A port sand extraction and dredging system
By collecting and processing multidimensional mud signals, identifying the mud-sand layer interface and automatically matching the operation mode, the problem of insufficient mud-sand layer identification in existing dredging technologies has been solved, realizing efficient and stable dredging operations in ports and waterways.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- QINGDAO JUNBAHAO TECHNOLOGY ENGINEERING CO LTD
- Filing Date
- 2026-06-03
- Publication Date
- 2026-07-03
AI Technical Summary
Existing dredging technologies cannot effectively distinguish between different physical properties of silt and sand layers, leading to over-dredging, under-dredging, and equipment failure. Furthermore, they lack real-time dynamic adjustment capabilities, affecting dredging accuracy and stability.
By collecting multidimensional physical signals from mud, performing standardized processing and weighted fusion, identifying the location of the mud-sand layer interface, and automatically matching adaptive operation modes, combined with topographic mapping and closed-loop control, layered dredging is achieved.
It enables accurate identification of different types of sediment layers and automatic matching of operation modes, ensuring that the dredging process is highly compatible with the characteristics of the sediment, avoiding problems caused by improper operation methods, and guaranteeing the stability and accuracy of dredging operations.
Smart Images
Figure CN122327770A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of port and waterway engineering technology, specifically to a port sand extraction and dredging system. Background Technology
[0002] As the core hub of the comprehensive transportation system, ports support the high-quality development of trade and regional economy. With the increasing trend of larger and more specialized ships in the shipping industry, the water depth and navigation guarantee capacity of port channels have become key factors restricting the efficiency of port operation. Affected by multiple factors such as river sediment transport, ocean tidal movement and coastal engineering construction, natural siltation is common in port channels and berth areas. The continuous accumulation of silt will directly lead to insufficient channel depth and affect the efficiency of ships entering and leaving the port. At present, dredging has become a necessary part of the daily operation and maintenance of ports.
[0003] However, existing dredging technologies mainly rely on traditional cutter suction dredging methods, which cannot effectively distinguish the stratified structure of sediment with different physical properties. It is difficult to accurately identify the interface between fluid silt, plastic silt, and compacted sand layers. Over-dredging and under-dredging are prone to occur during the operation. Over-dredging not only increases the amount of soil to be transported but may also damage the original geological structure of the channel base, leading to safety hazards such as subsequent bank instability. Under-dredging cannot meet the design navigation depth requirements and requires secondary rework. At the same time, existing technologies mostly use fixed operating parameters that are preset manually and cannot dynamically adjust the operating status according to real-time sediment characteristics. In areas where sediment characteristics change abruptly, equipment failures such as pipe blockage and air suction are prone to occur. The operation process lacks an effective closed-loop control mechanism, making it difficult to continuously guarantee dredging accuracy and operational stability. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a port sand extraction and dredging system. This invention collects multi-dimensional physical signals of mud, performs standardized processing and weighted fusion to obtain characteristic signals that can comprehensively reflect the overall characteristics of mud and sand. Based on these characteristic signals, feature information of different dimensions is extracted to achieve accurate identification of different types of mud and sand layers and their interface locations. Based on the identification results, an operation mode adapted to the physical characteristics of each layer of mud and sand is automatically matched, so that the dredging operation process is highly consistent with the characteristics of mud and sand, fundamentally avoiding problems caused by improper operation methods for different mud and sand layers.
[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a port sand extraction and dredging system, the system comprising: Topographic mapping module: Uses multibeam sonar units to scan the maintenance area of the port channel and the surrounding underwater topography, generate a three-dimensional elevation distribution model and extract three-dimensional elevation data to determine the boundary of the dredging operation area and the initial operation depth; Mud Sensing Module: Through a distributed sensor array integrated in the suction port area of the dredging head, the pressure, flow rate and density signals of mud at different depths are collected in real time during the vertical feeding process of the dredging head, and the signals are standardized and weighted to obtain the comprehensive characteristic signal of mud. Layer identification module: Based on the comprehensive characteristic signal of mud, after smoothing, the features of pressure drop, flow velocity fluctuation and density change are extracted to identify the layer interface positions of the upper fluid mud, the middle plastic mud and the lower crusted sand layer, and obtain the layering results; Layered operation module: Based on the layered results, it automatically matches the corresponding operation mode, controls the cutter head speed, mud pump flow rate and vibration device status, and obtains real-time operation parameters; Depth control module: Based on three-dimensional elevation data, layered results and real-time operation parameters, it continuously adjusts the vertical cutting depth and feed speed of the dredging head according to the real-time characteristics of the sediment, and performs closed-loop adaptive sand extraction and dredging.
[0006] Furthermore, in the topographic mapping module, the multibeam sonar unit transmits at a frequency of 200kHz and has a beam angle of 1°×1°. It performs a full-coverage scan of the port channel maintenance area and the underwater topographic area extending 5m outward. After coordinate correction and noise removal, the scan data generates a three-dimensional elevation distribution model with a grid resolution of 0.5m×0.5m. The three-dimensional elevation data of each grid point is extracted from the three-dimensional elevation distribution model. The set of grid points with three-dimensional elevation data lower than the design navigable water depth is taken as the siltation layer distribution range. The boundary of the dredging operation area is determined by extending 0.5m outward from the outer edge of the siltation layer distribution range. The average three-dimensional elevation data of the top surface of the siltation layer of all grid points in the dredging operation area is taken to determine the initial operation depth. During the dredging process, the already operated area is locally scanned and updated every 2 hours. The updated data is used to correct the subsequent operation path and depth parameters.
[0007] Furthermore, in the mud sensing module, the distributed sensor array is arranged in a ring with equal spacing. Pressure sensors, flow rate sensors, and density sensors are evenly arranged around the circumference of the sludge suction port. The sampling frequency is 100Hz. The pressure, flow rate, and density signals of the mud at different depths are standardized and then weighted and fused to obtain the comprehensive mud characteristic signal. The weight coefficients of each sensor during the weighted fusion process are determined by historical field operation data.
[0008] Furthermore, in the layer identification module, a sliding window filter is used to smooth the comprehensive mud feature signal, eliminating the influence of sensor noise and water flow disturbance. The window size is set to 50 sampling points, and the sliding step size is 1 sampling point. Pressure drop, velocity fluctuation, and density change features are extracted from the filtered comprehensive mud feature signal. The pressure drop is determined by the ratio of the pressure difference between two adjacent sampling points to the corresponding vertical feed depth difference. The velocity fluctuation is determined by the standard deviation of all velocity sampling values within the current sliding window. The density change is directly taken from the relative density value of the mud at the current sampling point, which is the ratio of the mud density to the standard clear water density. Based on the extracted three features, the stratification feature value of the sediment is calculated using the sediment stratification feature value calculation formula, which is: ,in, For hierarchical feature values, The pressure difference between two adjacent sampling points. This represents the difference in vertical feed depth between two corresponding sampling points. Characterized by flow velocity fluctuations. Characteristics of density variation The weighting coefficients are 0.4, 0.3, and 0.3, respectively, and are determined by principal component analysis using historical data of sediment samples under different working conditions.
[0009] Furthermore, in the layer identification module, when the layer feature value F of three consecutive sampling points is less than 1.2, it is determined to be the upper layer of fluid silt; when the layer feature value 1.2 ≤ F < 2.5 of three consecutive sampling points, it is determined to be the middle layer of plastic silt; and when the layer feature value F ≥ 2.5 of three consecutive sampling points, it is determined to be the lower layer of compacted sand. The position where the feature values of two adjacent layers cross consecutively is the layer interface position. After the layer interface position is determined, the current depth is recorded to obtain the layering result.
[0010] Furthermore, in the layered operation module, based on the layer interface position identified in the layering results, the corresponding operation mode is matched. When it is upper-layer fluidized sludge, the cutter head is locked, the rotation speed is 0 r / min, and the sludge pump is started for negative pressure suction, with the sludge pump flow rate set to the reference flow rate of 1200 m³ / min. 3 / h; When identified as mid-layer plastic sludge, start the cutter head for low-speed cutting at 30 r / min. Adjust the real-time sludge pump flow rate using an adaptive formula with a target pipe velocity of 2 m / s, controlling the range between 800-1500 m³ / s. 3 Between / h; when identified as a lower layer of compacted sand, activate the high-frequency vibration device at the front end of the cutter shaft, with the vibration direction consistent with the feed direction of the dredging head, setting the vibration frequency to 50Hz and the amplitude to 5mm. Simultaneously, increase the cutter speed to 80r / min and adjust the mud pump flow rate to 1000m³ / min. 3 / h, and during the operation, the vibration frequency and cutter speed are dynamically adjusted according to the magnitude of the stratification characteristic value. When the stratification characteristic value F≥3.0, the vibration frequency is increased to 60Hz and the cutter speed is increased to 90r / min. The operation parameters of the real-time flow velocity in the pipe are monitored in all operation modes.
[0011] Furthermore, in the layered operation module, the adaptive adjustment formula for the mud pump flow rate is: ,in, For real-time mud pump flow rate, As the baseline flow rate, The flow rate regulation coefficient was determined through statistical analysis of historical field operation data under different working conditions, and its value was set at 300m. 2 ·s / h, For the target flow velocity in the pipe, This represents the real-time flow velocity within the pipe.
[0012] Furthermore, in the depth control module, three-dimensional elevation data is used to determine the depth benchmark and safety boundary for dredging operations; stratification results are used to dynamically update the target cutting depth of the dredging head; real-time operating parameters are used to compensate for changes in cutting resistance under different operating modes; and the density gradient of adjacent sampling points is calculated in real time. The density gradient is determined by the ratio of the difference in mud density between two adjacent sampling points to the corresponding difference in vertical feed depth. When the absolute value of the density gradient is > 200 kg / m 4 When the area is identified as a transition zone between layers, the vertical feed speed of the dredging head is adjusted to 0.1 m / min. The vertical feed speed control quantity of the dredging head is calculated by the density gradient feedforward PID control algorithm. The vertical cutting depth of the dredging head is continuously adjusted by controlling the magnitude and direction of the feed speed.
[0013] Furthermore, in the depth control module, the calculation formula for the density gradient feedforward PID control algorithm is as follows: ,in, for The vertical feed rate of the sludge removal head is constantly controlled. for The deviation between the actual cutting depth and the target cutting depth at any given time. This is the proportionality constant, with a value of 2.0. This is the integral coefficient, with a value of 0.5. is the differential coefficient, with a value of 0.1. From the initial time 0 to the current time The integral value of the deviation, For integration time variable, for The rate of change of time deviation over time. This is the feedforward coefficient, with a value of 0.2m. 5 / (s·kg), All were determined through statistical analysis of historical data on depth control under different operating conditions. This represents the real-time density gradient.
[0014] Compared with existing technologies, this port sand extraction and dredging system has the following advantages: I. This invention collects multidimensional physical signals of mud, performs standardized processing and weighted fusion to obtain characteristic signals that can comprehensively reflect the overall characteristics of mud and sand. Based on these characteristic signals, feature information of different dimensions is extracted to achieve accurate identification of different types of mud and sand layers and their interface locations. Based on the identification results, an operation mode adapted to the physical characteristics of each layer of mud and sand is automatically matched, so that the dredging operation process is highly compatible with the characteristics of mud and sand, fundamentally avoiding problems caused by improper operation methods for different mud and sand layers.
[0015] Second, this invention integrates topographic mapping data, sediment layer identification results, and real-time operation parameters to construct a complete closed-loop control system. Based on real-time changes in sediment characteristics, it continuously adjusts the vertical position and movement state of the dredging head, enabling the dredging head to accurately follow changes in the sediment layer interface and ensuring that the dredging operation is always carried out within the target layer. At the same time, it compensates for resistance changes under different operation modes through a real-time feedback mechanism, ensuring the stability and consistency of the dredging operation and providing reliable technical support for port and waterway dredging projects.
[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0018] Figure 1 A flowchart of a port sand extraction and dredging system; Figure 2 This is a framework diagram of a layered identification module in a port sand extraction and dredging system. Figure 3 This is a framework diagram of a layered operation module in a port sand extraction and dredging system. Detailed Implementation
[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.
[0020] Example: In the scenario of routine dredging and maintenance of 10,000-ton-class waterways in coastal ports, the waterway is affected by river sediment transport, ocean tides and coastal currents, forming a typical layered siltation structure with an upper layer of fluid silt, a middle layer of plastic silt, and a lower layer of hardened sand. The physical properties of these layers of sediment are different. In this embodiment, a port sand extraction and dredging system is used to carry out full-process adaptive sand extraction and dredging operations for the layered siltation conditions of this waterway.
[0021] Topographic mapping module: A multi-beam sonar unit is used for the operation. The transmission frequency of the multi-beam sonar unit is set to 200kHz, and the beam angle is set to 1°×1°. A full-coverage scan is performed on the port channel maintenance area and the underwater topographic area extending 5m outward. After coordinate correction and noise removal, the raw topographic data acquired by the scan is used to generate a three-dimensional elevation distribution model with a grid resolution of 0.5m×0.5m. The three-dimensional elevation data of each grid point is extracted from this three-dimensional elevation distribution model. The set of grid points with three-dimensional elevation data lower than the design navigable water depth is determined as the siltation layer distribution range. The boundary of the dredging operation area is determined by extending 0.5m outward from the outer edge of the siltation layer distribution range. The initial operation depth is calculated by taking the average of the three-dimensional elevation data of the top surface of the siltation layer of all grid points within the dredging operation area. Figure 1 As shown, during the operation, the already operated area is scanned and updated every 2 hours. The updated 3D elevation data is transmitted to the depth control module in real time to correct the subsequent operation path and depth parameters.
[0022] Mud Sensing Module: A distributed sensor array is arranged in a ring with equal spacing around the circumference of the dredging head's suction port. Pressure sensors, flow velocity sensors, and density sensors are evenly distributed, and the sampling frequency of each sensor is set to 100Hz. When the dredging head begins vertical feeding, the sensor array moves synchronously with the dredging head, collecting raw signals of mud pressure, flow velocity, and density at different depths in real time. The three types of signals are standardized to eliminate interference caused by differences in signal dimensions and numerical ranges. The weighting coefficients of each sensor are determined based on historical field operation data. The standardized pressure, flow velocity, and density signals are then weighted and fused according to the weighting coefficients to obtain a comprehensive mud characteristic signal that fully reflects the physical properties of the mud. This comprehensive mud characteristic signal is then transmitted completely to the layer identification module to provide basic data support for subsequent layer identification.
[0023] Layered Identification Module: Receives the comprehensive mud feature signal and smooths it using a sliding window filtering algorithm. The window size is set to 50 sampling points, and the sliding step size is set to 1 sampling point. Filtering eliminates interference from sensor noise and water flow disturbances, ensuring the accuracy of feature extraction. Based on the filtered comprehensive mud feature signal, three core features are extracted: pressure drop, velocity fluctuation, and density change. Pressure drop is determined by the ratio of the pressure difference between two adjacent sampling points to the corresponding vertical feed depth difference. Velocity fluctuation is determined by the standard deviation of all velocity sampling values within the current sliding window. Density change is directly extracted by the relative density of the mud at the current sampling point, which is the ratio of mud density to standard clear water density. Based on the extracted three features, layered feature values are calculated using the mud-sand layered feature value calculation formula: ,in, For hierarchical feature values, The pressure difference between two adjacent sampling points. This represents the difference in vertical feed depth between two corresponding sampling points. Characterized by flow velocity fluctuations. Characteristics of density variation The weighting coefficients, with values of 0.4, 0.3, and 0.3, were determined through principal component analysis using historical data of sediment samples under different working conditions. Then, the stratification characteristic values of consecutive sampling points were used for judgment. When the stratification characteristic value F < 1.2 for three consecutive sampling points, it was determined to be the upper fluid silt layer; when the stratification characteristic value 1.2 ≤ F < 2.5 for three consecutive sampling points, it was determined to be the middle plastic silt layer; and when the stratification characteristic value F ≥ 2.5 for three consecutive sampling points, it was determined to be the lower compacted sand layer. The location where the characteristic values of two adjacent layers continuously cross each other is the layer interface location. The depth data corresponding to the layer interface location is recorded, and the stratification results are generated and synchronously transmitted to the stratification operation module and the depth control module. Figure 2 As shown.
[0024] Layered Operation Module: After receiving the layering results, it automatically matches the corresponding operation mode based on the layer interface position. When it identifies the upper layer as fluid sludge, it locks the cutter head to maintain its rotation speed at 0 r / min, starts the sludge pump to perform negative pressure suction operation, and sets the sludge pump flow rate to 1200 m³ / min. 3 The baseline flow rate is set at / h, and the flow velocity inside the pipe is monitored in real time to generate operating parameters. When the medium-layer plastic sludge is identified, the cutter head is started to perform low-speed cutting operation. The cutter head speed is set to 30 r / min. The real-time sludge pump flow rate is adjusted using an adaptive adjustment formula with a target flow velocity of 2 m / s inside the pipe. The adaptive adjustment formula for sludge pump flow rate is as follows: ,in, For real-time mud pump flow rate, As the baseline flow rate, The flow rate regulation coefficient was determined through statistical analysis of historical field operation data under different working conditions, and its value was set at 300m. 2 ·s / h, For the target flow velocity in the pipe, Real-time flow velocity within the pipe; flow rate adjustment range controlled between 800-1500 m³ / s. 3 Between / h; when the lower layer of compacted sand is identified, the high-frequency vibration device at the front end of the cutter shaft is activated, with the vibration direction consistent with the feed direction of the dredging head. The vibration frequency is set to 50Hz and the amplitude to 5mm. At the same time, the cutter speed is increased to 80r / min, and the mud pump flow rate is adjusted to 1000m³ / min. 3 During operation, the stratification characteristic value is continuously monitored. When the stratification characteristic value F≥3.0, the vibration frequency is increased to 60Hz and the cutter speed is increased to 90r / min. In all operating modes, the flow velocity inside the pipe is monitored in real time, generating real-time operating parameters and transmitting them to the depth control module. Figure 3 As shown.
[0025] The depth control module receives 3D elevation data from the topographic mapping module, layering results from the layering identification module, and real-time operation parameters from the layering operation module. The 3D elevation data is used to determine the depth benchmark and safety boundary for dredging operations. The layering results are used to dynamically update the target cutting depth of the dredging head. The real-time operation parameters are used to compensate for changes in cutting resistance under different operation modes. It also calculates the density gradient between adjacent sampling points in real time. The density gradient is determined by the ratio of the difference in mud density between two adjacent sampling points to the corresponding difference in vertical feed depth. When the absolute value of the density gradient is greater than 200 kg / m³, the gradient is adjusted accordingly. 4 When the current position is determined to be in the layer interface transition area, the vertical feed speed of the dredging head is adjusted to 0.1 m / min; and the density gradient feedforward PID control algorithm is used to calculate the control quantity of the vertical feed speed of the dredging head. The calculation formula of the density gradient feedforward PID control algorithm is as follows: ,in, for The vertical feed rate of the sludge removal head is constantly controlled. for The deviation between the actual cutting depth and the target cutting depth at any given time. This is the proportionality constant, with a value of 2.0. This is the integral coefficient, with a value of 0.5. is the differential coefficient, with a value of 0.1. From the initial time 0 to the current time The integral value of the deviation, For integration time variable, for The rate of change of time deviation over time. This is the feedforward coefficient, with a value of 0.2m.5 / (s·kg), All were determined through statistical analysis of historical data on depth control under different operating conditions. The system provides real-time density gradient control; by controlling the magnitude and direction of the feed speed, the vertical cutting depth of the dredging head is continuously adjusted to achieve closed-loop adaptive sand dredging, ensuring that the dredging head always adapts to changes in the mud and sand layer interface to complete the operation; during the operation, each module maintains continuous data interaction until the silt layer in the dredging operation area is cleared and the water depth requirements for navigation are met, at which point the operation stops.
[0026] In summary, in the scenario of dredging and maintenance of 10,000-ton-class waterways in coastal ports, the collaborative operation of the topographic mapping module, mud sensing module, layer identification module, layer operation module, and depth control module has fully realized the adaptive sand extraction and dredging process for layered siltation in ports, effectively adapting to the dredging operation requirements of layered siltation conditions.
[0027] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A port sand extraction and dredging system, characterized in that, The system includes: Topographic mapping module: Uses multibeam sonar units to scan the port channel maintenance area and surrounding underwater topography, generate a three-dimensional elevation distribution model and extract three-dimensional elevation data to determine the boundary of the dredging operation area and the initial operation depth; Mud Sensing Module: Through a distributed sensor array integrated in the suction port area of the dredging head, the pressure, flow rate and density signals of mud at different depths are collected in real time during the vertical feeding process of the dredging head, and the signals are standardized and weighted to obtain the comprehensive characteristic signal of mud. Layer identification module: Based on the comprehensive characteristic signal of mud, after smoothing, the features of pressure drop, flow velocity fluctuation and density change are extracted to identify the layer interface positions of the upper fluid mud, the middle plastic mud and the lower crusted sand layer, and obtain the layering results; Layered operation module: Based on the layered results, it automatically matches the corresponding operation mode, controls the cutter head speed, mud pump flow rate and vibration device status, and obtains real-time operation parameters; Depth control module: Based on three-dimensional elevation data, layered results and real-time operation parameters, it continuously adjusts the vertical cutting depth and feed speed of the dredging head according to the real-time characteristics of the sediment, and performs closed-loop adaptive sand extraction and dredging.
2. The port sand extraction and dredging system according to claim 1, characterized in that, In the topographic mapping module, the multibeam sonar unit transmits at a frequency of 200kHz and has a beam angle of 1°×1°. It performs a full-coverage scan of the port channel maintenance area and the underwater topographic area extending 5m outward. After coordinate correction and noise removal, the scan data generates a three-dimensional elevation distribution model with a grid resolution of 0.5m×0.5m. The three-dimensional elevation data of each grid point is extracted from the three-dimensional elevation distribution model. The set of grid points with three-dimensional elevation data lower than the design navigable water depth is taken as the siltation layer distribution range. The boundary of the dredging operation area is determined by extending 0.5m outward from the outer edge of the siltation layer distribution range. The initial operation depth is determined by taking the average three-dimensional elevation data of the top surface of the siltation layer of all grid points in the dredging operation area. During the dredging process, the already operated area is locally scanned and updated every 2 hours.
3. A port sand extraction and dredging system according to claim 1, characterized in that, In the mud sensing module, the distributed sensor array is arranged in a ring with equal spacing. Pressure sensors, flow velocity sensors and density sensors are evenly set in the circumference of the dredging head suction port. The sampling frequency is 100Hz. The pressure, flow velocity and density signals of mud at different depths are standardized and then weighted and fused to obtain the comprehensive mud feature signal. The weight coefficients of each sensor in the weighted fusion process are determined by historical field operation data.
4. A port sand extraction and dredging system according to claim 1, characterized in that, In the layer identification module, a sliding window filter is used to smooth the comprehensive mud feature signal, eliminating the influence of sensor noise and water flow disturbance. The window size is set to 50 sampling points, and the sliding step size is 1 sampling point. Three features—pressure drop, flow velocity fluctuation, and density change—are extracted from the filtered comprehensive mud feature signal. Based on these three extracted features, the layering feature value of the sediment is calculated using the sediment layering feature value calculation formula: ,in, For hierarchical feature values, The pressure difference between two adjacent sampling points. This represents the difference in vertical feed depth between two corresponding sampling points. Characterized by flow velocity fluctuations. Characteristics of density variation The weighting coefficients are 0.4, 0.3, and 0.3, respectively, and are determined by principal component analysis using historical data of sediment samples under different working conditions.
5. A port sand extraction and dredging system according to claim 4, characterized in that, In the layer identification module, when the layer characteristic value F of three consecutive sampling points is less than 1.2, it is determined to be the upper layer of fluid silt; when the layer characteristic value 1.2 ≤ F < 2.5 of three consecutive sampling points, it is determined to be the middle layer of plastic silt; when the layer characteristic value F of three consecutive sampling points is greater than or equal to 2.5, it is determined to be the lower layer of compacted sand. The position where the characteristic values of two adjacent layers are continuously crossed is the layer interface position. After the layer interface position is determined, the current depth is recorded to obtain the layering result.
6. A port sand extraction and dredging system according to claim 1, characterized in that, In the layered operation module, based on the layer interface position identified in the layering results, the corresponding operation mode is matched. When it is upper-layer fluidized sludge, the cutter head is locked, the rotation speed is 0 r / min, and the sludge pump is started for negative pressure suction, with the sludge pump flow rate set to the reference flow rate of 1200 m³ / min. 3 / h; When identified as mid-layer plastic sludge, start the cutter head for low-speed cutting at 30 r / min. Adjust the real-time sludge pump flow rate using an adaptive formula with a target pipe velocity of 2 m / s, controlling the range between 800-1500 m³ / s. 3 Between / h; when identified as a lower layer of compacted sand, activate the high-frequency vibration device at the front end of the cutter shaft, with the vibration direction consistent with the feed direction of the dredging head, setting the vibration frequency to 50Hz and the amplitude to 5mm. Simultaneously, increase the cutter speed to 80r / min and adjust the mud pump flow rate to 1000m³ / min. 3 / h, and during the operation, the vibration frequency and cutter speed are dynamically adjusted according to the magnitude of the stratification characteristic value. When the stratification characteristic value F≥3.0, the vibration frequency is increased to 60Hz and the cutter speed is increased to 90r / min. The operation parameters of the real-time flow velocity in the pipe are monitored in all operation modes.
7. A port sand extraction and dredging system according to claim 6, characterized in that, In the layered operation module, the adaptive adjustment formula for the mud pump flow rate is: ,in, For real-time mud pump flow rate, As the baseline flow rate, The flow rate regulation coefficient was determined through statistical analysis of historical field operation data under different working conditions, and its value was set at 300m. 2 ·s / h, For the target flow velocity in the pipe, This represents the real-time flow velocity within the pipe.
8. A port sand extraction and dredging system according to claim 1, characterized in that, In the depth control module, three-dimensional elevation data is used to determine the depth benchmark and safety boundary for dredging operations. Layering results are used to dynamically update the target cutting depth of the dredging head. Real-time operating parameters are used to compensate for changes in cutting resistance under different operating modes, and the density gradient of adjacent sampling points is calculated in real time. The density gradient is determined by the ratio of the difference in mud density between two adjacent sampling points to the corresponding difference in vertical feed depth. When the absolute value of the density gradient > 200 kg / m³, the density gradient is controlled. 4 When the area is identified as a transition zone between layers, the vertical feed speed of the dredging head is adjusted to 0.1 m / min. The vertical feed speed control quantity of the dredging head is calculated by the density gradient feedforward PID control algorithm. The vertical cutting depth of the dredging head is continuously adjusted by controlling the magnitude and direction of the feed speed.
9. A port sand extraction and dredging system according to claim 8, characterized in that, In the depth control module, the calculation formula for the density gradient feedforward PID control algorithm is as follows: ,in, for The vertical feed rate of the sludge removal head is constantly controlled. for The deviation between the actual cutting depth and the target cutting depth at any given time. This is the proportionality constant, with a value of 2.
0. This is the integral coefficient, with a value of 0.
5. is the differential coefficient, with a value of 0.
1. From the initial time 0 to the current time The integral value of the deviation, For integration time variable, for The rate of change of time deviation over time. This is the feedforward coefficient, with a value of 0.2m. 5 / (s·kg), All were determined through statistical analysis of historical data on depth control under different operating conditions. This represents the real-time density gradient.