A power transmission and transformation project river pond underwater terrain intelligent measurement and backfilling quantity precise calculation method and system

Abstract

The application discloses a kind of transmission and transformation engineering river pond underwater topography intelligent measurement and backfill quantity precision calculation method and system.The method is realized high-precision three-dimensional data acquisition by unmanned ship carrying multi-beam sounding system;Dynamic obstacle avoidance is realized by using improved ant colony algorithm combined with B-spline interpolation optimization path planning, setting safety steering angle and minimum turning path;Based on layered sound ray tracking algorithm, CUBE filtering and attitude correction technology process data, eliminate sound velocity error and environmental disturbance;Delaunay triangulation algorithm is used to optimize the triangular net topology structure of point cloud data, and DEM is generated by contour extraction technology based on region growing;Square grid method is used to divide calculation unit, and backfill quantity is accurately calculated by three pyramid volume formula.The application solves the problem of low precision and poor efficiency of traditional measurement method, reduces measurement error, and significantly improves the automation degree of underwater topography measurement and the accuracy of engineering quantity calculation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic engineering surveying, in particular to a method and system for intelligent measurement of underwater topography of rivers and ponds in power transmission and transformation projects and precise calculation of backfilling quantity, which is particularly suitable for underwater topography surveying and earthwork quantity calculation in power infrastructure construction. BACKGROUND

[0002] In the construction of power transmission and transformation projects, the accurate measurement of river and pond backfilling quantity has always been a technical problem. Traditional underwater topography measurement methods mainly rely on manual pumping for cross-section measurement or the use of single-beam sounding systems. These methods have the following defects:

[0003] Insufficient measurement accuracy: Traditional cross-section measurement method is a rough estimate by manual point calculation, with large errors. Single-beam sounding system can only obtain single-point water depth data each time, which cannot achieve full-coverage measurement, resulting in incomplete underwater topography data and affecting the accuracy of engineering quantity calculation. Low efficiency: Traditional methods require pumping out the water in rivers and ponds before measurement, which not only consumes time and effort, but also is limited by weather and hydrological conditions, seriously affecting project progress. In addition, manual operation and data processing are low in efficiency, which is difficult to meet the needs of large-scale projects. Safety hazards: Manual measurement or manned ship measurement in complex water areas (such as rapids and deep water areas) has high safety risks, especially in adverse weather conditions, the personal safety of measurement personnel is difficult to guarantee. Poor data reliability: Traditional sounding methods are easily affected by environmental factors such as water turbidity, waterweeds and fish schools, and lack effective noise filtering and error correction methods, resulting in large deviations between measurement results and actual topography.

[0004] In recent years, unmanned ships equipped with multi-beam sounding technology have been gradually applied to underwater topography measurement, but its practical application in power transmission and transformation projects still faces the following problems: the obstacle avoidance ability and path smoothing of the route planning algorithm in complex water areas are insufficient; multi-sensor data fusion and error correction technology is not perfect, especially the influence of sound velocity profile and attitude deviation on sounding accuracy has not been effectively solved; the efficiency and accuracy of point cloud data processing methods (such as contour extraction and three-dimensional modeling) need to be improved, which is difficult to meet the centimeter-level DEM modeling demand; the backfilling quantity calculation model lacks dynamic adaptation to high-precision topography data, resulting in significant differences between the calculation results and the actual construction quantity.

[0005] Therefore, it is urgent to develop an underwater topography measurement method for power transmission and transformation projects, which integrates high-precision data acquisition, intelligent path planning, multi-source data fusion processing and precise engineering quantity calculation, to solve the problems of low precision, low efficiency and insufficient safety in existing technology, and provide reliable technical support for engineering construction. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a safe and efficient, accurate and reliable power transmission project river pond underwater terrain intelligent measurement and backfill quantity accurate calculation method and system.

[0007] In a first aspect, the embodiments of the present application provide a power transmission project river pond underwater terrain intelligent measurement and backfill quantity accurate calculation method, which comprises:

[0008] S1, an unmanned ship is used to carry a multi-beam sounding system, a surface sound velocity meter, an attitude meter and a positioning system are integrated, high-precision three-dimensional data acquisition is performed on the underwater terrain of the river pond, water depth, terrain and obstacle information are obtained;

[0009] S2, the planned path is smoothed based on an improved ant colony algorithm and a B-spline interpolation method, a safety turning angle and a minimum turning path are set, dynamic obstacle avoidance and track optimization are realized;

[0010] S3, the collected underwater data are intelligently processed through a layered sound ray tracking algorithm, a CUBE filtering algorithm and an attitude deviation correction algorithm, the influence of sound velocity error, environmental noise interference and sounding deviation is eliminated;

[0011] S4, a Delaunay triangulation algorithm is used to optimize the triangular net topology structure of the point cloud data, and a high-precision underwater digital elevation model DEM is generated through a contour extraction technology based on region growing;

[0012] S5, based on the DEM model, a square grid method is used to divide the calculation unit, and the backfill quantity is calculated.

[0013] Optionally, in an implementation manner of the first aspect of the present application, S1, the unmanned ship is used to carry the multi-beam sounding system, the surface sound velocity meter, the attitude meter and the positioning system are integrated, the high-precision three-dimensional data acquisition is performed on the underwater terrain of the river pond, and the water depth, the terrain and the obstacle information are obtained, which comprises:

[0014] S1.1, the unmanned ship is used to carry the multi-beam sounding system, the surface sound velocity meter, the attitude meter and the positioning system are integrated, the underwater terrain data in the range of 0.15-300 meters of water depth are obtained through the multi-beam sounding technology, the sound velocity profile, the ship body attitude and the high-precision positioning information are synchronously collected, and the original underwater point cloud data set is constructed;

[0015] S1.2, the original underwater point cloud data set is preprocessed, which comprises:

[0016] S1.2.1, data validity verification: invalid sounding points with signal intensity lower than a threshold value are removed, and abnormal data caused by water surface reflection or instrument noise are filtered;

[0017] S1.2.2, space-time synchronization calibration: time alignment of multi-sensor data based on GNSS timestamp, unification of each measuring point to engineering coordinate system through coordinate transformation;

[0018] S1.2.3, attitude compensation correction: according to the real-time collected roll and pitch data, the position of the depth measuring point is dynamically compensated by using the quaternion rotation matrix;

[0019] S1.2.4, sound speed profile correction: according to the surface sound velocity instrument and CTD profile instrument data, a layered sound speed model is established to correct the original water depth value by sound ray bending;

[0020] S1.2.5, data standardization: the processed point cloud data is converted into a standardized format containing three-dimensional coordinates, intensity values and confidence, and a pre-processing point cloud database is constructed.

[0021] Optionally, in an implementation form of the first aspect of the present application, in the S2, the improved ant colony algorithm and B-spline interpolation method are used to smooth the planned path, comprising:

[0022] S2.1, constructing an improved ant colony algorithm path planning model:

[0023] S2.1.1, designing heuristic function :

[0024] ,

[0025] wherein,

[0026] ,

[0027] ,

[0028] ,

[0029] ,

[0030] ,

[0031] wherein, is a distance function, , is the maximum and minimum distance from all next selectable grid centers to the target grid center of the current ant represents the distance heuristic factor from the center of the grid to be walked to the center of the starting grid and the target grid; , , is a distance coefficient, is a distance heuristic information coefficient; and​ represent the next available grid Euclidean distance to the center of the start grid and the target grid, represent the available grid Euclidean distance to the center of the target grid, represent the ant a set of selectable next available grids, represent distance on the momentary path ;

[0032] S2.1.2, optimize the path transition probability:

[0033] ,

[0034] wherein, characterize the importance of pheromone, represent the importance of heuristic function, represent pheromone concentration on the momentary path ;

[0035] S2.2, introduce path smoothness constraint:

[0036] ,

[0037] wherein, is the distance heuristic information coefficient; is the coefficient representing the importance of straight driving; represent the turning direction of the previous step of the ant, represent the turning direction of the next step to be taken.

[0038] Optionally, in an implementation form of the first aspect of the present application, in the S2, a safety turning angle and a minimum turning path are set to realize dynamic obstacle avoidance and path optimization, comprising:

[0039] S2.3, set mechanical motion constraint:

[0040] ,

[0041] ,

[0042] wherein, is the heading angle of the unmanned ship, is the offset angle of the center axis of the ship and the turning direction, and the minimum turning path of the unmanned ship is , is the center axis distance difference of the ship;

[0043] S2.4, implement path optimization processing: adopt B-spline curve to carry out smooth interpolation to the planning path, establish trajectory planning guide line through Frenet coordinate system, and construct optimization objective function :

[0044] ,

[0045] wherein, , , for each discrete point along the coordinate system axis direction Lateral offset , the lateral offset , and of the point are selected as optimization variables, and are the first and second derivatives of the lateral offset relative to ; , and are the corresponding weight coefficients;

[0046] S2.5, dynamic obstacle avoidance processing: establish route-time obstacle map, adopt A* algorithm for global path search, and implement speed planning optimization:

[0047] ,

[0048] wherein, , , is the timestamp corresponding to the longitudinal distance, is the optimization decision variable.

[0049] Optionally, in an implementation form of the first aspect of the present application, the S3, the collected underwater data is intelligently processed through a hierarchical sound ray tracking algorithm, a CUBE filtering algorithm and a posture deviation correction algorithm, to eliminate the influence of sound velocity error, environmental noise interference and depth deviation, comprising:

[0050] S3.1, a hierarchical sound ray tracking algorithm is adopted, and a sound velocity profile model is established based on Snell's law;

[0051] S3.2, CUBE algorithm is adopted for automatic filtering, influence radius of depth measuring point is calculated, grid point capture radius is calculated, and Bayesian filtering is implemented;

[0052] S3.3, based on the roll and pitch data collected by the POS system, a quaternion rotation matrix is adopted for dynamic compensation, and the "crying face" or "laughing face" deformation caused by residual error is eliminated;

[0053] S3.4. Eliminate abnormal points with signal strength below the threshold, and perform manual interactive filtering to supplement the processing of complex terrain areas, outputting centimeter-level accuracy.

[0054] Optionally, in an implementation of the first aspect of the present invention, the step S3.1, using a layered ray tracing algorithm to establish a sound velocity profile model based on Snell's law, includes:

[0055] a) Constant sound velocity layered model: The sound velocity profile is divided into N layers. The sound velocity in each layer is constant, and the sound line propagates in a straight line. The propagation time of each layer is calculated. and horizontal distance :

[0056] ,

[0057] ,

[0058] in, is the initial incident angle of the beam, is the initial sound velocity, according to Snell's law and the sound velocity of each layer , the incident angle of the sound ray in each layer can be obtained, is the distance on the Z axis;

[0059] b) Constant gradient layered model: The speed of sound changes linearly within each layer, and the sound line propagates in a circular arc. Calculate the curvature of the sound line:

[0060] ,

[0061] in, The sound ray element The incident angle, is its grazing angle, for The speed of sound at the infinitesimal point is determined by Snell's law when the sound line is at Initial grazing angle within the layer and the initial sound speed , given time is a constant, is the distance element.

[0062] Optionally, in an implementation of the first aspect of the present invention, in S4, the Delaunay triangulation algorithm is used to optimize the triangulated network topology structure of the point cloud data, and a high-precision underwater digital elevation model (DEM) is generated by a contour extraction technique based on region growing, including:

[0063] S4.1, Delaunay triangulation optimization: Delaunay triangulation is performed on the point cloud data in the Ping of the multi-beam Ping, triangular facets are constructed by maximizing the minimum internal angle criterion to ensure the geometric uniformity and topological stability of the triangular mesh; the legality of the triangle is verified by the empty circumscribed circle criterion, and the triangle that does not meet the Delaunay condition is removed to optimize the structure of the triangular mesh;

[0064] S4.2, contour extraction based on region growing: the Ping point cloud data collected by the multi-beam sonar is divided into a spatial grid, and distance and density thresholds are set; taking the seed point as the core, the adjacent points meeting the threshold condition are divided into the same contour line by the Euclidean distance clustering and region growing algorithm, avoiding contour line breakage or distortion; for the contour line with the "branch" problem, a breakpoint detection method based on curvature analysis is used to split the complex contour into multiple simple sub-contours, ensuring the "one-to-one" correspondence;

[0065] S4.3, contour line facet filling: for the contour lines of adjacent Pings, a corresponding point matching algorithm based on the minimum spanning tree (MST) is used to establish the topological connection between the contour lines; the generation path of the triangular facet is optimized by dynamic programming to ensure smooth transition between adjacent contour lines; the optimized triangular mesh model and the measured micro-topographic data are fused to generate a centimeter-level underwater digital elevation model (DEM);

[0066] S4.4, DEM post-processing: Laplacian smoothing algorithm is used to eliminate local noise in the triangular mesh and improve the smoothness of the DEM surface; the data vacancy area is filled by Kriging interpolation to ensure the integrity and continuity of the DEM model.

[0067] Optionally, in an implementation form of the first aspect of the present application, in S5, the backfilling amount is calculated by combining the square grid method with the DEM model through the following steps:

[0068] S5.1, the DEM model is divided into uniform square grid units according to the preset resolution, and each unit corresponds to the actual plane area of the water area;

[0069] S5.2, the elevation data of each grid unit is extracted, and the unit filling and digging height difference is calculated in combination with the design backfilling elevation;

[0070] S5.3, the backfilling volume of each triangular pyramid unit is calculated by the bidirectional cutting triangular pyramid average value algorithm according to the formula

[0071] wherein is the side length of the square grid, is the unit vertex filling and digging height difference;

[0072] S5.4, accumulate all unit backfill amounts, output the total backfill earthwork amount, and compare and verify with the actual value of construction.

[0073] In a second aspect, the embodiments of the present application provide a power transmission and transformation project river and pond underwater topography intelligent measurement and backfill amount accurate calculation system, which is applied to the power transmission and transformation project river and pond underwater topography intelligent measurement and backfill amount accurate calculation method as described in the first aspect. The system comprises:

[0074] A data acquisition module is configured to use an unmanned ship to carry a multi-beam sounding system, integrate a surface sound velocity meter, an attitude meter and a positioning system, perform high-precision three-dimensional data acquisition on the river and pond underwater topography, and acquire water depth, topography and obstacle information.

[0075] A path planning module is configured to perform smoothing processing on a planned path based on an improved ant colony algorithm and a B-spline interpolation method, set a safety turning angle and a minimum turning path, and realize dynamic obstacle avoidance and track optimization.

[0076] A data processing module is configured to perform intelligent processing on the acquired underwater data through a layered sound ray tracking algorithm, a CUBE filtering algorithm and an attitude deviation correction algorithm, and eliminate the influence of sound velocity error, environmental noise interference and sounding deviation.

[0077] A three-dimensional modeling module is configured to perform triangular net topology optimization on point cloud data using a Delaunay triangulation algorithm, and generate a high-precision underwater digital elevation model DEM through a contour extraction technology based on region growing.

[0078] An engineering quantity calculation module is configured to divide calculation units using a square grid method based on the DEM model, and calculate the backfill amount.

[0079] In a third aspect, the embodiments of the present application provide an electronic device, which comprises:

[0080] a processor;

[0081] a memory for storing processor-executable instructions;

[0082] When the processor is configured to execute the instructions, the power transmission and transformation project river and pond underwater topography intelligent measurement and backfill amount accurate calculation method as described in the first aspect is implemented.

[0083] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, which stores a program. The program instructs a device to execute the power transmission and transformation project river and pond underwater topography intelligent measurement and backfill amount accurate calculation method as described in the first aspect.

[0084] The application discloses a kind of transmission and transformation engineering river pond underwater terrain intelligent measurement and backfill quantity precision calculation method and system.The method is carried by multi-beam sounding system by unmanned ship, integrates surface sound velocity meter, attitude instrument and positioning system, realizes the high-precision three-dimensional data acquisition of 0.15-300 meters water depth range;Dynamic obstacle avoidance is realized by using improved ant colony algorithm combined with B-spline interpolation optimization path planning, setting safety steering angle and minimum turning path;Data is processed based on layered sound ray tracking algorithm, CUBE filtering and attitude correction technology, and sound velocity error and environmental interference are eliminated;Delaunay triangulation algorithm is used to optimize the triangular net topology structure of point cloud data, and high-precision underwater digital elevation model DEM is generated by contour extraction technology based on region growing;Finally, the calculation unit is divided by square grid method, and the backfill quantity is accurately calculated by three pyramid volume formula.The application solves the problems of low precision and poor efficiency of traditional measurement method, reduces measurement error, significantly improves the automation degree of underwater terrain measurement and engineering quantity calculation accuracy, and provides reliable technical support for transmission and transformation engineering construction.

[0085] Beneficial effects:

[0086] 1、Measurement precision is significantly improved.Through the cooperative work of multi-beam sounding system and high-precision sensor, combined with layered sound ray tracking algorithm and attitude compensation technology, the underwater terrain measurement precision is improved, the backfill quantity calculation error is reduced, and the calculation precision is improved.

[0087] 2、Operation efficiency is greatly improved.Improved ant colony algorithm is used to realize autonomous path planning, and B-spline curve smoothing processing is used to improve the optimization efficiency of measurement track.

[0088] 3、Safety and reliability are enhanced.The dynamic obstacle avoidance algorithm (A* search+speed planning optimization) is used to realize the automatic avoidance of underwater obstacles of unmanned ship, reduce the operation risk, the CUBE filtering algorithm is combined with artificial interactive verification, effectively eliminate the interference of water grass, fish school and the like, improve the data reliability rate, and the mechanical motion constraint is set to ensure the navigation stability and avoid rollover accident. BRIEF DESCRIPTION OF DRAWINGS

[0089] Figure 1 The flowchart of the transmission and transformation engineering river pond underwater terrain intelligent measurement and backfill quantity precision calculation method provided by an embodiment of the application is shown.

[0090] Figure 2 The simplified model of simple unmanned ship provided by an embodiment of the application is shown.

[0091] Figure 3 The path comparison chart before and after smoothing provided by an embodiment of the application is shown.

[0092] Figure 4 The speed planning optimization chart provided by an embodiment of the application is shown.

[0093] Figure 5 A constant-velocity-in-layer model velocity layering diagram provided by an embodiment of the present application.

[0094] Figure 6 A constant-gradient-in-layer model velocity layering diagram provided by an embodiment of the present application.

[0095] Figure 7 A system architecture diagram of an intelligent measurement and precise calculation of backfilling quantity of river and pond underwater topography in a power transmission and transformation project provided by an embodiment of the present application.

[0096] Figure 8 A schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0097] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments of the present application.

[0098] It should be noted that “at least one” in the embodiments of the present application means one or more, and more means two or more than two. Unless otherwise defined, all technical and scientific terms used in the present application have the same meanings as those commonly understood by those skilled in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments of the present application, and are not intended to limit the present application.

[0099] It should be noted that “first”, “second” and the like in the embodiments of the present application are only used for distinguishing description purposes, and cannot be understood as indicating or implying relative importance, and cannot be understood as indicating or implying sequence. The features limited by “first”, “second” can be explicitly or implicitly included one or more of the features. In the description of the embodiments of the present application, the words “exemplary” or “for example” are used to mean as an example, illustration or description. Any embodiment or design scheme described as “exemplary” or “for example” in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words “exemplary” or “for example” are used to present the relevant concept in a specific manner.

[0100] Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0101] Embodiment one

[0102] Figure 1A flowchart of an intelligent measurement and precise calculation method of river and pond underwater topography for power transmission and transformation engineering is provided for an embodiment of the present application. As shown in Figure 1 The intelligent measurement and precise calculation method of river and pond underwater topography for power transmission and transformation engineering includes:

[0103] S1, an unmanned ship is used to carry a multi-beam sounding system, a surface sound velocity meter, an attitude meter, and a positioning system, high-precision three-dimensional data acquisition is performed on the underwater topography of the river and pond, and water depth, topography, and obstacle information are obtained.

[0104] In underwater topography measurement, using an unmanned ship to carry a multi-beam sounding system is a highly efficient and accurate method. The multi-beam sounding system can obtain high-density three-dimensional point cloud data by emitting fan-shaped beams and receiving water bottom reflection echo signals, thereby restoring complex underwater topography. At the same time, the integration of a surface sound velocity meter, an attitude meter, and a positioning system (such as RTK) can further improve the measurement accuracy and ensure the accuracy of the water depth data. In addition, the automatic operation capability of the unmanned ship makes the measurement process more efficient and reduces the need for manual intervention.

[0105] Specifically, on the basis of the DEM model, the underwater area can be divided into uniform calculation units using the grid method. Each unit corresponds to the planar area of the actual water area, and the elevation data thereof is extracted. Combined with the design backfill elevation, the height difference of each unit is calculated. The backfill volume of each triangular prism unit is calculated by the bidirectional cutting triangular prism average value algorithm, and the backfill volume of all units is accumulated to obtain the total backfill earthwork volume. Finally, the calculation results are compared and verified with the actual construction value to ensure that the error is controlled within an effective range.

[0106] In this embodiment, the S1, an unmanned ship is used to carry a multi-beam sounding system, a surface sound velocity meter, an attitude meter, and a positioning system, high-precision three-dimensional data acquisition is performed on the underwater topography of the river and pond, and water depth, topography, and obstacle information are obtained, including:

[0107] S1.1, an unmanned ship is used to carry a multi-beam sounding system, a surface sound velocity meter, an attitude meter, and a positioning system, underwater topography data within a water depth range of 0.15-300 meters is obtained by multi-beam sounding technology, sound velocity profile, ship attitude, and high-precision positioning information are synchronously collected, and an original underwater point cloud data set is constructed.

[0108] In the data preprocessing stage, the original underwater point cloud data needs to be verified for effectiveness first, invalid sounding points with signal intensity below a threshold value are removed, and abnormal data caused by water surface reflection or instrument noise is filtered. This process can be realized by professional post-processing software (such as CARIS), to ensure the accuracy and reliability of the data.

[0109] Specifically, in this embodiment, S1.2, the original underwater point cloud dataset is preprocessed, including: S1.2.1, data validity check: eliminating invalid sounding points with signal intensity below the threshold, filtering abnormal data caused by water surface reflection or instrument noise. S1.2.2, space-time synchronization calibration: time alignment of multi-sensor data based on GNSS timestamp, and coordinate conversion to unify each measuring point to the engineering coordinate system. In order to ensure the consistency of multi-sensor data, it is necessary to align the multi-sensor data based on GNSS timestamp, and to unify each measuring point to the engineering coordinate system through coordinate conversion. This step can be realized by the GNSS-RTK system carried by the unmanned ship, to ensure the space-time consistency of the data.

[0110] S1.2.3, attitude compensation correction: according to the real-time collected roll and pitch data, the sounding point position is dynamically compensated by using quaternion rotation matrix. In the measurement process, the attitude change of the ship body will affect the accuracy of the sounding data. Therefore, according to the real-time collected roll and pitch data, the sounding point position is dynamically compensated by using quaternion rotation matrix. This method can effectively reduce the influence of attitude error on the measurement results and improve the accuracy of the data.

[0111] S1.2.4, sound speed profile correction: according to the surface sound velocity instrument and CTD profile instrument data, a layered sound speed model is established to correct the original water depth value. Sound speed is one of the important factors affecting the accuracy of water depth measurement. In order to eliminate the influence of sound speed error on the measurement results, a layered sound speed model is established according to the surface sound velocity instrument and CTD profile instrument data to correct the original water depth value. This process can be realized by multi-beam post-processing software (such as CMS), to ensure the accuracy of the water depth data.

[0112] S1.2.5, data standardization: the processed point cloud data is converted into a standardized format containing three-dimensional coordinates, intensity values and confidence, and a preprocessed point cloud database is constructed. Finally, the processed point cloud data is converted into a standardized format containing three-dimensional coordinates, intensity values and confidence, and a preprocessed point cloud database is constructed. This process can be realized by professional post-processing software (such as CARIS), to ensure the standardization and processability of the data.

[0113] S2, based on the improved ant colony algorithm and B-spline interpolation method, the planned path is smoothed, the safety turning angle and the minimum turning path are set, and the dynamic obstacle avoidance and track optimization are realized.

[0114] The turning radius algorithm of unmanned ship refers to the minimum radius required when the ship needs to turn during navigation. This algorithm plays a very important role in the safety and efficiency of ship navigation. The calculation of the turning radius algorithm of unmanned ship needs to consider various parameters of the ship, such as length, width, displacement, speed, etc. These parameters will affect the turning radius of the ship, so accurate calculation is required. The ship turning radius algorithm also needs to consider the navigation environment of the ship, such as water depth, water flow, wind direction, etc. These environmental factors will also affect the turning radius of the ship, so they need to be considered comprehensively. The traditional ant colony algorithm does not consider the turning radius when turning, but in actual driving, the turning energy consumption needs to be considered and the rollover during turning needs to be avoided. Therefore, based on the improved ant colony algorithm for planning the navigation path, this study uses B-spline interpolation method to smooth the path, so as to ensure the stability of the ship driving.

[0115] During the measurement of unmanned ship, path planning is one of the key steps. In order to ensure the smooth progress of the measurement task, the planned path needs to be smoothed, and the safety turning angle and minimum turning path need to be set to realize dynamic obstacle avoidance and trajectory optimization. The improved ant colony algorithm and B-spline interpolation method can effectively optimize the path planning, so that the unmanned ship can efficiently and safely complete the measurement task in complex water areas. These algorithms not only improve the smoothness of the path, but also reduce unnecessary turning, thereby improving the measurement efficiency.

[0116] In the traditional ant colony algorithm, the heuristic function is only related to the path length, but in the actual path planning process, the evaluation of path quality cannot only consider the path length factor. The smoothness of the path, adaptive distance heuristic factor, and algorithm running time are all evaluation standards for the path. Therefore, this paper improves the heuristic function from the total path length, path smoothness, and adaptive distance heuristic factor.

[0117] Specifically, in S2, the planned path is smoothed based on the improved ant colony algorithm and B-spline interpolation method, including:

[0118] S2.1, construct an improved ant colony algorithm path planning model: improve the heuristic function according to the total path length, path smoothness, and adaptive distance heuristic factor. S2.1.1, design the heuristic function : .

[0119] Wherein, the path length factor is related to the distance factor between the to-be-walked grid and the target grid, and the distance factor between the to-be-walked grid and the starting grid, and the distance function is introduced, which is defined as follows:

[0120] ,

[0121] ,

[0122] ,

[0123] ,

[0124] ,

[0125] wherein, is the distance function, , is the current ant the maximum and minimum distance from all the next available grid centers to the target grid center; denotes the distance from the center of the grid to be walked to the center of the start grid and the target grid; , is the distance coefficient, is the distance heuristic coefficient; and denote the Euclidean distance from the next available grid to the center of the start grid and the target grid, denotes the Euclidean distance from the available grid to the center of the target grid, denotes the set of next available grids that the ant can choose, denotes the distance on the path at time t.

[0126] The ant searches the path according to the guidance of the heuristic function, the strength of the pheromone concentration on the path and the transition probability of the current node state. The traditional ant colony algorithm selects the transition probability.

[0127] S2.1.2, when the ant is located in the current grid and selects the next travel direction, the next path will be determined according to the pheromone concentration on the current path and the heuristic function to optimize the path transition probability:

[0128] ,

[0129] wherein, characterizes the importance of pheromone, denotes the importance of the heuristic function, denotes the pheromone concentration on the path at time t; ​​​The value is related to the global search ability of the algorithm, The value affects how the ants choose the next step adjacent grid; The value represents the ant The set of optional next step feasible grids, The value is inversely proportional to the Euclidean distance between the centers of the two grids, and the grid , The distance The smaller the distance, the greater the heuristic function of the path, and the formula is as follows: .

[0130] S2.2, introduce path smoothness constraint:

[0131] ,

[0132] Among them, is the distance heuristic information coefficient; is a coefficient representing the importance of straight line; The value represents the turning direction of the ant's last step, The value represents the turning direction to be walked next step.

[0133] S2.3, path smoothing step. As Figure 2 , the simple unmanned ship simplified model. Assuming that the unmanned ship is in a position at a certain time during the navigation process, at this time the heading of the unmanned ship is The offset angle between the ship's center axis and the turning direction is The turning angle of the unmanned ship has the constraint of mechanical characteristics, that is, the turning angle of the unmanned ship is set to have the constraint of mechanical characteristics:

[0134] ,

[0135] ,

[0136] Among them, is the heading angle of the unmanned ship, is the offset angle between the ship's center axis and the turning direction, and the minimum turning path of the unmanned ship is , is the center axis distance difference of the ship.

[0137] The smoothing result is shown in Figure 3 Without smoothing, the path inflection point is obvious, and some turning angles reach , which does not meet the actual navigation requirements; the smoothed path can effectively meet the requirements of safe turning angle and minimum turning path when turning.

[0138] Extract key nodes from the output optimal path and perform B-spline interpolation on the inflection points. Select and set the optimization objective function. First, for each discrete point along the axis of the coordinate system, the lateral offset should be as close to the guide line as possible without considering the influence of surrounding environmental factors. and the second-order derivative , as small as possible, which can reduce the acceleration of lateral motion, avoid frequent steering, and save energy. According to the above analysis, the objective function is defined as:

[0139] S2.4. Implement path optimization: Use B-spline curve to smoothly interpolate the planned path, establish trajectory planning guide lines through the Fresnel coordinate system, and construct the optimization objective function :

[0140] ,

[0141] In order to ensure the continuity of the path, the horizontal distance The relevant derivatives of are approximated by the difference at each discrete point, where , , for the coordinate system Each discrete point in the axis direction The lateral offset , select the lateral offset of the point 、 and As the optimization variable, and For horizontal Offset relative to The first and second derivatives of ; 、 and is the corresponding weight coefficient.

[0142] In the coordinate system of the route-time obstacle map, the cumulative distance along the time axis and the route is discretized to form a grid map of equal time and distance. Then, according to the planning requirements, various environmental factors are considered and different weights are used. A universal and unified cost function is established. Adjacent grid nodes in the grid map are assigned a cost. After the grid map is established, the entire solution process is equivalent to a typical graph search process for path planning. The search result is a globally optimal path. The search algorithm samples the typical A* algorithm. From the results of A*, it can be seen that a set of cumulative route distances S and time t can be obtained. Each point in this sequence represents the time (t) and the expected location (S) of the unmanned boat.

[0143] The search results are sequentially connected by multiple straight line segments, which do not meet the requirements of autonomous navigation smoothness of the unmanned ship and do not satisfy the kinematic constraints, and the results need to be optimized. First, calculate the timestamp The corresponding longitudinal distance , through the fixed timestamp Take as the optimization decision variable, and the objective function of the optimization problem is defined as follows according to the above analysis:

[0144] S2.5, dynamic obstacle avoidance processing: establish a route-time obstacle map, use A* algorithm for global path search, and implement speed planning optimization:

[0145] ,

[0146] Among them, in order to ensure the continuity of speed allocation, The related derivative is approximated by difference at each discrete point, that is , , The timestamp The corresponding longitudinal distance, Is the optimization decision variable.

[0147] The above constraints are all linear constraints, and the feasible region of the decision variable has become a convex set through the obstacle avoidance constraint relationship. The objective function of the optimization problem is a quadratic convex function, and the speed optimization becomes a quadratic programming problem. Using a quadratic programming solver can quickly solve the optimization problem, and the optimization result is as shown in Figure 4 .

[0148] S3, through the hierarchical sound line tracking algorithm, CUBE filtering algorithm and attitude deviation correction algorithm, the collected underwater data is intelligently processed to eliminate the influence of sound velocity error, environmental noise interference and depth deviation.

[0149] During underwater data processing, sound velocity error, environmental noise interference and depth deviation are the main factors affecting measurement accuracy. In order to eliminate these influences, hierarchical sound line tracking algorithm, CUBE filtering algorithm and attitude deviation correction algorithm can be used. These algorithms can effectively correct sound velocity error, filter environmental noise and correct depth deviation, thereby improving the accuracy and reliability of underwater data. In addition, the combination of these algorithms can realize intelligent processing of underwater data and improve the overall measurement quality.

[0150] Specifically, the S3, through the hierarchical sound line tracking algorithm, CUBE filtering algorithm, and attitude deviation correction algorithm, intelligently processes the collected underwater data to eliminate the influence of sound velocity error, environmental noise interference and depth deviation, including:

[0151] S3.1, a layered sound ray tracing algorithm is adopted, and a sound velocity profile model is established based on Snell's law; S3.2, CUBE algorithm is adopted for automatic filtering, the influence radius of the sounding point is calculated, the grid point capture radius is calculated, and Bayesian filtering is implemented; S3.3, based on the roll and pitch data collected by the POS system, a quaternion rotation matrix is adopted for dynamic compensation, and the "crying face" or "smiling face" deformation caused by residual error is eliminated; S3.4, the abnormal points with signal intensity lower than the threshold value are removed, and the complex terrain area is supplemented and processed by manual interactive filtering, and the output precision is centimeter level.

[0152] The influence of the sound velocity profile on the sounding result is reflected in the change of the sound ray trajectory, thereby affecting the result of the spatial homing of the beam footprint. In the multi-beam depth calculation process, the tracking of the sound ray propagation path in water is called sound ray tracing. The theoretical basis of sound ray tracing is the sound velocity stratification assumption, that is, any complex sound velocity profile structure can be approximately composed of multiple layers with simple sound velocity structure. This assumption replaces the continuous change of the entire sound velocity profile with the sound velocity broken line distribution in each independent layer. In specific applications, the commonly used sound velocity stratification forms include constant sound velocity stratification and constant sound velocity gradient stratification. The former considers that the sound velocity in each small layer is constant, and the sound ray propagates along a straight line. The latter considers that the sound velocity in the layer varies linearly, and the sound ray propagates along a curve.

[0153] The S3.1, the layered sound ray tracing algorithm is adopted, and the sound velocity profile model is established based on Snell's law, which comprises:

[0154] a) Constant sound velocity stratification model. The sound velocity stratification of the constant sound velocity assumption model in the layer is as shown in Figure 5 When the sound velocity in the layer is constant, the grazing angle of the sound ray in the layer is constant, and the trajectory is a straight line. It is assumed that the initial incidence angle of the beam is , and the initial sound velocity is The incidence angle of the sound ray in each layer can be obtained from Snell's law and the sound velocity of each layer .

[0155] b) The sound velocity profile is divided into N layers, the sound velocity in each layer is constant, the sound ray propagates in a straight line, the propagation time of each layer is calculated , and the horizontal distance :

[0156] ,

[0157] ,

[0158] wherein is the initial incidence angle of the beam, is the initial sound velocity, and the incidence angle of the sound ray in each layer can be obtained from Snell's law and the sound velocity of each layer . The distance on the Z axis.

[0159] The accumulated travel time and horizontal distance of the sound ray after traveling through the whole N-1 layers are as follows:

[0160] ,

[0161] .

[0162] If the sound ray still has time left after N-1 layers of refraction, denoted as but is not enough to cross the whole Nth layer, the depth and horizontal distance of the sound ray in the Nth layer are as follows:

[0163] ,

[0164] .

[0165] b) Constant gradient layered model. The sound velocity stratification of the constant gradient layered model is shown in FIG. 2. The sound ray trajectory curvature of the first layer, i.e., the sound velocity linearly changes in each layer, the sound ray propagates in a circular arc, and the sound ray curvature is calculated as follows: Figure 6

[0166] ,

[0167] wherein is the incidence angle of the sound ray microelement, is the grazing angle thereof, is the sound velocity at the microelement, is the distance element, and is a constant given by Snell's law when the initial grazing angle and the initial sound velocity of the sound ray in the first layer are given. is a constant. For the constant sound velocity gradient layer is a constant. Therefore, there is which is a constant, i.e., the sound ray curvature is equal everywhere in the Nth layer, and is a constant. The trajectory of the sound ray in the layer is a circular arc.

[0168] S4, the Delaunay triangulation algorithm is used to optimize the triangular net topology structure of the point cloud data, and a high-precision underwater digital elevation model DEM is generated through the contour extraction technology based on region growing.

[0169] ​​​​​In the S4, the point cloud data is optimized by Delaunay triangulation algorithm, and a high-precision underwater digital elevation model (DEM) is generated by the contour extraction technology based on region growing. The specific steps include:

[0170] S4.1, Delaunay triangulation optimization: Delaunay triangulation is performed on the point cloud data within the Ping of multi-beam, and the triangular patches are constructed by maximizing the minimum internal angle criterion to ensure the geometric uniformity and topological stability of the triangular mesh. The legality of the triangle is verified by the empty circumscribed circle criterion, and the triangles that do not meet the Delaunay condition are removed to optimize the structure of the triangular mesh.

[0171] Delaunay triangulation is an algorithm widely used in computational geometry, and its core idea is to construct triangular patches by maximizing the minimum internal angle criterion to ensure the geometric uniformity and topological stability of the triangular mesh. The empty circumscribed circle property of Delaunay triangulation (i.e. the circumscribed circle of any triangle does not contain other points) is an important basis for its legality. By removing triangles that do not meet the Delaunay condition, the structure of the triangular mesh can be optimized, and its applicability in underwater terrain modeling can be improved. In practical applications, Delaunay triangulation algorithm can be divided into several types, such as divide-and-conquer algorithm, triangular mesh growth method and random growth method. Among them, the random growth method is widely used in the triangulation of point cloud data due to its easy implementation and small memory occupation. In addition, when generating a triangular mesh, Delaunay triangulation can ensure that the angles of each triangle are as close to equilateral as possible, thereby avoiding the appearance of "slim" triangles and improving the stability and accuracy of the model.

[0172] S4.2, contour extraction based on region growing: the point cloud data within the Ping collected by multi-beam sonar is divided into spatial grid, and the distance threshold and density threshold are set; taking the seed point as the core, the adjacent points that meet the threshold condition are divided into the same contour line by the Euclidean distance clustering and region growing algorithm, avoiding the breakage or distortion of the contour line; for the contour line with "branch" problem, the breakpoint detection method based on curvature analysis is used to split the complex contour into multiple simple sub-contours, ensuring the "one-to-one" correspondence.

[0173] Contour extraction based on region growing is a common method for segmenting point cloud data, and its core idea is to set the distance threshold and density threshold to divide the adjacent points that meet the conditions into the same contour line, thereby avoiding the breakage or distortion of the contour line. This method usually takes the seed point as the core, and gradually expands the contour range by the Euclidean distance clustering and region growing algorithm, ensuring the continuity and integrity of the contour.

[0174] For the contour line with the "branch" problem, a breakpoint detection method based on curvature analysis can be used to split the complex contour into multiple simple sub-contours, ensuring a "one-to-one" correspondence. This method shows good results in processing complex building roof point cloud segmentation and can effectively solve the segmentation problem caused by point cloud scattering and error.

[0175] S4.3, contour line patch filling: for the contour lines of adjacent Pings, a corresponding point matching algorithm based on minimum spanning tree (MST) is used to establish the topological connection between the contour lines; the generation path of the triangular patch is optimized by dynamic programming to ensure smooth transition between adjacent contour lines; the optimized triangular mesh model is fused with the measured micro-topographic data to generate a centimeter-level underwater digital elevation model (DEM).

[0176] In the contour line patch filling stage, the contour lines of adjacent Pings need to be topologically connected through a corresponding point matching algorithm based on minimum spanning tree (MST). By optimizing the generation path of the triangular patch through dynamic programming, the smooth transition between adjacent contour lines can be ensured. Finally, the optimized triangular mesh model is fused with the measured micro-topographic data to generate a centimeter-level underwater digital elevation model (DEM).

[0177] S4.4, DEM post-processing: Laplacian smoothing algorithm is used to eliminate local noise in the triangular mesh and improve the smoothness of the DEM surface; Kriging interpolation is used to fill in the data gaps to ensure the integrity and continuity of the DEM model.

[0178] After the DEM is generated, in order to improve the surface smoothness and data integrity of the model, Laplacian smoothing algorithm is usually used to eliminate local noise in the triangular mesh. In addition, Kriging interpolation method is used to fill in the data gaps to ensure the continuity and consistency of the DEM model.

[0179] The optimized triangular mesh model is fused with the measured micro-topographic data to generate a centimeter-level underwater digital elevation model (DEM) for accurate calculation of the backfilling amount of the river pond and underwater terrain analysis.

[0180] S5, based on the DEM model, the square grid method is used to divide the calculation unit, and the backfilling amount is calculated.

[0181] Specifically, in the S5, the square grid method and the DEM model are combined to calculate the backfilling amount by the following steps: S5.1, the DEM model is divided into uniform square grid units according to the preset resolution, and each unit corresponds to the actual water area plane region;

[0182] S5.2, extract the elevation data of each grid unit, and calculate the unit fill-dig height difference combined with the design backfill elevation; S5.3, through the bidirectional cutting triangular pyramid average value algorithm, according to the formula

[0183] Calculate the backfill volume of each triangular pyramid unit, wherein is the square side length, is the unit vertex filling and digging height difference; S5.4, accumulate all unit backfill amounts, output the total backfill earthwork amount, and compare and verify with the actual construction value.

[0184] Embodiment two

[0185] As Figure 7 shown, the application provides a power transmission and transformation project river pond underwater topography intelligent measurement and backfill amount accurate calculation system architecture diagram, applied to the power transmission and transformation project river pond underwater topography intelligent measurement and backfill amount accurate calculation system as described in embodiment one, comprising a data acquisition module 11, a path planning module 12, a data processing module 13, a three-dimensional modeling module 14, and an engineering quantity calculation module 15.

[0186] The data acquisition module 11 is used to adopt an unmanned ship to carry a multi-beam sounding system, integrate a surface sound velocity meter, an attitude meter and a positioning system, perform high-precision three-dimensional data acquisition on the river pond underwater topography, and obtain water depth, topography and obstacle information;

[0187] The path planning module 12 is used to perform smoothing processing on the planned path based on an improved ant colony algorithm and a B-spline interpolation method, set a safety turning angle and a minimum turning path, and realize dynamic obstacle avoidance and track optimization;

[0188] The data processing module 13 is used to perform intelligent processing on the collected underwater data through a layered sound ray tracking algorithm, a CUBE filtering algorithm and an attitude deviation correction algorithm, and eliminate the influence of sound velocity error, environmental noise interference and sounding deviation;

[0189] The three-dimensional modeling module 14 adopts a Delaunay triangulation algorithm to perform triangular net topological structure optimization on point cloud data, and generates a high-precision underwater digital elevation model DEM through a contour extraction technology based on region growing;

[0190] The engineering quantity calculation module 15 is used to divide and calculate units based on the DEM model using a square grid method, and calculate the backfill amount.

[0191] Figure 8 The electronic device provided by an embodiment of the application. As Figure 8 shown, the electronic device at least includes the following parts: a processor 101 and a memory 100, a communication interface 103, and a bus 102.

[0192] In the embodiments of the application, the memory 100 is used to store processor 101 executable instructions, and the processor 101 is configured to implement the method of the first aspect when executing the instructions.

[0193] In an embodiment of the present application, a computer-readable storage medium includes instructions that instruct a device to perform the method of the first aspect. For example, the instructions instruct the device to perform the method of the first aspect. Figure 1 The method shown in the flow steps in the middle.

[0194] The program that works in the electronic device according to an embodiment of the present application can be a program (a program that makes a computer function) that controls a central processing unit (CPU) or the like to realize the functions of the above-described embodiments according to one aspect of the present application. Then, the information processed by these devices is temporarily stored in a random access memory (RAM) when it is processed, and then stored in various ROMs such as a read-only memory (Flash ROM), a hard disk drive (HDD), and the like, and read out, corrected, and written by a CPU as needed.

[0195] Note that a part of the electronic device according to the above-described embodiments can also be realized by a computer. In this case, a program for realizing the control function can be recorded in a computer-readable recording medium, and realized by reading the program recorded in the recording medium into a computer and executing it.

[0196] Note that the "computer" referred to here means a computer built into the electronic device, and a computer that includes an OS, a peripheral device, and the like as hardware. In addition, the "computer-readable recording medium" means a removable medium such as a floppy disk, a magneto-optical disk, a ROM, a CD-ROM, and the like, a storage device such as a hard disk built into a computer.

[0197] Furthermore, the "computer-readable recording medium" can include a medium that dynamically stores a program for a short period of time, such as a communication line in the case of transmitting a program via a network such as the Internet or a communication line such as a telephone line, and a medium that stores a program for a fixed period of time, such as a volatile memory inside a computer that is a server or a client in this case. In addition, the above-described program can be a program for realizing a part of the above-described functions, and can also be a program that can realize the above-described functions by being combined with a program already recorded in a computer.

[0198] In addition, the electronic device according to the above-described embodiments can also be realized as an assembly (a device group) composed of a plurality of devices. Each device that constitutes the device group can have a part or all of each function or each functional block of the electronic device according to the above-described embodiments. As the device group, all of each function or each functional block of the electronic device can be possessed.

[0199] Those skilled in the art should know that the above-mentioned embodiments are only used to explain the present application, but not as a limitation to the present application, as long as the changes and modifications made to the above embodiments are within the scope of the present application, they should fall within the scope of the present application.

Claims

1. A method for intelligent measurement of underwater terrain and accurate calculation of backfill volume in rivers and ponds for power transmission and transformation projects, characterized by: The method comprises: S1. Use an unmanned vessel equipped with a multi-beam bathymetric system, integrated with a surface sound velocity meter, attitude indicator, and positioning system, to collect high-precision three-dimensional data of the underwater terrain of rivers and ponds, and obtain information on water depth, terrain, and obstacles; S2: Smoothing the planned path based on the improved ant colony algorithm and B-spline interpolation method, setting the safe steering angle and minimum turning path, and achieving dynamic obstacle avoidance and trajectory optimization; S3, intelligently process the collected underwater data through layered ray tracking algorithm, CUBE filtering algorithm, and attitude deviation correction algorithm to eliminate the influence of sound velocity error, environmental noise interference and sounding deviation; S4. Use the Delaunay triangulation algorithm to optimize the triangulated network topology of the point cloud data, and generate a high-precision underwater digital elevation model (DEM) through contour extraction technology based on region growing; S5. Divide the calculation unit using the grid method based on the DEM model and calculate the backfill volume; S3, intelligently processing the collected underwater data through a layered ray tracking algorithm, a CUBE filtering algorithm, and an attitude deviation correction algorithm to eliminate the effects of sound velocity error, environmental noise interference, and sounding deviation, including: S3.

1. Using the layered ray tracing algorithm, a sound velocity profile model is established based on Snell's law. S3.

2. Use the CUBE algorithm for automatic filtering, calculate the influence radius of the sounding point, calculate the grid point capture radius, and implement Bayesian filtering; S3.

3. Based on the roll and pitch data collected by the POS system, dynamic compensation is performed using the quaternion rotation matrix to eliminate the "crying face" or "smiling face" deformation caused by residual errors; S3.

4. Eliminate outliers with signal strength below the threshold, perform manual interactive filtering to supplement processing of complex terrain areas, and output centimeter-level accuracy; In S4, the Delaunay triangulation algorithm is used to optimize the triangulated network topology of the point cloud data, and a high-precision underwater digital elevation model (DEM) is generated by a contour extraction technique based on region growing, including: S4.

1. Delaunay triangulation optimization: Perform Delaunay triangulation on the multi-beam Ping point cloud data, construct triangular facets by maximizing the minimum internal angle criterion, and ensure the geometric uniformity and topological stability of the triangulation network. Verify the legitimacy of the triangles using the empty circumscribed circle criterion, remove triangles that do not meet the Delaunay conditions, and optimize the triangulation network structure. S4.

2. Contour extraction based on region growing: The Ping point cloud data collected by multibeam sonar is spatially gridded, and distance and density thresholds are set. Using the seed point as the core, adjacent points that meet the threshold conditions are grouped into the same contour line using Euclidean distance clustering and region growing algorithms to avoid contour line breakage or distortion. For contour lines with "branching" problems, a breakpoint detection method based on curvature analysis is used to split complex contours into multiple simple sub-contours to ensure a "one-to-one" correspondence. S4.

3. Contour patch filling: For the contour lines of adjacent pings, a corresponding point matching algorithm based on the minimum spanning tree (MST) is used to establish topological connections between the contour lines. Dynamic programming is used to optimize the generation path of triangular patches to ensure smooth transitions between adjacent contour lines. The optimized triangulated network model is then integrated with the measured microtopography data to generate a centimeter-level underwater digital elevation model (DEM). S4.4, DEM post-processing: Use the Laplacian smoothing algorithm to eliminate local noise in the triangulation network and improve the smoothness of the DEM surface; use Kriging interpolation to fill in data gaps and ensure the integrity and continuity of the DEM model.

2. The method for intelligent measurement of underwater terrain and accurate calculation of backfill volume of rivers and ponds in power transmission and transformation projects according to claim 1 is characterized in that: S1 uses an unmanned vessel equipped with a multi-beam bathymetry system, integrated with a surface sound velocity meter, attitude meter, and positioning system, to collect high-precision three-dimensional data of the underwater terrain of rivers and ponds, and obtain information on water depth, terrain, and obstacles, including: S1.

1. Use an unmanned vessel equipped with a multi-beam bathymetry system, integrating a surface sound velocity meter, an attitude meter, and a positioning system. This system uses multi-beam bathymetry technology to acquire underwater topographic data within a water depth range of 0.15-300 meters. It also simultaneously collects sound velocity profiles, vessel attitude, and high-precision positioning information to construct an original underwater point cloud dataset. S1.

2. Preprocess the original underwater point cloud dataset, including: S1.2.

1. Data validity check: Eliminate invalid sounding points with signal strength below the threshold, and filter out abnormal data caused by water surface reflection or instrument noise; S1.2.2, Time and Space Synchronization Calibration: Time align multi-sensor data based on GNSS timestamps, and unify each measurement point to the engineering coordinate system through coordinate transformation; S1.2.3, Attitude compensation correction: Based on the real-time collected roll and pitch data, the quaternion rotation matrix is ​​used to dynamically compensate the sounding point position; S1.2.4, Sound velocity profile correction: Based on the surface sound velocity meter and CTD profiler data, a layered sound velocity model is established to perform sound line bending correction on the original water depth value; S1.2.

5. Data standardization: Convert the processed point cloud data into a standardized format containing three-dimensional coordinates, intensity values, and confidence levels to build a preprocessed point cloud database.

3. The method for intelligent measurement of underwater terrain and accurate calculation of backfill volume of rivers and ponds in power transmission and transformation projects according to claim 1 is characterized in that: In S2, the planned path is smoothed based on the improved ant colony algorithm and B-spline interpolation method, including: S2.

1. Constructing an improved ant colony algorithm path planning model: S2.1.

1. Design Heuristic Function : ， in, ， ， ， ， ， in, is the distance function, 、 Is the current ant The next step is to select the maximum and minimum distances from all grid centers to the target grid center; Indicates the grid to be walked The distance heuristic factor from the center of to the starting grid and the target grid center; 、 is the distance coefficient, is the distance heuristic information coefficient; and Represents the next feasible grid Euclidean distance to the center of the starting grid and the target grid, Represents a feasible grid Euclidean distance to the center of the target grid, Represents ants A set of optional next feasible grids, express Time Path distance on; S2.1.2、Optimize path transfer probability: ， in, Characterize the importance of pheromones, Indicates the importance of the heuristic function, express Time Path Pheromone concentration on; S2.

2. Introducing path smoothness constraints: ， in, is the distance heuristic information coefficient; is the coefficient representing the importance of straight travel; Indicates the direction the ant turned in the previous step. Indicates the direction to be turned next.

4. The method for intelligent measurement of underwater terrain and accurate calculation of backfill volume of rivers and ponds in power transmission and transformation projects according to claim 2 is characterized in that: In S2, a safe steering angle and a minimum turning path are set to achieve dynamic obstacle avoidance and track optimization, including: S2.

3. Set mechanical motion constraints: ， ， in, is the heading angle of the unmanned ship, is the offset angle between the ship's center axis and the steering direction, and the minimum turning path of the unmanned ship is , is the difference in center axis distance of the ship; S2.

4. Implement path optimization: Use B-spline curve to smoothly interpolate the planned path, establish trajectory planning guide lines through the Freynes coordinate system, and construct the optimization objective function : ， in, , , for the coordinate system Each discrete point in the axis direction The lateral offset , select the lateral offset of the point 、 and As the optimization variable, and For horizontal Offset relative to The first and second derivatives of ; 、 and is the corresponding weight coefficient; S2.5, Dynamic Obstacle Avoidance Processing: Create a route-time obstacle map, use the A* algorithm to perform global path search, and implement speed planning optimization: ， in, , , Timestamp The corresponding longitudinal distance, To optimize the decision variables.

5. The method for intelligent measurement of underwater terrain and accurate calculation of backfill volume of rivers and ponds in power transmission and transformation projects according to claim 4 is characterized in that: S3.1, using a layered ray tracing algorithm to establish a sound velocity profile model based on Snell's law, includes: a) Constant sound velocity layered model: The sound velocity profile is divided into N layers. The sound velocity in each layer is constant, and the sound line propagates in a straight line. The propagation time of each layer is calculated. and horizontal distance : ， ， in, is the initial incident angle of the beam, is the initial sound velocity, according to Snell's law and the sound velocity of each layer , the incident angle of the sound ray in each layer can be obtained, is the distance on the Z axis; b) Constant gradient layered model: The speed of sound changes linearly within each layer, and the sound line propagates in a circular arc. Calculate the curvature of the sound line: ， in, The sound ray element The incident angle, is its grazing angle, for The speed of sound at the infinitesimal point is determined by Snell's law when the sound line is at Initial grazing angle within the layer and the initial sound speed , given time is a constant, is the distance element.

6. The method for intelligent measurement of underwater terrain and accurate calculation of backfill volume for power transmission and transformation projects according to claim 5 is characterized in that: In S5, the backfill volume is calculated by combining the grid method and the DEM model through the following steps: S5.

1. Divide the DEM model into uniform grid cells at a preset resolution, with each cell corresponding to a planar area of ​​actual water; S5.

2. Extract the elevation data of each grid cell and calculate the height difference between the cell and the cut and fill height based on the designed backfill elevation. S5.3, by bidirectionally splitting the triangular pyramid average value algorithm, according to the formula Calculate the backfill volume of each triangular pyramid element, where is the side length of the square, Fill and cut height difference for cell vertices; S5.

4. Accumulate the backfill volume of all units, output the total backfill volume, and compare and verify it with the actual construction value.

7. A system for intelligent measurement of underwater terrain and precise calculation of backfill volume for power transmission and transformation projects, applied to the method for intelligent measurement of underwater terrain and precise calculation of backfill volume for power transmission and transformation projects as claimed in any one of claims 1 to 6, characterized in that: The system comprises: The data acquisition module is used to use an unmanned vessel equipped with a multi-beam bathymetric system, integrating a surface sound velocity meter, attitude indicator, and positioning system to collect high-precision three-dimensional data of underwater terrain in rivers and ponds, and obtain information on water depth, terrain, and obstacles; The path planning module is used to smooth the planned path based on the improved ant colony algorithm and B-spline interpolation method, set the safe steering angle and minimum turning path, and achieve dynamic obstacle avoidance and trajectory optimization; The data processing module is used to intelligently process the collected underwater data through the layered ray tracking algorithm, CUBE filtering algorithm, and attitude deviation correction algorithm to eliminate the influence of sound velocity error, environmental noise interference, and bathymetric deviation; The 3D modeling module uses the Delaunay triangulation algorithm to optimize the triangulated network topology of point cloud data and generates a high-precision underwater digital elevation model (DEM) through contour extraction technology based on region growing; The engineering quantity calculation module is used to divide the calculation units using the grid method based on the DEM model and calculate the backfill quantity.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program that instructs a device to execute the method for intelligent measurement of underwater terrain and precise calculation of backfill volume of rivers and ponds in power transmission and transformation projects as described in any one of claims 1 to 6.

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