Water conservancy facility intelligent scheduling system based on industrial internet platform

Abstract

The invention relates to the technical field of industrial internet and intelligent water conservancy crossing, and particularly discloses a water conservancy facility intelligent scheduling system based on an industrial internet platform. The system comprises a data acquisition device, a physical information fusion prediction device, a scheduling decision generation device and an industrial internet communication center, the physical information fusion prediction device adopts a physical information neural network fusing a Saint-Venant equation set and mass conservation constraint, and combines a convolution and long and short-term memory network architecture to realize high-precision space-time prediction of a water flow state; the scheduling decision generation device dynamically generates a cooperative scheduling instruction based on a prediction result and a multi-objective optimization engine, and ensures execution feasibility through a security verification layer; the industrial internet communication center supports an edge-cloud collaborative architecture and ensures low-delay response. According to the technical scheme, the flood prediction robustness, the scheduling decision scientificity and the system operation safety are improved, and the real-time intelligent scheduling requirement under the sudden water regimen is met.

Description

Technical Field

[0001] This invention belongs to the field of interdisciplinary technology of industrial internet and smart water conservancy, and specifically relates to an intelligent scheduling system for water conservancy facilities based on an industrial internet platform. Background Technology

[0002] With the deep integration of smart water conservancy and industrial internet technologies, building intelligent scheduling systems for water conservancy facilities has become a key means to improve flood control and drought resistance capabilities and optimize water resource allocation. Water conservancy engineering systems involve various complex structures such as rivers, reservoirs, and dams, and their operational status is affected by diverse environmental factors such as rainfall, topography, and upstream water inflow. Achieving accurate perception and prediction of the hydraulic state of large-scale watersheds is the core foundation for ensuring the coordinated operation of water conservancy facilities, optimizing resource allocation, and guaranteeing watershed safety.

[0003] Dynamic scheduling of water conservancy facilities based on hydraulic prediction models is a core component of water conservancy informatization. It aims to simulate and predict flood evolution and runoff processes using mathematical methods, providing scientific support for decision-making. Typically, this technical direction involves solving fluid dynamics equations and analyzing and processing massive amounts of real-time monitoring data. The system acquires key parameters such as water level, flow rate, and velocity collected by sensors to construct a digital mapping model that reflects the actual physical process, enabling quantitative characterization and real-time early warning of complex water flow trends.

[0004] Traditional hydraulic numerical simulations typically rely on solving complex partial differential equations, with computational costs increasing exponentially with spatial resolution and time step refinement. This makes it difficult to meet the millisecond-level real-time scheduling requirements in the context of sudden flood disasters. While purely data-driven AI models offer advantages in computational speed, their black-box mechanisms lack physical support. Under extreme weather conditions or boundary conditions lacking training samples, they are prone to producing predictions that violate the laws of mass conservation and energy balance. Existing systems cannot integrate the rigor of mechanistic models with the efficiency of deep learning, resulting in insufficient robustness of predictions in extreme scenarios and extremely poor physical interpretability, making it difficult to guide practical scheduling decisions.

[0005] There is an urgent need for an intelligent scheduling system for water conservancy facilities based on an industrial internet platform. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent scheduling system for water conservancy facilities based on an industrial internet platform, which can solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A smart scheduling system for water conservancy facilities based on an industrial internet platform includes a data acquisition device, a physical information fusion and prediction device, a scheduling decision generation device, and an industrial internet communication hub, as follows: The data acquisition device is configured to acquire water level, flow rate, flow velocity and rainfall data of each monitoring point in the basin in real time, and transmit the acquired data to the physical information fusion prediction device through the industrial internet communication hub. The physical information fusion prediction device is configured to receive real-time monitoring data from the data acquisition device and, based on a neural network model that incorporates constraints from fluid dynamics equations, predict the water flow state in the future period of the basin and output a hydraulic evolution trend that conforms to physical laws. The scheduling decision generation device is configured to automatically generate coordinated scheduling instructions for gates, pumping stations and reservoir spillways based on the prediction results output by the physical information fusion prediction device, combined with a preset scheduling rule base and safe operation boundary conditions. The industrial internet communication hub is configured to establish a low-latency, highly reliable data interaction channel between the data acquisition device, the physical information fusion prediction device, and the scheduling decision generation device, ensuring that the overall system response meets the real-time requirements in the event of a sudden flood.

[0008] Preferably, the neural network model built into the physical information fusion prediction device is a physical information neural network, whose loss function is composed of data fitting error term and physical equation residual term. The physical equation residual term is constructed based on Saint-Venant's equations and the law of conservation of mass, and is used to constrain the model output to meet the basic physical principles of water balance and energy conservation.

[0009] Furthermore, the physical information neural network adopts a hybrid architecture of convolutional neural network and long short-term memory network. The convolutional neural network is used to extract the spatial distribution features of the watershed, and the long short-term memory network is used to capture the temporal dependence of hydrological processes. The two work together to achieve spatiotemporal joint modeling of complex hydrodynamic processes.

[0010] Furthermore, during the training process, the physical information fusion prediction device not only uses historical monitoring data for supervised learning, but also uses automatic differentiation technology to calculate the partial derivatives of the neural network output with respect to spatial coordinates and time variables, so as to dynamically evaluate its degree of satisfaction with the fluid dynamics control equations, and uses the evaluation result as part of the optimization objective.

[0011] Preferably, the scheduling decision generation device has a built-in multi-objective optimization engine, which can dynamically adjust the opening and closing sequence and opening parameters of each water conservancy facility based on the predicted flood peak arrival time, inundation risk area and downstream carrying capacity, while ensuring the safety of the engineering structure, so as to achieve synergistic optimization of maximizing flood control benefits and water resource utilization efficiency.

[0012] Furthermore, the industrial internet communication hub adopts a distributed architecture of edge computing and cloud collaboration. Edge nodes are deployed near key water conservancy hubs to execute local data preprocessing and rapid issuance of emergency dispatch instructions, while the cloud platform is responsible for global model training, cross-basin collaborative analysis, and iterative updates of long-term dispatch strategies.

[0013] Furthermore, the data acquisition device integrates a variety of heterogeneous sensors, including radar water level gauges, ultrasonic flow meters, rain gauges, and video surveillance equipment. All sensors are sampled through a unified time synchronization protocol to ensure the consistency of multi-source data in the spatiotemporal dimension, providing high-quality input for physical information fusion prediction.

[0014] Preferably, the physical information fusion prediction device has online learning capabilities, enabling it to continuously receive new observation data during system operation and incrementally update model parameters to adapt to the influence of unsteady environmental factors such as changes in the underlying surface of the watershed, river siltation, or extreme weather events.

[0015] Furthermore, a safety verification layer is provided between the scheduling decision generation device and the execution mechanism of the water conservancy facility. This safety verification layer performs physical feasibility verification on the generated scheduling instructions. If the instructions may cause a sudden change in water level, structural overload, or violation of operating procedures, a correction mechanism will be automatically triggered or the system will switch to manual intervention mode.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The intelligent scheduling system for water conservancy facilities based on the industrial internet platform provided by this invention solves the contradiction between the poor real-time performance of traditional hydraulic numerical simulation calculations and the physical uninterpretability of pure artificial intelligence models by constructing a dual-drive prediction model that integrates physical mechanisms and data-driven approaches.

[0017] 2. The system embeds the fundamental laws of fluid mechanics as hard constraints during neural network training to ensure that the prediction results conform to the principles of mass conservation and energy balance under any working conditions, thereby improving the robustness and reliability of predictions in extreme weather or data-sparse scenarios.

[0018] 3. Relying on the low-latency transmission capabilities of the industrial internet communication hub and the edge-cloud collaborative architecture, the system can complete the entire link response from data perception to scheduling instruction generation within milliseconds, meeting the timeliness requirements of emergency scheduling for sudden floods.

[0019] 4. The scheduling decision generation device, combined with multi-objective optimization and safety verification mechanisms, realizes the intelligent, collaborative, and safe operation of water conservancy facilities. It not only improves the precise regulation and control capabilities for flood control and drought relief, but also provides scientific support for water resource allocation, demonstrating technological advancement and practical engineering value. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the neural network based on physical equation constraints in the physical information fusion prediction device of this invention; Figure 3 This is a logical flowchart of the scheduling decision generation device in this invention that combines multi-objective optimization and security verification; Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between the edge computing and cloud platform of the industrial internet communication hub in this invention; Figure 5 This is a flowchart of the multi-source heterogeneous data fusion and spatiotemporal synchronization processing process of the data acquisition device in this invention. Detailed Implementation

[0021] Example 1: Please refer to the appendix Figure 1 To be continued Figure 5 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0022] A smart scheduling system for water conservancy facilities based on an industrial internet platform includes a data acquisition device, a physical information fusion and prediction device, a scheduling decision generation device, and an industrial internet communication hub.

[0023] The data acquisition device is configured to acquire real-time water level, flow rate, velocity, and rainfall data from various monitoring points within the basin, and transmit the collected data to a physical information fusion prediction device via an industrial internet communication hub. The data acquisition device is constructed as a multi-source heterogeneous sensing system, distributed across river channels, reservoirs, tributary inlets, and key water conservancy projects within the basin. In terms of hardware, the data acquisition device includes radar level gauges deployed at reservoir edges, ultrasonic Doppler current meters installed at river cross-sections, tipping bucket rain gauges placed in open areas, and video surveillance equipment with night vision capabilities. To ensure the timeliness consistency of the output data from these heterogeneous sensors, the data acquisition device integrates a high-precision clock synchronization unit. This unit uses the BeiDou satellite timing protocol or network time protocol to perform nanosecond-level alignment processing on all sensor sampling pulses, ensuring that every data point for water level, flow rate, and rainfall is stamped with a unified global timestamp before entering the communication network. The data acquisition device is also equipped with a local signal conditioning circuit and an edge preprocessing board, which are used to filter and denoise the raw voltage signal or pulse signal, convert the range and remove outliers, filter out false data caused by water surface fluctuations, silt interference or electronic noise, and ensure that the physical parameters input to subsequent stages have confidence.

[0024] The physical information fusion prediction device is configured to receive real-time monitoring data from the data acquisition device and, based on a neural network model incorporating constraints from fluid mechanics equations, predict the water flow state in the basin over future periods, outputting a hydraulic evolution trend that conforms to physical laws. As the core computing unit of the system, the physical information fusion prediction device can be physically manifested as a GPU-accelerated computing cluster deployed in the cloud or a high-performance industrial control server located in a scheduling center. Internally, the device stores and runs a deeply integrated physical information neural network. This network no longer relies solely on correlation analysis of historical statistical data but embeds fluid mechanics partial differential equations into its algorithm architecture, ensuring the prediction process is rigorously constrained by physical laws. The device is configured to input real-time monitored water level and flow rate as boundary conditions into the neural network, using the network's nonlinear mapping capabilities to calculate flow rate process lines and water level evolution curves for the next 5, 12, and even 48 hours.

[0025] The physical information fusion prediction device incorporates a physical information neural network model, whose loss function comprises a data fitting error term and a physical equation residual term. The data fitting error term measures the deviation between the neural network's predicted value and the measured value acquired by the data acquisition device. The physical equation residual term is constructed based on Saint-Venant's equations and the law of conservation of mass, constraining the model output to satisfy the fundamental physical principles of water balance and energy conservation. The construction logic of the physical equation residual term involves calculating the first and second partial derivatives of the water depth and flow rate output by the neural network with respect to spatial coordinates and time variables using automatic differentiation techniques. The physical information fusion prediction device is configured to execute the following verification logic: substituting these partial derivatives into the continuity and momentum equations in Saint-Venant's equations; if the result is not equal to 0, the resulting residual value is fed back to the optimizer, forcing the neural network to adjust the weight parameters of its internal neurons until the prediction result both fits the observed data and reduces the residual value of the physical equation. This mechanism ensures that even in the event of a sudden surge in river flow, a dramatic increase in the riverbed surface, or sensor failure due to extreme weather leading to sparse data, the system can still deduce the scientifically logical trend of water flow evolution based on physical mechanisms, thus avoiding unrealistic numerical oscillations or sudden changes in flow.

[0026] The physical information neural network employs a hybrid architecture of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNNs are configured to extract spatial features from the digital elevation model of the watershed, the river channel topology, and the distribution of sensor stations using multi-layer convolutional kernels, generating feature vectors that reflect the spatial heterogeneity of the watershed. The LSTM network is configured with input, forget, and output gates to receive the feature vectors output by the CNNs and capture the long-range dependencies of rainfall and water level rise processes over time. This spatiotemporal joint modeling approach allows the physical information fusion prediction device to consider not only the time-lag effect of upstream rainfall on downstream flow but also the attenuation effect of river channel morphology on flood wave propagation.

[0027] During training, the physical information fusion prediction device also uses automatic differentiation technology to calculate the partial derivatives of the neural network output with respect to spatial coordinates and time variables, so as to dynamically evaluate its satisfaction with the fluid dynamics control equations. This automatic differentiation mechanism does not require complex mesh generation using finite difference or finite element methods as in traditional numerical calculations. Instead, it solves the derivatives in the function space, improving computational efficiency and enabling the system to complete the hydraulic dynamics prediction of the entire basin within seconds.

[0028] The scheduling decision generation device is configured to automatically generate coordinated scheduling instructions for gates, pumping stations, and reservoir spillways based on the prediction results output by the physical information fusion prediction device, combined with a preset scheduling rule base and safe operation boundary conditions. The scheduling decision generation device can physically manifest as a logic controller or an intelligent decision workstation. It incorporates a multi-objective optimization engine configured to minimize flood control risk, maximize water storage benefits, and minimize pump operating energy consumption as comprehensive objectives. This multi-objective optimization engine can dynamically calculate the optimal gate opening sequence and opening percentage based on the predicted flood peak arrival time, the sensitivity of the inundation risk area, and the real-time water level of the downstream receiving channel.

[0029] Furthermore, a safety verification layer is established between the scheduling decision generation device and the execution mechanism of the water conservancy facility. This safety verification layer is configured as a logic audit unit with the highest priority, internally storing safety operating procedures for different water conservancy projects. For example, when the scheduling decision generation device generates an instruction requiring the gate to close rapidly within a short period, the safety verification layer assesses whether this operation will cause a destructive water hammer effect in the river channel or cause the upstream water level to exceed the design flood limit. If the instruction violates preset safety indicators, the safety verification layer will automatically truncate the instruction and calculate an alternative instruction within a safe range based on the current physical boundary conditions, or trigger an alarm system to request manual intervention. This two-layer architecture ensures that while pursuing automated scheduling, the structural safety of the water conservancy project and the safety of downstream personnel are absolutely guaranteed.

[0030] The industrial internet communication hub is configured to establish a low-latency, highly reliable data interaction channel between the data acquisition device, the physical information fusion prediction device, and the scheduling decision generation device. The industrial internet communication hub adopts a distributed architecture of edge computing and cloud collaboration. Edge nodes are deployed near key water conservancy hubs to perform local data preprocessing, emergency threshold judgment, and rapid issuance of scheduling instructions. The cloud platform is responsible for maintaining the global physical information neural network model, performing large-scale historical data mining, cross-basin water resource collaborative analysis, and iterative updates of long-term scheduling strategies. This edge-cloud collaborative mechanism allows for closed-loop processing of emergency flood discharge scheduling with extremely high real-time requirements at local edge nodes, achieving millisecond-level response times, while comprehensive basin-wide balance scheduling requiring a global perspective is carefully considered in the cloud.

[0031] At the communication protocol level, the industrial internet communication hub supports multimodal communication methods including 5G, narrowband IoT, wired fiber optics, and satellite links. It is configured to sense link quality and dynamically switch between methods. For example, in the event of fiber optic communication disruption caused by flooding, the communication hub can automatically switch critical scheduling commands to satellite links for transmission, ensuring the uninterrupted lifeline of flood control scheduling commands. The communication hub also integrates a national-level encryption module to perform two-way authentication and encrypted storage of all hydrological data and control commands transmitted over the public internet, preventing malicious interference or unauthorized hijacking of scheduling permissions.

[0032] Example 2: As a further refinement and extension of Example 1, this example describes an intelligent scheduling system for water conservancy facilities based on an industrial internet platform with a distributed fault-tolerant architecture. This system is specifically optimized for high-reliability scheduling requirements in complex water systems with multiple tributaries.

[0033] In Embodiment 2, the data acquisition device not only includes basic sensing elements but also integrates a self-organizing network module. This self-organizing network module is configured to establish temporary communication links between monitoring points via wireless multi-hop technology when some communication base stations within the watershed are damaged by disasters, relaying water level and rainfall information to the nearest edge computing node. This self-healing acquisition network enhances the system's survivability under extreme flooding conditions. Each data acquisition terminal is also equipped with local storage capabilities, enabling it to record at least 30 days of complete hydrological data using a high-capacity flash memory chip in the extreme case of complete communication interruption, and synchronize it to the cloud via a breakpoint resume protocol as soon as communication is restored.

[0034] In Embodiment 2, the physical information fusion prediction device is further designed as a hierarchical prediction architecture. This device includes a local rapid prediction module and a cloud-based deep prediction module. The local rapid prediction module is deployed on the edge server of the water conservancy project, and internally runs a pruned and compressed lightweight physical information neural network. This lightweight physical information neural network focuses only on the hydrological evolution within a 5-kilometer range upstream of the water conservancy project. By reducing the number of hidden layer neurons and focusing on a simplified Saint-Venant one-dimensional dynamic equation, it can output local flow change trends in a very short time (e.g., within 200 milliseconds). The cloud-based deep prediction module runs a large-scale physical information neural network with full parameters, which integrates two-dimensional hydrodynamic equations with more complex three-dimensional topographic data, enabling refined simulation of the hydraulic evolution of the entire watershed.

[0035] In Example 2, the physical information fusion prediction device also introduces a domain decomposition mechanism when processing the residual terms of the physical equations. This mechanism divides the complex watershed system into multiple sub-watershed modules. Each sub-module calculates its internal mass conservation and energy balance relationships, and uses the Schwarz alternation method to perform continuous iteration of flow and water level at the boundaries of the sub-modules. The physical information fusion prediction device is configured to set a physical constraint boundary layer at the boundary of each sub-module, ensuring that the amount of water flowing out of the upstream sub-module is numerically equal to the amount of water entering the downstream sub-module, and that the energy gradient remains continuous. This divide-and-conquer computational strategy not only reduces the training difficulty of individual models but also improves the accuracy of physical constraints in complex terrain.

[0036] The scheduling decision generation device in Embodiment 2 not only considers preset scheduling rules but also integrates an evolutionary strategy unit based on reinforcement learning. This evolutionary strategy unit is configured to extract heuristic scheduling schemes that surpass traditional rule bases by performing deep reinforcement learning on tens of thousands of historical flood scheduling schemes. The evolutionary strategy unit can simulate thousands of possible rainfall evolution combinations that may occur in the next 24 hours, and through continuous self-play and strategy optimization, find the statistically optimal gate opening adjustment sequence. The scheduling decision generation device is configured to weightedly fuse this reinforcement learning-based proposed scheme with a deterministic scheme based on physical prediction, thereby tapping the water storage potential of water conservancy facilities while ensuring physical safety.

[0037] The industrial internet communication hub in Embodiment 2 further includes a software-defined network controller. This controller is configured to dynamically allocate bandwidth based on the priority of data streams. When the system detects a critical signal that the water level exceeds the warning level, the software-defined network controller will automatically increase the data transmission priority of that monitoring point and open a dedicated deterministic latency channel for control commands issued by the scheduling decision generation device. The communication hub also has cross-protocol conversion capabilities, enabling seamless connection to various industrial bus protocols such as Modbus, Profibus, and OPC-UA. This allows conventional control systems in older water conservancy facilities to be integrated into a unified scheduling platform, achieving deep compatibility between new and old equipment at the logical level.

[0038] In Embodiment 2, the safety verification layer is further enhanced. It not only verifies the compliance of physical laws but also integrates a fault tree analysis unit based on an industrial safety assessment model. This fault tree analysis unit is configured to monitor the gate motor current, pump station vibration frequency, and hydraulic system pressure in real time. If the safety verification layer detects that the current health status of the actuator is insufficient to support the required action frequency or amplitude of the scheduling command (for example, an abnormal gate motor current may indicate a jamming risk), it will automatically adjust the scheduling sequence, prioritizing the activation of backup hydraulic facilities, or reducing the action rate to ensure that the equipment does not suffer organic damage. This mechanism, which deeply couples equipment health monitoring with scheduling decisions, is a significant technological upgrade in this embodiment within the context of the Industrial Internet.

[0039] Example 3: This example focuses on describing an intelligent scheduling system for water conservancy facilities based on an industrial internet platform, which has multi-level disaster recovery and dynamic online learning capabilities.

[0040] In Example 3, the physical information fusion prediction device possesses enhanced online learning and adaptive calibration capabilities. The device is configured to run a sliding window training algorithm. As the data acquisition device continuously transmits the latest measured water level and flow rates, the device automatically calculates the real-time residual distribution between the predicted and measured values. When this residual distribution exceeds a preset confidence interval, the physical information fusion prediction device automatically triggers a weight update procedure, dynamically adjusting the weights of the physical residual term and the data fitting term in the neural network using newly added data from the last 24 hours. This mechanism enables the system to sensitively capture changes in the hydraulic characteristics of the watershed's underlying surface caused by human activities (such as reclamation and sand dredging) or natural factors (such as siltation and vegetation growth), ensuring that the prediction model remains highly synchronized with the real physical environment, achieving so-called "digital twin" dynamic alignment.

[0041] The data acquisition device in Embodiment 3 incorporates virtual sensor technology. This virtual sensor technology is configured such that when data is lost from a physical hardware sensor at a key location within the watershed due to lightning strikes, signal interference, or physical damage, the physical information fusion and prediction device utilizes its internally stored physical laws, combined with measured data from upstream and downstream adjacent stations of the missing monitoring point, and based on the spatial continuity constraints of the Saint-Venant equation, to reverse-calculate the approximate water level and flow rate of the missing monitoring point. This "mechanism-based data supplementation" logic ensures that the scheduling system can still obtain complete data stream support even when local sensing is impaired, reducing the system's dependence on a single hardware node.

[0042] Regarding the scheduling decision generation device, Example 3 introduces a game theory-based group decision-making module. This module is configured to treat each water conservancy hub (such as reservoirs, sluice gates, and pumping stations) within the basin as game participants with independent constraints. The group decision-making module establishes a Nash equilibrium model to ensure the overall flood control safety of the basin while also considering the local interests of each water conservancy hub (such as power generation revenue, navigation depth, and ecological flow demand). The scheduling decision generation device is configured to find the Pareto optimal solution for the entire system through multiple iterations, achieving an optimal balance between political, social, and economic benefits in the scheduling scheme.

[0043] The industrial internet communication hub in Example 3 also integrates a lightweight blockchain evidence storage module at the edge. This module is configured to encrypt and store every key scheduling instruction, every boundary parameter of every physical information prediction, and the execution feedback of every gate action in the form of a hash linked list, and synchronize it to multiple nodes of the watershed management department. This tamper-proof auditing mechanism provides absolutely reliable metadata support for accident retrospective analysis, responsibility determination, and subsequent optimization analysis of scheduling strategies. In the decision-making process for responding to sudden flood disasters, the basis for the generation of each instruction and the status of the physical parameters at that time can be retrieved at any time, ensuring transparency and accountability.

[0044] Regarding the execution phase of water conservancy facilities, Example 3 describes a closed-loop intelligent feedback adjustment logic. After issuing an instruction, the scheduling decision generation device does not simply wait for the task to complete, but is configured to continuously monitor the real-time location data or operational status data fed back by the executing mechanism. If a gate encounters an obstruction during opening, causing the location feedback to lag behind the instruction requirement, the scheduling decision generation device will use its internal predictive model to quickly assess the impact of this delay on the overall flood control plan and dynamically adjust the operating parameters of other related facilities to mitigate risks. This scheduling system with closed-loop control capabilities achieves end-to-end intelligence from perception, prediction, decision-making to execution, enabling the water conservancy facility cluster to operate like a collaborative intelligent robot swarm.

[0045] Example 3 also provides an interactive augmented reality command cockpit connected to an industrial internet communication hub. This cockpit can overlay the flood wave evolution trend output by the physical information fusion prediction device onto a real-world river map in a 3D visualization. The cockpit is configured to allow dispatchers to intervene in the automatic dispatching process via gestures or voice commands under extreme conditions, and to simulate the potential physical consequences of manual commands before intervention, providing precise decision-making assistance to command personnel.

[0046] Example 4: This example describes an intelligent scheduling system for water conservancy facilities based on an industrial internet platform, which is oriented towards cross-basin, large-scale water network collaborative scheduling.

[0047] In Example 4, the system is constructed as a hierarchical, gridded architecture. The data acquisition device not only covers the main channel of a single river but also extends to flood-prone areas in remote mountainous regions via a low-Earth orbit satellite constellation. The data acquisition device integrates a component called a "synthetic aperture radar image analysis module," which is configured to receive satellite remote sensing imagery and automatically identify the inundation extent, surface vegetation cover, and soil moisture content within the watershed. These macroscopic parameters are converted into initial input vectors for a physical information fusion prediction device, enabling the system to possess a wide-area perspective for understanding local flow changes from a macroscopic water cycle viewpoint.

[0048] In Example 4, the physical information fusion prediction device employs a deeply coupled hydrological-hydraulic integrated model. This model is configured to include not only the Saint-Venant equations describing river confluence but also a rainfall-runoff model describing land surface runoff. When calculating the loss function, the physical information fusion prediction device adds an additional runoff-runoff balance constraint term. This constraint term requires that the total rainfall, evaporation loss, soil infiltration, and total outflow at the outlet section within the watershed satisfy a mass conservation relationship over the time integral dimension. Through this full-cycle physical constraint, the system can accurately simulate the evolution of alternating drought and flood processes lasting several months, providing scientific data for cross-seasonal water resource allocation.

[0049] The scheduling decision generation device in Example 4 is further configured to possess "predictive control" capabilities. The predictive control module does not make decisions based on the current instantaneous state, but rather solves for the optimal trajectory over a future complete scheduling cycle (such as a week or a flood season). This module is configured to employ a rolling time-domain optimization algorithm. At each sampling time, it solves for a control sequence containing multiple step sizes based on the latest physical prediction results, but only executes the first step of the sequence, and re-solves at the next sampling time using the latest feedback information. This rolling optimization mechanism overcomes the long-term cumulative decision-making errors caused by weather forecast uncertainties or disturbances in physical model parameters.

[0050] To address the complexity of cross-basin scheduling, the industrial internet communication hub in Example 4 constructs a cross-regional data bus. This bus employs a microservice architecture, enabling logical interconnection of independent scheduling subsystems across different basins and administrative regions. The communication hub is configured to execute a logic called a "cross-regional resource negotiation protocol." When a region faces a threat of flooding exceeding standard levels, this protocol automatically searches for areas with flood storage capacity across the entire basin and automatically generates cross-regional water allocation and storage / discharge coordination instructions. The scheduling decision generation device then precisely controls the operating parameters of each level of water diversion canal headworks and cross-basin dams based on these global instructions.

[0051] At the hardware support level, Example 4 describes a long-endurance emergency power supply system based on hydrogen fuel cells to support the operation of the data acquisition and communication hub under extreme disasters. This power supply system uses an industrial internet platform for real-time energy efficiency monitoring to ensure that, even in the extreme case of grid failure, the perception and decision-making layers of the entire dispatch system can maintain full-load operation for at least 15 days.

[0052] The safety verification layer in Example 4 introduces a structural dynamics assessment module. This module is configured to receive data from vibration sensors and strain gauges deployed on the dam body and piers, and to perform real-time coupled analysis of the dynamic pressure load generated by hydraulic dispatch commands with the structural health modes of the hydraulic structures. If the generated dispatch commands cause resonance in the dam body or local stress exceeds the fatigue limit, the safety verification layer will forcibly modify the dispatch curve, transforming steep flow amplitudes into a smooth transition process, thereby extending the service life of large-scale hydraulic engineering projects while ensuring flood control benefits.

[0053] Example 5: This example describes in detail an intelligent scheduling system for water conservancy facilities based on an industrial internet platform that integrates digital twin technology and artificial intelligence autonomous evolution mechanism. The system is committed to realizing intelligent management and proactive response throughout the entire life cycle of water conservancy projects.

[0054] In Embodiment 5, the data acquisition device is endowed with the characteristic of continuous spatial perception. In addition to fixed monitoring stations, the system also includes a cluster of unmanned water quality / hydraulic inspection vessels and unmanned aerial vehicles (UAVs) equipped with lidar and multispectral cameras. These mobile monitoring nodes are real-time scheduled by the industrial internet communication hub and can autonomously perform encrypted sampling based on "high-risk evolution areas" or "areas of dramatic hydraulic gradient changes" identified by the physical information fusion prediction device. The data acquisition device is configured to instantly match the real-time cross-sectional morphological images of the river channel transmitted by the UAVs with a digital twin base in the cloud. If it is found that the river channel has partially collapsed or silted up due to scouring, resulting in a decrease in flow capacity, the data acquisition device will automatically update the terrain parameters and trigger the physical information fusion prediction device to recalculate the water flow evolution trend under constrained conditions.

[0055] In Embodiment 5, the physical information fusion prediction device introduces a physical constraint enhancement module based on a self-attention mechanism. This module is configured to abstract each control section, tributary, and reservoir within the watershed as a node in a neural network. The self-attention mechanism can automatically learn and quantify the correlation weights between different hydraulic nodes. For example, during flood evolution, the module can identify the contribution of a specific tributary's inflow to the main stream's flood peak evolution and specifically strengthen the constraint weight of that node in the discretized calculation of the Saint-Venant equations. This physical constraint method based on an attention mechanism enables the neural network to accurately identify key physical variables affecting global safety from massive amounts of data, much like a human expert, thus improving the model's convergence speed and prediction accuracy in large-scale complex systems.

[0056] In Embodiment 5, the scheduling decision generation device is configured as a sandbox simulation engine with "hypothesis analysis" functionality. This engine allows the system to concurrently run tens of thousands of virtual parallel worlds within the digital twin space before executing actual scheduling actions. In each parallel world, the system conducts simulation exercises based on different scheduling intensities, different start times, and different combinations of facilities. The scheduling decision generation device is configured to use a Monte Carlo search algorithm to find the most robust decision path among these parallel worlds. The most robustness refers to the decision path's ability to ensure that downstream dikes do not overflow and reservoir water levels do not exceed limits even when faced with a ±20% error disturbance in weather forecasts.

[0057] The industrial internet communication hub in Embodiment 5 employs a low-level transmission architecture based on deterministic networking technology. This architecture is configured to provide deterministic transmission services with "zero packet loss and microsecond-level jitter" for scheduling and control signaling amidst complex industrial internet traffic. This is achieved by reserving dedicated time slices on all switching devices along the communication path, ensuring that every "shutdown" or "pump start" command issued by the scheduling decision generation device reaches the actuator within a predetermined timeframe. The communication hub also integrates a semantic understanding-based data dictionary, which can automatically parse and map private protocols from actuators of different manufacturers and eras, unifying heterogeneous low-level actions into standardized logical primitives such as "flow regulation," "liquid level maintenance," and "emergency avoidance," thus simplifying the logical complexity of the scheduling decision generation device.

[0058] Regarding the security verification layer, Example 5 describes a "defender" model based on deep reinforcement learning. This model is configured to continuously attempt to find potential physical logic vulnerabilities in the scheduling strategy and automatically generate reinforced verification rules. For example, the defender model simulates various extreme equipment coordination failures (such as the superimposed impact of two reservoirs simultaneously releasing water on the midstream river channel), and based on this, establishes a multi-dimensional dynamic interlocking logic in the security verification layer. This self-evolving security mechanism enables the scheduling system to anticipate and defend against unknown risks.

[0059] The scheduling decision generation device in Example 5 also integrates a green scheduling module based on carbon footprint assessment. This module is configured to prioritize the scheduling of hydropower units with higher power generation efficiency, or to utilize the potential energy regulation function of reservoirs to meet the peak-shaving and frequency regulation needs of the regional power grid, while meeting flood control and water storage requirements. The system is configured to reduce fossil energy consumption without affecting water conservancy safety by fine-tuning the gate opening and turbine load, thus realizing low-carbon water conservancy operation empowered by the Industrial Internet.

[0060] Example 6: This example describes an intelligent scheduling system for water conservancy facilities based on an industrial internet platform with swarm intelligence perception. The system emphasizes data fusion and disaster emergency response collaboration with the participation of the whole society.

[0061] In Embodiment 6, the data sources for the data acquisition device are enriched. This includes not only specialized sensor networks but also crowdsourced data collection modules integrated into social mobile terminals (such as patrol officers' mobile phones and dashcams in vehicles). The data acquisition device is configured to extract textual information from social media or professional inspection apps using natural language processing technology, transforming it into qualitative descriptions of water conditions with geographical location characteristics (e.g., "the river water overflows the embankment by approximately 10 centimeters"). The system uses a component called a "soft and hard data fusion algorithm" to transform this unstructured qualitative data into fuzzy constraints recognizable by the physical information fusion prediction device. This approach provides the system with highly resilient external data supplementation even in the event of large-scale sensor outages due to disasters.

[0062] The physical information fusion prediction device in Embodiment 6 employs a distributed federated learning architecture. Considering the often administratively sensitive nature of water conservancy data, the system is configured to allow different provinces or water system management departments to jointly train a physical information neural network model covering the entire watershed by exchanging gradient parameters of the neural network, without exchanging the original underlying data. This architecture, while ensuring the data privacy and security of all parties, achieves cross-domain sharing of global learning and prediction capabilities for physical laws. During training, each local node fine-tunes the global model based on its own historical monitoring data, forming a prediction system that "has both a global perspective and takes into account local characteristics."

[0063] The scheduling decision generation device in Example 6 is configured to have "social response assessment" capabilities. When formulating flood discharge or high-flow scheduling plans, this device not only calculates the physical carrying capacity of the river channel, but also assesses the impact of the scheduling plan on the surrounding social operations in real time by connecting to traffic, power supply, municipal, and population distribution databases on an industrial internet platform. For example, the system will assess whether localized flooding caused by flood discharge will block critical emergency evacuation routes, or whether it will cause power transformers in low-lying areas to be flooded. The scheduling decision generation device is configured to automatically optimize the time window of the scheduling plan, avoiding peak periods or critical social activity periods, minimizing overall social losses while ensuring physical safety.

[0064] At the industrial internet communication hub level, Example 6 introduces a water situation early warning distribution mechanism based on a Content Delivery Network (CDN). The communication hub is configured such that once the scheduling decision generation device generates an emergency evacuation command, the command is rapidly pushed through edge nodes to every terminal device, smart street light, loudspeaker, and water conservancy facility within the affected area with extremely high concurrency. This geofencing-based precise distribution technology ensures unimpeded flow from decision generation to risk perception, addressing the "last mile" seamlessly.

[0065] The security verification layer in Embodiment 6 also integrates a physical consistency defense unit against network security threats. This unit is configured to monitor in real time whether the scheduling instructions transmitted by the communication hub are consistent with the predicted trends of the physical information fusion prediction device. If the system detects an instruction that logically completely violates the current physical laws (for example, suddenly requiring maximum flood discharge during the dry season), the security verification layer will determine that the instruction may originate from a malicious network attack and automatically cut off the execution path of the instruction, switching the system to a "restricted autonomous operation mode based on physical mechanisms" until manual intervention is required to eliminate the network security threat.

[0066] Example 6 also provides an automatic settlement module for water rights trading and compensation based on an industrial internet platform. This module is configured to automatically calculate water quota deviations between different stakeholders within the basin based on the actual water storage and release volumes of each hub generated by the scheduling decision generation device, and to perform automatic settlement using smart contract technology. This logic, which seamlessly integrates physical scheduling with economic regulation, provides closed-loop technical support for water resource allocation in complex water systems.

[0067] In summary, this invention, by deeply coupling physical information neural networks and fluid dynamics control equations within an industrial internet platform architecture, constructs an intelligent scheduling system for water conservancy facilities that possesses high physical interpretability, millisecond-level response speed, and extremely strong robustness. The system not only copes with complex hydraulic evolution predictions under extreme weather conditions but also achieves optimal allocation of water resources in the spatiotemporal dimensions through multi-objective optimization, edge cloud collaboration, multi-level security verification, and socialized data fusion, thereby improving the intelligent governance level of modern water conservancy projects.

[0068] Those skilled in the art should understand that, in one or more embodiments of the present invention, the configuration, connection method, and operating logic of each device, module, unit, or unit can be flexibly combined or replaced according to the actual watershed characteristics, project scale, and computing resources, all of which fall within the protection scope of the present invention. The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart scheduling system for water conservancy facilities based on an industrial internet platform, characterized in that, This includes data acquisition devices, physical information fusion and prediction devices, scheduling decision generation devices, and industrial internet communication hubs; The data acquisition device is configured to acquire real-time hydrological data from monitoring points within the basin and transmit the acquired data to the physical information fusion prediction device via the industrial internet communication hub. The physical information fusion prediction device is configured to receive the real-time hydrological data, predict the flow state based on a neural network model that incorporates fluid dynamics equation constraints, and output a hydraulic evolution trend that conforms to physical laws. The scheduling decision generation device is configured to generate coordinated scheduling instructions for the execution agency based on the hydraulic evolution trend, combined with the scheduling rule base and safe operation boundary conditions; The industrial internet communication hub is configured to establish a data interaction channel between the data acquisition device, the physical information fusion prediction device, and the scheduling decision generation device to ensure the real-time response of the overall system.

2. The intelligent scheduling system for water conservancy facilities based on an industrial internet platform according to claim 1, characterized in that: The data acquisition device is constructed as a multi-source heterogeneous sensing system including a radar water level gauge, an ultrasonic Doppler current meter, a tipping bucket rain gauge, and a video surveillance device with night vision function. The data acquisition device integrates a high-precision clock synchronization unit, which uses the BeiDou satellite timing protocol to perform nanosecond-level alignment processing on the sampling pulses of all sensors, ensuring that water level data, flow data and rainfall data have a unified global timestamp before entering the communication network. The data acquisition device is also equipped with a local signal conditioning circuit and an edge preprocessing board. The local signal conditioning circuit includes a preamplifier and an active filter circuit, which are used to perform gain compensation and high-frequency noise reduction on the raw electrical signal output by the sensor. The edge preprocessing board is used to perform range conversion and outlier removal on the digitized signal.

3. The intelligent scheduling system for water conservancy facilities based on an industrial internet platform according to claim 2, characterized in that: The neural network model built into the physical information fusion prediction device adopts a hybrid architecture of convolutional neural network and long short-term memory network. The convolutional neural network is configured with multiple feature extraction layers. Through multi-layer convolutional kernels, the spatial characteristics of the digital elevation model of the watershed, the river topology, and the distribution of sensor stations are reduced in dimensionality to generate feature vectors that reflect the spatial heterogeneity of the watershed. The Long Short-Term Memory (LSTM) network is configured to include an input gate, a forget gate, and an output gate structure to receive the feature vector and capture the long-distance dependence of the rainfall process and the water level rise process in the time series through iterative updates of the hidden layer states. The convolutional neural network and the LSM network are fused through a fully connected layer to achieve spatiotemporal joint modeling of complex hydrodynamic processes.

4. The intelligent scheduling system for water conservancy facilities based on an industrial internet platform according to claim 3, characterized in that: The physical information fusion prediction device is configured to constrain the neural network through a loss function, which is composed of a data fitting error term and a physical equation residual term. The residual terms of the physical equations are constructed based on the Saint-Venant equations and the law of conservation of mass. The Saint-Venant equations include the continuity equation and the momentum equation. The continuity equation is used to characterize the mass conservation relationship that the sum of the rate of change of flow with spatial distance and the rate of change of flow area with time is equal to zero. The physical information fusion prediction device uses automatic differentiation technology to directly calculate the first-order partial derivatives of the water depth and flow rate output by the neural network with respect to spatial coordinate variables and time variables in the function space, and substitutes the first-order partial derivatives into the continuity equation and momentum equation to calculate the residual value. The optimizer of the physical information fusion prediction device is configured to minimize the residual value during training, forcing the model output to strictly satisfy the water balance principle.

5. The intelligent scheduling system for water conservancy facilities based on an industrial internet platform according to claim 4, characterized in that: The physical information fusion prediction device is designed as a hierarchical prediction architecture, including a local fast prediction module deployed on the edge server of a key water conservancy hub and a cloud-based deep prediction module deployed in a data center. The local rapid prediction module runs a lightweight physical information neural network that has undergone model compression. This lightweight physical information neural network only focuses on the hydrological evolution within a preset distance range upstream of the water conservancy hub. By reducing the number of hidden layer neurons, it can output the local flow change trend in milliseconds. The cloud-based depth prediction module runs a full-parameter physical information neural network, which integrates two-dimensional hydrodynamic equations and three-dimensional topographic data to perform high-precision simulation of the hydraulic evolution of the entire basin. Meanwhile, the physical information fusion prediction device has online learning capabilities. It continuously receives new observation data through a sliding window algorithm and dynamically adjusts the weight ratio of the physical residual term and the data fitting term in the loss function to adapt to the unsteady environment caused by changes in the watershed underlying surface.

6. The intelligent scheduling system for water conservancy facilities based on an industrial internet platform according to claim 5, characterized in that: The scheduling decision generation device has a built-in multi-objective optimization engine, which is configured to minimize flood risk, maximize reservoir water storage benefits, and minimize the energy consumption of the actuator as comprehensive optimization objectives. The multi-objective optimization engine dynamically calculates the optimal gate opening sequence and opening percentage based on the predicted flood peak arrival time, the geographical sensitivity of the inundation risk area, and the real-time water level load of the downstream discharge channel using the Pareto optimization algorithm. A security verification layer is also provided between the scheduling decision generation device and the execution mechanism. The security verification layer is configured as a logical audit unit with the highest priority, and its internal storage contains safety operation procedures for different water conservancy projects. The security verification layer performs physical feasibility verification on the generated scheduling instructions and assesses whether the scheduling instructions will cause a destructive water hammer effect in the river or cause the water level rise rate to exceed the preset structural safety threshold.

7. The intelligent scheduling system for water conservancy facilities based on an industrial internet platform according to claim 6, characterized in that: The scheduling decision generation device also integrates an evolutionary strategy unit based on reinforcement learning and a group decision-making module based on game theory. The evolutionary strategy unit is configured to perform deep reinforcement learning on historical flood control schemes and use a self-game mechanism to find the optimal control sequence among various possible combinations of rainfall evolution within a preset time period. The group decision-making module treats multiple reservoirs, sluice gates, and pumping stations within the basin as game participants with independent constraints. By establishing a Nash equilibrium model, and under the premise of ensuring the overall safety of flood control in the entire basin, it seeks a scheduling solution that achieves the optimal balance between power generation revenue, navigation depth, and ecological flow demand through multiple iterations. The scheduling decision generation device is configured to perform weighted fusion of the proposed scheme of the evolution strategy unit and the prediction scheme of the physical information fusion prediction device to generate the final cooperative scheduling instruction.

8. The intelligent scheduling system for water conservancy facilities based on an industrial internet platform according to claim 7, characterized in that: The industrial internet communication hub adopts a distributed architecture of edge computing and cloud collaboration. The edge nodes are deployed at the water conservancy hub site to perform local data preprocessing, emergency threshold judgment and rapid issuance of scheduling instructions. The cloud platform is responsible for maintaining the global model and performing cross-basin collaborative analysis. The industrial internet communication hub supports multi-modal communication methods, including 5G mobile communication technology, narrowband IoT, wired fiber optics, and satellite links, and is configured to have link quality awareness capabilities. When the packet loss rate or latency of the primary communication link exceeds a preset threshold, the routing control module inside the communication hub automatically switches the critical scheduling instructions to the satellite link for transmission. Meanwhile, the communication hub integrates an encryption module to perform two-way authentication and encryption processing based on national cryptographic standards for all water information data and control commands transmitted over the public network.

9. The intelligent scheduling system for water conservancy facilities based on an industrial internet platform according to claim 8, characterized in that: The industrial internet communication hub further includes a software-defined network controller and an edge-side blockchain evidence storage module. The software-defined network controller is configured to dynamically allocate bandwidth based on the priority of data streams. When a warning signal indicating that the water level exceeds the warning level is detected, the software-defined network controller automatically increases the data transmission weight of the monitoring point and opens a deterministic delay channel for scheduling instructions. The edge-side blockchain evidence storage module is configured to encrypt and store the generation time of each scheduling instruction, physical prediction boundary parameters, and action feedback of the executing agency in the form of a hash chain list, and synchronize the hash root node to multiple distributed nodes of the watershed management department to achieve tamper-proof auditing and accountability throughout the scheduling process.

10. The intelligent scheduling system for water conservancy facilities based on an industrial internet platform according to claim 9, characterized in that: The intelligent scheduling system for water conservancy facilities also has data completion function based on virtual sensor technology and closed-loop intelligent feedback regulation logic; The virtual sensor technology is configured such that when a physical sensor at a monitoring point malfunctions, resulting in data loss, the physical information fusion prediction device uses the spatial continuity constraint of the Saint-Venant equation and combines it with the measured data of adjacent stations of the missing monitoring point to reverse-calculate the approximate water level and flow rate of the missing monitoring point. The closed-loop intelligent feedback adjustment logic is configured as follows: after issuing the instruction, the scheduling decision generation device continuously monitors the real-time location data fed back by the execution mechanism. If the action deviation of the execution mechanism exceeds the preset range, the scheduling decision generation device uses a prediction model to re-evaluate the impact of the action deviation on the flood control plan and dynamically adjusts the operating parameters of other related water conservancy facilities to hedge against risks.

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