Water power and grain generation cooperative system based on silt dam system

Abstract

The invention discloses a hydraulic energy and grain generation cooperative system based on a silt dam system. The system comprises four cooperative subsystems, namely a silt dam safety and regulation digital twin subsystem, a water resource scheduling instruction output based on real-time monitoring and simulation, and a driving energy and agriculture subsystem. The multi-energy collaborative and distributed pumped storage subsystem is used for receiving instructions and external signals, controlling and optimizing a power plan through model prediction, controlling a water pump / water turbine to realize conversion between electric energy and water potential energy, and feeding back an energy state; the water-fertilizer integration and ecological production subsystem receives the water distribution instruction, the energy signal and the platform strategy, fuses field data to generate a water-fertilizer scheme, controls execution and feeds back production ecological data; the system scheduling and ecological value platform integrates multi-source data, generates a global instruction through a coupling model, distributes the global instruction, calculates ecological benefits based on feedback, and realizes closed-loop optimization. According to the method, a'monitoring-decision-control-optimization 'closed loop is formed, and the water power and grain cooperation efficiency and ecological benefits can be improved.

Description

A hydropower-food synergistic system based on silt-retention dam systems Technical Field

[0001] This invention relates to the field of smart water conservancy and clean energy intersection technology, and more specifically to a water-energy-food synergistic system based on silt-retaining dam systems. Background Technology

[0002] The Yellow River Basin is a vital ecological barrier and grain-producing area in my country, and has long faced severe challenges such as water scarcity, imbalance between water and sediment, fragile ecological environment, and contradictions in hydropower development, which are particularly prominent in the Loess Plateau region. This region suffers from severe soil erosion, and traditional irrigation methods are inefficient and energy-intensive, hindering sustainable agricultural development and regional ecological security.

[0003] In terms of existing technologies, some studies have attempted to optimize irrigation management using new energy sources or digital means. For example, Jiang Huixia et al. [1] proposed an intelligent irrigation system based on wind and solar power, which realizes the local utilization of energy through time-sharing power supply and mobile irrigation structure. However, this scheme is not deeply integrated with the water resource coordination scheduling and digital twin management of the watershed siltation dam system, and the overall coordination of the system and the global control capability of water resources are insufficient. Wang Lin et al. [2] disclosed an irrigation area water resource control system, which realizes multi-source allocation and irrigation control based on water demand model with the help of cloud platform. However, it still lacks organic integration with new energy systems, soil and water conservation and ecological governance processes, and has failed to form a "water energy food The systematic coupling and overall optimization of multiple ecological objectives.

[0004] In summary, existing technologies in water There are still significant limitations in terms of synergistic efficiency, integration of ecological benefits, and refined system scheduling, making it difficult to meet the complex needs of the Yellow River Basin, especially the Loess Plateau region, in terms of efficient water resource utilization, clean energy consumption, and agricultural-ecological synergistic development.

[0005] Therefore, there is an urgent need to construct a systematic approach that integrates digital twins, multi-energy synergy, and ecological benefits to achieve multi-dimensional synergy and sustainable management of water resources, energy, and agricultural production. Summary of the Invention

[0006] In view of the above problems, the present invention is proposed to provide a hydropower-grain-life synergy system based on silt-retaining dam systems that overcomes or at least partially solves the above problems.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, embodiments of the present invention provide a water-energy-food-production synergistic system based on a silt-retention dam system, including a digital twin subsystem for silt-retention dam safety and regulation, a multi-energy synergy and distributed pumped-storage subsystem, a water-fertilizer integration and ecological production subsystem, and a system scheduling and ecological value platform; the digital twin subsystem for silt-retention dam safety and regulation is used to output water resource scheduling instructions based on real-time monitoring data and simulation models of the dam body and watershed, including pumping / discharge power instructions driving the multi-energy synergy and distributed pumped-storage subsystem, and water distribution instructions driving the water-fertilizer integration and ecological production subsystem; the multi-energy synergy and distributed pumped-storage subsystem is used to receive water resource scheduling instructions from the digital twin subsystem and external instructions from the system scheduling and ecological value platform. The system receives water distribution instructions from the digital twin subsystem and optimization strategy parameters from the system scheduling and ecological value platform. It integrates field environmental data to generate water and fertilizer application schemes, outputs control instructions for the irrigation network and fertilization equipment to achieve precise irrigation and fertilization, and feeds back production and ecological data to the platform. The system scheduling and ecological value platform accesses and integrates status data from all subsystems and external data. It generates global collaborative scheduling instructions through coupled model calculations and distributes them to each subsystem. Simultaneously, it calculates ecological benefits based on feedback data from each subsystem and achieves closed-loop optimization of system parameters.

[0008] Preferably, the digital twin subsystem for the safety and regulation of silt-retention dams includes: a three-dimensional geological and structural simulation module, used to construct a three-dimensional geological model of the dam system based on BIM+GIS technology and perform finite element safety calculations; a multi-source sensor monitoring network, deployed in the dam body and reservoir area, used to obtain monitoring data in real time, the monitoring data including at least seepage pressure, stress, water level and water quality data; and a dynamically coupled digital twin, used to integrate the three-dimensional geological model of the dam system, the hydrodynamic model of the reservoir area and the runoff generation and confluence model of the watershed, to simulate and predict based on the monitoring data, generate and issue pumping / discharge commands to the multi-energy collaborative and distributed pumped storage subsystem, and issue precise water allocation commands to the water-fertilizer integration and ecological production subsystem.

[0009] Preferably, the multi-energy synergy and distributed pumped storage subsystem includes: a distributed pumped storage facility, consisting of a high-level reservoir connected to a downstream silt-retention dam, and equipped with bidirectional pumps / turbine units; a wind-solar hybrid power station, including a photovoltaic array and a wind turbine; and a model predictive controller configured to: take future rolling time-domain wind and solar power output predictions and irrigation load predictions as inputs, take the reservoir's safe water level and downstream ecological flow as constraints, and take system operating economy and new energy absorption rate as optimization objectives to solve for the optimal pumping or power generation command, and send it to the bidirectional pumps / turbine units; the subsystem is integrated into a unified "schedulable unit" for simultaneously receiving and executing power commands from the power grid dispatch center, optimized dispatch commands from the system dispatch and ecological value platform, and real-time control commands from the silt-retention dam safety and regulation digital twin subsystem.

[0010] Preferably, the optimal instructions solved by the model predictive controller include at least the following: during off-peak hours when the grid electricity price is low or when there is excess output from new energy sources, executing a "valley pumping" instruction to convert electrical energy into the potential energy of water for storage; during peak hours when the grid electricity price is high or when there is a power shortage, executing a "peak generation" instruction to convert the potential energy of water into electrical energy for output; and during critical periods of crop water demand, responding to the needs from the integrated water and fertilizer and ecological production subsystem, executing an "emergency drought relief" water release instruction.

[0011] Preferably, the integrated water and fertilizer and ecological production subsystem includes: a field monitoring network, including soil moisture sensors, weather stations, soil physicochemical sensors, and UAV multispectral equipment deployed in the field for periodic aerial surveys; an intelligent decision-making module, configured to: integrate field monitoring data, new energy output predictions and electricity price signals from the multi-energy synergy and distributed pumped storage subsystem, and calculate the optimal irrigation time window, irrigation volume, and fertilizer formula through a dynamic irrigation threshold model and a machine learning optimization model; and a precision execution unit, including a solenoid valve group controlled by a programmable logic controller and a liquid fertilizer application device, for executing irrigation and fertilization commands issued by the intelligent decision-making module.

[0012] Preferably, the machine learning optimization model takes the change rate of crop transpiration efficiency and soil organic matter content as the optimization target, and is retrained regularly using the latest field monitoring data and historical operation data to dynamically correct irrigation quotas and fertilizer formulas, and feeds the optimization results back to the system scheduling and ecological value platform.

[0013] Preferably, the system scheduling and ecological value platform adopts a microservice architecture, including: a data layer, which deploys data pipeline tools for periodically or in real-time access to remote sensing images, weather forecasts, sensor data from various subsystems, and power grid data; a model layer, which deploys a water resource-energy-crop growth coupling model, dynamically corrects model parameters through a data assimilation algorithm, and generates optimal collaborative scheduling schemes across subsystems; a decision layer, which provides a visual control terminal and a scene-policy mapping engine for converting the optimal collaborative scheduling schemes into executable instructions and distributing them in batches to the corresponding subsystems; and an ecological layer, which implements a dual-track ecological value accounting method for both process and results, generates standardized data packets from the accounting results, stores them on a blockchain, and then uploads them to an external trading platform.

[0014] Preferably, the water resources-energy-crop growth coupling model has a built-in dynamic weight adjustment mechanism: when the real-time water level of the reservoir is lower than a set threshold, the weight of the irrigation strategy is automatically increased; when the real-time output of new energy is higher than a set threshold, the weight of the pumped storage strategy is automatically increased.

[0015] Preferably, the system scheduling and ecological value platform is also equipped with a closed-loop optimization mechanism, which regularly compares the execution results of each subsystem with the expected goals. When the deviation exceeds a set threshold, the parameters or strategy triggering conditions of the coupled models in the model layer are automatically adjusted.

[0016] This invention provides a water-energy-grain-life synergy system based on silt-retaining dam systems, aiming to solve the shortcomings of existing irrigation systems in the multi-dimensional synergy of "water-energy-grain-life", especially the problems of extensive scheduling, weak water-energy coupling, and lack of ecological value in dam system areas, so as to achieve the system integration of precise water resource allocation, local consumption of new energy, stable grain production and ecological benefits.

[0017] Specifically, this application achieves real-time perception and forward-looking decision-making of the overall situation through a digital twin subsystem for the safety and regulation of the dam, providing a unified intelligent foundation for the entire system. At its core, a multi-energy synergy and distributed pumped storage subsystem serves as the power engine, transforming unstable new energy sources into stable irrigation capacity and grid peak-shaving resources through a "water-energy dual constraint" mechanism, resolving the core temporal and spatial contradiction between energy and water use. Furthermore, a water-fertilizer integration and ecological production subsystem acts as a precise execution unit, directly responding to system-level scheduling commands to achieve the linkage of water, fertilizer, and energy, significantly reducing energy and fertilizer consumption. Finally, under the coordination of system scheduling and the ecological value platform, all subsystems achieve one-click intelligent management and control of flood control and drought relief, power generation and water distribution, and intelligent irrigation through coupled optimization models and scenario-based scheduling. Furthermore, through innovative ecological value accounting, ecological benefits are quantified into tradable assets, thus achieving the synergistic effect and value enhancement of multiple objectives: water security, energy consumption, stable grain production, and ecological gains. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 is a schematic diagram of the overall structure and process of the system of the present invention; Figure 2 is a flowchart of the digital twin subsystem for the safety and regulation of silt-retention dams of the present invention; Figure 3 is a flowchart of the multi-energy synergy and distributed pumped storage system of the present invention; Figure 4 is a flowchart of the integrated water and fertilizer system and ecological system of the present invention. Production collaboration system workflow diagram; Figure 5 is a schematic diagram of the system scheduling and ecological value platform architecture of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention discloses a water-energy-grain-production synergistic system based on silt-retention dams, aiming to achieve water conservation, energy consumption, stable grain production, and ecological benefits. Its structure, as shown in Figure 1, includes a digital twin subsystem for silt-retention dam safety and regulation, a multi-energy synergy and distributed pumped storage subsystem, a water-fertilizer integration and ecological production subsystem, and a system scheduling and ecological value platform. The silt-retention dam safety and regulation digital twin subsystem, as the fundamental core, is responsible for the safe storage and management of water resources. The multi-energy synergy and distributed pumped storage system, as the regulating hub, is responsible for the production, conversion, storage, and coordinated scheduling of energy. The water-fertilizer integration and ecological-production synergy system is used for precise execution, responsible for precise water and fertilizer application in the field and monitoring the ecological production effects. The system scheduling and ecological value platform, as the intelligent decision-making and value manifestation center, is responsible for summarizing all information, making optimal decisions, and calculating ecological value.

[0022] The following is a description through specific embodiments.

[0023] Figure 2 in Example 1 is a flowchart of the digital twin subsystem for the safety and regulation of silt-retaining dams in this application. First, the topographic map of the project area and historical soil erosion monitoring data are input. Then, using BIM and GIS technologies in collaboration with Revit and ArcGIS Pro, a three-dimensional geological model of the silt-retaining dam system, including topography, strata, and dam structure, is constructed. Furthermore, through finite element structural simulation, the stability and seepage safety of the dam body under different water levels (such as design water level and check flood level) are digitally verified to ensure that it meets the safety requirements of the "Technical Specification for Backbone Engineering of Soil and Water Conservation and Gully Control" (SL 289-2019). Preferably, the model is updated every 1 to 24 hours.

[0024] Simultaneously, water level sensors, seepage sensors, stress sensors, and water quality sensors are integrated into key parts of the dam body to construct a multi-source sensor network, with a sampling frequency of 1 to 10 minutes per sampling. In some implementation schemes, vibrating wire piezometers (range 0 to 1 MPa, accuracy ±0.1% FS) are deployed at key sections of the dam body to monitor the dam body's phreatic line; resistance strain gauge earth pressure cells (range 0 to 2 MPa) are installed on the dam slope and foundation to monitor stress distribution; piezoresistive water level sensors (range 0 to 50 m, accuracy ±0.5% FS) are installed in the water level fluctuation zone to collect reservoir capacity data in real time; and multi-parameter water quality sensors (monitoring pH, turbidity, conductivity, and dissolved oxygen) are deployed simultaneously, with a sampling frequency uniformly set to 5 minutes per sampling. The data is uploaded to the cloud platform's real-time database via a 4G / 5G IoT gateway.

[0025] This application develops a digital twin of a dam system based on the Unity 3D engine and a real-time database. Specifically, it generates a dynamically coupled digital twin by coupling a dam safety model, a reservoir hydrodynamic model, and a watershed runoff generation and confluence model. This digital twin is used to simulate and deduce optimal decisions in virtual space based on different scheduling strategies (such as the number of gate openings). In this application, the digital twin automatically verifies and updates the model every 6 hours.

[0026] The optimal decision includes: deploying collaborative energy facilities and executing "peak shaving and valley filling" commands: installing vertical axis wind turbines (rated power 50kW) in areas with good wind resources; configuring smart inverters and grid-connected cabinets that support MPPT; the operation of the power station directly receives commands from the digital twin: during peak power generation and non-irrigation periods, initiating pumping commands to convert electrical energy into the potential energy of water and store it in a high-level water tank; during critical periods of crop water demand, executing irrigation commands to complete the spatiotemporal transfer of energy and irrigation; see Implementation Example 2 for details.

[0027] Construct a precise water distribution network and respond to platform commands: Construct a reinforced concrete elevated water tank (100m³) at the highest point of the watershed, and connect it to distributed water cellars (30m³ each) in the terraced fields via UPVC pipes (DN150); Install electric butterfly valves (supporting Modbus communication) and electromagnetic flow meters (accuracy ±1%) at key nodes of the pipeline network. The control logic of this network comes directly from the decision commands issued by the digital twin and the "four predictions" system, realizing precise water distribution on demand, in different time periods, and in different quantities.

[0028] The four-prevention system (forecasting, early warning, rehearsal, and contingency plan) is used to achieve proactive safety management, forming a complete closed loop from intelligent early warning to contingency plan execution, ultimately achieving coordinated and efficient regulation of dam system safety and water resources. This includes: executing flood forecasts in a digital twin based on meteorological forecast data; automatically issuing safety warnings when sensor data triggers a set threshold (e.g., displacement rate > 5 mm / d); conducting dam-break rehearsals for extreme conditions, and intelligently matching the simulation results with disposal measures in the emergency plan database to generate specific dispatch or disposal instructions.

[0029] Example 2: To achieve precise response to coordinated dispatch instructions for "water-energy-grain," this system's assessment and decision-making for new energy power plants follow the integrated principle of "source-load-storage." In addition to routine resource assessment, the evaluation process focuses on analyzing the spatial relationship between the site location and elevated water tanks and irrigation networks to assess its potential as a distributed peak-shaving unit. Accordingly, as a flexible energy regulator, the power plant's capacity must meet the needs of pumped storage during critical periods, and its layout ensures rapid response to dispatch instructions issued by the digital twin system based on real-time water and agricultural conditions ("peak-hour power generation," "valley-hour pumping," "emergency drought relief"), rather than solely focusing on power generation.

[0030] In one specific embodiment, referring to Figure 3, which is a flowchart of the multi-energy synergy and distributed pumped storage subsystem of the present invention, to solve the problem of time misalignment between new energy output and agricultural irrigation, the wind-solar hybrid power station is designed as a dynamic matching unit of the water-energy-food-livelihood synergy system. Specifically, this application first uses Meteonorm and WAsP software to analyze historical data on total solar radiation and wind frequency distribution in the watershed, and overlays the locations of high-level water tanks, irrigation network layouts, and irrigation zones divided by the digital twin of the dam system in the GIS platform to calculate the distance and elevation difference from each potential site to the critical load point. Furthermore, with the optimization objectives of minimizing pumping energy consumption and transmission loss, the specific construction location, capacity configuration, and layout of the photovoltaic power station and wind turbine are comprehensively determined.

[0031] Secondly, the system is equipped with pumped storage facilities that can be coordinated and dispatched in response, including distributed pumped storage facilities consisting of a high-level reservoir connected to a downstream silt-retaining dam, and equipped with high-pressure submersible bidirectional pumps / turbine units; preferred performance indicators include: rated head 50-200m, flow rate 10-50m³ / h, and efficiency >85%.

[0032] The matching variable frequency drive enables soft start and stepless speed regulation; ensuring that the unit can receive and execute three types of dispatching instructions: "peak hour power generation", "valley hour water pumping", and "emergency drought relief".

[0033] Furthermore, the system also develops and deploys an MPC controller embedded with dual constraints on water resources. Within each 15-minute scheduling cycle, it performs the following operations: It acquires rolling forecast data for the next four hours from the digital twin system, including not only wind and solar power output and irrigation load, but also forcibly reads and verifies the upper limit of the reservoir's safe water level and the minimum downstream ecological flow threshold, embedding these two as hard constraints into the optimization model; simultaneously, it receives time-of-use electricity price signals from the power grid, using the maximization of the peak-valley electricity price difference and the highest renewable energy absorption rate as objective functions to solve for the optimal pumping / power generation command, and sends it to the power plant monitoring system and bidirectional pumps within one second via the OPC UA protocol, thereby achieving joint scheduling of water resources and energy. This application supports second-level (≤1 second) power response and multi-timescale energy scheduling.

[0034] In one implementation scheme, this application integrates distributed pumped storage facilities, photovoltaic power plants, and wind turbines into a unified "dispatchable unit" at the electrical and communication levels. This unit is configured with a 5G-based control system, enabling it to receive instructions from the upper-level power grid dispatch center, participate in the regulation of the large power grid, and achieve a transition from "grid connection" to "participation in power grid regulation," as well as safe grid connection and flexible operation. This application also includes anti-islanding protection devices and online power quality monitoring terminals, and submits an access system design report to the power grid company to ensure compliance with the "Technical Regulations for Distributed Power Source Access to the Power Grid" (Q / GDW 1480-2015), achieving grid-connected operation as a single controllable unit.

[0035] In this embodiment, the power station, based on the predictions of the digital twin system, actively converts electrical energy into the potential energy of water for storage during peak power generation; during the critical period of crop water demand, it releases the stored water for irrigation, thereby achieving "peak shifting and valley filling" and transforming unstable energy into reliable irrigation security; it is equipped with intelligent inverters and grid-connected controllers with a rated power of 50-200kW; it constructs a water distribution network connecting elevated water tanks and terraced water cellars, and is equipped with intelligent valves and high-precision flow meters to achieve precise water distribution on demand.

[0036] Example 3 addresses the practical application of water-energy synergy at the field scale by introducing a water-fertilizer-energy linkage decision-making mechanism. Unlike traditional fixed-threshold irrigation, this system dynamically adjusts the timing and intensity of irrigation based on 24-hour wind and solar power output forecasts provided by a digital twin and time-of-use electricity price signals from the collaborative controller in Example 2. This is matched with the peak / off-peak power generation periods of the renewable energy power station to optimize operating costs. Specifically, the system's network of soil moisture and weather stations provides input for the dynamic irrigation threshold model; the integrated water and fertilizer precision application system's activation command originates from the optimized scheduling scheme of the collaborative controller, ensuring priority operation when energy is abundant or costs are lowest. Simultaneously, an ecological effect feedback mechanism based on crop transpiration efficiency and soil organic matter content is established to dynamically adjust water and fertilizer strategies, thereby ensuring food production while minimizing energy consumption and improving soil fertility.

[0037] In one specific embodiment, Figure 4 is a flowchart of the water and fertilizer integration and ecological production subsystem in this embodiment; the subsystem adopts a three-layer architecture, forming a closed loop including perception, decision-making and execution.

[0038] For the sensing layer, this application first deploys soil moisture sensors based on the FDR principle (monitoring depth 0-60cm) in typical fields in a 50m×50m grid, based on the crop planting layout map, to perceive the crop's water requirements in real time; at the same time, small weather stations are deployed to monitor temperature, humidity, wind speed, light, and rainfall; soil salinity and pH sensors are further deployed to continuously monitor soil health and prevent secondary salinization, and drones equipped with multispectral cameras are used regularly for aerial surveys, such as using a DJI M300RTK drone equipped with a RedEdge-MX multispectral camera to conduct a field aerial survey every 7 days, flying at an altitude of 100m, and acquiring image data in 5 bands (blue, green, red, red edge, and near-infrared); the images are processed by PIX4Dfields software to obtain the leaf area index (LAI) and normalized difference vegetation index (NDVI) for each field to accurately assess crop growth, and finally all monitoring data are uploaded to the cloud platform via the LoRaWAN network at a frequency of 10 minutes / time.

[0039] For the decision-making level, based on the wind and solar power output forecasts for the next 24 hours and time-of-use electricity price signals (from Example 2), combined with real-time soil moisture and meteorological data, and according to the dynamic irrigation threshold model, the optimal irrigation time window and irrigation amount for each field in the next 6 hours are calculated, providing an execution basis for the intelligent drip irrigation system. Simultaneously, a machine learning optimization model is established and the strategy is updated. This includes integrating historical and real-time multispectral data, soil data, and irrigation and fertilization records to construct a training dataset with 15 feature factors. A random forest algorithm is used, targeting changes in crop transpiration efficiency and soil organic matter content, and retraining is performed every 15 days. Irrigation quotas and fertilizer formulas are dynamically adjusted based on the latest monitoring data, forming a complete closed loop of "monitoring-decision-execution-optimization," achieving soil fertility improvement and energy consumption reduction while ensuring crop yield.

[0040] For the execution layer, a zoned and controllable water distribution and supplementary irrigation device is designed, using an industrial-grade programmable logic controller (PLC) to control the field solenoid valve group to achieve independent control of irrigation for different fields. Specifically, when the system time enters the optimal irrigation window, and the collaborative controller in Example 2 confirms that the current period is either a peak in new energy output or a low electricity price period, the PLC immediately executes the irrigation command.

[0041] The PID control algorithm runs in the PLC. Its input is the real-time reading of the pipeline pressure sensor, and the setpoint is the pressure target (such as 0.25 MPa) issued by the co-controller. The algorithm solves the problem in real time through proportional, integral, and derivative operations, and outputs the control signal to the pulse width modulation module to dynamically adjust the opening of the solenoid valves of each branch, ensuring stable flow and pressure, and achieving a control accuracy of ±5%, thus realizing precise matching between irrigation operations and energy supply.

[0042] This application is equipped with a liquid fertilizer mixing tank (5m³) and a high-precision proportional injection pump (flow range 0.5-20L / h, repeatability ±1%). When the irrigation command is executed, the injection pump automatically retrieves the preset formula according to the current crop growth stage and soil nutrient status, and injects the concentrated liquid fertilizer into the irrigation main pipe in a set ratio, thereby achieving simultaneous water and fertilizer supply and precise supply. The ratio error of this application can reach 3%.

[0043] Example 4: This application constructs a four-layer platform based on microservices and digital twins, consisting of "data-model-decision-ecosystem". The core of the system lies in the following: The data layer integrates remote sensing data, IoT device data, energy system data, and meteorological data; the model layer develops a coupled optimization model for water resources, energy, and crop growth, which incorporates a dynamic weight adjustment mechanism. This model can dynamically adjust the priority of water resource allocation, power generation plans, and irrigation strategies based on real-time reservoir storage, new energy output, and crop growth stages. It also continuously corrects model parameters through real-time data assimilation technology and sensor network data interaction; the decision layer develops a visual control terminal with an embedded "scenario-strategy" mapping engine. This allows complex model outputs to be encapsulated into one-click scenario-based scheduling commands such as "drought resistance and irrigation," "peak-valley arbitrage," and "ecological water replenishment," directly driving the coordinated execution of dam systems, pumping stations, and field valves; and the ecological layer integrates an ecological value accounting module that adopts a "process-result" dual-track accounting method. This module not only calculates static benefits such as carbon sequestration and water conservation according to national standards but also uses digital twins to simulate and extrapolate comparative scenarios without system intervention, dynamically quantifying incremental ecological benefits such as flood control and disaster reduction, and sand retention and soil conservation. It also generates standardized data packages that can be connected to the green trading market.

[0044] In one exemplary embodiment, Figure 5 is a schematic diagram of the system scheduling and ecological value platform architecture of the present invention, which clearly shows the technology stack of the platform, including a four-layer platform architecture built with the Spring Cloud microservice framework, a data layer based on the Hadoop HDFS distributed storage system, a model layer that uses Docker containerization to deploy and package TensorFlow and SWAT models, a decision layer that uses Spring Boot to develop RESTful APIs, and an ecosystem layer that exchanges data with each subsystem through the Kafka message queue.

[0045] Specifically, in this embodiment, the platform's technical foundation and integration include: Spring Cloud microservice framework, Hadoop HDFS distributed storage, Docker containers (Tensor Flow / SWAT model), Spring Boot, RESTful API, and Kafka message queue.

[0046] Furthermore, Apache NiFi is used as the data pipeline for data deployment, responsible for the automated and visual access and preprocessing of heterogeneous data from various sources. This heterogeneous data includes daily scheduled acquisition of Gaofen-2 remote sensing images, analysis of vegetation index NDVI and land surface temperature using the GDAL library, real-time reception of dam sensor data from Example 1 and soil moisture data from Example 3 via the MQTT protocol, acquisition of renewable energy power generation and pumping power consumption data from Example 2 via the Modbus TCP protocol, and acquisition of GRAPES gridded weather forecast data from the China Meteorological Administration via API interfaces.

[0047] At the model layer, a water resource-energy-crop growth coupled model was developed based on the TensorFlow framework. This model has a built-in dynamic weight adjustment mechanism: when the real-time water level of the reservoir is 60% lower than the design water level, the weight coefficient of the irrigation strategy is automatically increased to 0.7; when the real-time output of new energy sources is 80% higher than the rated capacity, the weight of pumped storage is increased to 0.6. Every 4 hours, the measured data is assimilated using the Ensemble Kalman Filter algorithm to correct the model parameters and generate an optimal scheduling scheme that includes specific pumping power, irrigation flow rate, and power generation plan.

[0048] The decision-making level developed a visual control terminal based on Vue and ECharts technologies, which is intuitive to operate and has a built-in scene-policy mapping engine. Operators do not need to understand complex models. They only need to select a business scenario, such as drought relief and irrigation mode, and the system will automatically match and execute a complete set of predefined strategies. The strategies are decomposed and converted into specific instructions, including sending valve opening instructions to the electric butterfly valve in Example 1; sending water pumping instructions to the bidirectional water pump in Example 2; and sending irrigation start instructions to the PLC controller in Example 3. Preferably, before executing the actual instructions, a digital twin is used for pre-rehearsal, and the instructions are issued in batches after confirmation.

[0049] The ecological layer employs a dual-track accounting system: process accounting, based on real-time monitoring data, calculates carbon sequestration (based on new energy power generation × 0.97 kg / kWh) and water-saving benefits (based on actual water savings × local water price) according to the "Technical Specifications for Accounting of Gross Ecosystem Product". Result accounting simulates a no-system-intervention scenario in a digital twin, calculating flood control and disaster reduction benefits (based on reduced affected area × average loss per acre) and sediment retention and soil conservation benefits (based on measured sediment retention × dredging cost). Finally, a JSON data package conforming to green electricity trading standards is generated, stored on the blockchain, and uploaded to the trading platform.

[0050] In this embodiment, the platform automatically compares the execution results with the expected target every week. If a deviation is found, such as when the crop transpiration efficiency in Embodiment 3 is lower than the set threshold, the irrigation weight of the corresponding area in the model layer is automatically adjusted. When the new energy absorption rate in Embodiment 2 fails to reach the target, the triggering conditions of the pumped storage strategy are dynamically corrected to form a closed-loop management system for continuous optimization.

[0051] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0052] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A hydropower-grain-life synergistic system based on silt-retention dam systems, characterized in that, The system includes a digital twin subsystem for silt-retention dam safety and regulation, a multi-energy collaborative and distributed pumped-storage subsystem, a water-fertilizer integration and ecological production subsystem, and a system scheduling and ecological value platform. The digital twin subsystem for silt-retention dam safety and regulation is used to output water resource scheduling commands based on real-time monitoring data and simulation models of the dam body and watershed. These commands include pumping / discharge power commands to drive the multi-energy collaborative and distributed pumped-storage subsystem, and water distribution commands to drive the water-fertilizer integration and ecological production subsystem. The multi-energy collaborative and distributed pumped-storage subsystem receives water resource scheduling commands from the digital twin subsystem for silt-retention dam safety and regulation. The system receives external signals from the system scheduling and ecological value platform, solves the optimal power plan through model predictive control, outputs control commands for pumped storage facilities to perform bidirectional conversion between electrical energy and water potential energy, and provides feedback on energy execution status. The water and fertilizer integration and ecological production subsystem receives water distribution commands from the siltation dam safety and regulation digital twin subsystem and optimization strategy parameters from the system scheduling and ecological value platform, integrates field environmental data to generate water and fertilizer application schemes, outputs control commands for irrigation networks and fertilization equipment to achieve precise irrigation and fertilization, and provides feedback on production and ecological data to the system scheduling and ecological value platform. The system scheduling and ecological value platform is used to access and integrate status data and external data from all subsystems, generate global collaborative scheduling instructions through a coupled model and distribute them to each subsystem, and calculate ecological benefits based on feedback data from each subsystem and achieve closed-loop optimization of system parameters.

2. The water-energy-grain-production synergistic system according to claim 1, characterized in that, The digital twin subsystem for the safety and regulation of silt-retention dams includes: a three-dimensional geological and structural simulation module, used to construct a three-dimensional geological model of the dam system based on BIM+GIS technology and perform finite element safety calculations; a multi-source sensor monitoring network, deployed in the dam body and reservoir area, used to obtain monitoring data in real time; and a dynamically coupled digital twin, used to integrate the three-dimensional geological model of the dam system, the hydrodynamic model of the reservoir area, and the runoff generation and confluence model of the watershed, to simulate and predict based on the monitoring data, generate and issue pumping / discharge commands to the multi-energy collaborative and distributed pumped storage subsystem, and issue precise water allocation commands to the water-fertilizer integration and ecological production subsystem.

3. The water-energy-grain-production synergistic system according to claim 1, characterized in that, The multi-energy synergy and distributed pumped storage subsystem includes: a distributed pumped storage facility consisting of a high-level reservoir connected to a downstream silt-retaining dam, and equipped with a bidirectional pump / turbine unit; a wind-solar hybrid power station, including a photovoltaic array and a wind turbine; and a model predictive controller configured to: take future rolling time-domain wind and solar power output predictions and irrigation load predictions as inputs, take the reservoir's safe water level and downstream ecological flow as constraints, and take the system's operational economy and new energy absorption rate as optimization objectives to solve for the optimal pumping or power generation command, and send it to the bidirectional pump / turbine unit.

4. The water-energy-grain-production synergistic system according to claim 1, characterized in that, The integrated water and fertilizer and ecological production subsystem includes: a field monitoring network, including soil moisture sensors, weather stations, soil physicochemical sensors, and UAV multispectral equipment deployed in the field for periodic aerial surveys; an intelligent decision-making module, configured to: integrate field monitoring data, new energy output predictions and electricity price signals from the multi-energy synergy and distributed pumped storage subsystem, and calculate the optimal irrigation time window, irrigation volume, and fertilizer formula through a dynamic irrigation threshold model and a machine learning optimization model; and a precision execution unit, including a solenoid valve group and a liquid fertilizer application device controlled by a programmable logic controller, for executing irrigation and fertilization commands issued by the intelligent decision-making module.

5. The water-energy-grain-production synergistic system according to claim 4, characterized in that, The machine learning optimization model takes the change rate of crop transpiration efficiency and soil organic matter content as the optimization target. It is retrained regularly using the latest field monitoring data and historical operation data to dynamically correct irrigation quotas and fertilizer formulas, and feeds the optimization results back to the system scheduling and ecological value platform.

6. The hydropower-grain-production synergistic system according to claim 1, characterized in that, The system scheduling and ecological value platform adopts a microservice architecture, including: a data layer, which deploys data pipeline tools for periodic or real-time access to remote sensing images, weather forecasts, sensor data from various subsystems, and power grid data; a model layer, which deploys a water resource-energy-crop growth coupling model, dynamically corrects model parameters through a data assimilation algorithm, and generates optimal collaborative scheduling schemes across subsystems; a decision layer, which provides a visual control terminal and a scene-policy mapping engine for converting the optimal collaborative scheduling schemes into executable instructions and distributing them in batches to the corresponding subsystems; and an ecological layer, which implements a dual-track ecological value accounting method for both process and results, generates standardized data packages from the accounting results, stores them on a blockchain, and uploads them to an external trading platform.

7. The water-energy-grain-production synergistic system according to claim 6, characterized in that, The water resources-energy-crop growth coupling model has a built-in dynamic weight adjustment mechanism: when the real-time water level of the reservoir is lower than the set threshold, the weight of the irrigation strategy is automatically increased; when the real-time output of new energy is higher than the set threshold, the weight of the pumped storage strategy is automatically increased.

8. The water-energy-grain-production synergistic system according to claim 6, characterized in that, The system scheduling and ecological value platform also deploys a closed-loop optimization mechanism, which regularly compares the execution results of each subsystem with the expected goals. When the deviation exceeds a set threshold, it automatically adjusts the parameters or strategy triggering conditions of the coupled models in the model layer.

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