Online concentration feedback type pump speed control method and system for conveying high-concentration silt

Abstract

The invention relates to the technical field of underwater sediment conveying control, in particular to an online concentration feedback type pump speed control method and system for high-concentration sediment conveying, and the method comprises the steps that S1, monitoring data of a target pump body are obtained; s2, acquiring carrier attitude water and correcting monitoring data; s3, the target pump speed is obtained based on the coupling control model; and S4, control data of the target pump body are obtained based on the target pump speed. The system comprises a monitoring module, a correction module, an analysis module and a control module. Through attitude data correction and a concentration-power-pressure difference coupling control model, the problem of low pump speed control precision caused by sediment concentration fluctuation is solved.

Description

Technical Field

[0001] This invention relates to the field of underwater sediment transport control technology, and in particular to an online concentration feedback pump speed control method and system for transporting high-concentration sediment. Background Technology

[0002] Underwater dredging robots are specialized operational equipment designed for complex underwater environments. They typically employ a low-profile, compact tracked or wheeled structure, enabling autonomous or semi-autonomous movement in extreme conditions with low clearance and numerous obstacles, such as beneath docks and at the bottom of dry docks. Their core function is similar to that of a cutter suction dredger: they break up underwater sediment using their onboard suction head and then utilize a pumping system to stably transport the high-concentration silt mixture to a designated discharge point. They offer advantages such as high operational flexibility and minimal disruption to normal dock operations.

[0003] However, the pumping system of underwater dredging robots faces severe technical challenges in actual operation. Because underwater dredging robots are small and agile, and operate in confined spaces, the contact state between the suction head and the silt is unstable. Furthermore, the frequent start-stop cycles required to adapt to complex terrain lead to drastic fluctuations in silt concentration over time and space, resulting in significant variations in pump load. Currently, pump speeds are often set to fixed values ​​by operators and are not dynamically adjusted according to changing operating conditions. This easily leads to pump overload or no-load operation, causing increased energy consumption, accelerated wear, and even system failure.

[0004] Regarding the impact of sediment concentration fluctuations on pumping systems, when sediment concentration is too high, the slurry density increases, pipeline resistance increases, and the pump load rises sharply. If the pump speed remains constant, it will lead to pump overload operation, excessive motor current, and even serious accidents such as pump blockage and motor burnout. Simultaneously, equipment wear is accelerated, and its service life is significantly shortened. When sediment concentration is too low, the slurry density decreases, pipeline resistance decreases, and the pump load decreases. If the pump speed remains constant, it will lead to pump idling operation, low pumping efficiency, and a large amount of energy wasted in ineffective fluid circulation, resulting in high system energy consumption. Furthermore, drastic fluctuations in sediment concentration can also cause unstable operation of the pumping system, with fluctuating slurry flow rates, affecting the continuity and efficiency of dredging operations.

[0005] Regarding existing technologies for pump speed control, the following methods are currently mainly used for pump speed control in underwater dredging robots:

[0006] One method is to manually set a fixed pump speed, where the operator pre-sets a fixed pump speed value based on experience and keeps it unchanged throughout the operation. While this method is simple and easy to implement, it cannot adapt to dynamic changes in sediment concentration, easily leading to pump overload or no-load operation, and compromising system efficiency and safety.

[0007] The second method is a simple control method based on single-parameter feedback, which adjusts the pump speed only according to the change of a single parameter (such as motor current, outlet pressure, etc.). Although this method is an improvement over the fixed pump speed method, it cannot fully reflect the operating status of the pumping system because it only considers a single parameter, resulting in limited control accuracy and poor performance under complex operating conditions.

[0008] Thirdly, there is the traditional PID control-based regulation method, which uses a proportional-integral-derivative (PID) controller to adjust the pump speed. While this method can achieve a certain degree of automatic adjustment, because PID control is based on linear theory, it is difficult to cope with the strong nonlinearity and multivariate coupling characteristics in the sediment transport process. The control effect is often unsatisfactory, especially when the sediment concentration fluctuates drastically, the PID controller is prone to oscillation or overshoot, affecting the stability of the system.

[0009] Furthermore, existing sediment concentration measurement technologies also have significant drawbacks. Currently, there are two main methods for concentration measurement: one is direct measurement using online concentration meters, such as ultrasonic and gamma-ray concentration meters. While these dedicated concentration meters offer high accuracy, they suffer from high cost, large size, and installation difficulties, especially on space-constrained equipment like underwater dredging robots, where installing online concentration meters faces significant space limitations and cost pressures. The other method involves measuring concentration through sampling and analysis, i.e., periodically collecting mud samples and obtaining concentration values ​​through laboratory analysis. While this method is lower in cost, it lacks real-time performance, failing to reflect changes in sediment concentration promptly and making it unsuitable for real-time control.

[0010] Regarding the operating environment of underwater dredging robots, the unique nature of their working environment presents other technical challenges. Firstly, underwater dredging robots typically operate in complex terrain such as the bottom of docks and below piers. Uneven ground, slippery steel plates, and low-adhesion surfaces like silt cause tracked / wheeled chassis to easily slip or tilt, resulting in high-frequency vibration interference. These attitude changes and vibration interference severely affect the measurement accuracy of key parameters such as pump inlet / outlet pressure difference, sediment concentration, and pump power. For example, when the robot tilts, the relative height between the pump inlet and outlet changes, causing the pressure difference measurement to deviate from the true value; when the robot vibrates, the fluid state is disturbed, interfering with concentration and power measurements. Secondly, underwater dredging operations inevitably stir up large amounts of sediment, leading to water turbidity and further complicating concentration measurement. The combined effects of these environmental factors make existing pump speed control methods ill-suited to the complex operating environment of underwater dredging robots.

[0011] While some research and applications have been conducted regarding pump speed control for underwater dredging robots, each has its limitations. For example, in the field of cutter suction dredgers, although pump speed regulation technologies based on the coordinated control of multiple parameters such as concentration, flow rate, and pressure exist, these technologies are mainly designed for the stable operating conditions of large cutter suction dredgers and do not consider the unique attitude changes and vibration interference issues of underwater dredging robots, thus they cannot be directly applied to underwater dredging robots. In the field of robot control, although adaptive control methods based on intelligent control algorithms such as neural networks and fuzzy logic exist, these methods are mostly used for robot motion control and do not involve pump speed control of sediment transport systems, and lack specific optimization for sediment concentration fluctuations.

[0012] In summary, the existing technology has the following main problems:

[0013] First, existing pump speed control methods cannot adapt to drastic fluctuations in sediment concentration. Fixed pump speed cannot respond to concentration changes, and while single-parameter feedback and PID control methods can achieve a certain degree of automatic adjustment, their control accuracy is limited due to the lack of specific consideration for sediment concentration fluctuations, making it difficult to cope with complex operating conditions.

[0014] Secondly, existing sediment concentration measurement technologies suffer from high costs, installation difficulties, and poor real-time performance. While online concentration meters offer high accuracy, they are also expensive and bulky, and while sampling and testing methods are low-cost, they lack real-time performance. Neither of these methods can meet the needs of underwater dredging robots for low-cost, real-time concentration measurement.

[0015] Third, existing pump speed control methods do not consider the impact of unique attitude changes and vibration disturbances of underwater dredging robots on parameter measurements. The robot's pitch, roll, and other attitude changes can lead to errors in pressure difference measurement, while its vibrations can cause errors in concentration and power measurement. These measurement errors affect the accuracy of control decisions and reduce control effectiveness.

[0016] Fourth, existing pump speed control methods do not establish the coupling relationship between multiple parameters such as concentration, power, and pressure difference. In actual sediment transport processes, there is a complex coupling relationship between the three parameters of concentration, power, and pressure difference. Single-parameter or simple multi-parameter superposition control cannot reflect this coupling relationship, thus limiting control accuracy.

[0017] Fifth, existing pump speed control methods lack comprehensive optimization of energy consumption and equipment wear. Under complex operating conditions, pump overload or no-load operation will lead to increased energy consumption and aggravated equipment wear. Existing control methods are difficult to achieve comprehensive optimization of energy consumption and equipment wear while ensuring delivery efficiency.

[0018] Therefore, a pump speed control method for underwater dredging robots is needed to address the problem that existing control methods cannot adapt to fluctuations in sediment concentration, as well as the measurement errors caused by attitude changes and vibration interference, and the high cost and poor real-time performance of concentration measurement. This would enable intelligent and precise control of pump speed, thereby improving the efficiency and safety of underwater dredging operations. Summary of the Invention

[0019] To address the aforementioned issues, an online concentration feedback pump speed control method and system for transporting high-concentration sediment is provided. By using attitude data correction and a concentration-power-pressure difference coupled control model, the problem of low pump speed control accuracy caused by sediment concentration fluctuations is solved.

[0020] To address the problems of existing technologies, this invention provides an online concentration feedback pump speed control method for transporting high-concentration sediment, comprising the following steps:

[0021] Step S1: Obtain monitoring data for the target pump body.

[0022] Used to obtain the detection pressure difference between the inlet and outlet of the target pump body, the monitoring concentration at the outlet of the target pump body, and the monitoring power of the target pump body;

[0023] Step S2: Acquire carrier attitude water and correct monitoring data.

[0024] Acquire the attitude data of the carrier in which the target pump is located, and dynamically correct the monitoring differential pressure data, monitoring concentration and monitoring power obtained in step S1 based on the attitude data to obtain the actual differential pressure data, actual concentration data and actual power data.

[0025] Step S3: Obtain the target pump speed based on the coupled control model.

[0026] Based on the actual differential pressure data, actual concentration data, and actual power data obtained in step S2, the target pump speed is obtained based on the preset concentration-power-differential pressure coupling control model.

[0027] Step S4: Obtain control data for the target pump body based on the target pump speed.

[0028] Based on the target pump speed obtained in step S3, control data for the target pump body is generated.

[0029] In some examples of the present invention, step S1 further includes acquiring monitoring dynamic pressure data at the outlet of the target pump body, and obtaining the monitoring concentration based on the monitoring dynamic pressure data and the monitoring differential pressure data, using a preset concentration calculation model; wherein, the concentration calculation model formula is:

[0030] ;

[0031] in, This indicates the concentration, specifically the indirectly obtained monitoring concentration. Indicates monitoring dynamic pressure. Indicates the monitored pressure difference. and These are preset linear parameters, which can be obtained by calibration using liquids of different concentrations.

[0032] In some examples of the present invention, step S2 includes:

[0033] Step S2.1: Obtain the attitude data of the carrier on which the target pump is located.

[0034] The attitude data of the carrier is collected in real time by attitude sensors (such as IMU inertial measurement units) installed on the carrier. The collected parameters include vertical acceleration, pitch angle characteristics and roll angle characteristics.

[0035] In some examples of the present invention, step S2 includes:

[0036] Step S2.2: Correct the monitored pressure difference based on the attitude data to obtain the actual pressure difference.

[0037] The static position difference data of the pump body is obtained, and the pressure compensation value is obtained by combining the attitude data. The monitored pressure difference is corrected based on the obtained pressure compensation value to obtain the actual pressure difference.

[0038] In some examples of the present invention, step S2 includes:

[0039] Step S2.3: Obtain the vibration index based on the attitude data.

[0040] The statistical characteristic values ​​of vertical acceleration, pitch angle, and roll angle obtained in step S2.1 are statistically analyzed. The acceleration disturbance index is obtained based on the statistical characteristic value of vertical acceleration, and the angular velocity disturbance index is obtained based on the statistical characteristic values ​​of pitch angle and roll angle. Finally, the vibration index is obtained based on the acceleration disturbance index and the angular velocity disturbance index.

[0041] In some examples of the present invention, step S2 includes:

[0042] Step S2.4: Correct the monitored concentration and power based on the vibration index to obtain the actual concentration and actual power.

[0043] A preset first sensitivity coefficient is used, and the monitored concentration is corrected according to the vibration index to obtain the actual concentration;

[0044] A second sensitivity coefficient is preset, and the monitored power is corrected according to the vibration index to obtain the actual power.

[0045] In some examples of the present invention, in step S3, the concentration-power-pressure difference coupling control model is:

[0046] A neural network model trained using big data based on actual pressure difference, actual concentration, and actual power; or...

[0047] A multivariate nonlinear regression model was obtained by fitting historical data based on actual pressure difference, actual concentration, and actual power.

[0048] An online concentration feedback pump speed control system for high-concentration sediment transport, comprising:

[0049] The monitoring module is used to acquire the monitoring differential pressure, monitoring concentration, and monitoring power at the target pump port;

[0050] The correction module is used to acquire the attitude data of the carrier in which the target pump is located, and correct the monitoring pressure difference, monitoring concentration and monitoring power based on the attitude data to obtain the actual pressure difference, actual concentration and actual power;

[0051] The analysis module is used to obtain the target pump speed based on the actual pressure difference, actual concentration, and actual power, using a preset concentration-power-pressure difference coupling control model.

[0052] The control module is used to obtain control data for the target pump body based on the target pump speed.

[0053] A data processing device, comprising:

[0054] Memory, used to store computer programs;

[0055] A processor is used to implement an online concentration feedback pump speed control method for transporting high-concentration sediments when executing the computer program.

[0056] A computer-readable storage medium storing a computer program that, when executed by a processor, implements an online concentration feedback pump speed control method for transporting high-concentration sediment.

[0057] The advantages of this invention compared to the prior art are:

[0058] (1) Eliminate measurement interference, ensure data authenticity, and reduce sensor dependence.

[0059] This invention dynamically corrects the monitored pressure difference, concentration, and power using attitude data, effectively eliminating measurement interference caused by posture changes such as vibration and tilt of the underwater robot, ensuring data accuracy. Pressure difference is corrected by calculating pressure compensation values ​​using pitch angle and static position difference, and concentration and power are corrected using vibration index, thus eliminating contamination from carrier motion and fluid noise. Simultaneously, through an indirect concentration calculation method, concentration monitoring can be achieved using only pressure and flow sensors, significantly reducing reliance on expensive online concentration meters, solving the problems of high cost and limited installation space, and improving engineering practicality.

[0060] (2) Achieve intelligent and refined control, adapt to concentration fluctuations, and accurately match the delivery load.

[0061] This invention achieves intelligent and precise pump speed control through a concentration-power-pressure difference coupling control model, enabling adaptive control of sediment concentration fluctuations. This model accurately characterizes the complex coupling relationship between actual pressure difference, concentration, and power, overcoming the limitations of traditional control methods. When the sediment concentration is too high, the system automatically reduces the pump speed to prevent accidents such as motor overload, pump blockage, and motor burnout caused by pump overload; when the concentration is too low, the system automatically increases the pump speed to avoid inefficiency and energy waste caused by pump idling, ensuring efficient transport.

[0062] (3) Significantly reduce energy consumption, reduce equipment wear and tear, and improve system economy and reliability.

[0063] This invention, through precise pump speed control, ensures the pumping system always operates at its optimal point, significantly reducing system energy consumption. Automatically adjusting the pump speed based on actual operating conditions avoids unnecessary energy consumption and significantly reduces operating costs. Simultaneously, precise control prevents pump overload and no-load operation, reducing abnormal wear on critical components such as impellers and bearings, extending equipment lifespan, and reducing maintenance frequency and costs. By reducing failure rates and improving operational continuity, this invention significantly enhances the system reliability and economic efficiency of the underwater dredging robot. Attached Figure Description

[0064] Figure 1 The flowchart of the online concentration feedback pump speed control method for high-concentration sediment transportation provided by the present invention is shown below.

[0065] Figure 2 for Figure 1 A detailed step diagram of step S102 is shown below; Detailed Implementation

[0066] To further understand the features, technical means, and specific objectives and functions achieved by the present invention, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0067] Reference Figure 1 As shown, an online concentration feedback pump speed control method for transporting high-concentration sediment includes the following steps:

[0068] Step S1: Obtain monitoring data for the target pump body.

[0069] Used to obtain the detection pressure difference between the inlet and outlet of the target pump body, the monitoring concentration at the outlet of the target pump body, and the monitoring power of the target pump body;

[0070] Pressure sensors are installed at both the pump inlet and outlet to acquire the monitored pressure difference. The monitored concentration can be obtained using an ultrasonic concentration meter, and the monitored power can be directly acquired via the motor controller. To mitigate the high cost and limited installation space of online concentration meters, this step preferably employs an indirect concentration calculation method. Specifically, by acquiring the monitored pressure difference and power of the target pump, and simultaneously acquiring the monitored dynamic pressure at the pump outlet, the monitored concentration is obtained based on the monitored dynamic pressure and pressure difference, using a pre-defined concentration calculation model.

[0071] Step S2: Acquire carrier attitude water and correct monitoring data.

[0072] Acquire the attitude data of the carrier in which the target pump is located, and dynamically correct the monitoring differential pressure data, monitoring concentration and monitoring power obtained in step S1 based on the attitude data to obtain the actual differential pressure data, actual concentration data and actual power data.

[0073] Attitude data describes the underwater attitude and motion of the carrier (in this application, the carrier refers to any device capable of carrying a pump and performing underwater dredging operations, including underwater dredging robots), such as displacement, velocity, and acceleration in various directions. This step uses a layered and step-by-step refined correction strategy to remove the contamination of key parameters by carrier motion and fluid noise.

[0074] Step S3: Obtain the target pump speed based on the coupled control model.

[0075] Based on the actual differential pressure data, actual concentration data, and actual power data obtained in step S2, the target pump speed is obtained based on the preset concentration-power-differential pressure coupling control model.

[0076] Step S4: Obtain control data for the target pump body based on the target pump speed.

[0077] Based on the target pump speed obtained in step S3, control data for the target pump body is generated.

[0078] Once the target pump speed is obtained, the control system can dynamically adjust the pump speed by combining PID control with fuzzy logic to adjust the frequency converter or pump control module. This control method can be deployed on small underwater tracked dredging robots, using readily available embedded main control boards, along with three-channel analog input modules and low-power frequency converters to construct a control closed loop. Through precise control based on the target pump speed, it can adapt to fluctuations in sediment concentration, accurately match the transport load, effectively avoid pump overload and no-load operation, and ultimately significantly reduce system energy consumption and equipment wear, greatly improving the safety, stability, and operational efficiency of high-concentration sediment transport processes.

[0079] In some examples of the present invention, step S1 further includes acquiring monitoring dynamic pressure data at the outlet of the target pump body, and obtaining the monitoring concentration based on the monitoring dynamic pressure data and the monitoring differential pressure data, using a preset concentration calculation model; wherein, the concentration calculation model formula is:

[0080] ;

[0081] in, This indicates the concentration, specifically the indirectly obtained monitoring concentration. Indicates monitoring dynamic pressure. This indicates the monitored differential pressure (which can be controlled to be non-zero during implementation). and These are preset linear parameters, which can be obtained by calibration using liquids of different concentrations.

[0082] In some examples of the present invention, reference is made to Figure 2 As shown, step S2 includes:

[0083] Step S2.1: Obtain the attitude data of the carrier on which the target pump is located.

[0084] The attitude data of the carrier is collected in real time by attitude sensors (such as IMU inertial measurement units) installed on the carrier. The collected parameters include vertical acceleration, pitch angle characteristics and roll angle characteristics.

[0085] Attitude data includes vertical acceleration Pitch angle characteristics and roll angle characteristics The attitude data is acquired by attitude sensors (such as IMU inertial measurement units) mounted on the vehicle. Vertical acceleration refers to the acceleration of the vehicle (underwater dredging robot) in the vertical direction, reflecting the intensity of vertical vibration. Pitch angle is the angle at which the vehicle tilts forward / backward (climbing / descending). Roll angle is the angle at which the vehicle tilts left / right (crossing ditches). Pitch and roll angle features are data characteristics obtained based on these two angles; they can be the angles themselves, or angular velocity, angular acceleration, etc. This attitude data reflects the motion state of the vehicle in a complex underwater environment, including vibration interference caused by track slippage and high-frequency shaking, as well as attitude changes such as pitch and roll occurring in complex terrain such as the bottom of a dock.

[0086] In some examples of the present invention, reference is made to Figure 2 As shown, step S2 includes:

[0087] Step S2.2: Correct the monitored pressure difference based on the attitude data to obtain the actual pressure difference.

[0088] The static position difference data of the pump body is obtained, and the pressure compensation value is obtained by combining the attitude data. The monitored pressure difference is corrected based on the obtained pressure compensation value to obtain the actual pressure difference.

[0089] This step is used to correct the monitoring pressure difference obtained in step S1 based on the attitude data, so as to obtain the actual pressure difference and solve the problem that the static pressure difference at the pump inlet and outlet is misread as the flow resistance pressure difference due to the change of the carrier attitude.

[0090] Specifically, firstly, the static position difference data of the pump body is obtained. This static position difference data refers to the difference in position between the pump body's inlet and outlet, such as height difference, width difference, etc. Then, based on the attitude data and the static position difference data, a pressure compensation value is obtained. This pressure compensation value is the data obtained by eliminating pressure difference error based on the static position difference data and the actual attitude data. Finally, the monitored pressure difference is corrected based on the pressure compensation value to obtain the actual pressure difference.

[0091] The calculation method for pressure compensation can be flexibly adjusted according to the actual situation. For example, assuming that the pump's inlet and outlet are at the same height on a horizontal surface and are both located on the same side of the robot's forward direction, the specific process of obtaining the actual pressure difference can be represented by the following formula:

[0092] ;

[0093] in, This represents the actual pressure difference; To monitor the pressure difference (from step S1); The density of the mud can be approximated by the measured value of a pre-collected sample; g is the acceleration due to gravity. The lateral positional difference between the inlet and outlet is denoted by ; sin is the sine function; θ is the pitch angle (obtained from the attitude data in step S2.1). Where ... This is the pressure compensation value.

[0094] In some examples of the present invention, reference is made to Figure 2 As shown, step S2 includes:

[0095] Step S2.3: Obtain the vibration index based on the attitude data.

[0096] The statistical characteristic values ​​of vertical acceleration, pitch angle, and roll angle obtained in step S2.1 are statistically analyzed. The acceleration disturbance index is obtained based on the statistical characteristic value of vertical acceleration, and the angular velocity disturbance index is obtained based on the statistical characteristic values ​​of pitch angle and roll angle. Finally, the vibration index is obtained based on the acceleration disturbance index and the angular velocity disturbance index.

[0097] This step is used to obtain the vibration index based on the attitude data. The vibration index is used to quantify the vibration intensity of the carrier and to provide a basis for subsequent correction of the monitoring concentration and monitoring power.

[0098] Specifically, firstly, statistical characteristic values ​​of vertical acceleration, pitch angle characteristics, and roll angle characteristics are calculated. These statistical characteristic values ​​refer to statistical indicators such as variance and standard deviation, used to quantify the fluctuation degree of vertical acceleration, pitch angle characteristics, and roll angle characteristics. Then, based on the statistical characteristic value of vertical acceleration, an acceleration disturbance index is obtained; simultaneously, based on the statistical characteristic values ​​of pitch angle and roll angle characteristics, an angular velocity disturbance index is obtained. Finally, based on the acceleration disturbance index and the angular velocity disturbance index, a vibration index is obtained. Using the root mean square value as the statistical characteristic value and pitch angular velocity and roll angular velocity as attitude data, a more specific embodiment of the above process is as follows:

[0099] ;

[0100] in, The vibration index, and These are preset acceleration and angular velocity weights, which can be obtained through experimental calibration. This indicates the calculation of the root mean square value. For vertical acceleration, This refers to the pitch angular velocity (pitch angle characteristic). denoted as ω, where ω is the roll angular velocity (roll angular characteristic). In the formula... For acceleration disturbance index, The angular velocity disturbance index.

[0101] In some examples of the present invention, reference is made to Figure 2 As shown, refer to Figure 2As shown, step S2 includes:

[0102] Step S2.4: Correct the monitored concentration and power based on the vibration index to obtain the actual concentration and actual power.

[0103] A preset first sensitivity coefficient is used, and the monitored concentration is corrected according to the vibration index to obtain the actual concentration;

[0104] A second sensitivity coefficient is preset, and the monitored power is corrected according to the vibration index to obtain the actual power.

[0105] This step is used to correct the monitored concentration and monitored power obtained in step S1 based on the vibration index obtained in step S2.3, so as to obtain the actual concentration and actual power, in order to filter out noise from different sources and ensure that the data on which the control model depends remains true and reliable under severe and complex vibration environment.

[0106] Specifically, this step fully considers the difference in response between concentration and power signals under vibration. Concentration measurements are easily affected by particle scattering noise, while power readings may fluctuate due to mechanical vibration. Therefore, independent coefficient corrections are applied to both. First, a preset first sensitivity coefficient and a second sensitivity coefficient are obtained, which can be calibrated. Then, based on the first sensitivity coefficient α, the monitored concentration (from step S1) is corrected according to the vibration index to obtain the actual concentration; simultaneously, based on the second sensitivity coefficient β, the monitored power (from step S1) is corrected according to the vibration index to obtain the actual power. A more specific embodiment of the above process is as follows:

[0107] ;

[0108] ;

[0109] Wherein, Ccorr is the actual concentration; Cmeas is the monitored concentration (from step S1); e is the natural constant; α is the first sensitivity coefficient; VI is the vibration index (from step S2.3); Pcorr is the actual power; Pmeas is the monitored power (from step S1); and β is the second sensitivity coefficient.

[0110] In some examples of the present invention, in step S3, the concentration-power-pressure difference coupling control model is:

[0111] A neural network model trained using big data based on actual pressure difference, actual concentration, and actual power; or...

[0112] A multivariate nonlinear regression model was obtained by fitting historical data based on actual pressure difference, actual concentration, and actual power.

[0113] Specifically, the concentration-power-pressure difference coupling control model is a pre-designed calculation model that derives the optimal pump speed based on three variables. This model can accurately characterize the complex coupling relationship between actual pressure difference, actual concentration, and actual power, overcoming the limitations of traditional linear or single-variable control models in highly nonlinear and multi-disturbance scenarios such as high-concentration sediment transport. The pre-designed concentration-power-pressure difference coupling control model can be implemented using one of the following two methods:

[0114] The first approach is a multivariate nonlinear regression model fitted from historical data. This model, through learning and fitting a large amount of historical operating data, transforms the dynamic correlation of "concentration fluctuation—power change—pressure difference response," which was originally difficult to describe with explicit formulas, into a computable multivariate nonlinear function. This allows control decisions to simultaneously consider the real-time states of the three key parameters and their interactions. A specific formula for a concentration-power-pressure difference coupled control model is expressed as:

[0115] ;

[0116] in, For the target pump speed, , , and All are fitting coefficients; This is the actual pressure difference (from step S2.2); The actual concentration (from step S2.4); This is the actual power (from step S2.4).

[0117] The second approach involves using a neural network model trained on large datasets. This model relies on extensive operational data covering varying sediment concentrations, pump loads, and pipeline conditions. Through offline or online training, a neural network is constructed, enabling it to automatically learn the implicit high-order coupling relationships between actual pressure differentials, actual concentrations, and actual power. Upon inputting real-time data, it directly maps to the optimal target pump speed. Compared to the fixed structure of explicit mathematical models, neural networks can dynamically adjust their internal weights based on data characteristics, providing robust control outputs even under unknown or mixed operating conditions (such as sudden concentration changes coupled with vibration disturbances).

[0118] An online concentration feedback pump speed control system for high-concentration sediment transport, comprising:

[0119] The monitoring module is used to acquire the monitoring differential pressure, monitoring concentration, and monitoring power at the target pump port;

[0120] The correction module is used to acquire the attitude data of the carrier in which the target pump is located, and correct the monitoring pressure difference, monitoring concentration and monitoring power based on the attitude data to obtain the actual pressure difference, actual concentration and actual power;

[0121] The analysis module is used to obtain the target pump speed based on the actual pressure difference, actual concentration, and actual power, using a preset concentration-power-pressure difference coupling control model.

[0122] The control module is used to obtain control data for the target pump body based on the target pump speed.

[0123] A data processing device, comprising:

[0124] Memory, used to store computer programs;

[0125] A processor is used to implement an online concentration feedback pump speed control method for transporting high-concentration sediments when executing the computer program.

[0126] A computer-readable storage medium storing a computer program that, when executed by a processor, implements an online concentration feedback pump speed control method for transporting high-concentration sediment.

[0127] The above embodiments only illustrate one or more implementations of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims

1. An online concentration feedback pump speed control method for transporting high-concentration sediment, characterized in that, Includes the following steps: Step S1: Obtain monitoring data for the target pump body. Used to obtain the detection pressure difference between the inlet and outlet of the target pump body, the monitoring concentration at the outlet of the target pump body, and the monitoring power of the target pump body; Step S2: Acquire carrier attitude water and correct monitoring data. Acquire the attitude data of the carrier in which the target pump is located, and dynamically correct the monitoring differential pressure data, monitoring concentration and monitoring power obtained in step S1 based on the attitude data to obtain the actual differential pressure data, actual concentration data and actual power data. Step S3: Obtain the target pump speed based on the coupled control model. Based on the actual differential pressure data, actual concentration data, and actual power data obtained in step S2, the target pump speed is obtained based on the preset concentration-power-differential pressure coupling control model. Step S4: Obtain control data for the target pump body based on the target pump speed. Based on the target pump speed obtained in step S3, control data for the target pump body is generated.

2. The online concentration feedback pump speed control method for high-concentration sediment transport according to claim 1, characterized in that, Step S1 also includes acquiring the monitoring dynamic pressure data at the outlet of the target pump body, and obtaining the monitored concentration based on the monitoring dynamic pressure data and the monitoring differential pressure data, using a preset concentration calculation model; wherein, the concentration calculation model formula is: ; in, This indicates the concentration, specifically the indirectly obtained monitoring concentration. Indicates monitoring dynamic pressure. Indicates the monitored pressure difference. and These are preset linear parameters, which can be obtained by calibration using liquids of different concentrations.

3. The online concentration feedback pump speed control method for high-concentration sediment transport according to claim 1, characterized in that, Step S2 includes: Step S2.1: Obtain the attitude data of the carrier on which the target pump is located. The attitude data of the carrier is collected in real time by attitude sensors (such as IMU inertial measurement units) installed on the carrier. The collected parameters include vertical acceleration, pitch angle characteristics and roll angle characteristics.

4. The online concentration feedback pump speed control method for high-concentration sediment transport according to claim 3, characterized in that, Step S2 includes: Step S2.2: Correct the monitored pressure difference based on the attitude data to obtain the actual pressure difference. The static position difference data of the pump body is obtained, and the pressure compensation value is obtained by combining the attitude data. The monitored pressure difference is corrected based on the obtained pressure compensation value to obtain the actual pressure difference.

5. The online concentration feedback pump speed control method for high-concentration sediment transport according to claim 3, characterized in that, Step S2 includes: Step S2.3: Obtain the vibration index based on the attitude data. The statistical characteristic values ​​of vertical acceleration, pitch angle, and roll angle obtained in step S2.1 are statistically analyzed. The acceleration disturbance index is obtained based on the statistical characteristic value of vertical acceleration, and the angular velocity disturbance index is obtained based on the statistical characteristic values ​​of pitch angle and roll angle. Finally, the vibration index is obtained based on the acceleration disturbance index and the angular velocity disturbance index.

6. The online concentration feedback pump speed control method for high-concentration sediment transport according to claim 5, characterized in that, Step S2 includes: Step S2.4: Correct the monitored concentration and power based on the vibration index to obtain the actual concentration and actual power. A preset first sensitivity coefficient is used, and the monitored concentration is corrected according to the vibration index to obtain the actual concentration; A second sensitivity coefficient is preset, and the monitored power is corrected according to the vibration index to obtain the actual power.

7. The online concentration feedback pump speed control method for high-concentration sediment transport according to claim 1, characterized in that, In step S3, the concentration-power-pressure difference coupling control model is as follows: A neural network model trained using big data based on actual pressure difference, actual concentration, and actual power; or... A multivariate nonlinear regression model was obtained by fitting historical data based on actual pressure difference, actual concentration, and actual power.

8. An online concentration feedback pump speed control system for transporting high-concentration sediment, characterized in that, An online concentration feedback pump speed control method for transporting high-concentration sediment as described in any one of claims 1-7, comprising: The monitoring module is used to acquire the monitoring differential pressure, monitoring concentration, and monitoring power at the target pump port; The correction module is used to acquire the attitude data of the carrier in which the target pump is located, and correct the monitoring pressure difference, monitoring concentration and monitoring power based on the attitude data to obtain the actual pressure difference, actual concentration and actual power; The analysis module is used to obtain the target pump speed based on the actual pressure difference, actual concentration, and actual power, using a preset concentration-power-pressure difference coupling control model. The control module is used to obtain control data for the target pump body based on the target pump speed.

9. A data processing device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the online concentration feedback pump speed control method for high-concentration sediment transport as described in any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the online concentration feedback pump speed control method for high-concentration sediment transport as described in any one of claims 1 to 10.

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