River self-adaptive dredging system and method based on water level intelligent sensing

Abstract

The present application relates to the technical field of river dredging, and especially discloses a river self-adaptive dredging system and method based on water level intelligent sensing, which is executed by a central control unit and comprises four steps of model construction and benchmark calibration, real-time monitoring and siltation diagnosis, accurate survey and operation planning, closed-loop execution and model synchronization; the system comprises a central control unit, a water level intelligent sensing array, a terrain scanning device and a dredging machine; the present application dynamically determines the siltation state by constructing a three-dimensional digital riverbed model in combination with water level sensing data, generates an accurate dredging operation scheme, and realizes closed-loop control of the operation; the present application takes into account the dredging needs of navigable and ecological rivers, improves the accuracy and automation level of dredging, reduces the operation cost, promotes the intelligent transformation of river management, and provides an efficient solution for long-term river management.

Description

Technical Field

[0001] This invention relates to the field of river dredging technology, specifically to an adaptive river dredging system and method based on intelligent water level sensing. Background Technology

[0002] River siltation is a common problem in water conservancy projects and water environment management. Long-term accumulation of silt and debris in river channels raises the riverbed and reduces the cross-sectional area of ​​water flow, not only reducing the river's flood control and drainage capacity and threatening the lives and property of residents along the banks, but also affecting the river's navigation function and disrupting the balance of the aquatic ecosystem. Therefore, river dredging is a crucial means of maintaining the normal function of river channels.

[0003] Current river dredging technologies and operational methods still have many shortcomings, specifically in the following aspects:

[0004] The timeliness and accuracy of siltation monitoring are insufficient; traditional siltation monitoring relies heavily on manual inspections and periodic topographic surveys, which have long monitoring cycles and cannot keep track of the dynamic changes in river siltation in real time; manual judgment of the degree of siltation is highly subjective and prone to omissions and misjudgments, making it difficult to discover hidden siltation areas in time and delaying the opportunity for dredging.

[0005] The planning of dredging operations lacks scientific support; existing dredging operations mostly adopt a rough planning model, with dredging areas and depths determined by experience, lacking precise data support on silt thickness and spatial distribution; this often leads to over-dredging, increasing operating costs and disturbing the riverbed ecology, or incomplete dredging, failing to effectively restore the designed water flow capacity of the river channel.

[0006] The operation process lacks dynamic closed-loop control; during the dredging operation, the working position and digging depth of the dredging machinery mainly rely on manual operation, making it difficult to accurately match the planned parameters; deviations that occur during the operation cannot be corrected in a timely manner, affecting the quality of the dredging operation; after the operation is completed, the acceptance data is out of sync with the updated river topography model, which is not conducive to the long-term management of the river.

[0007] It is difficult to meet the diverse functional needs of waterways; the dredging standards for navigable waterways and ecological waterways differ significantly, and existing technologies have not developed differentiated dredging triggering conditions for different types of waterways, which can easily lead to conflicts between dredging operations and navigation safety and ecological protection requirements.

[0008] In summary, existing river dredging technologies have significant shortcomings in monitoring, planning, and control, making it difficult to meet the precision and intelligent requirements of modern river management. Therefore, there is an urgent need to develop a river dredging system and method capable of real-time monitoring of siltation status, precise planning of operation schemes, and dynamic control of the operation process, in order to improve the efficiency and quality of river dredging and achieve long-term maintenance of river functions. Summary of the Invention

[0009] The purpose of this invention is to provide a river adaptive dredging system and method based on intelligent water level sensing, so as to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] An adaptive dredging method for rivers based on intelligent water level sensing, executed by a central control unit, includes the following steps:

[0012] S1. Model Construction and Benchmark Calibration: Establish and maintain a three-dimensional digital riverbed model of the target river channel. The three-dimensional digital riverbed model integrates river channel cross-sectional morphology data and real-time riverbed elevation data. Based on the preset design riverbed cross-sectional data and the minimum navigable water level or ecological base flow water level, calculate the design flow area of ​​each monitoring section as the benchmark value for dredging triggering.

[0013] S2. Real-time monitoring and siltation diagnosis: Receives real-time water level data collected by a smart water level sensing array deployed at key river sections; dynamically calculates the real-time water flow area of ​​each section based on the real-time water level data and the corresponding section morphology data in the three-dimensional digital riverbed model; compares the real-time water flow area of ​​any section with its siltation triggering benchmark value; when the real-time water flow area is continuously less than the siltation triggering benchmark value and the difference exceeds the first preset threshold, and the duration exceeds the preset time window, the river section where the section is located is automatically determined to be an initial siltation area, and a siltation early warning signal is generated.

[0014] S3. Precise Survey and Operation Planning: In response to siltation early warning signals, a detailed topographic survey instruction for the initial siltation area is sent to the topographic scanning equipment mounted on a mobile vehicle; the detailed topographic survey data is received, and the precise siltation thickness and spatial distribution information of the initial siltation area are updated in the three-dimensional digital riverbed model; with the goal of restoring the designed riverbed cross-section, based on the precise siltation distribution information, and through earthwork balance calculation and operation path optimization algorithms, a set of dredging operation instructions is automatically generated, including the division of operation sub-areas, the planned dredging depth of each sub-area, and the optimal dredging machinery travel path;

[0015] S4. Closed-loop execution and model synchronization: The dredging operation instruction set is sent to the dredging machinery; the operation position and excavation depth data transmitted back in real time by the dredging machinery are received and compared in real time with the planned values ​​in the instruction set, and the subsequent control instructions sent to the dredging machinery are dynamically adjusted according to the deviation to form a closed-loop control of the operation; after the dredging operation is completed, the riverbed elevation data of the corresponding river section in the three-dimensional digital riverbed model is updated according to the acceptance measurement data.

[0016] As a preferred approach, step S1, model construction and benchmark calibration, specifically includes:

[0017] Acquire basic river topographic mapping data and preset river design cross-section data of the target river channel under historical conditions of no or low siltation, integrate the basic river topographic mapping data and the river design cross-section data, and construct a three-dimensional digital riverbed initial model that reflects the natural and designed morphology of the river channel.

[0018] Based on a preset maintenance cycle or triggering event, the system receives periodic riverbed topographic survey data of the target river channel and uses the periodic riverbed topographic survey data to dynamically update the real-time riverbed elevation data in the initial three-dimensional digital riverbed model in order to form and maintain the three-dimensional digital riverbed model.

[0019] Based on the three-dimensional digital riverbed model, the cross-sectional morphology data of each monitoring section is extracted; for navigable waterways, the first design flow area of ​​each monitoring section at the minimum navigable water level is calculated based on the cross-sectional morphology data and the preset minimum navigable water level; for ecological waterways, the second design flow area of ​​each monitoring section at the ecological base flow water level is calculated based on the cross-sectional morphology data and the preset ecological base flow water level.

[0020] The first or second designed water flow area is set as the dredging trigger benchmark value for the corresponding monitoring section.

[0021] As a preferred approach, the system receives real-time water level data collected by an intelligent water level sensing array deployed at key river sections; based on the real-time water level data and the corresponding cross-sectional morphology data in the three-dimensional digital riverbed model, it dynamically calculates the real-time water flow area of ​​each cross-section, specifically including:

[0022] Real-time reception of raw water level data streams periodically collected by multiple water level sensors in the intelligent water level sensing array;

[0023] Time series analysis is performed on the raw water level data stream. A sliding window filtering algorithm is used to remove noise points of random fluctuations and sudden changes in the raw water level data stream, generating stable water level time series data after filtering.

[0024] The stable water level time series data is input into the water level rationality verification module. Based on the historical water level relationship between adjacent sections and the river hydrodynamic characteristics, the stable water level time series data is checked for spatiotemporal consistency and the verified real-time water level value of each monitoring section under a unified timestamp is output.

[0025] Based on the unified timestamp and the identification code of each monitoring section, the cross-sectional morphology data corresponding to each monitoring section is automatically retrieved and called from the three-dimensional digital riverbed model. The cross-sectional morphology data consists of a continuous sequence of topographic point coordinates arranged in order of riverbed elevation along the cross section.

[0026] Using the verified real-time water level as the reference horizontal height, all valid topographic points with elevation values ​​lower than or equal to the reference horizontal height are identified in a continuous topographic point coordinate sequence.

[0027] Based on the effective shape point coordinate sequence, an adaptive polygon approximation algorithm is used to construct a closed geometric figure jointly enclosed by the reference horizontal plane, the effective shape point connection line, and the riverbank boundary. The area of ​​this closed geometric figure is then calculated as the real-time water flow area of ​​the monitoring section.

[0028] As a preferred approach, the real-time water flow area of ​​any cross-section is compared with its dredging trigger benchmark value. When the real-time water flow area is continuously less than the dredging trigger benchmark value and the difference exceeds a first preset threshold, and the duration exceeds a preset time window, the river section where the cross-section is located is automatically determined to be an initial siltation zone, and a siltation early warning signal is generated, specifically including:

[0029] Based on the monitoring section corresponding to the verified real-time water level value, the dredging trigger benchmark value bound to the section morphology data corresponding to the monitoring section is retrieved from the three-dimensional digital riverbed model, and the first area difference between the real-time water flow area of ​​the monitoring section and its dredging trigger benchmark value is calculated.

[0030] Determine whether the first area difference is greater than the first preset threshold, and record the continuous state of the first area difference being greater than the first preset threshold;

[0031] When the first area difference is greater than the first preset threshold for an extended period of time, an initial siltation determination result is generated for the monitored section.

[0032] Based on the initial siltation determination results, the upstream and downstream river sections associated with the monitoring section are marked as the initial siltation area, and a siltation early warning signal containing a timestamp, section identifier, and siltation degree is generated based on the verified real-time water level value, real-time water flow area, and first area difference.

[0033] As a preferred approach, in response to a siltation early warning signal, a detailed topographic survey command for the initial siltation zone is sent to a terrain scanning device mounted on a mobile vehicle; the detailed topographic survey data is received, and the precise siltation thickness and spatial distribution information of the initial siltation zone are updated in the three-dimensional digital riverbed model, specifically including:

[0034] In response to the siltation early warning signal, the spatial location range and siltation degree information of the initial siltation area are extracted;

[0035] Based on the spatial location and degree of siltation of the initial siltation area, targeted and refined terrain survey instructions are generated and issued to multibeam echo sounders or laser scanning equipment mounted on unmanned ships or unmanned survey vehicles. The refined terrain survey instructions include at least the survey range, survey path planning, and survey density and accuracy requirements dynamically set based on the degree of siltation.

[0036] Receive high-density point cloud topographic data covering the initial siltation area after the multibeam echo sounder or laser scanning equipment executes fine topographic survey instructions;

[0037] The high-density point cloud terrain data is subjected to coordinate calibration, noise filtering and data fusion processing to generate the current refined terrain surface of the initial siltation area;

[0038] Spatial overlay analysis is performed between the current refined topographic surface and the design riverbed cross-section data corresponding to the same spatial location in the three-dimensional digital riverbed model. The vertical elevation difference between the current refined topographic surface and the design riverbed cross-section data is calculated point by point to obtain the sedimentation thickness at each calculation point in the initial sedimentation area.

[0039] Based on the silt thickness and spatial coordinates of all calculation points, spatial interpolation calculation and regional cluster analysis are performed to generate accurate silt thickness and spatial distribution information that reflects the continuous spatial distribution of silt at different thickness levels. This information is then linked to the descriptive data of the initial siltation area in the three-dimensional digital riverbed model to complete the model update.

[0040] As a preferred approach, aiming to restore the riverbed to its designed cross-section, based on accurate siltation distribution information, and through earthwork balance calculations and operational path optimization algorithms, a set of dredging operation instructions is automatically generated. This instruction set includes the division of operational sub-regions, the planned dredging depth for each sub-region, and the optimal dredging machinery travel path. Specifically, it includes:

[0041] Based on the siltation thickness and its spatial coordinates in the accurate siltation distribution information, a density clustering-based spatial segmentation algorithm is used to divide the initial siltation area into multiple operational sub-regions. The variation in siltation thickness within each operational sub-region does not exceed a preset second threshold, and the spatial boundaries between operational sub-regions are automatically defined by the clustering analysis results.

[0042] For each sub-area of ​​operation, based on the siltation thickness of all calculation points within the sub-area, the total dredging volume of the sub-area is determined through earthwork balance calculation. Combined with the design riverbed cross-section data, the planned dredging depth of the sub-area is calculated to ensure that the riverbed elevation of the sub-area is restored to the design elevation after dredging.

[0043] Based on the planned dredging depth and spatial location of all operation sub-regions, a dredging operation path optimization model is constructed. The optimization objective of the dredging operation path optimization model is to minimize the total travel distance of the dredging machinery and the operation switching time. The constraints include the maximum operating capacity of the dredging machinery, the access order of each operation sub-region, and the execution requirements of the planned dredging depth.

[0044] A heuristic search algorithm is used to solve the dredging operation path optimization model to obtain the optimal dredging machinery travel path. The optimal dredging machinery travel path defines the order in which the dredging machinery visits each operation sub-area and the specific operation trajectory in each operation sub-area.

[0045] By integrating the division of work sub-areas, the planned dredging depth of each work sub-area, and the optimal dredging machinery travel path, a dredging operation instruction set is generated. The dredging operation instruction set includes dredging depth instructions for each work sub-area and travel control instructions for the dredging machinery.

[0046] As a preferred approach, the closed-loop execution and model synchronization in step S4 includes the following specific steps:

[0047] The dredging operation instruction set is wirelessly transmitted to the onboard controller of the dredging machinery; the onboard controller parses the dredging operation instruction set, generates control signals to drive the actuators of the dredging machinery, and controls the dredging machinery to move along the optimal dredging machinery travel path to the first operation sub-area, and starts the dredging operation according to the planned dredging depth corresponding to the operation sub-area;

[0048] The positioning module and depth sensor integrated on the dredging machinery collect real-time data on the machinery's operating position and excavation depth, and transmit this data back to the central control unit. The central control unit compares the received real-time excavation depth data with the planned dredging depth corresponding to the current operating position in the dredging operation instruction set, and calculates the depth execution deviation value. At the same time, it compares the received operating position data with the predetermined trajectory in the optimal dredging machinery travel path, and calculates the position deviation value.

[0049] The central control unit determines whether to trigger adjustment conditions based on the depth execution deviation value and the position deviation value. When the depth execution deviation value or the position deviation value exceeds the corresponding preset tolerance range, it dynamically generates a local dredging depth correction command or a path correction command based on the accurate silt distribution information of the remaining undredged area in the current operation sub-region, the real-time status of the dredging machinery, and the earthwork balance calculation principle. The local dredging depth correction command or path correction command is sent to the onboard controller of the dredging machinery to perform online correction of the dredging operation being performed, forming a closed-loop control of the operation.

[0050] After the dredging machinery completes the dredging work in all sub-areas, it sends an acceptance scan command for the dredged river section to the terrain scanning equipment mounted on the mobile vehicle; it receives the acceptance terrain data returned by the terrain scanning equipment after performing the acceptance scan; it compares the acceptance terrain data with the design riverbed cross-section data in the same coordinate system and calculates the riverbed elevation recovery error of the accepted area; when the riverbed elevation recovery error meets the preset acceptance standard, it updates the riverbed elevation data of the corresponding river section in the three-dimensional digital riverbed model based on the acceptance terrain data and marks the dredging of that river section as completed.

[0051] An adaptive dredging system for rivers based on intelligent water level sensing is provided. The system is used to execute an adaptive dredging method for rivers based on intelligent water level sensing. The system includes a central control unit, an intelligent water level sensing array, a terrain scanning device, and dredging machinery.

[0052] The central control unit includes a model building and benchmark calibration module, a real-time monitoring and siltation diagnosis module, a precise survey and operation planning module, and a closed-loop execution and model synchronization module connected in sequence. The latter module is configured to further process the processing results output by the former module.

[0053] The model building and benchmark calibration module is used to establish and maintain a three-dimensional digital riverbed model of the target river channel, and calculates the design flow area of ​​each monitoring section as the dredging trigger benchmark value based on the preset design riverbed cross-section data and the minimum navigable water level or ecological base flow water level.

[0054] The real-time monitoring and siltation diagnosis module is connected to the intelligent water level sensing array to receive real-time water level data collected by the intelligent water level sensing array. Based on the real-time water level data and the corresponding cross-sectional morphology data in the three-dimensional digital riverbed model, it dynamically calculates the real-time water flow area of ​​each cross-section and compares the real-time water flow area with the siltation triggering benchmark value. When the real-time water flow area is continuously less than the siltation triggering benchmark value and the difference exceeds the first preset threshold and the duration exceeds the preset time window, it automatically determines that the river section where the cross-section is located is the initial siltation area and generates a siltation early warning signal.

[0055] The precise survey and operation planning module communicates with the terrain scanning equipment. In response to the siltation early warning signal, it sends a detailed terrain survey instruction to the terrain scanning equipment for the initial siltation area, receives the detailed terrain survey data returned by the terrain scanning equipment, and updates the precise siltation thickness and spatial distribution information of the initial siltation area in the three-dimensional digital riverbed model. With the goal of restoring the designed riverbed cross section, it automatically generates a set of dredging operation instructions based on the precise siltation distribution information, through earthwork balance calculation and operation path optimization algorithm.

[0056] The closed-loop execution and model synchronization module communicates with the dredging machinery to send the dredging operation instruction set to the dredging machinery, receive the real-time operation position and excavation depth data transmitted back by the dredging machinery, compare it with the planned value in the dredging operation instruction set in real time, and dynamically adjust the subsequent control instructions sent to the dredging machinery according to the deviation. After the dredging operation is completed, the riverbed elevation data of the corresponding river section in the three-dimensional digital riverbed model is updated according to the acceptance measurement data.

[0057] The intelligent water level sensing array is deployed at key sections of the river channel to collect water level data in real time and transmit it to the real-time monitoring and siltation diagnosis module.

[0058] The terrain scanning equipment is mounted on a mobile vehicle and is used to conduct detailed terrain surveys of the initial siltation area according to detailed terrain survey instructions, and to transmit the detailed terrain survey data back to the precision survey and operation planning module.

[0059] The dredging machinery is used to execute dredging operations according to the dredging operation instruction set and to transmit the operation location and excavation depth data back to the closed-loop execution and model synchronization module.

[0060] As can be seen from the technical solution provided by the present invention above, the river adaptive dredging system and method based on intelligent water level sensing provided by the present invention has the following beneficial effects:

[0061] Improving the accuracy of dredging operations: The system relies on the dynamic updating capability of the three-dimensional digital riverbed model and combines it with real-time data obtained from the intelligent water level sensing array to accurately calculate the real-time water flow area of ​​the monitoring section and quantitatively determine the siltation area and degree. By obtaining information on siltation thickness and spatial distribution through refined topographic surveys, and then using closed-loop operation control technology to dynamically correct deviations, the system can avoid the problems of over-dredging or incomplete dredging, and ensure that the designed water flow capacity of the river is accurately restored after dredging.

[0062] Improve the level of automation and intelligence in operations: The system realizes fully automated operation from siltation diagnosis, precise surveying, operation planning to execution correction and model updating; it can complete siltation early warning generation, operation sub-area division, optimal path planning and dynamic adjustment of operation deviation without human intervention, which greatly reduces manual operation links and improves the overall efficiency of river dredging operations;

[0063] Reduce the overall cost of dredging operations: The path optimization algorithm aims to minimize the total travel distance of dredging machinery and the operation switching time, effectively reducing equipment energy consumption and wear; accurate siltation diagnosis and survey can reduce ineffective operations and rework frequency, saving manpower and material resources; closed-loop control technology further ensures that the operation meets the standards on the first attempt, avoiding the additional costs caused by repeated construction.

[0064] Balancing river function and ecological protection: The system sets minimum navigable water level and ecological base flow level as dredging benchmarks for navigable and ecological rivers respectively, which can meet the functional needs of different types of rivers; the precise dredging method reduces disturbance to the original topography of the riverbed, protects aquatic habitats, and achieves coordinated development of river function maintenance and ecological environment protection.

[0065] Promoting the intelligent transformation of river management: The three-dimensional digital riverbed model can be continuously updated with dredging operations. The accumulated siltation data and operation data can be used to analyze the siltation patterns of the river, providing data support for subsequent river maintenance and management planning. The digital archives generated during the system operation can realize the visualization and refinement of river management, helping the river management model to transform from traditional experience-based to intelligent data-based.

[0066] Ensuring operational quality stability and traceability: Closed-loop control technology monitors the operating position and excavation depth of dredging machinery in real time, and generates correction instructions in a timely manner when deviations occur, ensuring that the operation process always meets design requirements; the acceptance scan and model update after dredging are completed can verify the effect of riverbed elevation restoration, forming a complete data link from diagnosis to acceptance, making the operational quality traceable. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the steps of an adaptive dredging method for rivers based on intelligent water level sensing according to the present invention.

[0068] Figure 2 This is a schematic diagram of the structure of a river adaptive dredging system based on intelligent water level sensing according to the present invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0070] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific embodiments.

[0071] like Figure 1-2 As shown, this embodiment of the invention provides a river adaptive dredging method based on intelligent water level sensing. The method is executed by a central control unit and includes the following steps:

[0072] S1. Model Construction and Benchmark Calibration: Establish and maintain a three-dimensional digital riverbed model of the target river channel. The three-dimensional digital riverbed model integrates river channel cross-sectional morphology data and real-time riverbed elevation data. Based on the preset design riverbed cross-sectional data and the minimum navigable water level or ecological base flow water level, calculate the design flow area of ​​each monitoring section as the benchmark value for dredging triggering.

[0073] S2. Real-time monitoring and siltation diagnosis: Receives real-time water level data collected by a smart water level sensing array deployed at key river sections; dynamically calculates the real-time water flow area of ​​each section based on the real-time water level data and the corresponding section morphology data in the three-dimensional digital riverbed model; compares the real-time water flow area of ​​any section with its siltation triggering benchmark value; when the real-time water flow area is continuously less than the siltation triggering benchmark value and the difference exceeds the first preset threshold, and the duration exceeds the preset time window, the river section where the section is located is automatically determined to be an initial siltation area, and a siltation early warning signal is generated.

[0074] S3. Precise Survey and Operation Planning: In response to siltation early warning signals, a detailed topographic survey instruction for the initial siltation area is sent to the topographic scanning equipment mounted on a mobile vehicle; the detailed topographic survey data is received, and the precise siltation thickness and spatial distribution information of the initial siltation area are updated in the three-dimensional digital riverbed model; with the goal of restoring the designed riverbed cross-section, based on the precise siltation distribution information, and through earthwork balance calculation and operation path optimization algorithms, a set of dredging operation instructions is automatically generated, including the division of operation sub-areas, the planned dredging depth of each sub-area, and the optimal dredging machinery travel path;

[0075] S4. Closed-loop execution and model synchronization: The dredging operation instruction set is sent to the dredging machinery; the operation position and excavation depth data transmitted back in real time by the dredging machinery are received and compared in real time with the planned values ​​in the instruction set, and the subsequent control instructions sent to the dredging machinery are dynamically adjusted according to the deviation to form a closed-loop control of the operation; after the dredging operation is completed, the riverbed elevation data of the corresponding river section in the three-dimensional digital riverbed model is updated according to the acceptance measurement data.

[0076] In this embodiment, the core function of step S1 is to establish a three-dimensional digital riverbed model that accurately reflects the topographic features of the target river channel. The model's timeliness is maintained through dynamic updates, and the design flow area of ​​each monitoring section is calculated by combining the water level standards corresponding to the river type. A dredging trigger benchmark value is set, providing a quantitative basis for subsequent real-time siltation diagnosis. The detailed steps are as follows:

[0077] S1-1: Initial Model Construction of 3D Digital Riverbed

[0078] Two types of core data were collected for the target river channel under historically unsilted or low-silted conditions. The basic river channel topographic mapping data consisted of measured data of the natural topography of the river channel, while the river channel design cross-section data consisted of standard cross-section data preset during the engineering planning stage. The two types of data were imported into the topographic data fusion module, and a spatial coordinate registration algorithm was used to unify the coordinate systems of the two types of data. Through topographic feature point matching technology, the natural topographic features in the basic river channel topographic mapping data and the standard cross-section features in the river channel design cross-section data were fused. The fusion process must ensure the continuity and consistency of the topographic data, and finally generate a three-dimensional digital riverbed initial model that can simultaneously reflect the natural and designed morphology of the river channel. This model includes the topographic point coordinate sequence of the entire river channel and the morphological feature information of each cross-section.

[0079] S1-2: Dynamic Maintenance of 3D Digital Riverbed Model

[0080] The system loads preset model update rules, which include two types of triggering conditions: the first is periodic update conditions, which initiate model updates according to a preset maintenance cycle; the second is event-triggered update conditions, which initiate model updates when major changes in the river channel occur, such as flooding, landslides, or dredging. When an update condition is triggered, the system receives periodic riverbed topographic survey data of the target river channel. This data consists of measured data of the river channel topography within the update cycle, including the coordinates and elevation information of newly added topographic feature points. The system then performs spatial overlay analysis between the periodic riverbed topographic survey data and the initial 3D digital riverbed model, calculating the topographic elevation difference point by point using the following formula:

[0081] ,in, Coordinates in the model The difference in terrain elevation at the location, coordinates The measured elevation value at the location, coordinates The original elevation value of the model at that location;

[0082] The real-time riverbed elevation data in the initial 3D digital riverbed model is corrected point by point based on the terrain elevation difference. The correction formula is as follows:

[0083] ,in, coordinates The revised new elevation value;

[0084] The correction process must adhere to the principle of spatial continuity of topographic data to ensure that the updated model can accurately reflect the current topographic state of the river channel; through continuous periodic updates and event-triggered updates, a time-sensitive three-dimensional digital riverbed model is formed and maintained.

[0085] S1-3: Data extraction of monitoring section morphology and calculation of design water flow area:

[0086] Based on a three-dimensional digital riverbed model, cross-sectional morphology data for each pre-set monitoring section is extracted. The cross-sectional morphology data consists of a continuous sequence of topographic points arranged in order of riverbed elevation along the cross-section; this coordinate sequence is denoted as... ,in, For the first The distance between each topographic point along the transverse section. For the first Elevation values ​​of each topographic point;

[0087] Calculate the design flow area for different types of waterways;

[0088] Calculation of design flow area for navigable waterways:

[0089] Retrieve the preset minimum navigable water level Based on the cross-sectional morphology data of the monitoring section, the reference horizontal level corresponding to the water level is determined; using the reference horizontal level as the upper limit, the cross-sectional morphology data that meet the following conditions are statistically analyzed. All topographic points form an effective sequence of topographic point coordinates. ;

[0090] An adaptive polygon approximation algorithm is used to construct a closed geometric figure bounded by the effective lines connecting the points on the reference horizontal plane and the boundaries of the riverbanks. The area of ​​this closed geometric figure is calculated using the shoelace formula and denoted as the first design flow area. The calculation formula is:

[0091] ,in, , , The total number of effective points within the monitoring section at the lowest navigable water level;

[0092] Calculation of the flow area for ecological river channel design:

[0093] Retrieve the preset ecological base flow level Repeat the above effective point selection and closed geometric shape construction process, and calculate the area of ​​the closed geometric shape of the monitoring section under the ecological base flow level using the shoelace formula. This area is denoted as the second design flow area. The calculation formula is:

[0094] ,in, This is the effective shape point coordinate sequence under the ecological base flow water level. , , The total number of effective points within the monitoring section under the ecological baseline water level;

[0095] S1-4: Dredging trigger baseline setting:

[0096] Establish a mapping relationship between monitoring sections and design flow areas; for monitoring sections of navigable waterways, the first design flow area is... The dredging trigger benchmark value for this section is set; for the monitoring section of the ecological river, the second design flow area is set. The dredging trigger benchmark value for this section is set; the identification codes of all monitored sections are bound to the corresponding dredging trigger benchmark values ​​and stored in the attribute database of the three-dimensional digital riverbed model; at the same time, a benchmark value index table is generated to provide data support for the subsequent real-time monitoring and siltation diagnosis module to quickly retrieve benchmark values.

[0097] In this embodiment, the core function of step S2 is to acquire real-time water level data of key river sections through a smart water level sensing array, obtain reliable water level values ​​after filtering and spatiotemporal consistency verification, dynamically calculate the real-time water flow area using a three-dimensional digital riverbed model, and accurately determine the initial siltation area and generate an early warning signal by quantitative comparison with the dredging trigger benchmark value, providing a triggering basis for subsequent accurate surveys; the detailed steps are as follows:

[0098] S2-1: Raw water level data acquisition and sliding window filtering:

[0099] The system receives raw water level data streams periodically collected by multiple water level sensors in a smart water level sensing array deployed at key sections of the river channel in real time. These raw water level data streams are denoted as... ,in, For data collection timestamps;

[0100] Time series analysis was performed on the raw water level data stream, and a sliding window filtering algorithm was used to remove noise points caused by random fluctuations and abrupt changes. The filtering formula is as follows:

[0101] ,in, For timestamps The corresponding filtered stable water level value, The length of the sliding window. For timestamps The corresponding original water level value;

[0102] The above calculations generate filtered and stable water level time series data, which can effectively eliminate abnormal water level fluctuations caused by environmental interference.

[0103] S2-2: Spatiotemporal consistency verification of water level data:

[0104] The stable water level time series data is input into the water level rationality verification module. Based on the historical water level relationship between adjacent sections and the river hydrodynamic characteristics, the spatiotemporal consistency of the stable water level time series data is verified. The calculation of the water level rationality of two adjacent monitoring sections is also performed. and The correlation coefficient of water level, and the verification formula is:

[0105] ,in, cross-section and cross-section water level correlation coefficient, To verify the length of the time series, cross-section timestamp stable water level value, cross-section The mean of stable water level values, cross-section timestamp stable water level value, cross-section The average value of stable water levels;

[0106] Set correlation coefficient threshold ,like If the data is abnormal, the water level data for the corresponding time period is determined to be abnormal. The abnormal data is then corrected based on the river hydrodynamic model. The verified real-time water level values ​​for each monitoring section at a unified timestamp are output. ;

[0107] S2-3: Real-time dynamic calculation of water flow area:

[0108] Based on the unified timestamp and the identification code of each monitoring section, the cross-sectional morphology data corresponding to each monitoring section is automatically retrieved and called from the three-dimensional digital riverbed model. The cross-sectional morphology data consists of a continuous sequence of topographic point coordinates arranged in order of riverbed elevation along the cross section.

[0109] Based on verified real-time water level values Using the reference horizontal plane height as a reference, in a continuous sequence of terrain point coordinates, identify all valid terrain points whose elevation values ​​are lower than or equal to the reference horizontal plane height. The valid terrain point coordinate sequence is denoted as...

[0110] ;

[0111] Based on the effective shape point coordinate sequence, an adaptive polygon approximation algorithm is used to construct a closed geometric figure bounded by the lines connecting the effective shape points on the reference horizontal plane and the boundaries of the riverbanks on both sides. The area of ​​this closed geometric figure is calculated using the shoelace formula, which serves as the real-time water flow area of ​​the monitoring section. The calculation formula is as follows:

[0112] ,in, To monitor the real-time water flow area of ​​the cross-section, , ;

[0113] S2-4: Siltation Determination and Early Warning Signal Generation:

[0114] Based on the monitoring section corresponding to the verified real-time water level value, the dredging trigger benchmark value bound to the cross-sectional morphology data corresponding to the monitoring section is retrieved from the 3D digital riverbed model. Calculate the first area difference between the real-time water flow area of ​​the monitored section and its dredging trigger baseline value. The calculation formula is as follows:

[0115] ,in, This is the first area difference;

[0116] Set the first preset threshold Determine whether the first area difference is greater than the first preset threshold. Then record the duration of this state. ;

[0117] Set preset time window When the first area difference is greater than the first preset threshold, the continuous state satisfies At that time, an initial siltation determination result is generated for the monitored section;

[0118] Based on the initial siltation assessment results, the upstream and downstream river sections associated with the monitoring section are marked as initial siltation areas. Based on the verified real-time water level, real-time water flow area, and first area difference, a siltation early warning signal containing a timestamp section identifier and siltation degree is generated, thus completing the siltation diagnosis process.

[0119] In this embodiment, the core function of step S3 is to respond to the siltation early warning signal, obtain high-precision topographic data by conducting a detailed topographic survey of the initial siltation area, calculate the siltation thickness and spatial distribution characteristics, and generate a precise dredging operation instruction set by combining the earthwork balance principle and path optimization algorithm, providing a quantitative basis and action plan for the dredging machinery to perform operations; the detailed steps are as follows:

[0120] S3-1: Sedimentation of Early Warning Signals and Generation of Survey Commands:

[0121] The system receives siltation early warning signals from the real-time monitoring and siltation diagnosis module, extracts the spatial location and siltation degree information of the initial siltation area contained in the signals. The spatial location is defined by the coordinates of the river segment with a preset length upstream and downstream of the warning section, and the siltation degree is characterized by the ratio of the first area difference to the dredging trigger benchmark value. Based on the spatial location and siltation degree information of the initial siltation area, a refined topographic survey command is generated. The survey command includes three core components: the survey range is the entire coordinate boundary of the initial siltation area, the survey path planning uses the parallel route method to generate a continuous survey trajectory covering the entire area, and the survey density and accuracy requirements are dynamically set according to the siltation degree. The higher the siltation degree, the smaller the spacing between the survey points and the higher the accuracy requirement. The refined topographic survey command is then sent to a multibeam echo sounder or laser scanning device mounted on an unmanned surface vessel or unmanned survey vehicle to initiate the refined topographic survey operation of the initial siltation area.

[0122] S3-2: Refined Topographic Survey and Point Cloud Data Processing

[0123] The system receives high-density point cloud topographic data covering the initial sedimentation area, transmitted back from a topographic scanning device. This point cloud data consists of the three-dimensional coordinates of a massive number of topographic points, denoted as... ;

[0124] The high-density point cloud terrain data is sequentially processed by coordinate calibration, noise filtering, and data fusion.

[0125] Coordinate calibration:

[0126] A two-dimensional coordinate rotation transformation algorithm is used to convert the device coordinate system of the point cloud data to the global coordinate system of the river channel. The transformation formula is as follows:

[0127] ;

[0128] ;

[0129] in, Let x be the horizontal coordinate of the point cloud data in the device coordinate system. Let be the vertical coordinate of the point cloud data in the device coordinate system. Let be the angle between the device coordinate system and the global coordinate system. Let x be the horizontal coordinate of the point cloud data in the global coordinate system. The vertical coordinates of the point cloud data in the global coordinate system;

[0130] Noise filtering:

[0131] Statistical filtering algorithms are used to remove outlier noise points from the point cloud data, and the average elevation of all points in the neighborhood of each point is calculated. with standard deviation Set the filter threshold If the elevation value of a certain point satisfies If the point is identified as a noise point, it will be removed. The arithmetic mean of elevations within the neighborhood;

[0132] Data fusion:

[0133] The point cloud data, after coordinate calibration and noise filtering, is subjected to gridded interpolation to generate the current refined terrain surface of the initial siltation area. The elevation values ​​of the terrain surface are denoted as follows: ;

[0134] S3-3: Calculation of sediment thickness and spatial distribution information:

[0135] The design riverbed cross-section data corresponding to the spatial location of the initial sedimentation zone is retrieved from the 3D digital riverbed model, and the elevation value of the design riverbed is extracted and denoted as... ;

[0136] By spatially overlaying the current refined topographic surface data with the designed riverbed cross-section data, and calculating the vertical elevation difference between the two data point by point, the sedimentation thickness at each calculation point within the initial sedimentation zone is obtained. The calculation formula is as follows:

[0137] ,in, coordinates The thickness of the silt at that location, coordinates The elevation value of the current refined terrain surface, coordinates The elevation value of the designed riverbed;

[0138] The Kriging interpolation algorithm is used to spatially interpolate the sediment thickness at discrete calculation points, generating a continuous sediment thickness distribution surface. The interpolation formula is as follows:

[0139] ,in, The estimated sediment thickness at the interpolation point. For the first The weight coefficients of the known points For the first The thickness of the sediment at a known point ; The total number of valid known points participating in the weighted calculation;

[0140] Perform region cluster analysis on the interpolated sediment thickness distribution surface and calculate the Euclidean distance between any two calculation points using the following formula:

[0141] ,in, For calculation points With calculation point The Euclidean distance between them For calculation points The horizontal coordinate, For calculation points The vertical coordinate;

[0142] Based on the Euclidean distance and the difference in sediment thickness, accurate sediment thickness and spatial distribution information reflecting the continuous spatial distribution of sediment at different thickness levels is generated, and this information is associated with the descriptive data of the initial sedimentation zone in the three-dimensional digital riverbed model to complete the model update.

[0143] S3-4: Sub-region partitioning based on density clustering:

[0144] Load the preset second threshold This threshold is the maximum permissible variation in the siltation thickness within the operating sub-region;

[0145] Based on accurate information on sediment thickness and spatial distribution, a density clustering spatial segmentation algorithm is used to divide the operation sub-region. The clustering process takes the spatial coordinates of the calculation points and the sediment thickness as the core features.

[0146] The core criteria for clustering are defined as follows: if the number of other computational points in the neighborhood of a certain computational point is greater than the preset minimum number of points, and the difference in accumulation thickness among all computational points in the neighborhood is less than [a certain value], then [the criteria are as follows]. If so, then the calculation point is the core point;

[0147] Expand the clustering region outward from the core point until the difference in sediment thickness within the expanded region exceeds [a certain value]. Stop expanding and define the spatial boundaries of the area;

[0148] The core point determination and area expansion process is repeated, dividing the initial siltation area into multiple independent operational sub-areas. The siltation thickness variation within each operational sub-area does not exceed [a certain value]. The spatial boundaries of each sub-region are automatically defined by the cluster analysis results;

[0149] S3-5: Earthwork Balance Calculation and Determination of Planned Dredging Depth

[0150] For each work sub-region, earthwork balance calculations are performed; the work sub-region is divided into several regular calculation units, and the area of ​​each calculation unit is denoted as... The average sediment thickness within the calculation unit is denoted as The formula for calculating the total volume of dredging earthwork in the work sub-area is:

[0151] ,in, This represents the total volume of dredged earthwork in the work sub-area. To calculate the horizontal number of the unit. To calculate the vertical number of cells. To calculate the area of ​​the unit;

[0152] Extract the total area of ​​the sub-region of the operation The planned dredging depth for this sub-area is calculated based on the total dredging volume. The calculation formula is as follows:

[0153] ,in, The planned dredging depth for the sub-area. This represents the total volume of dredged earthwork in the work sub-area. This represents the total planar projected area of ​​a single sub-region; the calculation results need to be verified in conjunction with the design riverbed cross-section data to ensure that the riverbed elevation of the sub-region is restored to the design elevation after dredging.

[0154] S3-6: Construction and Solution of the Job Path Optimization Model

[0155] Based on the planned dredging depth and spatial location of all operational sub-regions, a dredging operation path optimization model is constructed.

[0156] Objective function:

[0157] The model aims to minimize the total travel distance of the dredging machinery and the operation switching time. The objective function is:

[0158] ,in, To comprehensively optimize the target value, The weighting coefficient for the total distance traveled. This is a weighting coefficient for job switching time. , This represents the total distance traveled by the dredging machinery. This refers to the total switching time of the dredging machinery between different work sub-areas.

[0159] Constraints:

[0160] The constraints include three aspects: the maximum operating capacity of the dredging machinery is constrained to ensure that the continuous operation time of the machinery in a single session does not exceed a preset value; the access order constraint for each operation sub-region is constrained to ensure that the spatial distance between adjacent operation sub-regions is less than the maximum movement distance of the machinery; and the planned dredging depth execution constraint is constrained to ensure that the actual dredging depth of the machinery within the sub-region is equal to the planned dredging depth. The deviation is less than the preset tolerance;

[0161] A heuristic search algorithm is used to solve the dredging operation path optimization model to obtain the optimal dredging machinery travel path; this path defines the order in which the dredging machinery visits each operation sub-area and the specific operation trajectory in each operation sub-area.

[0162] S3-7: Generation of Dredging Operation Instruction Set:

[0163] By integrating the results of sub-regional division of operations, the planned dredging depth for each sub-region, and the optimal dredging machinery travel path, a dredging operation instruction set is generated. The instruction set contains two core types of instructions: dredging depth instructions for each sub-region, with the instruction content specifying the dredging depth for that region. The coordinates of the dredging area are used to generate movement control commands for the dredging machinery. The commands specify the order in which the machinery visits each sub-area and the coordinates of its trajectory. The dredging operation command set is stored in the command database of the central control unit to provide data support for subsequent closed-loop execution and model synchronization modules.

[0164] In this embodiment, the core function of step S4 is to accurately send the dredging operation instruction set to the dredging machinery. By collecting real-time data on the operation location and excavation depth, comparing it quantitatively with the planned values, and dynamically adjusting the control instructions, a closed-loop control of the operation is formed to ensure the accuracy of the dredging operation. After the operation is completed, the three-dimensional digital riverbed model is updated through acceptance measurement to maintain the timeliness and accuracy of the model. The detailed steps are as follows:

[0165] S4-1: Issuance of Dredging Operation Instructions and Start of Operation:

[0166] The dredging operation instruction set generated by the precise survey and operation planning module is transmitted to the onboard controller of the dredging machinery via wireless communication; the onboard controller parses the dredging operation instruction set and extracts core information such as the location coordinates of the operation sub-area, the planned dredging depth, and the optimal travel path;

[0167] Based on the analysis results, control signals are generated to drive the actuators of the dredging machinery. The control signals include the travel command of the power system and the depth command of the digging system. Driven by the control signals, the dredging machinery moves along the optimal dredging machinery travel path to the first working sub-area. After reaching the designated position, the dredging operation is started according to the planned dredging depth corresponding to the working sub-area.

[0168] S4-2: Real-time Operation Data Acquisition and Deviation Calculation:

[0169] The positioning module and depth sensor integrated on the dredging machinery collect two core operational data in real time: the positioning module collects the real-time operational position coordinates of the dredging machinery, and the depth sensor collects the real-time digging depth value of the dredging machinery; both types of data are transmitted back to the central control unit according to the preset collection frequency.

[0170] After receiving the returned data, the central control unit compares it quantitatively with the planned values ​​in the dredging operation instruction set and calculates the deviation value.

[0171] Depth execution deviation calculation:

[0172] Retrieve the planned dredging depth value corresponding to the current working location, and calculate the absolute difference between the real-time excavation depth value and the planned dredging depth value. The formula is as follows:

[0173] ,in, For depth, the deviation value is executed. To mine depth values ​​in real time;

[0174] Position deviation calculation:

[0175] Retrieve the planned position coordinates of the current operation stage within the optimal dredging machinery travel path, and calculate the Euclidean distance between the real-time operation position coordinates and the planned position coordinates. The formula is:

[0176] ,in, This is the positional deviation value. The horizontal coordinates of the real-time operation location. The vertical coordinate of the real-time operation location. The horizontal coordinate of the planned work location. The vertical coordinate of the planned work location;

[0177] S4-3: Deviation Judgment and Dynamic Correction Closed-Loop Control:

[0178] Load the preset deviation tolerance thresholds, which are the depth tolerance thresholds. With position tolerance threshold The central control unit compares the calculated depth execution deviation value and position deviation value with the corresponding tolerance threshold to determine whether the adjustment condition is triggered.

[0179] like or If the conditions are met, the adjustment conditions are triggered; the central control unit retrieves the precise silt distribution information of the remaining silt-free areas in the current working sub-area, and dynamically generates local correction instructions based on the real-time operating status of the dredging machinery and the earthwork balance calculation principle.

[0180] For cases where the depth deviation exceeds the standard, a local dredging depth correction command is generated, which includes the adjusted excavation depth value; for cases where the position deviation exceeds the standard, a path correction command is generated, which includes the adjusted work trajectory coordinates; the local dredging depth correction command or path correction command is sent to the onboard controller of the dredging machinery, and the onboard controller adjusts the action of the actuator according to the correction command to realize online correction of the dredging operation and form a closed-loop control.

[0181] like and If the operation status meets the requirements, the dredging machinery will continue to perform the operation according to the original instructions.

[0182] S4-4: Acceptance Scan and 3D Digital Riverbed Model Update

[0183] After the dredging machinery completes the dredging work in all sub-areas, the central control unit sends an acceptance scan command for the dredged river section to the terrain scanning equipment mounted on the mobile vehicle; the scan command includes the coordinate range and scanning accuracy requirements of the dredged river section.

[0184] The terrain scanning equipment performs acceptance scanning operations, collecting acceptance terrain data for the dredged river section. The acceptance terrain data includes the elevation values ​​of various calculation points within the river section. After the topographic data is transmitted back to the central control unit, the central control unit compares it with the design riverbed cross-section data of the corresponding river section in the 3D digital riverbed model, and calculates the riverbed elevation recovery error at each calculation point. The formula is as follows:

[0185] ,in, To account for the error in restoring the riverbed elevation, The elevation value for designing the riverbed;

[0186] Load the preset acceptance standard threshold ,like If the percentage of calculation points that have passed acceptance is reached, the dredging operation of that section of the river is deemed to have passed acceptance.

[0187] Based on the accepted terrain data, the central control unit updates the riverbed elevation data of the corresponding river section in the three-dimensional digital riverbed model point by point, and marks the completion of dredging of the river section in the model, thus completing the synchronous maintenance of the model for the entire dredging process.

[0188] An adaptive dredging system for rivers based on intelligent water level sensing is an intelligent solution in the field of river dredging engineering. Through the coordinated linkage of an intelligent water level sensing array and a three-dimensional digital riverbed model, it can achieve automatic diagnosis of siltation status, precise planning of dredging operations, and closed-loop control of the operation process, ensuring that the river can maintain its designed water flow capacity under complex hydrological conditions.

[0189] The river adaptive dredging system based on intelligent water level sensing is primarily responsible for establishing and maintaining a 3D digital riverbed model of the target river. It collects real-time water level data from key sections using an intelligent water level sensing array, dynamically calculates the water flow area, and diagnoses siltation status. Upon identifying siltation risk, the system automatically initiates refined topographic surveys, generates precise dredging operation command sets, controls dredging machinery to complete the operation, and simultaneously updates the model. The system achieves fully automated and intelligent management, ensuring the river's navigation capacity and ecological functions, and improving the efficiency and accuracy of dredging operations. The system includes:

[0190] Central control unit:

[0191] The central control unit is the core command module of the system. It includes a model building and benchmark calibration module, a real-time monitoring and siltation diagnosis module, a precise survey and operation planning module, and a closed-loop execution and model synchronization module, which are connected in sequence. Each module completes data processing and command issuance in sequence.

[0192] Model building and benchmark calibration module:

[0193] 3D digital riverbed model construction: Obtain topographic mapping data and design cross-section data of the target river channel in its historical non-siltation state, and fuse them to generate an initial 3D digital riverbed model; Based on preset cycles or triggered events such as floods and landslides, receive periodic survey data, dynamically update the riverbed elevation data in the model, and maintain the model's timeliness;

[0194] Calculation of dredging trigger baseline: Extract morphological data of each monitoring section in the model. For navigable waterways, combine the minimum navigable water level; for ecological waterways, combine the ecological base flow water level. Calculate the design flow area as the dredging trigger baseline. The formula for calculating the first design flow area of ​​navigable waterways is as follows: (in, Design the water passage area for navigable waterways, For the first The effective distance of the forming point along the transverse direction of the cross section. For the first The elevation values ​​of an effective shape point, , ); Calculation formula for the second design flow area of ​​ecological river channels and The water level parameter is consistent; simply replace it with the ecological base flow water level.

[0195] Real-time monitoring and siltation diagnosis module:

[0196] The wavelet algorithm removes noise and generates stable water level time series data; based on the historical water level relationship and hydrodynamic characteristics of adjacent cross sections, the spatiotemporal consistency of the water level data is verified, and the verified real-time water level value is output; the sliding window filtering formula is as follows: (in, For timestamps The corresponding filtered stable water level value, The length of the sliding window. For timestamps (corresponding original water level value);

[0197] Real-time water flow area calculation: The morphological data of the monitoring section in the three-dimensional digital riverbed model is called. Based on the verified real-time water level, effective shape points are identified. A closed geometric figure is constructed through an adaptive polygon approximation algorithm to calculate the real-time water flow area.

[0198] Siltation diagnosis and early warning: Calculate the difference between the real-time water flow area and the dredging trigger benchmark value. When the difference exceeds the first preset threshold and the duration exceeds the preset time window, determine that the upstream and downstream river sections of the cross section are the initial siltation area and generate an early warning signal containing timestamp, cross section identifier and siltation degree.

[0199] Precision Survey and Operation Planning Module:

[0200] Refined Topographic Survey: In response to siltation early warning signals, survey instructions are sent down to the topographic scanning equipment mounted on the mobile vehicle. The instructions include the survey range, path, and dynamic accuracy requirements. The high-density point cloud data transmitted back by the equipment is received, and coordinate calibration, noise filtering, and data fusion are completed to generate a refined topographic surface of the initial siltation area.

[0201] Calculation of Sediment Thickness and Distribution: The sediment thickness is calculated by overlaying refined topographic surface data with designed riverbed cross-section data, and then calculating the vertical elevation difference point by point. Spatial interpolation and regional cluster analysis are used to generate spatial distribution information of the sediment thickness. The formula for calculating sediment thickness is as follows: (in, coordinates The thickness of the silt at that location, This represents the current refined elevation values ​​for the terrain surface. (For designing riverbed elevation values);

[0202] Dredging operation instruction generation: The density clustering algorithm is used to divide the operation into sub-regions to ensure that the change in silt thickness within the sub-region does not exceed the second preset threshold; the planned dredging depth of each sub-region is determined by earthwork balance calculation; a path optimization model is constructed with the goal of minimizing travel distance and operation switching time; the optimal path is solved by heuristic search algorithm; and the dredging operation instruction set is integrated and generated.

[0203] Closed-loop execution and model synchronization module:

[0204] Operation instruction issuance and execution: The dredging operation instruction set is wirelessly transmitted to the onboard controller of the dredging machinery, which controls the machinery to move along the optimal path and carry out the operation according to the planned depth;

[0205] Real-time deviation correction: Data on the operating position and digging depth of the dredging machinery are collected, and the depth execution deviation and position deviation are calculated. When the deviation exceeds the preset tolerance range, a correction command is dynamically generated to achieve closed-loop control of the operation. The formula for depth execution deviation is: (in, For depth, the deviation value is executed. (For real-time excavation depth); the formula for position deviation is... (in, This is the positional deviation value. The horizontal coordinates of the real-time work location. The vertical coordinates of the real-time operation location. The horizontal coordinate of the planned work location. (Vertical coordinates of the planned work location);

[0206] Model Update: After the dredging operation is completed, an acceptance scan is initiated to compare the acceptance topographic data with the design cross-section data. When the elevation recovery error meets the acceptance criteria, the three-dimensional digital riverbed model is updated and the dredging is marked as complete.

[0207] Intelligent water level sensing array:

[0208] The intelligent water level sensing array is deployed at key sections of the river channel and consists of multiple water level sensors. Its core function is to periodically collect real-time water level data of the river channel and transmit the raw data stream to the real-time monitoring and siltation diagnosis module of the central control unit. The array is the front-end sensing carrier for the system to acquire hydrological data and provides basic data support for subsequent water flow area calculation and siltation diagnosis.

[0209] Terrain scanning equipment:

[0210] The terrain scanning equipment is mounted on mobile vehicles such as unmanned boats or unmanned surveying vehicles, and includes two core instruments: multibeam echo sounders or laser scanning equipment. Its function is to respond to the instructions of the central control unit, conduct detailed terrain surveys of the initial siltation area, and transmit high-density point cloud terrain data back. After the dredging operation is completed, it performs acceptance scanning and submits acceptance terrain data. This equipment is a key tool for achieving accurate siltation surveys and acceptance of operation results.

[0211] Dredging machinery:

[0212] Dredging machinery is the execution vehicle for dredging operations, equipped with an onboard controller, positioning module, and depth sensor. Its functions include receiving the set of operation instructions issued by the central control unit, completing the dredging operation in each sub-area along the optimal path, transmitting the operation position and excavation depth data back in real time, receiving and executing deviation correction instructions, and assisting in completing the finishing work before acceptance. This equipment is the core carrier for realizing the automated execution of dredging operations.

[0213] Furthermore, the dynamic maintenance technology for the 3D digital riverbed model is based on the theory of multi-source topographic data fusion and dynamic updating. By integrating historical non-silting topographic data and design cross-section data, an initial model that can reflect the natural and designed morphology of the river channel is constructed. The system has a preset periodic or event-triggered mechanism to introduce periodic measured topographic data and completes the dynamic correction of the riverbed elevation of the model by calculating the elevation difference point by point. This technology ensures that the model can reflect the changes in river channel topography in real time, providing an accurate topographic basis for all subsequent analysis and calculation.

[0214] The real-time dynamic calculation technology for water flow area is based on the theory of water level data verification and geometric area calculation. First, noise in the water level data is eliminated through sliding window filtering. Then, spatiotemporal consistency verification is completed by combining river hydrodynamic characteristics to ensure the accuracy of the water level data. Using the verified water level value as a benchmark, cross-sectional topographic points are extracted from the three-dimensional digital riverbed model. Valid topographic points below the water level benchmark are selected, and a closed geometric figure is constructed using an adaptive polygon approximation algorithm. The area of ​​the figure is calculated using the shoelace formula to obtain the real-time water flow area. This technology achieves dynamic and accurate calculation of the water flow area and is the core basis for siltation diagnosis.

[0215] The precise siltation survey and spatial distribution analysis technology is based on high-density point cloud data processing and spatial overlay analysis theory. Point cloud data collected by terrain scanning equipment is converted to the global coordinate system of the river channel after coordinate calibration. Noise points are removed by statistical filtering, and then a refined terrain surface is generated by grid interpolation. This surface is overlaid with the design riverbed cross-section data, and the elevation difference is calculated point by point to obtain the siltation thickness. The Kriging interpolation algorithm is used to realize the continuity of the discrete point thickness data. Combined with regional cluster analysis, siltation areas of different thickness levels are delineated. This technology realizes the precise quantification of siltation status and visualization of spatial distribution.

[0216] The dredging operation path optimization technology is based on density clustering segmentation and heuristic search algorithm theory. It uses density clustering to divide the siltation area into several sub-regions with uniform siltation thickness, reducing the difficulty of mechanical operations. An optimization model is constructed with the goal of minimizing the total travel distance and operation switching time. The model constraints cover mechanical operation capacity, sub-region access order, and dredging depth requirements. A heuristic search algorithm is used to solve the model to obtain the optimal travel path. This technology enables intelligent planning of dredging operations and improves operational efficiency.

[0217] The closed-loop operation control technology is based on real-time data feedback and dynamic deviation correction theory. The positioning module and depth sensor of the dredging machinery collect operation data in real time, and the central control unit compares the real-time data with the planned value to calculate the deviation. When the deviation exceeds the limit, a correction command is generated by combining the remaining silt distribution information and the earthwork balance principle, and sent to the mechanical actuator to complete the online correction. This technology forms a closed-loop control link of "command-execution-feedback-correction", which ensures the accuracy of dredging operations.

[0218] The system's workflow is as follows:

[0219] Initialization phase:

[0220] The central control unit is activated, completing communication connections with the intelligent water level sensing array, terrain scanning equipment, and dredging machinery, and confirming that each device is in normal working condition;

[0221] Load basic parameters such as historical topographic mapping data, design cross-section data, preset water level thresholds, and deviation tolerance range of the target river channel;

[0222] Model building and benchmark calibration phase:

[0223] By integrating historical topographic mapping data with design cross-section data, an initial three-dimensional digital riverbed model is constructed.

[0224] Based on a preset cycle or triggered event, it receives periodic survey data and dynamically updates the model's riverbed elevation data.

[0225] Extract the morphological data of each monitoring section, combine it with the water level standard corresponding to the river type, calculate the design water flow area and set it as the benchmark value for dredging triggering;

[0226] Real-time monitoring and siltation diagnosis stage:

[0227] The intelligent water level sensing array periodically collects raw water level data streams and transmits them to the central control unit;

[0228] The raw data is filtered and spatiotemporal consistency is verified, and the verified real-time water level value is output.

[0229] Call the cross-sectional morphology data, calculate the real-time water flow area, and compare it with the dredging trigger benchmark value;

[0230] When the real-time difference in water flow area exceeds the threshold and continues for a specified duration, the initial siltation zone is determined and an early warning signal is generated.

[0231] Precise surveying and operation planning phase:

[0232] The central control unit sends a survey command to the terrain scanning equipment, which completes the initial siltation area survey and transmits point cloud data back.

[0233] Process point cloud data to generate refined terrain surfaces, calculate sediment thickness and spatial distribution information, and update the three-dimensional digital riverbed model;

[0234] Divide the work into sub-regions, calculate the planned dredging depth for each sub-region, and construct and solve the path optimization model;

[0235] Integrate sub-region division, dredging depth, and optimal path information to generate a dredging operation instruction set;

[0236] Closed-loop execution and model synchronization phase:

[0237] The work instruction set is issued to the dredging machinery, which then starts up and carries out dredging operations according to the instructions.

[0238] Real-time acquisition of work location and excavation depth data, calculation of deviation values, and generation of correction commands when exceeding limits to achieve closed-loop control;

[0239] After the work is completed, start the acceptance scan, compare the acceptance data with the design data, and update the three-dimensional digital riverbed model and mark the dredging as completed after the standard is met;

[0240] End phase:

[0241] When the system receives a stop command or completes dredging operations across the entire river section, it disconnects the communication connections of all devices, saves the operation records and model data, and releases system resources.

[0242] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A river channel adaptive dredging method based on intelligent water level sensing, characterized in that: The method is executed by the central control unit and includes the following steps: S1. Model Construction and Benchmark Calibration: Establish and maintain a three-dimensional digital riverbed model of the target river channel. The three-dimensional digital riverbed model integrates river channel cross-sectional morphology data and real-time riverbed elevation data. Based on the preset design riverbed cross-sectional data and the minimum navigable water level or ecological base flow water level, calculate the design flow area of ​​each monitoring section as the benchmark value for dredging triggering. S2. Real-time monitoring and siltation diagnosis: Receives real-time water level data collected by a smart water level sensing array deployed at key river sections; dynamically calculates the real-time water flow area of ​​each section based on the real-time water level data and the corresponding section morphology data in the three-dimensional digital riverbed model; compares the real-time water flow area of ​​any section with its siltation triggering benchmark value; when the real-time water flow area is continuously less than the siltation triggering benchmark value and the difference exceeds the first preset threshold, and the duration exceeds the preset time window, the river section where the section is located is automatically determined to be an initial siltation area, and a siltation early warning signal is generated. S3. Precise Survey and Operation Planning: In response to siltation early warning signals, a detailed topographic survey instruction for the initial siltation area is sent to the topographic scanning equipment mounted on a mobile vehicle; the detailed topographic survey data is received, and the precise siltation thickness and spatial distribution information of the initial siltation area are updated in the three-dimensional digital riverbed model; with the goal of restoring the designed riverbed cross-section, based on the precise siltation distribution information, and through earthwork balance calculation and operation path optimization algorithms, a set of dredging operation instructions is automatically generated, including the division of operation sub-areas, the planned dredging depth of each sub-area, and the optimal dredging machinery travel path; S4. Closed-loop execution and model synchronization: The dredging operation instruction set is sent to the dredging machinery; the operation position and excavation depth data transmitted back in real time by the dredging machinery are received and compared in real time with the planned values ​​in the instruction set, and the subsequent control instructions sent to the dredging machinery are dynamically adjusted according to the deviation to form a closed-loop control of the operation; after the dredging operation is completed, the riverbed elevation data of the corresponding river section in the three-dimensional digital riverbed model is updated according to the acceptance measurement data.

2. The adaptive dredging method for rivers based on intelligent water level sensing according to claim 1, characterized in that: Step S1, model construction and benchmark calibration, specifically includes: Acquire basic river topographic mapping data and preset river design cross-section data of the target river channel under historical conditions of no or low siltation, integrate the basic river topographic mapping data and the river design cross-section data, and construct a three-dimensional digital riverbed initial model that reflects the natural and designed morphology of the river channel. Based on a preset maintenance cycle or triggering event, the system receives periodic riverbed topographic survey data of the target river channel and uses the periodic riverbed topographic survey data to dynamically update the real-time riverbed elevation data in the initial three-dimensional digital riverbed model in order to form and maintain the three-dimensional digital riverbed model. Based on the three-dimensional digital riverbed model, the cross-sectional morphology data of each monitoring section is extracted; for navigable waterways, the first design flow area of ​​each monitoring section at the minimum navigable water level is calculated based on the cross-sectional morphology data and the preset minimum navigable water level; for ecological waterways, the second design flow area of ​​each monitoring section at the ecological base flow water level is calculated based on the cross-sectional morphology data and the preset ecological base flow water level. The first or second designed water flow area is set as the dredging trigger benchmark value for the corresponding monitoring section.

3. The adaptive dredging method for rivers based on intelligent water level sensing according to claim 1, characterized in that: It receives real-time water level data collected by an intelligent water level sensing array deployed at key river sections; based on the real-time water level data and the corresponding cross-sectional morphology data in the three-dimensional digital riverbed model, it dynamically calculates the real-time water flow area of ​​each cross-section, specifically including: Real-time reception of raw water level data streams periodically collected by multiple water level sensors in the intelligent water level sensing array; Time series analysis is performed on the raw water level data stream. A sliding window filtering algorithm is used to remove noise points of random fluctuations and sudden changes in the raw water level data stream, generating stable water level time series data after filtering. The stable water level time series data is input into the water level rationality verification module. Based on the historical water level relationship between adjacent sections and the river hydrodynamic characteristics, the stable water level time series data is checked for spatiotemporal consistency and the verified real-time water level value of each monitoring section under a unified timestamp is output. Based on the unified timestamp and the identification code of each monitoring section, the cross-sectional morphology data corresponding to each monitoring section is automatically retrieved and called from the three-dimensional digital riverbed model. The cross-sectional morphology data consists of a continuous sequence of topographic point coordinates arranged in order of riverbed elevation along the cross section. Using the verified real-time water level as the reference horizontal height, all valid topographic points with elevation values ​​lower than or equal to the reference horizontal height are identified in a continuous topographic point coordinate sequence. Based on the effective shape point coordinate sequence, an adaptive polygon approximation algorithm is used to construct a closed geometric figure jointly enclosed by the reference horizontal plane, the effective shape point connection line, and the riverbank boundary. The area of ​​this closed geometric figure is then calculated as the real-time water flow area of ​​the monitoring section.

4. The adaptive dredging method for rivers based on intelligent water level sensing according to claim 3, characterized in that: The real-time water flow area of ​​any cross-section is compared with its dredging trigger benchmark value. When the real-time water flow area is continuously less than the dredging trigger benchmark value and the difference exceeds the first preset threshold, and the duration exceeds the preset time window, the river section where the cross-section is located is automatically determined to be an initial siltation zone, and a siltation early warning signal is generated, specifically including: Based on the monitoring section corresponding to the verified real-time water level value, the dredging trigger benchmark value bound to the section morphology data corresponding to the monitoring section is retrieved from the three-dimensional digital riverbed model, and the first area difference between the real-time water flow area of ​​the monitoring section and its dredging trigger benchmark value is calculated. Determine whether the first area difference is greater than the first preset threshold, and record the continuous state of the first area difference being greater than the first preset threshold; When the first area difference is greater than the first preset threshold for an extended period of time, an initial siltation determination result is generated for the monitored section. Based on the initial siltation determination results, the upstream and downstream river sections associated with the monitoring section are marked as the initial siltation area, and a siltation early warning signal containing a timestamp, section identifier, and siltation degree is generated based on the verified real-time water level value, real-time water flow area, and first area difference.

5. The adaptive dredging method for rivers based on intelligent water level sensing according to claim 1, characterized in that: In response to a siltation early warning signal, a detailed topographic survey command for the initial siltation zone is sent to a terrain scanning device mounted on a mobile vehicle; the detailed topographic survey data is received, and the precise siltation thickness and spatial distribution information of the initial siltation zone are updated in the three-dimensional digital riverbed model, specifically including: In response to the siltation early warning signal, the spatial location range and siltation degree information of the initial siltation area are extracted; Based on the spatial location and degree of siltation of the initial siltation area, targeted and refined terrain survey instructions are generated and issued to multibeam echo sounders or laser scanning equipment mounted on unmanned ships or unmanned survey vehicles. The refined terrain survey instructions include at least the survey range, survey path planning, and survey density and accuracy requirements dynamically set based on the degree of siltation. Receive high-density point cloud topographic data covering the initial siltation area after the multibeam echo sounder or laser scanning equipment executes fine topographic survey instructions; The high-density point cloud terrain data is subjected to coordinate calibration, noise filtering and data fusion processing to generate the current refined terrain surface of the initial siltation area; Spatial overlay analysis is performed between the current refined topographic surface and the design riverbed cross-section data corresponding to the same spatial location in the three-dimensional digital riverbed model. The vertical elevation difference between the current refined topographic surface and the design riverbed cross-section data is calculated point by point to obtain the sedimentation thickness at each calculation point in the initial sedimentation area. Based on the silt thickness and spatial coordinates of all calculation points, spatial interpolation calculation and regional cluster analysis are performed to generate accurate silt thickness and spatial distribution information that reflects the continuous spatial distribution of silt at different thickness levels. This information is then linked to the descriptive data of the initial siltation area in the three-dimensional digital riverbed model to complete the model update.

6. The adaptive dredging method for rivers based on intelligent water level sensing according to claim 5, characterized in that: With the goal of restoring the riverbed to its designed cross-section, and based on accurate siltation distribution information, an automatic dredging operation instruction set is generated using earthwork balance calculations and operation path optimization algorithms. This set includes sub-regional divisions, planned dredging depths for each sub-region, and optimal dredging machinery travel paths. Specifically, it includes: Based on the siltation thickness and its spatial coordinates in the accurate siltation distribution information, a density clustering-based spatial segmentation algorithm is used to divide the initial siltation area into multiple operational sub-regions. The variation in siltation thickness within each operational sub-region does not exceed a preset second threshold, and the spatial boundaries between operational sub-regions are automatically defined by the clustering analysis results. For each sub-area of ​​operation, based on the siltation thickness of all calculation points within the sub-area, the total dredging volume of the sub-area is determined through earthwork balance calculation. Combined with the design riverbed cross-section data, the planned dredging depth of the sub-area is calculated to ensure that the riverbed elevation of the sub-area is restored to the design elevation after dredging. Based on the planned dredging depth and spatial location of all operation sub-regions, a dredging operation path optimization model is constructed. The optimization objective of the dredging operation path optimization model is to minimize the total travel distance of the dredging machinery and the operation switching time. The constraints include the maximum operating capacity of the dredging machinery, the access order of each operation sub-region, and the execution requirements of the planned dredging depth. A heuristic search algorithm is used to solve the dredging operation path optimization model to obtain the optimal dredging machinery travel path. The optimal dredging machinery travel path defines the order in which the dredging machinery visits each operation sub-area and the specific operation trajectory in each operation sub-area. By integrating the division of work sub-areas, the planned dredging depth of each work sub-area, and the optimal dredging machinery travel path, a dredging operation instruction set is generated. The dredging operation instruction set includes dredging depth instructions for each work sub-area and travel control instructions for the dredging machinery.

7. The adaptive dredging method for rivers based on intelligent water level sensing according to claim 1, characterized in that: Step S4, closed-loop execution and model synchronization, includes the following specific steps: The dredging operation instruction set is wirelessly transmitted to the onboard controller of the dredging machinery; the onboard controller parses the dredging operation instruction set, generates control signals to drive the actuators of the dredging machinery, and controls the dredging machinery to move along the optimal dredging machinery travel path to the first operation sub-area, and starts the dredging operation according to the planned dredging depth corresponding to the operation sub-area; The positioning module and depth sensor integrated on the dredging machinery collect real-time data on the machinery's operating position and excavation depth, and transmit this data back to the central control unit. The central control unit compares the received real-time excavation depth data with the planned dredging depth corresponding to the current operating position in the dredging operation instruction set, and calculates the depth execution deviation value. At the same time, it compares the received operating position data with the predetermined trajectory in the optimal dredging machinery travel path, and calculates the position deviation value. The central control unit determines whether to trigger the adjustment condition based on the depth execution deviation value and the position deviation value; When the depth deviation or position deviation exceeds the corresponding preset tolerance range, based on the accurate silt distribution information of the remaining undredged area in the current operation sub-region, the real-time status of the dredging machinery, and the earthwork balance calculation principle, a local dredging depth correction command or path correction command is dynamically generated. Local dredging depth correction commands or path correction commands are sent to the onboard controller of the dredging machinery to perform online correction of the dredging operation and form a closed-loop control of the operation. After the dredging machinery completes the dredging work in all sub-areas, it sends an acceptance scan command for the dredged river section to the terrain scanning equipment mounted on the mobile vehicle; it receives the acceptance terrain data returned by the terrain scanning equipment after performing the acceptance scan; it compares the acceptance terrain data with the design riverbed cross-section data in the same coordinate system and calculates the riverbed elevation recovery error of the accepted area; when the riverbed elevation recovery error meets the preset acceptance standard, it updates the riverbed elevation data of the corresponding river section in the three-dimensional digital riverbed model based on the acceptance terrain data and marks the dredging of that river section as completed.

8. A river adaptive dredging system based on intelligent water level sensing, characterized in that: The system is used to execute any one of the river adaptive dredging methods based on intelligent water level sensing according to claims 1-7, and the system includes a central control unit, an intelligent water level sensing array, a terrain scanning device, and dredging machinery; The central control unit includes a model building and benchmark calibration module, a real-time monitoring and siltation diagnosis module, a precise survey and operation planning module, and a closed-loop execution and model synchronization module connected in sequence. The latter module is configured to further process the processing results output by the former module. The model construction and benchmark calibration module is used to establish and maintain a three-dimensional digital riverbed model of the target river channel, and calculate the design flow area of ​​each monitoring section as the dredging trigger benchmark value based on the preset design riverbed cross-section data and the minimum navigable water level or ecological base flow water level. The real-time monitoring and siltation diagnosis module is connected to the intelligent water level sensing array for receiving real-time water level data collected by the intelligent water level sensing array. Based on the real-time water level data and the corresponding cross-sectional morphology data in the three-dimensional digital riverbed model, it dynamically calculates the real-time water flow area of ​​each cross-section and compares the real-time water flow area with the siltation triggering benchmark value. When the real-time water flow area is continuously less than the siltation triggering benchmark value and the difference exceeds the first preset threshold and the duration exceeds the preset time window, it automatically determines that the river section where the cross-section is located is the initial siltation area and generates a siltation early warning signal. The precise survey and operation planning module is communicatively connected to the terrain scanning device. In response to the siltation early warning signal, it sends a refined terrain survey instruction to the terrain scanning device for the initial siltation area, receives the refined terrain survey data returned by the terrain scanning device, and updates the precise siltation thickness and spatial distribution information of the initial siltation area in the three-dimensional digital riverbed model. With the goal of restoring the designed riverbed cross section, it automatically generates a set of dredging operation instructions based on the precise siltation distribution information and through earthwork balance calculation and operation path optimization algorithms. The closed-loop execution and model synchronization module is connected to the dredging machinery and is used to send the dredging operation instruction set to the dredging machinery, receive the operation position and excavation depth data transmitted back by the dredging machinery in real time, compare them with the planned values ​​in the dredging operation instruction set in real time, and dynamically adjust the subsequent control instructions sent to the dredging machinery according to the deviation. After the dredging operation is completed, the riverbed elevation data of the corresponding river section in the three-dimensional digital riverbed model is updated according to the acceptance measurement data. The intelligent water level sensing array is deployed at key sections of the river channel to collect water level data in real time and transmit it to the real-time monitoring and siltation diagnosis module. The terrain scanning device is mounted on a mobile vehicle and is used to conduct a detailed terrain survey of the initial siltation area according to the detailed terrain survey instructions, and to transmit the detailed terrain survey data back to the precision survey and operation planning module. The dredging machinery is used to execute dredging operations according to the dredging operation instruction set and to transmit the operation location and excavation depth data back to the closed-loop execution and model synchronization module.

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