated by the method of the present invention are all measured water depth data of the measuring points, which avoids the loss of data accuracy caused by the gridding method, thereby affecting the drawbacks of the accuracy evaluation results. The calculation results are reliable and efficient, and can be used to operate on big data, greatly improving the reliability of the accuracy evaluation, so as to better evaluate the applicability of the data, and have strong engineering practical significance.

[0275] In summary, in order to solve the shortcoming of gridded data for accuracy assessment, the present invention proposes a method for directly and quickly calculating the accuracy of multi-beam water depth using multi-beam water depth data point cloud. According to the maximum distance value of the overlapping point, the data is partitioned and stored within the range of all data according to several multiples of the distance value, and each partition corresponds to a row ID and a column ID; when calculating the non-conformity value of the point cloud of the main survey line and the connecting survey line, the overlapping points are searched partition by partition. First, all point clouds of the connecting survey line of a certain partition and all point cloud data of the partition corresponding to the main survey line are extracted point by point, and the distance between the comparison points of the main survey line measurement point and the connecting survey line measurement point in the partition is calculated. The point with the minimum distance is taken. If the distance between the point and the comparison point meets the requirements of the overlapping point, the depth non-conformity value of the overlapping point is calculated and saved. After the comparison of the main survey line and the connecting survey line measurement points of all partitions is completed according to the above method, the accuracy information is statistically calculated. This method can quickly perform accuracy evaluation and calculation on multi-beam water depth data. Since the data used for evaluation is measured point cloud data, it avoids the shortcomings of inaccurate accuracy calculation caused by inaccurate water depth in the gridding method in the mainstream gridding accuracy evaluation method, and can accurately evaluate the accuracy of multi-beam measurement results data.

[0276] On the other hand, Fig. 9 As shown, an embodiment of the present invention provides a multi-beam bathymetric point cloud data accuracy assessment device 900, which may include:

[0277] The first module 901 is used to obtain the multi-beam bathymetric point cloud data to be evaluated as the first point cloud data; the first point cloud data includes the second point cloud data of the main survey line and the third point cloud data of the contact survey line; each point in the first point cloud data includes the data plane coordinates of the corresponding survey point and the depth value of the water depth;

[0278] The second module 902 is used to determine the coordinate range according to the maximum value of the data plane coordinates of all points in the first point cloud data;

[0279] The third module 903 is used to partition the coordinate range based on the preset block side length to obtain multiple partition blocks; each partition block is marked with a partition number, and the partition number includes a row number and a column number;

[0280] The fourth module 904 is used to match the partition number based on the data plane coordinates of each point in the first point cloud data, and store the first point cloud data point by point into the corresponding partition block;

[0281] The fifth module 905 is used to traverse the partition blocks based on a preset order, and sequentially search for the coincidence points of the second point cloud data and the third point cloud data in each partition block, and then determine the water depth discrepancy value of the coincidence point;

[0282] The sixth module 906 is used to count the water depth discrepancies corresponding to all the overlapping points, and obtain the accuracy evaluation result of the multi-beam bathymetric point cloud data according to the statistical results.

[0283] In some embodiments, the fifth module is specifically used for:

[0284] The partition block of the first row and the first column is taken as the target block;

[0285] Acquire the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block;

[0286] The first point in the fifth point cloud data is used as the first comparison point;

[0287] Searching the fourth point cloud data for a point closest to the first comparison point as the second comparison point;

[0288] When the distance between the first comparison point and the second comparison point is less than the preset maximum distance between the coincidence points, the first comparison point and the second comparison point are determined to be coincidence points;

[0289] Determine the difference between the depth value of the second comparison point and the depth value of the first comparison point as the water depth discrepancy value of the corresponding coincident point;

[0290] The next point of the first comparison point in the fifth point cloud data is used as the first comparison point, and the step of searching the fourth point cloud data for the point closest to the first comparison point as the second comparison point is returned to be executed until all the fifth point cloud data are traversed;

[0291] Based on the preset row and column traversal order, the next partition block of the target block is taken as the target block, and the step of obtaining the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block is returned to execute until all partition blocks are traversed.

[0292] In some embodiments, when no coincidence point is found after traversing all fifth point cloud data, the device may further include:

[0293] The seventh module is used to obtain the block coordinate range of the target block based on the partition number of the target block combined with the block side length processing; the block coordinate range includes the minimum horizontal coordinate value of the block, the maximum horizontal coordinate value of the block, the minimum vertical coordinate value of the block and the maximum vertical coordinate value of the block;

[0294] An eighth module is used to use the first point in the fifth point cloud data as a third comparison point;

[0295] A ninth module, configured to perform conditional comparison on each adjacent block of the target block based on the data plane coordinates of the third comparison point and the block coordinate range;

[0296] Among them, the expression of conditional comparison is:

[0297]

[0298] The tenth module is used to obtain the sixth point cloud data of the main survey line in the adjacent block that meets the preset conditions when there is an adjacent block that meets the preset conditions;

[0299] An eleventh module is used to search the sixth point cloud data for a point closest to the third comparison point as a fourth comparison point;

[0300] A twelfth module is used to determine that the third comparison point and the fourth comparison point are coincident points when the distance value between the third comparison point and the fourth comparison point is less than a preset maximum distance value of coincident points;

[0301] A thirteenth module is used to determine the difference between the depth value of the fourth comparison point and the depth value of the third comparison point as the water depth discrepancy value of the corresponding coincidence point;

[0302] The fourteenth module is used to take the next point of the third comparison point in the fifth point cloud data as the third comparison point, and return to execute the ninth module until all the fifth point cloud data are traversed.

[0303] The contents of the method embodiments of the present invention are all applicable to the device embodiments. The functions specifically implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0304] On the other hand, an embodiment of the present invention further provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned hydrate stability region bottom boundary prediction method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0305] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0306] like Fig.10 As shown, Fig.10 The hardware structure of an electronic device 1000 of another embodiment is illustrated. The electronic device 1000 includes:

[0307] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0308] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the network node population optimization method of the embodiment of the present invention;

[0309] Input / output interface 1003, used to implement information input and output;

[0310] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0311] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0312] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0313] The electronic device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0314] The contents of the method embodiments of the present invention are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0315] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the above method.

[0316] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read to Only Memory, CD to ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0317] The contents of the method embodiments of the present invention are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0318] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0319] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0320] It should be noted that, although several modules of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0321] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD to ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation of the present invention.

[0322] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented by the present invention. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part described as a larger operation is performed independently.

[0323] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of various functional modules in the device disclosed in the present invention, the actual implementation of the module will be understood within the conventional skills of engineers. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0324] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0325] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution device, apparatus or device (such as a computer-based device, a device including a processor, or other device that can fetch instructions from an instruction execution device, apparatus or device and execute the instructions), or in conjunction with such instruction execution device, apparatus or device. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution device, apparatus or device, or in conjunction with such instruction execution device, apparatus or device.

[0326] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0327] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0328] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0329] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0330] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for evaluating the accuracy of multi-beam bathymetric point cloud data, characterized in that: The following steps are involved: Acquire the multi-beam bathymetric point cloud data to be evaluated as the first point cloud data; the first point cloud data includes the second point cloud data of the main survey line and the third point cloud data of the contact survey line; each point in the first point cloud data includes the data plane coordinates of the corresponding survey point and the depth value of the water depth; Determine a coordinate range according to the maximum values ​​of the data plane coordinates of all points in the first point cloud data; Partitioning the coordinate range based on a preset block side length to obtain a plurality of partition blocks; each of the partition blocks is marked with a partition number, and the partition number includes a row number and a column number; Based on matching the data plane coordinates of each point in the first point cloud data with the partition number, the first point cloud data is stored point by point in the corresponding partition block; Traversing the partition blocks based on a preset order, searching for coincident points of the second point cloud data and the third point cloud data in each of the partition blocks in turn, and then determining a water depth discrepancy value of the coincident point; The water depth discrepancies corresponding to all the coincident points are counted, and the accuracy evaluation result of the multi-beam bathymetric point cloud data is obtained according to the statistical results.

2. The multi-beam bathymetric point cloud data accuracy assessment method according to claim 1, characterized in that: The extreme values ​​include a first maximum value and a first minimum value corresponding to the plane horizontal coordinate and a second maximum value and a second minimum value corresponding to the plane vertical coordinate; the step of determining the coordinate range according to the extreme values ​​of the data plane coordinates of all points in the first point cloud data includes the following steps: Determine a plane horizontal coordinate range according to the first maximum value and the first minimum value; Determine a plane vertical coordinate range according to the second maximum value and the second minimum value; The coordinate range is determined based on the plane abscissa range and the plane ordinate range.

3. The multi-beam bathymetric point cloud data accuracy assessment method according to claim 1, characterized in that: The coordinate range includes a plane horizontal coordinate range and a plane vertical coordinate range; partitioning the coordinate range based on a preset block side length to obtain a plurality of partition blocks includes the following steps: Obtaining the block side length based on a preset multiple value of a preset maximum distance value of coincidence points; Performing a rounding operation on the ratio of the first range value of the plane vertical coordinate range to the side length of the block, and then adding 1 to the result of the rounding operation to obtain the number of partition rows; Performing a rounding operation on the ratio of the second range value of the plane horizontal coordinate range to the side length of the block, and then adding 1 to the result of the rounding operation to obtain the number of partition columns; Perform the partitioning process on the coordinate range based on the number of partition rows and the number of partition columns to obtain a plurality of the partition blocks; The partition number is determined based on the row and column of the partition block; the total number of the partition blocks is the product of the number of partition rows and the number of partition columns.

4. The multi-beam bathymetric point cloud data accuracy assessment method according to claim 1, characterized in that: The step of matching the partition number based on the data plane coordinates of each point in the first point cloud data and storing the first point cloud data point by point in the corresponding partition block comprises the following steps: Traversing the second point cloud data of all the main survey lines, matching the partition number based on the data plane coordinates of each point, and storing the second point cloud data point by point into the corresponding partition block; The third point cloud data of all the contact survey lines are traversed, the partition number is matched based on the data plane coordinates of each point, and the third point cloud data is stored point by point in the corresponding partition block.

5. The multi-beam bathymetric point cloud data accuracy assessment method according to claim 1, characterized in that: The matching of the partition number based on the data plane coordinates of each point comprises the following steps: Taking the traversed point as the target point, obtaining the data plane coordinates of the target point; the data plane coordinates include a horizontal coordinate and a vertical coordinate; Determine a first interval value according to the ordinate of the target point and the minimum ordinate value of the coordinate range; Performing a rounding-down operation on the ratio of the first interval value to the side length of the block, and then adding 1 to the result of the rounding-down operation to obtain the row number of the partition block where the target point will be stored; Determine a second interval value according to the horizontal coordinate of the target point and the minimum horizontal coordinate value of the coordinate range; A ratio of the second interval value to the block side length is rounded down, and then 1 is added to the result of the rounding down operation to obtain the column number of the partition block where the target point will be stored.

6. The multi-beam bathymetric point cloud data accuracy assessment method according to claim 1, characterized in that: The step of traversing the partition blocks based on a preset order, searching for coincident points of the second point cloud data and the third point cloud data in each partition block in turn, and then determining the water depth discrepancy value of the coincident point, comprises the following steps: Taking the partition block in the first row and the first column as the target block; Acquire the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block; Using the first point in the fifth point cloud data as a first comparison point; Searching the fourth point cloud data for a point closest to the first comparison point as a second comparison point; When the distance between the first comparison point and the second comparison point is less than a preset maximum distance between the coincidence points, determining that the first comparison point and the second comparison point are the coincidence points; Determine the difference between the depth value of the second comparison point and the depth value of the first comparison point as the water depth discrepancy value of the corresponding coincident point; Taking the next point of the first comparison point in the fifth point cloud data as the first comparison point, and returning to the step of searching the fourth point cloud data for a point closest to the first comparison point as the second comparison point, until all the fifth point cloud data are traversed; Based on the preset row and column traversal order, the next partition block of the target block is taken as the target block, and the step of obtaining the fourth point cloud data of the main survey line and the fifth point cloud data of the contact survey line in the target block is returned to execute until all the partition blocks are traversed.

7. The multi-beam bathymetric point cloud data accuracy assessment method according to claim 6, characterized in that: When the coincidence point is not found after traversing all the fifth point cloud data, the method further comprises the following steps: Based on the partition number of the target block and the block side length, a block coordinate range of the target block is obtained; the block coordinate range includes a minimum block horizontal coordinate value, a maximum block horizontal coordinate value, a minimum block vertical coordinate value, and a maximum block vertical coordinate value; Using the first point in the fifth point cloud data as a third comparison point; Based on the data plane coordinates of the third comparison point and the block coordinate range, performing conditional comparison on each adjacent block of the target block; Wherein, the expression of the condition comparison is: Where, (X1, Y1) represents the data plane coordinates of the third comparison point; Xmin_n represents the minimum horizontal coordinate value of the block; Xmax_n represents the maximum horizontal coordinate value of the block; Ymin_n represents the minimum vertical coordinate value of the block; Ymax_n represents the maximum vertical coordinate value of the block; d represents the maximum distance value of the coincident point; (n row ,n col ) represents the row number and column number corresponding to the partition number of the target block; (n ′ row ,n ′ col ) represents the row and column numbers corresponding to the partition numbers of adjacent blocks in sequence; When there is an adjacent block that meets the preset condition, obtaining the sixth point cloud data of the main survey line in the adjacent block that meets the preset condition; Searching the sixth point cloud data for a point closest to the third comparison point as a fourth comparison point; When the distance between the third comparison point and the fourth comparison point is less than a preset maximum distance between the coincidence points, determining that the third comparison point and the fourth comparison point are the coincidence points; Determine the difference between the depth value of the fourth comparison point and the depth value of the third comparison point as the water depth discrepancy value of the corresponding coincident point; The next point of the third comparison point in the fifth point cloud data is used as the third comparison point, and the step of performing conditional comparison on each adjacent block of the target block based on the data plane coordinates of the third comparison point and the block coordinate range is returned to be executed until all the fifth point cloud data are traversed.

8. A multi-beam bathymetric point cloud data accuracy assessment device, characterized in that: include: The first module is used to obtain the multi-beam bathymetric point cloud data to be evaluated as the first point cloud data; the first point cloud data includes the second point cloud data of the main survey line and the third point cloud data of the contact survey line; each point in the first point cloud data includes the data plane coordinates of the corresponding survey point and the depth value of the water depth; A second module is used to determine a coordinate range according to the maximum value of the data plane coordinates of all points in the first point cloud data; The third module is used to partition the coordinate range based on a preset block side length to obtain a plurality of partition blocks; each of the partition blocks is marked with a partition number, and the partition number includes a row number and a column number; A fourth module is used to match the partition number based on the data plane coordinates of each point in the first point cloud data, and store the first point cloud data point by point into the corresponding partition block; A fifth module is used to traverse the partition blocks based on a preset order, and sequentially search for the coincidence points of the second point cloud data and the third point cloud data in each of the partition blocks, and then determine the water depth discrepancy value of the coincidence point; The sixth module is used to count the water depth discrepancies corresponding to all the overlapping points, and obtain the accuracy evaluation result of the multi-beam bathymetric point cloud data according to the statistical results.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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