Auxiliary dam blocking type silt reduction system of sandy river reservoir and optimization design method of auxiliary dam blocking type silt reduction system

Abstract

The invention provides an auxiliary dam blocking type silt reduction system of a sandy river reservoir and an optimization design method of the auxiliary dam blocking type silt reduction system, and relates to the technical field of water conservancy projects. The system comprises a simulation riverbed, a simulation main dam body, a V-shaped flow guide auxiliary dam body and an intelligent monitoring and control system, and an efficient sand discharge execution unit comprises submersible sand discharge pumps arranged at low-lying positions of sediment collection areas on the two sides and an operation optimization software platform; the method comprises the following steps: S1, acquiring an initial parameter data set representing an operating environment; s2, calculating and determining the optimal V-shaped angle of the V-shaped diversion auxiliary dam body; s3, determining an optimized spatial layout of the intelligent monitoring and control system; and S4, an intelligent start-stop control strategy of the efficient desilting execution unit is generated through the desilting pump control model, scientific design, accurate prediction and economic decision are deeply fused, the system becomes a self-adaptive and self-optimized intelligent economical body, and fundamental improvement of the operation efficiency and the full-cycle economic benefit is achieved.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, specifically to a secondary dam-type silt reduction system for reservoirs in sandy rivers and its optimized design method. Background Technology

[0002] Siltation in reservoirs is a common technical challenge faced by water conservancy projects worldwide. Siltation drastically reduces the effective storage capacity of reservoirs, diminishes their flood control and water utilization capabilities, and ultimately leads to premature reservoir decommissioning. Existing solutions mainly include: Setting up sand discharge holes: This is the most commonly used engineering measure. However, their location and elevation are often difficult to cover the entire siltation area, resulting in limited sand discharge efficiency. Furthermore, the water level needs to be lowered during sand discharge, affecting the reservoir's normal power generation and water supply benefits.

[0003] Mechanical dredging: Dredging using equipment such as dredgers and excavators is costly, has a long operation cycle, and may cause secondary damage to the reservoir ecosystem, making it difficult to serve as a long-term sustainable solution.

[0004] Hydraulic sediment removal: This method utilizes water power to remove sediment from the reservoir through reservoir operation and scheduling. Its effectiveness depends heavily on inflow and sediment conditions, lacks flexibility, and is ineffective for severely silted reservoirs.

[0005] In the design and operation of large-scale engineering systems such as water conservancy, energy, and transportation, a common technical challenge exists: the physical layout design and subsequent operational control strategies are often disconnected. The design of the physical layout (such as the angle of the sand-guiding embankment in a reservoir or the location of sensors in the power grid) relies heavily on static historical data and engineering experience, making it difficult to achieve optimal configuration for dynamic environments. Meanwhile, operational control strategies (such as pump start-up and shutdown, and power grid dispatch) are based on the fixed physical layout, and their optimization effectiveness is limited by the quality of the initial design. This "design first, control later" model lacks global perspective and foresight, resulting in low overall system efficiency, high energy consumption, and suboptimal economic benefits. Existing technologies often focus on localized and passive processing, lacking global control and proactive intervention capabilities regarding the laws governing sediment movement in reservoir areas. In particular, there is a lack of a systematic design theory and methodology that deeply integrates engineering structures, sediment movement mechanisms, and intelligent control, leading to insufficient efficiency and adaptability of solutions. Therefore, there is an urgent need for a systematic methodology that can deeply integrate early-stage optimization design with later-stage intelligent control to form a closed loop, achieving optimal overall performance throughout the system's entire lifecycle. Summary of the Invention

[0006] The purpose of this invention is to provide a secondary dam-type silt reduction system for reservoirs in sandy rivers and its optimized design method, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The secondary dam-type sediment reduction system of the Duosha River Reservoir includes a simulated riverbed and a simulated main dam body, as well as a V-shaped diversion secondary dam body set on the simulated riverbed and built upstream of the simulated main dam body. The V-shaped diversion secondary dam body is arranged in a V shape with its tip pointing towards the water flow direction of the reservoir. It is used to change the water flow pattern in the reservoir area and divert bedload and suspended sediment accumulated in front of the main dam to the banks. The intelligent monitoring and control system is deployed behind the V-shaped diversion secondary dam and in the confluence areas on both banks. The intelligent monitoring and control system includes a sediment deposition sensor network, a water level sensor, and a central controller. The sediment deposition sensor network collects the sediment deposition thickness and distribution in real time, and the water level sensor is used to collect water level data and transmit it to the central controller. The high-efficiency sand removal unit includes a submersible sand removal pump located in the low-lying area of ​​the sediment collection area on both banks, and a sand conveying pipeline system connected to the outlet of the submersible sand removal pump, which is used to transport the pumped sediment to a designated storage yard or utilization area. The operation optimization software platform integrates theoretical calculation models for system design, simulation, and real-time operation optimization.

[0008] Furthermore, the operation optimization software platform includes a V-shaped angle optimization design model for the secondary dam body, a spatiotemporal prediction model for sediment deposition thickness, a sediment pump control model, and a system life-cycle economic evaluation model.

[0009] Furthermore, the operation optimization software platform includes a non-volatile storage medium storing computer programs. When the computer programs are executed by the processor, they can perform calculation and optimization functions for the V-shaped angle optimization design model of the secondary dam body, the spatiotemporal prediction model of sediment deposition thickness, the control model of the sediment pump, and the economic evaluation model of the entire system life cycle. The non-volatile storage medium is used to store computer programs and data information.

[0010] Furthermore, the calculation logic of the V-shaped angle optimization design model for the secondary dam body is as follows: obtain the product of the characteristic inflow rate and the inflow sediment concentration to obtain the first calculation result representing the sediment flux; obtain the product of the average reservoir width, sediment settling velocity, and flow regime correction coefficient to obtain the second calculation result representing the sediment settling and transport capacity; divide the first calculation result by the second calculation result to obtain the dimensionless characteristic ratio; perform an arctangent function operation on the characteristic ratio and multiply the operation result by a constant 2 to determine the optimal V-shaped angle.

[0011] Furthermore, the calculation logic of the spatiotemporal prediction model for sediment deposition thickness is as follows: the rate of change of sediment deposition thickness over time is equal to the product of the following four factors: the first factor is the topographic adaptability coefficient; the second factor is the sedimentation efficiency factor related to flow velocity, which is obtained by dividing the local flow velocity by the critical starting flow velocity to obtain the flow velocity ratio, and then the flow velocity ratio is multiplied by an empirical exponent, and finally the result of the exponentiation is subtracted by a constant 1; the third factor is the local sediment content; and the fourth factor is the effective settlement probability.

[0012] Furthermore, the objective function of the sand pump control model is to maximize the system's net benefit. This is achieved by multiplying the unit dredging revenue by the cumulative dredging volume to obtain the total dredging revenue. Integrating the pump power over time yields the total power consumption, which is then multiplied by the unit electricity cost to obtain the total operating cost. Subtracting the total operating cost from the total dredging revenue gives the system's net benefit. The sand pump control model maximizes this net benefit by adjusting start-up and shutdown timings and operating power.

[0013] Furthermore, the calculation logic of the system's full life-cycle economic evaluation model is as follows: For each year in the project's life cycle, subtract the annual cost from the annual benefit of the calculated year to obtain the net benefit of that year; divide the net benefit of that year by the discount factor, which is obtained by adding a constant 1 to the discount rate and then raising the sum to the power of the year; sum the net benefits of all years in the project's life cycle after discounting to obtain the total discounted revenue; subtract the initial investment from the total discounted revenue, and the final result is the project's net present value.

[0014] The optimized design method for the secondary dam-type sediment reduction system of a reservoir in a sandy river includes the following steps: S1. Obtain the initial parameter dataset representing the operating environment. The initial parameter dataset includes physical environment parameters and economic operating parameters. S2. Based on physical environment parameters, the optimal V-angle of the V-shaped diversion secondary dam is determined by calculation using the V-shaped angle optimization design model of the secondary dam body, so as to achieve the optimal guiding efficiency of the V-shaped diversion secondary dam body for specific media in the system. S3. Based on the determined geometric configuration parameters and physical environment parameters of the V-shaped diversion secondary dam, the spatiotemporal distribution evolution of a specific medium in the system is simulated by a spatiotemporal prediction model of sediment deposition thickness, and the optimized spatial layout of the intelligent monitoring and control system is determined based on the simulation results. S4. Based on the optimized spatial layout of the intelligent monitoring and control system and combined with economic operating parameters, an intelligent start-stop control strategy for the efficient sand discharge execution unit is generated through the sand discharge pump control model. The control strategy takes the maximization of the preset global economic benefit index of the system as the optimization objective, and the intelligent start-stop control strategy generates command signals.

[0015] Furthermore, the economic operating parameters include the net benefit of the year, the discount factor, the total discounted income, and the net present value of the project. The net present value of the project is generated by inputting the net benefit of the year, the discount factor, and the total discounted income into the system's full life cycle economic evaluation model.

[0016] Furthermore, the specific calculation steps for the sand-discharging pump control model are as follows: Data from the intelligent monitoring and control system is collected via industrial bus, and after analysis and calculation, the real-time volume of removable sediment is obtained. Obtain the current unit cost of electricity and unit revenue from dredging through the network interface; The built-in prediction model module is invoked to calculate the probability of predicted high-yield events and the amplification factor of predicted event returns within a preset future time window. Calculate the expected net benefit of running the decision cycle at maximum power at the current moment; Multiply the predicted probability of a high-yield event by the predicted event's return multiplier to obtain the expected return multiplier. The instant net benefit index and the time-domain opportunity value index are integrated into a single, final intelligent start-stop control strategy. The calculated intelligent start-stop control strategy is compared with the preset start-up decision threshold; If the intelligent start-stop control strategy exceeds the start decision threshold, it is determined that the current or near future is a favorable time to start sand removal. At this time, the central controller will enter the power optimization subroutine, aiming to maximize the intelligent start-stop control strategy, to find the optimal power level among the available power levels, and output the command signal corresponding to the optimal power level. If the intelligent start-stop control strategy is less than or equal to the start decision threshold, the central controller determines that the overall economic benefits of starting sand removal are not good and should wait for a better opportunity to output a command signal to keep the machine stopped or maintain the minimum power. The central controller sends the generated command signals to the high-efficiency sand removal execution unit and stores all input parameters, intermediate calculation results and command signals of this decision into the historical database for subsequent sand removal pump control model iteration and auditing.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention transforms the silt reduction system from a qualitative model relying on personal experience to a completely data-driven scientific paradigm by introducing a series of interconnected mathematical models. The V-shaped angle optimization design model of the secondary dam body ensures that the physical form of the diversion structure achieves maximum efficiency at the source; the spatiotemporal prediction model of siltation thickness enables precise and forward-looking layout of monitoring resources; and the refined design based on scientific computing enables the system to have silt capture and sensing capabilities at the hardware level, laying a solid physical foundation for subsequent intelligent control. The sand-dredging pump control model of this invention goes beyond passive control logic based solely on physical thresholds; it integrates real-time physical state, dynamic economic parameters, and probability prediction of future high-yield events to construct an optimal decision with the goal of maximizing long-term economic benefits; this significantly reduces system operating costs while greatly increasing dredging output per unit of energy consumption, thereby maximizing economic benefits. This invention tightly couples together the three originally separate stages of early-stage structural design, mid-stage process prediction, and late-stage operation control, forming an organic whole. The final control command is the embodiment of the efficient physical conditions created by the optimal design in the early stage, the high-value decision-making information provided by the accurate prediction in the mid-stage, and the final comprehensive judgment of multi-dimensional information in the late stage. It simultaneously drives multiple technical links and synergistically achieves the effect of multiple optimization goals such as economy, efficiency, and safety. This makes the entire silt reduction system no longer a cold facility that passively performs tasks, but an intelligent economic entity that can actively sense the environment, predict the future, and self-regulate based on the principle of economic optimization. It demonstrates system resilience, economy, and foresight that traditional technical solutions cannot achieve. Attached Figure Description

[0018] Figure 1 This is a side view of the model of the present invention. Figure 2 This is a top view of the structure of the model of the present invention; Figure 3 This is a schematic diagram of the overall process flow of the present invention; Figure 4 This is a schematic diagram illustrating the specific calculation steps of the sand-discharging pump control model of the present invention.

[0019] In the diagram: 1. Simulated riverbed; 2. Simulated main dam body; 3. V-shaped diversion secondary dam body; 4. Intelligent monitoring and control system; 5. Submersible sand pump; 6. Sand conveying pipeline system. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] Example 1: Please see Figures 1 to 2 This invention provides a technical solution: a secondary dam-type silt reduction system for a reservoir in a sandy river, comprising a simulated riverbed 1 and a simulated main dam 2, and a V-shaped diversion secondary dam 3 installed on the simulated riverbed 1 and built upstream of the simulated main dam 2. The V-shaped diversion secondary dam 3 is arranged in a V-shape, with its tip pointing towards the water flow direction of the reservoir; it is used to change the water flow pattern in the reservoir area and guide bedload and suspended sediment deposited in front of the main dam to the banks. The intelligent monitoring and control system 4 is deployed behind the V-shaped diversion secondary dam 3 and in the confluence areas on both banks. The intelligent monitoring and control system 4 includes a sediment deposition sensor network, a water level sensor, and a central controller. The sediment deposition sensor network collects the sediment deposition thickness and distribution in real time, and the water level sensor is used to collect water level data and transmit it to the central controller. The high-efficiency sand discharge unit includes a submersible sand discharge pump 5 located in the low-lying area of ​​the sediment collection area on both banks, and a sand conveying pipeline system 6 connected to the outlet of the submersible sand discharge pump 5, which is used to transport the pumped sediment to a designated storage yard or utilization area. The operation optimization software platform integrates theoretical calculation models for system design, simulation, and real-time operation optimization.

[0023] In this embodiment, preferably, the operation optimization software platform includes a secondary dam body V-shaped angle optimization design model, a siltation thickness spatiotemporal prediction model, a sand discharge pump control model, and a system full life cycle economic evaluation model. It should be noted that by integrating the V-shaped angle optimization design model of the secondary dam body, the spatiotemporal prediction model of sediment deposition thickness, the sediment pump control model, and the system's full life-cycle economic evaluation model into the operation optimization software platform, a closed-loop, integrated intelligent optimization system has been constructed, covering the entire process from early decision-making and mid-term design to later operation and maintenance. This not only ensures that the physical structure has optimal performance from the beginning of construction and achieves precise layout and efficient operation of monitoring and execution units through predictive models, but more importantly, it unifies all technical aspects with the ultimate goal of optimal economic efficiency throughout the entire life cycle. This systematic and consistent top-level design makes the project's investment decisions based on evidence, the engineering design scientifically quantified, and the operation and control economically efficient.

[0024] In this embodiment, preferably, the running optimization software platform includes a non-volatile storage medium storing computer programs. When the computer programs are executed by the processor, they can realize the calculation and optimization functions of the auxiliary dam body V-shaped angle optimization design model, the spatiotemporal prediction model of sediment deposition thickness, the sand discharge pump control model, and the system full life cycle economic evaluation model. The non-volatile storage medium is used to store computer programs and data information. It should be noted that by embedding the complex calculation and optimization functions of the auxiliary dam body V-angle optimization design model, the spatiotemporal prediction model of sediment deposition thickness, the sediment pump control model, and the system's full life-cycle economic evaluation model into a non-volatile storage medium in the form of a computer program, a stable, reliable, and reproducible physical carrier is provided. This allows the core intelligence of the entire system to be independent of specific, customized hardware, achieving decoupling between software and hardware. It ensures the secure and persistent storage of core algorithm logic and key data information, unaffected by external factors such as power outages, guaranteeing the stability of system operation and the consistency of decision-making. This greatly enhances the portability, maintainability, and scalability of this technical solution, enabling the entire optimization method to be easily deployed at different project sites and to be continuously iterated and optimized through software upgrades.

[0025] In this embodiment, the preferred calculation logic of the V-shaped angle optimization design model for the secondary dam body is as follows: The product of the characteristic inflow rate and the inflow sediment concentration is obtained to get the first calculation result representing the sediment flux; the product of the average reservoir width, sediment settling velocity, and flow regime correction coefficient is obtained to get the second calculation result representing the sediment settling and transport capacity; the first calculation result is divided by the second calculation result to obtain the dimensionless characteristic ratio; the arctangent function operation is performed on the characteristic ratio, and the result is multiplied by a constant 2 to determine the optimal V-shaped angle. It should be noted that in the design scenario of a sediment-laden river reservoir silt reduction system, the design flood flow or multi-year average flow of the target reservoir is collected as the characteristic inflow flow, the average sediment content of the inflow measured by the hydrological station is collected, the average reservoir width of the section where the secondary dam is located is measured by topographic map, and the representative settling velocity is determined by sediment particle size distribution experiments. The flow regime correction coefficient is determined by referring to tables or empirical formulas based on the specific hydraulic conditions of the reservoir and the roughness of the secondary dam material. Thus, the optimal V-angle that can guide the water flow to the designated collection area with maximum efficiency in carrying sediment can be calculated. This provides a direct and calculable scientific method for optimizing the V-angle of the abstract secondary dam body, replacing the empirical estimation in traditional engineering design. This ensures that the core geometric parameters of the V-shaped diversion secondary dam body 3 have optimal physical performance from the design source, laying a solid foundation for the efficient operation of the entire system and significantly improving the efficiency of sediment collection.

[0026] In this embodiment, the preferred calculation logic of the spatiotemporal prediction model for sediment deposition thickness is as follows: the rate of change of sediment deposition thickness over time is equal to the product of the following four factors: the first factor is the terrain adaptability coefficient; the second factor is the sedimentation efficiency factor related to flow velocity, which is obtained by dividing the local flow velocity by the critical starting flow velocity to obtain the flow velocity ratio, and then performing a power operation on the flow velocity ratio using an empirical exponent, and finally subtracting the result of the power operation from the constant 1; the third factor is the local sediment content; and the fourth factor is the effective settlement probability. It should be noted that after determining the optimal V-angle and using it as the boundary condition, computational fluid dynamics software, combined with the reservoir area digital elevation model, was used to numerically simulate the water and sediment movement in the reservoir area under different inflow and sediment conditions. By solving the above calculation logic, a dynamic three-dimensional cloud map showing the change in sediment thickness at various points on the reservoir bottom over time within one or more future hydrological cycles was obtained. The optimized spatial layout of the intelligent monitoring and control system 4 was determined based on the hotspot areas revealed by the dynamic three-dimensional cloud map, where the sedimentation rate was the fastest and the final sedimentation thickness was the largest. The system was then densely deployed in these areas. This ensured that the deployment of the intelligent monitoring and control system 4 was no longer blind but precisely focused on the most critical areas, thereby obtaining the highest value monitoring data at the most economical cost.

[0027] In this embodiment, preferably, the objective function of the sand pump control model is to maximize the system's net benefit. The unit dredging revenue is multiplied by the cumulative dredging volume to obtain the total dredging revenue. The pump power over time is integrated to obtain the total power consumption, which is then multiplied by the unit electricity cost to obtain the total operating cost. The total operating cost is subtracted from the total dredging revenue, and the difference is the system's net benefit. The sand pump control model adjusts the start-stop timing and operating power to maximize the system's net benefit. It should be noted that two types of heterogeneous data are received in real time or periodically: one is physical state data collected by the intelligent monitoring and control system 4, such as the real-time silt thickness at each monitoring point, from which the current volume of silt that can be removed can be calculated; the other is economic operating parameters input from the outside, such as the comprehensive benefits brought by the unit silt removal and the real-time electricity price. The background optimization solver of the sand pump control model, such as dynamic programming or heuristic algorithms, simulates various possible control strategies, including immediate start, delayed start, and start with different power levels, for each decision moment. For each strategy, the sand pump control model: predicts the cumulative volume of sand removed over a future period after implementing the strategy, and multiplies it by the unit dredging benefit to obtain the expected benefit; calculates the instantaneous power of the sand pump group required to implement the strategy, integrates it over time, and multiplies it by the unit electricity cost to obtain the estimated operating cost; obtains the system net benefit by weighted summation of benefits and costs (weights are 1 and -1); the optimization solver finally selects the strategy that maximizes the system net benefit and converts it into specific start / stop and power adjustment commands, which are then issued to the efficient sand pump execution unit for execution; its quantifiable performance indicators are the lowest average unit sand dredging cost or the highest dredging benefit per unit energy consumption after long-term operation.

[0028] In this embodiment, the preferred calculation logic of the system's full life-cycle economic evaluation model is as follows: For each year in the project's life cycle, the annual cost is subtracted from the annual benefit of the calculated year to obtain the net benefit of that year; the net benefit of that year is divided by the discount factor, which is obtained by adding a constant 1 to the discount rate and then multiplying the sum by the number of years; the net benefits of all years in the project's life cycle after discounting are summed to obtain the total discounted revenue; the initial investment is subtracted from the total discounted revenue, and the final result is the project's net present value. It should be noted that when the calculation result for maximizing the system's net benefit indicates that the current period is the golden window for sand removal, the central controller will generate a set of structured command signals; these command signals are not simply broadcast to initiate the process, but rather: Command decomposition: The total power demand (e.g., 1000kW) is decomposed into specific commands for multiple sand pumps. In this embodiment, "Pump No. 1 starts and operates at 80% of rated power; Pump No. 2 starts and operates at 90% of rated power; Pump No. 3 is on standby"; Timing coordination: The command signal includes the start-up timing. In this embodiment, "pump 1 starts first, and pump 2 starts 30 seconds later" to avoid excessive impact on the power grid. Control Manifestation: When monitoring data shows uneven siltation in the confluence areas on both banks, the command signal will be directly reflected in the command. In this embodiment, "the left bank No. 1 and No. 2 pumps will be activated with priority, and the power of the right bank No. 4 pump will be halved", so that a core decision can drive multiple physical links to carry out differentiated and coordinated operations at the same time. When a sensor or pump malfunctions, an abnormal status is received. At this point, the system's net benefit is recalculated with remaining available resources as a constraint, and a degraded optimal control command is generated to ensure that the system can still operate optimally under abnormal conditions, rather than simply shutting down.

[0029] Please see Figures 3 to 4 The optimized design method for the secondary dam-type silt reduction system of a reservoir in a sandy river includes the following steps: S1. Obtain the initial parameter dataset representing the operating environment. The initial parameter dataset includes physical environment parameters and economic operating parameters. S2. Based on physical environment parameters, the optimal V-angle of the V-shaped diversion secondary dam 3 is determined by calculation using the V-shaped angle optimization design model of the secondary dam body, so as to achieve the optimal guiding efficiency of the V-shaped diversion secondary dam 3 for the specific medium in the system. S3. Based on the determined geometric configuration parameters and physical environment parameters of the V-shaped diversion secondary dam body 3, the spatiotemporal distribution evolution of a specific medium in the system is simulated by a spatiotemporal prediction model of sediment deposition thickness, and the optimized spatial layout of the intelligent monitoring and control system 4 is determined based on the simulation results. S4. Based on the optimized spatial layout of the intelligent monitoring and control system 4 and combined with economic operating parameters, an intelligent start-stop control strategy for the efficient sand discharge execution unit is generated through the sand discharge pump control model. The control strategy takes the maximization of the preset global economic benefit index of the system as the optimization objective, and the intelligent start-stop control strategy generates command signals.

[0030] In this embodiment, preferably, the economic operating parameters include the net benefit of the current year, the discount factor, the total discounted income, and the net present value of the project. The net present value of the project is generated by inputting the net benefit of the current year, the discount factor, and the total discounted income into the system's full life cycle economic evaluation model. It should be noted that by explicitly using the net benefit of the year, the discount factor, and the total discounted revenue as core input parameters, and by using the system's full life-cycle economic evaluation model to ultimately generate the project's net present value, a scientific, rigorous, and standardized financial quantitative framework has been established for the entire system's investment decisions and strategic evaluation. This elevates the evaluation of complex engineering projects from vague qualitative judgments to precise quantitative analysis, and formally introduces the core economic concept of time value.

[0031] In this embodiment, preferably, the sand pump control model involves the following key parameters: Parameter symbol for unit dredging revenue It represents the comprehensive economic value that can be brought about by removing one cubic meter of sediment; it includes not only the direct increase in power generation head and restoration of water supply capacity brought about by the restoration of reservoir capacity, but also the indirect socio-economic benefits such as avoiding downstream river siltation and reducing dredging costs. Real-time removable sediment volume parameter symbols ; indicates at the moment of decision-making If the submersible sand pump 5 is started, the volume of mud and sand effectively removed within a unit decision cycle; The calculation logic is as follows: By performing time-of-flight analysis on the signal returned by the ultrasonic mud level gauge and combining it with the preset reservoir bottom elevation, the average sediment thickness near the discharge outlet is calculated; by performing fast Fourier transform and characteristic frequency energy spectrum analysis on the fluid acoustic signal collected by the DAS array, it is compared with the pre-calibrated acoustic characteristic sediment concentration database to calculate the current sediment volume concentration near the discharge outlet in real time; based on the performance curve of the discharge pump, the flow rate of the clean water corresponding to the given pump power at the current head is obtained; the real-time sediment removal volume is the product of the clean water flow rate and the sediment volume concentration, and then multiplied by the unit decision cycle time. The symbol for the total operating cost parameter The monetary cost per unit of electricity consumed by the submersible sand pump 5 during operation; The sign of the parameter used to predict the probability of high-yield events It represents the probability of a high-yield sediment discharge event occurring within a preset time window in the future; a high-yield event is defined as an event in which both the upstream water flow and sediment concentration are significantly higher than normal levels, such as floods during the flood season. The calculation logic is as follows: Rainfall forecast data for the next 72 hours is obtained from the meteorological center and input into a pre-trained hydrological forecasting model based on a long short-term memory network to obtain a predicted inflow process line for the next 72 hours. Rainfall forecasts and upstream soil moisture and vegetation cover data monitored by remote sensing satellites are input into a watershed sediment yield model modified based on a general soil loss equation to obtain a predicted inflow sediment concentration process line for the next 72 hours. A threshold for high-yield events is set: the flow rate must exceed the 5-year flood standard, and the sediment concentration must be more than three times the historical average for the same period. By analyzing the predicted process line, the proportion of the cumulative duration of predicted values ​​exceeding the threshold within the entire prediction time window is calculated, and this proportion is used as the probability of predicting high-yield events. The sign of the parameter of the predictive event return amplification factor Used to quantify the amplification factor of sediment removal benefits of predicted high-yield events compared to current operating conditions; The calculation logic is as follows: Calculate the average sediment concentration during the predicted high-yield event period, and calculate the current measured sediment concentration, and the predicted event yield amplification factor. This is the ratio of average sediment concentration to sediment concentration; The specific calculation steps for the sand-discharging pump control model are as follows: Data from the intelligent monitoring and control system 4 is collected via industrial bus, and after analysis and calculation, the real-time removable sediment volume is obtained. ; Obtain the current unit cost of electricity and unit revenue from dredging through the network interface; The built-in prediction model module is invoked to calculate the probability of predicted high-yield events within a preset future time window. and the amplification factor of predicted event returns ; The expected net benefit of operating the decision cycle at maximum power at the current moment is calculated using the following logic: unit dredging revenue. Multiply by the volume of silt that can be removed in real time Then, subtract the maximum power multiplied by the decision cycle duration multiplied by the unit energy cost, and process this net benefit value through a preset benefit normalization function. The function maps the actual benefit value to the [0,1] interval to obtain the instantaneous net benefit index. This normalization process aims to eliminate the influence of dimensions and make the benefits under different operating conditions comparable. Predicting the probability of high-yield events Amplification factor for predicted event returns Multiplying these together yields the expected return multiplier. This result is also processed by a preset opportunity value normalization function and mapped to the [0,1] interval to obtain the time-domain opportunity value index. The higher the time-domain opportunity value index, the greater the cost-effectiveness of future sand removal compared to the present. The instant net benefit index and the time-domain opportunity value index are integrated into a single, final intelligent start-stop control strategy. The calculated intelligent start-stop control strategy is compared with the preset start-up decision threshold (in this embodiment, the start-up decision threshold is 0.6); If the intelligent start-stop control strategy exceeds the start-up decision threshold, it is determined that the current or near future is a favorable time to start sand removal. At this time, the central controller will enter the power optimization subroutine, aiming to maximize the intelligent start-stop control strategy, and search for the optimal power level among the selectable power levels. In this embodiment, the power levels include 0%, 25%, 50%, 75%, and 100%, and output the command signal corresponding to the optimal power level. In this embodiment, the command signal is sent to the central controller through the Modbus protocol to control the frequency converter to run the submersible sand removal pump 5 at 75% of its rated power. If the intelligent start-stop control strategy is less than or equal to the start decision threshold, the central controller determines that the overall economic benefits of starting sand removal are not good and should wait for a better opportunity to output a command signal to keep the machine stopped or maintain the minimum power. The central controller sends the generated command signals to the high-efficiency sand discharge execution unit and stores all input parameters, intermediate calculation results and command signals of this decision into the historical database for subsequent sand discharge pump control model iteration and auditing. It should be noted that this system can generate counterintuitive but optimal control strategies in the long run. When sediment concentration is acceptable and electricity prices are average, traditional models would choose to initiate sediment discharge. However, if a high-sediment-content flood is predicted to pass through in 24 hours, the system will decide to remain silent and accumulate energy to discharge sediment at several times the efficiency during the future golden window. By incorporating future predictions into decision-making, the system shifts from passively responding to environmental changes to actively utilizing them. This greatly enhances the system's resilience and profitability in the face of extreme weather events. Furthermore, it provides a calculable and optimizable mathematical framework, transforming qualitative descriptions into quantitative indicators that can guide engineering practice.

[0032] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, Min-Max Normalization and Z-Score standardization. The technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of this invention.

[0033] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0034] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A secondary dam-type silt reduction system for a reservoir in a sandy river, characterized in that: It includes a simulated riverbed (1) and a simulated main dam body (2), as well as a V-shaped diversion secondary dam body (3) set on the simulated riverbed (1) and built upstream of the simulated main dam body (2). The V-shaped diversion secondary dam body (3) is arranged in a V shape with its tip pointing towards the water flow direction of the reservoir. It is used to change the water flow pattern in the reservoir area and guide the bedload and suspended sediment accumulated in front of the main dam to the areas on both banks. The intelligent monitoring and control system (4) is deployed behind the V-shaped diversion secondary dam (3) and in the confluence area on both banks. The intelligent monitoring and control system (4) includes a sedimentation sensor network, a water level sensor and a central controller. The sedimentation sensor network collects the sedimentation thickness and distribution in real time, and the water level sensor is used to collect water level data and transmit it to the central controller. The high-efficiency sand discharge unit includes a submersible sand discharge pump (5) located in the low-lying area of ​​the sediment collection area on both banks, and a sand conveying pipeline system (6) connected to the outlet of the submersible sand discharge pump (5) for transporting the pumped sediment to a designated storage yard or utilization area. The operation optimization software platform integrates theoretical calculation models for system design, simulation, and real-time operation optimization.

2. The secondary dam-type silt reduction system for a sandy river reservoir according to claim 1, characterized in that: The operation optimization software platform includes a V-shaped angle optimization design model for the secondary dam body, a spatiotemporal prediction model for sediment deposition thickness, a sediment pump control model, and a system life-cycle economic evaluation model.

3. The secondary dam-type silt reduction system for a sandy river reservoir according to claim 2, characterized in that: The operation optimization software platform includes a non-volatile storage medium that stores computer programs. When the computer programs are executed by the processor, they can perform calculation and optimization functions for the V-shaped angle optimization design model of the secondary dam body, the spatiotemporal prediction model of sediment deposition thickness, the control model of the sediment pump, and the economic evaluation model of the entire system life cycle. The non-volatile storage medium is used to store the computer programs and data information.

4. The secondary dam-type silt reduction system for a sandy river reservoir according to claim 3, characterized in that: The calculation logic of the V-shaped angle optimization design model of the secondary dam body is as follows: obtain the product of the characteristic inflow rate and the inflow sediment concentration to obtain the first calculation result characterizing the sediment flux; obtain the product of the average reservoir width, sediment settling velocity and flow correction coefficient to obtain the second calculation result characterizing the sediment settling and transport capacity; divide the first calculation result by the second calculation result to obtain the dimensionless characteristic ratio. Perform an arctangent function operation on the characteristic ratio and multiply the result by a constant 2 to determine the optimal V-angle.

5. The secondary dam-type silt reduction system for a sandy river reservoir according to claim 4, characterized in that: The calculation logic of the spatiotemporal prediction model for sediment deposition thickness is as follows: the rate of change of sediment deposition thickness over time is equal to the product of the following four factors: the first factor is the topographic adaptability coefficient; the second factor is the sedimentation efficiency factor related to flow velocity, which is obtained by dividing the local flow velocity by the critical starting flow velocity to obtain the flow velocity ratio, and then the flow velocity ratio is multiplied by an empirical exponent, and finally the result of the exponentiation is subtracted by a constant 1; the third factor is the local sediment content; and the fourth factor is the effective settlement probability.

6. The secondary dam-type silt reduction system for a sandy river reservoir according to claim 5, characterized in that: The objective function of the sand pump control model is to maximize the net benefit of the system. The unit dredging benefit is multiplied by the cumulative dredging volume to obtain the calculation result representing the total dredging benefit. The total power consumption is obtained by integrating the function of pump power change over time. The total power consumption is then multiplied by the unit electricity cost to obtain the calculation result representing the total operating cost. The total operating cost is subtracted from the total dredging revenue, and the difference is the net benefit of the system. The sand pump control model adjusts the start-up and shutdown timing and operating power to maximize the net benefit of the system.

7. The secondary dam-type silt reduction system for a sandy river reservoir according to claim 6, characterized in that: The calculation logic of the system's full life-cycle economic evaluation model is as follows: For each year in the project's life cycle, subtract the annual cost from the annual benefit of the calculated year to obtain the net benefit for that year; divide the net benefit for that year by the discount factor, which is obtained by adding a constant 1 to the discount rate and then raising the sum to the power of the year; sum up the net benefits of all years in the project's life cycle after discounting to obtain the total discounted revenue; subtract the initial investment from the total discounted revenue, and the final result is the project's net present value.

8. An optimized design method for a secondary dam-type silt reduction system in a reservoir on a sandy river, the method being used to implement the system described in any one of claims 1-7, characterized in that, Includes the following steps: S1. Obtain the initial parameter dataset representing the operating environment. The initial parameter dataset includes physical environment parameters and economic operating parameters. S2. Based on physical environment parameters, the optimal V-angle of the V-shaped diversion secondary dam body (3) is determined by calculation using the V-shaped angle optimization design model of the secondary dam body, so that the guiding efficiency of the V-shaped diversion secondary dam body (3) for the specific medium in the system reaches the optimal level. S3. Based on the determined geometric configuration parameters and physical environment parameters of the V-shaped diversion secondary dam body (3), the spatiotemporal distribution evolution of a specific medium in the system is simulated by the spatiotemporal prediction model of sediment deposition thickness, and the optimized spatial layout of the intelligent monitoring and control system (4) is determined based on the simulation results. S4. Based on the optimized spatial layout of the intelligent monitoring and control system (4) and combined with the economic operation parameters, the intelligent start-stop control strategy of the efficient sand discharge execution unit is generated through the sand discharge pump control model. The control strategy takes the maximization of the preset global economic benefit index of the system as the optimization goal, and the intelligent start-stop control strategy generates command signals.

9. The optimized design method for the secondary dam-type silt reduction system of a reservoir in a sandy river according to claim 8, characterized in that: The economic operating parameters include the net benefit of the year, the discount factor, the total discounted income, and the net present value of the project. The net present value of the project is generated by inputting the net benefit of the year, the discount factor, and the total discounted income into the system's full life cycle economic evaluation model.

10. The optimized design method for the secondary dam-type silt reduction system of a reservoir in a sandy river according to claim 8, characterized in that: The specific calculation steps for the sand-discharging pump control model are as follows: Data from the intelligent monitoring and control system (4) is collected via industrial bus, and after analysis and calculation, the real-time volume of removable sediment is obtained. Obtain the current unit cost of electricity and unit revenue from dredging through the network interface; The built-in prediction model module is invoked to calculate the probability of predicted high-yield events and the amplification factor of predicted event returns within a preset future time window. Calculate the expected net benefit of running the decision cycle at maximum power at the current moment; Multiply the predicted probability of a high-yield event by the predicted event's return multiplier to obtain the expected return multiplier. The instant net benefit index and the time-domain opportunity value index are integrated into a single, final intelligent start-stop control strategy. The calculated intelligent start-stop control strategy is compared with the preset start-up decision threshold; If the intelligent start-stop control strategy exceeds the start decision threshold, it is determined that the current or near future is a favorable time to start sand removal. At this time, the central controller will enter the power optimization subroutine, aiming to maximize the intelligent start-stop control strategy, to find the optimal power level among the available power levels, and output the command signal corresponding to the optimal power level. If the intelligent start-stop control strategy is less than or equal to the start decision threshold, the central controller determines that the overall economic benefits of starting sand removal are not good and should wait for a better opportunity to output a command signal to keep the machine stopped or maintain the minimum power. The central controller sends the generated command signals to the high-efficiency sand removal execution unit and stores all input parameters, intermediate calculation results and command signals of this decision into the historical database for subsequent sand removal pump control model iteration and auditing.

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