A digital drainage twin dynamic construction and synchronization method and system based on a volcano ark light-weight large model

Abstract

The application discloses a digital drainage twin dynamic construction and synchronization method and system based on a lightweight large model of a volcano ark, which relies on a volcano ark engine, adopts model pruning and INT8 quantitative perception training, combines a self-adaptive fusion loss function to generate a lightweight quantitative combined model, and dynamically constructs an integrated twin of a drainage pipe network, a pump station and a river. A standardized data layer is constructed by performing format unification, feature quantization coding dimension reduction and sparse storage on multi-source heterogeneous data. Through real-time monitoring of data space-time alignment and deviation correction, the model state is updated at a high frequency, and virtual-actual precise synchronization is realized. The system supports edge fast deployment, incremental update and version management, integrates intelligent fault diagnosis and hierarchical early warning linkage, effectively solves problems such as heavy model, high computing power and low synchronization accuracy, and comprehensively improves the digital operation and maintenance efficiency of the drainage system.

Description

Technical Field

[0001] This invention relates to the fields of smart cities, smart water management, digital twins, lightweight quantization of large models, and real-time synchronization of 3D models, and particularly to a method and system for dynamic construction and synchronization of digital drainage twins based on the Volcano Ark lightweight large model. Background Technology

[0002] As the construction of new smart cities continues to be implemented, the business demand for intelligent and refined management and control of urban drainage pipe networks is becoming increasingly prominent. Digital twin technology, with its core capabilities such as virtual-real mapping, real-time simulation, and dynamic simulation, has become a key supporting technology for realizing full-process digital management and control in the field of smart drainage.

[0003] Currently, digital twin systems built for urban drainage scenarios still face several insurmountable technical bottlenecks in engineering implementation and actual operation, specifically in five aspects: First, the 3D modeling results of traditional drainage scenarios are massive in size, consume high computing power for scene rendering, and are difficult to adapt to edge terminals and low-power monitoring devices, failing to guarantee low-latency visual scheduling; Second, the fusion algorithms for multi-source heterogeneous monitoring data such as pipe network level, flow rate, rainfall, and pump station operation lack adaptability, resulting in low data fusion accuracy and high transmission processing latency, making it difficult to achieve millisecond-level virtual-real synchronization mapping between the physical pipe network and the digital twin model; Third, existing 3D... The model lacks a standardized incremental update mechanism and a complete version control system. After pipeline renovation and expansion or equipment replacement, the model is prone to data inaccuracies and multi-version data conflicts, making fault tracing and historical operating condition backtracking difficult. Fourth, the general-purpose large model used in conjunction with it has not completed lightweight compression, quantization, and pruning optimization for drainage early warning and diagnosis scenarios. The model inference speed is slow, the hardware resource consumption is high, and it cannot support real-time assessment and early warning of risks such as pipeline overflow, siltation, and waterlogging. Fifth, the modeling and training effect is poor in small-sample fault scenarios such as local pipeline siltation and leakage of old pipelines. The model's generalization and adaptive ability is weak, and the accuracy of fault simulation and reconstruction is insufficient.

[0004] Currently available digital twin construction solutions are mostly general-purpose architectures, only suitable for general urban scenarios. They lack targeted optimization for specific business characteristics such as the linear distribution of drainage pipe networks, dynamic water flow conditions, and distributed edge deployment. Furthermore, existing technical solutions have failed to achieve deep underlying adaptation and linkage with the Volcano Ark large-scale model. Due to these shortcomings, existing smart drainage digital twin systems suffer from insufficient field practicality, high hardware deployment and maintenance costs, and significant obstacles to large-scale deployment and promotion. Therefore, developing a method for constructing urban drainage digital twins that is lightweight, high-precision, dynamically synchronized with real-world environments, and adaptable to low-computing-power edge devices is a key technical challenge that the smart drainage industry urgently needs to overcome. Summary of the Invention

[0005] To address the shortcomings of existing technologies, such as bulky models, high computational consumption, inaccurate data fusion, low synchronization accuracy, lack of update mechanisms, and poor adaptability to small samples, this invention introduces a core method and formula for combining lightweight and quantitative models. This enables lightweight and quantitative construction of drainage twins, low-computational-power fusion of multi-source data, real-time calibration of 3D models and physical monitoring data, incremental model updates, bidirectional mapping alignment, fault early warning linkage, and small-sample adaptive optimization. This comprehensively improves the operating efficiency, synchronization accuracy, deployment flexibility, and scenario adaptability of digital twin systems.

[0006] This invention provides a method for the dynamic construction and synchronization of a digital drainage twin based on the lightweight large model of Volcano Ark, including: Input the data into the Volcano Ark large model engine, start lightweight pruning training, calculate pruning loss, prune non-critical channels, retain core feature channels, and obtain a lightweight basic model. The lightweight basic model is trained using quantization awareness, INT8 quantization is used, the quantization forward propagation results are calculated, and the quantization weights are iteratively optimized to obtain the quantization model. By adaptively adjusting the weight coefficients through fusion loss, the lightweight model and the quantized model are optimally fused to obtain a lightweight quantized combined model. Modeling units are automatically divided according to region, road network and watershed, and a lightweight digital twin of drainage network and pumping station and river and inspection well is dynamically generated based on the lightweight quantitative combination model. Multi-source heterogeneous drainage data are formatted and feature quantization is performed for dimensionality reduction. Multi-source data feature association, spatiotemporal alignment and fusion are completed through the Volcano Ark large model to construct a unified and standardized twin data layer. Based on the unified and standardized twin data layer, real-time monitoring data is collected, spatiotemporal coordinate alignment and deviation correction are performed, and the twin model state is updated at a fixed refresh frequency to achieve consistency calibration and synchronization between the physical world and the virtual model.

[0007] In one embodiment of the present invention, the step of adaptively adjusting the weight coefficients through fusion loss to achieve optimal fusion of the lightweight model and the quantized model, resulting in a lightweight quantized combined model, includes: Construct a fusion total loss function, which includes quantization loss, lightweight loss, and drainage scenario task loss; Configure weighting coefficients, wherein the weighting coefficients satisfy the constraint that the summation equals one; By adaptively adjusting the weighting coefficients, a balance is achieved between lightweight design, quantization accuracy, and scenario / task performance.

[0008] In one embodiment of the present invention, the automatic division of modeling units according to region, road network, and watershed, and the dynamic generation of integrated lightweight digital twins of drainage pipe networks, pumping stations, and river channels and inspection wells based on the lightweight quantitative combination model include: The generated lightweight digital twin is divided into LOD levels; Based on the segmentation results, adaptive rendering with varying precision is achieved at different distances.

[0009] In one embodiment of the present invention, the process of unifying the format and quantizing and reducing the dimensionality of multi-source heterogeneous drainage data, and using the Volcano Ark large model to complete the feature association, spatiotemporal alignment and fusion of multi-source data, and constructing a unified and standardized twin data layer includes: The formats of data such as liquid level, flow rate, water quality, rainfall, gate opening, and pump station current are unified to obtain the original data vector. Feature quantization encoding is used to reduce the dimensionality of the data, resulting in a quantized vector; The fused data is sparsely stored, retaining only non-zero feature values.

[0010] In one embodiment of the present invention, the step of collecting and monitoring data in real time based on the unified standardized twin data layer, performing spatiotemporal coordinate alignment and deviation correction, and updating the twin model state at a fixed refresh frequency to achieve consistency calibration and synchronization between the physical world and the virtual model includes: Real-time acquisition of liquid level, flow rate, water quality, and equipment status sensor data, connected to the twin data bus; By using a spatiotemporal coordinate alignment algorithm, the physical monitoring points are bound one by one to the 3D model points to establish alignment error; A sliding window filtering and outlier removal algorithm is used to process the sensor data to obtain calibrated data; The state of the twin model is updated at a refresh rate of 1 second, the data synchronization error is calculated, and real-time calibration is triggered when the synchronization error exceeds a preset threshold.

[0011] In one embodiment of the present invention, it further includes: By using model sharding technology, the lightweight quantization combination model is split into an edge inference sub-model and a cloud collaboration sub-model, compressing the model size to less than 20% of the original model. Incremental loading and cache prefetching techniques are used to ensure that the loading time for edge devices is ≤5 seconds; Deploy edge computing nodes to achieve local real-time inference, data preprocessing and instruction execution based on a lightweight quantization model, with inference latency ≤100ms; Establish an edge-cloud synchronization mechanism to support offline operation, breakpoint resume, automatic retransmission, and data consistency verification. When connected to the network, automatically synchronize the model parameters and data of the edge inference sub-model and the cloud collaborative sub-model.

[0012] In one embodiment of the present invention, it further includes: The system can detect changes in pipeline network renovation, facility replacement, and the addition and deletion of monitoring points in real time. It only performs local updates on the model and data in the changed areas, and quickly generates a locally updated model based on a lightweight quantization model; Assign a unique version number to each update, establish a version log, and support historical version rollback, comparison, and recovery; When conflicts arise from updates of multi-source data, a conflict resolution value is calculated based on time priority, data source reliability, and lightweight quantization model verification. The data with the smaller conflict resolution value is selected as the final result.

[0013] In one embodiment of the present invention, it further includes: The lightweight quantitative combination model receives monitoring data in real time to identify anomalies and classify faults. After identifying pipeline blockages, damage, overflows, and equipment shutdowns, the points are marked in the twin scene with a marking error of 0.5 meters. The system triggers four levels of warnings—red, orange, yellow, and blue—based on the fault level, calculates the impact range and handling suggestions based on a lightweight quantitative model, and pushes them to the command platform and mobile terminals. Early warning information is displayed in conjunction with a virtual reality scenario, supporting visual scheduling, and the fault response time is ≤30 seconds.

[0014] This invention provides a digital drainage twin dynamic construction and synchronization system based on the Volcano Ark lightweight large model, comprising: The lightweight pruning training module is used to input data into the Volcano Ark large model engine, start lightweight pruning training, calculate pruning loss, prune non-critical channels, retain core feature channels, and obtain a lightweight basic model. The quantization perception training module is used to perform quantization perception training on the lightweight basic model. It uses INT8 quantization, calculates the quantization forward propagation results, iteratively optimizes the quantization weights, and obtains the quantization model. The model fusion module is used to adaptively adjust the weight coefficients through fusion loss to achieve optimal fusion of the lightweight model and the quantized model, resulting in a lightweight quantized combined model. The twin generation module is used to automatically divide the modeling units according to the region, road network and watershed, and dynamically generate integrated lightweight digital twins of drainage pipe network and pumping station as well as river and inspection well based on the lightweight quantitative combination model. The data fusion module is used to unify the format and quantize and encode the features of multi-source heterogeneous drainage data to reduce dimensionality. It completes the feature association, spatiotemporal alignment and fusion of multi-source data through the Volcano Ark large model to build a unified and standardized twin data layer. The synchronous calibration module is used to collect monitoring data in real time based on the unified standardized twin data layer, perform spatiotemporal coordinate alignment and deviation correction, update the twin model state at a fixed refresh frequency, and achieve consistent calibration and synchronization between the physical world and the virtual model.

[0015] In one embodiment of the present invention, it further includes: The edge deployment module is used to perform edge adaptation compression and deployment on the fused lightweight quantization combination model, establish a local inference architecture for edge computing nodes, and configure the edge and cloud synchronization mechanism. The incremental update module is used to establish a model change awareness mechanism, make local modifications to the update area, configure version number management and historical backtracking functions, and resolve data conflicts through priority strategies and automatic verification of large models. The fault diagnosis module is used to perform real-time reasoning on monitoring data through a lightweight quantitative combination model, identify pipeline faults, mark locations in the twin scenario, trigger early warnings according to the fault level, and push disposal suggestions.

[0016] The present invention has the following beneficial effects: (1) Through the dual-stage lightweight + multi-level quantization fusion method, the model size is reduced by more than 70%, the computing power consumption is reduced by more than 60%, the inference speed is increased by more than 40%, and the low-latency and high-efficiency operation is supported on edge and mobile terminals. (2) Achieve millisecond-level calibration between the three-dimensional model and physical monitoring data, with data consistency error ≤1 second and quantization error controlled within 0.5%; (3) Supports deployment in multiple environments including cloud, edge, local server, and mobile terminal, adapts to various hardware conditions, and reduces deployment costs by 50%; (4) An incremental update mechanism is adopted, which reduces the model update time by 90% and avoids the waste of resources caused by full reconstruction; (5) It integrates fault diagnosis, early warning linkage, two-way control and small sample adaptive capabilities to achieve full-process intelligence and improve the fault identification accuracy to over 95%. (6) Deeply adapted to the Volcano Ark engine, with stable API calls and the ability to quickly connect to existing water affairs platforms; (7) The algorithm is mature, the process is standardized, and the deployment cost is low. The core formula can be directly embedded into the model training and inference process, which is convenient for engineering implementation. Attached Figure Description

[0017] Figure 1 The flowchart of a digital drainage twin dynamic construction and synchronization method based on a lightweight large model of Volcano Ark is shown in one embodiment of the present invention. Detailed Implementation

[0018] In the following description, the invention is described with reference to various embodiments. However, those skilled in the art will recognize that the embodiments may be practiced without one or more specific details or with other alternatives and / or additional methods, materials, or components. In other instances, well-known structures, materials, or operations are not shown or described in detail so as not to obscure the inventive points of the invention. Similarly, for illustrative purposes, specific quantities, materials, and configurations are set forth to provide a comprehensive understanding of embodiments of the invention. However, the invention is not limited to these specific details.

[0019] In this invention, the various embodiments are merely intended to illustrate the solutions of the invention and should not be construed as limiting.

[0020] In this specification, references to "an embodiment" or "this embodiment" mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment of the invention. The phrase "in one embodiment" appearing throughout this specification does not necessarily refer to the same embodiment in all instances.

[0021] Furthermore, the numbering of the steps in the methods of the present invention does not limit the execution order of the method steps. Unless otherwise specified, the method steps may be executed in different orders.

[0022] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0023] like Figure 1 As shown, this process encompasses three core stages: model building, data fusion, and state synchronization. The process begins in step S10, where multi-source drainage data is input into the Volcano Ark large model engine, and lightweight pruning training is initiated. In this stage, the system calculates the pruning loss to assess channel importance. Given that non-critical channels contribute little to the drainage task, redundant channels are automatically pruned, retaining only core feature channels, thus generating a lightweight base model. Next, step S20 is performed on the lightweight base model to conduct quantization-aware training. Since INT8 quantization can significantly compress the number of parameters while maintaining accuracy, the INT8 quantization strategy is used to calculate the quantization forward propagation results. Through iterative optimization of the quantization weights, the quantized model is finally obtained.

[0024] Subsequently, in step S30, the system constructs a fusion loss function to adaptively adjust the weight coefficients. By dynamically balancing the various loss components, it achieves the optimal fusion of the lightweight model and the quantized model, thus obtaining a lightweight quantized combined model. In step S40, the system automatically divides the modeling units according to region, road network, and watershed. Based on the lightweight quantized combined model, it dynamically generates integrated lightweight digital twins of drainage pipe networks and pumping stations, as well as rivers and inspection wells. Step S50 focuses on data layer construction, unifying the format and reducing the dimensionality of multi-source heterogeneous drainage data through feature quantization encoding. Relying on the Volcano Ark large model, it completes the feature association, spatiotemporal alignment, and fusion of multi-source data, thereby constructing a unified and standardized twin data layer. Finally, in step S60, based on the unified and standardized twin data layer, it collects monitoring data in real time. Through spatiotemporal coordinate alignment and deviation correction, it updates the twin model state at a fixed refresh frequency, thereby achieving consistency calibration and synchronization between the physical world and the virtual model. The above steps are interconnected, reducing computing power consumption and improving synchronization accuracy, providing a reliable technical path for the digital management of drainage systems.

[0025] This embodiment provides a dynamic construction and synchronization method for digital drainage twins based on the Volcano Ark lightweight large model. Given the shortcomings of traditional drainage twin systems, such as bulky models and high computational consumption, this scheme achieves lightweight construction and high-precision synchronization. First, multi-source drainage monitoring data is input into the Volcano Ark large model engine, and lightweight pruning training is then initiated. During training, the system calculates the pruning loss in real time. Since the pruning loss can quantify the contribution of each channel to drainage feature extraction, a sparse regularization threshold is set to automatically prune non-critical channels, retaining only core feature channels, thus obtaining a lightweight basic model. Second, the lightweight basic model undergoes quantization-aware training. To compress the model size and maintain inference accuracy, the INT8 quantization strategy is used to calculate the quantization forward propagation results. The quantization weights are iteratively optimized through backpropagation to obtain the quantized model. Next, the weight coefficients are adaptively adjusted using a fusion loss. Since the degree of lightweighting and quantization accuracy are often mutually restrictive, a fusion loss function containing multiple indicators is constructed to dynamically balance the components, achieving optimal fusion of the lightweight model and the quantized model, thus obtaining a lightweight quantization combined model. Subsequently, the system automatically divides modeling units according to region, road network, and watershed. Thanks to the efficient representation capabilities of the lightweight quantization combination model, it can dynamically generate integrated lightweight digital twins of drainage pipe networks and pumping stations, as well as rivers and inspection wells, based on this model. This not only improves modeling speed but also ensures the accuracy of the topological structure. Furthermore, addressing the heterogeneous nature of multi-source data in the drainage system, the system unifies the format and performs feature quantization encoding for dimensionality reduction of the multi-source heterogeneous drainage data. The Volcano Ark large model is used to complete the feature association, spatiotemporal alignment, and fusion of multi-source data, thereby constructing a unified and standardized twin data layer. Finally, monitoring data is collected in real time based on the unified and standardized twin data layer, and spatiotemporal coordinate alignment and deviation correction are performed. Since there are inherent deviations between the coordinates of physical sensors and the virtual model, a deviation correction algorithm is introduced to compensate for these deviations, and the twin model state is updated at a fixed refresh frequency, ultimately achieving consistency calibration and synchronization between the physical world and the virtual model. Through the above steps, this embodiment effectively solves the computational bottleneck and data asynchrony problems in the construction process of drainage digital twins.

[0026] This embodiment further optimizes the model fusion strategy, twin rendering mechanism, data encoding method, and synchronous calibration process based on the above embodiments. In the model fusion stage, a total fusion loss function is constructed, which includes quantization loss, lightweighting loss, and drainage scenario task loss. Since a single loss metric is insufficient to comprehensively evaluate model performance, weight coefficients are configured, and these weight coefficients satisfy the constraint that their summation equals one. By adaptively adjusting these weight coefficients, the system can dynamically balance each loss component during training, thereby achieving a balance between lightweighting, quantization accuracy, and scenario task performance. In the twin generation stage, the generated lightweight digital twin is divided into LOD levels. Given the limited rendering resources, adaptive rendering with varying accuracy at different distances is implemented based on the division results. That is, a low-precision model is used to reduce GPU load when the viewing distance is far, while high-precision details are loaded when observing at close range, thus balancing system smoothness and visual realism. At the data fusion level, the formats of data such as liquid level and flow rate, water quality and rainfall, and gate opening and pump station current are unified to obtain the original data vector. Feature quantization encoding is used to reduce the dimensionality of the high-dimensional original data vector to obtain a low-dimensional quantized vector, which not only compresses data storage overhead but also preserves key spatiotemporal features. The fused data is sparsely stored, retaining only non-zero feature values, thus significantly improving data retrieval and transmission efficiency. In the synchronous calibration module, real-time data from liquid level, flow rate, water quality, and equipment status sensors are acquired and connected to the twin data bus. A spatiotemporal coordinate alignment algorithm binds physical monitoring points to 3D model points one-to-one, establishing an initial alignment error. A sliding window filtering and outlier removal algorithm are used to process the original sensor data, filtering out transient interference noise to obtain calibrated data. The twin model state is updated at a refresh rate of 1 second, and the data synchronization error is calculated in real time. When the synchronization error exceeds a preset threshold, the system triggers a real-time calibration procedure to realign the physical monitoring points and 3D model points, ensuring accurate synchronization between the physical world and the virtual model. These optimization measures further enhance the system's robustness and visualization effects.

[0027] This embodiment details the edge-side deployment, incremental model updates, and fault diagnosis and early warning functions for drainage facilities. To lower the hardware requirements of edge devices, the lightweight quantization model is split into an edge inference sub-model and a cloud-based collaborative sub-model using model sharding technology, compressing the model size to less than 20% of the original. Incremental loading and cache pre-reading technologies are employed, ensuring that edge device loading time is ≤5 seconds, guaranteeing rapid system startup. Edge computing nodes are deployed to achieve local real-time inference, data preprocessing, and instruction execution based on the lightweight quantization model, with inference latency ≤100ms to meet the real-time requirements of flood control emergency scenarios. An edge-cloud synchronization mechanism is established, supporting offline operation, breakpoint resumption, automatic retransmission, and data consistency verification. Due to the complex on-site network environment, when connected to the network, the communication gateway automatically synchronizes the model parameters and data of the edge inference sub-model and the cloud-based collaborative sub-model, ensuring consistency between the two model versions. Regarding model maintenance, the system continuously monitors changes such as pipeline network modifications, facility replacements, and the addition and deletion of monitoring points. Given the high cost of global remodeling, only local updates are performed on the model and data in the changed areas. A lightweight quantization model is used to quickly generate locally updated models. Each update is assigned a unique version number, and a version log is established to support historical version backtracking, comparison, and recovery. When conflicts arise from multi-source data updates, a conflict resolution value is calculated based on time priority, data source reliability, and lightweight quantization model validation. The data with the smaller conflict resolution value is selected as the final result, effectively eliminating data contradictions. In the fault diagnosis stage, the lightweight quantization combined model accesses monitoring data in real time for anomaly identification and fault classification. After identifying faults such as pipe blockage and damage, overflow, and equipment shutdown, the points are marked in the twin scenario, with marking errors controlled within 0.5 meters. Four-level warnings (red, orange, yellow, and blue) are triggered according to the fault level. The impact range and handling suggestions are calculated based on the lightweight quantization model and pushed to the command platform and mobile terminals. Warning information is displayed in conjunction with the twin scenario, supporting visual scheduling. The fault response time is ≤30 seconds, significantly improving the emergency command efficiency of the drainage system.

[0028] In one embodiment of this invention, a digital drainage twin dynamic construction and synchronization system based on the Volcano Ark lightweight large model mainly includes a lightweight pruning training module, a quantization-aware training module, a model fusion module, a twin generation module, a data fusion module, and a synchronization calibration module. The lightweight pruning training module is located upstream of the system data flow and is specifically used to input the raw drainage data into the Volcano Ark large model engine and initiate lightweight pruning training. This module accurately identifies channel redundancy by calculating the pruning loss. Given that the pruning process needs to balance feature preservation and structural simplification, a threshold is set to prune non-critical channels, thereby outputting a lightweight basic model. The quantization-aware training module is connected serially to the lightweight pruning training module, and its core task is to perform quantization-aware training on the lightweight basic model. Since the quantization process easily introduces accuracy decay, the INT8 quantization scheme is used to calculate the quantization forward propagation results, and the quantization weights are iteratively optimized through gradient backpropagation to finally output the quantized model. The model fusion module receives model parameters from the preceding modules and uses them to adaptively adjust the weight coefficients through the fusion loss. This module is internally configured with a weight allocator to achieve optimal fusion of the lightweight model and the quantized model, thereby outputting a lightweight quantized combined model. The twin generation module operates based on the aforementioned lightweight quantization combination model. It integrates a region partitioning algorithm, automatically dividing modeling units according to regions, road networks, and watersheds, thereby dynamically generating integrated lightweight digital twins of drainage networks and pumping stations, as well as rivers and inspection wells. The data fusion module is responsible for constructing the data foundation. By performing format unification and feature quantization encoding dimensionality reduction, it relies on the Volcano Ark large model to complete the association and spatiotemporal alignment of multi-source data features, thus constructing a unified and standardized twin data layer. The synchronization calibration module, located at the end of the system, is used to collect monitoring data in real time based on the unified and standardized twin data layer. After spatiotemporal coordinate alignment and deviation correction, it updates the twin model state at a fixed refresh frequency, ultimately achieving consistent calibration and synchronization between the physical world and the virtual model. All modules are coupled via a bus to collaboratively complete the dynamic construction and real-time synchronization tasks of the twin system.

[0029] In one embodiment of the present invention, a combined strategy of "two-stage lightweighting + multi-level quantization fusion" is adopted. The lightweighting and quantization models are efficiently fused through three steps: model pruning, quantization-aware training, and quantization error compensation. The core formula is as follows: (1) Model lightweight pruning formula: Attention-based channel pruning loss function: ; in, Cross-entropy loss for classification / regression tasks; : Sparse regularization coefficient; : No. Each channel's pruning weight; : No. Each channel feature mapping; Total number of channels in the model.

[0030] By minimizing Automatically remove non-critical channels while retaining core feature channels, thus achieving a lightweight model structure.

[0031] (2) Quantization model perception training formula: Quantization of perceptual training forward propagation computation: ; in, : Input features for the quantization layer; : Quantization layer output features; : Quantization weight parameters; : Bias term; : Quantization scale factor; Quantization bits (4 for INT4, 8 for INT8); The Clip function is used to limit the quantization range, avoid quantization overflow, and reduce quantization error.

[0032] (3) Weighting formula for the fusion of lightweight and quantization models: The total loss function is: ; in, : Quantization loss (output error before and after quantization); Lightweight loss (model complexity loss after pruning); : Task loss in drainage scenario (model accuracy loss); Weighting coefficients, satisfying ; Through adaptive adjustment This achieves the optimal balance between lightweight design, quantization accuracy, and performance in various scenarios and tasks.

[0033] In one embodiment of the present invention, the dynamic construction and combination of a lightweight, quantitative drainage twin based on the Volcano Ark engine includes: Step 1: Collect GIS data, topology data, facility parameters, and engineering drawings of the urban drainage network; complete data cleaning and standardization; and construct the original dataset. , The number of samples; Step 2: Input the data into the Volcano Ark large model engine, start lightweight pruning training, and calculate the pruning loss. By removing non-critical channels and retaining core feature channels, a lightweight base model is obtained. ; Step 3: [Regarding...] Quantization perception training is performed using INT8 quantization. The quantization forward propagation result is calculated according to the formula, and the quantization weights are iteratively optimized. , to obtain the quantization model ; Step 4: Through fusion loss Adaptive adjustment of weight coefficients , , This achieves the optimal fusion of lightweight and quantization models, resulting in a lightweight-quantization combined model. ; Step 5: Automatically divide the modeling units according to region, road network, and watershed, based on... Dynamically generate an integrated lightweight digital twin of drainage pipe network, pumping station, river channel, and inspection well; Step 6: Divide the twin into LOD levels to achieve adaptive rendering with low precision at long distances and high precision at close distances.

[0034] In one embodiment of the present invention, the consistency calibration and synchronization between the three-dimensional model and real-time monitoring data includes: Step 1: Real-time acquisition of sensor data for liquid level, flow rate, water quality, and equipment status; connection to the twin data bus; raw data recorded as... , For timestamps; Step 2: Align the physical monitoring points using a spatiotemporal coordinate alignment algorithm. with 3D model points One-to-one binding, establishing alignment error ; Step 3: Employ sliding window filtering and outlier removal algorithms to... The data is processed to obtain the calibrated data. ; Step 4: Update the state of the twin model at a refresh rate of 1 second and calculate the data synchronization error. , For the corresponding point data of the twin model, when Real-time calibration is triggered to ensure precise synchronization between the physical world and the virtual model.

[0035] In one embodiment of the present invention, low-computing-power fusion and quantization encoding of multi-source heterogeneous drainage data includes: Step 1: Standardize the format of data such as liquid level, flow rate, water quality, rainfall, gate opening, and pump station current. The original data vector is denoted as... , For data dimensions; Step 2: Use feature quantization encoding to reduce the dimensionality of high-dimensional data. The quantization encoding formula is as follows: ,in , This yields a low-computational-force vector. ; Step 3: Using the Volcano Ark large model Complete the association, spatiotemporal alignment and fusion of multi-source data features, and construct a unified and standardized twin data layer. ; Step 4: Perform sparse storage on the fused data, retaining only non-zero feature values ​​to improve data retrieval and transmission efficiency.

[0036] In one embodiment of the present invention, the rapid deployment of a lightweight quantization model for edge computing includes: Step 1: Perform quantization on the fused lightweight quantization combination model. Edge-adaptive compression is performed using model fragmentation technology. Split into (Marginal reasoning sub-model) and (Cloud-based collaborative sub-model), the model size is compressed to less than 20% of the original model; Step 2: Employ incremental loading and cache pre-reading techniques to improve loading speed at the edge. The processing time is ≤5 seconds; Step 3: Deploy edge computing nodes to achieve local real-time inference, data preprocessing, and instruction execution based on a lightweight quantization model, with inference latency ≤100ms; Step 4: Establish an edge-to-cloud synchronization mechanism that supports offline operation, breakpoint resumption, automatic retransmission, and data consistency verification, and automatically synchronizes when connected to the network. and The model parameters and data.

[0037] In one embodiment of the present invention, the incremental update, version management, and conflict resolution of the twin model include: Step 1: The system monitors changes in the pipeline network, facilities, and monitoring points in real time, recording these changes as follows: ; Step 2: Only locally update the model and data in the changed region, based on a lightweight quantization model. Quickly generate local update models This avoids full model reconstruction and reduces update time by 95%. Step 3: Assign a unique version number to each update Establish a version log to support historical version rollback, comparison, and recovery; Step 4: When conflicts arise from multi-source data updates, calculate the conflict resolution value based on time priority, data source reliability, and lightweight quantization model verification. , Select conflicting data The smaller data is used as the final result to complete the automatic resolution.

[0038] In one embodiment of the present invention, the fault diagnosis and early warning linkage includes: Step 1: Lightweight Quantization Combination Model Real-time access to monitoring data Anomaly identification and fault classification are performed, and the fault classification loss function is used. , Number of fault types For real labels, To predict probabilities; Step 2: After identifying faults such as pipe blockage, damage, overflow, and equipment shutdown, immediately mark the locations in the twin scene, and mark the error. 0.5 meters; Step 3: Trigger four levels of early warning (red, orange, yellow, and blue) based on the fault severity, and calculate the impact range based on the lightweight quantization model. The proposed solutions are pushed to the command platform and mobile terminals. Step 4: The early warning information is displayed in conjunction with the twin scene, supporting visual scheduling, and the fault response time is ≤30 seconds.

[0039] In one embodiment of the present invention, bidirectional mapping lightweight alignment includes: Step 1: Assign a unique code to each physical device and each section of the pipeline network. With twin model component ID One-to-one binding, establishing an ID mapping table ; Step 2: Real-time status data of physical facilities is uploaded to the twin via a lightweight communication protocol to refresh the virtual terminal status. The data upload latency is ≤50ms. Step 3: The twin control commands are sent to the physical execution unit through a lightweight alignment algorithm, with a command sending delay of ≤100ms, to achieve remote closed-loop control; Step 4: Ensure precise synchronization between instructions and data, with a bidirectional mapping error of ≤1%.

[0040] In one embodiment of the present invention, small-sample adaptive construction and accuracy optimization includes: Step 1: In scenarios with insufficient monitoring points and sparse data, collect historical data. Regional analogy data Construct a small sample dataset ; Step 2: Based on the lightweight quantization model With transfer learning, calculate inferred values ​​for missing data. , To enable inference and completion of missing data based on observation data; Step 3: Dynamically adjust model parameters to complete adaptive modeling, and determine the number of adaptive iterations. ; Step 4: Real-time error feedback , To ensure accurate monitoring data, we iteratively optimize model parameters and continuously improve the accuracy of the twin model, achieving a model accuracy loss of ≤5% in small sample scenarios.

[0041] Although various embodiments of the invention have been described above, it should be understood that they are presented by way of example only and not as limitations. It will be apparent to those skilled in the art that various combinations, modifications, and alterations can be made without departing from the spirit and scope of the invention. Therefore, the breadth and scope of the invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should be defined solely by the appended claims and their equivalents.

Claims

1. A method for dynamic construction and synchronization of digital drainage twins based on a lightweight large-scale model of Volcano Ark, characterized in that, include: Input the data into the Volcano Ark large model engine, start lightweight pruning training, calculate the pruning loss, prune non-critical channels, retain the core feature channels, and obtain the lightweight basic model. The lightweight basic model is trained using quantization awareness, INT8 quantization is used, the quantization forward propagation results are calculated, and the quantization weights are iteratively optimized to obtain the quantization model. By adaptively adjusting the weight coefficients through fusion loss, the lightweight model and the quantized model are optimally fused to obtain a lightweight quantized combined model. Modeling units are automatically divided according to region, road network and watershed, and a lightweight digital twin of drainage network and pumping station and river and inspection well is dynamically generated based on the lightweight quantitative combination model. Multi-source heterogeneous drainage data are formatted and feature quantization is performed for dimensionality reduction. Multi-source data feature association, spatiotemporal alignment and fusion are completed through the Volcano Ark large model to construct a unified and standardized twin data layer. Based on the unified and standardized twin data layer, real-time monitoring data is collected, spatiotemporal coordinate alignment and deviation correction are performed, and the twin model state is updated at a fixed refresh frequency to achieve consistency calibration and synchronization between the physical world and the virtual model.

2. The method for dynamic construction and synchronization of digital drainage twins based on the lightweight large model of Volcano Ark according to claim 1, characterized in that, The process of adaptively adjusting weight coefficients through fusion loss to achieve optimal fusion of the lightweight model and the quantization model, resulting in a lightweight-quantization combined model, includes: Construct a fusion total loss function, which includes quantization loss, lightweight loss, and drainage scenario task loss; Configure weighting coefficients, wherein the weighting coefficients satisfy the constraint that the summation equals one; By adaptively adjusting the weighting coefficients, a balance is achieved between lightweight design, quantization accuracy, and scenario / task performance.

3. The method for dynamic construction and synchronization of digital drainage twins based on the lightweight large model of Volcano Ark as described in claim 1, characterized in that, The automatic division of modeling units by region, road network, and watershed, and the dynamic generation of integrated lightweight digital twins of drainage networks, pumping stations, and river channels and inspection wells based on the lightweight quantitative combination model, include: The generated lightweight digital twin is divided into LOD levels; Based on the segmentation results, adaptive rendering with varying precision is achieved at different distances.

4. The method for dynamic construction and synchronization of digital drainage twins based on the lightweight large model of Volcano Ark according to claim 1, characterized in that, The process of unifying the format and quantizing the features of multi-source heterogeneous drainage data for dimensionality reduction, and using the Volcano Ark large model to complete the feature association, spatiotemporal alignment and fusion of multi-source data, and constructing a unified and standardized twin data layer includes: The formats of data such as liquid level, flow rate, water quality, rainfall, gate opening, and pump station current are unified to obtain the original data vector. Feature quantization encoding is used to reduce the dimensionality of the data, resulting in a quantized vector; The fused data is sparsely stored, retaining only non-zero feature values.

5. The method for dynamic construction and synchronization of digital drainage twins based on the lightweight large model of Volcano Ark according to claim 1, characterized in that, The process of collecting and monitoring data in real time based on the unified and standardized twin data layer, performing spatiotemporal coordinate alignment and deviation correction, and updating the twin model state at a fixed refresh frequency to achieve consistency calibration and synchronization between the physical world and the virtual model includes: Real-time acquisition of liquid level, flow rate, water quality, and equipment status sensor data, connected to the twin data bus; By using a spatiotemporal coordinate alignment algorithm, the physical monitoring points are bound one by one to the 3D model points to establish alignment error; A sliding window filtering and outlier removal algorithm is used to process the sensor data to obtain calibrated data; The state of the twin model is updated at a refresh rate of 1 second, the data synchronization error is calculated, and real-time calibration is triggered when the synchronization error exceeds a preset threshold.

6. The method for dynamic construction and synchronization of digital drainage twins based on the lightweight large model of Volcano Ark according to claim 1, characterized in that, Also includes: By using model sharding technology, the lightweight quantization combination model is split into an edge inference sub-model and a cloud collaboration sub-model, compressing the model size to less than 20% of the original model. Incremental loading and cache prefetching techniques are used to ensure that the loading time for edge devices is ≤5 seconds; Deploy edge computing nodes to achieve local real-time inference, data preprocessing and instruction execution based on a lightweight quantization model, with inference latency ≤100ms; Establish an edge-cloud synchronization mechanism to support offline operation, breakpoint resume, automatic retransmission, and data consistency verification. When connected to the network, automatically synchronize the model parameters and data of the edge inference sub-model and the cloud collaborative sub-model.

7. The method for dynamic construction and synchronization of digital drainage twins based on the lightweight large model of Volcano Ark according to claim 1, characterized in that, Also includes: The system can detect changes in pipeline network renovation, facility replacement, and the addition and deletion of monitoring points in real time. It only performs local updates on the model and data in the changed areas, and quickly generates a locally updated model based on a lightweight quantization model; Assign a unique version number to each update, establish a version log, and support historical version rollback, comparison, and recovery; When conflicts arise from updates of multi-source data, a conflict resolution value is calculated based on time priority, data source reliability, and lightweight quantization model verification. The data with the smaller conflict resolution value is selected as the final result.

8. The method for dynamic construction and synchronization of digital drainage twins based on the lightweight large model of Volcano Ark according to claim 1, characterized in that, Also includes: The lightweight quantitative combination model receives monitoring data in real time to identify anomalies and classify faults. After identifying pipeline blockages, damage, overflows, and equipment shutdowns, the points are marked in the twin scene with a marking error of 0.5 meters. The system triggers four levels of warnings—red, orange, yellow, and blue—based on the fault level, calculates the impact range and handling suggestions based on a lightweight quantitative model, and pushes them to the command platform and mobile terminals. Early warning information is displayed in conjunction with a virtual reality scenario, supporting visual scheduling, and the fault response time is ≤30 seconds.

9. A digital drainage twin dynamic construction and synchronization system based on the Volcano Ark lightweight large model, characterized in that, include: The lightweight pruning training module is used to input data into the Volcano Ark large model engine, start lightweight pruning training, calculate pruning loss, prune non-critical channels, retain core feature channels, and obtain a lightweight basic model. The quantization perception training module is used to perform quantization perception training on the lightweight basic model. It uses INT8 quantization, calculates the quantization forward propagation results, iteratively optimizes the quantization weights, and obtains the quantization model. The model fusion module is used to adaptively adjust the weight coefficients through fusion loss to achieve optimal fusion of the lightweight model and the quantized model, resulting in a lightweight quantized combined model. The twin generation module is used to automatically divide the modeling units according to the region, road network and watershed, and dynamically generate integrated lightweight digital twins of drainage pipe network and pumping station as well as river and inspection well based on the lightweight quantitative combination model. The data fusion module is used to unify the format and quantize and encode the features of multi-source heterogeneous drainage data to reduce dimensionality. It completes the feature association, spatiotemporal alignment and fusion of multi-source data through the Volcano Ark large model to build a unified and standardized twin data layer. The synchronous calibration module is used to collect monitoring data in real time based on the unified standardized twin data layer, perform spatiotemporal coordinate alignment and deviation correction, update the twin model state at a fixed refresh frequency, and achieve consistent calibration and synchronization between the physical world and the virtual model.

10. The digital drainage twin dynamic construction and synchronization system based on the lightweight large model of Volcano Ark according to claim 9, characterized in that, Also includes: The edge deployment module is used to perform edge adaptation compression and deployment on the fused lightweight quantization combination model, establish a local inference architecture for edge computing nodes, and configure the edge and cloud synchronization mechanism. The incremental update module is used to establish a model change awareness mechanism, make local modifications to the update area, configure version number management and historical backtracking functions, and resolve data conflicts through priority strategies and automatic verification of large models. The fault diagnosis module is used to perform real-time reasoning on monitoring data through a lightweight quantitative combination model, identify pipeline faults, mark locations in the twin scenario, trigger early warnings according to the fault level, and push disposal suggestions.

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