Detection data refinement processing method and system, electronic equipment and storage medium

Abstract

The invention discloses a detection data refinement processing method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining detection data of a target sea area, carrying out the preprocessing of the detection data, and obtaining depth measurement data and corresponding positioning and attitude data; wherein the detection data comprises sounding original data obtained based on a laser radar and corresponding positioning and attitude original data obtained based on a positioning and orientation system, and the sounding data comprises sounding echoes of each water area of the target sea area; performing waveform decomposition on the sounding data, and constructing a target optical path slant distance based on a waveform decomposition result; error correction is carried out on a target optical path slant distance based on internal parameters and positioning and attitude data of the laser radar, and then an initial point cloud is constructed; and performing three-dimensional filtering on the initial point cloud to obtain a target point cloud. The method can efficiently realize refined processing of detection data, and 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, system, electronic device and storage medium for refined processing of detection data. Background Art

[0002] With the growing demand for marine resource development and marine environmental monitoring, higher requirements are being placed on high-precision seabed topography detection technology. However, existing technologies still face the following bottlenecks in practical application: the terrain around land-sea interfaces, such as coastal zones and island reefs, varies dramatically and the environment is extremely complex. Traditional surveying uses different technical means and carriers for sea and land areas, dividing the survey area into subtidal, intertidal, and supratidal zones. Airborne three-dimensional laser surveying is used for intertidal and supratidal zones, while unmanned boats / manned motherships equipped with acoustic equipment such as single-beam, multi-beam, side-scan, and shallow-profile sonar are used for topographical surveying of the subtidal zone. However, this method is inefficient, costly, difficult, and risky, and there are measurement gaps at the land-sea interface. Summary of the Invention

[0003] The present invention aims to at least partially address the limitations of related technologies. To this end, the present invention provides a method, system, electronic device, and storage medium for refined processing of detection data, which can efficiently perform refined processing of detection data.

[0004] In one aspect, an embodiment of the present invention provides a method for fine-grained processing of detection data, comprising the following steps:

[0005] Acquire detection data of the target sea area, pre-process the detection data, and obtain bathymetric data and its corresponding positioning and attitude data; wherein the detection data includes raw bathymetric data acquired by the lidar and its corresponding raw positioning and attitude data acquired by the positioning and orientation system; the bathymetric data includes bathymetric echoes of various water areas in the target sea area;

[0006] Perform waveform decomposition on the sounding data and construct the target optical path slant distance based on the waveform decomposition result;

[0007] Based on the internal parameters, positioning and attitude data of the laser radar, the target optical path slant distance is corrected to obtain the initial point cloud.

[0008] Perform three-dimensional stereo filtering on the initial point cloud to obtain the target point cloud.

[0009] Optionally, preprocessing includes data sorting, data importing, and data parsing; preprocessing the detection data to obtain bathymetric data includes the following steps:

[0010] Perform statistical sorting of the bathymetric raw data and the positioning and attitude raw data based on the preset data sorting format;

[0011] The data format of the bathymetric raw data includes the route file name, record length, sampling rate, and route start and end time; the data format of the positioning and attitude raw data includes the route file name and the start and end time of positioning and attitude;

[0012] Import the statistically organized bathymetric raw data and positioning and attitude raw data into the preset processing system;

[0013] Based on the processing system calling the preset programming language function, the statistically sorted bathymetric raw data and the positioning and attitude raw data are preliminarily analyzed to obtain the bathymetric data and its corresponding positioning and attitude data.

[0014] Optionally, waveform decomposition is performed on the sounding data, and a target optical path slant distance is constructed based on the waveform decomposition result, including the following steps:

[0015] The high-frequency noise in the sounding echo is denoised based on the frequency domain filtering algorithm to obtain a smooth denoised waveform.

[0016] Based on the smoothed and denoised waveform, the waveform parameters are optimized using a pre-built waveform fitting model to obtain the waveform fitting results. The echo signals of each water area are extracted based on the waveform fitting results.

[0017] Based on the echo signals of each water area, the first slant distance from the laser radar's emission source to the sea surface and the second slant distance from the sea surface to the seabed are constructed.

[0018] Optionally, a pre-built waveform fitting model is used to optimize waveform parameters to obtain waveform fitting results, and echo signals of various water areas are extracted based on the waveform fitting results, including the following steps:

[0019] The waveform signal received by the lidar is fitted by means of sliding average integration to construct a waveform fitting model;

[0020] Among them, the full waveform of the waveform fitting model is obtained by adopting the nonlinear factor decomposition method and modeling in the form of summing the damping exponents;

[0021] When the number of detected waveform signals is greater than or equal to 2, the first signal is recorded as the sea surface echo signal, and the last signal is recorded as the seabed echo signal;

[0022] When the number of detected waveform signals is equal to 1, an evaluation function is constructed based on the waveform fitting model, and the optimal solution is obtained through the nonlinear least squares algorithm to extract the echo signals of each water area.

[0023] Optionally, error correction of the target optical path slant distance is performed based on the internal parameters of the laser radar in combination with the positioning and attitude data, thereby constructing an initial point cloud, including the following steps:

[0024] According to the laser radar code disk rotation information, the laser radar emission vector in the scanner coordinate system is obtained;

[0025] The three-dimensional point cloud of the sea surface and seabed is obtained by combining the launch vector and waveform time information with the positioning and attitude data. The waveform time information is determined based on the bathymetric data.

[0026] Based on the transmission characteristics of laser on the sea surface and water, a laser transmission model and a water scattering error correction model are constructed;

[0027] Construct a seabed reflection echo model based on the local terrain parameters within the laser spot range;

[0028] Based on the three-dimensional point clouds of the sea surface and seabed, wave refraction correction, water scattering error correction and seabed terrain slope correction are performed accordingly through the laser transmission model, water scattering error correction model and seabed reflection echo model. Then, the features of the points with the same name are matched according to the overlapping areas of adjacent flight strips in the point cloud. Based on the results of the feature matching of the same name points, the relative drift between flight strips is corrected, and the strip point clouds are seamlessly spliced ​​to construct the initial point cloud.

[0029] Optionally, performing three-dimensional stereo filtering on the initial point cloud to obtain a target point cloud includes the following steps:

[0030] Based on the spatial distribution characteristics of noise points in the initial point cloud, filtering models of the sea surface, water body and seabed are constructed, and then the filtering models are used to realize three-dimensional filtering to obtain the target point cloud.

[0031] Optionally, the method further comprises the following steps:

[0032] Target products are obtained based on the target point cloud; target products include terrain products and data evolution products;

[0033] Perform visual output processing on target data;

[0034] The target data includes one or more combinations of bathymetric echoes, waveform decomposition results, initial point clouds, target point clouds, and target products.

[0035] In another aspect, an embodiment of the present invention provides a system for fine-grained processing of detection data, including:

[0036] The first module is used to obtain detection data of the target sea area, pre-process the detection data, and obtain bathymetric data and its corresponding positioning and attitude data; wherein the detection data includes the original bathymetric data obtained by the lidar and its corresponding original positioning and attitude data obtained by the positioning and orientation system; the bathymetric data includes the bathymetric echoes of various waters in the target sea area;

[0037] The second module is used to perform waveform decomposition on the bathymetric data and construct the target optical path slant distance based on the waveform decomposition result;

[0038] The third module is used to perform error correction on the target optical path slant distance based on the internal parameters, positioning and attitude data of the lidar, and then construct an initial point cloud;

[0039] The fourth module is used to perform three-dimensional stereo filtering on the initial point cloud to obtain the target point cloud.

[0040] Optionally, the system further includes a fifth module, specifically configured to perform the following operations:

[0041] Target products are obtained based on the target point cloud; target products include terrain products and data evolution products;

[0042] Perform visual output processing on target data;

[0043] The target data includes one or more combinations of bathymetric echoes, waveform decomposition results, initial point clouds, target point clouds, and target products.

[0044] 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 detection data refined processing method.

[0045] On the other hand, an embodiment of the present invention provides a computer storage medium storing a program executable by a processor. The program executable by the processor is used to implement the above-mentioned method for fine-tuning detection data when executed by the processor.

[0046] The embodiment of the present invention obtains detection data of the target sea area, pre-processes the detection data, and obtains bathymetric data and its corresponding positioning and attitude data; wherein the detection data includes the original bathymetric data obtained based on the laser radar and its corresponding positioning and attitude original data obtained based on the positioning and orientation system, and the bathymetric data includes the bathymetric echoes of each water area in the target sea area; waveform decomposition is performed on the bathymetric data, and the target optical path slant distance is constructed based on the result of the waveform decomposition; the target optical path slant distance is error-corrected based on the internal parameters and positioning and attitude data of the laser radar, and then an initial point cloud is constructed; the initial point cloud is three-dimensionally filtered to obtain a target point cloud. The embodiment of the present invention proposes a complete set of laser radar detection data refinement processing procedures based on the bathymetric data of the laser radar, specifically based on the waveform analysis of the bathymetric data and the error correction of the target optical path slant distance to construct a point cloud and finally realize point cloud filtering. Compared with traditional acoustic methods, the present invention can comprehensively cover all water areas and can effectively avoid the tedious processing of multi-source data. The present invention can efficiently realize the refinement of detection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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 to the technical solution of the present invention.

[0048] Figure 1 This is a schematic diagram of an implementation environment for the method for fine-grained processing of detection data provided by an embodiment of the present invention;

[0049] Figure 2 This is a flow chart of a method for fine-grained processing of detection data provided by an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of the expanded process of step S100 provided in an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of the expanded process of step S200 provided in an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of the expansion process of step S202 provided in an embodiment of the present invention;

[0053] Figure 6 A schematic diagram of the expanded process of step S300 provided in an embodiment of the present invention;

[0054] Figure 7 A schematic diagram of an example of coastal terrain, which is one of the measurement application scenarios provided by an embodiment of the present invention;

[0055] Figure 8A schematic diagram of the overall application process of the detection data refinement processing method provided by an embodiment of the present invention;

[0056] Figure 9 A schematic diagram of the structure of a detection data refinement processing system provided by an embodiment of the present invention;

[0057] Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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 intended to limit the present invention.

[0059] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate a logical sequence, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first / S100," "second / S200," and the like in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily intended to describe a specific sequence or precedence.

[0060] References to "embodiments" in this disclosure mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the disclosure. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0061] It is understandable that the method for fine-grained processing of detection data 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 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.

[0062] To facilitate understanding of the technical solutions of the present invention, the following are first explained regarding the technical features that may appear in the embodiments of the present invention:

[0063] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present 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 the network in a wireless or wired manner to complete data transmission and exchange.

[0064] 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.

[0065] In addition, server 101 can also be a node server in a 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.

[0066] The terminal 102 may be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be connected directly or indirectly via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0067] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a method for fine-grained processing of detection data. The following is explained using the application of the method for fine-grained processing of detection data in the server 101 as an example. It can be understood that the method for fine-grained processing of detection data can also be applied to the terminal 102.

[0068] Reference Figure 2 , Figure 2 The flowchart of the method for fine-grained processing of detection data applied to a server provided in an embodiment of the present invention is provided. The execution subject of the method for fine-grained processing of detection data can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps:

[0069] S100, acquiring detection data of the target sea area, preprocessing the detection data, and obtaining bathymetric data and corresponding positioning and attitude data;

[0070] The detection data includes the original bathymetric data obtained by the laser radar and its corresponding original positioning and attitude data obtained by the positioning and orientation system. The bathymetric data includes the bathymetric echoes of various waters in the target sea area.

[0071] It should be noted that preprocessing includes data sorting, data importing and data parsing; in some embodiments, such as Figure 3 As shown, preprocessing the detection data to obtain the bathymetric data may include the following steps: S101, statistically collating the bathymetric raw data and the positioning and attitude raw data based on a preset data sorting format; wherein the data sorting format of the bathymetric raw data includes the route file name, record length, sampling rate, and route start and end time; the data sorting format of the positioning and attitude raw data includes the route file name and the positioning and attitude start and end time; S102, importing the statistically sorted bathymetric raw data and the positioning and attitude raw data into a preset processing system; S103, based on the processing system calling a preset programming language function, performing preliminary analysis on the statistically sorted bathymetric raw data and the positioning and attitude raw data to obtain the bathymetric data and its corresponding positioning and attitude data.

[0072] For example, in some specific embodiments, the detection data compilation includes the bathymetric raw data acquired by the airborne lidar and the positioning and attitude raw data acquired by the POS (Position and Orientation System). Specifically, the bathymetric raw data compilation includes statistical compilation of the route file name, record length, sampling rate, and route start and end times, while the positioning and attitude raw data compilation includes statistical compilation of the route file name and the start and end times of the positioning and attitude. Because the bathymetric raw data and the positioning and attitude raw data belong to different acquisition systems, their file names, recording time lengths, and recording formats may all differ. To ensure the smooth execution of subsequent data analysis and processing, the raw data statistical compilation is used to unify the file names and further confirm the relevant information. This step can achieve standardized operations.

[0073] Furthermore, the statistically organized bathymetric raw data and positioning and attitude raw data can be imported into the processing system;

[0074] Finally, a preliminary analysis of raw depth data, positioning data, and attitude data is achieved. A programming language can be used to parse the raw depth data according to the specified LiDAR data protocol to obtain the required relevant data. A programming language can also be used to parse the raw positioning data obtained by the GNSS board and the raw attitude data obtained by the MEMS inertial measurement unit to obtain the required relevant data.

[0075] S200, performing waveform decomposition on the sounding data, and constructing a target optical path slant distance based on the waveform decomposition result;

[0076] It should be noted that the target optical path slant distance includes a first slant distance and a second slant distance; in some embodiments, such as Figure 4 As shown, step S200 may include the following steps: S201, denoising the high-frequency noise in the bathymetric echo based on the frequency domain filtering algorithm to obtain a smooth denoised waveform; S202, based on the smooth denoised waveform, optimizing the waveform parameters using a pre-built waveform fitting model to obtain a waveform fitting result, and extracting the echo signal of each water area based on the waveform fitting result; S203, constructing a first slant distance from the emission source of the laser radar to the sea surface and a second slant distance from the sea surface to the seabed based on the echo signal of each water area.

[0077] For example, in some specific implementations, the waveform of the bathymetric data after data analysis can be decomposed, the laser echo waveform in ultra-shallow water and relatively turbid water can be processed, and the waveform of each laser beam in the original laser beam can be displayed, denoised, and decomposed and extracted to obtain the sea surface-seabed optical path slant distance.

[0078] Specifically, airborne lidar can directly acquire signals from the sea surface, seawater, and seabed echoes. To obtain accurate sea surface and seabed echo signals, the raw bathymetric echo signals must be processed as follows: Frequency-domain filtering algorithms are used to remove high-frequency noise present in the airborne lidar bathymetric echoes, resulting in a smoothed, denoised waveform. A waveform fitting model is constructed, and waveform parameter optimization algorithms are used to obtain waveform fitting results for extracting seabed echo signals in ultra-shallow and turbid waters. Finally, echo information from the sea surface, seabed, and water column is obtained to extract the surface-seabed optical path slant distance.

[0079] The high-frequency noise present in the airborne LiDAR echo can be processed using a frequency-domain filtering algorithm to obtain a smooth, denoised waveform. When acquiring LiDAR point cloud data, the raw point cloud data contains a certain proportion of invalid noise point clouds due to environmental influences, instrument accuracy, field operator operations, reflection, refraction, and scattering from the sea surface and water, and subsequent data processing. In addition to noisy point clouds, LiDARs are also subject to obstruction and interference from obstacles during operation. At this time, the point clouds collected from non-target objects are also invalid point clouds. Filtering and denoising point cloud data is the basis for subsequent point cloud processing.

[0080] Furthermore, a waveform fitting model can be constructed, and a waveform parameter optimization algorithm can be used to obtain waveform fitting results to extract seabed echo signals in ultra-shallow water and relatively turbid waters.

[0081] Furthermore, the echo information of the sea surface, seabed, and water body is obtained to extract the sea surface-seabed optical path slant distance. After extracting the sea surface, seabed, and water body echo information, the time position of the surface, underwater target, or seabed is determined based on the echo information.

[0082] Specifically, the distance difference between the emission source and the target reflected in the air and water surfaces is calculated by multiplying the flight time of the laser radar by the speed of light in air and water respectively. air and the slant distance L in water water Respectively expressed as:

[0083]

[0084] Where t1 and t2 represent the time interval between the laser being emitted from the water surface and the bottom and returning, respectively, n a and n w are the refractive indices of air and water respectively, and c is the speed of light in air.

[0085] Among them, in some embodiments, such as Figure 5 As shown, a pre-constructed waveform fitting model is used to optimize waveform parameters to obtain waveform fitting results, and the echo signals of each water area are extracted based on the waveform fitting results. The following steps can be included: S2021. The waveform signal received by the lidar is waveform fitted by a sliding average integral method to construct a waveform fitting model; wherein, the full waveform of the waveform fitting model is modeled in the form of summing the damping exponents using a nonlinear factor decomposition method; S2022. When the number of detected waveform signals is greater than or equal to 2, the first signal is recorded as a sea surface echo signal, and the last signal is recorded as a seabed echo signal; S2023. When the number of detected waveform signals is equal to 1, an evaluation function is constructed based on the waveform fitting model, and the optimal solution is obtained by a nonlinear least squares algorithm to extract the echo signals of each water area.

[0086] For example, in some specific implementations, for the signal received by the laser radar, waveform fitting can be performed by a sliding average integral method to construct a waveform fitting model:

[0087]

[0088] Where f(t) is the fitted waveform function, c(τ) is the differential backscatter cross section, and m(t) is the full waveform, which refers to the time series data of the complete emission and echo signals recorded by the lidar system.

[0089] The full waveform m(t) is modeled by using the nonlinear factor decomposition method and summing the damping exponents:

[0090]

[0091] Among them, α i , β i , γ i is a complex-valued parameter, u(t) is a step function, when t<0, u(t)=0, when t≥0, u(t)=1.

[0092] Based on the waveform fitting model, construct the evaluation function:

[0093]

[0094] Among them, y k is the digitized sampling signal, vector Used to group positions τ, differential backscatter cross sections E, and delays γ.

[0095] The nonlinear least squares algorithm is used to evaluate the evaluation function The minimum value of is solved to obtain the vector The optimal solution is to further obtain the seabed echo signal

[0096] Furthermore, when the number of detected echo signals is greater than or equal to 2, the first signal is recorded as the sea surface echo signal, and the last signal is recorded as the seabed echo signal; when the number of detected echo signals is equal to 1, an evaluation function is constructed based on the waveform fitting model, and the optimal solution is obtained through the nonlinear least squares algorithm to extract the seabed echo signals in ultra-shallow water and relatively turbid waters.

[0097] S300, performing error correction on the target optical path slant distance based on the internal parameters, positioning, and attitude data of the laser radar, thereby constructing an initial point cloud;

[0098] It should be noted that, in some embodiments, Figure 6As shown, step S300 may include the following steps: S301, according to the rotation information of the laser radar code disk, processing to obtain the emission vector of the laser radar in the scanner coordinate system; S302, according to the emission vector and waveform time information, combined with the positioning and attitude data, to obtain the three-dimensional point cloud of the sea surface and the seabed; wherein, the waveform time information is determined based on the bathymetric data; based on the transmission characteristics of the laser on the sea surface and the water body, a laser transmission model and a water scattering error correction model are constructed; S303, based on the local terrain parameters within the laser spot range, a seabed reflection echo model is constructed; S304, based on the three-dimensional point cloud of the sea surface and the seabed, wave refraction correction, water scattering error correction and seabed terrain slope influence correction are performed correspondingly through the laser transmission model, the water scattering error correction model and the seabed reflection echo model, and then the same-name point feature matching is performed according to the overlapping areas of adjacent flight strips of the point cloud, and the relative drift between flight strips is corrected based on the result of the same-name point feature matching, and the strip point cloud is seamlessly spliced ​​to construct the initial point cloud.

[0099] For example, in some specific implementations, error correction can be performed on the distance from the laser to the sea surface and the slant distance of the sea surface-seabed optical path. This includes beam launch vector calculation, measurement point reduction, refraction error correction, water scattering error correction, and flight track adjustment. Specifically, this can be achieved as follows:

[0100] Based on the code disk rotation information, the precise launch vector in the LiDAR scanner coordinate system is calculated. First, the internal parameters of the LiDAR, including the launch angle range and scanning resolution, are determined. The motor rotation angle calculated by the LiDAR's external code disk is obtained. The LiDAR scanner coordinate system is generally established with the LiDAR's launch point as the origin, and the launch vector in the scanner coordinate system is further calculated.

[0101] Based on the emission vector and waveform time information, combined with the positioning and attitude data acquired by the POS system, a three-dimensional point cloud of the sea surface and seafloor is calculated. Specifically, a coordinate transformation model is established from the LiDAR scanner coordinate system to the target coordinate system based on the positioning and attitude data provided by the POS system. For each laser pulse, the local coordinates of the LiDAR point cloud are calculated based on the emission vector and the extracted waveform time information. These local coordinates are then transformed to the target coordinate system using the coordinate transformation model to obtain the calculated three-dimensional point cloud of the sea surface and seafloor. The target coordinate system can be selected as the WGS-84 coordinate system, the CGCS2000 coordinate system, or some local independent coordinate system as needed.

[0102] Based on the characteristics of laser transmission between the sea surface and water, a laser transmission model is constructed to eliminate refraction errors between the sea surface and water. When the laser passes through the sea-air interface, the instantaneous sea surface tilt will cause the laser propagation path to deviate, seriously affecting the accuracy of detecting the geographical location of the seabed. To improve the depth measurement accuracy of the lidar, wave refraction correction is required for the lidar depth measurement data.

[0103] To reduce the bathymetric uncertainty caused by the interaction of laser scattering and absorption in water due to suspended particles, water scattering error correction can also be performed. Specifically, when calculating the sea surface and seabed slant range, the interaction of laser scattering and absorption in water due to suspended particles can reduce bathymetric accuracy and lead to bathymetric uncertainty. Water scattering error correction is necessary to reduce the impact of multiple scattering from water on the calculation of LiDAR sea surface and seabed slant range data. The water scattering error correction model consists of three parts: environmental and system parameter determination, a semi-analytical simulation model, and a deep-sea detection error model.

[0104] Based on the local terrain parameters within the bathymetric laser spot, a seabed reflection echo model is constructed to correct for the influence of the seabed terrain slope. Specifically, due to the diffusion effect, the laser pulse is expanded into a cone in the water body. When it reaches the seabed, it irradiates a certain range of light spots. The slope of the seabed terrain will cause changes in the vertical depth of the seabed space within the spot, which will then cause phenomena such as broadening of the seabed reflection echo and peak position shift. In order to accurately describe the seabed reflection echo formed by the continuous seabed terrain reflection within the lidar laser spot, the seabed terrain can be linked to the three-dimensional seabed information such as slope and water depth by establishing a local terrain parameter model within the laser spot to realize the construction of the seabed reflection echo model, and then analyze the influence of the seabed terrain on the lidar bathymetry. By constructing the relationship between the water depth value within the laser spot and the seabed slope, the changes in the seabed terrain can be accurately described.

[0105] Based on the overlapping areas of adjacent strips, point cloud features are matched with the same name, relative drift between strips is corrected, and seamless stitching of strip point clouds is achieved. In order to reduce the inconsistency between the overlapping areas of adjacent strips and improve the quality of airborne lidar bathymetric data, point cloud registration is required between adjacent strips to obtain complete and continuous point cloud data.

[0106] S400: Perform three-dimensional stereo filtering on the initial point cloud to obtain a target point cloud.

[0107] It should be noted that, in some embodiments, step S400 may include the following steps: constructing a filtering model of the sea surface, water body and seabed based on the spatial distribution characteristics of the noise points of the initial point cloud, and then using the filtering model to implement three-dimensional stereo filtering to obtain the target point cloud.

[0108] For example, in some specific implementations, the noise point cloud (i.e., the initial point cloud) obtained by measuring point calculations can be used to construct sea surface, water body, and seabed filtering models based on the spatial distribution characteristics of the noise points to achieve three-dimensional filtering. Specifically, the airborne lidar bathymetric data is affected by suspended particles and sunlight in the water body, and a certain number of noise point clouds will be generated during waveform decomposition. The filtering of these noise point clouds is a key step in obtaining the true seabed topography. In this application, based on the spatial distribution characteristics of the noise points, sea surface, water body, and seabed filtering models are constructed, and the filtering of the noise point cloud is achieved through three-dimensional visualization software.

[0109] In some embodiments, the method may further include the following steps: constructing a target product based on the target point cloud; the target product includes a terrain product and a data evolution product; performing visual output processing on the target data; wherein the target data includes one or more combinations of bathymetric echoes, waveform decomposition results, initial point clouds, target point clouds and target products.

[0110] For example, some specific implementations can generate seafloor contours and digital bathymetric models (DDM) terrain products from point clouds and output these products. Other products can be optionally output, expanding the output capabilities to include digital surface models (DSMs), water quality inversion, and seafloor sediment classification. Furthermore, visualization processing can include: real-time processing and display of raw echo waveforms, denoised waveforms, and fitted waveforms; color-coded display of reduced point clouds, filtered point clouds, and user-interactive point cloud results; selective output of sea surface and seafloor echo time, amplitude, and pulse width waveform parameters; and selective output of reduced point clouds, filtered point clouds, and point cloud accompanying information.

[0111] 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.

[0112] First of all, it should be noted that the terrain around the land-sea boundary areas such as coastal areas, islands and reefs changes dramatically and the environment is very complex. Traditional measurements use different technical means and carriers to measure sea areas and land areas respectively. Figure 7 The schematic diagram of coastal topography shown here divides the survey area into the subtidal, intertidal, and supratidal zones. Airborne 3D laser surveys are used for the intertidal and supratidal zones, while unmanned vehicles and manned motherships equipped with acoustic equipment such as single-beam, multi-beam, side-scan, and shallow-profile sonars are used for topographic and geomorphological surveys of the subtidal zone. However, this method is inefficient, costly, difficult, and risky, and there are gaps in measurement at the land-sea interface. Therefore, a marine surveying and mapping technology is urgently needed to provide technical support for integrated land-sea surveys in coastal areas.

[0113] Specifically, the emergence and rapid development of airborne ocean lidar bathymetry technology has made it an effective means of addressing these issues. It simultaneously measures land and water topography, boasting numerous advantages, including high efficiency, wide coverage, amphibious operation, and high mobility. It is particularly well-suited for rapid and precise surveying of complex terrain, such as shallow coastal waters and areas near islands and reefs. It can seamlessly stitch together the above- and below-water terrain of a coastline, resolving the challenge of full shallow-water coverage that traditional acoustic methods struggle to achieve.

[0114] In view of this, if Figure 8 As shown, the present invention provides a solution for fine-grained processing of airborne lidar sea and land detection data. Specifically, the embodiments of the present invention can achieve the following:

[0115] S110 , performing data sorting, data importing, and data analysis on the acquired detection data.

[0116] In this embodiment of the present invention, the detection data collected includes raw bathymetric data acquired by an airborne LiDAR (LiDAR) and raw positioning and attitude data acquired by a Position and Orientation System (POS). The LiDAR scanner is divided into a land laser detection module and an ocean laser detection module, each equipped with a red and green laser (1064nm and 532nm) to detect land topography and water depth, respectively. It is capable of simultaneously scanning both land and sea terrain, thus achieving seamless integration of land and sea terrain. The LiDAR scanner is well-adapted to the relatively complex water quality conditions in China and is designed to meet domestic water quality detection requirements. It is equipped with sufficient power interfaces, including a DC power input, a network port, and a positioning signal input, and is equipped with a removable solid-state drive to store the collected raw waveform data. The LiDAR scanner must support raw data output and make all raw data, including waveforms, and data format descriptions available to the user. This facilitates subsequent research on detection data processing algorithms, enabling refined processing of different water quality detection data and the development of automated control software. The POS system selects customized high-precision GNSS boards suitable for aerial surveys, supports multiple GNSS constellations such as Beidou, GPS, GLONASS, Galileo, and supports technologies such as GDCORS, RTK, PPP, and PPK; and selects customized high-performance MEMS inertial measurement units.

[0117] Specifically, the arrangement of the bathymetric raw data includes the statistical arrangement of the route file name, record length, sampling rate, and route start and end time. The arrangement of the positioning and attitude raw data includes the statistical arrangement of the route file name, and the start and end time of positioning and attitude. Since the bathymetric raw data and the positioning and attitude raw data belong to different acquisition systems, their file names, recording time lengths, and recording formats may all be different. In order to facilitate the subsequent data analysis and processing, the file names are unified and the relevant information is further confirmed through the statistical arrangement of the raw data. This step can achieve standardized operations. The segmented bathymetric raw data files may be temporarily offline due to instrument equipment failure during the acquisition process, as well as automatic segmentation due to data size limitations. Generally speaking, if the recording time difference is not large, such as a few seconds, the bathymetric raw file data generally needs to be merged. If the recording time difference is large, such as several hours, it is generally believed to be caused by instrument equipment failure, and the bathymetric raw file data files cannot be merged at this time.

[0118] Furthermore, the statistically organized bathymetric raw data and positioning and attitude raw data are imported into the processing system;

[0119] Furthermore, preliminary analysis of raw depth data, positioning data, and attitude data is performed. Using a programming language, raw depth data is parsed according to the specified LiDAR data protocol to obtain the required relevant data. Raw positioning data acquired by the GNSS board and raw attitude data acquired by the MEMS inertial measurement unit are also parsed using a programming language to obtain the required relevant data.

[0120] S120. Perform waveform decomposition on the bathymetric data after data analysis, process the laser echo waveforms in ultra-shallow water and relatively turbid water, display, denoise, and decompose and extract the waveform of each laser beam in the original laser beam, and obtain the sea surface-seabed optical path slant distance.

[0121] In the technical solution of the present invention, the signals that can be directly acquired by the airborne laser radar include sea surface, seawater, and seabed echo signals. To obtain accurate sea surface and seabed echo signals, the acquired raw bathymetric echo signals need to be processed as follows: the high-frequency noise present in the airborne laser radar bathymetric echo is processed using a frequency-domain filtering algorithm to obtain a smoothed, denoised waveform; a waveform fitting model is constructed, and a waveform parameter optimization algorithm is used to obtain waveform fitting results for extracting seabed echo signals in ultra-shallow and relatively turbid waters; and echo information from the sea surface, seabed, and water body is obtained to extract the sea surface-seabed optical path slant distance.

[0122] Furthermore, a frequency-domain filtering algorithm is used to process the high-frequency noise present in the airborne LiDAR bathymetric echoes to obtain a smooth, denoised waveform. When acquiring LiDAR point cloud data, the raw point cloud data contains a certain proportion of invalid noise point clouds due to environmental influences, instrument accuracy, field operator operations, reflection, refraction, and scattering from the sea surface and water, and the effects of post-processing data. In addition to noisy point clouds, LiDARs are also subject to obstruction and interference from obstacles during operation. At this time, the point clouds collected from non-target objects are also considered invalid point clouds. Filtering and denoising point cloud data is the basis for subsequent point cloud processing.

[0123] Specifically, the bandwidth difference between the low-frequency target echo and the high-frequency signal noise can be used for denoising. According to the basic principle of frequency domain filtering, the low-pass filter can be expressed as:

[0124]

[0125] Where j represents the imaginary unit, T is the echo signal period, h(e jλT ) is the approximate frequency response of the filter, λ c is the cutoff frequency, λ f is the sampling frequency.

[0126] Therefore, obtaining a suitable cutoff frequency is the key to improving the signal filtering effect. After using the Gaussian function to perform Fourier transform on the simulated echo signal, the following expression can be obtained:

[0127]

[0128] Where b is the bandwidth control parameter, λ s =(λ f T) / (2π) is the standardized sampling frequency, b is used to reflect the width of the target echo signal and is related to its physical properties. The frequency range of the echo signal can be calculated using formula (2). Then the low-pass filter can separate the echo signal frequency from the target and remove or attenuate other frequencies.

[0129] Furthermore, a waveform fitting model is constructed and a waveform parameter optimization algorithm is used to obtain waveform fitting results and extract seabed echo signals in ultra-shallow and turbid waters. The specific steps include:

[0130] For the signal received by the lidar, waveform fitting can be performed by sliding average integration to build a waveform fitting model:

[0131]

[0132] Where f(t) is the fitted waveform function, c(τ) is the differential backscatter cross section, and m(t) is the full waveform, which refers to the time series data of the complete emission and echo signals recorded by the lidar system.

[0133] The full waveform m(t) is modeled by using the nonlinear factor decomposition method and summing the damping exponents:

[0134]

[0135] Among them, α i , β i , γ i is a complex-valued parameter, u(t) is a step function, when t<0, u(t)=0, when t≥0, u(t)=1.

[0136] Based on the waveform fitting model, construct the evaluation function:

[0137]

[0138] Among them, y k is the digitized sampling signal, vector Used to group positions τ, differential backscatter cross sections E, and delays γ.

[0139] The nonlinear least squares algorithm is used to evaluate the evaluation function The minimum value of is solved to obtain the vector The optimal solution is to further obtain the seabed echo signal

[0140] Furthermore, when the number of detected echo signals is greater than or equal to 2, the first signal is recorded as the sea surface echo signal, and the last signal is recorded as the seabed echo signal; when the number of detected echo signals is equal to 1, an evaluation function is constructed based on the waveform fitting model, and the optimal solution is obtained through the nonlinear least squares algorithm to extract the seabed echo signals in ultra-shallow water and relatively turbid waters.

[0141] This technical solution combines direct extraction of sea surface and seabed echo signals with the proposed method of constructing a waveform fitting model for seabed echo signals in ultra-shallow and relatively turbid waters. This allows for rapid and accurate LiDAR velocity data acquisition in waters deeper than 2 meters and with good water quality. For ultra-shallow and relatively turbid waters, constructing a waveform fitting model for seabed echo signal extraction is selected based on actual processing requirements.

[0142] Furthermore, the echo information of the sea surface, seabed, and water body is obtained to extract the sea surface-seabed optical path slant distance. After extracting the sea surface, seabed, and water body echo information, the time position of the surface, underwater target, or seabed is determined based on the echo information.

[0143] Specifically, the distance difference between the emission source and the target reflected in the air and water surfaces is calculated by multiplying the flight time of the laser radar by the speed of light in air and water respectively. air and the slant distance L in water water Respectively expressed as:

[0144]

[0145] Where t1 and t2 represent the time interval between the laser being emitted from the water surface and the bottom and returning, respectively, n a and n w are the refractive indices of air and water respectively, and c is the speed of light in air.

[0146] S130. Correct the distance from the laser to the sea surface and the slant distance of the sea surface-seabed optical path.

[0147] In the technical solution of the present invention, errors of the distance from the laser to the sea surface and the slant distance of the sea surface-seabed optical path are corrected, specifically including light beam emission vector calculation, measurement point reduction, refraction error correction, water scattering error correction and track adjustment.

[0148] Furthermore, the precise emission vector in the laser radar scanner coordinate system is calculated based on the code disk rotation information.

[0149] Specifically, the motor's rotation angle is calculated based on the LiDAR's external encoder, which in turn calculates the precise emission vector in the LiDAR scanner's coordinate system. Similarly, an encoder mounted on the motor's shaft can also be used to obtain the motor's rotation angle.

[0150] Specifically, the method steps for calculating the emission vector in the laser radar scanner coordinate system may include: determining the internal parameters of the laser radar, which include the emission angle range and scanning resolution; obtaining the motor rotation angle calculated by the external code disk of the laser radar; generally establishing the laser radar scanner coordinate system with the emission point of the laser radar as the origin, and further calculating the emission vector in the scanner coordinate system.

[0151] Furthermore, based on the emission vector and waveform time information, combined with the positioning and attitude data obtained by the POS system, the three-dimensional point cloud of the sea surface and seabed is obtained;

[0152] Specifically, based on the positioning and attitude data provided by the POS system, a coordinate transformation model is established from the LiDAR scanner coordinate system to the target coordinate system. For each laser pulse, the local coordinates of the LiDAR point cloud are calculated based on the emission vector and the extracted waveform time information. These local coordinates are then converted to the target coordinate system using the coordinate transformation model, resulting in the calculated 3D point clouds of the sea surface and seabed. The target coordinate system can be selected as WGS-84, CGCS2000, or some other local independent coordinate system as needed.

[0153] Furthermore, according to the laser transmission characteristics of sea surface and water body, a laser transmission model is constructed to eliminate the refraction error of sea surface and water body.

[0154] When the laser passes through the interface between the sea and air, the instantaneous sea surface tilt will cause the laser propagation path to deviate, seriously affecting the detection accuracy of the seabed geographic location. In order to improve the depth measurement accuracy of the lidar, it is necessary to perform wave refraction correction on the lidar depth measurement data. Specifically, the initial shape of the sea surface is unknown. At this time, for the initially generated lidar point cloud, the sea surface is assumed to be flat, and the incident angle and scanning angle are set to the same, that is, n = [0, 0, 1]. T First, wave correction is performed using the sea surface topography obtained from the initial point cloud. Due to the wave and laser ranging errors, the initial sea surface point cloud is not planar. The incident angle of the laser spot is estimated by estimating the local tangent plane of the wave, and the local tangent plane is reconstructed by fitting it to the adjacent point cloud data. The tangent plane is obtained by minimizing the cost function:

[0155]

[0156] Among them, n is the normal vector, l is the distance from the origin to the plane M, p i is the three-dimensional coordinate of the i-th point.

[0157] In order to reduce the influence of point cloud position error, the centroid of local adjacent points is set to the position tangent to the corresponding reflection plane. The normal vector n is estimated by applying the optimization estimation principle based on principal component analysis (PCA) to the dimension-based laser incident correction.

[0158] Furthermore, the bathymetric uncertainty caused by the interaction of scattering and absorption of laser light by suspended particles in water is reduced, and water scattering error correction is performed.

[0159] When calculating the slant distance between the sea surface and the seabed, the interaction between the scattering and absorption of the laser in the water body due to suspended particles will reduce the sounding accuracy and lead to sounding uncertainty. It is necessary to correct the water scattering error to reduce the impact of multiple scattering of the water body on the calculation of the lidar sea surface and seabed slant distance data.

[0160] Specifically, a water scattering error correction model is established. The model consists of three parts: environmental and system parameter determination, semi-analytical simulation model, and deep-sea detection error model. The error correction steps are as follows:

[0161] 1. Run the water scattering error correction model, select and input system parameters (IFOV, laser divergence angle, ALB flight altitude, transmission pulse width, receiving aperture radius, etc.) and environmental parameters (LiDAR attenuation coefficient, particle size distribution, actual refractive index of particles, water depth, wind speed above sea surface, etc.) according to user definition.

[0162] 2. The semi-analytical simulation model consists of two main parts. The first part is to generate and propagate a large number of photon packets and track their interactions with particles in the water column. The second part is to record the photon packet information, including the seabed position, residual weight, forward scattering number and propagation distance.

[0163] 3. By tracking the propagation of photon packets in the water column and recording their information, the seafloor reflection waveform can be accurately simulated based on the results of step 2. Under given system and environmental parameters, the bathymetric error caused by scattering effects can be calculated using the seafloor reflection waveforms with and without scattering effects.

[0164] Furthermore, a seabed reflection echo model is constructed based on the local terrain parameters within the range of the bathymetric laser spot to correct the influence of the seabed terrain slope.

[0165] Specifically, due to the diffusion effect, the laser pulse is expanded into a cone shape in the water body, and when it reaches the seabed, it irradiates a certain range of light spots. The slope of the seabed terrain will cause changes in the vertical depth of the seabed space within the spot, which will then cause phenomena such as the broadening of the seabed reflection echo and the shift of the peak position. In order to accurately describe the seabed reflection echo formed by the continuous seabed terrain reflection within the lidar laser spot, the seabed terrain can be linked to the slope, water depth and other three-dimensional seabed information by establishing a local terrain parameter model within the laser spot to realize the construction of the seabed reflection echo model, and then analyze the influence of the seabed terrain on the lidar depth measurement. By constructing the relationship between the water depth value and the seabed slope within the laser spot, the changes in the seabed terrain can be accurately described.

[0166] Furthermore, feature matching of points with the same name is performed based on the overlapping areas of adjacent flight strips in the point cloud, and the relative drift between flight strips is corrected to achieve seamless splicing of the strip point cloud.

[0167] The design overlap ratio for adjacent strips of airborne LiDAR bathymetric data is generally 20%-50%, meeting requirements for point cloud density and flight coverage completeness. However, due to the influence of scanning system hardware, the environment, and target properties, there may be deviations in measured depth in the overlapping areas of adjacent strips. To reduce inconsistencies between overlapping strips and improve the quality of airborne LiDAR bathymetric data, point cloud registration is required between adjacent strips to obtain complete and continuous point cloud data.

[0168] Specifically, the method uses terrain feature information extracted from seafloor point clouds, adaptively selects a feature similarity threshold based on the number of point clouds in the overlapping area of ​​adjacent strips, and then adaptively matches corresponding points. This method mainly involves terrain feature parameter extraction, adaptive matching of corresponding points, and random sample consistency-iterative closest point registration.

[0169] Specifically, terrain feature parameter extraction involves constructing a covariance matrix based on the points within the domain radius to calculate the eigenvalues ​​and eigenvectors of the covariance matrix; then, calculating the entropy function. When the entropy function reaches its minimum value, the uncertainty is lowest, and the corresponding radius is the optimal neighborhood radius.

[0170] Specifically, the adaptive matching of corresponding points includes: adaptively selecting a feature similarity threshold according to the normal vector zenith angle, curvature change and omnidirectional variance, finding the distance between corresponding points within the threshold range, and obtaining corresponding points in the overlapping area of ​​adjacent strips.

[0171] Specifically, random sample consistency-iterative closest point registration involves roughly registering the seafloor point cloud using a random sample consistency algorithm based on adaptive matching of corresponding point pairs to obtain an initial transformation matrix. Iterative closest point registration is then used to reduce errors between strips and improve registration accuracy.

[0172] This technical solution fully considers the effects of water refraction, water scattering, seabed slope, and inter-strip drift on the precise measurement of slant distance during error correction for the laser-to-surface distance and the sea-surface-to-seabed optical path. By constructing corresponding models for water refraction, water scattering, seabed slope, and inter-strip relative drift, we achieve relatively accurate laser-to-surface distance and sea-surface-to-seabed optical path slant distance.

[0173] S140, realize waveform and point cloud display.

[0174] Furthermore, for the noise point cloud obtained by measurement point calculation, sea surface, water body and seabed filtering models are constructed according to the spatial distribution characteristics of the noise points to achieve three-dimensional filtering;

[0175] Specifically, airborne LiDAR bathymetric data is affected by suspended particles in the water and sunlight, generating a certain amount of noise point clouds during waveform decomposition. Filtering these noise point clouds is a key step in obtaining the true seabed topography. In this application, based on the spatial distribution characteristics of the noise points, sea surface, water, and seabed filtering models are constructed, and noise point cloud filtering is achieved through 3D visualization software.

[0176] Furthermore, the original echo waveform, denoised waveform and fitted waveform are processed and displayed in real time;

[0177] Furthermore, the calculated point cloud, filtered point cloud and user interaction point cloud results are displayed in colors.

[0178] S150, output point cloud data, waveform parameters, seabed topography and other products.

[0179] Furthermore, the sea surface and seabed echo time, amplitude, and pulse width waveform parameters are selectively output;

[0180] Furthermore, the reduced point cloud, the filtered point cloud and the point cloud with attached information are selectively output;

[0181] Furthermore, it can output terrain products, realize point cloud generation of seabed isobaths and digital water depth model DDM terrain products, and output products; it can choose to output other products to expand the output of digital surface model DSM, water quality inversion, and seabed sediment classification results.

[0182] In summary, the embodiments of the present invention organize, import and analyze the acquired detection data; perform waveform decomposition on the depth measurement data after data analysis, process the laser echo waveform in ultra-shallow water and relatively turbid water, display, denoise and decompose and extract the waveform of each laser beam in the original laser beam to obtain the sea surface-seabed optical path slant distance; perform error correction on the distance from the laser to the sea surface and the sea surface-seabed optical path slant distance; realize waveform and point cloud display; output point cloud data, waveform parameters, seabed topography and other products. The technical solution of the present invention proposes an algorithm model for efficiently extracting seabed echo signals in ultra-shallow water and relatively turbid water, realizing rapid and precise detection of complex terrains such as coastal shallow waters and areas near islands and reefs, solving the problem of full coverage of shallow waters that is difficult to solve with traditional acoustic means, and providing a complete set of refined processing procedures for airborne lidar sea and land detection data.

[0183] On the other hand, Figure 9 As shown, an embodiment of the present invention provides a detection data refinement processing system 900, which may include:

[0184] The first module 901 is used to obtain detection data of the target sea area and pre-process the detection data to obtain bathymetric data and its corresponding positioning and attitude data; wherein the detection data includes raw bathymetric data obtained by the lidar and its corresponding raw positioning and attitude data obtained by the positioning and orientation system; the bathymetric data includes bathymetric echoes of various water areas in the target sea area;

[0185] The second module 902 is used to perform waveform decomposition on the depth measurement data and construct a target optical path slant distance based on the waveform decomposition result;

[0186] The third module 903 is used to perform error correction on the target optical path slant distance based on the internal parameters and positioning and attitude data of the laser radar, thereby constructing an initial point cloud;

[0187] The fourth module 904 is used to perform three-dimensional stereo filtering on the initial point cloud to obtain a target point cloud.

[0188] In some embodiments, the system may further include a fifth module, specifically configured to perform the following operations:

[0189] Target products are obtained based on the target point cloud; target products include terrain products and data evolution products;

[0190] Perform visual output processing on target data;

[0191] The target data includes one or more combinations of bathymetric echoes, waveform decomposition results, initial point clouds, target point clouds, and target products.

[0192] The contents of the method embodiments of the present invention are all applicable to the system embodiments. The functions specifically implemented by the system 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 methods.

[0193] In another aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method for fine-grained processing of detection data. The electronic device can be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0194] It can be understood that the contents of the above method embodiments are 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.

[0195] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is shown. The electronic device 1000 includes:

[0196] The processor 1001 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention.

[0197] 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 solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called by the processor 1001 to execute the network node population optimization method of the embodiment of the present invention.

[0198] Input / output interface 1003, used to implement information input and output;

[0199] Communication interface 1004, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0200] Bus 1005 , which transmits information between various components of the device (e.g., processor 1001 , memory 1002 , input / output interface 1003 , and communication interface 1004 );

[0201] 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 a bus 1005 .

[0202] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0203] 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.

[0204] 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.

[0205] It should be noted that the computer-readable medium shown in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0206] 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.

[0207] The present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the above method.

[0208] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that 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 flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0209] 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 concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0210] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments 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, and includes a number of 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 embodiments of the present invention.

[0211] In some optional 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 in an exemplary manner 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. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0212] Furthermore, while the present invention has been described in the context of functional modules, it should be understood that, unless otherwise indicated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the system disclosed in the present invention, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention as set forth in the claims using ordinary skill without undue experimentation. It will also be 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.

[0213] 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 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute 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, and other media that can store program code.

[0214] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, system, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch instructions from and execute instructions on an instruction execution system, system, or device). For purposes of this specification, a "computer-readable medium" can be any system that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, system, or device.

[0215] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), 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 disc read-only memory (CDROM). In addition, the computer-readable medium may even be 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 processing in another suitable manner as necessary, and then stored in a computer memory.

[0216] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0217] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0218] While 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 invention, and that the scope of the invention is defined by the claims and their equivalents.

[0219] 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 fine-tuning detection data, characterized in that: The following steps are involved: Acquire detection data of the target sea area, pre-process the detection data, and obtain bathymetric data and corresponding positioning and attitude data; wherein the detection data includes raw bathymetric data acquired by a laser radar and corresponding raw positioning and attitude data acquired by a positioning and orientation system, and the bathymetric data includes bathymetric echoes of various water areas in the target sea area; performing waveform decomposition on the depth measurement data, and constructing a target optical path slant distance based on a result of the waveform decomposition; Performing error correction on the target optical path slant distance based on the internal parameters of the laser radar and the positioning and attitude data, thereby constructing an initial point cloud; Perform three-dimensional stereo filtering on the initial point cloud to obtain a target point cloud.

2. The method for fine-tuning detection data according to claim 1, characterized in that: The preprocessing includes data sorting, data importing and data parsing; the preprocessing of the detection data to obtain the bathymetric data includes the following steps: Performing statistical processing on the depth measurement raw data and the positioning and attitude raw data based on a preset data processing format; The data arrangement format of the bathymetric raw data includes the route file name, record length, sampling rate, and route start and end time; the data arrangement format of the positioning and attitude raw data includes the route file name and positioning and attitude start and end time; Importing the statistically sorted bathymetric raw data and the positioning and attitude raw data into a preset processing system; Based on the processing system calling a preset programming language function, the statistically sorted depth measurement raw data and the positioning and attitude raw data are preliminarily analyzed to obtain the depth measurement data and the corresponding positioning and attitude data.

3. The method for fine-tuning detection data according to claim 1, characterized in that: The target optical path slant distance includes a first slant distance and a second slant distance; performing waveform decomposition on the depth measurement data and constructing the target optical path slant distance based on the result of the waveform decomposition includes the following steps: De-noising the high-frequency noise in the sounding echo based on a frequency domain filtering algorithm to obtain a smooth de-noised waveform; Based on the smoothed denoised waveform, a pre-built waveform fitting model is used to optimize waveform parameters to obtain a waveform fitting result, and echo signals of each water area are extracted according to the waveform fitting result; The first slant distance from the emission source of the laser radar to the sea surface and the second slant distance from the sea surface to the seabed are constructed based on the echo signals of each water area.

4. The method for fine-tuning detection data according to claim 3, characterized in that: The method of optimizing waveform parameters using a pre-built waveform fitting model to obtain a waveform fitting result and extracting echo signals of various water areas according to the waveform fitting result comprises the following steps: Performing waveform fitting on the waveform signal received by the laser radar by means of a sliding average integral method to construct the waveform fitting model; The full waveform of the waveform fitting model is obtained by adopting a nonlinear factor decomposition method and modeling in the form of summing damping exponents; When the number of the detected waveform signals is greater than or equal to 2, the first signal is recorded as a sea surface echo signal, and the last signal is recorded as a seabed echo signal; When the number of the detected waveform signals is equal to 1, an evaluation function is constructed based on the waveform fitting model, and the optimal solution is obtained through a nonlinear least squares algorithm to extract the echo signals of each water area.

5. The method for fine-tuning detection data according to claim 1, characterized in that: The error correction of the target optical path slant distance based on the internal parameters of the laser radar combined with the positioning and attitude data, and then constructing an initial point cloud, includes the following steps: According to the rotation information of the code disk of the laser radar, a transmission vector of the laser radar in the scanner coordinate system is obtained; According to the emission vector and waveform time information, combined with the positioning and attitude data, a three-dimensional point cloud of the sea surface and the seabed is obtained; wherein the waveform time information is determined based on the bathymetric data; Based on the transmission characteristics of laser on the sea surface and water, a laser transmission model and a water scattering error correction model are constructed; Construct a seabed reflection echo model based on the local terrain parameters within the laser spot range; Based on the three-dimensional point clouds of the sea surface and the seabed, wave refraction correction, water scattering error correction and seabed terrain slope influence correction are performed correspondingly through the laser transmission model, the water scattering error correction model and the seabed reflection echo model, and then the features of the points of the same name are matched according to the overlapping areas of adjacent flight strips of the point cloud. Based on the results of the feature matching of the points of the same name, the relative drift between flight strips is corrected, and the strip point clouds are seamlessly spliced ​​to construct the initial point cloud.

6. The method for fine-tuning detection data according to claim 1, characterized in that: The step of performing three-dimensional stereo filtering on the initial point cloud to obtain a target point cloud comprises the following steps: Based on the spatial distribution characteristics of the noise points of the initial point cloud, a filtering model of the sea surface, water body and seabed is constructed, and then the three-dimensional stereo filtering is implemented using the filtering model to obtain the target point cloud.

7. The method for fine-tuning detection data according to claim 1, characterized in that: The method further comprises the following steps: Constructing a target product based on the target point cloud; the target product includes a terrain product and a data evolution product; Perform visual output processing on target data; The target data includes one or more combinations of the bathymetric echo, the result of the waveform decomposition, the initial point cloud, the target point cloud and the target product.

8. A detection data refinement processing system, characterized in that: include: The first module is used to obtain detection data of the target sea area and pre-process the detection data to obtain bathymetric data and its corresponding positioning and attitude data; wherein the detection data includes raw bathymetric data obtained by the laser radar and its corresponding raw positioning and attitude data obtained by the positioning and orientation system, and the bathymetric data includes bathymetric echoes of various water areas in the target sea area; The second module is used to perform waveform decomposition on the bathymetric data and construct a target optical path slant distance based on the result of the waveform decomposition; A third module is configured to perform error correction on the target optical path slant distance based on the internal parameters of the laser radar and the positioning and attitude data, thereby constructing an initial point cloud; The fourth module is used to perform three-dimensional stereo filtering on the initial point cloud to obtain a target point cloud.

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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