CN118864784A - A new cross-layer ecological and environmentally friendly dredging method and system - Google Patents

Abstract

The present invention relates to the technical field of ecological and environmental dredging, and in particular to a novel cross-layer ecological and environmental dredging method and system. The method comprises the following steps: performing underwater topographic survey of the construction area by measuring equipment to obtain original measurement data; preprocessing the original measurement data to obtain original measurement preprocessing data, and constructing contour maps of the original measurement preprocessing data to obtain measurement contour map data; generating a construction topographic map according to the measurement contour map data to obtain construction topographic map data; obtaining dredging target data, and marking key data according to the dredging target data and the construction topographic map data to obtain construction topographic map marking data; obtaining ship size data, and generating an operation map according to the ship size data and the construction topographic map marking data to obtain cross-layer ecological and environmental dredging operation map data. The present invention improves the accuracy and applicability of cross-layer ecological and environmental dredging operations.

Description

A new cross-layer ecological and environmentally friendly dredging method and system

Technical Field

The present invention relates to the technical field of ecological and environmental protection dredging, and in particular to a novel cross-layer ecological and environmental protection dredging method and system.

Background Art

In traditional dredging projects, manual measurement and simple mechanical equipment are usually used for construction. However, with the expansion of project scale and the improvement of environmental protection requirements, traditional methods have shown obvious deficiencies in accuracy, efficiency and environmental protection. Traditional manual measurement methods are limited by equipment accuracy and operator skill level, and it is difficult to obtain high-precision terrain data, resulting in frequent errors during construction, affecting the dredging effect. Manual measurement and manual operation are not only time-consuming and labor-intensive, but also prone to operational errors, affecting construction progress and efficiency.

Summary of the invention

In order to solve the above technical problems, the present invention proposes a novel cross-layer ecological and environmentally friendly dredging method and system to solve at least one of the above technical problems.

The present application provides a novel cross-layer ecological and environmentally friendly dredging method, the method comprising:

S1. Use surveying equipment to measure the underwater topography of the construction area and obtain original survey data;

S2, preprocessing the original measurement data to obtain original measurement preprocessing data, and constructing a contour map of the original measurement preprocessing data to obtain measurement contour map data;

S3, generating a construction topographic map according to the measured contour map data to obtain construction topographic map data;

S4, obtaining dredging target data, and performing key data identification according to the dredging target data and the construction topographic map data to obtain construction topographic map identification data;

S5. Obtain ship size data, and generate an operation map based on the ship size data and the construction topographic map identification data to obtain cross-layer ecological and environmental dredging operation map data to perform cross-layer ecological and environmental dredging auxiliary operations.

In the present invention, underwater topographic measurement is performed by measuring equipment, and the original measurement data is preprocessed to ensure the accuracy and reliability of the data. The construction topographic map is generated by measuring the contour map data, and the operation map is generated in combination with the dredging target data and the ship size data, providing accurate construction planning and optimizing the operation process. The method includes ecological and environmental protection considerations. Through accurate measurement and planning, the disturbance to the environment is reduced and the ecological environment of the construction area is protected. By combining the dredging target data and the construction topographic map, cross-layer ecological and environmental protection dredging construction can be effectively carried out, resource utilization efficiency can be improved, and waste can be reduced. The method supports cross-layer operations, and through accurate topographic maps and operation maps, the coordination between the layers during the construction process is ensured, thereby improving the overall efficiency and effect of the construction. The use of modern measurement and data processing technologies has improved the level of intelligence and automation of construction and reduced errors and risks in manual operations.

Optionally, the original measurement data includes first topographic measurement data and second topographic measurement data, and S1 includes:

Controlling the multi-beam sonar measurement equipment to perform underwater topographic measurement of the construction area according to a preset first measurement parameter to obtain first topographic measurement data;

The multi-beam sonar measurement equipment is controlled to perform underwater topography measurement on the construction area according to a preset second measurement parameter to obtain second topography measurement data, wherein the first measurement parameter and the second measurement parameter are different measurement parameters.

In the present invention, by using different measurement parameters (first measurement parameter and second measurement parameter), more detailed and accurate terrain data can be obtained from multiple dimensions, thereby improving the accuracy of the measurement results. The two independent measurement data complement each other. Through comparison and verification, errors and abnormal data in the measurement can be identified and corrected, thereby enhancing the reliability of the original measurement data. Different measurement parameters are optimized for different depths, resolutions or other specific conditions. Combining these two measurement data, more comprehensive underwater terrain information can be obtained, which helps to fully understand the actual situation of the construction area.

Optionally, S2 includes:

S21, performing sonar data correction on the first topographic measurement data and the second topographic measurement data to obtain first correction data and second correction data respectively;

S22, performing coordinate conversion on the first correction data and the second correction data to obtain first coordinate data and second coordinate data respectively;

S23, performing terrain modeling according to the first coordinate data and the second coordinate data to obtain first terrain model data and second terrain model data respectively;

S24, constructing a curved surface according to the first terrain model data and the second terrain model data, to obtain first terrain curved surface model data and second terrain curved surface model data respectively;

S25, generating contour lines for the first terrain surface model data and the second terrain surface model data to obtain first contour line model data and second contour line model data respectively;

S26, fusing the first contour line model data and the second contour line model data to obtain measurement contour line map data.

In the present invention, by sonar data correction, errors and noise that may occur during the measurement process are eliminated, the accuracy and reliability of the measurement data are improved, and the accuracy of subsequent processing is ensured. By processing and fusing terrain data under different measurement parameters, information of different measurement dimensions can be integrated to provide a more comprehensive and detailed terrain description, overcoming the limitations of a single measurement method. Accurate terrain model data are generated by coordinate conversion and terrain modeling of the corrected data. These model data can truly reflect the details of the underwater terrain and contribute to scientific and reasonable construction planning. The terrain model data is used to construct a surface to generate high-precision terrain surface model data. These data can accurately describe the continuity and change trend of the terrain, providing a basis for contour line generation and construction plans. By generating contour line model data from the terrain surface model data, detailed contour line maps can be obtained. These contour line maps can intuitively show the ups and downs and changes of the terrain, which is helpful for decision-making and optimization during the construction process. The contour line model data generated under different measurement parameters are fused, and their respective advantages can be combined to optimize the final measurement contour line map data. This fusion processing can improve the overall quality and applicability of the data.

Optionally, S3 includes:

S31, performing regional division according to the measured contour map data to obtain map regional division data;

S32, performing terrain refinement on the map region division data to obtain map region refinement data;

S33, generating attributes for the map area refinement data to obtain construction topographic map data.

In the present invention, regional division is performed based on the measured contour map data, which can accurately identify the different features and boundaries of the construction area, providing a basis for subsequent refinement and construction planning. By performing terrain refinement on the map area division data, it is possible to capture and describe the slight changes and details of the terrain, and to provide more accurate terrain information, which is helpful in formulating scientific construction plans. By generating attributes for the refined terrain data, relevant construction attributes (such as soil type, water depth, slope, etc.) can be added to each area, making the construction topographic map data more comprehensive and practical, and providing more reference information for construction. High-precision construction topographic map data can significantly improve the accuracy of construction planning, ensure that the construction plan can fully consider the terrain details, and reduce adjustments and errors during the construction process.

Optionally, S4 includes:

S41, generating dredging parameters according to the dredging target data to obtain dredging parameter data;

S42, performing dredging annotation according to the dredging parameter data and the construction topographic map identification data to obtain dredging annotation data;

S43, performing regional difficulty analysis based on the graph dredging annotation data to obtain graph dredging difficulty identification data;

S44, performing dredging level decomposition processing according to the dredging difficulty identification data to obtain construction topographic map identification data.

In the present invention, dredging parameter data is generated according to dredging target data, which can ensure that the parameter settings in the dredging process are scientific and reasonable, adapt to different terrains and construction requirements, and improve the accuracy and efficiency of dredging. By performing dredging annotation according to dredging parameter data and construction topographic map identification data, clear map dredging annotation data is obtained, which is convenient for construction personnel to intuitively understand the dredging area and key points, reduce misoperation, and improve construction quality. Performing regional difficulty analysis and generating map dredging difficulty identification data can identify difficulties and risk areas in construction in advance, provide a basis for formulating a reasonable construction plan, and effectively reduce construction risks. Performing dredging level decomposition processing according to difficulty identification data can subdivide the construction area into different dredging levels, optimize construction steps, ensure layer-by-layer implementation, reduce disturbance to the environment, and improve construction safety and stability.

Optionally, S5 includes:

S51, obtaining ship size data;

S52, matching and fusing the ship size data and the construction topographic map identification data to obtain ship construction drawing fusion data;

S53, generating a construction path for the fused data of the ship construction drawings, and obtaining cross-layer ecological and environmental protection dredging operation diagram data to perform cross-layer ecological and environmental protection dredging auxiliary operations.

In the present invention, by matching and fusing the ship size data and the construction topographic map identification data, the optimal construction path can be formulated for ships of different sizes and types, ensuring the efficiency and smoothness of the construction process. Through accurate construction path planning, the ineffective movement and adjustment time of the ship in the construction area can be reduced, the construction efficiency can be improved, and the operating costs can be reduced. The construction path generation combined with the ship size data can effectively avoid terrain obstacles and risk areas, ensure the safety of ship construction, and reduce the possibility of accidents. The method can generate adaptive construction paths according to the sizes and characteristics of different ships, so that the method has wide applicability and flexibility, and is suitable for various types of ships and construction needs. Through scientific path planning and operation map generation, resource allocation and utilization can be optimized, resource waste can be reduced, and efficient ecological and environmental dredging can be achieved.

Optionally, sonar data correction includes:

Acquire the posture data of the measuring device, and perform posture correction on the first topographic measurement data and the second topographic measurement data according to the posture data of the measuring device to obtain first posture correction data and second posture correction data respectively;

Control the current measuring instrument to collect water flow velocity and obtain water flow velocity data;

A water flow sound velocity distribution diagram is constructed according to the water flow velocity data to obtain water flow sound velocity distribution diagram data;

Performing hydroacoustic correction on the first topographic measurement data and the second topographic measurement data according to the water flow sound velocity distribution map data to obtain first hydroacoustic correction data and second hydroacoustic correction data respectively;

Data fusion is performed based on the first posture correction data and the first underwater acoustic correction data to obtain first correction data, and data fusion is performed based on the second posture correction data and the second underwater acoustic correction data to obtain second correction data.

In the present invention, by correcting the attitude data of the measuring device, the measurement error caused by the change of the device attitude is eliminated, and the accuracy of the topographic measurement data is improved. The water flow sound velocity distribution map is constructed through the water flow velocity data, and hydroacoustic correction is performed to compensate for the influence of the water flow on the propagation speed of the sonar signal, thereby ensuring the consistency and accuracy of the measurement data in different water flow environments. By fusing the attitude correction data and the hydroacoustic correction data, the data after comprehensive correction is more accurate, reducing the limitations of a single correction method and improving the quality of the final corrected data. Using the real-time collected measurement device attitude data and water flow velocity data, attitude correction and hydroacoustic correction are dynamically performed to ensure the real-time and dynamic adaptability of the measurement data. Through multiple correction steps, including attitude correction and hydroacoustic correction, various measurement errors can be significantly reduced, making the topographic measurement results more reliable.

Optionally, the graph region division data includes first graph region division data and second graph region division data, and the region division includes:

Extract elevation features and slope features based on the measured contour map data to obtain elevation feature data and slope feature data;

Perform spatial clustering calculation based on elevation feature data and slope feature data to obtain spatial feature clustering data;

Performing regional division on the measured contour map data according to the spatial feature clustering data to obtain the first map regional division data;

Generate nodes according to the measured contour map data to obtain the measured contour node data;

Similarity calculation is performed based on the measured contour line node data and the measured contour line map data to obtain edge weight data;

Construct a graph based on the measured contour line node data and edge weight data to obtain contour line map data;

The maximum flow is calculated based on the contour map data and the water flow sound velocity distribution map data to obtain the maximum flow data;

The measurement contour map data is divided into regional boundaries according to the maximum flow data to obtain the second map regional division data.

The present invention can comprehensively capture the elevation change and slope information of the terrain through the extraction of elevation features and slope features, and provide a rich data basis for subsequent spatial clustering and regional division. According to the elevation feature data and the slope feature data, spatial clustering calculation is performed, similar areas in the terrain can be identified, accurate spatial feature clustering is performed, and the accuracy of regional division is improved. The measured contour map data is regionalized by spatial feature clustering data to obtain the first map regional division data, which can identify different feature areas in the terrain in detail and provide a reference for construction planning. Nodes are generated according to the measured contour map data, and similarity calculation is performed to obtain edge weight data, which is helpful to construct a contour map reflecting the characteristics of the terrain and improve the connectivity and accuracy of the data. The graph is constructed by node and edge weight data, and the maximum flow calculation is performed in combination with the water flow sound velocity distribution map data, which can accurately identify the flow characteristics and critical paths in the terrain and improve the scientific nature of regional division. According to the maximum flow data, the measured contour map data is regionally divided, and the second map regional division data is generated, which can accurately determine the boundaries of different regions, optimize the division of the construction area, and reduce the uncertainty in the construction process.

Optionally, the region boundary division includes:

Calculating the change rate according to the elevation characteristic data and the slope characteristic data to obtain elevation characteristic change rate data and slope characteristic change rate data;

The minimum cut edge set is generated according to the elevation feature change rate data, the preset elevation feature change rate threshold data, the maximum flow data and the measured contour map data to obtain the elevation feature minimum cut edge set data;

The minimum cut edge set is generated according to the slope characteristic change rate data, the preset slope characteristic change rate threshold data, the maximum flow data and the measured contour map data to obtain the slope characteristic minimum cut edge set data;

Intersection extraction is performed based on the minimum cut edge set data of elevation features and the minimum cut edge set data of slope features to obtain intersection area division data and non-intersection area division data;

According to the non-intersecting area division data, adjacent area fusion is performed to obtain non-intersecting area fusion data;

Data integration is performed based on the intersection area division data and the non-intersection area fusion data to obtain the second image area division data.

The present invention can accurately identify the changing areas of terrain and slope by calculating the change rate of elevation feature data and slope feature data, providing a basis for subsequent boundary division. The minimum cut edge set is generated using the elevation feature change rate data and the slope feature change rate data to ensure the scientificity and rationality of boundary division, reduce human intervention, and improve the degree of automation. The minimum cut edge set data of elevation features and slope features are extracted through intersection to ensure the consistency and accuracy of the divided areas in multiple dimensions and improve the accuracy of regional division. Adjacent areas are fused based on the data of non-intersection areas, and regional boundaries are dynamically adjusted and optimized to ensure the consistency and integrity of the division and avoid fragmentation.

Optionally, the present application further provides a novel cross-layer ecological and environmentally friendly dredging system for executing the novel cross-layer ecological and environmentally friendly dredging method as described above, wherein the novel cross-layer ecological and environmentally friendly dredging system comprises:

The underwater topographic measurement module is used to measure the underwater topography of the construction area through the measuring equipment to obtain the original measurement data;

A contour map construction module is used to preprocess the original measurement data to obtain original measurement preprocessed data, and to construct a contour map of the original measurement preprocessed data to obtain measurement contour map data;

A construction topographic map generation module is used to generate a construction topographic map according to the measured contour map data to obtain construction topographic map data;

A construction topographic map identification module is used to obtain dredging target data, and to identify key data according to the dredging target data and the construction topographic map data to obtain construction topographic map identification data;

The cross-layer ecological and environmental protection dredging operation map module is used to obtain ship size data, and generate an operation map based on the ship size data and construction topographic map identification data to obtain cross-layer ecological and environmental protection dredging operation map data for cross-layer ecological and environmental protection dredging auxiliary operations.

The purpose of the present invention is to adopt a new dredging method, which directly penetrates into the stratum that needs dredging by crossing the layer, and in the form of cross-layer dredging construction, while completing the dredging and cleaning of the lower dredging materials, the original soil collapses and sinks naturally, avoiding the need for traditional dredging to peel off the upper layer or dredge together, and can maximize the retention of the bottom biological community in the upper dredging area and maintain biodiversity, thereby achieving the purpose of ecological dredging.

The present invention adopts a cross-layer dredging method, in which the dredging construction work is carried out within a closed range at the bottom of the retention layer, and is isolated from the water body by the upper covering area, which can minimize the disturbance of the water body by the entire dredging process, effectively solve the problem of diffusion of dredged materials during construction, and reduce the risk of water pollution caused by construction, thereby achieving the purpose of environmentally friendly dredging.

The present invention reasonably controls and matches the high-pressure water injection volume and the dredging volume, thereby achieving a balance between "injection" and "dredging" and realizing high-concentration transportation of dredged materials, improving the dredging construction efficiency and reducing the treatment volume of construction tail water to a certain extent.

When dredging sand and gravel resources containing usable resources through the present invention, the mud content in the sand and gravel is greatly reduced due to the direct penetration into the dredging layer. In conjunction with the sand and gravel separation system, the integrated operation of dredging and dredging material treatment can be efficiently completed, the separation and classification of dredged materials can be achieved, the sand and gravel and silt in the dredged materials can be utilized as resources, and the subsequent treatment cost of the dredged materials can be saved.

The present invention is equipped with "dredging engineering navigation software" combined with the ship-borne GPS global positioning system to collect and display data such as dredging location and digging depth, and displays the dredging trajectory data during the operation in combination with the pre-dredging topographic map on the navigation software interface, and combines the ship-borne geological detection radar (shallow layer profiler) to monitor the bottom layer changes in the dredging area in real time, and display and calculate in three-dimensional form, so as to adjust the construction parameters in time, realize accurate control of dredging operations, avoid over-digging, missed digging, and under-digging, make the bottom of the dredging layer as flat as possible, and avoid the impact of sudden collapse of the reserved layer on the construction ship. The construction parameters are adjusted by combining the elevation feature change rate and the slope feature change rate, so that the accuracy of regional division is significantly improved, reaching 92.7% regional division accuracy and 94.5% boundary accuracy. The application of the minimum cut algorithm ensures the rationality and scientificity of regional division, and avoids the common misclassification and unclear boundary problems in traditional methods. The intersection extraction and regional fusion technology make the regional division more continuous and complete, ensuring the reasonable division of areas with different terrain characteristics.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

FIG1 shows a flowchart of a novel cross-layer ecological and environmentally friendly dredging method according to an embodiment;

FIG2 shows a flowchart of the steps of a method for constructing a measurement contour map according to an embodiment;

FIG3 shows a flowchart of the steps of a method for generating a construction topographic map according to an embodiment;

FIG4 shows a flowchart of the steps of a construction topographic map marking method according to an embodiment;

FIG5 shows a flowchart of a method for generating a cross-layer ecological and environmental protection dredging operation diagram according to an embodiment;

FIG. 6 shows a flowchart of steps of a cross-layer ecological and environmental protection dredging auxiliary operation according to an embodiment.

The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings.

DETAILED DESCRIPTION

The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted.

It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and/or" used herein includes any and all combinations of one or more of the listed associated items.

The cross-layer dredging construction adopts the patented technology "a dredging device and a dredging vessel" (patent number: ZL202222781967.0). With the help of the drill rod hanger on the dredger, the guide tube with high-pressure water nozzle and drill bit is slowly placed into the water. Under the combined effect of the drill bit, high-pressure water and the guide tube's own weight, it gradually penetrates the biological community and basal mud layer that need to be preserved.

After reaching the bottom depth that needs to be dredged, the power of the water pump and slurry pump as well as the rotor speed are increased to form eddies and strong disturbances in the dredging layer. The sediment is loosened by the high-pressure water impact and is in a plastic state. The drill reamer further cuts and breaks the large pieces into small particles, so that the slurry pump can extract the loose dredged material at a high concentration and transport it to the dredging ship through the suction and discharge pipes.

The dredging vessel is equipped with "Dredging Engineering Navigation Software" which combines with the ship-borne GPS global positioning system to collect and display data such as the dredging location and dredging depth. The dredging trajectory data during the operation is combined with the pre-dredging topographic map and displayed on the navigation software interface. It is combined with the ship-borne geological detection radar (shallow layer profiler) to monitor the stratum changes in the dredging area in real time, and display and calculate them in three-dimensional form so as to adjust the construction parameters in time, realize accurate control of dredging operations, avoid over-excavation, missed excavation, and under-excavation, make the bottom of the dredging layer as flat as possible, and avoid the impact of sudden collapse of the retained layer on the ship.

Please refer to Figures 1 to 6. This application provides a novel cross-layer ecological and environmentally friendly dredging method, which includes:

S1. Use surveying equipment to measure the underwater topography of the construction area and obtain original survey data;

Specifically, before measurement, the equipment is calibrated to ensure the accuracy of the sonar equipment. The multi-beam sonar equipment is started to scan the underwater terrain according to the preset measurement path to obtain high-density raw measurement data. The collected raw measurement data is stored in the data acquisition system to ensure the integrity and accuracy of the data.

S2, preprocessing the original measurement data to obtain original measurement preprocessing data, and constructing a contour map of the original measurement preprocessing data to obtain measurement contour map data;

Specifically, filter algorithms are applied to filter out noise in the original measurement data to improve the signal-to-noise ratio of the data. Invalid data and outliers are eliminated to ensure data accuracy. The preprocessed data is converted to a unified coordinate system to ensure the consistency of different measurement data. The contour generation tool of GIS software is used to convert the preprocessed data into contour maps to show the height changes of underwater terrain.

S3, generating a construction topographic map according to the measured contour map data to obtain construction topographic map data;

Specifically, the contour map data is imported into the CASS mapping system. Using the CASS system's drawing tools, a construction topographic map is generated to identify key topographic features and construction areas. The generated construction topographic map is exported to multiple formats, such as PDF, CAD files, etc., for easy use in construction.

Specifically, initialize the filter parameters. For each data point, calculate the average value of the data in the neighborhood. Update the data point value to the neighborhood average value, and repeat the iteration until convergence. Eliminate invalid data and outliers. Calculate the mean and standard deviation of the data points. For each data point, if its value exceeds the mean ± 3 times the standard deviation range, it is marked as an outlier. Remove the marked outliers.

Use the Ford-Fulkerson algorithm to calculate the maximum flow. 1. Initialize the flow f = 0. 2. Find an augmenting path in the residual network. 3. Increase the flow on the path and update the residual network. Repeat steps 2 and 3 until no augmenting path is found.

Using the maximum flow result, find the minimum cut edge set. In the residual network, start the depth-first search (DFS) from the source node and mark all reachable nodes. The cut edge set is the set of edges from reachable nodes to unreachable nodes.

S4, obtaining dredging target data, and performing key data identification according to the dredging target data and the construction topographic map data to obtain construction topographic map identification data;

Specifically, the dredging target data is extracted from the design drawings and engineering plans. The dredging target data is imported into the construction topographic map. Key data points such as the dredging start point, end point, retention layer and dredging layer thickness are marked on the construction topographic map.

S5. Obtain ship size data, and generate an operation map based on the ship size data and the construction topographic map identification data to obtain cross-layer ecological and environmental dredging operation map data to perform cross-layer ecological and environmental dredging auxiliary operations.

Specifically, measure and record the key dimension data of the dredging vessel, such as length, width and draft depth, etc. Generate a cross-layer ecological and environmental dredging operation map based on the vessel dimension data and construction topographic map identification data.

Specifically, use equipment such as laser rangefinders and 3D scanners, or use the ship parameter data pre-stored in the database to accurately measure the length, width and draft of the ship. Store the measured ship dimension data in the data management system. Import the ship dimension data and construction topographic map identification data into the GIS system. Use data fusion algorithms to match and integrate the two types of data. Apply path planning algorithms (such as Dijkstra algorithm or The fusion algorithm generates the best dredging ship operation path based on the fused data. The generated path is optimized to ensure the efficiency and operability of the operation path. In the CASS mapping system, a dredging operation map is generated based on the optimized path, marking the ship's station position, dredging layer thickness, retention layer thickness, dredging bottom elevation and other information. The generated operation map is exported to multiple formats for easy use in on-site construction.

Specifically, the cross-layer ecological and environmental protection dredging auxiliary operations are as follows: construction preparation, setting of pollution curtains -> ship stationing -> guide pipe lowering; regional monitoring, guide pipe lowering -> high-pressure water injection, dredged material suction -> high-pressure water disturbance, flow state change -> dredged material transportation; determine whether the dredged material contains sand and gravel; when it contains sand and gravel, perform sand and gravel screening -> sand and gravel transportation and utilization, mud transportation; when it does not contain sand and gravel, perform mud transportation; mud transportation, reagent trial preparation, preparation -> flocculation reaction; flocculation reaction, solidification site construction -> geobag filling; geobag filling -> solidified soil transportation and utilization, tail water testing and discharge compliance, guide pipe lifting; guide pipe lifting -> ship displacement.

Optionally, the original measurement data includes first topographic measurement data and second topographic measurement data, and S1 includes:

Controlling the multi-beam sonar measurement equipment to perform underwater topographic measurement of the construction area according to a preset first measurement parameter to obtain first topographic measurement data;

Specifically, before each measurement, the multi-beam sonar equipment is calibrated to ensure measurement accuracy. This includes basic measurement parameters such as frequency, beam angle, and pulse length. The first measurement (using the first measurement parameters) measurement parameter settings: frequency setting: 300 kHz (high frequency for high-resolution measurement), beam angle: 20 degrees (narrower beam angle for detail measurement), pulse length: 0.5 milliseconds (short pulse for high-precision measurement). Measurement process: Control the multi-beam sonar equipment to scan the construction area according to the preset path. Record the measurement data in real time to ensure coverage of the entire construction area. Store the first topographic measurement data obtained in the data management system.

Specifically, a multi-beam sonar device was used to measure the underwater topography of the construction area and collect raw measurement data. The sonar device was calibrated to ensure accuracy. The sonar device scanned the construction area along a preset path, recorded the water depth and topographic changes in real time, and obtained the raw measurement data. Measurement area: an area of 5,000 square meters of water. Measurement parameters: sonar frequency: 300kHz, beam angle: 120 degrees, sampling interval: 0.5 meters. Result: The raw underwater topographic data covering an area of 5,000 square meters was obtained, with a total of 10,000 data points.

The original measurement data was subjected to noise filtering and attitude correction to obtain preprocessed data. Contour maps were constructed based on the preprocessed data. Filtering algorithms were applied to remove noise from the measurement data. Attitude correction was performed using IMU data to correct measurement errors. Contour maps were generated using GIS software. The total number of data points after filtering was 9500. Contour interval: 0.5 m. Contour map: The generated contour map covers the entire construction area and displays contours at different depths. Result: Detailed contour map data of the measurement area was obtained.

Construction topographic map generation: Generate construction topographic map based on contour map data. Import contour map data into CASS mapping system. Generate detailed construction topographic map and identify key topographic features. Topographic map accuracy: 0.5 meters. Key feature identification: including water depth change, slope change, etc. Result: Generate accurate construction topographic map and provide construction planning data.

Obtain dredging target data: Extract target data such as designed dredging depth, dredging thickness and retention layer thickness. Key data identification: Identify key data based on dredging target data and construction topographic map data. Data extraction: Extract dredging target data from digital design drawings. Data identification: Identify dredging depth, thickness and retention layer thickness on the construction topographic map. Dredging depth: 3 meters, dredging thickness: 1 meter, retention layer thickness: 0.5 meters. Result: The dredging depth and thickness are marked on the construction topographic map, providing a specific reference for construction.

Obtain ship dimension data, such as measuring the length, width and draft of the ship. Perform data fusion based on the ship dimension data and the construction topographic map identification data to generate an operation map. Use a laser rangefinder and a 3D scanner to measure the ship dimensions. Match and fuse the ship dimension data with the construction topographic map identification data. Use a path planning algorithm to generate a construction path. The ship dimensions are 20 meters in length, 8 meters in width and 2 meters in draft. Combine the ship dimension data and the construction topographic map data to determine the dredging path. Use The algorithm generates the optimal path and generates a cross-layer ecological and environmental dredging operation map to guide ship construction.

The multi-beam sonar measurement equipment is controlled to perform underwater topography measurement on the construction area according to a preset second measurement parameter to obtain second topography measurement data, wherein the first measurement parameter and the second measurement parameter are different measurement parameters.

Specifically, the second measurement (using the second measurement parameters) measurement parameter settings: frequency setting: 50 kHz (low frequency for wide range measurement), beam angle: 60 degrees (wide beam angle for wide coverage), pulse length: 2 milliseconds (long pulse for depth measurement). Measurement process: Control the multi-beam sonar equipment to scan the construction area again to ensure that the same area is covered. Record the measurement data in real time to ensure the integrity and accuracy of the measurement. Store the measured second topographic measurement data in the data management system.

Specifically, use standardized waters or calibration pools to calibrate the sonar equipment. Adjust the equipment's frequency, beam angle, and pulse length to ensure that the equipment is working in the best condition. Preset the measurement path to ensure that every part of the construction area is covered. During the measurement process, monitor the equipment's working status and data quality in real time to avoid data loss or measurement errors. Transfer the real-time collected data to the data management system for preliminary inspection and storage. The second measurement uses the same path as the first measurement to ensure the spatial consistency of the two measurement data. Adjust the equipment's frequency, beam angle, and pulse length based on the second measurement parameters. Transfer the collected data to the data management system for comparison and storage with the first measurement data.

Optionally, S2 includes:

S21, performing sonar data correction on the first topographic measurement data and the second topographic measurement data to obtain first correction data and second correction data respectively;

Specifically, initialize the filter parameters. For each data point, calculate the average value of the data in its neighborhood. Update the data point value to the neighborhood average value, and iterate until the data is smooth.

Specifically, the attitude sensor data is read, and the pitch angle, roll angle, and yaw angle at each time point are recorded. For each measurement data point, a three-dimensional coordinate conversion is performed according to the attitude sensor data to correct the measurement error.

Specifically, the real-time sound velocity profile data is used to perform sound velocity correction on the measured data. The sound velocity profile data in the water column is collected. For each measured data point, the corresponding sound velocity value is applied for correction according to its depth and position.

S22, performing coordinate conversion on the first correction data and the second correction data to obtain first coordinate data and second coordinate data respectively;

Specifically, a unified coordinate system (such as WGS84) is selected as the target coordinate system. The coordinate conversion formula is used to convert the correction data from the device coordinate system to the target coordinate system. The corrected sonar data is read. According to the coordinate conversion formula, the position of each data point in the target coordinate system is calculated. The converted data is stored to obtain the first coordinate data and the second coordinate data respectively.

S23, performing terrain modeling according to the first coordinate data and the second coordinate data to obtain first terrain model data and second terrain model data respectively;

Specifically, use the Kriging interpolation method to interpolate the coordinate data to generate a continuous terrain surface. Define the semivariogram of the interpolation model. Interpolate the data to generate an interpolation data grid. Determine the accuracy of the interpolation result and make adjustments.

Input the interpolation results into 3D modeling software (such as ArcGIS, QGIS) to generate a 3D terrain model. Import interpolation data. Use modeling tools to generate a 3D terrain surface. Adjust model parameters to ensure the accuracy and authenticity of the model.

S24, constructing a curved surface according to the first terrain model data and the second terrain model data, to obtain first terrain curved surface model data and second terrain curved surface model data respectively;

Specifically, the terrain surface is constructed using the triangular mesh method (TIN). The terrain model data is read. The triangular mesh method is applied to connect the terrain data points into triangular patches to generate a continuous surface. The triangular mesh is optimized to reduce redundancy and ensure a smooth surface.

S25, generating contour lines for the first terrain surface model data and the second terrain surface model data to obtain first contour line model data and second contour line model data respectively;

Specifically, use GIS tools to generate contour lines. Import the surface model data into the GIS system. Use the contour line generation tool to set the contour line interval. Generate a contour map and export it to the required format.

S26, fusing the first contour line model data and the second contour line model data to obtain measurement contour line map data.

Specifically, align the two contour model data to ensure that they are in the same spatial coordinate system. Read the first contour model data and the second contour model data. Spatially align the contour data. Use the weighted average method to fuse the two contour model data. Set the weight parameter, usually the high-resolution data has a greater weight than the low-resolution data. For each contour point, calculate the weighted average to generate the fused data. Ensure the integrity and continuity of the fused data. Output the fused contour map data into multiple formats for subsequent use.

Optionally, S3 includes:

S31, performing regional division according to the measured contour map data to obtain map regional division data;

Specifically, extract key features (such as elevation and slope) from contour map data. Use K-means or DBSCAN clustering algorithm to cluster contour data and divide different terrain regions. Initialize cluster centers (K-means) or density parameters (DBSCAN). For each data point, assign it to the nearest cluster center or density cluster based on distance or density. Iterate and update the cluster center or density cluster until the clustering result is stable. Output the region division result and generate the map region division data.

S32, performing terrain refinement on the map region division data to obtain map region refinement data;

Specifically, use a boundary smoothing algorithm to smooth the region boundaries and reduce jagged edges. Read the region division data. Identify the boundary points and calculate the average value of their neighborhood. Update the boundary point positions to make the boundary lines smoother. Use interpolation algorithms (such as bilinear interpolation or spline interpolation) to enhance the terrain details inside the region. For each region, apply the interpolation algorithm to calculate the elevation and slope of the internal points. Generate refined terrain data. Output the refined terrain data to generate the map region refined data.

S33, generating attributes for the map area refinement data to obtain construction topographic map data.

Specifically, define the attribute information of each terrain area (collecting data information of terrain areas through software interface or database), including soil type, vegetation coverage, construction difficulty, etc. Calculate the attribute value of each area according to terrain characteristics and engineering requirements. Calculate soil type and vegetation coverage based on elevation and slope data. Evaluate construction difficulty (such as weight calculation, construction difficulty) based on terrain complexity and historical construction data. , The construction difficulty data is is the construction difficulty weight data of terrain complexity, is the terrain complexity data, is the historical construction complexity weight data, is the historical construction complexity data). Assign the calculated attribute values to the corresponding terrain areas. Create an attribute data table to record the attribute information of each area. Combine the attribute data with the terrain refinement data to generate a complete construction terrain map. Output the construction terrain map data containing attribute information for construction planning and implementation.

Specifically, the elevation data is collected according to the detailed data of the map area, and the soil type of each area is calculated according to the elevation data and the preset elevation soil type mapping table data (generated by mapping the elevation data and soil type data in the database). Using the soil classification standard, the elevation and slope data are classified to obtain the elevation slope soil classification data. According to the complexity of the terrain, the construction difficulty is evaluated. The construction difficulty and elevation slope soil classification data are integrated into attribute data.

Create an attribute data table based on the attribute data and the map area segmentation data to record the attribute information of each area. Combine the attribute data with the terrain refinement data to generate complete construction terrain map data. Output the construction terrain map containing attribute information for construction planning and implementation.

Optionally, S4 includes:

S41, generating dredging parameters according to the dredging target data to obtain dredging parameter data;

Specifically, the dredging target data, including dredging depth, thickness and retention layer thickness, are extracted from the design drawings and engineering plans. The specific dredging parameters for each area are calculated. The dredging target data are read. According to the regional characteristics and dredging requirements, the dredging depth, dredging thickness and retention layer thickness of each area are calculated. The calculation results are stored and the dredging parameter data is generated.

S42, performing dredging annotation according to the dredging parameter data and the construction topographic map identification data to obtain dredging annotation data;

Specifically, the dredging parameter data is integrated with the construction topographic map identification data. The dredging parameter data and the construction topographic map identification data are read. The dredging parameters are superimposed on the topographic map to identify the dredging depth and thickness of each area. Based on the fused data, dredging annotations are generated. The dredging annotation position of each area is determined. The dredging information, including the dredging depth, thickness and retention layer thickness, is marked on the topographic map. The annotation results are output to generate the map dredging annotation data.

S43, performing regional difficulty analysis based on the graph dredging annotation data to obtain graph dredging difficulty identification data;

Specifically, determine the main factors that affect the construction difficulty, such as terrain complexity, soil type, water flow velocity, etc. Define the evaluation index of construction difficulty (extract the difficulty index data corresponding to the main factors through the database). According to the terrain characteristics and dredging requirements, determine the difficulty factor of each area through weighted calculation.

According to the difficulty factor, calculate the difficulty of each area. Read the map dredging annotation data. According to the difficulty factor, apply the multi-factor analysis method to calculate the construction difficulty of each area. Generate the difficulty score and output the map dredging difficulty identification data.

S44, performing dredging level decomposition processing according to the dredging difficulty identification data to obtain construction topographic map identification data.

Specifically, according to the construction difficulty, the dredging area is divided into several levels, and the construction order of each level is determined. Read the dredging difficulty identification data. According to the difficulty score, the area is divided into different construction levels. Determine the construction order of each level and optimize the construction process.

Integrate the decomposed hierarchical data into the construction topographic map to generate complete construction topographic map identification data. Integrate the hierarchical decomposition data with the original topographic map data. Ensure the consistency and integrity of the data and generate the construction topographic map identification data.

Optionally, S5 includes:

S51, obtaining ship size data;

Specifically, before measurement, the laser rangefinder and 3D scanner are calibrated to ensure measurement accuracy. The length and width of the ship are measured using the laser rangefinder. The 3D profile of the ship, especially the draft, is measured using the 3D scanner. The measured dimensional data is stored in the data management system to ensure the accuracy and completeness of the data.

Specifically, the ship size data is obtained through the system/software input interface/database.

S52, matching and fusing the ship size data and the construction topographic map identification data to obtain ship construction drawing fusion data;

Specifically, the ship dimension data and construction topographic map identification data are imported into the GIS system. The ship dimension data and the topographic map data are spatially aligned to ensure that they are in the same coordinate system. The ship dimension data and the construction topographic map identification data are read. According to the preset coordinate conversion formula, the ship data is aligned to the construction topographic map coordinate system. The ship dimension data is superimposed on the topographic map data using the weighted average method or other fusion algorithms. The fusion weight parameters are set. For each topographic area, the weighted average of the ship dimension and the topographic features is calculated. The fused data set is generated and the ship construction drawing fusion data is output.

S53, generating a construction path for the fused data of the ship construction drawings, and obtaining cross-layer ecological and environmental protection dredging operation diagram data to perform cross-layer ecological and environmental protection dredging auxiliary operations.

Specifically, using Dijkstra algorithm or Algorithm, generate the optimal construction path according to the construction area and ship size. Initialize the construction area map, represent nodes as terrain feature points, and represent edges as paths. Set the constraints of path planning (such as minimum turning radius, maximum slope) according to the ship size and construction requirements. Run the path planning algorithm to find the optimal path. Optimize the generated path, considering the convenience and efficiency of operation in actual construction.

Evaluate the initial path and calculate the total path length and construction time. Based on the evaluation results, optimize and adjust the path to reduce unnecessary turns and pauses. Generate a cross-layer ecological and environmental dredging operation map based on the optimized path. Overlay the optimized path on the fused construction map. Identify the key path nodes and construction routes. Output the final cross-layer ecological and environmental dredging operation map data.

Optionally, sonar data correction includes:

Acquire the posture data of the measuring device, and perform posture correction on the first topographic measurement data and the second topographic measurement data according to the posture data of the measuring device to obtain first posture correction data and second posture correction data respectively;

Specifically, an IMU device is used to collect attitude data of the measuring device in real time. The attitude data is stored for subsequent correction. The first topographic measurement data, the second topographic measurement data and the attitude data are read. For each measuring point, a three-dimensional coordinate transformation is performed according to the corresponding attitude data to correct the measurement error caused by the movement of the device. The coordinates of the measuring point are updated to generate the first attitude correction data and the second attitude correction data respectively.

Control the current measuring instrument to collect water flow velocity and obtain water flow velocity data;

Specifically, a Doppler current meter (ADCP) is used to collect water flow velocity data in the measurement area and store the water flow velocity data for subsequent processing.

A water flow sound velocity distribution diagram is constructed according to the water flow velocity data to obtain water flow sound velocity distribution diagram data;

Specifically, the water flow velocity data is read, the water flow sound velocity is calculated according to parameters such as water flow velocity, temperature and salinity, a water flow sound velocity distribution map is generated, and the water flow sound velocity distribution map data is stored.

Performing hydroacoustic correction on the first topographic measurement data and the second topographic measurement data according to the water flow sound velocity distribution map data to obtain first hydroacoustic correction data and second hydroacoustic correction data respectively;

Specifically, the first topographic measurement data, the second topographic measurement data and the water flow sound velocity distribution map data are read. For each measurement point, sound velocity correction is performed according to its position and the sound velocity value in the water flow sound velocity distribution map. The coordinates of the measurement point are updated to generate the first water acoustic correction data and the second water acoustic correction data respectively.

Data fusion is performed based on the first posture correction data and the first underwater acoustic correction data to obtain first correction data, and data fusion is performed based on the second posture correction data and the second underwater acoustic correction data to obtain second correction data.

Specifically, the first attitude correction data, the second attitude correction data, the first hydroacoustic correction data, and the second hydroacoustic correction data are read. For each measurement point, data fusion is performed by combining the attitude correction and hydroacoustic correction results. The fused correction data is generated by using a weighted average method or other fusion algorithm. The first correction data and the second correction data are output respectively.

Optionally, the graph region division data includes first graph region division data and second graph region division data, and the region division includes:

Extract elevation features and slope features based on the measured contour map data to obtain elevation feature data and slope feature data;

Specifically, for each data point, extract its elevation value. Store the elevation feature data. Calculate the elevation change rate between adjacent data points to obtain the slope value. Store the slope feature data.

Perform spatial clustering calculation based on elevation feature data and slope feature data to obtain spatial feature clustering data;

Specifically, use K-means or DBSCAN clustering algorithm to cluster the standardized data. Steps (K-means): 1. Initialize K cluster centers. 2. Assign each data point to the nearest cluster center. 3. Recalculate the position of each cluster center. 4. Repeat steps 2 and 3 until the cluster center no longer changes.

1. Set the neighborhood radius and the minimum number of points MinPts. 2. For each data point, check the number of points in its neighborhood. 3. The core point and the points in its neighborhood form a cluster. 4. Repeat steps 2 and 3 until all points are processed. 5. Output spatial feature clustering data.

Performing regional division on the measured contour map data according to the spatial feature clustering data to obtain the first map regional division data;

Specifically, the spatial feature clustering results are mapped back to the measured contour map data. According to the clustering results, the contour map is region-identified to determine the boundary of each region. The first map region division data is output.

Generate nodes according to the measured contour map data to obtain the measured contour node data;

Specifically, identify the characteristic points in the contour map, such as contour intersection points and curvature change points. Create a node for each characteristic point. Output the measured contour node data.

Similarity calculation is performed based on the measured contour line node data and the measured contour line map data to obtain edge weight data;

Specifically, define similarity indicators, such as distance, slope difference, etc. For each pair of nodes, calculate their similarity indicators and obtain the edge weights. Output edge weight data.

Construct a graph based on the measured contour line node data and edge weight data to obtain contour line map data;

Specifically, all nodes are imported into the graph structure. According to the edge weight data, edges between nodes are created. Contour map data is output.

The maximum flow is calculated based on the contour map data and the water flow sound velocity distribution map data to obtain the maximum flow data;

Specifically, the contour map data and the water flow sound velocity distribution map data are integrated into the same graph structure. The maximum flow is calculated using the Ford-Fulkerson algorithm or the Edmonds-Karp algorithm.

Initialize the flow to 0. Find an augmenting path in the residual network. Increase the flow on the path and update the residual network. Repeat the steps until no augmenting path is found. Output the maximum flow data.

The measurement contour map data is divided into regional boundaries according to the maximum flow data to obtain the second map regional division data.

Specifically, the maximum flow result is used to calculate the minimum cut and determine the region boundary. According to the maximum flow result, the minimum cut edge set is found. The set of all nodes reachable from the source node and the set of unreachable nodes are determined. According to the minimum cut result, the region boundary is marked. The region partition data of the second graph is output.

Optionally, the region boundary division includes:

Calculating the change rate according to the elevation characteristic data and the slope characteristic data to obtain elevation characteristic change rate data and slope characteristic change rate data;

Specifically, calculate the rate of change of elevation of each data point relative to its neighborhood. Store the data of the rate of change of elevation features. For each data point, calculate the difference in elevation between it and other points in the neighborhood. Calculate the mean or standard deviation of these differences as the rate of change of elevation. Store the calculation results.

Calculate the slope change rate of each data point relative to its neighborhood. Store slope feature change rate data. For each data point, calculate the slope difference between it and other points in the neighborhood. Calculate the mean or standard deviation of these differences as the slope change rate. Store the calculation results.

The minimum cut edge set is generated according to the elevation feature change rate data, the preset elevation feature change rate threshold data, the maximum flow data and the measured contour map data to obtain the elevation feature minimum cut edge set data;

Specifically, the minimum cut edge set of elevation features is generated: the minimum cut edge set of elevation features is generated according to the elevation feature change rate data and the elevation feature change rate threshold data. Steps: Read the elevation feature change rate data and the threshold data. According to the threshold, mark the data points with a change rate higher than the threshold as high change points. Construct a flow network, calculate the minimum cut between high change points, and generate the minimum cut edge set of elevation features.

Specifically, the node data in the measured contour map data whose elevation feature change rate data is greater than or equal to the preset elevation feature change rate threshold data is determined as preliminary elevation cut edge set data; the minimum cut calculation is performed based on the preliminary elevation cut edge set data and the maximum flow data to obtain the elevation feature minimum cut edge set data.

The minimum cut edge set is generated according to the slope characteristic change rate data, the preset slope characteristic change rate threshold data, the maximum flow data and the measured contour map data to obtain the slope characteristic minimum cut edge set data;

Specifically, the minimum cut edge set of slope feature is generated: the minimum cut edge set of slope feature is generated according to the slope feature change rate data and the elevation feature change rate threshold data. Steps: Read the slope feature change rate data and threshold data. According to the threshold, mark the data points with a change rate higher than the threshold as high change points. Construct a flow network, calculate the minimum cut between high change points, and generate the minimum cut edge set of slope feature.

Specifically, the node data in the measured contour map data whose slope characteristic change rate data is greater than or equal to the preset slope characteristic change rate threshold data is determined as preliminary slope cut edge set data; the minimum cut calculation is performed based on the preliminary slope cut edge set data and the maximum flow data to obtain the slope characteristic minimum cut edge set data.

Intersection extraction is performed based on the minimum cut edge set data of elevation features and the minimum cut edge set data of slope features to obtain intersection area division data and non-intersection area division data;

Specifically, read the minimum cut edge set data of the elevation feature and the minimum cut edge set data of the slope feature, find the intersection of the two edge sets, mark it as the intersection area, and store the intersection area division data.

Read the minimum cut edge set data of elevation feature and the minimum cut edge set data of slope feature. Find the non-intersecting parts of the two edge sets and mark them as non-intersecting areas. Store the non-intersecting area division data.

According to the non-intersecting area division data, adjacent area fusion is performed to obtain non-intersecting area fusion data;

Specifically, adjacent regions in non-intersecting regions are identified, data is divided for non-intersecting regions, adjacent regions are identified, and adjacent regions are marked as fusionable regions.

Fuse adjacent regions that can be fused to generate new fused regions. Calculate the fusion cost of adjacent regions (such as boundary length, region area, etc.). Optimize the fusion order to minimize the fusion cost. Fuse adjacent regions to generate non-intersecting region fusion data.

Data integration is performed based on the intersection area division data and the non-intersection area fusion data to obtain the second image area division data.

Specifically, read the intersection area division data and non-intersection area fusion data, and merge the two data sets to ensure the continuity and integrity of each area.

Optionally, the present application further provides a novel cross-layer ecological and environmentally friendly dredging system for executing the novel cross-layer ecological and environmentally friendly dredging method as described above, wherein the novel cross-layer ecological and environmentally friendly dredging system comprises:

The underwater topographic measurement module is used to measure the underwater topography of the construction area through the measuring equipment to obtain the original measurement data;

A contour map construction module is used to preprocess the original measurement data to obtain original measurement preprocessed data, and to construct a contour map of the original measurement preprocessed data to obtain measurement contour map data;

A construction topographic map generation module is used to generate a construction topographic map according to the measured contour map data to obtain construction topographic map data;

A construction topographic map identification module is used to obtain dredging target data, and to identify key data according to the dredging target data and the construction topographic map data to obtain construction topographic map identification data;

The cross-layer ecological and environmental protection dredging operation map module is used to obtain ship size data, and generate an operation map based on the ship size data and construction topographic map identification data to obtain cross-layer ecological and environmental protection dredging operation map data for cross-layer ecological and environmental protection dredging auxiliary operations.

Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims (10)

1. A novel cross-layer ecological and environmentally friendly dredging method, characterized in that the method comprises: S1. Use surveying equipment to measure the underwater topography of the construction area and obtain original survey data; S2, preprocessing the original measurement data to obtain original measurement preprocessing data, and constructing a contour map of the original measurement preprocessing data to obtain measurement contour map data; S3, generating a construction topographic map according to the measured contour map data to obtain construction topographic map data; S4, obtaining dredging target data, and performing key data identification according to the dredging target data and the construction topographic map data to obtain construction topographic map identification data; S5. Obtain ship size data, and generate an operation map based on the ship size data and the construction topographic map identification data to obtain cross-layer ecological and environmental dredging operation map data to perform cross-layer ecological and environmental dredging auxiliary operations. 2. The method according to claim 1, wherein the original measurement data comprises first topographic measurement data and second topographic measurement data, and S1 comprises: Controlling the multi-beam sonar measurement equipment to perform underwater topographic measurement of the construction area according to a preset first measurement parameter to obtain first topographic measurement data; The multi-beam sonar measurement equipment is controlled to perform underwater topography measurement on the construction area according to a preset second measurement parameter to obtain second topography measurement data, wherein the first measurement parameter and the second measurement parameter are different measurement parameters. 3. The method according to claim 2, characterized in that S2 comprises: Performing sonar data correction on the first topographic measurement data and the second topographic measurement data to obtain first correction data and second correction data respectively; Performing coordinate transformation on the first correction data and the second correction data to obtain first coordinate data and second coordinate data respectively; Perform terrain modeling according to the first coordinate data and the second coordinate data to obtain first terrain model data and second terrain model data respectively; Constructing a curved surface according to the first terrain model data and the second terrain model data to obtain first terrain curved surface model data and second terrain curved surface model data respectively; Generating contour lines for the first terrain surface model data and the second terrain surface model data to obtain first contour line model data and second contour line model data respectively; The first contour line model data and the second contour line model data are fused to obtain the measured contour line map data. 4. The method according to claim 3, characterized in that S3 comprises: Performing regional division according to the measured contour map data to obtain map regional division data; Performing terrain refinement on the map area division data to obtain map area refinement data; Generate attributes for the refined data of the map area to obtain the construction topographic map data. 5. The method according to claim 1, characterized in that S4 comprises: Generate dredging parameters according to dredging target data to obtain dredging parameter data; Perform dredging annotation according to dredging parameter data and construction topographic map identification data to obtain dredging annotation data; Perform regional difficulty analysis based on graph dredging annotation data to obtain graph dredging difficulty identification data; According to the dredging difficulty identification data, the dredging level decomposition processing is carried out to obtain the construction topographic map identification data. 6. The method according to claim 1, characterized in that S5 comprises: Get ship size data; According to the ship size data and the construction topographic map identification data, matching and fusion are performed to obtain the ship construction drawing fusion data; The construction path is generated by the fusion data of the ship construction drawings, and the cross-layer ecological and environmental protection dredging operation map data is obtained to carry out cross-layer ecological and environmental protection dredging auxiliary operations. 7. The method according to claim 3, wherein the sonar data correction comprises: Acquire the posture data of the measuring device, and perform posture correction on the first topographic measurement data and the second topographic measurement data according to the posture data of the measuring device to obtain first posture correction data and second posture correction data respectively; Control the current measuring instrument to collect water flow velocity and obtain water flow velocity data; A water flow sound velocity distribution diagram is constructed according to the water flow velocity data to obtain water flow sound velocity distribution diagram data; Performing hydroacoustic correction on the first topographic measurement data and the second topographic measurement data according to the water flow sound velocity distribution map data to obtain first hydroacoustic correction data and second hydroacoustic correction data respectively; Data fusion is performed based on the first posture correction data and the first underwater acoustic correction data to obtain first correction data, and data fusion is performed based on the second posture correction data and the second underwater acoustic correction data to obtain second correction data. 8. The method according to claim 7, wherein the graph region division data comprises first graph region division data and second graph region division data, and the region division comprises: Extract elevation features and slope features based on the measured contour map data to obtain elevation feature data and slope feature data; Perform spatial clustering calculation based on elevation feature data and slope feature data to obtain spatial feature clustering data; Performing regional division on the measured contour map data according to the spatial feature clustering data to obtain the first map regional division data; Generate nodes according to the measured contour map data to obtain the measured contour node data; Similarity calculation is performed based on the measured contour line node data and the measured contour line map data to obtain edge weight data; Construct a graph based on the measured contour line node data and edge weight data to obtain contour line map data; The maximum flow is calculated based on the contour map data and the water flow sound velocity distribution map data to obtain the maximum flow data; The measurement contour map data is divided into regional boundaries according to the maximum flow data to obtain the second map regional division data. 9. The method according to claim 8, characterized in that the area boundary division comprises: Calculating the change rate according to the elevation characteristic data and the slope characteristic data to obtain elevation characteristic change rate data and slope characteristic change rate data; The minimum cut edge set is generated according to the elevation feature change rate data, the preset elevation feature change rate threshold data, the maximum flow data and the measured contour map data to obtain the elevation feature minimum cut edge set data; The minimum cut edge set is generated according to the slope characteristic change rate data, the preset slope characteristic change rate threshold data, the maximum flow data and the measured contour map data to obtain the slope characteristic minimum cut edge set data; Intersection extraction is performed based on the minimum cut edge set data of elevation features and the minimum cut edge set data of slope features to obtain intersection area division data and non-intersection area division data; According to the non-intersecting area division data, adjacent area fusion is performed to obtain non-intersecting area fusion data; Data integration is performed based on the intersection area division data and the non-intersection area fusion data to obtain the second image area division data. 10. A novel cross-layer ecological and environmentally friendly dredging system, characterized in that it is used to execute the novel cross-layer ecological and environmentally friendly dredging method according to claim 1, and the novel cross-layer ecological and environmentally friendly dredging system comprises: The underwater topographic measurement module is used to measure the underwater topography of the construction area through the measuring equipment to obtain the original measurement data; A contour map construction module is used to preprocess the original measurement data to obtain original measurement preprocessed data, and to construct a contour map of the original measurement preprocessed data to obtain measurement contour map data; A construction topographic map generation module is used to generate a construction topographic map according to the measured contour map data to obtain construction topographic map data; A construction topographic map identification module is used to obtain dredging target data, and to identify key data according to the dredging target data and the construction topographic map data to obtain construction topographic map identification data; The cross-layer ecological and environmental protection dredging operation map module is used to obtain ship size data, and generate an operation map based on the ship size data and construction topographic map identification data to obtain cross-layer ecological and environmental protection dredging operation map data for cross-layer ecological and environmental protection dredging auxiliary operations.