River dredging flow control method and system

Abstract

The embodiment of the invention provides a riverway dredging flow control method and system, and relates to the technical field of riverway dredging flow control, the method comprises the steps that operation data of a riverway dredging system are collected, and the operation data comprise a flow meter feedback value, mud pump operation parameters, pipeline pressure, valve opening and mud concentration; based on the operation data, the abnormal types of the river dredging system are recognized, and the abnormal types comprise flowmeter measurement errors, valve mechanical response hysteresis and slurry rheological characteristic anomalies; and adjusting a control strategy according to the exception type. The river dredging operation efficiency, the equipment safety and the environmental protection level can be improved.

Description

Technical Field

[0001] This application relates to the field of river dredging flow control technology, and in particular to a method and system for river dredging flow control. Background Technology

[0002] In river dredging operations, precise control of slurry flow rate is crucial for ensuring operational efficiency, equipment safety, and environmental protection. Traditional flow control systems typically rely on real-time feedback from flow meters to adjust pump speed and valve opening. However, in actual operations, especially when dredged slurry contains highly viscous fine-particle clay and fibrous residues, existing methods face a series of complex and interconnected challenges, leading to inaccurate flow control and the risk of pipeline sedimentation or even blockage. Specifically, when dredged slurry contains high viscosity and fibrous residues, it causes mechanical lag in the flow control valve actuator and measurement errors in the flow meter. When the valve actuator receives an opening adjustment command, the accumulation of slurry and fibrous deposits on the internal sealing surfaces and valve stem sliding parts causes a slight lag in its mechanical response, preventing it from accurately achieving the required opening. Meanwhile, the flow meter's sensing probe is immersed and washed in the mud for a long time, and its measuring window surface is covered with highly viscous mud and fine particles, forming a fouling film. This interferes with the normal measurement principle of the flow meter, causing the flow feedback value fed back by the flow meter to be lower than the actual mud flow, resulting in a persistent measurement error. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method and system for controlling river dredging flow, aiming to improve the efficiency, equipment safety, and environmental protection level of river dredging operations.

[0004] In a first aspect, embodiments of this application provide a method for controlling river dredging flow, including: Collect operational data of the river dredging system, including flow meter feedback values, mud pump operating parameters, pipeline pressure, valve opening degree, and mud concentration; Based on the operational data, the abnormality types of the river dredging system are identified, including flow meter measurement errors, valve mechanical response hysteresis, and abnormal mud rheological properties. Based on the type of anomaly, the control strategy is adjusted, and the control strategy includes at least one of the following: When the anomaly type is the flow meter measurement error, switch the flow feedback source; When the abnormality type is the valve mechanical response hysteresis, valve feedforward compensation adjustment is performed; When the anomaly type is the aforementioned mud rheological characteristic anomaly, the critical flow velocity target value is adaptively adjusted.

[0005] According to some embodiments of this application, the step of adaptively adjusting the target value of the critical flow velocity when the anomaly type is the abnormality of the mud rheological properties includes: When the rheological properties of the mud are abnormal, collect the acoustic characteristic data of the pipeline and the monitoring data of the pipeline wall thickness. Based on the pipeline acoustic characteristic data and the pipeline wall thickness monitoring data, the degree of pipeline erosion and the intensity of abrasive particles are inferred. The pipe roughness parameters are adjusted based on the degree of pipe erosion and the intensity of the abrasive particles. The actual flow rate is estimated based on the corrected pipe roughness parameters, mud pump operating parameters, pipe pressure, and mud concentration. Assess the reliability of the flow meter's feedback values ​​and the estimated actual flow rates; Based on the trust level, the flow meter feedback value and the estimated actual flow value are weighted and fused to obtain the decision flow. The decision flow rate is used as the flow feedback source, and the critical flow velocity target value is dynamically adjusted according to the mud concentration, abrasive particle intensity and pipeline erosion degree to maintain the mud flow velocity always above the critical settling velocity.

[0006] According to some embodiments of this application, the step of adjusting the control strategy according to the anomaly type further includes: When there are two or more types of the aforementioned anomalies, it is determined to be a combined anomaly; Based on the combined anomalies, the activation sequence, intensity, or coordination parameters of the control strategy are adjusted. The control strategy includes: adjusting the weight of the flow feedback source switching, the advance and overshoot of valve feedforward compensation, the controller gain coefficient, and the critical flow velocity target value according to the most important anomaly mode and the degree of their mutual influence.

[0007] According to some embodiments of this application, the step of adjusting the activation sequence, intensity, or coordination parameters of the control strategy based on the said combination anomaly includes: When a combined anomaly is identified, collect data on mud flow velocity, critical settling velocity, pipeline sediment thickness variation trend, and the difference between mud pump power consumption and target energy consumption. When the mud flow rate is close to or below the critical settling velocity and the thickness of the pipe deposits shows an increasing trend, the control strategy of increasing the flow rate and preventing deposition is preferentially activated or enhanced. When the mud flow rate is consistently higher than the critical settling velocity and the thickness of the sediment in the pipeline shows a decreasing trend, the control strategy is adjusted to reduce energy consumption based on the difference between the power consumption of the mud pump and the target energy consumption.

[0008] According to some embodiments of this application, adjusting the activation sequence, intensity, or coordination parameters of the control strategy based on combined anomalies includes: Based on the combined anomalies, assess the most prominent anomaly patterns and the degree of their mutual influence; Based on the most prevalent abnormal patterns and the degree of their mutual influence, the activation sequence, intensity, and coordination parameters of the control strategy are dynamically adjusted.

[0009] According to some embodiments of this application, after the step of adjusting the activation sequence, intensity, or coordination parameters of the control strategy based on the combination anomaly, the method further includes: Acquire the trends in mud flow rate, pipe sediment thickness, and mud pump power consumption; The adjusted parameters are fine-tuned based on the mud flow rate, the trend of sediment thickness variation in the pipeline, and the power consumption of the mud pump. While fine-tuning the parameters, the changing trends of all remaining parameters are monitored simultaneously; When unexpected fluctuations are detected in the remaining parameters, immediately pause the current fine-tuning and analyze the cause of the fluctuations; Based on the cause of the fluctuations, adjust the fine-tuning parameters or activation sequence to eliminate or reduce nonlinear coupling effects.

[0010] According to some embodiments of this application, the step of adjusting the fine-tuning parameters or activation sequence according to the cause of the fluctuation to eliminate or reduce the nonlinear coupling effect includes: Continuously collect data on mud viscosity, mud density, particle size distribution, pipe inner wall roughness, and the impact frequency and intensity of abrasive particles; Based on the mud viscosity, mud density, particle size distribution, pipe inner wall roughness, and impact frequency and intensity of abrasive particles, calculate the dynamic characteristic parameters of the nonlinear coupling effect; Based on the dynamic characteristic parameters of the nonlinear coupling effect, adjust the fine-tuning parameters, including the adjustment step size, the adjustment strategy activation interval, and the adjustment strategy activation sequence.

[0011] According to some embodiments of this application, the steps following adjusting the fine-tuning parameter adjustment step size, adjusting the strategy activation interval, and adjusting the strategy activation sequence based on the dynamic characteristic parameters of the nonlinear coupling effect further include: Continuously monitor the instantaneous change rate of mud flow rate, pipeline pressure, and mud pump current; When the instantaneous rate of change of the mud flow rate, the pipeline pressure, and the mud pump current simultaneously exceeds a preset threshold, the update of the current dynamic characteristic parameters of the nonlinear coupling effect is frozen, and a backup stable parameter set is activated. When the instantaneous change rates of the mud flow rate, the pipeline pressure, and the mud pump current are all found to be below a preset threshold, the freezing of the dynamic characteristic parameters of the nonlinear coupling effect is lifted, and the update of the current dynamic characteristic parameters of the nonlinear coupling effect is restored.

[0012] According to some embodiments of this application, the steps of obtaining the mud flow rate, the trend of pipe sediment thickness variation, and the power consumption of the mud pump include: Collect the output values ​​of redundant mud flow rate sensors, sediment thickness sensors, and mud pump operating parameter sensors; The output values ​​of the redundant mud flow rate sensor are checked for consistency, and the output values ​​of the sediment thickness sensor and the mud pump operating parameter sensor are analyzed over time to identify abnormal sensors. Based on the identified abnormal sensors, adjust the weight of the abnormal sensors or exclude the data from the abnormal sensors; Based on the adjusted sensor data, the mud flow rate, the trend of changes in pipeline sediment thickness, and the power consumption of the mud pump are obtained.

[0013] Secondly, embodiments of this application provide a river dredging flow control system, comprising: The data acquisition module is used to collect operational data of the river dredging system, including flow meter feedback values, mud pump operating parameters, pipeline pressure, valve opening degree, and mud concentration. An anomaly identification module is used to identify anomaly types in the river dredging system based on the operational data. The anomaly types include flow meter measurement errors, valve mechanical response hysteresis, and abnormal mud rheological properties. The control strategy adjustment module is used to adjust the control strategy according to the type of exception.

[0014] The technical solution according to the embodiments of this application has at least the following beneficial effects: The river dredging flow control method disclosed in this application obtains the cutting force between the cutter and the sound-absorbing cotton board in real time, and determines the local material characteristics at the cutting point of the sound-absorbing cotton board accordingly. It then dynamically adjusts the vibration frequency and pressing depth of the cutter, thereby achieving precise cutting of the sound-absorbing cotton board while maintaining a constant cutter forward speed. This method effectively solves the problems in the prior art caused by the non-uniformity of the sound-absorbing cotton board material, such as decreased cutting quality, rough or hardened cut edges, dimensional deviations, and accelerated cutter wear. Through real-time perception and adaptive adjustment of local material characteristics, this application can ensure that the cutter always cuts with optimal parameters, avoiding the limitations of traditional fixed-parameter open-loop control, significantly improving cutting accuracy and product quality, while extending the cutter's service life and reducing production costs.

[0015] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0016] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0017] Figure 1 A schematic flowchart of a river dredging flow control method provided in one embodiment of this application; Figure 2 This is a schematic diagram of a river dredging flow control system provided in one embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0020] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: the existence of a alone, the existence of b alone, the existence of c alone, the simultaneous existence of a and b, the simultaneous existence of a and c, the simultaneous existence of b and c, or the simultaneous existence of a, b, and c, where a, b, and c can be single or multiple.

[0021] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0022] Based on the above, this application proposes a method and system for controlling river dredging flow, aiming to improve the efficiency, equipment safety and environmental protection level of river dredging operations.

[0023] The river dredging flow control method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application that implements the river dredging flow control method, but is not limited to the above forms.

[0024] This application can be applied to numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via communication networks. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices. It should be noted that in various specific embodiments of this invention, when processing is required based on data related to the characteristics of an object (e.g., user attributes or sets of attribute information), permission or consent from the corresponding object is obtained first, and the collection, use, and processing of this data comply with relevant laws and standards. Furthermore, when the embodiments of the present invention need to obtain the attribute information of an object, they will obtain the separate permission or separate consent of the corresponding object through pop-up windows or redirection to a confirmation page. After obtaining the separate permission or separate consent of the corresponding object, they will then obtain the relevant data of the object necessary for the embodiments of the present invention to operate normally.

[0025] See Figure 1 , Figure 1This is a flowchart illustrating a river dredging flow control method according to an embodiment of this application. The river dredging flow control method provided in this embodiment includes, but is not limited to, steps S110 to S130, which will be described in detail below.

[0026] Step S110: Collect the operation data of the river dredging system. The operation data includes flow meter feedback value, mud pump operation parameters, pipeline pressure, valve opening degree and mud concentration. Step S120: Based on the operational data, identify the anomaly types of the river dredging system. The anomaly types include flow meter measurement errors, valve mechanical response hysteresis, and abnormal mud rheological properties. Step S130: Adjust the control strategy according to the type of exception.

[0027] In one embodiment, the control strategy includes at least one of the following: When the anomaly type is flow meter measurement error, switch the flow feedback source; When the anomaly type is valve mechanical response hysteresis, valve feedforward compensation adjustment is performed; When the anomaly type is mud rheological property anomaly, the critical flow velocity target value is adaptively adjusted.

[0028] It should be noted that river dredging systems typically include equipment such as mud pumps, pipelines, valves, flow meters, and various sensors, used to transport riverbed sediment to designated areas via pipelines. Operational data refers to the various physical quantities and equipment status parameters collected in real time during the dredging process; this data forms the basis for the system's anomaly identification and strategy adjustments. Flow meter feedback values ​​refer to the mud flow rate data measured by the flow meter; mud pump operating parameters include pump speed and power; pipeline pressure refers to the pressure generated when mud flows within the pipeline; valve opening refers to the degree to which the valve controlling the mud flow rate is open; and mud concentration refers to the content of solid particles in the mud. Anomaly types refer to various faults or abnormal states that may occur during dredging that lead to inaccurate flow control, such as flow meter measurement errors, valve mechanical response lag, and abnormal mud rheological properties. Control strategies refer to the countermeasures taken by the system based on the identified anomaly types, aiming to restore or optimize flow control.

[0029] In one embodiment, the river dredging flow control method of this application first involves collecting operational data of the river dredging system. This operational data forms the basis for anomaly identification and control strategy adjustment. For example, flowmeter feedback values ​​can be obtained using devices such as ultrasonic flowmeters or electromagnetic flowmeters installed on the pipeline. Mud pump operating parameters can be obtained using sensors integrated into the mud pump or externally installed sensors. Pipeline pressure can be obtained using pressure sensors; for example, pressure sensors can be installed at different locations in the pipeline to monitor pressure changes in the mud flow. Valve opening can be obtained using position sensors on the valve actuator; for example, the valve opening angle can be monitored in real time using a potentiometer or encoder. Mud concentration can be obtained using an online densitometer or mud concentration sensor; for example, the solids content of the mud can be measured using a gamma-ray densitometer or ultrasonic densitometer. This operational data can be collected in real time and transmitted to the control system for subsequent processing. Based on the collected operational data, the method of this application can identify anomaly types in the river dredging system. Anomaly types include flowmeter measurement errors, valve mechanical response hysteresis, and abnormal mud rheological properties. For example, flowmeter measurement errors can be identified by analyzing the correlation between flowmeter feedback values ​​and sludge pump operating parameters, pipeline pressure, valve opening, and sludge concentration. A persistent deviation between the flowmeter feedback value and the flow rate calculated based on other parameters may indicate a measurement error. Similarly, valve mechanical response hysteresis can be identified by monitoring the response time difference between the valve opening command and the actual valve opening. If the actual valve opening lags behind the commanded opening, mechanical response hysteresis may exist. Furthermore, abnormal sludge rheological properties can be identified by analyzing the nonlinear relationship between sludge concentration, pipeline pressure, and flowmeter feedback values. Significant changes in the sludge's viscosity, density, and other rheological properties may lead to inaccurate flowmeter measurements or abnormal pipeline resistance. Based on the identified anomaly type, the method of this application can adjust the control strategy. When the anomaly type is a flowmeter measurement error, the flow feedback source can be switched. For example, when the system detects a measurement error in the currently used flowmeter, the feedback value of a backup flowmeter can be switched as the primary flow feedback source, or a virtual flow rate calculated based on sludge pump operating parameters and pipeline pressure can be used as the feedback source. When the anomaly is a valve mechanical response hysteresis, valve feedforward compensation can be implemented. For example, when sending an opening command to the valve, the command can be sent earlier or in excess based on historical data and a hysteresis model to compensate for the valve's mechanical response hysteresis, ensuring that the valve can reach the target opening faster and more accurately. When the anomaly is an abnormality in the rheological properties of the slurry, the critical velocity target value can be adaptively adjusted. For example, when the viscosity or solid particle content of the slurry changes, the critical settling velocity of the slurry will also change. In this case, the system can dynamically adjust the critical velocity target value based on the slurry rheological property parameters to ensure that the slurry flow rate is always higher than the critical settling velocity, preventing solid particles from settling in the pipeline.

[0030] It should be noted that once abnormal mud rheological properties are identified, the system actively collects pipeline acoustic characteristic data and pipeline wall thickness monitoring data. The pipeline acoustic characteristic data reflects the impact frequency and intensity of particles in the mud, thus indirectly indicating the activity of abrasive particles; the pipeline wall thickness monitoring data directly reflects the wear and tear of the pipeline material, used to assess the degree of pipeline erosion. The purpose of collecting this data is to obtain real-time information on the internal wear state of the pipeline and the abrasiveness of the mud. Based on the collected pipeline acoustic characteristic data and pipeline wall thickness monitoring data, the degree of pipeline erosion and the intensity of abrasive particles can be inferred. For example, by analyzing the spectrum and intensity changes of the acoustic signal, the type and concentration of abrasive particles can be identified; by comparing pipeline wall thickness data at different time points, the rate of thinning of the pipeline wall can be calculated, thus quantifying the degree of erosion. These inferences provide a basis for subsequent parameter correction. Based on the inferred pipeline erosion degree and the quantified abrasive particle intensity, the pipeline roughness parameters are corrected. Pipeline roughness is a key parameter affecting fluid resistance, and it changes with the erosion of the pipeline inner wall. By correcting the roughness parameters, the actual hydraulic characteristics of the pipeline can be more accurately reflected. Based on the corrected pipeline roughness parameters, slurry pump operating parameters, pipeline pressure, and slurry concentration, the actual flow rate can be calculated. This calculated flow rate is derived from a physical model and real-time operating parameters, aiming to provide a flow reference independent of the flowmeter feedback value. To obtain more reliable flow information, the system evaluates the confidence level of both the flowmeter feedback value and the calculated actual flow rate value. Confidence level assessment can be based on factors such as historical data, sensor calibration status, anomaly identification results, and the differences between the two. For example, when a flowmeter measurement error is identified, its confidence level decreases. Based on the evaluated confidence level, the flowmeter feedback value and the calculated actual flow rate value are weighted and fused to obtain the decision flow rate. The purpose of weighted fusion is to comprehensively utilize the advantages of both flow information, reduce the error impact of a single data source, and provide a more accurate and robust flow feedback. Finally, the decision flow rate is used as the flow feedback source, and the critical velocity target value is dynamically adjusted according to slurry concentration, abrasive particle intensity, and pipeline erosion degree. This dynamic adjustment not only considers the influence of mud concentration on the critical settling velocity, but also introduces abrasive particle strength and pipeline erosion degree as correction factors to ensure that the mud flow rate can always be maintained above the critical settling velocity under different wear and rheological conditions, thereby effectively preventing deposition and slowing down pipeline wear.

[0031] In one embodiment, assuming that during river dredging operations, the system detects a sudden increase in mud concentration and abnormal fluctuations in flow meter feedback values, indicating a possible abnormality in mud rheological properties, the control system will immediately initiate the detailed adjustment process of this scheme. First, the system collects data from the pipe acoustic sensors and pipe wall thickness monitoring sensors. For example, the acoustic sensors detect an increase in high-frequency impact signals, indicating enhanced activity of abrasive particles; the wall thickness sensor data shows an accelerated rate of local wall thinning, indicating intensified erosion. Next, based on this data, the system infers that the current pipe erosion level is moderate and the abrasive particle intensity is high. Based on this, the pipe roughness parameter is corrected, for example, from an initial 0.0015 to 0.0020. Subsequently, using the corrected pipe roughness parameter, mud pump operating parameters (such as speed and power), pipe pressure, and real-time mud concentration, the system calculates the current actual flow rate as 1500 cubic meters per hour using a fluid dynamics model.

[0032] Meanwhile, the system assesses the flow meter feedback value (e.g., 1300 cubic meters / hour) with a low confidence level (e.g., 0.6), while the estimated actual flow value has a higher confidence level (e.g., 0.9) because the estimated value considers more real-time operating conditions. Based on the confidence level, the system performs a weighted fusion of the two values, for example: Decision Flow = (1300... 0.6 + 1500 (0.9) / (0.6 + 0.9) = 1420 cubic meters / hour. Ultimately, 1420 cubic meters / hour will be used as the new flow feedback source. Simultaneously, based on the current high mud concentration, high abrasive particle intensity, and moderate pipeline erosion, the system dynamically adjusts the critical flow velocity target value, for example, from the initial 2.5 m / s to 2.8 m / s, to ensure that under the current complex operating conditions, the mud flow velocity remains above the critical settling velocity, effectively preventing sedimentation while also controlling pipeline wear.

[0033] It should be noted that a combined anomaly is defined as the simultaneous presence of two or more of the following anomalies: flowmeter measurement error, valve mechanical response lag, and abnormal mud rheological properties. For example, if both flowmeter measurement error and valve mechanical response lag are detected simultaneously, the system will identify this as a combined anomaly mode. Adjusting the activation sequence, intensity, or coordination parameters of the control strategy based on combined anomalies means that when multiple anomalies coexist, the system no longer simply executes control strategies independently for each anomaly. Instead, it comprehensively adjusts the control strategies based on the hierarchy of these anomaly modes and their mutual influence. Specifically, this includes: weighting of flow feedback source switching: When a combination of flowmeter measurement error and other anomalies exists, the system dynamically adjusts the weighting ratio between the flowmeter feedback value and other backup flow sources (such as estimated flow based on mud pump operating parameters and pipeline pressure) according to the severity of the other anomalies and their impact on flow measurement, in order to obtain more reliable flow data. Valve feedforward compensation lead and overshoot: When there is a combination of valve mechanical response lag and other anomalies, the system will adjust the valve action lead and overshoot according to the influence of other anomalies (such as pipeline pressure fluctuations caused by abnormal mud rheological properties) on the valve response, so as to ensure that the valve can reach the target opening more accurately and timely, and avoid new problems caused by over-compensation or under-compensation.

[0034] Controller Gain Coefficient: When multiple anomalies exist, traditional PID or other controller parameters may no longer be applicable. The system dynamically adjusts the proportional, integral, and derivative gain coefficients of the controller based on the characteristics of the combined anomalies to optimize the controller's response speed and stability, preventing control oscillations or slow response. Critical Flow Velocity Target Value: When anomalies in mud rheological properties are combined with other anomalies, the system comprehensively considers the impact of all anomalies on mud settling characteristics and dynamically adjusts the critical flow velocity target value to ensure that the mud flow velocity is always higher than the critical settling velocity, effectively preventing pipeline blockage and avoiding unnecessary energy consumption.

[0035] In one embodiment, it is assumed that during river dredging operations, the system simultaneously detects flowmeter measurement errors and abnormal mud rheological properties. First, the anomaly identification module identifies these two anomalies as a combined anomaly based on the collected operational data. Next, the control strategy adjustment module assesses the most significant anomaly pattern and the degree of their mutual influence based on this combined anomaly. In this example, abnormal mud rheological properties may pose a more direct and severe threat to critical flow velocity and pipeline pressure, while flowmeter measurement errors affect the accuracy of control decisions. Therefore, the system may prioritize adjusting the critical flow velocity target value to ensure that the mud flow velocity is always higher than the corrected critical settling velocity, preventing pipeline blockage. Simultaneously, to address flowmeter measurement errors, the system adjusts the weighting of flow feedback source switching; for example, reducing the weighting of the flowmeter feedback value and increasing the weighting of the actual flow rate calculated based on mud pump operating parameters and pipeline pressure, to provide more reliable flow data as a control basis. Furthermore, the controller gain coefficient may also be fine-tuned to adapt to new flow feedback sources and dynamically changing critical flow velocity target values, ensuring that the system maintains stable and efficient operation even when both anomalies coexist. Through this coordinated adjustment, the system can effectively avoid the risk of mud settling and control based on more accurate flow information, thereby ensuring the smooth progress of dredging operations.

[0036] It should be noted that after a combined anomaly is identified, the system continuously collects a series of key operating parameters. Among these, mud flow velocity refers to the actual flow speed of mud within the pipeline; its accurate measurement is crucial for determining the mud transport status within the pipeline. Critical settling velocity is the minimum flow velocity at which solid particles in the mud begin to settle; maintaining a mud flow velocity above this value is key to preventing pipeline blockage. The trend in pipeline sediment thickness reflects the dynamic process of solid particle deposition or scouring within the pipeline; an increasing trend usually indicates an increased risk of blockage. The difference between the mud pump power consumption and the target energy consumption is used to assess the energy efficiency level of the current operating status. Furthermore, when the mud flow velocity is detected to be close to or below the critical settling velocity and the pipeline sediment thickness shows an increasing trend, this indicates a serious risk of sedimentation within the pipeline, and blockage may have already begun. In this case, the system will prioritize activating or enhancing control strategies aimed at increasing mud flow velocity and preventing sedimentation. For example, it may increase the mud pump speed, adjust valve opening to increase flow rate, or activate pipeline flushing procedures, with the aim of rapidly increasing mud transport capacity and avoiding or alleviating pipeline blockage. Furthermore, when the slurry flow rate is consistently above the critical settling velocity and the sediment thickness in the pipeline shows a decreasing trend, this indicates that the pipeline is currently operating well, with high slurry transport efficiency and no significant risk of sedimentation. Under these safe and stable operating conditions, the system will adjust its control strategy to reduce energy consumption based on the difference between the slurry pump power consumption and the target energy consumption. For example, the slurry pump speed can be appropriately reduced, valve opening optimized, or other operating parameters adjusted to minimize energy consumption while ensuring safe operation, with the aim of improving the economics of dredging operations.

[0037] It's important to note that assessing the dominant anomaly patterns and their interactions means that after identifying combined anomalies, the system further analyzes the impact and interactions of these anomaly patterns on the river dredging system. For example, by analyzing historical data, real-time sensor data, and pre-defined expert rules, it can be determined which anomaly pattern has the most significant impact on the current dredging operation when multiple anomalies coexist—this is the dominant anomaly pattern. Simultaneously, it's necessary to quantify the interactions between different anomaly patterns. For instance, flowmeter measurement errors may occur simultaneously with valve mechanical response hysteresis, but one may have a more direct or significant impact on system flow control. The assessment process can utilize machine learning models, expert systems, or rule-based inference engines to comprehensively analyze the characteristic data of various anomaly patterns, thereby deriving a quantitative assessment result of the dominant anomaly pattern and its interactions. The dynamic adjustment of the activation sequence, intensity, and coordination parameters of the control strategy means that, based on the above evaluation results, the system will no longer simply activate or adjust the control strategy, but will intelligently adjust the execution priority, intensity, and coordination of various control strategies (such as the weight of flow feedback source switching, the advance and overshoot of valve feedforward compensation, the controller gain coefficient, and the critical velocity target value) according to the dominant abnormal mode and the degree of their mutual influence. For example, if the evaluation results show that the flowmeter measurement error is the main abnormality and it interacts with the abnormality of mud rheological properties, the weight of the flow feedback source switching can be adjusted first, and the critical velocity target value can be adjusted simultaneously to ensure that the main problem is solved while taking into account the influence of other related abnormalities.

[0038] In one embodiment, it is assumed that the river dredging system simultaneously detects flowmeter measurement errors and abnormal mud rheological properties, forming a combined anomaly. First, the system collects additional diagnostic data, such as cross-validation using redundant sensors or physical models, to assess the actual degree of deviation in the flowmeter measurement error. Simultaneously, it analyzes data such as mud viscosity, mud density, and particle size distribution to assess the impact of the abnormal mud rheological properties on the critical velocity. Through these assessments, the system may identify the abnormal mud rheological properties as the most significant anomaly mode, as it directly affects the mud settling behavior, while the flowmeter measurement error, although present, may have a relatively minor impact or can be corrected through other means. Furthermore, the system analyzes the interaction between these two anomalies; for example, the abnormal mud rheological properties may exacerbate flowmeter measurement instability. Based on this assessment, the control strategy will prioritize addressing the abnormal mud rheological properties. For example, the critical velocity target value may be dynamically adjusted to ensure that the mud flow velocity remains above the corrected critical settling velocity, preventing pipe deposition. Meanwhile, to address flow meter measurement errors, the system adjusts the weighting of flow feedback source switching, potentially relying more on estimated actual flow values, and fine-tunes the advance of valve feedforward compensation to adapt to the new flow feedback source. This dynamic adjustment ensures that when dealing with complex combined anomalies, the system can prioritize resolving the most critical issues and coordinate other control strategies to achieve optimal flow control performance.

[0039] It should be noted that obtaining data on mud flow rate, pipeline sediment thickness variation trends, and mud pump power consumption can be understood as real-time data acquisition through various sensors in the system. For example, mud flow rate can be obtained through flow meters or redundant velocity sensors; pipeline sediment thickness variation trends can be monitored through sediment thickness sensors or acoustic sensors; and mud pump power consumption can be calculated by monitoring mud pump operating parameters such as current, voltage, and speed. This data acquisition aims to provide real-time feedback for subsequent fine-tuning. Fine-tuning the adjusted parameters based on mud flow rate, pipeline sediment thickness variation trends, and mud pump power consumption refers to making small, refined corrections to these adjusted parameters after initial adjustments to the control strategy (e.g., the weight of flow feedback source switching, valve feedforward compensation lead and overshoot, controller gain coefficient, and critical flow rate target value) based on combined anomalies, using these real-time monitored key operating indicators. The purpose is to further optimize the control effect, making the system operate more smoothly and efficiently. When fine-tuning parameters, the system simultaneously monitors the changing trends of all remaining parameters. This means that while fine-tuning one or a group of parameters, the system continuously and comprehensively monitors all other relevant operating parameters, such as pipeline pressure, mud concentration, and valve opening. The purpose of this simultaneous monitoring is to promptly detect any unexpected chain reactions or nonlinear coupling effects that may be triggered by the fine-tuning operation. When unexpected fluctuations in remaining parameters are detected, the current fine-tuning is immediately paused, and the cause of the fluctuation is analyzed. This means that once an unexpected fluctuation is detected, the system immediately stops the current fine-tuning operation to prevent further deterioration of the problem. Subsequently, the system will initiate a diagnostic mechanism to analyze the underlying causes of the fluctuations, which may involve comparing and reasoning about data patterns, historical trends, and system models. Based on the cause of the fluctuation, the fine-tuning parameters or activation sequence are adjusted to eliminate or weaken nonlinear coupling effects. This means that after analyzing the cause of the fluctuation, the system will specifically adjust the fine-tuning parameters, the adjustment step size, or the activation sequence of the control strategy.

[0040] In one embodiment, it is assumed that the river dredging system simultaneously experiences a combination of anomalies: valve mechanical response lag and abnormal mud rheological properties. According to the above scheme, the system first makes preliminary adjustments to the valve feedforward compensation's lead and overshoot, as well as the critical flow velocity target value, based on the most predominant anomaly mode and their mutual influence. After completing the preliminary adjustments, the system continuously acquires data such as mud flow velocity, pipeline sediment thickness trends, and mud pump power consumption. For example, the system monitors stable mud flow velocity and a decreasing trend in pipeline sediment thickness, but the mud pump power consumption exhibits slight, periodic, unexpected fluctuations. At this point, the system immediately suspends fine-tuning of the critical flow velocity target value and analyzes the cause of this power consumption fluctuation. Analysis reveals that this fluctuation is due to a nonlinear coupling effect between the mud concentration fluctuating within a specific range and the valve feedforward compensation response time, causing the mud pump's power consumption to oscillate when attempting to maintain a stable flow rate. Based on the cause of this fluctuation, the system will adjust the fine-tuning step size of the valve feedforward compensation, or adjust the activation order of the valve feedforward compensation and the critical flow velocity target value adjustment. For example, it will prioritize stabilizing the valve response and then fine-tune the critical flow velocity, thereby effectively eliminating or weakening this nonlinear coupling effect, restoring the power consumption of the mud pump to a stable level, and ensuring the stability and energy efficiency of the entire dredging process.

[0041] It should be noted that continuously collecting data on mud viscosity, mud density, particle size distribution, pipe inner wall roughness, and the impact frequency and intensity of abrasive particles refers to acquiring key physicochemical parameters of the mud and the wear state of the pipeline in real time by deploying appropriate sensors or employing online analysis technology. Mud viscosity reflects the resistance characteristics of the mud's internal flow; mud density affects the balance between inertial force and gravity; particle size distribution determines the mud's settling characteristics and abrasiveness; pipe inner wall roughness directly affects fluid resistance; and the impact frequency and intensity of abrasive particles quantifies the dynamic process of pipeline wear. These parameters are key inputs for understanding and quantifying nonlinear coupling effects. Calculating the dynamic characteristic parameters of the nonlinear coupling effect based on mud viscosity, mud density, particle size distribution, pipe inner wall roughness, and the impact frequency and intensity of abrasive particles can be understood as using this real-time collected data, through preset physical models, empirical formulas, or machine learning algorithms, to comprehensively evaluate the strength, direction, and trend of the nonlinear coupling effect within the current system. For example, this parameter can be a comprehensive index reflecting the degree of interaction between mud rheology, pipeline wear, and flow control. The aim is to transform complex nonlinear interactions into a quantifiable indicator to facilitate subsequent control decisions. Adjusting the fine-tuning parameter adjustment step size, adjustment strategy activation interval, and adjustment strategy activation sequence based on the dynamic characteristic parameters of the nonlinear coupling effect means that the system can intelligently adjust the specific execution method of the fine-tuning operation based on the calculated dynamic characteristic parameters. For example, when the dynamic characteristic parameters of the nonlinear coupling effect indicate that the system is in a highly unstable or sensitive state, the adjustment step size of the fine-tuning parameter can be reduced to avoid excessive oscillation; simultaneously, the activation interval of the adjustment strategy can be extended to give the system sufficient response time; and, according to the main manifestations of the nonlinear coupling effect, the activation sequence of different control strategies can be optimized, prioritizing the factors causing the main fluctuations. The aim is to make the fine-tuning process smoother and more efficient, and effectively suppress nonlinear coupling effects.

[0042] In one embodiment, assuming that during fine-tuning of the river dredging system, unexpected fluctuations in slurry flow velocity are detected, along with abnormalities in pipeline pressure and slurry pump current, the system immediately pauses the current fine-tuning and initiates the calculation process for the dynamic characteristic parameters of the nonlinear coupling effect. Specifically, the system continuously collects data such as slurry viscosity, slurry density, particle size distribution, pipeline inner wall roughness, and the impact frequency and intensity of abrasive particles. For example, the slurry viscosity is obtained as 1.5 Pa·s using an online viscometer, the slurry density as 1200 kg / m³ using a densitometer, the particle size distribution as concentrated in the 50-200 micrometer range using a laser particle size analyzer, the pipeline inner wall roughness as 0.5 mm using an ultrasonic thickness gauge, and the impact frequency of abrasive particles as 100 Hz with an intensity of 0.8 using an acoustic sensor. Based on this real-time data, the system uses a preset nonlinear coupling model to calculate the dynamic characteristic parameter of the nonlinear coupling effect as 0.75 (assuming this parameter ranges from 0 to 1, with higher values ​​indicating stronger coupling effects and greater system sensitivity). Based on the dynamic characteristic parameter of 0.75, the system determines that the current nonlinear coupling effect is strong and requires careful adjustment. Therefore, the system automatically adjusts the adjustment step size of the fine-tuning parameter, reducing it from the default 0.1% to 0.05%; simultaneously, the activation interval of the adjustment strategy is extended from 5 seconds to 10 seconds to give the system more response and stabilization time; furthermore, based on model analysis, the main source of fluctuation is determined to be the coupling between mud rheology and pipeline wear, so the control strategy targeting abnormal mud rheological characteristics is activated first, followed by the activation of the valve feedforward compensation adjustment strategy. Through this dynamic and refined adjustment, the system can more effectively suppress unexpected fluctuations and restore stable operation.

[0043] It should be noted that after adjusting the fine-tuning parameters, including the step size, activation interval, and activation sequence, based on the dynamic characteristic parameters of the nonlinear coupling effect, the system continuously monitors the instantaneous rate of change of mud flow velocity, pipeline pressure, and mud pump current. Mud flow velocity, pipeline pressure, and mud pump current are core indicators reflecting the operational status and stability of the river dredging system; their instantaneous rate of change can sensitively indicate whether the system is in a state of severe fluctuation or instability. For example, a sharp change in mud flow velocity may indicate pipeline blockage or mud pump idling; rapid fluctuations in pipeline pressure may indicate valve malfunction or pipeline damage; and instantaneous changes in mud pump current directly reflect drastic changes in mud pump load. When the instantaneous rate of change of mud flow velocity, pipeline pressure, and mud pump current simultaneously exceeds a preset threshold, it indicates that the system may be experiencing significant transient disturbances or has entered an unstable region. In this case, to avoid misjudgment or over-adjustment of the control strategy due to updating the dynamic characteristic parameters of the nonlinear coupling effect based on unstable data, the system immediately freezes the update of the current dynamic characteristic parameters of the nonlinear coupling effect. Simultaneously, a pre-stored backup stable parameter set is activated. This parameter set is typically optimized and verified under stable system operation conditions, providing a relatively reliable and safe control benchmark to maintain basic system stability. Conversely, when the instantaneous rate of change of mud flow velocity, pipeline pressure, and mud pump current simultaneously does not exceed preset thresholds, it indicates that the system has recovered to a relatively stable or controllable operating state. At this point, the freezing of the dynamic characteristic parameters of the nonlinear coupling effect is lifted, and the current update of the dynamic characteristic parameters of the nonlinear coupling effect is resumed. This allows the system to continue to adaptively adjust based on real-time, reliable dynamic characteristic parameters to more accurately eliminate or reduce nonlinear coupling effects and optimize control performance.

[0044] In one embodiment, it is assumed that a river dredging system is transporting slurry and fine-tuning parameters are adjusted based on dynamic characteristic parameters of nonlinear coupling effects. Suddenly, due to the appearance of large deposits in the pipeline or air intake by the slurry pump, the slurry flow rate, pipeline pressure, and slurry pump current experience drastic fluctuations within a short period of time, with their instantaneous rates of change simultaneously exceeding preset thresholds (e.g., flow rate exceeding 5 m / s², pressure exceeding 1 MPa / s, and current exceeding 10 A / s). At this point, the system immediately identifies this unstable state and freezes the currently updated dynamic characteristic parameters of nonlinear coupling effects, instead activating a preset set of standby stable parameters. This standby parameter set may contain a set of conservative, validated control parameters to ensure that the system can maintain basic safe operation during periods of instability, such as temporarily fixing the slurry pump speed at a safe value or adjusting the valve opening to a preset position. After a period of self-regulation or manual intervention, the instantaneous rates of change of the slurry flow rate, pipeline pressure, and slurry pump current gradually decrease and simultaneously fall below the preset thresholds. At this point, the system will unfreeze the dynamic characteristic parameters of the nonlinear coupling effect and resume their updates, thereby allowing the system to continue to make fine adjustments based on real-time, accurate dynamic characteristic parameters to eliminate or reduce the nonlinear coupling effect and optimize dredging efficiency.

[0045] It should be noted that acquiring the output values ​​of redundant mud flow velocity sensors, sediment thickness sensors, and mud pump operating parameter sensors refers to deploying multiple mud flow velocity sensors in a redundant configuration within a river dredging system, along with sediment thickness sensors and mud pump operating parameter sensors. These sensors continuously monitor and output their respective measurement data, such as mud flow velocity, sediment thickness on the inner wall of the pipe, and operating parameters of the mud pump, including current, voltage, and rotational speed. The redundant configuration of mud flow velocity sensors can provide multiple independent flow velocity measurements, improving measurement reliability. The consistency check of the output values ​​of the redundant mud flow velocity sensors can be understood as comparing the measurements of multiple mud flow velocity sensors at the same or close moments to determine if there are significant differences between them. For example, the average, median, or standard deviation of each sensor reading can be calculated, and a threshold can be set. If the reading of a sensor deviates from the average by more than this threshold, it is considered potentially abnormal. Simultaneously, the time-series analysis of the output values ​​of the sediment thickness sensor and mud pump operating parameter sensor refers to performing trend analysis, anomaly detection, or pattern recognition on the historical data of these sensors. For example, monitoring the rate of change, fluctuation range, or deviation from a preset normal operating mode of data over a period of time can identify situations such as sensor drift, jamming, or abnormal output values. The aim is to improve the accuracy of identifying abnormal sensors through multi-dimensional data verification. In practical applications, based on the identified abnormal sensors, the weight of the abnormal sensors can be adjusted or their data can be excluded. Specifically, when a sensor is identified as abnormal, its weight in data fusion or calculation can be reduced to minimize its impact on the final result; or, if the anomaly is severe, its data can be directly excluded from the dataset and no longer participate in subsequent calculations. For example, if one of three redundant mud flow rate sensors has a reading that significantly deviates from the other two, the weight of the abnormal sensor can be reduced, or the average of the other two normal sensors can be used directly as the valid mud flow rate data. The goal is to maximize the use of valid data through a flexible data processing mechanism while avoiding the negative impact of abnormal data on system decisions. Based on the adjusted sensor data, the process of acquiring mud flow rate, pipeline sediment thickness variation trends, and mud pump power consumption involves processing abnormal sensor data (such as adjusting weights or excluding abnormal data) and then using the remaining reliable sensor data to calculate and output the current mud flow rate, pipeline sediment thickness variation trends over time, and actual mud pump power consumption. This verified and processed data will serve as input for subsequent fine-tuning of the control strategy, ensuring the accuracy and effectiveness of control decisions.

[0046] In one embodiment, assuming that during river dredging operations, the system deploys three redundant mud flow velocity sensors A, B, and C, one sediment thickness sensor D, and one mud pump power consumption sensor E. At a certain moment, sensors A and B report mud flow velocities of 3.2 m / s and 3.3 m / s, respectively, while sensor C suddenly reports a velocity of 1.5 m / s. At this point, the system initiates a consistency check, finding a significant difference between the reading of sensor C and those of sensors A and B, thus identifying sensor C as an abnormal sensor. Simultaneously, historical data from sediment thickness sensor D shows a steady upward trend in its reading over the past hour, but a sudden, anomalous jump occurs at a certain moment; time-series analysis will mark this as abnormal. The reading of mud pump power consumption sensor E remains stable. Based on the identification results, the system immediately excludes the abnormal data from sensors C and D, or significantly reduces their weight. Ultimately, the system will primarily rely on the average values ​​of sensors A and B (e.g., 3.25 m / s) as valid data for mud flow velocity. This will be combined with normal power consumption data from sensor E, and the sediment thickness variation trend after correcting or interpolating abnormal data from sensor D, to obtain accurate parameters for subsequent fine-tuning. In this way, even if some sensors malfunction, the system can still obtain reliable operating data, ensuring the effective execution of the control strategy and avoiding the risk of decreased dredging efficiency or pipeline blockage due to sensor failure.

[0047] See Figure 2 , Figure 2 This is a schematic diagram of a river dredging flow control system according to one embodiment of this application. The river dredging flow control system 200 includes: Data acquisition module 210 is used to collect the operation data of the river dredging system. The operation data includes flow meter feedback value, mud pump operation parameters, pipeline pressure, valve opening degree and mud concentration. The anomaly identification module 220 is used to identify the anomaly types of the river dredging system based on operational data. The anomaly types include flow meter measurement errors, valve mechanical response hysteresis, and abnormal mud rheological properties. The control strategy adjustment module 230 is used to adjust the control strategy according to the type of exception.

[0048] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0049] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0050] The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A method for controlling the flow rate of river dredging, characterized in that, include: Collect operational data of the river dredging system, including flow meter feedback values, mud pump operating parameters, pipeline pressure, valve opening degree, and mud concentration; Based on the operational data, the abnormality types of the river dredging system are identified, including flow meter measurement errors, valve mechanical response hysteresis, and abnormal mud rheological properties. Based on the type of anomaly, the control strategy is adjusted, and the control strategy includes at least one of the following: When the anomaly type is the flow meter measurement error, switch the flow feedback source; When the abnormality type is the valve mechanical response hysteresis, valve feedforward compensation adjustment is performed; When the anomaly type is the aforementioned mud rheological characteristic anomaly, the critical flow velocity target value is adaptively adjusted.

2. The method according to claim 1, characterized in that, The step of adaptively adjusting the target value of the critical flow velocity when the anomaly type is the mud rheological property anomaly includes: When the rheological properties of the mud are abnormal, collect the acoustic characteristic data of the pipeline and the monitoring data of the pipeline wall thickness. Based on the pipeline acoustic characteristic data and the pipeline wall thickness monitoring data, the degree of pipeline erosion and the intensity of abrasive particles are inferred. The pipe roughness parameters are adjusted based on the degree of pipe erosion and the intensity of the abrasive particles. The actual flow rate is estimated based on the corrected pipe roughness parameters, mud pump operating parameters, pipe pressure, and mud concentration. Assess the reliability of the flow meter's feedback values ​​and the estimated actual flow rates; Based on the trust level, the flow meter feedback value and the estimated actual flow value are weighted and fused to obtain the decision flow. The decision flow rate is used as the flow feedback source, and the critical flow velocity target value is dynamically adjusted according to the mud concentration, abrasive particle intensity and pipeline erosion degree to maintain the mud flow velocity always higher than the critical settling velocity.

3. The method according to claim 1, characterized in that, The step of adjusting the control strategy according to the anomaly type further includes: When there are two or more types of the aforementioned anomalies, it is determined to be a combined anomaly; Based on the combined anomalies, the activation sequence, intensity, or coordination parameters of the control strategy are adjusted. The control strategy includes: adjusting the weight of the flow feedback source switching, the advance and overshoot of valve feedforward compensation, the controller gain coefficient, and the critical flow velocity target value according to the most important anomaly mode and the degree of their mutual influence.

4. The method according to claim 3, characterized in that, The steps for adjusting the activation sequence, intensity, or coordination parameters of the control strategy based on the aforementioned combined anomalies include: When a combined anomaly is identified, collect data on mud flow velocity, critical settling velocity, pipeline sediment thickness variation trend, and the difference between mud pump power consumption and target energy consumption. When the mud flow rate is close to or below the critical settling velocity and the thickness of the pipe deposits shows an increasing trend, the control strategy of increasing the flow rate and preventing deposition is preferentially activated or enhanced. When the mud flow rate is consistently higher than the critical settling velocity and the thickness of the sediment in the pipeline shows a decreasing trend, the control strategy is adjusted to reduce energy consumption based on the difference between the power consumption of the mud pump and the target energy consumption.

5. The method according to claim 3, characterized in that, The adjustment of the activation sequence, intensity, or coordination parameters of the control strategy based on combined anomalies includes: Based on the combined anomalies, assess the most prominent anomaly patterns and the degree of their mutual influence; Based on the most prevalent abnormal patterns and the degree of their mutual influence, the activation sequence, intensity, and coordination parameters of the control strategy are dynamically adjusted.

6. The method according to claim 3, characterized in that, The step of adjusting the activation sequence, intensity, or coordination parameters of the control strategy based on the combined anomaly further includes: Acquire the trends in mud flow rate, pipe sediment thickness, and mud pump power consumption; The adjusted parameters are fine-tuned based on the mud flow rate, the trend of sediment thickness variation in the pipeline, and the power consumption of the mud pump. While fine-tuning the parameters, the changing trends of all remaining parameters are monitored simultaneously; When unexpected fluctuations are detected in the remaining parameters, immediately pause the current fine-tuning and analyze the cause of the fluctuations; Based on the cause of the fluctuations, adjust the fine-tuning parameters or activation sequence to eliminate or reduce nonlinear coupling effects.

7. The method according to claim 6, characterized in that, The step of adjusting the fine-tuning parameters or activation sequence according to the cause of the fluctuation to eliminate or reduce the nonlinear coupling effect includes: Continuously collect data on mud viscosity, mud density, particle size distribution, pipe inner wall roughness, and the impact frequency and intensity of abrasive particles; Based on the mud viscosity, mud density, particle size distribution, pipe inner wall roughness, and impact frequency and intensity of abrasive particles, calculate the dynamic characteristic parameters of the nonlinear coupling effect; Based on the dynamic characteristic parameters of the nonlinear coupling effect, adjust the fine-tuning parameters, including the adjustment step size, the adjustment strategy activation interval, and the adjustment strategy activation sequence.

8. The method according to claim 6, characterized in that, The steps following adjusting the fine-tuning parameter adjustment step size, strategy activation interval, and strategy activation sequence based on the dynamic characteristic parameters of the nonlinear coupling effect further include: Continuously monitor the instantaneous change rate of mud flow rate, pipeline pressure, and mud pump current; When the instantaneous rate of change of the mud flow rate, the pipeline pressure, and the mud pump current simultaneously exceeds a preset threshold, the update of the current dynamic characteristic parameters of the nonlinear coupling effect is frozen, and a backup stable parameter set is activated. When the instantaneous change rates of the mud flow rate, the pipeline pressure, and the mud pump current are all found to be below a preset threshold, the freezing of the dynamic characteristic parameters of the nonlinear coupling effect is lifted, and the update of the current dynamic characteristic parameters of the nonlinear coupling effect is restored.

9. The method according to claim 6, characterized in that, The steps for obtaining the mud flow rate, the trend of pipeline sediment thickness variation, and the power consumption of the mud pump include: Collect the output values ​​of redundant mud flow rate sensors, sediment thickness sensors, and mud pump operating parameter sensors; The output values ​​of the redundant mud flow rate sensor are checked for consistency, and the output values ​​of the sediment thickness sensor and the mud pump operating parameter sensor are analyzed over time to identify abnormal sensors. Based on the identified abnormal sensors, adjust the weight of the abnormal sensors or exclude the data from the abnormal sensors; Based on the adjusted sensor data, the mud flow rate, the trend of changes in pipeline sediment thickness, and the power consumption of the mud pump are obtained.

10. A river dredging flow control system, characterized in that, include: The data acquisition module is used to collect operational data of the river dredging system, including flow meter feedback values, mud pump operating parameters, pipeline pressure, valve opening degree, and mud concentration. An anomaly identification module is used to identify anomaly types in the river dredging system based on the operational data. The anomaly types include flow meter measurement errors, valve mechanical response hysteresis, and abnormal mud rheological properties. The control strategy adjustment module is used to adjust the control strategy according to the type of exception.

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