A method and system for intelligent scheduling, collaborative reasoning and security protection of a large drainage model based on a volcanic ark

Abstract

The application discloses a kind of based on volcano ark's drainage large model intelligent scheduling, collaborative reasoning and security protection method and system, the method adopts light pruning and INT4 / INT8 quantization perception training, constructs fusion scheduling loss function to realize resource adaptive allocation, system combines dynamic priority queue and node load migration technology, utilize time window batch reasoning and incremental cache mechanism to reduce delay, through parallel scheduling prediction, diagnosis and optimization model are fused decision with weighting, and introduce RBAC authentication and light homomorphic encryption guarantee data isolation security, rely on edge-cloud collaborative architecture and high concurrency precision automatic switching strategy, the application effectively breaks through drainage scene computing power bottleneck, improves the scheduling efficiency of large model system, reasoning performance and flood season collaborative control ability.

Description

Technical Field

[0001] This invention relates to the fields of smart city, smart water management, digital twin, artificial intelligence large model scheduling and collaborative reasoning technology, and in particular to a method and system for intelligent scheduling, collaborative reasoning and security protection of drainage large model based on Volcano Ark. Background Technology

[0002] Urban drainage systems are the core lifeline engineering projects ensuring the normal operation of cities. Their safety, stability, and efficiency are directly related to urban flood control and drainage, water environment management, and the safety of residents' lives and production. They are a key link in urban infrastructure construction and intelligent operation and maintenance. With the rapid iteration of smart water management technologies, cutting-edge technologies such as digital twins and artificial intelligence large models are gradually and deeply empowering the urban drainage field. The demand for refined applications such as intelligent prediction, fault diagnosis, dynamic optimization, and intelligent scheduling of drainage systems continues to rise, placing higher demands on the accuracy, efficiency, coordination, and safety of drainage network scheduling.

[0003] Currently, existing intelligent urban drainage scheduling technologies still suffer from numerous core technological bottlenecks, making it difficult to adapt to the large-scale and refined implementation requirements of smart drainage. Firstly, traditional scheduling models suffer from low computational efficiency and insufficient lightweight quantization inference performance, failing to meet the timeliness requirements of real-time dynamic scheduling in drainage systems. Secondly, existing technologies mostly employ a single-model independent computation mode, lacking a robust multi-model collaborative fusion and linkage scheduling mechanism, resulting in poor data interoperability and functional complementarity, and limiting the comprehensiveness and accuracy of scheduling decisions. Simultaneously, the existing drainage scheduling system has a weak security architecture, lacking security control mechanisms for scheduling data transmission, model computation, and command execution, posing security risks such as data leakage, command tampering, and system attacks.

[0004] In addition, the existing edge-cloud collaborative scheduling architecture suffers from insufficient adaptability. The linkage efficiency between edge data collection and preprocessing and cloud model training and decision inference is low, with prominent issues of data transmission redundancy and response latency. Furthermore, facing the complex operating scenarios of drainage systems characterized by high concurrency, multiple nodes, and dynamic changes, existing scheduling methods lack adaptive optimization capabilities and cannot adjust scheduling strategies in real time based on dynamic parameters such as pipeline operating status and rainfall conditions. More importantly, current general-purpose large-scale model scheduling methods are mostly designed for general scenarios and have not been specifically optimized in conjunction with the characteristics of urban drainage pipeline network topology, operating conditions, and the need for lightweight and quantitative implementation. This results in low adaptability to actual operation and maintenance scenarios of drainage systems, and an inability to effectively solve practical technical pain points such as real-time on-site scheduling, accurate judgment, and safe operation and maintenance.

[0005] In summary, current intelligent scheduling technologies for urban drainage generally suffer from problems such as low scheduling efficiency, poor inference performance, weak multi-model collaboration capabilities, lack of security protection, inefficient edge-cloud collaboration, and insufficient adaptability to high-concurrency scenarios. General large-model scheduling solutions cannot meet the specific application requirements of the drainage field. Therefore, developing an intelligent scheduling method for drainage systems that is compatible with lightweight quantization models, supports multi-model collaborative inference, and combines security protection capabilities with efficient edge-cloud collaboration has become a key technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] This invention aims to address the shortcomings of existing drainage large-scale model scheduling technologies, such as low scheduling efficiency, weak quantization inference performance, insufficient multi-model collaboration capabilities, inadequate security protection, inefficient edge-cloud collaboration, and inability to adaptively optimize in high-concurrency scenarios. It provides a method for intelligent scheduling, collaborative inference, and security protection of drainage large-scale models based on Volcano Ark. This invention employs a lightweight, quantization model combination architecture to achieve model load balancing and efficient scheduling, optimized batch caching of quantization inference, multi-model collaborative decision fusion, unified security authentication and data isolation, edge-cloud collaborative inference, and high-concurrency adaptive performance optimization. This effectively improves the scheduling efficiency, inference performance, security, and collaboration level of drainage systems, meeting the intelligent scheduling requirements of smart drainage systems for high real-time performance, high security, and high concurrency.

[0007] This invention provides a method for intelligent scheduling, collaborative reasoning, and security protection of a large-scale drainage model based on Volcano Ark, including: Collect monitoring data from urban drainage pipe networks to construct a dataset; Lightweight pruning was performed on the prediction model, diagnostic model, and optimization model to obtain a lightweight base model. The lightweight basic model is trained with INT4 or INT8 quantization awareness, and the quantization formula is iteratively optimized and the quantization range is limited to obtain the quantization model. A fusion scheduling loss function is constructed, and the weight coefficients of load balancing loss, inference performance loss, and security isolation loss are adaptively adjusted. The quantization model is then fused and scheduled to obtain a lightweight quantization combination model. The lightweight quantization combination model is deployed to the Volcano Ark engine scheduling module to initialize the task scheduling queue and load balancing monitoring.

[0008] In one embodiment of the present invention, the lightweight pruning of the prediction model, diagnostic model, and optimization model to obtain a lightweight basic model includes: Calculate the cross-entropy loss and sparse regularization term for drainage prediction or diagnostic tasks; A pruning loss function is constructed by combining the attention weights of each channel with the feature maps; The feature channels for the drainage scenario are preserved by minimizing the pruning loss function.

[0009] In one embodiment of the present invention, the step of performing INT4 or INT8 quantization-aware training on the lightweight basic model, iteratively optimizing the quantization formula and limiting the quantization range to obtain the quantization model includes: Set the quantization scale factor and the number of INT4 or INT8 quantization bits; The forward propagation formulas for the input and output features of the quantization layer are calculated based on the quantization scale factor and the number of quantization bits. The Clip function is used to limit the quantization range to suppress quantization errors.

[0010] In one embodiment of the present invention, the step of constructing a fusion scheduling loss function, adaptively adjusting the weight coefficients of load balancing loss, inference performance loss, and security isolation loss, and performing fusion scheduling on the quantization model to obtain a lightweight quantization combination model includes: Construct a fusion scheduling loss function that is weighted by load balancing loss, inference performance loss, and security isolation loss; Set the weight coefficients of each loss term to satisfy the normalization condition; The weighting coefficients are dynamically adjusted to optimize resource allocation in terms of scheduling efficiency, performance, and security.

[0011] In one embodiment of the present invention, the method further includes: obtaining the task type, model lightweighting degree, and node computing power load information of the drainage scheduling task, calculating dynamic priority, and allocating scheduling resources and task queues.

[0012] In one embodiment of the present invention, the method further includes: performing batch calculations on aggregated inputs of monitoring data, and managing the high-frequency prediction result cache queue using a time window combined with an incremental update method.

[0013] In one embodiment of the present invention, the invention further includes: a prediction model, a diagnostic model, and an optimization model deployed in parallel, wherein each model independently outputs local decision results and then generates a final scheduling decision through a weighted fusion mechanism.

[0014] In one embodiment of the present invention, it further includes: real-time monitoring of the number of concurrent requests, GPU utilization and inference latency; automatically switching the model precision from INT8 to INT4 when the concurrency exceeds a threshold; and automatically resuming INT8 inference when the concurrency decreases. The INT4 lightweight quantization sub-model is deployed at the edge, and the INT8 combined model is deployed in the cloud. Inference nodes are allocated according to the task complexity. After the inference is completed at the edge, the feature data is uploaded to the cloud for supplementary inference.

[0015] This invention also provides an intelligent scheduling, collaborative reasoning, and security protection system for a large-scale drainage model based on Volcano Ark, comprising: The data acquisition module is used to collect monitoring data of urban drainage pipe network and construct a dataset; A lightweight pruning module, connected to the data acquisition module, is used to calculate the pruning loss function based on channel attention for the prediction model, diagnostic model, and optimization model respectively to prune channels and output a lightweight basic model. A quantization perception training module, which is connected to the lightweight pruning module, is used to perform INT4 or INT8 quantization perception training on the lightweight base model and output a quantization model. The model fusion scheduling module is connected to the quantization perception training module. It is used to construct a fusion scheduling loss function, adaptively adjust the weight coefficients of load balancing loss, inference performance loss and security isolation loss, perform fusion scheduling on the quantization model, and output a lightweight quantization combination model. The engine deployment module, which is connected to the model fusion scheduling module, is used to deploy the lightweight quantization combination model to the Volcano Ark engine scheduling module and initialize the task scheduling queue and load balancing monitoring.

[0016] In one embodiment of the present invention, it further includes: The task scheduling and load balancing module is connected to the engine deployment module. It is used to obtain the task type, model lightweighting degree and node computing power load information of the drainage scheduling task, calculate dynamic priority and allocate scheduling resources and task queues. The batch inference and cache optimization module is connected to the engine deployment module. It is used to aggregate and input monitoring data to perform batch calculations and manage the high-frequency prediction result cache queue using a time window combined with incremental update. A multi-model collaborative fusion module, which is connected to the engine deployment module, is used to call the prediction model, diagnostic model and optimization model in parallel, and generate the final scheduling decision by weighted fusion of the local decision results output by each model. The security protection module connects the data acquisition module and the engine deployment module. It is used to perform lightweight homomorphic encryption on sensitive data such as liquid level, flow rate, and water quality, generate time-sensitive tokens with limited call counts, interfaces, and validity periods, and record call logs in real time to intercept high-frequency abnormal access. The edge-cloud collaboration module and the high-concurrency adaptive tuning module are configured to deploy a lightweight INT4 quantization sub-model at the edge and a complete INT8 combined model in the cloud, and perform inference splitting and feature uploading according to task complexity. The high-concurrency adaptive tuning module is connected to the edge-cloud collaboration module and is used to monitor the number of concurrent requests, GPU utilization and inference latency in real time, and control the model accuracy to switch from INT8 to INT4 when the concurrency exceeds the threshold.

[0017] The present invention has the following beneficial effects: (1) Lightweight and highly adaptable: By combining implementation methods, the model size is reduced by more than 60% and the computing power consumption is reduced by more than 50%, which is suitable for the multi-node and low computing power deployment requirements of drainage scenarios. (2) Significantly improved scheduling efficiency: Dynamic priority scheduling and load balancing optimization reduce task response latency by more than 40% and support millisecond-level scheduling decisions; (3) Inference performance optimization: Batch computing and caching optimization strategies improve inference throughput by more than 30% and remain stable under high concurrency scenarios; (4) Multi-model collaborative precision: The result fusion mechanism solves the problem of inconsistent decision-making, and the accuracy of scheduling decisions is improved to over 95%; (5) Comprehensive security protection system: unified authentication and data isolation effectively prevent data leakage and unauthorized access, and meet the data security standards of the water industry; (6) High efficiency of edge-cloud collaboration: The inference offloading strategy reduces cloud computing power pressure by more than 30% and improves edge response speed by 50%; (7) Strong engineering feasibility: The core formula can be directly embedded into the Volcano Ark engine scheduling module, the process is standardized, and it can be quickly adapted to various drainage digital twin systems. Attached Figure Description

[0018] Figure 1 The flowchart of a method for intelligent scheduling, collaborative reasoning and security protection of a large drainage model based on Volcano Ark in one embodiment of the present invention is shown. Detailed Implementation

[0019] 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.

[0020] In this invention, the various embodiments are merely intended to illustrate the solutions of the invention and should not be construed as limiting.

[0021] 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.

[0022] 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.

[0023] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0024] Figure 1 The flowchart of a method for intelligent scheduling, collaborative reasoning and security protection of a large drainage model based on Volcano Ark in one embodiment of the present invention is shown.

[0025] like Figure 1 As shown, the method begins with S101, the data acquisition stage, where the system collects urban drainage network monitoring data in real time and constructs a standardized dataset. Then, in S102, the model lightweighting stage, structured pruning operations are performed on the prediction model, diagnostic model, and optimization model respectively, eliminating redundant channels to obtain a lightweight base model. Next, in S103, quantization-aware training, INT4 or INT8 quantization-aware training is performed on the lightweight base model, introducing quantization formulas for iterative optimization and strictly limiting the quantization range to suppress error propagation, thereby outputting a high-precision quantization model. Based on this, in S104, quantization model fusion scheduling, the system constructs a fusion scheduling loss function and dynamically adjusts the weight coefficients of load balancing loss, inference performance loss, and security isolation loss through an adaptive algorithm to implement fusion scheduling of the quantization model, ultimately generating a lightweight quantization combined model. In S105, the lightweight quantization combined model is deployed to the Volcano Ark engine scheduling module, simultaneously initializing the task scheduling queue and load balancing monitoring mechanism to complete the entire closed-loop control process. Each step is closely linked through data flow and parameter feedback to ensure the efficient operation of the drainage model in complex business scenarios.

[0026] This embodiment provides a method for intelligent scheduling, collaborative reasoning, and security protection of a large-scale drainage model based on Volcano Ark, aiming to solve the technical bottlenecks of low scheduling efficiency, insufficient quantization inference performance, and inefficient edge-cloud collaboration in existing drainage systems. The method first collects urban drainage network monitoring data to construct a dataset covering multi-dimensional parameters such as liquid level, flow rate, rainfall, and equipment status. Then, lightweight pruning is performed on the prediction model, diagnostic model, and optimization model to obtain a lightweight base model. Given the high real-time requirements of drainage operations, the lightweight base model is trained using INT4 or INT8 quantization perception, and iterative optimization using quantization formulas with limited quantization range is performed to obtain a quantized model. To balance system stability and resource utilization, a fusion scheduling loss function is constructed, adaptively adjusting the weight coefficients of load balancing loss, inference performance loss, and security isolation loss, and the quantized model is fused and scheduled to obtain a lightweight quantized combined model. Finally, the lightweight quantized combined model is deployed to the Volcano Ark engine scheduling module, initializing the task scheduling queue and load balancing monitoring to achieve integrated management of multi-model collaborative inference and security protection.

[0027] In the lightweight pruning stage, the cross-entropy loss and sparsity regularization term for the drainage prediction or diagnosis task are calculated, and a pruning loss function is constructed by combining the attention weights and feature maps of each channel. By minimizing the pruning loss function, feature channels for the drainage scenario are preserved, thereby significantly compressing the number of parameters while maintaining model accuracy. In the quantization-aware training stage, a quantization scale factor and the number of INT4 or INT8 quantization bits are set, and the forward propagation formula for the input and output features of the quantization layer is calculated based on the quantization scale factor and the number of quantization bits. To suppress quantization noise, the Clip function is used to limit the quantization range, enabling the model to maintain stable gradient propagation even in low-bit environments.

[0028] In the model fusion scheduling phase, a fusion scheduling loss function is constructed, weighted by load balancing loss, inference performance loss, and security isolation loss. The weight coefficients of each loss term are set to satisfy normalization conditions. When node computing power distribution is uneven or inference latency fluctuates, these weight coefficients are dynamically adjusted to optimize resource allocation for scheduling efficiency, performance, and security. Furthermore, the system acquires the task type, model lightweighting level, and node computing power load information for drainage scheduling tasks, calculates dynamic priorities, and allocates scheduling resources and task queues. For high-frequency monitoring data, aggregated input is used for batch computation. A time window combined with incremental updates is employed to manage the high-frequency prediction result cache queue, significantly reducing the computing power overhead caused by repeated inference. Thanks to these optimization strategies, the system can not only achieve millisecond-level scheduling decisions but also maintain stable inference throughput in high-concurrency scenarios.

[0029] In terms of deployment architecture, the system monitors the number of concurrent requests, GPU utilization, and inference latency in real time. When the concurrency exceeds a threshold, the model precision is automatically switched from INT8 to INT4, and when the concurrency decreases, INT8 inference is automatically restored. For the sake of decentralized computing power and security isolation, a lightweight INT4 quantization sub-model is deployed at the edge, while an INT8 combined model is deployed in the cloud. Inference nodes are allocated according to task complexity, and after inference is completed at the edge, feature data is uploaded to the cloud for supplementary inference. Optionally, the system adds a security protection module to perform lightweight homomorphic encryption on sensitive data such as liquid level, flow rate, and water quality, generating time-limited tokens with limited call counts, interfaces, and validity periods, and recording call logs in real time to intercept high-frequency abnormal access. Once an illegal call request is detected, the system immediately terminates the session and triggers an alarm mechanism. In summary, this embodiment, through deep collaboration between the edge and cloud, combined with dynamic precision switching and a unified security authentication system, comprehensively improves the engineering implementation capability of the large-scale drainage model in complex urban water management scenarios.

[0030] To meet the computational demands of multi-tasking and high concurrency in drainage scenarios, this invention proposes a combined implementation method of "lightweight pruning + quantization-aware training + model fusion scheduling". The core steps and formulas are as follows: (1) Lightweight model pruning formula: Automatic pruning of non-core channels is achieved using a channel attention-based pruning loss function. ; in, Cross-entropy loss for drainage prediction / diagnosis tasks; : Sparse regularization coefficient, which controls the degree of pruning; : No. Attention weights for each channel; : No. Feature mapping of each channel; Total number of channels in the model.

[0031] By minimizing This preserves key feature channels relevant to drainage scenarios, achieving a lightweight model structure.

[0032] (2) Quantization model perception training formula: INT4 / INT8 quantization is used to achieve low-bit weight quantization and error suppression. The forward propagation calculation formula is as follows: ; : Input features for the quantization layer; : Quantization layer output features; : Quantized 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.

[0033] (3) Scheduling formula for the fusion of lightweight and quantization models: To achieve optimal resource allocation for different models in scheduling, a fusion scheduling loss function is constructed: ; Load balancing loss measures the difference in computing power load among nodes. Inference performance loss, a measure of inference latency versus throughput; : Security isolation loss, measuring data isolation and authentication compliance; Weighting coefficients, satisfying ; By adaptively adjusting weights, an optimal balance between scheduling efficiency, performance, and security can be achieved.

[0034] In one embodiment of the present invention, the task scheduling and load balancing optimization method for the lightweight large model of Volcano Ark for drainage scenarios includes: Based on the implementation results of the lightweight quantization model combination, a task scheduling queue is constructed, and a dynamic priority scheduling algorithm is adopted to dynamically allocate scheduling resources according to the task type (prediction / diagnosis / optimization), the degree of model lightweighting, and the computing load, so as to achieve load balancing.

[0035] Core scheduling formula: ; Task scheduling priority; Task type weight (predictive tasks have a higher weight than diagnostic tasks). : Weight of model lightweighting degree (the higher the quantization degree, the higher the weight); Node computing power load weight (the lower the load, the higher the weight); Weighting coefficients, satisfying .

[0036] In one embodiment of the present invention, the lightweight optimization method for quantitative inference, batch computation, and result caching of drainage prediction models includes: For the quantized drainage prediction model, a batch inference and caching optimization strategy is designed: The monitoring data is aggregated in batches using a batch quantization inference formula: ; Batch input of data; Batch inference results; High-frequency prediction results are cached using a time window + incremental update caching strategy. The cache replacement formula is as follows: ; : Cache queue; : Length of time window; : Actual value; : Predicted value.

[0037] In one embodiment of the present invention, the method for intelligent scheduling decision generation and priority ranking of drainage pipe networks based on a large model includes: Based on the Volcano Ark lightweight quantitative model, multi-source monitoring data (liquid level, flow rate, rainfall, etc.) are integrated to generate intelligent scheduling decisions for drainage pipe networks, and priority is sorted according to the scope of impact and urgency.

[0038] Decision Priority Formula: ; Decision priority; : Influence range coefficient (the larger the range, the higher the coefficient); Emergency level coefficient (the higher the risk of flooding / waterlogging, the higher the coefficient). : Decision confidence coefficient (the higher the confidence level, the higher the coefficient); Weighting coefficient.

[0039] In one embodiment of the present invention, the prediction / diagnosis / optimization multi-model collaborative reasoning and result fusion method includes: A multi-model collaborative reasoning architecture is constructed, in which the prediction model provides trend prediction, the diagnostic model identifies anomalies, and the model generation scheduling scheme is optimized. The final result is obtained through weighted fusion.

[0040] Fusion formula: ; : Fusion results; : No. Model weights (prediction model) =0.5, diagnostic model =0.3, optimized model =0.2); : Predictive model results; Diagnostic model results; : Optimize model results.

[0041] In one embodiment of the present invention, the method for authentication, data isolation, and security protection of a large model for a drainage twin system includes: A unified authentication and data isolation system is constructed, employing role-based access control (RBAC) + lightweight encryption scheme: Authentication process: User / device initiates a request → Verifies identity credentials and permissions → Generates a temporary authentication token; Data isolation: Data domains are divided by department / business scenario, and lightweight homomorphic encryption formulas are used to achieve encrypted data computation. ; : Encrypted data; : Generator; : Random number; : Large prime number; Security protection: Record model call logs, set call frequency limits, and prevent illegal attacks.

[0042] In one embodiment of the present invention, the lightweight inference offloading method for large-scale drainage models with edge-cloud collaboration includes: Based on the implementation results of the lightweight quantization model combination, an edge-cloud inference offloading strategy is designed: Deploying lightweight quantization sub-models at the edge Responsible for real-time data preprocessing and simple inference; Deploy a complete lightweight quantization combination model in the cloud It is responsible for complex prediction, diagnosis, and optimization; Diversion decision formula: ; : Model to be assigned; Inference delay; Delay threshold; Model complexity threshold.

[0043] In one embodiment of the present invention, the method for adaptively optimizing the inference performance of a drainage AI model in a high-concurrency scenario includes: For high-concurrency monitoring data scenarios, an adaptive inference performance tuning mechanism is constructed to dynamically adjust batch size, quantization accuracy, and computing power allocation. Concurrency monitoring: Real-time statistics of concurrent request counts ; Batch size adjustment formula: ; Maximum batch size; Basic throughput; Target delay; Dynamic adjustment of quantization precision: When concurrency is too high, reduce the number of quantization bits (e.g., INT8→INT4) to improve inference speed.

[0044] In one embodiment of the present invention, the lightweight quantization model combination implementation and scheduling initialization based on Volcano Ark includes: Step 1: Collect monitoring data of urban drainage pipe network (liquid level, flow rate, rainfall, equipment status, etc.) and construct a dataset. , The number of samples; Step 2: Develop the prediction model Diagnostic models Optimization Model Perform lightweight pruning separately and calculate pruning losses. By cutting off non-critical channels, a lightweight basic model is obtained. , , ; Step 3: Perform INT8 quantization-aware training on the lightweight base model, according to the quantization formula. Iterative optimization yields a quantization model. , , ; Step 4: By fusing scheduling losses The weights are adaptively adjusted to obtain a lightweight quantization combination model. , , ; Step 5: Deploy the combined model to the Volcano Ark engine scheduling module and initialize the task scheduling queue and load balancing monitoring.

[0045] In one embodiment of the present invention, the optimization of drainage large-scale model task scheduling and load balancing includes: Step 1: The Volcano Ark engine receives drainage scheduling tasks in real time (such as flood season waterlogging prediction, pump station scheduling optimization, etc.) and obtains task information. ; Step 2: According to the task scheduling priority formula Calculate the priority of each task; Step 3: Based on the computing power load of each node Distribute high-priority tasks to nodes with lower loads; Step 4: Monitor node load in real time. When the load difference exceeds 20%, trigger load migration to migrate some tasks from high-load nodes to low-load nodes to achieve dynamic balancing.

[0046] In one embodiment of the present invention, quantization inference, batch computation, and cache optimization include: Step 1: Collect real-time monitoring data at the edge and aggregate it into batch data according to time windows (e.g., 1 second). ; Step 2: Input lightweight quantization prediction model Perform batch quantization inference according to the formula. Obtain batch prediction results; Step 3: Store the prediction results in a cache queue, using the cache replacement formula. Eliminate cached data that has not been used for a long time and has large errors; Step 4: When a new request requires a prediction result, retrieve it from the cache first. If the cache is not found, perform inference to improve response speed.

[0047] In one embodiment of the present invention, prediction / diagnosis / optimization multi-model collaborative inference and result fusion includes: Step 1: The Volcano Ark engine simultaneously calls the lightweight quantization prediction model. Diagnostic models Optimization Model Perform collaborative reasoning; Step 2: The prediction model outputs the predicted value of the urban flooding trend. The diagnostic model identifies anomalies and their types in the pipeline network. Optimize the model to generate schemes for pump station scheduling, gate opening and closing, etc. ; Step 3: According to the fusion formula The outputs of the three types of models are weighted and fused to obtain a unified scheduling decision result; Step 4: Input the fusion results into the decision ranking module and sort them according to the priority formula. Generate a priority list; Step 5: Output the final scheduling command and push it to the pumping station, gate, and river monitoring unit for execution to achieve collaborative closed-loop control.

[0048] In one embodiment of the present invention, large model call authentication, data isolation, and security protection include: Step 1: The user / device initiates a model call request, and the system verifies the identity, role, and scope of operation based on the RBAC role-based access control mechanism; Step 2: Employ a lightweight homomorphic encryption formula Sensitive data such as liquid level, flow rate, and water quality are encrypted during transmission and encrypted calculation to achieve data isolation; Step 3: Generate an expiration token for legitimate requests to limit the number of model calls, interfaces, and validity period, preventing unauthorized access; Step 4: Record call logs in real time to intercept high-frequency abnormal access and malicious requests, forming a closed loop of security protection.

[0049] In one embodiment of the present invention, lightweight inference offloading for edge-cloud collaboration includes: Step 1: Deploy the INT4 lightweight quantization sub-model at the edge Deploy the complete INT8 combinatorial model in the cloud ; Step 2: The system automatically allocates inference tasks according to the triage decision formula: simple real-time inference (such as data preprocessing and status monitoring) is allocated to the edge; complex inference (such as flood prediction and topology optimization) is allocated to the cloud. Step 3: After the edge device completes the inference, it uploads the feature data to the cloud, where it performs high-precision supplementary inference and decision enhancement. Step 4: Maintain millisecond-level synchronization between the edge and the cloud, support offline caching and online resume transmission, and ensure uninterrupted inference.

[0050] In one embodiment of the present invention, adaptive optimization of inference performance in high-concurrency scenarios includes: Step 1: Real-time monitoring of the number of high-concurrency requests during the flood season GPU utilization and inference latency; Step 2: Adjust the formula according to batch size Dynamically adjust batch processing scale; Step 3: When the concurrency exceeds the threshold, automatically switch the model precision from INT8 to INT4 to improve inference speed; Step 4: Automatically resume high-precision inference when concurrency decreases, achieving the optimal balance between speed and accuracy.

[0051] In one embodiment of the invention, the system adopts a modular, layered design, with a data acquisition module, a lightweight pruning module, a quantization-aware training module, a model fusion scheduling module, and an engine deployment module distributed sequentially from top to bottom. The data acquisition module, located at the bottom layer of the system, is responsible for aggregating multi-source monitoring data such as liquid level, flow rate, and rainfall, and constructing a structured dataset. Its output is directly coupled to the input of the lightweight pruning module. The lightweight pruning module integrates a channel attention evaluation unit to calculate a feature-mapping-based pruning loss function. By filtering and retaining key channels in the drainage scenario layer by layer, it generates a lightweight basic model and passes it to the quantization-aware training module. The quantization-aware training module is connected to the lightweight pruning module and mainly performs quantization scaling factor calculation and forward propagation simulation, outputting an INT4 or INT8 quantization model. After receiving the quantized model, the model fusion scheduling module incorporates a multi-objective optimizer to construct a fusion scheduling loss function. Through real-time feedback, it adjusts the weights of each loss term to complete model parameter alignment. The engine deployment module, located at the top layer of the system and directly connected to the model fusion scheduling module, is responsible for loading the optimized lightweight quantization combination model into the Volcano Ark engine and initiating task queue management and load monitoring functions. The modules interact with each other through a standard data bus, forming an end-to-end automated processing link.

[0052] The multi-model collaborative fusion module internally deploys a prediction model, a diagnostic model, and an optimization model in parallel. These three models independently perform local feature extraction and decision generation, and then the output results are merged into a weighted fusion unit to generate the final scheduling instructions. At the computing power allocation level, the system divides into edge inference nodes and cloud inference nodes. Edge inference nodes deploy the INT4 lightweight quantization sub-model, prioritizing high-concurrency, low-latency real-time monitoring tasks; cloud inference nodes carry the INT8 combined model, responsible for complex topology deduction and global policy optimization. Two-way data synchronization is established between the two through a feature upload channel. After initial inference is completed at the edge, high-dimensional feature data is transmitted along the channel to the cloud for supplementary calculations. Furthermore, a high-concurrency adaptive tuning module spans between the edge and the cloud, monitoring GPU utilization and inference latency in real time. Once a concurrency threshold is triggered, the model accuracy is dynamically switched. This topology effectively achieves on-demand distribution of computing resources and precise alignment of inference results.

[0053] 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 intelligent scheduling, collaborative reasoning, and security protection of a large-scale drainage model based on Volcano Ark, characterized in that, include: Collect monitoring data from urban drainage pipe networks to construct a dataset; Lightweight pruning was performed on the prediction model, diagnostic model, and optimization model to obtain a lightweight base model. The lightweight basic model is trained with INT4 or INT8 quantization awareness, and the quantization formula is iteratively optimized and the quantization range is limited to obtain the quantization model. A fusion scheduling loss function is constructed, and the weight coefficients of load balancing loss, inference performance loss, and security isolation loss are adaptively adjusted. The quantization model is then fused and scheduled to obtain a lightweight quantization combination model. The lightweight quantization combination model is deployed to the Volcano Ark engine scheduling module to initialize the task scheduling queue and load balancing monitoring.

2. The method according to claim 1, characterized in that, The lightweight pruning of the prediction model, diagnostic model, and optimization model to obtain the lightweight base model includes: Calculate the cross-entropy loss and sparse regularization term for drainage prediction or diagnostic tasks; A pruning loss function is constructed by combining the attention weights of each channel with the feature maps; The feature channels for the drainage scenario are preserved by minimizing the pruning loss function.

3. The method according to claim 1, characterized in that, The step of performing INT4 or INT8 quantization-aware training on the lightweight base model, iteratively optimizing it using the quantization formula, and limiting the quantization range to obtain the quantization model includes: Set the quantization scale factor and the number of INT4 or INT8 quantization bits; The forward propagation formulas for the input and output features of the quantization layer are calculated based on the quantization scale factor and the number of quantization bits. The Clip function is used to limit the quantization range to suppress quantization errors.

4. The method according to claim 1, characterized in that, The construction of the fusion scheduling loss function, adaptively adjusting the weight coefficients of load balancing loss, inference performance loss, and security isolation loss, and performing fusion scheduling on the quantization model to obtain the lightweight quantization combination model includes: Construct a fusion scheduling loss function that is weighted by load balancing loss, inference performance loss, and security isolation loss; Set the weight coefficients of each loss term to satisfy the normalization condition; The weighting coefficients are dynamically adjusted to optimize resource allocation in terms of scheduling efficiency, performance, and security.

5. The method according to claim 1, characterized in that, Also includes: Obtain the task type, model lightweighting level, and node computing load information of the drainage scheduling task, calculate the dynamic priority, and allocate scheduling resources and task queues.

6. The method according to claim 1, characterized in that, Also includes: The monitoring data is aggregated and input to perform batch calculations. A time window combined with incremental update method is used to manage the cache queue of high-frequency prediction results.

7. The method according to claim 1, characterized in that, Also includes: Predictive models, diagnostic models, and optimization models are deployed in parallel. Each model independently outputs local decision results, and the final scheduling decision is generated through a weighted fusion mechanism.

8. The method according to claim 1, characterized in that, Also includes: Real-time monitoring of concurrent request count, GPU utilization, and inference latency; when concurrency exceeds the threshold, the model precision is automatically switched from INT8 to INT4; when concurrency decreases, INT8 inference is automatically restored. The INT4 lightweight quantization sub-model is deployed at the edge, and the INT8 combined model is deployed in the cloud. Inference nodes are allocated according to the task complexity. After the inference is completed at the edge, the feature data is uploaded to the cloud for supplementary inference.

9. A large-scale drainage model intelligent scheduling, collaborative reasoning, and security protection system based on Volcano Ark, characterized in that, include: The data acquisition module is used to collect monitoring data of urban drainage pipe network and construct datasets; A lightweight pruning module, connected to the data acquisition module, is used to calculate the pruning loss function based on channel attention for the prediction model, diagnostic model, and optimization model respectively to prune channels and output a lightweight basic model. A quantization perception training module, which is connected to the lightweight pruning module, is used to perform INT4 or INT8 quantization perception training on the lightweight base model and output a quantization model. The model fusion scheduling module is connected to the quantization perception training module. It is used to construct a fusion scheduling loss function, adaptively adjust the weight coefficients of load balancing loss, inference performance loss and security isolation loss, perform fusion scheduling on the quantization model, and output a lightweight quantization combination model. The engine deployment module, which is connected to the model fusion scheduling module, is used to deploy the lightweight quantization combination model to the Volcano Ark engine scheduling module and initialize the task scheduling queue and load balancing monitoring.

10. The system according to claim 9, characterized in that, Also includes: The task scheduling and load balancing module is connected to the engine deployment module. It is used to obtain the task type, model lightweighting degree and node computing power load information of the drainage scheduling task, calculate dynamic priority and allocate scheduling resources and task queues. The batch inference and cache optimization module is connected to the engine deployment module. It is used to aggregate and input monitoring data to perform batch calculations and manage the high-frequency prediction result cache queue using a time window combined with incremental update. A multi-model collaborative fusion module, which is connected to the engine deployment module, is used to call the prediction model, diagnostic model and optimization model in parallel, and generate the final scheduling decision by weighted fusion of the local decision results output by each model. The security protection module connects the data acquisition module and the engine deployment module. It is used to perform lightweight homomorphic encryption on sensitive data such as liquid level, flow rate, and water quality, generate time-sensitive tokens with limited call counts, interfaces, and validity periods, and record call logs in real time to intercept high-frequency abnormal access. The edge-cloud collaboration module and the high-concurrency adaptive tuning module are configured to deploy a lightweight INT4 quantization sub-model at the edge and a complete INT8 combined model in the cloud, and perform inference splitting and feature uploading according to task complexity. The high-concurrency adaptive tuning module is connected to the edge-cloud collaboration module and is used to monitor the number of concurrent requests, GPU utilization and inference latency in real time, and control the model accuracy to switch from INT8 to INT4 when the concurrency exceeds the threshold.

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