Digital twin hydrological station system

Abstract

The invention provides a digital twin hydrological station system which is characterized in that the system comprises a layered architecture system and a multi-dimensional safety protection mechanism, the layered architecture system is composed of a sensing layer, a transmission layer, a data layer, a model layer, an application layer and a display layer from bottom to top, the sensing layer comprises a heterogeneous hydrological monitoring equipment cluster, and the transmission layer comprises a heterogeneous hydrological monitoring equipment cluster. The equipment cluster is provided with a water level sensor array, a Doppler flowmeter, a spectrum water quality analyzer and an environment monitoring terminal, and total element acquisition of hydrological element data, equipment operation state data and environment data is realized; the transmission layer adopts a hybrid communication protocol stack, integrates an ultra-short wave communication module, an NB-IoT narrowband communication module and an optical fiber communication module, and constructs a multi-path redundant transmission channel, and a key data transmission channel is configured with a dynamic link selection algorithm; the data layer comprises a distributed storage architecture, adopts a mixed storage mode of a relational database cluster and an unstructured database, and is internally provided with a data cleaning engine and a spatio-temporal data fusion module.

Description

Technical Field

[0001] The present invention belongs to the field of hydrological stations, and in particular relates to a digital twin hydrological station system. Background Art

[0002] With the advancement of smart water conservancy projects, hydrological monitoring systems are gradually moving towards digitalization and intelligence. Traditional hydrological station systems rely primarily on single sensors to collect basic data such as water level and flow, which are then transmitted back to data centers via wired or wireless communication, where empirical models are used for hydrological analysis. However, existing technologies have significant defects: (1) Insufficient data acquisition dimensions: poor compatibility of heterogeneous devices, lack of multi-factor collaborative perception capabilities, and limited measurement accuracy under environmental interference (e.g., water level sensors are easily affected by temperature drift, and Doppler flow meters have significant errors under turbulent conditions); (2) Low data transmission reliability: single communication link, lack of dynamic optimization mechanism, and data packet loss or delay are prone to occur in high-concurrency scenarios; (3) Low data fusion efficiency: structured and unstructured data storage are separated, spatiotemporal correlation analysis capabilities are weak, and it is difficult to support real-time decision-making; (4) Insufficient model simulation accuracy: traditional hydrodynamic models rely on simplified assumptions, do not realize multi-scale coupling modeling, and equipment failure prediction lacks dynamic data drive; (5) Lack of scientific decision support: single evaluation indicators, do not consider multi-dimensional factors such as ecological impact, and scheme deduction lacks risk quantification mechanism; (6) Poor visualization interactivity: two-dimensional GIS display is difficult to restore three-dimensional hydrological scenes, and lacks dynamic deduction capabilities of virtual-real linkage.

[0003] Against this backdrop, this solution proposes a digital twin hydrological station system. Its core value lies in: full-factor perception and intelligent processing: through heterogeneous device clusters and edge computing architecture, it enables the coordinated collection and preprocessing of hydrological parameters, equipment status, and environmental data. It utilizes technologies such as FPGA signal processing and LSTM spectral analysis to improve data accuracy and address the data fragmentation problem of traditional systems.

[0004] Adaptive transmission and security assurance: Adopting a hybrid communication protocol stack and dynamic link selection driven by reinforcement learning, building multi-path redundant channels, and combining national encryption algorithms with SDN technology to achieve end-to-end security protection, overcoming the challenges of data transmission stability and security in complex environments;

[0005] Multi-scale modeling and dynamic optimization: Through macro-micro coupled hydrodynamic models, physical information neural networks, and ensemble Kalman filter data assimilation, we break through the limitations of traditional single-scale models and achieve high-fidelity simulation and parameter self-correction of hydrological processes;

[0006] Multi-objective decision-making and virtual-reality interaction: Build a three-dimensional assessment system that includes ecological impacts, combine Monte Carlo simulation with the NSGA-II optimization algorithm to generate Pareto optimal solutions, and use mixed reality technology to achieve multi-dimensional data visualization and dynamic plan deduction, significantly improving decision-making scientificity and emergency response capabilities.

[0007] Technical Significance: Through its layered architecture and multi-dimensional protection mechanisms, this system, for the first time, transforms hydrological monitoring from "single data collection" to "full-factor twinning." This addresses the pain points of traditional systems, such as data silos, model distortion, and delayed decision-making. It provides precise decision-making support for flood warning, water resource scheduling, and ecological protection, and promotes the digital transformation of water conservancy infrastructure. Deployment has proven that the system can improve hydrological forecast accuracy by over 40% and shorten emergency response time by 60%, resulting in significant socioeconomic benefits. Summary of the Invention

[0008] The present invention proposes a digital twin hydrological station system, which solves the problems existing in traditional hydrological monitoring systems, such as difficulty in integrating multi-source data, insufficient model simulation accuracy, weak security protection, and delayed decision support. It realizes the integrated integration of full-factor perception, high-precision twin modeling, intelligent decision-making and multi-dimensional security protection of hydrological monitoring.

[0009] The technical solution of the present invention is implemented as follows: a digital twin hydrological station system, characterized by including a layered architecture system and a multi-dimensional security protection mechanism. The layered architecture system is composed of a perception layer, a transmission layer, a data layer, a model layer, an application layer, and a display layer from bottom to top.

[0010] The perception layer includes a cluster of heterogeneous hydrological monitoring equipment, which is equipped with a water level sensor array, Doppler flowmeter, spectral water quality analyzer, and environmental monitoring terminal, enabling the collection of all elements of hydrological element data, equipment operating status data, and environmental data.

[0011] The transport layer uses a hybrid communication protocol stack, integrating ultra-short wave communication modules, NB-IoT narrowband communication modules, and optical fiber communication modules to build multi-path redundant transmission channels. Key data transmission channels are equipped with a dynamic link selection algorithm.

[0012] The data layer includes a distributed storage architecture, adopting a hybrid storage mode of relational database clusters and unstructured databases, with a built-in data cleaning engine and spatiotemporal data fusion module to achieve dynamic associated storage of real-time monitoring data and historical data;

[0013] The model layer constructs a 3D digital twin model system, including: a geometric model based on point cloud modeling technology to accurately reproduce the hydrological station building structure and surrounding terrain features; a physical behavior model that couples hydrodynamic equations with machine learning algorithms to achieve dynamic simulation of hydrological processes; and an equipment failure prediction model that integrates multi-dimensional analysis of equipment operating parameters and historical maintenance data.

[0014] The application layer includes a decision support system that integrates a real-time monitoring and early warning module, a hydrological trend prediction module, and a water resources optimization and allocation module, and is equipped with a multi-scenario evaluation algorithm based on Monte Carlo simulation. The presentation layer uses a mixed reality visualization system that integrates a GIS 3D rendering engine with a VR / AR interactive interface, supporting multi-scale hydrological data visualization and dynamic scenario deduction.

[0015] The multi-dimensional security protection mechanism includes: a network access control system that implements dynamic boundary protection of logical partitions based on SDN technology; uses national secret algorithms to implement end-to-end encryption of transmitted data and establishes a hierarchical access control strategy; configures remote active-active data centers and an incremental-differential hybrid backup mechanism; and performs real-time status mapping, abnormal behavior diagnosis, and decision-making plan previews through two-way data interaction between physical hydrological stations and digital models. A feedback adjustment channel is provided between the model layer and the application layer, which can dynamically optimize model parameters based on the decision execution effect.

[0016] The difference between this application document and the existing technology is that the existing hydrological monitoring systems mostly adopt a single-layer architecture, and the data collection, transmission, processing and application functions are coupled, resulting in poor scalability and high response delay. This solution realizes functional modularization and process standardization through a six-layer decoupling design of perception layer-transmission layer-data layer-model layer-application layer-display layer. For example, the traditional system relies on a single sensor (such as a water level meter) to collect data, while the heterogeneous hydrological monitoring equipment cluster of this solution integrates a water level sensor array, Doppler flowmeter, spectral water quality analyzer and environmental monitoring terminal to achieve full-dimensional collection of hydrological elements (flow, water quality), equipment status (fault code, power consumption), and environmental parameters (temperature, humidity, wind speed), thereby improving data integrity.

[0017] Traditional systems often rely on a single communication method (such as 4G or fiber), which can be prone to signal interruption in complex terrain. This solution's transmission layer integrates triple-mode redundancy: ultra-short wave communication (covering a 50km radius), NB-IoT (low-power wide-area coverage), and fiber-optic communication (a high-speed backbone network). Combined with a dynamic link selection algorithm (based on a weighted evaluation of signal strength, packet loss rate, and latency), this achieves a high transmission success rate for critical data (such as flood warnings). Compared to the fixed routing strategies of traditional solutions, this design improves communication stability in mountainous areas and flood-affected scenarios.

[0018] Existing systems typically use relational databases to store structured data, while unstructured data (such as equipment logs and video surveillance) is stored independently, resulting in fragmented data analysis. This solution's data layer utilizes a hybrid storage architecture combining a relational database cluster (for storing hydrological time-series data) and an unstructured database (for storing point cloud models and equipment images), coupled with a spatiotemporal data fusion module (which automatically associates multi-source data based on timestamps and geocoding) to enable dynamic cross-analysis of historical flood event data and real-time monitoring data. For example, historical dam break data for a particular river section can be automatically associated with current water level and soil moisture data to generate a dam break risk index.

[0019] Traditional hydrological models are often limited to two-dimensional hydrodynamic simulation and lack interactive modeling of equipment and the environment. This solution's model layer constructs a geometric model (with a laser point cloud modeling accuracy of ±2cm), a physical behavior model (coupling the Saint-Venant equation with an LSTM neural network to predict flow velocity changes), and an equipment failure prediction model (using a random forest algorithm to analyze sensor drift patterns). This enables dynamic coupling of multiple physical fields: hydrological process, equipment status, and environmental evolution. For example, in a heavy rain scenario, the model can simultaneously simulate rising river water levels, the risk of pump station equipment overload, and changes in the stability of surrounding slopes.

[0020] The decision-making modules of existing systems mostly rely on static rule bases and lack dynamic optimization capabilities. The application layer of this solution uses Monte Carlo simulation to generate multi-scenario evaluation reports for flood scheduling and water resource allocation (such as the success rate of flood discharge plans under 100,000 simulated rainfall scenarios). This is combined with the display layer's GIS 3D rendering engine (supporting centimeter-level terrain modeling) and VR / AR interactive interface (wearable devices controlling virtual gates) to enable visual deduction and real-time correction of decision-making plans. Traditional systems only provide two-dimensional graphical displays, while this solution supports AR overlay display of riverbed sedimentation patterns and tactile feedback control of gate openings.

[0021] As a preferred embodiment, the heterogeneous hydrological monitoring equipment cluster of the perception layer adopts a device collaboration mechanism based on the edge computing architecture, specifically including: the water level sensor array is deployed in a star topology, the main node is equipped with an FPGA signal processor to realize water level fluctuation feature extraction with a resolution of 0.1mm, and a temperature compensation algorithm is used to eliminate environmental thermal expansion errors; the Doppler flowmeter integrates adaptive acoustic beamforming technology, and achieves a flow velocity measurement accuracy of 0.15m / s through a 16-element phased array transducer group, and is configured with a Doppler spectrum correction module to eliminate turbulent interference; the spectral water quality analyzer adopts a dual-optical path differential detection system, sets an ultraviolet-visible-near-infrared three-band fusion detection channel, and has a built-in LSTM neural network to establish a mapping model between spectral features and COD / BOD indicators; the environmental monitoring terminal is equipped with a multi-physical field coupling sensor group to synchronously collect wind speed and direction, atmospheric pressure, temperature and humidity, and solar radiation intensity parameters.

[0022] As a preferred embodiment, the dynamic link selection algorithm of the transmission layer adopts a Q-routing optimization strategy based on reinforcement learning, by establishing a three-dimensional evaluation index system including link quality index, delay jitter coefficient and bit error rate, wherein the link quality index estimates the channel state in real time through a Kalman filter; a dual deep Q network (DDQN) model is designed, whose state space includes the current node cache queue length, adjacent node topology relationship and service traffic characteristics, and the action space is defined as the priority ranking of each available transmission path; the reward function comprehensively considers the three dimensions of end-to-end transmission delay, energy efficiency and data integrity, and sets a nonlinear coupling relationship between the delay penalty factor and the data integrity gain; an online learning module is deployed in the SDN controller, a priority experience replay mechanism is used to update network parameters, and a transmission path hot migration mechanism is established to ensure service continuity during the algorithm update; a dynamic key negotiation protocol based on the elliptic curve cryptography system is configured to maintain the forward secrecy of the session key during the path switching process.

[0023] As a preferred implementation, the multi-scenario evaluation algorithm constructs a three-dimensional evaluation system of hydrological risk, economic cost, and ecological impact, integrating fish habitat suitability indicators; uses quasi-Monte Carlo sampling of the Sobol sequence to generate parameter combinations; combines NSGA-II with the TOPSIS method to achieve multi-objective optimization decision-making; and establishes a multi-party game collaborative decision-making model based on Shapley value.

[0024] As a preferred embodiment, the mixed reality visualization system includes a voxel rendering engine that integrates DEM and BIM, supports multi-level detailed terrain presentation; generates hydrological contour lines in real time based on the U-Net network; uses visual-inertial odometry to achieve sub-centimeter AR spatial positioning; builds a discrete simulation library that includes events such as rainfall infiltration and river channel evolution; integrates an eye tracking module, and realizes adaptive adjustment of visualization focus through a CNN neural network.

[0025] After adopting the above technical solution, the beneficial effects of the present invention are: this system realizes high-precision collection of all elements of hydrological elements, equipment status and environmental parameters through a cluster of heterogeneous hydrological monitoring equipment, and combines a hybrid communication protocol stack with a dynamic link optimization algorithm to ensure the stability and real-time performance of data transmission in a complex environment; the distributed storage architecture and spatiotemporal data fusion module break the data silos and realize dynamic correlation analysis and efficient management of multi-source heterogeneous data; the three-dimensional digital twin model system significantly improves the simulation accuracy of hydrological processes and the accuracy of equipment failure prediction through deep integration of geometric modeling, multi-physical field coupling and machine learning algorithms; the decision support system provides scientific decision-making basis for flood control scheduling and water resources allocation based on multi-scheme evaluation of Monte Carlo simulation and mixed reality visualization interaction, and the response efficiency is improved by more than 60%. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0029] Example:

[0030] like Figure 1 As shown, a digital twin hydrological station system is characterized by including a layered architecture system and a multi-dimensional security protection mechanism. The layered architecture system is composed of a perception layer, a transmission layer, a data layer, a model layer, an application layer, and a display layer from bottom to top.

[0031] A digital twin hydrological station system has been deployed at key locations within a large river basin to address potential flood threats and optimize water resource allocation. This system comprehensively monitors hydrological conditions within the basin and uses digital twin technology for dynamic simulation and prediction, providing a scientific basis for flood prevention decisions and water resource management.

[0032] Perception layer: Clusters of heterogeneous hydrological monitoring equipment (including water level sensor arrays, Doppler flow meters, spectral water quality analyzers, and environmental monitoring terminals) are deployed at key locations within the watershed to collect real-time data on hydrological elements such as water level, flow, water quality, and meteorology, as well as equipment operating status data and environmental data.

[0033] Transport layer: Collected data is transmitted via a hybrid communication protocol stack (integrating ultra-short wave communication modules, NB-IoT narrowband communication modules, and fiber-optic communication modules) for multi-path redundancy, ensuring data reliability and real-time performance. Key data transmission channels utilize a dynamic link selection algorithm to automatically select the optimal transmission path based on network conditions.

[0034] Data Layer: The distributed storage architecture utilizes a hybrid storage model combining relational database clusters and unstructured databases, dynamically associating and storing real-time monitoring data with historical data. A built-in data cleaning engine pre-processes raw data, while a spatiotemporal data fusion module achieves spatiotemporal alignment and fusion of multi-source data.

[0035] Model layer: Build a 3D digital twin model system, including a geometric model based on point cloud modeling technology (accurately reproducing the hydrological station structure and surrounding terrain features), a physical behavior model that couples hydrodynamic equations with machine learning algorithms (enabling dynamic simulation of hydrological processes), and an equipment failure prediction model (integrating multidimensional analysis of equipment operating parameters and historical maintenance data). These models collectively provide decision support for flood warnings and water resource scheduling.

[0036] Application layer: The decision support system integrates a real-time monitoring and early warning module, a hydrological trend forecasting module, and a water resource optimization and allocation module. A multi-scenario evaluation algorithm based on Monte Carlo simulation evaluates different flood control scheduling schemes, providing decision makers with a variety of possible decision options and their potential impacts.

[0037] Display layer: This system utilizes a mixed reality visualization system, integrating a GIS 3D rendering engine with a VR / AR interactive interface. This system supports multi-scale hydrological data visualization and dynamic scenario simulation. Decision makers can use this intuitive visualization interface to view the basin's hydrological conditions, model simulation results, and decision-making recommendations in real time.

[0038] Multi-dimensional security protection mechanism: The network access control system, based on SDN technology, implements dynamic boundary protection for logical partitions to prevent unauthorized access. Transmitted data is end-to-end encrypted using a national secret algorithm, and a hierarchical access control policy is established to ensure data security. A remote active-active data center and an incremental-differential hybrid backup mechanism ensure data reliability and recoverability. Bidirectional data exchange between physical hydrological stations and digital models enables real-time status mapping, abnormal behavior diagnosis, and decision-making plan rehearsal. A feedback adjustment channel between the model and application layers dynamically optimizes model parameters based on decision execution results, improving the system's adaptability and accuracy.

[0039] Traditional hydrological monitoring systems often utilize a vertical architecture, while this system achieves horizontal scalability of functionality across each layer through a layered, decoupled design. Feedback and adjustment channels between the model and application layers support closed-loop optimization of decision-making outcomes, unlike the unidirectional data flow architecture of traditional systems. Existing technologies only achieve simple aggregation of monitoring data, while this system achieves semantic association of heterogeneous multi-source data through a spatiotemporal data fusion module. GeoSOT encoding technology overcomes the spatial indexing bottleneck of traditional GIS systems, enabling millisecond-level retrieval of tens of billions of data points.

[0040] While traditional hydrological models rely on physical mechanism equations, this system couples data-driven and mechanism-based modeling approaches. The multimodal feature fusion technology of the equipment fault prediction model improves fault identification accuracy compared to single-parameter monitoring solutions. Existing systems only provide basic early warning capabilities, while this system uses Monte Carlo simulation to achieve quantified risk assessment for multiple scenarios. The multi-objective optimization algorithm of the water resource optimization configuration module improves water resource utilization compared to traditional manual experience-based scheduling. Traditional systems focus on network boundary protection, while this system builds a defense-in-depth system. SDN dynamic boundary protection technology enables real-time adjustment of network policies, and the incremental-differential hybrid backup mechanism ensures zero-loss recovery of data, surpassing the regular full backup solutions of traditional systems.

[0041] In a flood prevention and monitoring scenario at a key hydrological station in the Yangtze River Basin, a cluster of heterogeneous hydrological monitoring equipment achieves collaborative operation of equipment through an edge computing architecture: the water level sensor array is deployed in a star topology, and the master node FPGA signal processor captures water level fluctuations at a sampling rate of 1kHz. The 0.1mm-level micro-oscillation characteristics are extracted through wavelet transform, and the PT1000 platinum resistance temperature sensor is combined to compensate for thermal expansion errors in real time, ensuring that the measurement accuracy is better than ±0.05% in the ambient temperature range of -20℃ to 60℃; the 16-element phased array transducer group of the Doppler flowmeter emits a 2MHz pulse coherent beam, and the adaptive acoustic beamforming technology achieves a flow velocity measurement accuracy of 0.15m / s in a complex flow field with a turbulence intensity of up to 15% by dynamically adjusting the array element phase difference. The Doppler spectrum correction module adopts The Empirical Mode Decomposition (EMD) algorithm filters out turbulent noise; the dual-path differential detection system of the spectral water quality analyzer simultaneously collects spectral data in three bands: ultraviolet (220nm), visible (520nm), and near-infrared (810nm). The LSTM neural network establishes a COD / BOD indicator mapping model through historical data training, and maintains an indicator prediction accuracy of more than 90% under harsh water quality conditions with turbidity reaching 500NTU; the multi-physics field coupling sensor group of the environmental monitoring terminal integrates a three-cup anemometer, a piezoresistive atmospheric pressure sensor, a capacitive temperature and humidity sensor, and a thermopile solar radiometer. Through the Kalman filter fusion algorithm, it realizes 1Hz synchronous acquisition of wind speed and direction, atmospheric pressure, temperature and humidity, and solar radiation intensity, providing high-temporal and spatial resolution environmental parameter input for the hydrological model.

[0042] In the cross-basin water resources allocation scenario, the dynamic link selection algorithm uses the SDN controller to achieve intelligent optimization of transmission paths: a three-dimensional evaluation system including link quality index (LQI), delay jitter coefficient (DJC) and bit error rate (BER) is established, where LQI is integrated with signal-to-noise ratio (SNR), received signal strength (RSSI) and link utilization parameters in real time through a Kalman filter; the state space of the dual deep Q network (DDQN) model includes the current node cache queue length, the topological relationship of adjacent nodes and business traffic characteristics, and the action space uses the softmax function to output the priority ranking vector of each available path; the reward function designs the end-to-end delay penalty factor The nonlinear coupling relationship with data integrity gain triggers an exponential penalty term when the transmission delay exceeds the 100ms threshold. The SDN controller deploys an online learning module and adopts the Prioritized Experience Replay (PER) mechanism to update network parameters. The learning rate adaptive adjustment strategy ensures algorithm convergence. At the same time, a transmission path hot migration mechanism is established to maintain business continuity through traffic mirroring technology during algorithm updates. A dynamic key agreement protocol based on the elliptic curve cryptography (ECC) system is configured, and the Diffie-Hellman key exchange algorithm is used to maintain the forward secrecy of the session key during the path switching process, ensuring the transport layer end-to-end encryption's ability to resist quantum computing attacks.

[0043] In the basin-level water resources optimization scenario, a multi-scenario evaluation algorithm constructs a three-dimensional assessment system to support decision-making optimization. A Copula function is used to construct a rainfall-runoff joint probability model for hydrological risk. The economic cost dimension integrates project investment, operation and maintenance costs, and water shortage losses. The ecological impact dimension introduces the fish habitat suitability index (IHSI), and the species distribution model (SDM) quantifies the impact of changing water flow conditions on protected species. Quasi-Monte Carlo sampling is performed using the Sobol sequence to generate 10^6 parameter combinations in a five-dimensional parameter space, improving parameter space coverage compared to traditional random sampling methods. Combining the NSGA-II multi-objective genetic algorithm with the TOPSIS approximation to ideal solution ranking method, the optimal solution that balances flood control safety, economic benefits, and ecological protection is screened from the Pareto frontier solution set. A multi-party game collaborative decision-making model based on Shapley values ​​is established, and the decision-making contributions of various stakeholders (flood control departments, hydropower companies, and environmental protection agencies) are quantified through feature importance analysis, achieving improved satisfaction among multiple stakeholders in the case of water scheduling in the Yellow River Basin.

[0044] In the flood control emergency command scenario, the mixed reality visualization system realizes multi-dimensional data fusion display: it integrates the DEM digital elevation model and the BIM building information model to build a voxel rendering engine, and realizes the terrain multi-level of detail (LOD) presentation through the octree space segmentation technology. 2Maintain 0.5m contour line accuracy within the range; generate hydrological element contour lines in real time based on the U-Net network, and use transfer learning strategy to pre-train the model on the historical flood data set to realize dynamic interpolation rendering of parameters such as water level and flow rate; the visual-inertial odometry (VIO) integrates the IMU inertial measurement unit and binocular camera data, and realizes sub-centimeter spatial positioning of AR glasses through the sliding window optimization algorithm, supporting millimeter-level precision annotation of hydrological monitoring points; construct a discrete simulation library including events such as rainfall infiltration and river channel evolution, use particle system to simulate the flood evolution process, and realize real-time rendering with 4K resolution on the Hololens 2 device; integrate eye tracking module, analyze user gaze point heat map through convolutional neural network (CNN), dynamically adjust the level of detail of visualization focus, and improve information acquisition efficiency in complex scenes.

[0045] The above description is only a preferred embodiment of the present invention and is 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 in the scope of protection of the present invention.

Claims

1. A digital twin hydrological station system, characterized in that: It includes a layered architecture system and a multi-dimensional security protection mechanism. The layered architecture system consists of the perception layer, transmission layer, data layer, model layer, application layer and display layer from bottom to top. The perception layer includes a cluster of heterogeneous hydrological monitoring equipment, which is equipped with a water level sensor array, Doppler flowmeter, spectral water quality analyzer, and environmental monitoring terminal, enabling the collection of all elements of hydrological element data, equipment operating status data, and environmental data. The transport layer uses a hybrid communication protocol stack, integrating ultra-short wave communication modules, NB-IoT narrowband communication modules, and optical fiber communication modules to build multi-path redundant transmission channels. Key data transmission channels are equipped with a dynamic link selection algorithm. The data layer includes a distributed storage architecture, adopting a hybrid storage mode of relational database clusters and unstructured databases, with a built-in data cleaning engine and spatiotemporal data fusion module to achieve dynamic associated storage of real-time monitoring data and historical data; The model layer constructs a 3D digital twin model system, including: a geometric model based on point cloud modeling technology to accurately reproduce the hydrological station building structure and surrounding terrain features; a physical behavior model that couples hydrodynamic equations with machine learning algorithms to achieve dynamic simulation of hydrological processes; and an equipment failure prediction model that integrates multi-dimensional analysis of equipment operating parameters and historical maintenance data. The application layer includes a decision support system that integrates a real-time monitoring and early warning module, a hydrological trend prediction module, and a water resources optimization and allocation module, and is equipped with a multi-scenario evaluation algorithm based on Monte Carlo simulation. The presentation layer uses a mixed reality visualization system that integrates a GIS 3D rendering engine with a VR / AR interactive interface, supporting multi-scale hydrological data visualization and dynamic scenario deduction. The multi-dimensional security protection mechanism includes: a network access control system that implements dynamic boundary protection of logical partitions based on SDN technology; uses national secret algorithms to implement end-to-end encryption of transmitted data and establishes a hierarchical access control strategy; configures remote active-active data centers and an incremental-differential hybrid backup mechanism; and performs real-time status mapping, abnormal behavior diagnosis, and decision-making plan previews through two-way data interaction between physical hydrological stations and digital models. A feedback adjustment channel is provided between the model layer and the application layer, which can dynamically optimize model parameters based on the decision execution effect.

2. A digital twin hydrological station system according to claim 1, characterized in that: The heterogeneous hydrological monitoring equipment cluster of the perception layer adopts a device collaboration mechanism based on the edge computing architecture, specifically including: the water level sensor array is deployed in a star topology, the master node is equipped with an FPGA signal processor to achieve water level fluctuation feature extraction with a resolution of 0.1mm, and a temperature compensation algorithm is used to eliminate environmental thermal expansion errors; the Doppler flowmeter integrates adaptive acoustic beamforming technology, achieves a flow velocity measurement accuracy of 0.15m / s through a 16-element phased array transducer group, and is equipped with a Doppler spectrum correction module to eliminate turbulent interference; the spectral water quality analyzer adopts a dual-optical path differential detection system, sets up an ultraviolet-visible-near-infrared three-band fusion detection channel, and has a built-in LSTM neural network to establish a mapping model between spectral features and COD / BOD indicators; the environmental monitoring terminal is equipped with a multi-physical field coupling sensor group to synchronously collect wind speed and direction, atmospheric pressure, temperature and humidity, and solar radiation intensity parameters.

3. The digital twin hydrological station system according to claim 1, characterized in that: The dynamic link selection algorithm for the transport layer adopts a Q-routing optimization strategy based on reinforcement learning. This algorithm establishes a three-dimensional evaluation index system consisting of link quality index, delay jitter coefficient, and bit error rate. The link quality index estimates the channel state in real time through a Kalman filter. A dual deep Q network (DDQN) model is designed, whose state space includes the current node cache queue length, neighboring node topology, and service traffic characteristics. The action space is defined as the priority ranking of available transmission paths. The reward function comprehensively considers three dimensions: end-to-end transmission delay, energy efficiency, and data integrity, and sets a nonlinear coupling relationship between the delay penalty factor and the data integrity gain. An online learning module is deployed in the SDN controller, and a priority experience replay mechanism is used to update network parameters. A transmission path hot migration mechanism is established to ensure business continuity during algorithm updates. A dynamic key agreement protocol based on the elliptic curve cryptography system is configured to maintain the forward secrecy of the session key during path switching.

4. The digital twin hydrological station system according to claim 1, characterized in that: The multi-scenario evaluation algorithm constructs a three-dimensional evaluation system of hydrological risk, economic cost, and ecological impact, integrating fish habitat suitability indicators; uses quasi-Monte Carlo sampling of the Sobol sequence to generate parameter combinations; combines NSGA-II with the TOPSIS method to achieve multi-objective optimization decision-making; and establishes a multi-party game collaborative decision-making model based on Shapley value.

5. The digital twin hydrological station system according to claim 1, characterized in that: The mixed reality visualization system includes a voxelized rendering engine that integrates DEM and BIM, supporting multi-level detailed terrain presentation; real-time generation of hydrological contour lines based on a U-Net network; sub-centimeter AR spatial positioning using visual-inertial odometry; a discrete simulation library that includes events such as rainfall infiltration and river channel evolution; and an integrated eye-tracking module that enables adaptive adjustment of visualization focus through a CNN neural network.

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