Environment-friendly wharf front edge dredging method

Abstract

The invention discloses a wharf front edge environment-friendly desilting method, belongs to the technical field of port and channel engineering desilting, and aims at solving the technical problems that traditional wharf desilting is low in precision, poor in efficiency, high in energy consumption and prone to causing secondary pollution of water. A mud concentration sensor, a water depth sensor and a GPS / RTK positioning module are adopted to collect operation data in real time, a desilting decision model in a central controller carries out fusion analysis on multi-source signals, and a cooperative control instruction is output to dynamically adjust the rotating speed of a sand pump, the opening degree of a valve port of a high-pressure water pump and the action of a winch. The mud concentration, the desilting elevation and the floating displacement pose are accurately controlled, the desilting precision is guaranteed, meanwhile, the efficiency is improved to the maximum extent, energy consumption and disturbance are reduced, and the method is suitable for environment-friendly desilting operation of narrow water areas such as wharf front edges and harbor basins.

Description

Technical Field

[0001] This invention belongs to the field of dredging technology for port and waterway engineering, specifically an environmentally friendly dredging method for the front edge of a wharf. Background Technology

[0002] Maintenance dredging of ports and waterways, especially in the wharf area, is a crucial aspect of ensuring normal port operations. This area experiences frequent vessel berthing and departure, complex underwater structures, and limited operating space, placing extremely high demands on the precision, efficiency, and environmental friendliness of dredging technologies. Traditional dredging methods have long faced several pressing technical challenges in applications to this type of scenario.

[0003] First, significant challenges exist in controlling the accuracy of dredging. Dredging at the wharf front must be carried out strictly according to the design elevation. It's crucial to avoid under-dredging to ensure berth depth while preventing over-dredging to protect the underwater foundation structure and control costs. Traditional methods often rely on operator experience, estimating water depth through intermittent water-filled dipstick measurements. This point-based, lagging measurement method makes it difficult to grasp real-time and comprehensive changes in seabed topography. Controlling the smoothness of the dredged surface is also a weak point, easily resulting in a situation where shallow areas are missed while deep pits are over-dredged, leading to unsatisfactory dredging quality. Subsequent acceptance phases often require repeated shallowing and rework, significantly impacting construction efficiency.

[0004] Secondly, operational efficiency and energy consumption control are prominent issues. The operating efficiency of dredging equipment, especially sand pump units, is closely related to the concentration of the pumped medium. Too low a concentration means a large amount of energy is consumed in pumping clean water, resulting in low efficiency and significant energy waste; too high a concentration easily causes blockage in the discharge pipeline, leading to unplanned downtime and potentially damaging the equipment. In traditional operations, judging the mud concentration relies on the operator observing the color and viscosity of the mud at the discharge port. This experience-based, subjective method cannot quantify and stably control the concentration, causing the entire system to operate under suboptimal conditions for extended periods, making it difficult to achieve an optimal balance between efficiency and energy consumption.

[0005] Third, controlling secondary pollution of the water body during construction is extremely difficult. Environmentally friendly dredging requires minimizing the disturbance to the surrounding aquatic environment and preventing the spread of suspended sediment. Traditional dredging processes, such as grab bucket or simple suction dredging, cause severe disturbance to the bottom sediment during operation and lack effective environmental isolation measures. More importantly, due to the inability to precisely control the suction concentration, the concentration of the sludge transported to the sedimentation tank fluctuates greatly. When the concentration is too low, excessive water flows into the sedimentation tank, increasing the treatment load and easily leading to overflow; when the concentration is too high, there is a risk of pipeline leakage. Both of these situations can cause sediment to spread and pollute the waters around the wharf.

[0006] The root cause of these technical challenges lies in the fact that traditional dredging operations are an open-loop process reliant on manual experience, lacking real-time perception, intelligent decision-making, and collaborative execution capabilities throughout the entire dredging and transportation process. While sensing and automatic control technologies are constantly advancing, integrating them into the complex and dynamic system of dredging to achieve multi-source information fusion and collaborative optimization control of multiple actuators—simultaneously addressing the challenges of accuracy, efficiency, and environmental protection—remains a persistent technical bottleneck in the industry. How to construct a comprehensive solution capable of adapting to changing operating conditions and operating in a closed loop has been a long-term focus for those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to address the following problems: Traditional dock dredging operations often suffer from problems such as inaccurate dredging elevation control, large fluctuations in mud concentration, low equipment operating efficiency, high energy consumption, and susceptibility to pipeline blockage and secondary water pollution due to the lack of real-time, multi-source data acquisition and intelligent decision-making mechanisms. Manual operation relies on experience-based judgment, making it impossible to achieve closed-loop control and multi-equipment collaboration in the dredging process, resulting in unstable operation quality, extended project duration, and high environmental risks. This invention constructs an intelligent dredging system based on a floating raft system, integrating multiple sensors and a central controller to achieve data fusion and collaborative control, thereby improving dredging accuracy, efficiency, and environmental friendliness.

[0008] During dredging, unstable mud concentration is one of the main causes of low efficiency and equipment failure. Too low a concentration leads to energy waste and water disturbance, while too high a concentration easily causes pipeline blockage. Traditional methods lack quantitative control over the concentration, making it impossible to stabilize it within the optimal range. This invention sets an optimal operating range for the mud concentration and achieves automatic adjustment based on real-time feedback, thereby ensuring the system operates efficiently, stably, and safely.

[0009] Improperly set upper and lower limits for mud concentration can still lead to unstable equipment operation or pipeline blockage. Traditional methods lack a scientific basis for setting the upper and lower limits of concentration, and cannot dynamically adjust the control strategy according to equipment performance and pipeline characteristics. This invention links the lower limit of concentration to the minimum solids content requirement of the sand pump and the upper limit to the critical siltation velocity of the pipeline, and designs a corresponding control response mechanism to adjust the equipment status in a timely manner when the concentration is abnormal, thereby avoiding operational risks.

[0010] In dredging operations, single-signal control is insufficient to handle complex and changing conditions, such as terrain undulations and vessel drift, easily leading to control mismatch and decreased operational quality. Traditional methods lack coordination mechanisms between multiple signals, failing to achieve dynamic coordination between primary and secondary control variables. This invention establishes a multi-level control logic with concentration as the primary factor and water depth and position as secondary factors, enabling real-time compensation and optimization of control commands, thereby improving the system's adaptability and stability in complex environments.

[0011] After dredging operations, uneven surfaces often appear, with shallow spots and deep pits. Traditional methods rely on manual measurement and intervention, which is inefficient, inaccurate, and prone to missed detections. This invention uses a multibeam echo sounder to scan in real time and construct a digital elevation model, automatically identifying uneven areas and controlling the slurry inlet pipe to precisely fill or avoid them. This achieves high-precision, automated contour-following dredging, improving surface flatness and overall work quality.

[0012] Floating rafts are susceptible to positional deviations due to external factors such as water flow and wind during operation, leading to deviations in the dredging path, missed dredging, or repeated dredging. Traditional methods rely on manual operation of winches for correction, resulting in slow response and low accuracy. This invention uses high-precision positioning and comparison with a preset path to automatically calculate a correction scheme and control a small winch to coordinate the operation, achieving rapid and accurate correction of the floating raft's position and ensuring the accuracy of the dredging path.

[0013] The slurry inlet pipe is easily blocked by underwater foreign objects during operation. Traditional methods rely on manual observation or shutdown for troubleshooting, resulting in delayed response and affecting the continuity of operations. This invention introduces dual-signal monitoring of vision and back pressure, using image recognition and trend analysis to assess blockage risk in real time, enabling early warning and rapid response, and improving the reliability and automation level of the system.

[0014] If a blockage risk is identified, failure to execute clearing actions quickly and in a coordinated manner can still lead to severe blockage or even equipment damage. Traditional clearing methods rely on manual operation, which is inefficient and risky. This invention achieves rapid and automated blockage handling through coordinated control commands such as automatically raising the pipe opening and flushing with maximum flow, minimizing downtime and manual intervention.

[0015] Slurry discharge pipelines are prone to gradual siltation during long-distance transport. Traditional methods rely on periodic shutdowns and manual cleaning, which affects operational efficiency. This invention achieves online assessment and early warning of pipeline status by real-time monitoring of pressure and flow rate and dynamic calculation of the slurry flow index, thereby improving the reliability and continuity of pipeline operation.

[0016] If pipes become clogged and cannot be cleared online, shutdown is still required, impacting the overall project schedule. Traditional unclogging methods are inefficient and ineffective. This invention controls the sand pump and high-pressure water valve to operate in periodic pulses, creating a pulsed flow pattern within the pipes. This achieves automatic, online unclogging, avoiding downtime for maintenance and improving system operating efficiency.

[0017] To achieve the above objectives, the present invention provides an environmentally friendly dredging method for wharf frontage, implemented based on a floating raft system. The floating raft system is equipped with a sand pumping unit for extracting slurry, a high-pressure water pumping unit for agitating the slurry, a winch unit consisting of a large winch for controlling the depth of the slurry inlet pipe and a small winch for controlling the displacement of the floating raft, and a slurry discharge pipeline. Specifically, the method includes the following steps: Step 1: Deploy a mud concentration sensor at the inlet of the sand pump to collect mud concentration data in real time; install water depth sensors at the four corners of the floating raft and at the head of the inlet pipe to monitor the elevation of the dredging area in real time; obtain the precise planar coordinates and attitude data of the floating raft through the GPS / RTK positioning module mounted on the floating raft. Step 2: Set up a central controller with a built-in dredging decision model. This dredging decision model receives and integrates data signals from mud concentration sensors, water depth sensors, and positioning modules to perform real-time analysis and decision-making. Step 3: The central controller outputs coordinated control commands to each execution unit, including: dynamically adjusting the speed of the sand pumping pump through the frequency converter based on the deviation between the real-time mud concentration and the preset concentration threshold, and simultaneously controlling the opening of the electric regulating valve on the high-pressure water pump pipeline to coordinately adjust the disturbance intensity and the extraction flow rate, so that the mud concentration is stabilized in the optimal working range; automatically controlling the large winch to lift or lower the slurry inlet pipe based on the difference between the real-time water depth and the design elevation, combined with the floating raft position information, and controlling the small winch to reel in and release the positioning cable to perform lateral movement of the floating raft and fixed-depth sludge removal operations at the slurry inlet pipe, in order to ensure the flatness of the sludge removal surface; Step 4: The mud concentration signal, water depth signal and position signal form a closed loop feedback. The central controller's dredging decision model is based on the multi-source signal fusion results and calculates the optimal coordinated control parameters for the sand pump, high-pressure water pump and winch system in real time. While ensuring dredging accuracy, it maximizes dredging efficiency and minimizes energy consumption and water disturbance. Step 5: The operating status of all equipment in the floating conveyor system, sensor data, and control command logs are uploaded to the remote monitoring center in real time, enabling full-process visual tracking, data storage, and intelligent analysis of the dredging operation.

[0018] Preferably, the optimal operating range of the present invention is a mud concentration of 10% to 30%; the dredging decision model takes this concentration range as the control target and controls the coordinated operation of the frequency converter and the electric regulating valve through the feedback signal of the mud concentration sensor.

[0019] Preferably, the lower limit of the concentration range of the present invention, 10%, is determined by the minimum solids content required for the sand pump group to maintain stable operation, and the upper limit of 30% is determined by the maximum transport concentration corresponding to the critical siltation flow rate of the slurry discharge pipeline. When the real-time mud concentration is below 10%, it indicates that the water content in the pumped material is too high. The central controller reduces the speed of the sand pump and simultaneously reduces the opening of the electric regulating valve to reduce unnecessary energy consumption and water disturbance, while avoiding the risk of overflow of the downstream sedimentation tank due to excessive flow. When the real-time mud concentration is higher than 30%, it indicates that the mud and sand content is too high, and there is a risk of blockage in the slurry discharge pipeline and pump overload. The central controller increases the opening of the electric regulating valve to enhance the dilution of disturbance, and at the same time increases the speed of the sand pump to increase the conveying capacity, thereby ensuring the continuous and stable operation of the system.

[0020] Preferably, the dredging decision model of the present invention performs the fusion and coordinated control of multi-source signals in the following manner: A multi-level control logic is established with mud concentration signal as the main control variable and water depth signal and position signal as auxiliary correction variables. The main control variable is used to generate basic control commands for sand pump speed and electric regulating valve opening to stabilize mud concentration within the target range. The auxiliary correction variables are used to compensate and optimize the basic control commands in real time: when the water depth sensor detects significant unevenness on the dredging surface, the dredging decision model prioritizes controlling the large winch to perform contour dredging and simultaneously fine-tunes the speed of the sand pump to match the concentration fluctuations caused by elevation changes; when the positioning module detects a deviation between the floating hull displacement speed and the planned path, the dredging decision model prioritizes controlling the small winch to correct the position and simultaneously fine-tunes the disturbance intensity of the high-pressure water pump to compensate for the changes in the amount of sediment supplied per unit time caused by changes in the speed of the moving vessel.

[0021] Preferably, the contour-following dredging of the present invention is achieved through the following specific steps: the water depth sensor is a multibeam echo sounder, which quickly scans the just-completed work surface during the interval of single-point dredging operation to obtain high-precision seabed elevation data and sends it to the central controller; the dredging decision model has a built-in terrain generation algorithm, which constructs a local seabed digital elevation model in real time based on the elevation data, and identifies shallow areas above the design elevation and deep pit areas below the design elevation; For shallow areas, the dredging decision model generates a supplementary excavation command, controlling the large winch to precisely position the slurry inlet pipe at that point and appropriately reduce the speed for fine excavation; for deep pit areas, the dredging decision model generates an avoidance command, controlling the large winch to raise the pipe opening to avoid over-excavation.

[0022] Preferably, the position correction of the present invention is achieved through the following specific steps: the dredging decision model pre-stores a planned operation path based on the grid division of the dredging area, and the planned operation path specifies the standard lateral movement sequence and dwell time of the floating hull; The GPS / RTK positioning module provides real-time feedback on the planar coordinates and heading angle of the floating raft's center of mass at a frequency of 1Hz. When the lateral deviation of the real-time position from the planned path exceeds a set threshold or the heading angle deviation exceeds a set threshold, the dredging decision model determines that there is an attitude deviation. Based on the magnitude and direction of the deviation, the dredging decision model calculates the optimal force distribution scheme required for correction and generates differentiated control commands, which are sent to the small winches responsible for the bow and stern, respectively. By winding and unwinding cables of different lengths, rotational torque or lateral tension is generated to bring the floating raft back to the planned path.

[0023] Preferably, the present invention further includes an underwater camera unit and a water pressure sensor at the slurry inlet of the sand pump; the underwater camera unit is used to capture visual images of the silt in front of the slurry inlet in real time, and the water pressure sensor is used to monitor the back pressure changes at the slurry inlet. The central controller's dredging decision model also receives and integrates visual image signals and back pressure signals. When the dredging decision model determines that there are large foreign objects in the visual image through image recognition algorithms, or determines through trend analysis that the back pressure value abnormally increases beyond the preset threshold within a set time, the dredging decision model immediately determines that there is a risk of blockage.

[0024] Preferably, after determining the risk of blockage, the dredging decision model of the present invention outputs a collaborative control command: it prioritizes controlling the large winch to quickly lift the slurry inlet pipe to remove it from the current silt layer, and at the same time controls the high-pressure water pump set to increase the opening of the electric regulating valve to the maximum to flush the pipe area with the maximum flow rate; after the back pressure value returns to normal and the image recognition confirms that the risk has been eliminated, the dredging decision model then controls the winch set and the high-pressure water pump set to automatically return to the normal dredging operation state.

[0025] Preferably, in this invention, a pipeline pressure sensor is installed on the slurry discharge pipeline near the outlet section of the sand pump unit, and an ultrasonic flow meter is installed at the inlet of the onshore sedimentation tank. The central controller receives signals from the pipeline pressure sensor and ultrasonic flow meter in real time. The dredging decision model has a built-in pipeline status monitoring submodule. Based on real-time pressure and flow data, this submodule dynamically evaluates the pipeline's patency index by calculating the ratio of pressure drop per unit pipe length to flow velocity. When the pressure value is continuously rising and the flow value is decreasing simultaneously, and the patency index is lower than the preset threshold, the dredging decision model determines that there is a risk of progressive siltation in the pipeline.

[0026] Preferably, when determining the risk of siltation, the dredging decision model of the present invention outputs a collaborative unblocking control command: controlling the frequency converter of the sand pump to alternately execute high-speed and low-speed operation within a preset cycle, while controlling the electric regulating valve on the high-pressure water pump pipeline to perform synchronous pulse width modulation (PWM), thereby forming a periodic pulsed flow state in the slurry discharge pipeline; this pulsed flow state generates shearing and scouring effects on the pipe wall deposits, realizing online unblocking; after the unobstructedness index returns to the normal range, it automatically switches back to the stable operation mode.

[0027] The present invention has at least the following beneficial effects: By constructing an intelligent dredging system based on a floating hull system, real-time acquisition, fusion, and closed-loop control of multi-source data are achieved, significantly improving the accuracy and efficiency of dredging operations. The system can dynamically adjust the operating status of the sand pump, high-pressure water pump, and winch to ensure precise control of mud concentration, dredging elevation, and floating hull position, thereby reducing over-dredging and under-dredging, lowering energy consumption and water disturbance, avoiding secondary pollution, and meeting the high-standard environmental dredging needs of narrow waterways.

[0028] By stabilizing the mud concentration within the optimal range of 10% to 30%, the system can efficiently transport mud and sand while avoiding pipeline blockage and equipment overload. This control strategy, based on real-time sensor feedback, enables coordinated adjustment of the sand pump and high-pressure water valve, ensuring continuous and stable system operation, improving dredging efficiency, reducing energy consumption, and enhancing operational reliability.

[0029] By scientifically setting upper and lower limits for concentration and implementing a dynamic response mechanism, the system can promptly adjust equipment status when concentrations are abnormal, avoiding energy waste and water disturbance caused by excessively low concentrations, or pipeline blockage and equipment damage caused by excessively high concentrations. This design enhances the system's adaptability and operational safety, ensuring the continuity and stability of dredging operations.

[0030] By establishing multi-level control logic, the system can optimize control commands in real time based on changes in primary and secondary control variables, enabling coordinated operation of multiple actuators. This mechanism effectively addresses complex working conditions during dredging, such as terrain undulations and vessel misalignment, improving the system's adaptability and control accuracy, and ensuring operational quality and equipment coordination.

[0031] By using automated terrain scanning and digital elevation model construction, the system can accurately identify uneven areas and automatically perform supplementary excavation or avoidance actions, significantly improving the smoothness of the dredged bottom surface. This method reduces manual intervention and rework, improves operational efficiency and acceptance rate, and is suitable for high-standard dredging projects.

[0032] Through high-precision positioning and automatic correction control, the system can quickly correct the floating raft's position and ensure its precise movement along the predetermined path. This mechanism reduces missed or repeated dredging caused by positional deviations, improves operational continuity and integrity, reduces operational intensity, and enhances the accuracy of the dredging trajectory.

[0033] By monitoring both visual and back pressure signals, the system can identify the risk of blockage at the slurry inlet pipe at an early stage, enabling early warning and rapid response. This design improves the system's reliability and automation level, reduces downtime caused by blockage, and ensures the continuity and safety of dredging operations.

[0034] Through coordinated unblocking control commands, the system can quickly execute pipe lifting and high-pressure flushing actions after identifying blockage risks, achieving automated unblocking. This mechanism significantly reduces manual intervention and downtime, improves the system's ability to respond to sudden failures, and ensures operational efficiency and equipment safety.

[0035] By monitoring pipeline pressure and flow in real time, the system can dynamically assess the patency index and provide early warning of siltation. This design improves the reliability and continuity of pipeline operation, avoids sudden blockages caused by gradual siltation, and reduces maintenance frequency and downtime.

[0036] Through pulsed flow control, the system can achieve online unblocking without shutting down the system, effectively removing deposits from the pipe walls. This mechanism enhances the self-maintenance capability of the pipeline system, ensures the continuity and efficiency of dredging operations, and reduces the frequency of manual cleaning and operating costs.

[0037] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the floating system of the present invention; Figure 2 This is a flowchart illustrating the environmentally friendly dredging method for the wharf frontage of the present invention. Figure 3 This is a flowchart illustrating the multi-source signal fusion and collaborative control method of the dredging decision model of the present invention. Figure 4 This is a flowchart illustrating the contour-following dredging process of the present invention. Figure 5 This is a flowchart illustrating the position correction process of the present invention. Figure 6 This is a plan view of the dredging area.

[0039] In the diagram: Floating hull body 1; Sand pump set 10; High-pressure water pump set 20; Large winch 30; Small winch 40; Slurry discharge pipeline 60; Slurry inlet pipe 101; Anchor body 401. Detailed Implementation

[0040] The present invention will be further described in detail below with reference to examples, so that those skilled in the art can implement it based on the description.

[0041] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0042] For ease of understanding and implementation, such as Figure 1 The diagram shows the structure of the floating raft system of the present invention. The floating raft body 1 is equipped with a sand dredging pump unit 10, a high-pressure water pump unit 20, a winch unit, and a slurry discharge pipeline 60 connected to the sand dredging pump unit. The winch unit includes a large winch 30 and a small winch 40. The small winch 40 is connected to an anchor 401 submerged in the water. The slurry inlet pipe 101 of the sand dredging pump unit 10 is connected to the large winch. The large winch is mounted on one side of the floating raft body via an A-frame and autonomously raises the slurry inlet pipe to control the dredging depth. Four small winches are installed at the four corners of the floating raft and connected to the anchor 401 in the water via traction cables for hull fixation and precise hull movement. The high-pressure water pump unit provides high-pressure water through a high-pressure pipeline to agitate the sediment.

[0043] like Figure 1 and 2 As shown, this invention provides an example of an environmentally friendly dredging method for the wharf front. This method is implemented using a floating raft system equipped with a sand dredging pump unit, a high-pressure water pump unit, a winch unit, and a slurry discharge pipeline. The floating raft can be constructed using steel pontoon structures, with dimensions of 7 meters wide and 20 meters long. The sand dredging pump can be a wear-resistant gravel pump with a flow rate of 1250 cubic meters per hour, the high-pressure water pump with a flow rate of 700 cubic meters per hour, and the small water pump with a flow rate of 100 cubic meters per hour. The winch unit includes one 12-ton large winch and four 3-ton small winches. The large winch can be installed on the A-frame of the floating raft, and the small winches can be arranged at the four corners of the floating raft. The slurry discharge pipeline can consist of 10-inch white plastic pipes and 12-inch black rubber pipes, with a total length of approximately 400 meters.

[0044] In practice, a mud concentration sensor is first deployed at the inlet of the sand pump. This sensor can be an online density meter with a measurement range of 0-50%. Water depth sensors are installed at the four corners of the floating raft and at the head of the inlet pipe. Single-beam echo sounders can be used, with a measurement accuracy of 0.1 meters. Precise planar coordinates and attitude data of the floating raft are obtained through a GPS / RTK positioning module mounted on the raft, achieving centimeter-level positioning accuracy. This sensor data is transmitted via cable to a central controller, which can be an industrial PLC system with a built-in dredging decision model.

[0045] The central controller outputs control commands based on real-time monitoring data. When the mud concentration deviates from the optimal range of 10%-30%, the speed of the sand pump is adjusted via a frequency converter, with the speed adjustment range set to 0-1500 rpm. Simultaneously, the opening of the electric regulating valve on the high-pressure water pump pipeline is controlled, with an opening adjustment range of 0-100%. Based on the difference between the real-time water depth and the design elevation of -17.5 meters, the large winch is automatically controlled to raise or lower the slurry inlet pipe, with the raising speed controllable at 0.5-2 meters per minute. By controlling the small winch to raise and lower the positioning cable (which can be a 50mm diameter nylon rope), the lateral movement and positioning of the float are achieved, with a movement step distance set to 2-3 meters.

[0046] This method achieves precise management of the dredging process through multi-source signal closed-loop feedback control. Compared with traditional manual dredging, it can maintain a stable mud concentration, avoid pipeline blockage, improve dredging efficiency, reduce over-dredging or under-dredging, and simultaneously reduce energy consumption and water disturbance. The entire system has a reasonable structure and reliable connections between components, making it suitable for environmentally friendly dredging operations in the wharf front area.

[0047] Furthermore, in another embodiment, the mud concentration control method is further defined. The dredging decision model uses a mud concentration of 10% to 30% as the control target for the optimal operating range. The lower limit of this concentration range, 10%, is determined by the minimum solids content required for the sand pump unit to maintain stable operation, and the upper limit, 30%, is determined by the maximum delivery concentration corresponding to the critical siltation flow velocity of the discharge pipeline. Real-time monitoring data from the mud concentration sensor is transmitted to the central controller via a 4-20mA analog signal. The central controller can be configured with a PID control algorithm to control the coordinated operation of the frequency converter and the electric regulating valve based on the feedback signal from the mud concentration sensor.

[0048] When real-time monitoring shows a mud concentration below 10%, it indicates that the water content in the pumped material is too high. The central controller reduces the speed of the sand pump by 20%-30% of the rated speed and simultaneously reduces the opening of the electric regulating valve by 15%-25% of the current opening. When the real-time concentration is above 30%, it indicates that the sediment content is too high. The central controller increases the opening of the electric regulating valve by 20%-30% of the current opening and simultaneously increases the speed of the sand pump by 15%-25% of the rated speed.

[0049] Furthermore, in another implementation, such as Figure 3 As shown, the multi-source signal fusion and collaborative control method of the dredging decision model is further optimized. The dredging decision model adopts a hierarchical control architecture, establishing a multi-level control logic with mud concentration signal as the main control variable and water depth signal and position signal as auxiliary correction variables. The sampling period of the main control variable can be set to 100 milliseconds, mainly used to generate basic control commands for the sand pump speed and the opening of the electric regulating valve. The sand pump speed adjustment range can be set to 500-1500 revolutions per minute, and the electric regulating valve opening adjustment range can be set to 20%-100%. The control objective is to stabilize the mud concentration within the target range of 10%-30%.

[0050] Auxiliary correction variables are used to compensate and optimize the basic control commands in real time. When the depth sensor detects a height difference of more than 0.3 meters on the dredging surface, the dredging decision model prioritizes controlling the large winch for contour dredging. The winch's lifting or lowering speed can be controlled at 0.3-0.8 meters per minute, while simultaneously fine-tuning the sand pump speed. The speed adjustment can be set to 5%-10% of the current speed to match the concentration fluctuations caused by elevation changes. When the positioning module detects that the floating hull displacement speed deviates from the planned path by more than 0.5 meters or the heading angle deviates by more than 5 degrees, the dredging decision model prioritizes controlling the small winch for position correction. The cable winding and unwinding speed can be controlled at 2-5 meters per minute, while simultaneously fine-tuning the high-pressure water pump disturbance intensity. The flow rate adjustment can be set to 10%-20% of the current flow rate to compensate for changes in the sediment supply per unit time caused by changes in the vessel's speed.

[0051] This multi-level control logic is implemented through a central controller, which can be an industrial-grade PLC system with a processor frequency of at least 1.2 GHz and a memory capacity of at least 4 GB. Sensor signals are transmitted to the central controller via a Profibus-DP fieldbus, and control commands are sent to each actuator via a CANopen bus. The system sampling period can be set to 50 milliseconds, and the control period can be set to 100 milliseconds to ensure real-time response performance.

[0052] This implementation method, by establishing a multi-level control architecture and a real-time compensation mechanism, can effectively coordinate the actions of various actuators and solve the problem of control parameter mismatch caused by changes in working conditions during dredging operations. Compared with traditional single-parameter control methods, it improves the stability and adaptability of system operation, ensuring the quality and efficiency of dredging operations.

[0053] Furthermore, in another implementation, such as Figure 4 As shown, the contour-following dredging method has been further optimized. A multibeam echo sounder is used as the depth sensor and can be installed in the middle of the floating raft. Its scanning range can be set to 120 degrees, and the scanning frequency can be set to 10 Hz. During the intervals between single-point dredging operations, the multibeam echo sounder rapidly scans the recently completed work surface. The scanning interval can be set to 5-10 minutes. The acquired high-precision seabed elevation data is transmitted to the central controller via Ethernet, and the elevation measurement accuracy can reach ±2 cm.

[0054] The central controller incorporates a terrain generation algorithm, which can employ triangular mesh interpolation to construct a local seabed digital elevation model based on real-time acquired elevation data. The model's grid resolution can be set to 0.5 meters × 0.5 meters. The dredging decision model identifies shallow areas above the design elevation (0.2 meters) and deep pits below the design elevation (0.3 meters) by comparing DEM data with the design elevation -17.5 meters. The identification threshold can be adjusted according to actual working conditions.

[0055] For identified shallow areas, the dredging decision model generates a supplementary excavation command, controlling the large winch to precisely position the slurry inlet pipe at that point, with a positioning accuracy controllable within ±0.3 meters. During supplementary excavation, the sand pump speed can be appropriately reduced to 70%-80% of its rated speed, and the slurry inlet pipe lowering speed can be controlled at 0.3-0.5 meters per minute. For deep pit areas, the dredging decision model generates an avoidance command, controlling the large winch to raise the pipe opening, maintaining a safe distance of at least 1.0 meter between the pipe opening and the mud surface to avoid over-excavation.

[0056] This implementation method effectively solves the common problem of uneven bottom surfaces in dredging operations through automated terrain scanning and recognition. Compared with traditional methods that rely on manual measurement, it improves the accuracy and efficiency of bottom surface flatness control, reduces rework, and ensures that the dredging quality meets design requirements. The entire process is automated, reducing manual intervention and improving operational safety.

[0057] Furthermore, in another implementation, such as Figure 5As shown, the position correction method is further optimized. The pre-stored planned operation path in the dredging decision model is based on a grid division of the dredging area, which is 97.5 meters long and 42 meters wide. The grid cell size can be set to 2 meters × 2 meters, and the dwell time for each grid cell can be set to 10-15 minutes. The planned path specifies the standard lateral movement sequence of the floating raft, with a movement step distance of 2 meters, and the overlap rate between adjacent operation zones can be controlled at 20%-30%.

[0058] The GPS / RTK positioning module can be a dual-frequency receiver, installed at the geometric center of the floating raft. The antenna height can be set at approximately 2 meters, providing real-time feedback of the floating raft's center of mass's planar coordinates and heading angle at a 1 Hz frequency. The planar positioning accuracy can reach ±2 cm, and the heading angle measurement accuracy can reach ±0.5 degrees. When real-time monitoring shows a lateral position deviation exceeding 0.5 meters or a heading angle deviation exceeding 5 degrees, the dredging decision model determines that there is an attitude deviation.

[0059] The dredging decision model uses a PID control algorithm to calculate the optimal tension distribution scheme required for correction based on the magnitude and direction of the deviation. For lateral position deviation, the required length of each cable can be calculated, with a controllability within ±0.1 meters. For heading angle deviation, the differentiated tension value of each cable can be calculated, with a tension control accuracy within ±5%. Control commands are sent via CAN bus to four 3-ton winches at the bow and stern. These winches are driven by servo motors and generate rotational torque or lateral tension by winding and unwinding cables of different lengths. The correction operation can be completed within 2-5 minutes.

[0060] This implementation method effectively maintains the floating raft along a predetermined path through high-precision positioning monitoring and automated correction control. Compared with traditional methods relying on manual observation and operation, it improves the accuracy and stability of the floating raft's posture control, reduces leakage or repeated dredging caused by positional deviations, and ensures the continuity and integrity of dredging operations. The entire correction process is automated, reducing the workload of operators.

[0061] Furthermore, in another embodiment, the anti-clogging control method for the slurry inlet is further optimized. An underwater camera unit and a water pressure sensor can be installed at the slurry inlet of the sand pump. The underwater camera unit can be a pressure-resistant high-definition camera with an IP68 protection rating, installed approximately 0.5 meters above and to the side of the slurry inlet, with its viewing angle facing the area in front of the inlet. The water pressure sensor can be a diffused silicon pressure transmitter with a range of 0-1 MPa and an accuracy class of 0.5, installed on the inner wall of the slurry inlet approximately 0.3 meters from the inlet.

[0062] The underwater camera unit acquires real-time visual images of the sediment in front of the slurry inlet at a rate of 25 frames per second, with an image resolution of 1280×1024 pixels. A water pressure sensor monitors back pressure changes at the slurry inlet at a sampling frequency of 10 Hz. The visual image signals are transmitted to the central controller via an underwater cable; the cable length can be selected according to actual needs, typically 20-30 meters. The pressure signals are transmitted to the central controller via a 4-20 mA analog signal, with a signal transmission delay of no more than 100 milliseconds.

[0063] The central controller's dredging decision model employs a multi-signal fusion processing approach. Visual image signals are analyzed in real-time using deep learning algorithms, enabling the identification of large foreign objects exceeding 0.2 meters in size. Pressure signals are processed using trend analysis algorithms; when the back pressure rises at a rate exceeding 0.5 bar / second within 10 seconds and the absolute pressure exceeds 0.7 MPa, the model determines a blockage risk. The warning thresholds for image recognition and pressure monitoring can be adjusted according to actual operating conditions to ensure system adaptability.

[0064] When the dredging decision model determines there is a risk of blockage, the emergency control procedure is immediately activated. First, the large winch is controlled to lift the slurry inlet pipe at maximum speed, up to 2 meters per minute, quickly removing the pipe from the current silt layer. Simultaneously, the electric regulating valve of the high-pressure water pump unit is increased to 100% to flush the pipe area at maximum flow rate for 30-60 seconds. Once the pressure sensor detects that the back pressure has dropped below 0.3 MPa and image recognition confirms there are no foreign objects in front of the pipe, the dredging decision model controls each actuator to gradually return to normal dredging operation. This recovery process can be controlled within 2-3 minutes.

[0065] This implementation method effectively prevents and addresses blockages at the slurry inlet pipe through dual monitoring and coordinated control of visual and pressure signals. Compared to traditional single-signal monitoring methods, it improves the accuracy and reliability of blockage warnings, reduces downtime caused by blockages, and ensures the continuity and stability of dredging operations. The entire process is automated, reducing reliance on operators.

[0066] Furthermore, in another embodiment, the method for preventing siltation in the slurry discharge pipeline is further optimized. A pipeline pressure sensor can be installed on the section of the slurry discharge pipeline near the outlet of the sand pump unit. The installation location is selected on a straight pipe section 5-10 meters from the pump outlet. The pressure sensor can be a diaphragm pressure transmitter with a range of 0-2.5 MPa and an accuracy class of 0.5. An ultrasonic flow meter can be installed at the inlet of the onshore sedimentation tank, installed on a vertical pipe section. The flow meter can be a Doppler ultrasonic flow meter with a measurement range of 0-2000 cubic meters per hour and a measurement accuracy of ±1%.

[0067] The central controller receives real-time monitoring data from pipeline pressure sensors and ultrasonic flow meters via 4-20 mA analog signals, with a signal sampling interval that can be set to 1 second. The pipeline status monitoring submodule built into the dredging decision model uses real-time pressure and flow data to dynamically assess the pipeline's patency index by calculating the ratio of pressure drop per unit pipe length to flow velocity. The calculation formula can be expressed as: patency index equals the square of the real-time flow rate divided by the pressure difference, where the pressure difference is the difference between the pump outlet pressure and the pressure at the end of the pipe. The normal range for the patency index can be set between 0.8 and 1.2; an index value below 0.6 indicates a risk of progressive siltation.

[0068] When the pressure value continuously rises by more than 0.3 MPa within 5 minutes and the flow rate decreases by more than 15% simultaneously, while the flowability index is below 0.6, the dredging decision model determines that there is a risk of progressive siltation in the pipeline. At this time, the model outputs a coordinated dredging control command, controlling the frequency converter of the sand pump to alternate between high-speed and low-speed operation within a 60-second cycle. The high-speed operation frequency can be set to 45-50 Hz, and the low-speed operation frequency can be set to 20-25 Hz. Simultaneously, the electric regulating valve on the high-pressure water pump pipeline is controlled to perform synchronous pulse width modulation, with the modulation frequency set to 0.5 Hz and the duty cycle set to 60%, thereby forming a periodic pulsed flow pattern in the slurry discharge pipeline.

[0069] This pulsed flow pattern generates alternating flow velocities and pressures to shear and scour deposits on the pipe wall, achieving online unblocking. The unblocking operation can be set to last 3-5 minutes, during which the patency index is continuously monitored. Once the patency index returns to a normal range of above 0.8 and remains stable for 2 minutes, the system automatically switches back to a stable operation mode, and the parameters of each device gradually return to their normal operating state before unblocking.

[0070] This implementation method effectively prevents sludge discharge pipeline blockage by real-time monitoring of pipeline status and automatic unblocking control. Compared with traditional periodic shutdown cleaning methods, it improves the operational reliability of the pipeline system, reduces maintenance downtime, and ensures the continuity and stability of dredging operations. The entire unblocking process is automated, reducing reliance on manual intervention.

[0071] Example 1 like Figure 6 As shown, taking a dredging project at the wharf of a certain project as an example, the dredging area is 97.5 meters long and 42 meters wide, with a total dredging area of ​​4095 square meters. The dredging needs to be carried out to the design elevation of -17.5 meters. In Example 1, the dredging volume is approximately 2000 cubic meters. The bottom sediment in this area is a mixture of backfilled fine sand and silt, and the water flow velocity is approximately 0.5 meters per second.

[0072] The construction was carried out using the environmentally friendly dredging method for the wharf front described in this invention.

[0073] A 7m x 20m steel floating platform was used as the working platform. It was equipped with a vessel with a rated flow rate of 1250 m³ / h. 3 / h wear-resistant gravel pump (equipped with a 456KW diesel engine), one 700m 3 / h high-pressure water pump (equipped with a 230KW diesel engine) and a 100m 3 The floating hull is equipped with an auxiliary small water pump ( / h). It also has one 12t large winch (for controlling the slurry inlet pipe) and four 3t small winches (for positioning and moving the hull). The total length of the slurry discharge pipeline is approximately 400 meters. After the pump outlet, a 200-meter 10-inch white plastic floating pipe (with buoys) is connected, followed by a 200-meter 12-inch black rubber hose (without buoys) to transport the slurry to a 1000-cubic-meter sedimentation tank behind the dock.

[0074] An online microwave concentration meter (range 0-50%) and a water pressure sensor with a range of 1 MPa are installed at the inlet of the gravel pump. Single-beam depth sensors are installed at the four corners of the float and at the head of the inlet pipe. A GPS / RTK positioning receiver is installed at the center of the float. A pressure sensor is installed on the discharge pipe 5 meters after the pump outlet, and an ultrasonic flow meter is installed at the inlet of the sedimentation tank. All sensor signals are connected to an industrial PLC central controller.

[0075] The core parameters of the dredging decision model in the central controller are set as follows: the target range for mud concentration is 10%-30%; the threshold for the unobstructed flow index is set to 0.6; the threshold for the lateral displacement deviation of the floating hull is set to 0.5 meters; the threshold for the heading angle deviation is set to 5 degrees; and the threshold for the back pressure rise rate of the blockage risk is set to 0.5 bar / second.

[0076] After construction began, the dredging decision model used mud concentration as the main control variable, and stabilized the concentration at around 20% by adjusting the speed of the sand pump and the opening of the high-pressure water valve.

[0077] During the operation, GPS real-time positioning showed that the floating raft had deviated 0.6 meters from the planned path to the right due to the influence of the tidal current. The dredging decision model immediately prioritized controlling the port side small winch to retrieve the cable and the starboard side small winch to release the cable to correct the deviation. At the same time, the high-pressure water pump flow rate was slightly increased by 15% to compensate for the amount of silt supplied during the deceleration period of the shifting vessel.

[0078] A multibeam echo sounder scanned and found a shallow spot of about 3 square meters, 0.25 meters above the design elevation. The model-controlled large winch precisely positioned the pipe opening at this point, and the sand pump speed was reduced to 1000 rpm for fine excavation until the elevation was reached.

[0079] The pressure sensor detected that the pressure in the slurry discharge pipe increased from 0.8 MPa to 1.2 MPa within 3 minutes, and the flow rate increased from 1200 m³ / s. 3 / h dropped to 900m 3 The flow rate was 1180 m³ / h, and the flow rate index dropped to 0.5. The model determined that there was a risk of siltation and immediately initiated the unblocking procedure: the sand pump was controlled to operate alternately at 30Hz (low speed) and 48Hz (high speed), and the high-pressure water valve was simultaneously controlled by PWM with a duty cycle of 60%. After 4 minutes, the pressure dropped to 0.85 MPa, the flow rate recovered to 1180 m³ / h, the index recovered to 1.0, and the system automatically resumed normal operation.

[0080] The water pressure sensor detected a sudden and abnormal increase in back pressure, and the image recognition simultaneously showed that a discarded woven bag had appeared in front of the pipe opening. The model immediately instructed the large winch to quickly lift the pipe opening by 2 meters and fully open the high-pressure water valve to flush it for 30 seconds. After the foreign object was dispersed, normal operation resumed.

[0081] This embodiment dredged a total of 2119 cubic meters of silt in 4 days, one day ahead of schedule. Final multibeam bath inspection confirmed a smooth dredged bottom surface with no over-excavation or over-depth work, and all areas reached the design elevation of -17.5 meters. No pipe blockages or machine downtime occurred during construction; the slurry concentration remained stable; the sedimentation tank showed good settling effect; and no significant turbidity was observed in the surrounding water, achieving the goals of efficient, high-quality, and environmentally friendly silt removal.

[0082] Comparative Example 1 Comparative Example 1 uses traditional manual control methods to carry out dredging operations in the remaining dredging area of ​​the same wharf, with the same geological and hydrological conditions as the area in Example 1. The dredging volume is also approximately 2000 cubic meters.

[0083] The same floating hull, pump set, winch, and sludge discharge pipeline equipment as in Example 1 were used. The key difference was that concentration, pressure, and flow sensors, underwater cameras, and GPS positioning systems were not installed, and a central controller and dredging decision model were not used. All equipment operation relied entirely on the operator's visual observation and experience judgment.

[0084] During construction, operators estimated the concentration based on experience by observing the color and form of the slurry at the outlet, manually adjusted the diesel engine throttle to control the pump speed via walkie-talkie, and manually operated the valve openings. Moving the floating raft relied on the assistance of transport boats and manual operation of the winch by the crew, making precise positioning and attitude adjustments impossible. The dredging elevation was measured using traditional water-dusting methods, measured after each point was cleared, which was inefficient and prevented real-time monitoring of overall flatness. During the operation, the operator failed to notice the slow increase in slurry discharge pressure, which ultimately led to a sudden surge in pump outlet pressure, causing a pipe burst and forcing a two-hour shutdown for repairs and cleaning.

[0085] The slurry inlet pipe was repeatedly entangled by unidentified underwater objects, forcing the pump to be stopped and divers to clean it up. Each operation took about 1.5 hours.

[0086] Comparative Example 1 involved dredging 2000 cubic meters of silt, with the actual operation taking 7 days. Acceptance surveys revealed multiple shallow spots, with some areas under-dredging to a depth of 0.4 meters, requiring rework. During construction, unexpected downtime due to pipe bursts and debris removal totaled 12 hours. The sludge concentration fluctuated greatly, sometimes thin and sometimes thick, resulting in poor treatment in the sedimentation tank and some silt overflow, causing some pollution to the surrounding waters. In terms of construction efficiency, quality, and environmental friendliness, it was far inferior to the exemplary example.

[0087] By comparing Example 1 with Comparative Example 1, it can be seen that the intelligent dredging method and system provided by the present invention effectively solves the technical problems of low dredging accuracy, unstable efficiency, easy blockage and shutdown accidents, and poor environmental performance of traditional manual control methods through multi-sensor data fusion and coordinated control of actuators, and significantly improves efficiency, quality and environmental performance.

[0088] Example 2 This embodiment is applied to a small area in the dredging project at the wharf front of the aforementioned project. The bottom of the dredging area is a mixture of backfilled fine sand and sticky silt, with a planned dredging volume of 500 cubic meters and a design elevation of -17.5 meters.

[0089] The intelligent mud concentration control method described in this invention is adopted, with the core control target set within a mud concentration range of 10%-30%. The same floating hull, pump set, and sensing system (concentration, pressure, and flow sensors) as in Example 1 are used. The central controller's dredging decision model strictly controls the concentration with a lower limit of 10% and an upper limit of 30%. The PID control algorithm parameters have been adjusted to respond quickly to concentration changes. The sand pump has a rated speed of 1500 rpm, and the high-pressure water valve has a rated opening of 70%.

[0090] During the operation, for low-concentration treatment, the initial concentration reading was 8%. Based on the algorithm, the controller reduced the sand pump speed by 22% from the initial 1200 rpm to approximately 935 rpm, while simultaneously reducing the high-pressure water valve opening by 18% to 57%. This reduced the amount of clean water pumped and the intensity of disturbance; after approximately 3 minutes, the concentration rose and stabilized at 12%. For high-concentration treatment, when a dense, silted area was encountered, the concentration sensor reading jumped to 33%. The controller immediately responded, increasing the high-pressure water valve opening by 28% to 90% (to maximize disturbance dilution), while simultaneously increasing the sand pump speed by 18% to approximately 1100 rpm (to enhance delivery capacity). After approximately 2 minutes, the concentration dropped back to 25%.

[0091] Throughout the entire 500-cubic-meter dredging process, the system continuously maintained the mud concentration within the target range of 10%-30% through real-time fine-tuning, with an average concentration of approximately 20%.

[0092] In Example 2, the entire dredging process was smooth and continuous, with no pipe blockages or equipment overload. The 500 cubic meter dredging task was completed efficiently within the planned time. Energy consumption was within the expected range because the equipment always operated within the high-efficiency concentration range. The sludge concentration delivered to the sedimentation tank was stable, with high settling efficiency, no turbidity, and excellent environmental performance.

[0093] Comparative Example 2 Comparative Example 2 was conducted in an adjacent area with identical geological conditions to Example 2, with the same dredging volume of 500 cubic meters. The exact same equipment was used, but the concentration control target was artificially set in two non-optimal ranges by modifying the settings of the central controller. In the first test (low concentration range), the concentration control target was set at 5%-8% for the first 250 cubic meters of dredging. In the second test (high concentration range), the concentration control target was set at 35%-40% for the remaining 250 cubic meters of dredging.

[0094] For operation in the low-concentration range (5%-8%), the system attempts to maintain the concentration at a low level. This is achieved by the controller maintaining a high high-pressure water valve opening (above 85%) and a low sand pump speed (approximately 800 rpm). Throughout the process, the slurry at the outlet is very thin, resembling a mud-water mixture. The time required to complete 250 cubic meters of dredging far exceeded the plan because the amount of solids extracted per unit time was very small. Energy efficiency was extremely poor, with a large amount of fuel consumed primarily for pumping seawater.

[0095] For operation in the high-concentration range (35%-40%), the system attempted to maintain a high concentration. The controller achieved this by reducing the opening of the high-pressure water valve (~50%) and increasing the speed of the sand pump (~1300 rpm). After approximately 30 minutes of operation, the pressure sensor in the slurry discharge pipeline showed a continuous, slow increase in pressure and a slow decrease in flow rate. Although the system attempted fine-tuning, the excessively high concentration caused sediment to gradually accumulate in the long-distance pipeline. When the dredging volume reached approximately 300 cubic meters (i.e., after operating for approximately 50 cubic meters in this range), the pipeline patency index dropped below 0.5. Although a pulse unblocking procedure was triggered, the severe sedimentation resulted in poor unblocking effectiveness, ultimately requiring a shutdown for manual cleaning, causing a 2-hour work interruption.

[0096] The efficiency in the low concentration range is extremely low; the time required to complete dredging 250 cubic meters is 1.8 times that of completing 500 cubic meters in Example 2. Energy costs increase significantly, and the subsequent sedimentation tank is overloaded. The high concentration range directly leads to severe pipeline siltation and downtime accidents; therefore, concentrations exceeding 30% significantly increase the risk of pipe blockage. Although the efficiency per unit time appears high, the overall project duration is actually extended due to handling accidents.

[0097] Therefore, controlling the concentration below 10% or above 30% will lead to low efficiency, increased energy consumption, or a surge in the risk of equipment failure. Maintaining the concentration in the 10%-30% range is essential and superior for ensuring efficient, continuous, and safe operation.

[0098] Example 3 This example was applied to a geologically complex area during dredging work at the project's wharf. The area contained not only backfilled fine sand but also localized hard clay clumps, and the dredging bottom was slightly undulating with a height difference of approximately 0.5 meters. The planned dredging volume was 300 cubic meters, with a design elevation of -17.5 meters. A lateral water flow of 0.4 meters per second was observed during the operation.

[0099] Construction was carried out using the multi-source signal fusion and collaborative control method described in this invention. All sensors and actuators were fully utilized in the system. The dredging decision model in the central controller (using a high-performance PLC) strictly followed the described multi-level control logic. The sampling period for the main control loop (concentration control) was set to 100 milliseconds, and the control period to 200 milliseconds. The sampling period for the auxiliary correction loop (water depth, position) was set to 50 milliseconds. Key thresholds were set as follows: concentration range 10-30%, bottom surface elevation difference threshold 0.3 meters, lateral displacement deviation threshold 0.5 meters, and heading angle deviation threshold 5 degrees.

[0100] Collaborative control process: The system uses mud concentration as the primary control variable, and a PID algorithm stabilizes the concentration at around 22%. The sand pump speed is maintained at 1100 rpm, and the high-pressure water valve opening is 65%. Water depth signals trigger contour-following dredging. When the multibeam echo sounder detects a hard clay area of ​​approximately 2m x 2m, 0.4m above the design elevation (elevation difference > 0.3m), the model immediately intervenes. It prioritizes controlling the large winch to lower the slurry inlet at a speed of 0.5 m / min, focusing on cutting this shallow point. Simultaneously, the model predicts that lowering the inlet into the dense soil layer will cause a sudden increase in concentration, so it synchronously fine-tunes the sand pump speed, reducing it by 8% (to approximately 1010 rpm) to mitigate anticipated concentration fluctuations and prevent pipe blockage. Throughout the entire excavation process, the concentration remains below 28%.

[0101] Position signals triggered dynamic compensation. During contour dredging, lateral water flow caused a change in the floating raft's attitude, and GPS / RTK detected a 7-degree (>5°) deviation between the raft's bow and the planned path. The model intervened again. It prioritized controlling the four small winches to wind up and unwind their cables for correction, generating reverse torque to correct the course. Simultaneously, the model determined that the correction action would cause the raft to decelerate momentarily, reducing the amount of dredging per unit time. To prevent a drop in concentration, it simultaneously fine-tuned the electric regulating valve of the high-pressure water pump, momentarily increasing its opening by 15% (to approximately 75%) to enhance disturbance and maintain sediment supply. After the correction was completed, all parameters returned to normal.

[0102] Throughout the entire operation, the main control and auxiliary control loops seamlessly switch and work together to ensure the quality and efficiency of the operation under complex working conditions.

[0103] The dredging task, involving 300 cubic meters of silt under complex geological conditions, was completed safely and efficiently. Final acceptance testing showed that the bottom surface flatness fully met requirements, with no over-excavation or under-excavation. The equipment operated smoothly, without any parameter mismatch issues caused by changes in operating conditions (such as pipe blockage or drastic concentration fluctuations). Construction efficiency increased by approximately 20% compared to the planned schedule, demonstrating the significant advantages of the multi-source signal collaborative control system.

[0104] Comparative Example 3 Comparative Example 3 was conducted in an area adjacent to Example 3 with identical geological and hydrological conditions, and the dredging volume was also 300 cubic meters. The exact same hardware was used, but the logic of the central controller was modified so that it could only perform closed-loop control of a single parameter, failing to achieve signal fusion and coordination. This invention conducted two sets of comparative experiments.

[0105] Comparative Example 3A (Concentration Control Only): The controller only receives the mud concentration signal and stabilizes the concentration between 10-30% by adjusting the pump speed and valves. Water depth and position signals are only displayed and do not participate in automatic control.

[0106] Comparative Example 3B (Position Control Only): The controller only receives GPS position signals and maintains the float's attitude solely by controlling the small winch. Concentration and water depth signals are not involved in automatic control.

[0107] In Comparative Example 3A (concentration control only), when excavating to a shallow point in the hard clay, the slurry inlet was blocked, preventing automatic excavation. The operator had to manually intervene, overriding the system command to force the large winch to descend. Because the system only had concentration control, when the inlet suddenly cut into the clay, causing the concentration to spike to 40%, the system only then drastically reduced the pump speed and opened the valves. However, this was too late, still causing severe pressure fluctuations in the pipeline and nearly causing a blockage. The system did not respond to the vessel's positional deviation caused by water flow, resulting in a 1.2-meter deviation between the dredging trajectory and the planned path. This led to the discovery of a missed area after dredging was completed, necessitating rework.

[0108] In Comparative Example 3B (position control only), the system effectively maintained the precise position and orientation of the floating raft. However, when encountering shallow spots, the system failed to automatically control the large winch to perform contour-following operations, resulting in these shallow spots being completely missed. More importantly, during the precise positioning and movement of the floating raft, the mud concentration fluctuated drastically between 15% and 38% due to changes in the bottom sediment. The system remained unresponsive to this fluctuation, and ultimately, after operating at a consistently high concentration (>35%) for approximately half an hour, the slurry discharge pipeline became severely clogged, leading to a work interruption. Subsequent surveys revealed extremely poor bottom surface flatness, along with the presence of missed shallow spots and deep pits caused by excessive concentration and over-pumping.

[0109] Both comparative examples failed. Comparative example 3A ensured concentration but sacrificed flatness and trajectory accuracy, and the control actions were abrupt; Comparative example 3B ensured position but sacrificed concentration control and bottom surface accuracy, directly leading to equipment failure. Both required significant manual intervention and rework, with total time and cost far exceeding the examples, and the work quality was substandard.

[0110] A comparison of Example 3 and Comparative Example 3 shows that the technical effect of the multi-source signal fusion and collaborative control of the present invention stems from the synergistic effect of concentration, water depth, position signals, and their corresponding actuators. Controlling a single parameter cannot cope with the real and complex dredging operation environment, and will inevitably lead to serious problems in one or more dimensions of efficiency, quality, and safety due to neglecting one aspect for another.

[0111] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Further modifications can be readily implemented by those skilled in the art.

Claims

1. A method for environmentally friendly dredging of a wharf front, implemented based on a floating raft system, wherein the floating raft system is equipped with a sand pumping unit for extracting slurry, a high-pressure water pumping unit for agitating the slurry, a winch unit consisting of a large winch for controlling the depth of the slurry inlet pipe and a small winch for controlling the displacement of the floating raft, and a slurry discharge pipeline, characterized in that, Specifically, the following steps are included: Step 1: Deploy a mud concentration sensor at the inlet of the sand pump to collect mud concentration data in real time; install water depth sensors at the four corners of the floating raft and at the head of the inlet pipe to monitor the elevation of the dredging area in real time; obtain the precise planar coordinates and attitude data of the floating raft through the GPS / RTK positioning module mounted on the floating raft. Step 2: Set up a central controller with a built-in dredging decision model. This dredging decision model receives and integrates data signals from mud concentration sensors, water depth sensors, and positioning modules to perform real-time analysis and decision-making. Step 3: The central controller outputs coordinated control commands to each execution unit, including: dynamically adjusting the speed of the sand pumping pump through the frequency converter based on the deviation between the real-time mud concentration and the preset concentration threshold, and simultaneously controlling the opening of the electric regulating valve on the high-pressure water pump pipeline to coordinately adjust the disturbance intensity and the extraction flow rate, so that the mud concentration is stabilized in the optimal working range; automatically controlling the large winch to lift or lower the slurry inlet pipe based on the difference between the real-time water depth and the design elevation, combined with the floating raft position information, and controlling the small winch to reel in and release the positioning cable to perform lateral movement of the floating raft and fixed-depth sludge removal operations at the slurry inlet pipe, in order to ensure the flatness of the sludge removal surface; Step 4: The mud concentration signal, water depth signal and position signal form a closed loop feedback. The central controller's dredging decision model is based on the multi-source signal fusion results and calculates the optimal coordinated control parameters for the sand pump, high-pressure water pump and winch system in real time. While ensuring dredging accuracy, it maximizes dredging efficiency and minimizes energy consumption and water disturbance. Step 5: The operating status of all equipment in the floating conveyor system, sensor data, and control command logs are uploaded to the remote monitoring center in real time, enabling full-process visual tracking, data storage, and intelligent analysis of the dredging operation.

2. The environmentally friendly dredging method for the wharf frontage according to claim 1, characterized in that, In step 3, the optimal working range is a mud concentration of 10% to 30%. The dredging decision model takes this concentration range as the control target and controls the coordinated action of the frequency converter and the electric regulating valve through the feedback signal of the mud concentration sensor.

3. The environmentally friendly dredging method for the wharf front as described in claim 2, characterized in that, The lower limit of this concentration range of 10% is determined by the minimum solids content required for the sand pumping unit to maintain stable operation, and the upper limit of 30% is determined by the maximum transport concentration corresponding to the critical siltation flow rate of the slurry discharge pipeline. When the real-time mud concentration is below 10%, it indicates that the water content in the pumped material is too high. The central controller reduces the speed of the sand pump and simultaneously reduces the opening of the electric regulating valve to reduce unnecessary energy consumption and water disturbance, while avoiding the risk of overflow of the downstream sedimentation tank due to excessive flow. When the real-time mud concentration is higher than 30%, it indicates that the mud and sand content is too high, and there is a risk of blockage in the slurry discharge pipeline and pump overload. The central controller increases the opening of the electric regulating valve to enhance the dilution of disturbance, and at the same time increases the speed of the sand pump to increase the conveying capacity, thereby ensuring the continuous and stable operation of the system.

4. The environmentally friendly dredging method for the wharf front as described in claim 1, characterized in that, In step 4, the dredging decision model performs multi-source signal fusion and coordinated control in the following manner: A multi-level control logic is established with mud concentration signal as the main control variable and water depth signal and position signal as auxiliary correction variables. The main control variable is used to generate basic control commands for sand pump speed and electric regulating valve opening to stabilize mud concentration within the target range. The auxiliary correction variables are used to compensate and optimize the basic control commands in real time: when the water depth sensor detects significant unevenness on the dredging surface, the dredging decision model prioritizes controlling the large winch to perform contour dredging and simultaneously fine-tunes the speed of the sand pump to match the concentration fluctuations caused by elevation changes; when the positioning module detects a deviation between the floating hull displacement speed and the planned path, the dredging decision model prioritizes controlling the small winch to correct the position and simultaneously fine-tunes the disturbance intensity of the high-pressure water pump to compensate for the changes in the amount of sediment supplied per unit time caused by changes in the speed of the moving vessel.

5. The environmentally friendly dredging method for the wharf front as described in claim 4, characterized in that, The contour-following dredging is achieved through the following specific steps: The water depth sensor is a multibeam echo sounder, which quickly scans the newly completed work surface during the intervals of single-point dredging operations to acquire high-precision seabed elevation data and send it to the central controller; The dredging decision model has a built-in terrain generation algorithm, which constructs a local seabed digital elevation model in real time based on the elevation data, and identifies shallow areas above the design elevation and deep pit areas below the design elevation. For shallow areas, the dredging decision model generates a supplementary excavation command, controlling the large winch to precisely position the slurry inlet pipe at that point and appropriately reduce the speed for fine excavation; for deep pit areas, the dredging decision model generates an avoidance command, controlling the large winch to raise the pipe opening to avoid over-excavation.

6. The environmentally friendly dredging method for the wharf front as described in claim 5, characterized in that, Position correction is achieved through the following specific steps: The dredging decision model has a pre-stored planned operation path based on the grid division of the dredging area. This planned operation path specifies the standard lateral movement sequence and dwell time of the floating hull. The GPS / RTK positioning module provides real-time feedback on the planar coordinates and heading angle of the floating raft's center of mass at a frequency of 1Hz. When the lateral deviation of the real-time position from the planned path exceeds a set threshold or the heading angle deviation exceeds a set threshold, the dredging decision model determines that there is an attitude deviation. Based on the magnitude and direction of the deviation, the dredging decision model calculates the optimal force distribution scheme required for correction and generates differentiated control commands, which are sent to the small winches responsible for the bow and stern, respectively. By winding and unwinding cables of different lengths, rotational torque or lateral tension is generated to bring the floating raft back to the planned path.

7. The environmentally friendly dredging method for the wharf frontage according to claim 1, characterized in that, An underwater camera unit and a water pressure sensor are also installed at the slurry inlet of the sand pump; the underwater camera unit is used to capture visual images of the silt in front of the slurry inlet in real time, and the water pressure sensor is used to monitor the back pressure changes at the slurry inlet. The central controller's dredging decision model also receives and integrates visual image signals and back pressure signals. When the dredging decision model determines that there are large foreign objects in the visual image through image recognition algorithms, or determines through trend analysis that the back pressure value abnormally increases beyond the preset threshold within a set time, the dredging decision model immediately determines that there is a risk of blockage.

8. The environmentally friendly dredging method for the wharf frontage according to claim 7, characterized in that, After determining the risk of blockage, the dredging decision model outputs a collaborative control command: prioritizes controlling the large winch to quickly lift the slurry inlet pipe to remove it from the current silt layer, and simultaneously controls the high-pressure water pump set to increase the opening of the electric regulating valve to the maximum to flush the pipe area with the maximum flow rate; after the back pressure value returns to normal and the image recognition confirms that the risk has been eliminated, the dredging decision model then controls the winch set and the high-pressure water pump set to automatically return to the normal dredging operation state.

9. The environmentally friendly dredging method for the wharf frontage according to claim 1, characterized in that, A pipeline pressure sensor is installed on the slurry discharge pipeline near the outlet section of the sand pump unit, and an ultrasonic flow meter is installed at the inlet of the onshore sedimentation tank. The central controller receives signals from the pipeline pressure sensor and ultrasonic flow meter in real time. The dredging decision model has a built-in pipeline status monitoring submodule. Based on real-time pressure and flow data, this submodule dynamically evaluates the pipeline's patency index by calculating the ratio of pressure drop per unit pipe length to flow velocity. When the pressure value is continuously rising and the flow value is decreasing simultaneously, and the patency index is lower than the preset threshold, the dredging decision model determines that there is a risk of progressive siltation in the pipeline.

10. The environmentally friendly dredging method for the wharf frontage according to claim 9, characterized in that, When assessing the risk of siltation, the dredging decision model outputs a collaborative unblocking control command: controlling the frequency converter of the sand pump to alternate between high-speed and low-speed operation within a preset cycle, while simultaneously controlling the electric regulating valve on the high-pressure water pump pipeline to perform synchronous pulse width modulation, thereby forming a periodic pulsed flow pattern in the slurry discharge pipeline; this pulsed flow pattern generates shearing and scouring effects on the pipe wall deposits, achieving online unblocking; once the unobstructed flow index returns to the normal range, it automatically switches back to the stable operation mode.

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