River sediment treatment effect monitoring method and system based on dynamic change of water flow

Abstract

The invention discloses a river sediment treatment effect monitoring method and system based on water flow dynamic change. The method comprises the following steps: acquiring river sediment distribution data, constructing a sediment distribution model, and constructing a sediment treatment monitoring network; obtaining water flow dynamic data and bottom mud monitoring data, evaluating a bottom mud suspension state, combining the water flow data to construct a hydrodynamic force-bottom mud suspension model, and predicting the bottom mud suspension state; according to the influence of different suspension states of the sediment on the accuracy of the monitoring sensor, the accuracy of the sensor in the monitoring network is evaluated, so that the monitoring of the sediment treatment effect is optimized. The river sediment treatment effect can be monitored in real time, the monitoring accuracy and real-time performance are improved, the application value is high, and a system architecture convenient to deploy is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of sediment treatment and monitoring, and particularly to a method and system for monitoring the effect of river sediment treatment based on dynamic changes in water flow. Background Art

[0002] With the advancement of urbanization, the problem of river sediment pollution has become increasingly serious, affecting the water quality and ecological environment of rivers. Traditional river sediment treatment methods usually rely on manual dredging or physical and chemical treatment, but these methods are often difficult to achieve continuous and effective treatment in a dynamic water flow environment, and there are also problems such as difficult quantification of treatment effects and high treatment costs. Therefore, developing a method that can monitor and evaluate the effect of sediment treatment in real time has become an urgent need in the current field of water environmental protection.

[0003] Existing river sediment treatment monitoring methods mostly focus on water quality monitoring by a single sensor or sediment sample collection, ignoring the impact of dynamic changes in water flow on the suspended state of sediment, resulting in a certain deviation between the monitoring results and the actual sediment treatment effect. Especially under complex water flow conditions, the suspended state of sediment shows dynamic changes, and traditional monitoring methods are difficult to comprehensively and accurately evaluate the sediment treatment effect.

[0004] Therefore, there is an urgent need for a monitoring method that comprehensively considers the dynamic changes in water flow and the suspended state of sediment. Through real-time data collection and analysis, the sediment treatment effect can be accurately evaluated to provide more scientific and effective treatment strategies. This not only helps to achieve sustainable management of the river environment but also provides data support and theoretical basis for relevant decision-making.

[0005] The present invention proposes a method and system for monitoring the effect of river sediment treatment based on dynamic changes in water flow. By constructing a water flow-sediment suspension model and a monitoring network, it can accurately monitor the sediment treatment effect in a dynamic water flow environment and provide an innovative solution for water environment treatment. Summary of the Invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes a method and system for monitoring the effect of river sediment treatment based on dynamic changes in water flow.

[0007] In the first aspect of the present invention, a method for monitoring the effect of river sediment treatment based on dynamic changes in water flow is provided, including:

[0008] Obtaining the sediment distribution data of the target river, constructing a sediment distribution model of the target river according to the sediment distribution data, and constructing a sediment treatment monitoring network according to the sediment distribution model;

[0009] Obtain the water flow dynamic data and sediment monitoring data of the target river according to the sediment treatment monitoring network, evaluate the suspension state of the sediment according to the sediment monitoring data, and construct a hydrodynamic-sediment suspension model according to the water flow dynamic data and the suspension state;

[0010] Predict the sediment suspension state of the target river according to the hydrodynamic-sediment suspension model to obtain sediment suspension prediction state data;

[0011] Obtain the data on the influence of different sediment suspension states on the monitoring accuracy of each sensor in the sediment treatment monitoring network, evaluate the monitoring accuracy of each sensor in the sediment treatment monitoring network according to the monitoring accuracy influence data and the sediment suspension prediction state data, and determine the monitoring sensors for sediment treatment effect monitoring according to the monitoring accuracy.

[0012] In this solution, the obtaining of the sediment distribution data of the target river, constructing the sediment distribution model of the target river according to the sediment distribution data, and constructing the sediment treatment monitoring network according to the sediment distribution model are specifically as follows:

[0013] Obtain the multi-beam sounding data of the target river based on the multi-beam sounding system, determine the topographic and geomorphic data of the target river according to the multi-beam sounding data, and construct a three-dimensional model of the target river;

[0014] Obtain the side-scan sonar data at the bottom of the target river, extract the texture features and distribution boundaries of the sediment at the bottom of the target river according to the side-scan sonar data, and extract the depth change information of the multi-beam sounding data and the image brightness and texture change information of the side-scan sonar data;

[0015] Identify the sediment thickness change of the target river according to the depth change information and the image brightness and texture change information, and map the texture features, distribution boundaries, and sediment thickness change to the three-dimensional model to construct the sediment distribution model of the target river;

[0016] Divide the sediment at the bottom of the target river into N sub-regions according to the sediment distribution model, and obtain the historical hydrological information of each sub-region in the target river. The historical hydrological information includes the water flow velocity change and water depth change data;

[0017] Obtain the sediment type data of the target river, obtain the sediment particle information of the target river according to the sediment type data. The sediment particle information includes particle viscosity, mass, and buoyancy, and obtain the impact coefficient of different water flow velocities on the sediment particles according to the sediment particle information;

[0018] Introduce a numerical simulation model of fluid mechanics, import the historical hydrological information of each sub-region into the numerical simulation model of fluid mechanics to conduct hydrological simulation on the target river channel, and integrate the numerical simulation model of fluid mechanics with the sediment distribution model to obtain a hydrological simulation model of the target river channel;

[0019] Construct a preset number of virtual tracer particles based on the sediment particle information, put the virtual tracer particles into the hydrological simulation model, and analyze the virtual tracer particles based on the particle tracking algorithm of the Lagrangian description method to determine the distribution information of each virtual tracer particle within a preset future time period;

[0020] Determine the sediment accumulation hot spots in the target river channel according to the distribution information, arrange sediment treatment monitoring sensors according to the sediment accumulation hot spots, and construct a sediment treatment monitoring network with the sediment treatment monitoring sensors.

[0021] In this solution, the water flow dynamic data and sediment monitoring data of the target river channel are obtained according to the sediment treatment monitoring network, the suspension state of the sediment is evaluated according to the sediment monitoring data, and a hydrodynamic-sediment suspension model is constructed according to the water flow dynamic data and the suspension state, specifically as follows:

[0022] Obtain the water flow dynamic data and sediment monitoring data of the target river channel within a preset monitoring time period according to the sediment treatment monitoring network. The water flow dynamic data includes changes in water flow velocity, changes in water flow direction, and changes in water depth. The sediment monitoring data includes sediment thickness and sediment particle distribution;

[0023] Analyze the sediment suspension degree of the target river channel within a preset monitoring time period according to the sediment monitoring data, and evaluate the suspension state of the sediment according to the sediment suspension degree;

[0024] Construct a correlation analysis matrix with the water flow dynamic data and the suspension state, and calculate the Pearson correlation coefficient between the water flow dynamic data and the sediment suspension state according to the correlation analysis matrix;

[0025] Determine the influence of the hydrodynamic force of the target river channel on the sediment suspension state according to the Pearson correlation coefficient between the water flow dynamic data and the sediment suspension state to obtain hydrodynamic-suspension state influence data;

[0026] Construct a hydrodynamic-sediment suspension model of the target river channel according to the support vector machine algorithm, and import the hydrodynamic-suspension state influence data into the hydrodynamic-sediment suspension model for model training.

[0027] In this solution, the sediment suspension state of the target river channel is predicted according to the hydrodynamic-sediment suspension model to obtain sediment suspension prediction state data, specifically as follows:

[0028] Obtain the real-time water flow dynamic data at the layout position of each sediment treatment monitoring sensor in the target river channel according to the sediment treatment monitoring network;

[0029] Import the real-time water flow dynamic data into the hydrodynamic-sediment suspension model to predict the sediment suspension state at the layout position of each sediment treatment monitoring sensor in the target river channel, and obtain the sediment suspension prediction state data, where the sediment suspension prediction state data includes the sediment suspension state position and the suspended sediment particle concentration.

[0030] In this solution, obtain the data on the influence of different sediment suspension states on the monitoring accuracy of each sensor in the sediment treatment monitoring network, evaluate the monitoring accuracy of each sensor in the sediment treatment monitoring network according to the monitoring accuracy influence data and the sediment suspension prediction state data, and determine the monitoring sensors for sediment treatment effect monitoring according to the monitoring accuracy. Specifically:

[0031] Obtain the influence of different occlusion degrees on the monitoring sensitivity of each sensor in the sediment treatment monitoring network to obtain the sensitivity influence data, and determine the influence of different occlusion degrees on the monitoring accuracy of each sensor according to the sensitivity influence data to obtain the occlusion-monitoring accuracy influence data;

[0032] Obtain the occlusion degree data of different sediment suspension states on the sensor, and determine the monitoring accuracy of different sediment suspension states on each sensor in the sediment treatment monitoring network according to the occlusion degree data and the occlusion-monitoring accuracy influence data to obtain the monitoring accuracy influence data;

[0033] Evaluate the monitoring accuracy of each sensor in the sediment treatment monitoring network according to the sediment suspension prediction state data and the monitoring accuracy data to obtain the monitoring accuracy data of each sensor;

[0034] Determine the monitoring sensors for sediment treatment effect monitoring according to the monitoring accuracy data.

[0035] In this solution, the determining the monitoring sensors for sediment treatment effect monitoring according to the monitoring accuracy data is specifically:

[0036] Calibrate the monitoring sensors with monitoring accuracy greater than the preset monitoring accuracy as first-class monitoring sensors according to the monitoring accuracy data, and obtain the quantity information of the first-class monitoring sensors;

[0037] According to the quantity information of the first-class monitoring sensors, if the quantity of the first-class monitoring sensors is greater than the preset value, monitor the sediment treatment effect of the target river channel according to the first-class monitoring sensors to obtain the first sediment treatment effect monitoring plan;

[0038] Determine the degree of occlusion of the sediment suspension in the target river channel on each monitoring sensor according to the monitoring accuracy data and the monitoring accuracy impact data. If the degree of occlusion is less than the preset degree of occlusion and it is not a type-I monitoring sensor, calibrate the monitoring sensor as a type-II monitoring sensor, and calibrate the remaining monitoring sensors as type-III monitoring sensors;

[0039] If the number of type-I monitoring sensors is less than the preset value, calculate the difference between the type-I monitoring sensors and the preset value, obtain the quantity information of the type-II monitoring sensors. If the number of type-II monitoring sensors is greater than the absolute value of the difference, determine the monitoring accuracy error of the type-II monitoring sensors according to the degree of occlusion of each type-II monitoring sensor and the occlusion-monitoring accuracy impact data, perform a monitoring data correction operation on the type-II monitoring sensors according to the monitoring accuracy error, and monitor the sediment treatment effect of the target river channel based on the type-I monitoring sensors and the type-II monitoring sensors with corrected monitoring data to obtain a second sediment treatment effect monitoring plan;

[0040] If the number of type-I monitoring sensors is less than the preset value and the number of type-II monitoring sensors is less than the absolute value of the difference, calibrate the sediment treatment effect monitoring data collected by the sediment treatment monitoring network as abnormal data, and suspend the monitoring of the sediment treatment effect to obtain a third sediment treatment effect monitoring plan;

[0041] Monitor the sediment treatment effect of the target river channel according to the first sediment treatment effect monitoring plan, the second sediment treatment effect monitoring plan, and the third sediment treatment effect monitoring plan.

[0042] The second aspect of the present invention also provides a monitoring system for the sediment treatment effect of a river channel based on the dynamic change of water flow. The system includes: a memory and a processor. The memory includes a program for the method of monitoring the sediment treatment effect of a river channel based on the dynamic change of water flow. When the program for the method of monitoring the sediment treatment effect of a river channel based on the dynamic change of water flow is executed by the processor, the following steps are implemented:

[0043] Obtain the sediment distribution data of the target river channel, construct a sediment distribution model of the target river channel according to the sediment distribution data, and construct a sediment treatment monitoring network according to the sediment distribution model;

[0044] Obtain the water flow dynamic data and sediment monitoring data of the target river channel according to the sediment treatment monitoring network, evaluate the suspension state of the sediment according to the sediment monitoring data, and construct a hydrodynamic-sediment suspension model according to the water flow dynamic data and the suspension state;

[0045] Predict the sediment suspension state of the target river channel according to the hydrodynamic-sediment suspension model to obtain sediment suspension prediction state data;

[0046] Obtain data on the impact of different suspended states of sediment on the monitoring accuracy of each sensor in the sediment treatment monitoring network. Evaluate the monitoring accuracy of each sensor in the sediment treatment monitoring network according to the monitoring accuracy impact data and the sediment suspension prediction state data, and determine the monitoring sensors for monitoring the sediment treatment effect according to the monitoring accuracy.

[0047] The present invention discloses a method and system for monitoring the effect of river sediment treatment based on the dynamic change of water flow. The method includes obtaining river sediment distribution data, constructing a sediment distribution model, and building a sediment treatment monitoring network; obtaining water flow dynamic data and sediment monitoring data, evaluating the sediment suspension state, and constructing a hydrodynamic-sediment suspension model in combination with the water flow data to predict the sediment suspension state; evaluating the accuracy of the sensors in the monitoring network according to the impact of different sediment suspension states on the accuracy of the monitoring sensors, so as to optimize the monitoring of the sediment treatment effect. The present invention can monitor the effect of river sediment treatment in real time, improve the accuracy and timeliness of monitoring, has high application value, and provides a system architecture convenient for deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Shows a flowchart of a method for monitoring the effect of river sediment treatment based on the dynamic change of water flow according to the present invention;

[0049] Figure 2 Shows a flowchart of obtaining the sediment suspension prediction state data according to the present invention;

[0050] Figure 3 Shows a flowchart of determining the monitoring sensors for monitoring the effect of sediment treatment according to the present invention;

[0051] Figure 4 Shows a block diagram of a system for monitoring the effect of river sediment treatment based on the dynamic change of water flow according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0053] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein, and therefore, the scope of the present invention is not limited by the specific embodiments disclosed below.

[0054] Figure 1 Shows a flowchart of a method for monitoring the effect of river sediment treatment based on the dynamic change of water flow according to the present invention.

[0055] As Figure 1 shown, the first aspect of the present invention provides a monitoring method for the treatment effect of riverbed sediment based on the dynamic change of water flow, including:

[0056] S102, obtaining the sediment distribution data of the target river, constructing a sediment distribution model of the target river according to the sediment distribution data, and constructing a sediment treatment monitoring network according to the sediment distribution model;

[0057] S104, obtaining the water flow dynamic data and sediment monitoring data of the target river according to the sediment treatment monitoring network, evaluating the suspension state of the sediment according to the sediment monitoring data, and constructing a hydrodynamic-sediment suspension model according to the water flow dynamic data and the suspension state;

[0058] S106, predicting the sediment suspension state of the target river according to the hydrodynamic-sediment suspension model to obtain sediment suspension prediction state data;

[0059] S108, obtaining the data on the influence of different sediment suspension states on the monitoring accuracy of each sensor in the sediment treatment monitoring network, evaluating the monitoring accuracy of each sensor in the sediment treatment monitoring network according to the monitoring accuracy influence data and the sediment suspension prediction state data, and determining the monitoring sensors for monitoring the sediment treatment effect according to the monitoring accuracy.

[0060] It should be noted that by obtaining the riverbed sediment distribution data and constructing a sediment distribution model, the spatial distribution characteristics of the sediment can be accurately understood. Based on the sediment distribution model, the sediment treatment monitoring network is reasonably arranged to realize the comprehensive monitoring of the riverbed sediment. Real-time obtaining of water flow dynamic data (such as water flow velocity, direction, water depth, etc.) and sediment monitoring data (such as sediment particle distribution, sediment thickness, etc.) can comprehensively evaluate the suspension state of the sediment and consider the influence of water flow on sediment suspension. By constructing a hydrodynamic-sediment suspension model, the mutual relationship between water flow and sediment suspension can be quantitatively described; by predicting the sediment suspension state of the riverbed through the hydrodynamic-sediment suspension model, the prediction data of sediment suspension can be obtained according to the water flow data of the target river; by evaluating the influence of different sediment suspension states on the monitoring accuracy of the sensor, the specific influence of sediment suspension on the performance of the monitoring sensor can be analyzed. Combining with the sediment suspension prediction state data, the monitoring strategy can be dynamically adjusted under different sediment suspension conditions, the monitoring accuracy of the sensor can be evaluated in real time, ensuring that the selected sensor can efficiently and accurately monitor the sediment treatment effect in a complex hydrological environment, avoiding data errors caused by poor sensor performance, and thus improving the monitoring accuracy and data reliability.

[0061] According to an embodiment of the present invention, the method of obtaining the sediment distribution data of the target river channel, constructing a sediment distribution model of the target river channel based on the sediment distribution data, and constructing a sediment treatment monitoring network based on the sediment distribution model is specifically as follows:

[0062] Obtain multi-beam sounding data of the target river channel based on a multi-beam sounding system, determine the topographic and geomorphic data of the target river channel according to the multi-beam sounding data, and construct a three-dimensional model of the target river channel according to the topographic and geomorphic data;

[0063] Obtain side-scan sonar data at the bottom of the target river channel, extract the texture features and distribution boundaries of the sediment at the bottom of the target river channel according to the side-scan sonar data, and extract the depth change information of the multi-beam sounding data and the image brightness and texture change information of the side-scan sonar data;

[0064] Identify the sediment thickness change of the target river channel according to the depth change information and the image brightness and texture change information, map the texture features, distribution boundaries, and sediment thickness change to the three-dimensional model, and construct a sediment distribution model of the target river channel;

[0065] Divide the sediment at the bottom of the target river channel into N sub-regions according to the sediment distribution model, and obtain the historical hydrological information of each sub-region in the target river channel. The historical hydrological information includes water flow velocity change and water depth change data;

[0066] Obtain the sediment type data of the target river channel, obtain the sediment particle information of the target river channel according to the sediment type data. The sediment particle information includes particle viscosity, mass, and buoyancy, and obtain the impact coefficient of different water flow velocities on the sediment particles according to the sediment particle information;

[0067] Introduce a hydrodynamic numerical simulation model, import the historical hydrological information of each sub-region into the hydrodynamic numerical simulation model to perform hydrological simulation on the target river channel, and integrate the hydrodynamic numerical simulation model with the sediment distribution model to obtain a hydrological simulation model of the target river channel;

[0068] Construct a preset number of virtual tracer particles based on the sediment particle information, put the virtual tracer particles into the hydrological simulation model, and analyze the virtual tracer particles based on the particle tracking algorithm of the Lagrangian description method to determine the distribution information of each virtual tracer particle within a preset future time period;

[0069] Determine the sediment accumulation hot spot area of the target river channel according to the distribution information, arrange sediment treatment monitoring sensors according to the sediment accumulation hot spot area, and construct a sediment treatment monitoring network with the sediment treatment monitoring sensors.

[0070] It should be noted that the hydrodynamic numerical simulation model is based on the basic principles of hydrodynamics and can describe the dynamic behavior of fluids through mathematical formulas (such as the Navier-Stokes equations). These equations comprehensively consider factors such as the conservation of momentum, mass, and energy of the fluid and can accurately describe the flow, turbulence, velocity field, pressure field of the water flow, as well as the interaction between the water flow and the solid surface (such as the riverbed sediment). In the simulation of river channel water flow, the hydrodynamic numerical simulation can not only simulate the water flow itself but also model in detail the interaction between the water flow and the sediment. In river channel hydrological simulation, the involved factors are complex and variable, including the velocity, direction, depth change of the water flow, the type of sediment, particle characteristics (such as particle viscosity, mass, buoyancy, etc.), and the movement behavior of sediment particles (such as suspension, sedimentation, erosion, etc.). Through the hydrodynamic numerical simulation model, these various factors can be integrated to simulate the complex interaction between the water flow and the sediment and accurately predict the impact of the water flow on the distribution and movement of the sediment. This process can reveal how the sediment accumulates or migrates with the change of the water flow through numerical calculation. After the hydrodynamic numerical simulation model is combined with the sediment distribution model, it can reflect the dynamic relationship between the water flow and the sediment in real time. Through the numerical simulation of the water flow, the impact of the water flow on the sediment under different hydrological conditions (such as water flow velocity, direction, water depth, etc.) can be analyzed, and the suspension, sedimentation, and migration processes of the sediment can be accurately predicted. In hydrodynamic numerical simulation, sediment particles, as solid particles in the "fluid", are regarded as part of hydrodynamics, and the change of the water flow directly affects the movement of sediment particles. By introducing the characteristics of sediment particles such as viscosity, mass, and buoyancy, how sediment particles move with the water flow under different water flow conditions can be simulated, including the sedimentation, suspension, and flow of sediment particles. To more accurately simulate the behavior of sediment particles, the hydrodynamic numerical simulation can also combine virtual tracer particles, add these particles in the water flow simulation, and analyze them based on the particle tracking algorithm (Lagrangian Particle Tracking) of the Lagrangian description method. This method can predict the distribution of sediment particles in the future period of time by simulating the movement trajectories of virtual particles, and further determine the hot spots of sediment accumulation in the river channel. The deployment of sensors in the hot spot areas can collect key data targeted, avoid bringing too much redundant data to the comprehensive monitoring of the entire river channel, and improve the efficiency and reliability of the monitoring network. Monitoring only in the areas where the sediment accumulation is relatively concentrated can achieve more efficient data collection and analysis.The multi-beam sounding data includes water depth data, underwater terrain data, acoustic reflection intensity data, underwater tilt angle data, positioning and attitude data, and time stamp data; the bottom image data, echo intensity data, underwater texture features, bottom boundary data, and sonar beam angle and direction data; the impact coefficient is the degree of influence of the hydrodynamic force of the water flow on the sediment particles, and is usually used to describe the magnitude of the impact force of the fluid on the sediment particles; the sediment monitoring sensor includes a hydrological monitoring sensor and a sediment treatment monitoring sensor, and the hydrological monitoring sensor includes a flow velocity sensor and a water level sensor, and the sediment treatment monitoring sensor includes a turbidity sensor, a sediment interstitial water nutrient sensor, a sediment Eh (oxidation-reduction potential) sensor, a sediment thickness sensor, a sediment material spectrum sensor, and a particle size sensor.

[0071] According to an embodiment of the present invention, the water flow dynamic data and sediment monitoring data of the target river channel are obtained according to the sediment treatment monitoring network, the suspension state of the sediment is evaluated according to the sediment monitoring data, and a hydrodynamic-sediment suspension model is constructed according to the water flow dynamic data and the suspension state, specifically:

[0072] The water flow dynamic data and sediment monitoring data of the target river channel in a preset monitoring time period are obtained according to the sediment treatment monitoring network, the water flow dynamic data includes changes in water flow velocity, changes in water flow direction, and changes in water depth, and the sediment monitoring data includes sediment thickness and sediment particle distribution;

[0073] The sediment suspension degree of the target river channel in the preset monitoring time period is analyzed according to the sediment monitoring data, and the suspension state of the sediment is evaluated according to the sediment suspension degree;

[0074] A correlation analysis matrix is constructed between the water flow dynamic data and the suspension state, and the Pearson correlation coefficient between the water flow dynamic data and the sediment suspension state is calculated according to the correlation analysis matrix;

[0075] The influence of the hydrodynamic force of the target river channel on the sediment suspension state is determined according to the Pearson correlation coefficient between the water flow dynamic data and the sediment suspension state, and the hydrodynamic-suspension state influence data is obtained;

[0076] A hydrodynamic-sediment suspension model of the target river channel is constructed according to the support vector machine algorithm, and the hydrodynamic-suspension state influence data is imported into the hydrodynamic-sediment suspension model for model training.

[0077] It should be noted that by combining the water flow dynamic data and the sediment monitoring data, the suspension degree of the riverbed sediment can be accurately evaluated, and an accurate sediment state analysis can be provided. Secondly, by constructing a correlation analysis matrix of the water flow dynamic data and the sediment suspension state and calculating the Pearson correlation coefficient, the actual impact of water flow changes on sediment suspension can be clarified, helping to quantify the relationship between hydrodynamic force and sediment suspension. Furthermore, using the support vector machine (SVM) algorithm to train the hydrodynamic-sediment suspension model enables the model to accurately simulate and predict the sediment suspension state, improving the pertinence and effectiveness of riverbed sediment treatment. In addition, continuously collecting water flow and sediment monitoring data can real-time evaluate the sediment suspension state of the target river, enhancing the timeliness and accuracy of monitoring. The suspension state includes the suspension concentration and suspension degree of sediment particles.

[0078] Figure 2 The flowchart of obtaining the predicted sediment suspension state data of the present invention is shown.

[0079] According to an embodiment of the present invention, predicting the sediment suspension state of the target river according to the hydrodynamic-sediment suspension model to obtain the predicted sediment suspension state data specifically includes:

[0080] S202, obtaining the real-time water flow dynamic data of each sediment treatment monitoring sensor layout position in the target river according to the sediment treatment monitoring network;

[0081] S204, importing the real-time water flow dynamic data into the hydrodynamic-sediment suspension model to predict the sediment suspension state of each sediment treatment monitoring sensor layout position in the target river, and obtaining the predicted sediment suspension state data, where the predicted sediment suspension state data includes the sediment suspension state position and the concentration of suspended sediment particles.

[0082] It should be noted that by inputting the real-time water flow dynamic data into the hydrodynamic-sediment suspension model, the system can accurately predict the sediment suspension state at different positions in the target river. This helps to identify the specific positions of sediment suspension and locate the areas in the river that are most vulnerable to the influence of water flow.

[0083] Figure 3 The flowchart of determining the monitoring sensors for monitoring the sediment treatment effect of the present invention is shown.

[0084] According to an embodiment of the present invention, obtaining the data on the influence of different sediment suspension states on the monitoring accuracy of each sensor in the sediment treatment monitoring network, evaluating the monitoring accuracy of each sensor in the sediment treatment monitoring network according to the monitoring accuracy influence data and the predicted sediment suspension state data, and determining the monitoring sensors for monitoring the sediment treatment effect according to the monitoring accuracy specifically includes:

[0085] S302. Obtain the influence of different degrees of occlusion on the monitoring sensitivity of each sensor in the sediment treatment monitoring network to obtain sensitivity influence data, and determine the influence of different degrees of occlusion on the monitoring accuracy of each sensor according to the sensitivity influence data to obtain occlusion-monitoring accuracy influence data;

[0086] S304. Obtain the occlusion degree data of different suspended states of the sediment on the sensor, and determine the monitoring accuracy of each sensor in the sediment treatment monitoring network for different suspended states of the sediment according to the occlusion degree data and the occlusion-monitoring accuracy influence data to obtain monitoring accuracy influence data;

[0087] S306. Evaluate the monitoring accuracy of each sensor in the sediment treatment monitoring network according to the sediment suspension prediction state data and the monitoring accuracy data to obtain the monitoring accuracy data of each sensor;

[0088] S308. Determine the monitoring sensors for monitoring the sediment treatment effect according to the monitoring accuracy data.

[0089] It should be noted that determining the monitoring sensors for monitoring the sediment treatment effect based on the monitoring accuracy data can accurately screen out the sensors with the least influence from interference factors such as sediment suspension and occlusion, ensure that the collected data is closest to the real state of the river bottom sediment, avoid misjudgment caused by sensor errors, and greatly improve the credibility of the monitoring data. On the other hand, by analyzing the monitoring accuracy data to select appropriate sensors, the resource allocation of the entire monitoring network can be optimized, enabling limited monitoring resources to be concentrated on the most reliable monitoring points, reducing unnecessary redundant monitoring, improving the monitoring efficiency, and reducing the monitoring cost.

[0090] According to an embodiment of the present invention, the determining the monitoring sensors for monitoring the sediment treatment effect according to the monitoring accuracy data is specifically:

[0091] Calibrate the monitoring sensors with a monitoring accuracy greater than the preset monitoring accuracy as the first-class monitoring sensors according to the monitoring accuracy data, and obtain the quantity information of the first-class monitoring sensors;

[0092] According to the quantity information of the first-class monitoring sensors, if the quantity of the first-class monitoring sensors is greater than the preset value, monitor the sediment treatment effect of the target river according to the first-class monitoring sensors to obtain the first sediment treatment effect monitoring plan;

[0093] It should be noted that when the number of a certain type of monitoring sensor is greater than the preset value, it means that there are a sufficient number of high-precision and high-accuracy sensors available. These first-class monitoring sensors, due to their monitoring accuracy being greater than the preset monitoring accuracy, are less affected by adverse factors such as sediment suspension and occlusion, and can stably and accurately collect data reflecting the true state of the river bottom sediment. Directly relying on them to monitor the sediment treatment effect of the target river can not only give full play to the advantages of these high-quality sensors, avoid introducing excessive data from other sensors that may have errors, and minimize data deviation caused by sensor problems to the greatest extent.

[0094] Determine the occlusion degree of the sediment suspension in the target river on each monitoring sensor according to the monitoring accuracy data and the monitoring accuracy influence data. If the occlusion degree is less than the preset occlusion degree and it is a non-first-class monitoring sensor, label the monitoring sensor as a second-class monitoring sensor, and label the remaining monitoring sensors as third-class monitoring sensors;

[0095] If the number of first-class monitoring sensors is less than the preset value, calculate the difference between the number of first-class monitoring sensors and the preset value, obtain the quantity information of the second-class monitoring sensors. If the number of second-class monitoring sensors is greater than the absolute value of the difference, determine the monitoring accuracy error of the second-class monitoring sensors according to the occlusion degree and the occlusion-monitoring accuracy influence data of each second-class monitoring sensor, perform a monitoring data correction operation on the second-class monitoring sensors according to the monitoring accuracy error, and monitor the sediment treatment effect of the target river according to the first-class monitoring sensors and the second-class monitoring sensors after the monitoring data correction to obtain the second sediment treatment effect monitoring plan;

[0096] It should be noted that although the monitoring accuracy of the second-class sensors does not reach the high standard of the first-class sensors, its occlusion degree is less than the preset occlusion degree, which indicates that the occlusion interference caused by sediment suspension is relatively controllable and not completely unreliable. Secondly, combining the known monitoring accuracy influence data and the occlusion-monitoring accuracy influence data can relatively accurately quantify the monitoring accuracy error generated by the current environmental factors, which provides a feasible basis for the correction operation. Compared with the first-class sensors, the first-class sensors have high accuracy and do not require correction; compared with the third-class sensors, the third-class sensors are severely affected by occlusion and other interferences, and the errors are difficult to accurately quantify and correct, and even the correction effect is not good. Since the number of first-class monitoring sensors is less than the preset value and cannot meet the requirements of sediment treatment effect monitoring, it is necessary to perform data correction on the second-class monitoring sensors and then conduct collaborative monitoring, which optimizes the monitoring resource allocation and greatly improves the reliability and fault tolerance of the entire monitoring system under limited cost and equipment conditions.

[0097] If the number of the first type of monitoring sensors is less than a preset value and the number of the second type of monitoring sensors is less than the absolute value of the difference, calibrate the sediment treatment effect monitoring data collected by the sediment treatment monitoring network as abnormal data, suspend the monitoring of the sediment treatment effect, and obtain a third sediment treatment effect monitoring plan;

[0098] Monitor the sediment treatment effect of the target river channel according to the first sediment treatment effect monitoring plan, the second sediment treatment effect monitoring plan, and the third sediment treatment effect monitoring plan.

[0099] It should be noted that when the number of the first type of monitoring sensors is less than the preset value and the number of the second type of monitoring sensors is less than the absolute value of the difference, the operation of calibrating the monitoring data collected by the sediment treatment monitoring network as abnormal data and suspending the monitoring is adopted to avoid being misled by incorrect data due to too few available reliable sensors, prevent rashly promoting the treatment work based on inaccurate data, and reduce resource waste and ineffective treatment actions. Because in this case, the error and uncertainty of the data collected by the existing small number of unstable sensors are too large to truly reflect the sediment condition of the river channel. The third type of monitoring sensors are the monitoring sensors that are not calibrated as the first type and the second type of monitoring sensors. Actually, the third type of sensors are the monitoring sensors with a shielding degree greater than the preset shielding degree.

[0100] Figure 4 The block diagram of a sediment treatment effect monitoring system for a river channel based on the dynamic change of water flow according to the present invention is shown.

[0101] The second aspect of the present invention also provides a sediment treatment effect monitoring system 4 for a river channel based on the dynamic change of water flow. The system includes: a memory 41 and a processor 42. The memory includes a sediment treatment effect monitoring method program based on the dynamic change of water flow. When the sediment treatment effect monitoring method program based on the dynamic change of water flow is executed by the processor, the following steps are implemented:

[0102] Obtain the sediment distribution data of the target river channel, construct a sediment distribution model of the target river channel according to the sediment distribution data, and construct a sediment treatment monitoring network according to the sediment distribution model;

[0103] Obtain the water flow dynamic data and sediment monitoring data of the target river channel according to the sediment treatment monitoring network, evaluate the suspension state of the sediment according to the sediment monitoring data, and construct a hydrodynamic-sediment suspension model according to the water flow dynamic data and the suspension state;

[0104] Predict the sediment suspension state of the target river channel according to the hydrodynamic-sediment suspension model to obtain sediment suspension prediction state data;

[0105] Obtain data on the impact of different suspended states of sediment on the monitoring accuracy of each sensor in the sediment treatment monitoring network. Evaluate the monitoring accuracy of each sensor in the sediment treatment monitoring network according to the monitoring accuracy impact data and the sediment suspension prediction state data, and determine the monitoring sensors for monitoring the sediment treatment effect based on the monitoring accuracy.

[0106] The present invention discloses a method and system for monitoring the effect of river sediment treatment based on the dynamic change of water flow. The method includes obtaining river sediment distribution data, constructing a sediment distribution model, and building a sediment treatment monitoring network; obtaining water flow dynamic data and sediment monitoring data, evaluating the sediment suspension state, and constructing a hydrodynamic-sediment suspension model in combination with the water flow data to predict the sediment suspension state; evaluating the accuracy of the sensors in the monitoring network according to the impact of different sediment suspension states on the accuracy of the monitoring sensors, so as to optimize the monitoring of the sediment treatment effect. The present invention can monitor the sediment treatment effect of the river in real time, improve the accuracy and timeliness of monitoring, has high application value, and provides a system architecture convenient for deployment.

[0107] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical, or other forms.

[0108] The units described as separate components above may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0110] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0111] Alternatively, if the above integrated units of the present invention are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

[0112] As described above, the above are only the specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for monitoring the effect of riverbed sediment management based on dynamic changes of water flow, characterized in that: The following steps are involved: Acquire sediment distribution data of a target river channel, construct a sediment distribution model of the target river channel according to the sediment distribution data, and construct a sediment management monitoring network according to the sediment distribution model; Acquiring water flow dynamics data and sediment monitoring data of a target river channel according to the sediment management monitoring network, evaluating the suspension state of sediment according to the sediment monitoring data, and constructing a hydrodynamic-sediment suspension model according to the water flow dynamics data and the suspension state; Predicting the sediment suspension state of the target river channel according to the hydrodynamic-sediment suspension model to obtain sediment suspension prediction state data; Obtain data on the impact of different sediment suspension states on the monitoring accuracy of each sensor in the sediment management monitoring network, evaluate the monitoring accuracy of each sensor in the sediment management monitoring network based on the monitoring accuracy impact data and the sediment suspension prediction state data, and determine the monitoring sensor for monitoring the sediment management effect based on the monitoring accuracy.

2. A method for monitoring the effect of riverbed sediment management based on dynamic changes of water flow according to claim 1, characterized in that: The step of obtaining sediment distribution data of a target river channel, constructing a sediment distribution model of the target river channel according to the sediment distribution data, and constructing a sediment management monitoring network according to the sediment distribution model is specifically as follows: Acquire multi-beam bathymetric data of a target river channel based on a multi-beam bathymetric system, determine topographic and geomorphic data of the target river channel according to the multi-beam bathymetric data, and construct a three-dimensional model of the target river channel according to the topographic and geomorphic data; Acquire side-scan sonar data of the bottom of the target river channel, extract texture features and distribution boundaries of the bottom mud of the target river channel according to the side-scan sonar data, and extract depth change information of the multi-beam bathymetric data and image brightness and texture change information of the side-scan sonar data; Identify the change in sediment thickness of the target river channel according to the depth change information and the image brightness and texture change information, map the texture features, distribution boundaries, and sediment thickness changes to the three-dimensional model, and construct a sediment distribution model of the target river channel; Divide the target riverbed sediment into N sub-areas according to the sediment distribution model, and obtain historical hydrological information of each sub-area in the target riverbed, wherein the historical hydrological information includes water flow velocity change and water depth change data; Acquire sediment type data of the target river channel, and acquire sediment particle information of the target river channel according to the sediment type data, wherein the sediment particle information includes particle viscosity, mass, and buoyancy, and acquire impact coefficients of different water flow velocities on sediment particles according to the sediment particle information; Introducing a fluid mechanics numerical simulation model, importing the historical hydrological information of each sub-region into the fluid mechanics numerical simulation model to perform hydrological simulation on the target river channel, and integrating the fluid mechanics numerical simulation model with the sediment distribution model to obtain a hydrological simulation model of the target river channel; Constructing a preset number of virtual tracer particles based on the sediment particle information, putting the virtual tracer particles into the hydrological simulation model, analyzing the virtual tracer particles based on a particle tracking algorithm of the Lagrangian description method, and determining the distribution information of each virtual tracer particle within a future preset time period; The target riverbed sediment accumulation hotspot areas are determined based on the distribution information, sediment management monitoring sensors are deployed based on the sediment accumulation hotspot areas, and the sediment management monitoring sensors are used to construct a sediment management monitoring network.

3. The method for monitoring the riverbed sediment management effect based on the dynamic change of water flow according to claim 1 is characterized in that: The method of obtaining the water flow dynamic data and sediment monitoring data of the target river channel according to the sediment management monitoring network, evaluating the suspension state of the sediment according to the sediment monitoring data, and constructing a hydrodynamic-sediment suspension model according to the water flow dynamic data and the suspension state is specifically as follows: Acquire the water flow dynamic data and sediment monitoring data of the target river during a preset monitoring period according to the sediment management monitoring network, wherein the water flow dynamic data includes changes in water flow velocity, water flow direction, and water depth, and the sediment monitoring data includes sediment thickness and sediment particle distribution; Analyzing the sediment suspension degree of the target river channel within a preset monitoring time period according to the sediment monitoring data, and evaluating the suspension state of the sediment according to the sediment suspension degree; Constructing a correlation analysis matrix between the water flow dynamics data and the suspension state, and calculating the Pearson correlation coefficient between the water flow dynamics data and the sediment suspension state according to the correlation analysis matrix; Determine the influence of the target river channel hydrodynamics on the sediment suspension state according to the Pearson correlation coefficient between the water flow dynamics data and the sediment suspension state, and obtain the hydrodynamics-suspension state influence data; A hydrodynamic-sediment suspension model of the target river channel is constructed according to the support vector machine algorithm, and the hydrodynamic-suspension state influencing data is imported into the hydrodynamic-sediment suspension model for model training.

4. A method for monitoring the riverbed sediment management effect based on dynamic changes of water flow according to claim 1, characterized in that: The sediment suspension state of the target river channel is predicted according to the hydrodynamic-sediment suspension model to obtain sediment suspension prediction state data, specifically: Acquire real-time water flow dynamic data of each sediment management monitoring sensor deployment location in the target river channel according to the sediment management monitoring network; The real-time water flow dynamics data is imported into the hydrodynamic-sediment suspension model, and the sediment suspension state at each sediment management monitoring sensor deployment location in the target river channel is predicted to obtain sediment suspension prediction state data, which includes the sediment suspension state position and the suspended sediment particle concentration.

5. The method for monitoring the riverbed sediment management effect based on the dynamic change of water flow according to claim 1 is characterized in that: The acquisition of the data on the influence of different suspension states of sediment on the monitoring accuracy of each sensor in the sediment management monitoring network, the monitoring accuracy of each sensor in the sediment management monitoring network is evaluated according to the monitoring accuracy influence data and the sediment suspension prediction state data, and the monitoring sensor for monitoring the sediment management effect is determined according to the monitoring accuracy, specifically: Obtain the influence of different obstruction degrees on the monitoring sensitivity of each sensor in the sediment management monitoring network to obtain sensitivity influence data, and determine the influence of different obstruction degrees on the monitoring accuracy of each sensor according to the sensitivity influence data to obtain obstruction-monitoring accuracy influence data; Obtaining data on the degree of shielding of sensors by different suspension states of sediment, determining the monitoring accuracy of each sensor in the sediment management monitoring network by different suspension states of sediment based on the shielding degree data and the shielding-monitoring accuracy impact data, and obtaining monitoring accuracy impact data; Performing a monitoring accuracy evaluation on each sensor in the sediment management monitoring network according to the sediment suspension prediction state data and the monitoring accuracy data to obtain monitoring accuracy data of each sensor; The monitoring sensor for monitoring the sediment treatment effect is determined based on the monitoring accuracy data.

6. A method for monitoring the riverbed sediment management effect based on the dynamic change of water flow according to claim 5, characterized in that: The monitoring sensor for monitoring the sediment treatment effect is determined according to the monitoring accuracy data, specifically: According to the monitoring accuracy data, the monitoring sensors having a monitoring accuracy greater than a preset monitoring accuracy are calibrated as a type of monitoring sensors, and the quantity information of the type of monitoring sensors is obtained; According to the quantity information of the first type of monitoring sensors, if the quantity of the first type of monitoring sensors is greater than a preset value, the sediment management effect of the target river channel is monitored according to the first type of monitoring sensors to obtain a first sediment management effect monitoring plan; Determine the degree of obstruction of each monitoring sensor by the sediment suspension of the target river channel according to the monitoring accuracy data and the monitoring accuracy impact data; if the obstruction degree is less than the preset obstruction degree and the monitoring sensor is not a Class I monitoring sensor, calibrate the monitoring sensor as a Class II monitoring sensor, and the remaining monitoring sensors are calibrated as Class III monitoring sensors; If the number of Class I monitoring sensors is less than a preset value, calculate the difference between the Class I monitoring sensors and the preset value, and obtain the number information of the Class II monitoring sensors; if the number of Class II monitoring sensors is greater than the absolute value of the difference, determine the monitoring accuracy error of the Class II monitoring sensors according to the occlusion degree and occlusion-monitoring accuracy impact data of each Class II monitoring sensor, perform monitoring data correction operation on the Class II monitoring sensors according to the monitoring accuracy error, monitor the sediment treatment effect of the target river channel according to the Class I monitoring sensors and the Class II monitoring sensors after the monitoring data correction, and obtain a second sediment treatment effect monitoring plan; If the number of the first type of monitoring sensors is less than the preset value and the number of the second type of monitoring sensors is less than the absolute value of the difference, the sediment treatment effect monitoring data collected by the sediment treatment monitoring network is calibrated as abnormal data, the monitoring of the sediment treatment effect is suspended, and the third sediment treatment effect monitoring plan is obtained; The sediment treatment effect of the target river channel is monitored according to the first sediment treatment effect monitoring plan, the second sediment treatment effect monitoring plan and the third sediment treatment effect monitoring plan.

7. A riverbed sediment management effect monitoring system based on dynamic changes of water flow, characterized in that: The riverbed sediment management effect monitoring system based on dynamic changes of water flow includes a storage device and a processor. The storage device includes a riverbed sediment management effect monitoring method program based on dynamic changes of water flow. When the riverbed sediment management effect monitoring method program based on dynamic changes of water flow is executed by the processor, the following steps are implemented: Acquire sediment distribution data of a target river channel, construct a sediment distribution model of the target river channel according to the sediment distribution data, and construct a sediment management monitoring network according to the sediment distribution model; Acquiring water flow dynamics data and sediment monitoring data of a target river channel according to the sediment management monitoring network, evaluating the suspension state of sediment according to the sediment monitoring data, and constructing a hydrodynamic-sediment suspension model according to the water flow dynamics data and the suspension state; Predicting the sediment suspension state of the target river channel according to the hydrodynamic-sediment suspension model to obtain sediment suspension prediction state data; Obtain data on the impact of different sediment suspension states on the monitoring accuracy of each sensor in the sediment management monitoring network, evaluate the monitoring accuracy of each sensor in the sediment management monitoring network based on the monitoring accuracy impact data and the sediment suspension prediction state data, and determine the monitoring sensor for monitoring the sediment management effect based on the monitoring accuracy.

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