Fast Retrieval of Hydropower Interaction Large Models Based on Long-Sequence Temporal Embedding

Cao Chunlan¹, Zheng Zhiwen¹, Zhang Feng², Liu Jiarong², Jiang Hao³, Zheng Wenhao², Chen Fuhai²*

Abstract

The safety early warning and optimal scheduling of hydropower systems rely on efficient analysis of long-term, high-precision temporal monitoring data (such as minute-level dam water levels, inflow rates, etc.). However, existing monitoring modes still rely primarily on manual retrieval, suffering from lagged response (missing critical event handling windows) and insufficient accuracy (relying on empirical judgment). Traditional temporal embedding models (e.g., TS2Vec) are constrained by fixed context windows of limited scope, making it difficult to capture long-cycle characteristics of hydropower systems, resulting in low recall rates for critical events such as rainstorms and failing to meet intelligent monitoring requirements. To address this, we propose HTE, a long-sequence temporal embedding retrieval framework fused with positional encoding, aiming to construct a large model-based hydropower interaction data monitoring system that enables efficient and precise data retrieval through natural language dialogue. The core designs of this framework include: (1) constructing the first hydropower temporal retrieval benchmark dataset, HydroT-Bench, encompassing 12k sequence-query pairs (11,293 multi-parameter temporal data series and 5,000 engineering-level queries); (2) innovating a hydropower cycle-aware dynamic NTK interpolation mechanism, combined with Rotary Positional Encoding (RoPE), to extend the context window to 32k points while balancing long-cycle feature capture and local detail preservation; and (3) designing a text-temporal cross-modal embedding space to support direct association between natural language queries and long temporal data. Experimental results demonstrate that in 32k sequence retrieval, critical event Recall@10 reaches 91%, a 68% improvement over TS2Vec; long-cycle pattern matching RMSE drops to 0.13; and inference latency for a single annual sequence of 1,425 points is less than 100 seconds. This framework provides minute-level or even second-level retrieval capabilities for intelligent hydropower monitoring, with open-source datasets and pre-trained models offering support for domain research.

Keywords: Large Models; Temporal Embedding; Hydropower Data Monitoring

Introduction

As the core pillar of the clean energy system, the safe and stable operation of hydropower projects directly relates to watershed flood control, energy supply, and ecological protection. Intelligent monitoring of such projects heavily depends on long-term, high-frequency, multi-dimensional temporal monitoring data—from minute-level dam water levels and inflow rates to real-time changing power generation flow and spillage flow. These data constitute the "nerve endings" for perceiving hydropower system states. For instance, a sudden increase in inflow rate may indicate flood risks from upstream rainstorms, while minute fluctuations in dam water level may imply structural safety hazards. Capturing and interpreting these signals requires precise correlation analysis of continuous temporal data spanning months or even years.

However, current hydropower monitoring still predominantly relies on traditional manual modes: dispatchers must manually filter and compare massive temporal curves across multiple terminal systems, judging abnormal patterns through experience. This approach has significant limitations: on one hand, a single hydropower station generates tens of thousands of temporal data points daily, making manual processing unable to achieve minute-level or even second-level response, easily missing the golden handling window for sudden critical events like rainstorms or equipment failures; on the other hand, manual analysis depends on individual experience, showing insufficient consistency in capturing cross-cycle correlation features (such as the implicit coupling between spillage volume in wet seasons and power generation in dry seasons), severely constraining the intelligent upgrading of hydropower systems.

To address these pain points, this paper aims to construct a large model-based hydropower interaction data monitoring system that achieves an efficient retrieval mode of "replacing operations with dialogue" through cross-modal interaction between natural language and temporal data. Specifically, users can directly locate target information from massive temporal data through natural language queries (e.g., "Dates in 2023 flood season when daily maximum inflow exceeded 500m³/s" or "Time periods when upstream water level rose continuously for 3 hours"). This mode breaks the passive situation of "people adapting to systems" in traditional monitoring, enabling systems to actively understand human intentions, substantially improving the efficiency and accuracy of anomaly identification and trend prediction, and providing "ask-and-answer" intelligent support for dispatching decisions.

The core challenge in achieving this goal lies in efficient retrieval and key information extraction from long-sequence temporal data. Hydropower system temporal data exhibits significant characteristics of "long-cycle, strong coupling, and dispersed events": cyclically, it contains multi-scale patterns including intra-day (e.g., power generation flow fluctuations with electricity load), intra-month (e.g., reservoir storage and release regulation), intra-year (e.g., wet and dry season alternation), and even inter-annual patterns; regarding critical event distribution, important signals such as water level surges caused by rainstorms or flow mutations caused by equipment failures are often dispersed across long sequences of tens of thousands of points, with low distinguishability from normal fluctuations.

Existing temporal embedding models struggle to handle such scenarios. Taking the typical model TS2Vec as an example, its fixed 512-point context window can only cover about 8 hours of minute-level data, unable to capture cross-month or cross-season long-cycle correlations, resulting in only 28% retrieval recall rate for critical events like rainstorms—numerous abnormal signals hidden in the middle segments of long sequences are missed due to being "outside the window" [1]. Other methods like piecewise aggregation can extend processing length but lose local details; sparse attention mechanisms struggle to balance computational efficiency and feature retention, all failing to meet the dual demands of "long-cycle coverage + fine-grained identification" in hydropower monitoring.

To break through this bottleneck, this paper proposes the first long-sequence temporal embedding retrieval framework for the hydropower domain (Hydro-Temporal Embedding, HTE). The framework's core design philosophy is "extending context windows, combining cycle-aware dynamic NTK interpolation with RoPE positional encoding, and achieving cross-modal interaction": addressing data scale and retrieval needs, we construct the first hydropower temporal retrieval benchmark dataset HydroT-Bench, covering 11,293 multi-parameter long temporal series and 5,000 engineering-level queries, providing standardized support for model training and evaluation; we innovatively introduce a cycle-aware dynamic NTK interpolation mechanism combined with Rotary Positional Embeddings (RoPE) to extend the context window from 512 points to 32k points, capturing both inter-annual long-cycle patterns and local features of minute-level data; through text-temporal cross-modal embedding design, we enable direct association between natural language queries and long temporal data, allowing engineering problems like "retrieving spillage flow peaks in a certain period" or "comparing power generation efficiency for the same period across two years" to be solved quickly through conversational interaction.

The core contributions of this study are fourfold: (1) Pioneering a new domain problem: For the first time, we apply long-sequence temporal embedding retrieval technology to hydropower monitoring scenarios, clarifying the technical requirements for "natural language-temporal data" cross-modal interaction and filling the domain gap; (2) Constructing a benchmark dataset: We release HydroT-Bench, containing 12k sequence-query pairs covering 8 core monitoring parameters and 8 engineering query templates, providing a reusable evaluation benchmark for subsequent research; (3) Proposing the innovative HTE framework: Through the fusion of dynamic NTK interpolation and RoPE encoding, we break through context limitations in long-sequence retrieval, enabling efficient processing of 32k-point data; (4) Verifying practical effectiveness: Experiments show the framework achieves 91% recall rate Recall@10 in critical event retrieval (68% improvement over TS2Vec), reduces long-cycle pattern matching RMSE to 0.13, and maintains inference latency for a single annual sequence under 100 seconds, fully demonstrating its practicality in industrial scenarios.

2.1 Time Series Representation Learning

Time series representation learning aims to transform raw temporal data into low-dimensional, information-rich vector representations. General temporal embedding models such as TS2Vec [1] generate segment-level embeddings through hierarchical contrastive learning, but their maximum context window is limited to 512 points, making it difficult to capture important dependencies spanning thousands of points in ultra-long sequences and unable to model long-cycle dependencies exceeding 8k points in hydropower scenarios (such as multi-year water level-power generation correlations). Similarly, TS-TCC [2] relies on temporal consistency contrast, and CoST [3] combines frequency domain decomposition to improve robustness, but both are constrained by inherent short-context modeling mechanisms and fail to break through the short-context bottleneck. Attempts for long sequences include: piecewise aggregation methods [4] compress sequences for dimensionality reduction but lose critical temporal details and local patterns; sparse attention mechanisms like Longformer [5] significantly reduce computational complexity, yet when processing ultra-long sequences exceeding 4k points, their sparse patterns still struggle to effectively maintain the ability to capture fine-grained, localized features.

2.2 Long Context Positional Encoding Technology

Traditional absolute positional encodings such as Sinusoidal [6] suffer from severe extrapolation degradation (positional representation distortion beyond training length). Relative positional encoding schemes like T5 Bias [7] and ALiBi [8] improve extrapolation by introducing bias terms proportional to relative distance, but their preset static distance decay patterns are insufficiently adaptable to periodic temporal fluctuations such as daily/quarterly cycles. Rotary Positional Encoding (RoPE) [9] introduces rotation matrices to maintain linear dependency of relative positional relationships, which has been verified to possess excellent extrapolation capabilities in Transformer-based language models, but its effectiveness in long-range modeling of complex periodicity-dominated multivariate industrial temporal data remains lacking systematic verification.

2.3 Context Window Extension Methods

Positional Interpolation (PI) [10] achieves window extension by compressing position indices but introduces high-frequency information loss. NTK-aware Scaling [11] dynamically adjusts base frequency based on neural tangent kernel theory, significantly outperforming fixed interpolation, but its scaling factor is a static hyperparameter that cannot adapt to the time-varying periodicity requirements determined by wet and dry seasons in hydropower data. Such methods have not been validated in hydropower monitoring scenarios.

Positional Interpolation (PI) [10] extends the window by uniformly compressing position indices, but this introduces significant high-frequency component loss due to excessive downsampling. NTK-aware Scaling [11] dynamically expands the frequency domain distribution based on neural tangent kernel theory, demonstrating significantly superior performance compared to fixed interpolation; however, its preset global scaling factor is a static hyperparameter that struggles to adapt to the dynamically changing periodic demands driven by strong seasonality and determined by wet/dry periods in hydropower data. Currently, the specific effectiveness of such advanced context extension methods in complex hydropower monitoring scenarios remains unverified.

2.4 Hydropower Temporal Intelligent Analysis

Traditional methods rely on statistical features such as mean and variance along with machine learning models like LSTM [12] and TCN [13] for prediction or anomaly detection, but most involve single-task independent modeling (e.g., some literature only predicts water level [14]), lacking end-to-end long-sequence semantic retrieval capabilities. More critically, the hydropower domain lacks publicly available large-scale retrieval datasets, with existing work only validated on private small data, constraining algorithm reproducibility and public benchmarking.

This study's positioning is shown in Table 1 [TABLE:1]. Existing work has three major limitations: (1) Model limitations: General embedding models like TS2Vec are constrained by 512-point windows, while long-sequence methods like NTK interpolation neglect hydropower periodic characteristics; (2) Task limitations: Hydropower research focuses on prediction/classification without specialized retrieval frameworks; (3) Data limitations: No publicly available long-sequence retrieval benchmarks exist. HTE pioneers the integration of dynamic NTK interpolation with RoPE encoding and constructs the first hydropower retrieval dataset, filling these gaps.

Table 1: Comparison of Related Work and Our Innovations
| Representative Methods | Limitations in Hydropower Scenarios | Our Innovation |
|------------------------|-------------------------------------|----------------|
| Temporal Embedding Models (TS2Vec [1]) | Window ≤ 512 points | Extended to 32k points |
| Sinusoidal NTK-aware Scaling [11] LSTM [12] | Static scaling factor | RoPE rotation encoding retention |
| Hydropower cycle-aware dynamic NTK interpolation | Single-task prediction, no retrieval | Text-temporal cross-modal retrieval |
| Domain Dataset | No public retrieval dataset | Release HydroT-Bench (12k samples) |

The core workflow of the HTE framework consists of three interconnected components, as shown in Figure 1 [FIGURE:1]: First, the input module receives multivariate long-term hydrological data, typically collected by various sensors in watersheds or hydraulic projects, forming a set of time series signals with long-term dependencies. Typical variables include but are not limited to: upstream dam water level (reflecting water storage conditions), downstream water level (reflecting river conveyance capacity or backwater effects), inflow rate (total water entering the reservoir), outflow rate (total water released from the reservoir, possibly including flood discharge and water transfer), power generation flow rate (flow specifically used for turbine power generation), etc. These multi-source heterogeneous yet highly correlated time series are then fed into the temporal adaptive encoder module, the core processor of the framework, which uses adaptive encoders to learn complex spatiotemporal dynamic associations and nonlinear features among multivariate sequences with different frequencies, dimensions, and change patterns, extracting high-dimensional, information-rich hidden states. Finally, features refined by the adaptive encoder are mapped and output to a unified semantic embedding space module, which serves as a shared, low-dimensional vector representation environment to generate dense, semantically rich embedding vectors for all input variables and their interactions. These embeddings capture not only each variable's historical evolution patterns but, more importantly, encode their cross-variable, cross-timestep intrinsic relationships and hydrological physical meanings, providing a powerful, unified feature foundation for downstream tasks (such as forecasting, anomaly detection, pattern recognition, etc.).

3.1 HTE Overall Framework

Leveraging the strong semantic understanding capability of the text embedding model E5, we introduce RoPE positional encoding adapted to hydropower temporal features to construct a cross-modal retrieval bridge from text to temporal data. The text encoder adopts the E5-base pre-trained model (12-layer Transformer) as the backbone. Input Layer: Converts natural language queries q such as "What was the maximum inflow rate in July 2023?" into token sequences; replaces the original positional encoding with the RoPE base framework (Equation (1)) and integrates hydropower cycle-aware dynamic NTK interpolation to achieve window extension, enhancing positional awareness for long queries exceeding 512 tokens. A temporal alignment layer adds a projection network to map text embeddings into the temporal space.

For time step t corresponding to dimension d output, for the i-th group of rotation angle base frequencies that decay with dimension index i, where d is the total vector dimension, i is the rotation dimension group index (0≤i