Reservoir emergency prediction and management method and system based on cloud data, and electronic equipment

Abstract

The invention relates to the technical field of data processing, discloses a reservoir emergency prediction and management method and system based on cloud data, and electronic equipment, and is used for solving the problem that the reservoir emergency prediction precision is reduced due to the fact that a prediction model in a traditional method is insufficient in generalization ability when processing a complex environment and an extreme event. According to the method, firstly, multi-source data of meteorology, hydrology, remote sensing, historical disasters, geology and the like are acquired through a cloud data access gateway, space-time grids and time granularity are unified, a complex environment feature subset and an extreme event index subset are constructed, an environment variation mode is mined, an extreme sample is enhanced, an extended training set is formed, and hierarchical verification is carried out; a deviation mode knowledge base is extracted and coupled with prediction logic, self-adaptive correction and risk deduction are performed on real-time data, flood discharge scheduling, storage capacity control, personnel transfer, material allocation and other instructions are automatically generated and issued through multiple channels, and therefore prediction precision and emergency decision reliability under the conditions of complex environments and extreme events are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method, system, and electronic equipment for reservoir emergency prediction and management based on cloud data. Background Technology

[0002] Reservoirs, as important water conservancy infrastructure, play a crucial role in flood control, water supply, and ecological protection. With climate change and the increasing frequency of extreme weather events, emergency forecasting and management of reservoirs have become a core requirement for ensuring public safety. Traditional methods rely on cloud data platforms to integrate multi-source information, such as meteorological data, remote sensing images, and real-time monitoring data, to predict and manage reservoir water levels, floods, and geological disasters. However, existing forecasting models have significant limitations in handling complex environments and extreme events, leading to reduced accuracy in emergency forecasting and failing to meet practical needs. For example, CN103489053A discloses an intelligent water resource management platform based on cloud computing and expert systems. This platform collects data through field monitoring stations and the Internet of Things (IoT), and uses cloud computing for real-time analysis and expert decision-making to support emergency prediction of reservoirs. Although this method achieves data sharing and preliminary prediction, its model relies on fixed rules and historical data. When facing complex environments (such as variable terrain and sudden geological changes), its generalization ability is insufficient, and it is prone to overfitting or prediction bias. Especially under extreme events (such as cascading floods caused by rainstorms), the accuracy drops significantly and it cannot effectively adapt to dynamic variables. CN116819025B discloses a water quality monitoring system and method based on the Internet of Things. This system integrates a big data cloud platform and maintenance terminals to achieve real-time monitoring and emergency early warning of reservoir water quality parameters. Through multi-source data fusion, this method improves the timeliness of prediction. However, its prediction model also faces generalization problems in complex environments. For example, in extreme scenarios with sparse data or noise interference, the algorithm has poor robustness, resulting in reduced emergency prediction accuracy and an inability to accurately simulate the interaction of multiple parameters, such as the cascading effect of water pollution coupled with floods. The common shortcoming of these existing technologies is that the predictive models lack the ability to generalize to complex environments and extreme events. Specifically, existing neural network or regression models (such as LSTM or support vector machines) struggle to capture the nonlinear relationships between high-dimensional variables under limited or non-stationary training data, leading to increased prediction errors in real-world reservoir scenarios, with the coefficient of determination fluctuating by 10%–15%. This not only affects the accuracy of emergency response but may also lead to decision-making errors, causing economic losses and safety hazards. Therefore, a reservoir emergency prediction and management method, system, and electronic equipment based on cloud data are needed to address the insufficient generalization ability of existing predictive models under complex environments and extreme events, and to achieve higher prediction accuracy and more reliable management decisions. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method, system, and electronic equipment for reservoir emergency prediction and management based on cloud data. This addresses the problem that traditional methods suffer from insufficient generalization ability of prediction models when dealing with complex environments and extreme events, leading to reduced accuracy in reservoir emergency prediction.

[0004] To achieve the goals of higher prediction accuracy and more reliable management decisions mentioned in the background section, this invention provides the following technical solution: Cloud-based methods for reservoir emergency prediction and management include: S1. Collect relevant cloud data of the reservoir, obtain real-time meteorological data, water level monitoring data and remote sensing image data from the cloud platform, and construct an initial dataset by combining historical disaster records and geological parameter data; S2. Classify and organize the initial dataset, group the data according to time series and spatial distribution, divide it into complex environmental characteristic data and extreme event indicator data according to the type of environmental variable, and perform standardization processing on each type of data. S3. Based on the classified and organized dataset, construct an extended training set, extract environmental variation patterns from complex environmental feature data, screen highly variable samples from extreme event index data, and combine these samples with the initial dataset. S4. Implement stratified validation on the extended training set by dividing the dataset into a normal environment subset and a complex extreme environment subset, and conduct cross-validation within each subset. S5. Adjust the prediction logic based on the results of hierarchical verification, identify deviation patterns under complex environments and extreme events, integrate these patterns into the main prediction process through iterative feedback, and gradually optimize the overall logical structure. S6. Apply the adjusted prediction logic to generate management output, import real-time input data into the optimized logic flow, and calculate emergency scenario parameters according to the flow sequence to form a reservoir management instruction sequence.

[0005] Preferably, relevant cloud data about the reservoir is collected, including real-time meteorological data, water level monitoring data, and remote sensing image data obtained from the cloud platform. This data is then combined with historical disaster records and geological parameter data to construct an initial dataset, including: Configure a distributed data access gateway in the cloud to establish encrypted communication channels with meteorological, hydrological, remote sensing, historical disaster and geological data sources and collect multi-source data; The collected data is processed to unify timestamps and align spatial coordinates, and real-time streaming data and historical batch data are encoded into a structured initial dataset according to a preset time step and spatial grid. The initial dataset is partitioned according to the time dimension and watershed identifier, written to distributed object storage, and its time range, spatial range, number of samples and feature dimensions are recorded for each partition; The metadata records the local relief thresholds, extreme event frequency thresholds, and intensity thresholds determined based on digital elevation models, long-term disaster records, and extreme meteorological and hydrological statistics, and describes the complex environmental markings of the grid cells.

[0006] Preferably, the initial dataset is categorized and organized, grouped according to time series and spatial distribution, and divided into complex environmental characteristic data and extreme event indicator data based on the type of environmental variable. Standardization processing is then performed on each type of data, including: The classification and organization process is triggered by the cloud-based data classification and organization module after receiving the initial dataset, and computing and storage resources are dynamically allocated based on elastic computing capabilities. Data is organized in multiple granularities in the time dimension, and multi-layer sequences ranging from fine granularity to daily cycles are generated according to a preset aggregation window. In the spatial dimension, multi-source information is grouped and mapped according to a grid. Thresholds are extracted based on the statistical distribution of long-term observation data to generate environmental complexity labels for each grid, and extreme event markers are constructed by scanning at multiple time scales based on extreme factors. The system standardizes numerical features, independently encodes categorical features, handles missing values ​​through interpolation and sample supplementation, and divides complex environment feature subsets and extreme event indicator subsets according to labels, attaching corresponding metadata to each subset.

[0007] Preferably, an expanded training set is constructed based on the categorized and organized dataset to extract environmental variation patterns from complex environmental feature data, including: Environmental variation pattern mining is performed on a subset of complex environmental features. The grid cells marked as environmentally complex are traversed sequentially by grid number, and the feature vectors in each time window are scanned at multiple time granularities. By combining and analyzing relevant indicators, when all relevant indicators exceed the threshold set based on long-term statistical distribution and industry standards within a certain time window, that time window is determined to be the interval corresponding to the environmental variation pattern. The system records the variation patterns of each environment in vector form and constructs a high-variability feature library based on this.

[0008] Preferably, highly variable samples are screened from extreme event index data and these samples are combined with the initial dataset, including: Sample augmentation is performed on a subset of extreme event indicators. First, the number distribution of normal scenario samples and extreme scenario samples is statistically analyzed, and then highly variable samples are selected from them. The key evolution segments of extreme events are replicated, random perturbations are applied to preset driving factors to generate synthetic samples, the synthetic samples are aligned with the original events on the time axis and spatial grid index, and an enhanced set of extreme events is constructed. The complex environment high-variability feature library and extreme event augmentation set are aligned and vertically stitched with the initial dataset according to grid number and time order to form an expanded training set. The extended training set is stored in a columnar format in a cloud distributed file system, partitioned according to time dimension and scene type, and feature statistics and sample proportion metadata are recorded for each partition.

[0009] Preferably, stratified validation is implemented on the expanded training set, dividing the dataset into a normal environment subset and a complex extreme environment subset, and cross-validation is performed within each subset, including: The extended training set is divided into training set, validation set and test set according to a preset ratio, and the proportion of the three types of samples is balanced by adding or removing normal scene samples, complex environment samples and extreme event samples. A preliminary prediction framework is constructed using the training set, the sample sequences are organized by batch, the correlation between features is extracted, and the correlation is solidified into a hierarchical prediction structure. The validation set is subdivided into a normal environment subset, a complex environment subset, and a complex extreme mixed subset, and cross-validation is used in each subset, while maintaining temporal order and spatial continuity during the cross-partitioning process; Calculate metrics such as time series matching degree and spatial grid accuracy, summarize the metric vectors of each fold for fluctuation analysis, and generate a structured report.

[0010] Preferably, the prediction logic is adjusted based on the results of hierarchical validation, bias patterns under complex environments and extreme events are identified, and these patterns are integrated into the main prediction process through iterative feedback, gradually optimizing the overall logical structure, including: Based on the index distribution in the stratified verification report, an error threshold is set, and samples with scores below the threshold in complex environments and extreme subsets are marked as high-error samples. Tracing the high-error sample identifiers and environmental characteristics, extracting the deviation vector to generate structured entries, and writing them into the key-value database after deduplication and merging; The knowledge base is loaded at the prediction front end, and multi-dimensional matching of real-time feature vectors is performed. When the bias pattern is hit, the correction path is used for compensation, and when the pattern is not hit, the general path is used. Periodically trigger iterative tasks, compare samples with observations to update deviation entries and decay expired records, atomically release new versions and record audit logs.

[0011] Preferably, the adjusted prediction logic is used to generate management output, importing real-time input data into the optimized logic flow, and calculating emergency scenario parameters according to the flow sequence to form a reservoir management instruction sequence, including: Real-time data is subjected to protocol identification and integrity verification, and meteorological, water level, gate status and remote sensing data are parsed into standard event streams and feature vectors are generated according to standardization. The feature vector is fed into the adaptive kernel, and the bias pattern is matched according to the grid and time. When a match is found, a correction offset is applied; otherwise, prediction is made along the general path, and a risk distribution matching plan is calculated. The system generates instruction sequences, standardizes the arrangement of various scheduling and early warning instructions, and attaches a decision basis chain and digital signature; The push service distributes instructions to terminals based on geofencing and permission matrix, records delivery status and writes feedback back to the database, triggers a second push when timeout, and periodically archives instruction sequences, test data and feedback information.

[0012] Preferred cloud-based reservoir emergency prediction and management system includes: Data acquisition module: responsible for acquiring meteorological, water level and remote sensing data from the cloud platform in real time, and integrating historical disaster records and geological parameters to form an initial dataset for subsequent processing; Data classification and organization module: Groups the initial dataset by time series and spatial grid, marks complex regions and extreme event periods according to environmental complexity indicators, performs standardization processing, and outputs structured subsets; Extended training set construction module: Extracts environmental variation patterns based on the cleaned data subset, generates synthetic samples based on this, and concatenates the synthetic samples with the initial dataset vertically to form an extended training set covering normal, complex and extreme scenarios; The stratified validation module performs subset partitioning of the extended training set and conducts stratified cross-validation, calculates various performance indicators and records their fluctuations, and generates a validation report. Prediction logic adjustment module: Extract deviation patterns based on the error distribution recorded in the hierarchical verification report, build and update the deviation pattern knowledge base, and dynamically correct the main prediction logic through online feedback loop and periodic iterative tasks; Management output generation module: It is used to process real-time data based on the optimized prediction logic, and sequentially complete feature matching, deviation correction, risk calculation and emergency simulation, generate management instruction sequence and push it to various terminals.

[0013] Preferably, the cloud-based reservoir emergency prediction electronic device includes: Processor: The core computing unit, used to perform data acquisition, classification and organization, training set construction, hierarchical validation, prediction logic adjustment and output generation management steps; Temporary storage space: used to cache the initial dataset, various subsets, and validation reports; Storage devices: used for persistent storage of the initial dataset, expanded training set, and bias pattern knowledge base; Communication interface: Used to support MQTT, HTTPS, 5G, and BeiDou short message communication methods to realize multi-source data access, management command push and multi-terminal distribution; Input / output interface: Used for data exchange with water level monitoring sensors, meteorological data acquisition equipment and management terminals.

[0014] Compared with existing technologies, this invention provides a method, system, and electronic equipment for reservoir emergency prediction and management based on cloud data, which has the following beneficial effects: 1. This invention constructs a unified spatiotemporal grid and multi-timescale data foundation covering the reservoir area and its upstream and downstream regions. Real-time meteorological, hydrological, remote sensing, historical disaster, and geological parameters are co-encoded into a structured initial dataset. Environmental complexity labels and extreme event markers are used to differentiate and finely organize complex environmental characteristics and extreme event indicators. Based on this, an extended training set covering normal, complex, and extreme scenarios is constructed through environmental variation pattern mining and extreme event sample enhancement. High-error samples are filtered through hierarchical cross-validation and deposited into a bias pattern knowledge base. This knowledge base is then mounted to the main prediction logic front-end. Combined with online matching and periodic iterative maintenance, an adaptively corrected prediction kernel is formed. Simultaneously, an emergency plan library and scheduling constraints are linked on top of this kernel. The prediction results are automatically compiled into various reservoir management instructions and distributed to terminals at all levels through permission and auditing mechanisms. This improves the accuracy of reservoir emergency prediction and the reliability of management decisions under complex environmental and extreme event conditions, solving the problem of insufficient generalization ability of prediction models in traditional methods when dealing with complex environments and extreme events, which leads to reduced accuracy in reservoir emergency prediction.

[0015] 2. This invention constructs an integrated data governance architecture in the cloud. This architecture encompasses the access of multi-source hydrological, meteorological, and remote sensing geological data, unified storage partitioning and metadata tagging, as well as the construction of extended training sets, hierarchical verification, and iterative deviation knowledge base. It also connects the prediction kernel with the emergency plan library, permission system, and push channel, enabling the automatic generation and distribution of flood discharge scheduling, reservoir capacity control, personnel transfer, and material allocation instructions under a unified framework. At the same time, a complete audit chain is formed through delivery confirmation, execution feedback, and regular archiving, thereby achieving closed-loop linkage and standardized management of reservoir emergency prediction operations across the entire process of data, models, and command. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the process of the reservoir emergency prediction and management method, system and electronic equipment based on cloud data of the present invention; Figure 2 This is a schematic diagram of the reservoir emergency prediction and management method, system, and electronic equipment modules based on cloud data according to the present invention; Figure 3 This is a schematic diagram of the reservoir emergency prediction and management method, system, and electronic equipment structure based on cloud data according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1: Figure 1 A method for reservoir emergency prediction and management based on cloud data is presented, including: S1. Collect relevant cloud data of the reservoir, obtain real-time meteorological data, water level monitoring data and remote sensing image data from the cloud platform, and construct an initial dataset by combining historical disaster records and geological parameter data; S2. Classify and organize the initial dataset, group the data according to time series and spatial distribution, divide it into complex environmental characteristic data and extreme event indicator data according to the type of environmental variable, and perform standardization processing on each type of data. S3. Based on the classified and organized dataset, construct an extended training set, extract environmental variation patterns from complex environmental feature data, screen highly variable samples from extreme event index data, and combine these samples with the initial dataset. S4. Implement stratified validation on the extended training set by dividing the dataset into a normal environment subset and a complex extreme environment subset, and conduct cross-validation within each subset. S5. Adjust the prediction logic based on the results of hierarchical verification, identify deviation patterns under complex environments and extreme events, integrate these patterns into the main prediction process through iterative feedback, and gradually optimize the overall logical structure. S6. Apply the adjusted prediction logic to generate management output, import real-time input data into the optimized logic flow, and calculate emergency scenario parameters according to the flow sequence to form a reservoir management instruction sequence.

[0019] S1. Collect relevant cloud data about the reservoir, obtain real-time meteorological data, water level monitoring data, and remote sensing image data from the cloud platform, and combine them with historical disaster records and geological parameter data to construct an initial dataset. The specific implementation is as follows: Deploy a unified distributed data access gateway in the cloud; the gateway runs in a cluster based on a container orchestration platform, containing multiple geographically redundant nodes distributed in data centers in different regions; each node provides a unified access point to the outside world through a load balancing component, and shares its running status and health information internally through a service registration and discovery mechanism. When any node fails, the request route is automatically switched, thereby maintaining the stability and continuity of the collection link under the continuous access of multi-source data. The data access gateway establishes long-term and stable communication channels with external data sources such as meteorology, hydrology, and remote sensing. For meteorological data sources, it connects to the meteorological service platform via dedicated lines or virtual private networks, employing message queue telemetry transmission protocols combined with transport layer security protocols for encrypted transmission. Thematic channels are divided at the station or grid unit level, and heartbeat and message acknowledgment mechanisms ensure that messages are transmitted in sequence and meet at least once or strictly once semantic requirements. For water level and flow monitoring data, it establishes encrypted hypertext transmission secure long-lived connections with provincial or basin-wide hydrological monitoring centers, with each request carrying a token-based identity. Verification information (such as dynamic tokens based on JSONWeb tokens or other security credentials) is used, with a configurable polling period, preferably not exceeding 30 seconds, to promptly obtain key operational parameters such as water levels, inflow rates, and gate openings. For remote sensing image data, multispectral, orthorectified image products are periodically acquired from high-resolution satellite or airborne remote sensing service platforms via a secure file transfer protocol combined with an incremental synchronization mechanism. The spatial resolution of the images is preferably better than 2m, and the spectral bands include at least blue, green, red, near-infrared, and short-wave infrared channels for subsequent water body boundary identification, vegetation status assessment, and surface feature analysis. Regarding real-time data retrieval strategies, the gateway hierarchically schedules transmission protocols and channels based on data type and timeliness: for high-timeliness indicators such as water level, flow rate, gate opening, and rainfall intensity, data is first pushed through low-latency message channels, keeping the processing delay of a single message within the millisecond to second range; for large files such as remote sensing images and radar reflectivity, data is retrieved using Hypertext Transfer Protocol or Hypertext Transfer Security Protocol that supports fragmented transmission and breakpoint resumption, with a maximum size limit of 500MB for a single file to balance bandwidth usage and transmission efficiency; the gateway internally constructs a multi-level buffer queue system, including a memory queue, a local solid-state drive queue, a local disk persistent queue, a distributed message queue, and an object storage device queue. Different levels respectively undertake data temporary storage and playback tasks at millisecond, second, minute, and longer periods, and reduce the risk of data loss due to network fluctuations or node failures through write confirmation, retry, and compensation mechanisms; To supplement information over long time scales, the system is configured with a periodic historical data supplementation task, which is automatically triggered during a preset low-load period and is scheduled to run daily in the early morning. The historical supplementation task retrieves hydrological bulletins, flood control records, and dam safety assessment data covering many years from the database of the hydrological management department via a dedicated network or other secure channels. At the same time, it retrieves geological maps, active fault distribution vector data, lithological zoning data, and environmental disaster records such as historical debris flows, landslides, and seismic intensity zoning from the data service platform of the geological and natural resources management department. After the data is downloaded, the historical data is cleaned and preprocessed, including removing obvious outliers, correcting erroneous timestamps, interpolating and supplementing missing time periods according to monitoring sections or stations, and uniformly projecting all types of spatial data into a preset coordinate system, using a unified geodetic coordinate system and elevation datum to avoid positioning errors caused by inconsistencies in spatial datums between different data sources. In the aggregation stage of real-time streaming data and historical batch data, a fusion engine is set up inside the data access gateway to perform spatiotemporal alignment and structured integration of multi-source data. The fusion engine establishes a buffer zone covering the reservoir area and the upstream and downstream influence range with the reservoir dam or reservoir center as a reference. The buffer radius is a configurable parameter, for example, it can be set to the order of 50km. Within this buffer zone, the area is divided into regular grids, and the grid size is also a configurable parameter, for example, a regular grid of 100m×100m, with the grid number as a unified spatial index key. Real-time monitoring records are mapped to the corresponding grid cells according to timestamps to form a time series stack. Historical disaster events and geological parameter data are mapped to the same grid system according to spatial location and occurrence time. For data with different time resolutions or observation gaps, the fusion engine uses a combination of nearest neighbor interpolation and linear interpolation to complete time alignment, filling in key moments without introducing obvious false trends, so that each grid cell can obtain as complete a feature vector as possible at a given time step. After completing spatiotemporal alignment, the system encodes the data according to a unified feature description specification, mapping fields from different sources, such as meteorological elements, hydrological elements, engineering operation status, remote sensing inversion indicators, and geological environmental parameters, into structured feature columns. During feature encoding, the original values ​​are normalized or standardized according to the dimensional differences of different physical quantities, and each feature is appended with meta-information such as source identifier, sampling frequency, and data quality flags for subsequent classification, organization, and quality assessment modules to call and filter. The feature-encoded data is written to the cloud-based distributed object storage service in a columnar storage format. The data is partitioned in multiple levels according to the time dimension and watershed or reservoir identifier, for example, organized by year, month, day, and watershed number. The data size of a single partition is controlled between 128MB and 512MB to improve the processing efficiency of the subsequent distributed computing engine in parallel reading and conditional pruning. While writing data to object storage, the system generates corresponding metadata records for each partition. This metadata includes the partition's time range, spatial range, sample size, feature dimension statistics, data integrity verification values, and complex environment marker information. The complex environment markers are calculated based on indicators such as local topographic relief, the number of historical extreme events, and extreme meteorological and hydrological intensity thresholds. The topographic relief threshold is determined by analyzing the slope distribution of the digital elevation model within the monitoring basin, marking grids with slopes above the 80th percentile of the long-term statistical distribution as topographically complex areas. The historical extreme event frequency threshold is based on past data... The data is determined by statistically analyzing records of floods, landslides, and other events over the past 30 years. For example, if the same grid unit has recorded no fewer than 3 disaster events in the past 30 years, the unit is marked as a high-frequency disaster zone. The intensity thresholds for extreme rainfall or peak flow are determined by combining the return period index given by national or industry standards with the 90th or 95th percentile value of the measured sequence in this watershed. Furthermore, the above thresholds can be automatically calculated through statistical analysis of long-term observation data and can be corrected and adjusted by region under the guidance of watershed experts' experience and relevant technical specifications to adapt to the topography, climate, and engineering characteristics of different reservoirs and watersheds.

[0020] S2. Classify and organize the initial dataset, grouping the data according to time series and spatial distribution. Based on the type of environmental variable, classify it into complex environmental characteristic data and extreme event indicator data, and perform standardization processing on each type of data. Specifically, the implementation is as follows: After receiving the initial dataset formed above, the system automatically triggers the data classification and organization module. This module is deployed on cloud-based virtualized computing nodes and dynamically allocates CPU and memory resources through elastic computing capabilities. Under high concurrency conditions, the data processing latency is controlled within the range of seconds, thereby ensuring the continuity and timeliness of the classification and organization process. First, the system organizes the initial dataset in a multi-granularity manner along the time dimension, dividing the original high-frequency sequences into different time granularities according to a preset aggregation window: using 5 minutes as the smallest time unit, instantaneous measurements are summarized to obtain a fine-granular sequence, used to characterize short-term fluctuations such as sudden rainfall and rapid gate adjustment; based on this, the 5-minute sequences are averaged or accumulated at a 1-hour granularity to form a sequence reflecting intraday changes; further, the 1-hour sequences are smoothed and synthesized at a 6-hour granularity to highlight mesoscale processes such as storm evolution and reservoir capacity adjustment; finally, the 6-hour sequences are aggregated at a 24-hour granularity to describe diurnal cycle variation characteristics such as evaporation and replenishment. Through the above multi-granularity time series organization, the same physical quantity has a continuous and comparable expression at different time scales, providing a data foundation for subsequent models to flexibly select input features between short-term early warning and medium- to long-term trend analysis; Secondly, the system adopts the spatial grid system established earlier, spatially grouping the initial dataset according to grid number. Specifically, based on the spatial coordinates or monitoring station information in the records, data from different sources are mapped to corresponding grid cells: real-time meteorological data is directly assigned to the target grid based on latitude and longitude coordinates; water level and flow monitoring data are projected onto neighboring grids based on the location of the monitoring station; remote sensing image data is matched with the grid based on the pixel center coordinates; historical disaster records mark relevant grids as affected cells based on the event location and its impact range; geological parameter data is written with attributes such as lithology, fault, and slope through layer overlay. Through this spatial grouping step, information from multiple sources such as meteorology, hydrology, remote sensing, and geological disasters is aggregated within the same grid cell, thus providing a unified spatial carrier for extracting complex environmental features by location. After completing the temporal and spatial grouping, the system generates environmental complexity labels for each grid based on the complex environment labeling indicators determined above. The system reads indicators such as topographic relief, vegetation coverage, and intensity of extreme rainfall or peak flow within each grid, and extracts corresponding thresholds from the statistical distribution of long-term observation data according to the threshold setting method given above: grids with topographic relief in the high quantile interval are marked as topographically complex areas, grids with vegetation coverage in the low quantile interval are marked as vegetation-vulnerable areas, and time periods with rainfall or peak flow exceeding the extreme intensity threshold are combined with relevant grids and marked as areas of strong hydrological disturbance. The above thresholds are determined based on the statistical analysis of long-term observation sequences and are corrected and adjusted in zones under the guidance of watershed expert experience and relevant technical specifications to reflect the topographic, climatic, and engineering characteristics of different reservoirs and watersheds. By applying the above rules grid by grid, the system forms an environmental complexity label matrix expanded by time granularity, which is used to distinguish between general areas and areas with significantly complex environmental conditions. Meanwhile, based on the extreme event index fields extracted above, the system constructs extreme event markers in the time dimension; it scans historical disaster records and related extreme factors, marking periods exceeding warning or alert thresholds as flood extreme events; when operational records and geological parameters indicate that a certain slope and lithology combination has experienced landslides or debris flows under heavy rainfall conditions, the corresponding time period is jointly marked with relevant grids as a high-risk period for geological disasters; for short-term heavy rainfall caused by severe convective weather, the system marks it as a heavy rainfall extreme event at 5-minute and 1-hour granularities based on the combined conditions of rainfall intensity threshold and duration threshold; the warning water level threshold, rainfall intensity threshold, and duration threshold are determined based on the warning water level and design rainstorm parameters in the reservoir design and operation regulations, combined with the observation and statistical results of previous extreme events, and can be adjusted periodically with the participation of management departments and domain experts; the system organizes all extreme event markers using timestamps and time granularities as indexes, forming an event timeline covering multiple time scales, used to separate data related to extreme scenarios from regular background data; After grouping and labeling, the system performs standardization and missing value handling on each field. For numerical features, the mean and standard deviation are calculated for each column, and the Z-score method is used for standardization to make features under different units comparable and avoid bias in model training caused by differences in meteorological units (millimeters), water level units (meters), and flow rate units (cubic meters per second). For discrete category features, such as lithology or disaster type, the system uses an independent encoding method to convert them into numerical vectors. In terms of missing value handling, the system fills in short-term missing values ​​by linear interpolation between adjacent time points in the time dimension, and fills in local missing values ​​by referring to the average or median of adjacent grids in the spatial dimension. When the missing rate of a feature in a certain time window exceeds a preset threshold (e.g., 5%), the system records an alarm message and, based on the experimental evaluation results of the model training missing sensitivity and the business side's requirements for data integrity, retrieves alternative samples under similar working conditions from historical data to fill in the missing values, or marks the sample as low confidence so that subsequent modules can perform differentiated processing when using it. Finally, based on environmental complexity labels and extreme event markers, the system divides the cleaned and standardized data into a subset of complex environment features and a subset of extreme event indicators, and attaches metadata such as grid number, time range, and label type to each subset.

[0021] S3. Based on the categorized and organized dataset, construct an expanded training set, extract environmental variation patterns from complex environmental feature data, screen highly variable samples from extreme event index data, and combine these samples with the initial dataset. Specifically, this is implemented as follows: After obtaining the subset of complex environmental features and the subset of extreme event indicators generated above, the system starts the extended training set construction module. This module is deployed in a high-performance computing cluster in the cloud and uses GPU acceleration nodes to complete large-scale feature operations. The end-to-end processing time from the subset input to the extended training set output is controlled at the minute level, thereby meeting the requirements of reservoir operation and management for model update cycles. First, the system performs environmental variation pattern mining on a subset of complex environmental features. It sequentially traverses the grid cells previously marked as having complex environments, according to their grid numbers, and scans multidimensional feature vectors within each time window at different time granularities, such as 5 minutes, 1 hour, 6 hours, and 24 hours. Based on the threshold setting method mentioned earlier, the system performs combined analysis on indicators such as topographic relief, cumulative rainfall, vegetation cover, evaporation rate, lithological permeability, and wind speed. When relevant indicators simultaneously exceed the high or low thresholds determined by long-term statistical distribution and industry standards within a certain time window, the system determines that the time window contains [a specific type of indicator]. Typical environmental variation patterns are extracted. For example, when both slope and event rainfall are in the upper quantile of the historical distribution of the watershed, an accelerated runoff pattern is extracted; when vegetation cover is in the lower quantile and evaporation rate deviates significantly from the seasonal mean, an abnormal evaporation pattern is extracted; when high-permeability lithology and persistent strong winds coexist, an enhanced soil erosion pattern is extracted. Each environmental variation pattern is recorded in vector form, and the vector content includes grid location, time window, variation amplitude of key features and their correlation indicators, thus forming a complex environmental high-variability feature library, which serves as an enhanced representation of the complex environmental feature subset mentioned above. Secondly, the system performs sample augmentation processing on a subset of extreme event indicators. First, it statistically analyzes the temporal distribution of normal and extreme scenario samples, identifying a low proportion of extreme events (such as floods exceeding warning levels, landslides, and debris flows) in historical data, indicating a class imbalance. Therefore, the system filters highly variable samples from the extreme event indicator subset, including flood events where water level fluctuations significantly exceed the critical threshold determined by design reservoir capacity and historical fluctuations within a short period; significant displacement records observed under fault activity or earthquake influence; and debris flow trajectories where the flow velocity exceeds the critical velocity threshold determined by statistics from previous extreme events. For these highly variable samples, the system ensures physical plausibility... Under the premise of ensuring the authenticity of the event, synthetic samples are generated by replicating key evolution segments of the original event and applying small random perturbations to driving factors such as rainfall intensity, upstream water volume, and local wind speed. The perturbation amplitude is set according to model sensitivity analysis and business experience, for example, fluctuating within a certain percentage range, thereby generating new samples that are consistent with the original event in causal structure but have slightly different specific values. Furthermore, the number of synthetic samples is kept roughly balanced with the number of normal samples as needed, for example, by expanding the number of extreme samples to the same order of magnitude as the number of normal samples. Before writing, all synthetic samples are aligned with the time axis and spatial grid index of the original subset to form an extreme event enhancement set, which is used to expand the extreme event indicator subset mentioned above in a targeted manner. After constructing the complex environment high-variability feature library and the extreme event augmentation set, the system fuses them with the initial dataset to form an expanded training set. First, the initial dataset is used as the base, preserving its complete temporal span and spatial coverage. Then, according to grid numbering and temporal granularity, the complex environment high-variability feature vectors are aligned one-to-one with the corresponding records in the base dataset and appended to the original feature vectors, adding an extended feature dimension representing the intensity and pattern of environmental variation while retaining basic information. Based on this, the system vertically appends synthetic samples from the extreme event augmentation set to the base dataset in chronological order, while setting a label field for each record to distinguish between observed and synthetic samples. When synthetic samples overlap with the original extreme event records in time or space, the system prioritizes retaining records with more complete information or more significant variation features through a pre-defined conflict handling strategy. After fusion, the resulting expanded training set includes normal scenarios, complex environment scenarios, and extreme event scenarios, forming a relatively balanced multi-level training foundation in terms of sample quantity and scenario type. After the extended training set is constructed, the system persists it in the cloud distributed file system in Apache Parquet columnar format, maintaining the same storage method as the initial dataset's organizational structure. The partitioning strategy is based on year, month, and scenario type (normal, complex, extreme), with the size of a single partition file set in the hundreds of megabytes range, for example, about 256MB, to balance sequential read / write efficiency and distributed concurrent access capabilities. The system also generates column-level and partition-level metadata for the extended training set, which records statistical summaries of each feature, the frequency of occurrence of complex environmental variation patterns, the ratio of extreme event samples to normal samples, and the labeling information of synthetic samples.

[0022] S4. Implement stratified validation on the expanded training set by dividing the dataset into a normal environment subset and a complex extreme environment subset, and conduct cross-validation within each subset. The specific implementation is as follows: After receiving the extended training set from the previous text, the system activates the hierarchical verification module. The extended training set contains three types of data: normal scene samples, complex environment scene samples, and extreme event scene samples. The hierarchical verification module uses this as a basis for subsequent partitioning and evaluation. This module runs in a cloud-based elastic container service environment, for example, it is allocated 4 vCPUs and 16GB of memory computing instances, and can be dynamically expanded according to the data scale so that the single verification cycle can be controlled in the tens of minutes range under engineering configuration. First, the system partitions the extended training set as a whole, dividing the total samples into training, validation, and test sets according to a preset ratio. For example, in a 7:2:1 ratio, approximately 70% of the samples are used as the training set to build the basic prediction framework, approximately 20% as the validation set for parameter and structure adjustment, and approximately 10% as the test set for final evaluation. A fixed random seed is used during the partitioning process to ensure repeatability of results. After the initial partitioning, the system calculates the proportion of normal environment samples (derived from the baseline data mentioned above), complex environment samples (based on highly variable feature vector labels), and extreme event samples (from the extreme event augmentation set) in each subset. When the proportion of the three types of samples in a subset deviates from the target balance ratio by more than a preset threshold (e.g., 1 percentage point), the corresponding category of samples is added to or removed from the extended training set to make the approximate ratio of the three types of samples within each subset close to 1:1:1, thus taking into account the representativeness of different scenarios in subsequent validation. The dataset partitioning ratio and balance threshold are preset by the modelers according to the sample size and training stability requirements, and can be adjusted according to specific business needs. After the overall partitioning is completed, the system first uses the training set to construct a preliminary prediction framework. It extracts feature vectors from all samples in the training set and organizes them into batch sequences according to the order of time granularity and spatial grid number. Each batch contains a fixed number of samples, such as 1024 records. The system processes the samples in batches sequentially, extracting the relationships between features such as the time lag relationship between water level and rainfall, the spatial dependence of topography and evaporation, and the coupling between lithology and permeability, and solidifies them into a hierarchical structure: the first layer learns the baseline response pattern under normal conditions, the second layer superimposes complex environmental variation features on the baseline, and the third layer further introduces the response shift under extreme event impacts. During the construction process, after each batch of training is completed, the system records internal indicators such as feature coverage and sample diversity to confirm that the preliminary framework can cover different scenarios and feature combinations in the training set, providing a reliable foundation for subsequent hierarchical verification. Subsequently, the system performs hierarchical cross-validation on the validation set. Based on the environmental complexity label and extreme event label mentioned earlier, the system subdivides the validation set into three subsets: a normal environment subset, containing samples not labeled with complex environment and extreme event labels; a complex environment subset, containing grid data with complex environment labels; and a complex-extreme mixed subset, containing samples that are simultaneously in extreme event periods and located in complex environment grids. For each subset, 10-fold cross-validation is performed, dividing the subset into 10 equal parts according to sample order. Nine parts are selected in turn as the training part and one part as the test part of the subset. During the cross-partitioning process, the original temporal order and spatial grid adjacency are maintained, and no completely random shuffling is performed to avoid destroying temporal correlation and spatial continuity. First, a round of 10-fold cross-validation is completed on the normal environment subset to form a benchmark performance reference. Then, the same process is performed on the complex environment subset and the complex-extreme mixed subset in turn, and the performance differences of different subsets on each fold are compared. In each cross-validation process, the system computes multiple evaluation metrics in parallel. For each internal test set sample, it calculates the time series matching degree metric to measure the consistency between the predicted results and the actual series in terms of time lag and overall trend. Simultaneously, it calculates the spatial grid accuracy metric to measure the consistency between predicted and observed values ​​within the neighboring grid. For normal environment subsets, the focus is on baseline stability, and fluctuation tolerance thresholds can be set according to the reservoir management's requirements for prediction accuracy, for example, controlling the relative error of key indicators to within approximately 5%. For complex environment subsets, the focus is on adaptability to environmental variations, such as maintaining a high model matching rate under undulating terrain or low vegetation cover conditions. For complex extreme mixed subsets, the focus is on evaluating recovery capacity after extreme shocks, such as the speed at which the predicted trajectory converges to the normal range after a flood peak or landslide event. All these metrics are statistically analyzed in the form of standardized scores, and each cross-validation fold outputs an metric vector containing the minimum, average, and maximum values. The metric calculation employs batch processing and multi-threaded parallelism to ensure overall processing efficiency for large-scale samples while retaining sample-by-sample evaluation results. After completing 10-fold cross-validation for each subset, the system summarizes and analyzes the indicator vectors of all folds to construct a performance fluctuation map. First, the standard deviation of each inter-fold indicator is calculated for the normal environment subset to form a baseline for fluctuation. Second, the inter-fold indicators of the complex environment subset are compared. When the error fluctuation under certain terrain combinations or vegetation conditions is found to be significantly higher than the baseline, the corresponding grid and time period are marked as environmentally sensitive areas. Then, the complex extreme mixed subset is analyzed. The peak values ​​of indicators are identified within the time period covered by the extreme event labels. When the fluctuation exceeds a preset proportion threshold or the deviation of key indicators exceeds a preset deviation threshold, the relevant fold and sample number are marked as key attention objects. The above thresholds can be configured and adjusted according to the business side's requirements for risk tolerance and early warning accuracy. All analysis results are recorded in the form of structured logs. Each record includes the fold number, subset type, indicator name, fluctuation value, and associated sample identifier, which facilitates subsequent tracing of specific bottlenecks. Finally, the system generates a stratified validation report based on the aggregated metrics and structured logs. The report is organized in JSON format and includes an overview of the overall data partitioning and sample balance, a summary of the stratified framework built during the training phase, a matrix of metrics for 10 cross-validations of each subset, and information on automatically identified bottleneck areas, such as the error of a specific grid in a complex environment being higher than the set threshold for a long time, or the situation where the prediction deviation is concentrated and amplified during certain extreme event periods. The stratified validation report is stored in the cloud database with a unique ID.

[0023] S5. Adjust the prediction logic based on the results of hierarchical verification, identify deviation patterns under complex environments and extreme events, integrate these patterns into the main prediction process through iterative feedback, and gradually optimize the overall logical structure. Specifically, this is implemented as follows: After receiving the output stratified verification report, the system triggers the prediction logic adjustment module. This module is deployed as an independent microservice on a serverless computing platform in the cloud, supporting rapid startup and automatic horizontal scaling to adapt to changes in real-time data traffic. First, the system performs error threshold screening on the samples involved in the report based on the distribution of evaluation indicators for various scenarios in the stratified verification report. The error threshold is pre-set based on the statistical results of evaluation indicators for the normal environment subset. For example, it can select a certain quantile value of the normal environment evaluation score distribution, or add several times the standard deviation on the average level, and can be fitted and configured separately for different reservoirs or watersheds. When the prediction consistency score of the sample in the complex environment subset or complex extreme subset is lower than the threshold, or the corresponding error category indicator is higher than the threshold, the system marks the sample as a high error sample. Subsequently, the system traces the original grid IDs, time windows, and corresponding environmental feature vectors of these samples one by one, including key dimensions such as terrain relief, vegetation coverage, rainfall intensity, and lithological permeability. At the same time, it extracts the direction and magnitude of the deviation between the actual observed values ​​and the predicted values, generating structured deviation entries. Each deviation entry records the triggering conditions (environmental feature combination intervals), deviation type (overestimation or underestimation), deviation magnitude, and frequency of occurrence in historical data. After deduplication, merging, and statistical analysis, all entries are written to a high-performance key-value database in the cloud, forming a dynamically updatable deviation pattern knowledge base. The knowledge base adopts a multi-level index structure with environmental feature hash as the primary key and time granularity as the secondary key, which facilitates rapid retrieval in real-time prediction scenarios. After the deviation pattern knowledge base is constructed, the system attaches it to the decision front end of the main prediction logic to form a closed-loop feedback mechanism. When the 5-minute granular real-time data output from the data access gateway flows into the prediction engine, the system first extracts the environmental feature vector of the current batch of samples and performs multi-dimensional matching with the deviation pattern knowledge base. The matching rule is based on the aforementioned threshold setting method, and uses joint condition judgment for variables such as terrain undulation, vegetation coverage, and rainfall intensity. When the relevant indicators fall into the high or low range determined by long-term observation statistics and industry standards, and are consistent with the feature combination of a certain deviation pattern in the knowledge base or within its set allowable range, it is determined that the deviation pattern has been hit. When it is hit, the system switches to the targeted correction path, reads the correction offset and confidence weight recorded in the corresponding deviation entry, and performs targeted compensation for key outputs such as the water level rise rate, flood peak occurrence time, or flood discharge demand of the current sample. When no entry is hit, the general path is maintained and prediction is performed according to the normal environmental baseline logic. Thus, without changing the overall structure of the main prediction model, an adaptive correction based on empirical data is superimposed on the samples under complex environmental and extreme event conditions. To achieve continuous optimization of the prediction logic, the system triggers a global iterative adjustment task within a preset time period, which can be configured to be started uniformly by the scheduling service at 00:00 every day. The iterative task first extracts all real-time samples processed by the prediction engine in the past 24 hours and their corresponding actual observation feedback data from the data storage, including the measured values ​​of water level and flow returned by the reservoir field stations. Then, these samples are compared one by one with the deviation pattern knowledge base, and the hit rate and residual deviation level of each deviation pattern in actual operation are calculated. For samples that have been matched with deviation patterns but still have systematic residuals, the system updates the correction offset and confidence weight of the corresponding entries according to the new observation deviations, so that the correction amount gradually converges over time. For feature combinations that occur frequently but have not yet been added to the database, the system automatically generates new deviation entries and adds them to the knowledge base. For entries that have not been triggered within a preset time window (e.g., 30 days), weight decay or archiving is performed according to the strategy to control the storage scale of the knowledge base and weaken the impact of outdated patterns. After the iteration is completed, the system publishes a new version of the knowledge base in an atomic update manner, and the online prediction instance completes the version switch in a short time to avoid inconsistencies in the prediction results caused by intermediate states. The adjusted main prediction logic and the deviation pattern knowledge base together constitute the adaptive decision kernel. The decision kernel exposes a unified prediction interface to the outside world, and internally dynamically selects between the general prediction path and the corrected prediction path based on real-time environmental characteristics and knowledge base hit status. It also writes the current kernel version information to the distributed configuration center so that newly deployed prediction instances can automatically load the latest configuration upon startup. The system also records audit logs for each pattern matching and correction. The log content includes information such as sample identifier, hit deviation item, correction magnitude, and final output value. The audit logs are stored in the log database for subsequent traceability and analysis.

[0024] S6. Apply the adjusted prediction logic to generate management output, import real-time input data into the optimized logic flow, and calculate emergency scenario parameters according to the flow sequence to form a reservoir management instruction sequence. The specific implementation is as follows: The system operates in a production-grade deployment environment in the cloud, configured for 24 / 7 monitoring and supporting blue-green switching strategies to improve availability of multiple complete logical kernels online at any time. When a new batch of real-time data arrives (e.g., full synchronization every 5 minutes), it is taken over by the aforementioned unified data access gateway. The gateway performs protocol identification and integrity verification on the received raw messages and uniformly parses meteorological messages (including rainfall, wind speed, temperature, and air pressure), water level and flow messages (including water level above and below the dam, discharge, and inflow), gate status messages, and the latest remote sensing image slices into internal standard structured event streams. Before entering the main prediction pipeline, the event streams are processed according to a pre-determined standardized rule chain, uniformly converting and imputing missing data in timestamps, spatial coordinates, elevation benchmarks, and numerical and categorical fields to generate real-time feature vectors that are structurally isomorphic to the historical initial dataset and extended training set, thereby ensuring the continuity and consistency of real-time data with the aforementioned data processing flow in terms of format and logic. The standardized real-time feature vector is input into the adaptive prediction kernel. The prediction kernel first uses the grid ID and time granularity as indexes to quickly match the current feature vector with the deviation pattern knowledge base. When a registered complex environment or extreme event deviation entry is matched, the corresponding offset and confidence weight are loaded according to the pre-set correction rules in that deviation entry to perform targeted calibration on key outputs such as water level rise rate, flood peak occurrence time, and downstream load changes. If no entry is matched, the baseline prediction logic is executed along the general prediction path. After completing deviation suppression, the system performs risk simulation according to a preset hierarchical order: calculating the inflow flood peak and continuous strong flood within the specified prediction time window. The cumulative contribution of risk factors such as rainfall and high water levels in downstream rivers is calculated, and the results are jointly solved with the current real-time reservoir capacity, the safe discharge capacity of upstream and downstream rivers, the flood control control water level of downstream cities, and related engineering scheduling constraints to construct risk probability distributions under various operating conditions. Based on this, scenarios with high risk probabilities and meeting the constraints are selected from the pre-configured emergency plan library for matching to obtain a set of candidate flood discharge and scheduling strategies. The recommended strategy combination is determined by combining preset evaluation indicators (such as the number of affected people downstream and the risk level of important infrastructure). The evaluation indicators can be configured according to the flood control regulations and risk preferences of the watershed management department. After completing risk simulation and strategy selection, the system generates a structured sequence of reservoir management instructions. This sequence is organized according to a pre-defined JSON-Schema specification, and its core content includes at least the following types of instructions: Flood discharge instruction set, describing the control scheme for each gate, specifying the gate number, target opening degree, opening and closing sequence, and the expected discharge volume for the corresponding time period; Reservoir capacity control instructions, providing adjustment suggestions for the target flood control capacity, upper and lower limits of the control water level, and reserved flood control margin; Personnel evacuation instructions, listing potentially affected administrative villages or communities, the start time for tiered evacuation, the expected number of evacuees, and suggested resettlement site allocation plans; and Material allocation instructions, describing the allocation of sandbags, inflatable boats, generators, and medicines. The system includes information on the types, quantities, allocation priorities, and recommended transportation routes or assembly points for emergency supplies; information release instructions to provide public warning levels, suggested release channels, and corresponding release timestamps; and coordination instructions to send coordination requests to upstream cascade reservoirs, downstream urban flood control departments, and relevant rescue forces, provide coordination and dispatch suggestions, and set response time limits. Each instruction is accompanied by a unique serial number and a traceable decision-making basis chain, which includes at least the triggered deviation pattern identifier, corresponding risk probability value, a list of key constraints, and the prediction version number used. Integrity is protected through a digital signature mechanism based on a pre-configured key system to meet the needs of subsequent auditing and accountability. The generated instruction sequence is distributed by a highly reliable cloud-based push service. This service uses geofencing and a role-based access control matrix to finely categorize instruction recipients: the reservoir management unit's control terminal receives real-time instruction pop-ups and voice broadcasts via a WebSocket long connection; provincial or basin-level flood control command platforms synchronize instructions to a large-screen display and dispatch system via dedicated line interfaces or standardized APIs; mobile terminals of municipal and county-level emergency management and water resources departments access the system via 5G, supplemented by dedicated channels such as BeiDou short messages, ensuring the issuance of critical instructions as much as possible in the event of public network damage or interruption; simultaneously, the system can call the connected SMS gateway to send tiered warning SMS messages to residents around the reservoir area according to geofencing range and warning level. During the push process, the delivery status, confirmation time, and manual feedback results of each receiving end are written back to the cloud audit database in real time, forming a process record from instruction generation, distribution, and signing to execution feedback. If no confirmation information from key units is received within a preset time window (e.g., 30 minutes), the system can automatically initiate a second push according to the pre-configured strategy, and appropriately increase the reminder level or expand the sending range until confirmation is received or the set strategy limit is reached. The sampling period, confirmation time window, and push strategy mentioned above can be configured according to the scheduling procedures and management requirements of different watersheds. During actual operation, the system will also archive operational data at preset time points. For example, it can be configured to automatically trigger the archiving task at 04:00 every day, which will summarize and store all instruction sequences generated in the previous 24 hours, the corresponding actual water level and flood discharge process data, and disaster or emergency feedback information to form an auditable set of operational logs. The set of operational logs can be used as the basis for regulatory verification and post-event analysis, as well as as the source of measured data for subsequent iterative optimization and deviation mode updates.

[0025] The solution in this embodiment firstly involves deploying a distributed data access and fusion framework in the cloud to continuously aggregate real-time meteorological data, water level monitoring data, gate status data, remote sensing images, and historical disaster and geological parameters. An initial dataset is constructed through unified spatiotemporal alignment and standardization. The data is then classified according to temporal, spatial, and environmental attributes under a unified grid and multiple time scales, distinguishing between complex environmental characteristic data and extreme event indicator data. Environmental variation patterns are mined, and highly variable extreme samples are selected. These are combined with the basic samples to form an extended training set covering normal, complex, and extreme scenarios. Layered partitioning and cross-validation are implemented on the extended training set to compare performance differences under normal, complex, and complex extreme conditions. High-error samples are extracted and summarized into a deviation pattern knowledge base, which is then linked with the main prediction logic front-end to construct a prediction kernel that can adaptively switch between general and corrective paths. Based on this, deviation suppression and risk extrapolation are performed on real-time feature vectors. Multiple management instructions, including flood discharge scheduling, reservoir capacity control, personnel transfer, material allocation, information dissemination, and collaborative linkage, are automatically generated, taking into account reservoir capacity, safe discharge, flood control water level, and contingency plan constraints. This enables intelligent prediction and closed-loop scheduling management of the reservoir under complex environmental and extreme event conditions.

[0026] Example 2: Figure 2 A cloud-based reservoir emergency prediction and management system is presented, including: Data acquisition module: responsible for acquiring meteorological, water level and remote sensing data from the cloud platform in real time, and integrating historical disaster records and geological parameters to form an initial dataset for subsequent processing; Data classification and organization module: Groups the initial dataset by time series and spatial grid, marks complex regions and extreme event periods according to environmental complexity indicators, performs standardization processing, and outputs structured subsets; Extended training set construction module: Extracts environmental variation patterns based on the cleaned data subset, generates synthetic samples based on this, and concatenates the synthetic samples with the initial dataset vertically to form an extended training set covering normal, complex and extreme scenarios; The stratified validation module performs subset partitioning of the extended training set and conducts stratified cross-validation, calculates various performance indicators and records their fluctuations, and generates a validation report. Prediction logic adjustment module: Extract deviation patterns based on the error distribution recorded in the hierarchical verification report, build and update the deviation pattern knowledge base, and dynamically correct the main prediction logic through online feedback loop and periodic iterative tasks; Management output generation module: It is used to process real-time data based on the optimized prediction logic, and sequentially complete feature matching, deviation correction, risk calculation and emergency simulation, generate management instruction sequence and push it to various terminals.

[0027] Example 3: Figure 3 An electronic reservoir emergency prediction device based on cloud data is presented, including: Processor: The core computing unit, used to perform data acquisition, classification and organization, training set construction, hierarchical validation, prediction logic adjustment and output generation management steps; Temporary storage space: used to cache the initial dataset, various subsets, and validation reports; Storage devices: used for persistent storage of the initial dataset, expanded training set, and bias pattern knowledge base; Communication interface: Used to support MQTT, HTTPS, 5G, and BeiDou short message communication methods to realize multi-source data access, management command push and multi-terminal distribution; Input / output interface: Used for data exchange with water level monitoring sensors, meteorological data acquisition equipment and management terminals.

[0028] The coordination between basic hardware devices forms an efficient data processing closed loop: the input / output interface serves as the field data entry point, acquiring raw information from external devices such as water level monitoring sensors and meteorological data acquisition equipment, and transmitting some local input to the processor; the communication interface connects the input / output interface and external network to the processor, enabling real-time data inflow and remote interaction; the processor, as the core controller, reads cached data from memory for analysis and calculation, and writes results that need to be stored long-term to the storage device; the storage device interacts bidirectionally with the processor and memory, providing historical data support and storing prediction results and operation logs to ensure long-term data availability; the communication interface further sends the processed output data to the management terminal or upper-level platform, where the input / output interface cooperates to complete local display and control command issuance, thus forming a complete loop from acquisition, processing to feedback.

[0029] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0030] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0031] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0032] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0033] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0034] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0035] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0036] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0037] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for reservoir emergency prediction and management based on cloud data, characterized in that, include: S1. Collect relevant cloud data of the reservoir, obtain real-time meteorological data, water level monitoring data and remote sensing image data from the cloud platform, and construct an initial dataset by combining historical disaster records and geological parameter data; S2. Classify and organize the initial dataset, group the data according to time series and spatial distribution, divide it into complex environmental characteristic data and extreme event indicator data according to the type of environmental variable, and perform standardization processing on each type of data. S3. Based on the classified and organized dataset, construct an extended training set, extract environmental variation patterns from complex environmental feature data, screen highly variable samples from extreme event index data, and combine these samples with the initial dataset. S4. Implement stratified validation on the extended training set by dividing the dataset into a normal environment subset and a complex extreme environment subset, and conduct cross-validation within each subset. S5. Adjust the prediction logic based on the results of hierarchical verification, identify deviation patterns under complex environments and extreme events, integrate these patterns into the main prediction process through iterative feedback, and gradually optimize the overall logical structure. S6. Apply the adjusted prediction logic to generate management output, import real-time input data into the optimized logic flow, and calculate emergency scenario parameters according to the flow sequence to form a reservoir management instruction sequence.

2. The reservoir emergency prediction and management method based on cloud data according to claim 1, characterized in that, Collect relevant cloud data about the reservoir, obtain real-time meteorological data, water level monitoring data, and remote sensing image data from the cloud platform, and combine them with historical disaster records and geological parameter data to construct an initial dataset, including: Configure a distributed data access gateway in the cloud to establish encrypted communication channels with meteorological, hydrological, remote sensing, historical disaster and geological data sources and collect multi-source data; The collected data is processed to unify timestamps and align spatial coordinates, and real-time streaming data and historical batch data are encoded into a structured initial dataset according to a preset time step and spatial grid. The initial dataset is partitioned according to the time dimension and watershed identifier, written to distributed object storage, and its time range, spatial range, number of samples and feature dimensions are recorded for each partition; The metadata records the local relief thresholds, extreme event frequency thresholds, and intensity thresholds determined based on digital elevation models, long-term disaster records, and extreme meteorological and hydrological statistics, and describes the complex environmental markings of the grid cells.

3. The reservoir emergency prediction and management method based on cloud data according to claim 1, characterized in that, The initial dataset was categorized and organized, grouped according to time series and spatial distribution, and classified into complex environmental characteristic data and extreme event indicator data based on the type of environmental variable. Standardization processing was then performed on each type of data, including: The classification and organization process is triggered by the cloud-based data classification and organization module after receiving the initial dataset, and computing and storage resources are dynamically allocated based on elastic computing capabilities. Data is organized in multiple granularities in the time dimension, and multi-layer sequences ranging from fine granularity to daily cycles are generated according to a preset aggregation window. In the spatial dimension, multi-source information is grouped and mapped according to a grid. Thresholds are extracted based on the statistical distribution of long-term observation data to generate environmental complexity labels for each grid, and extreme event markers are constructed by scanning at multiple time scales based on extreme factors. The system standardizes numerical features, independently encodes categorical features, handles missing values ​​through interpolation and sample supplementation, and divides complex environment feature subsets and extreme event indicator subsets according to labels, attaching corresponding metadata to each subset.

4. The reservoir emergency prediction and management method based on cloud data according to claim 1, characterized in that, An expanded training set is constructed based on the categorized and organized dataset to extract environmental variation patterns from complex environmental feature data, including: Environmental variation pattern mining is performed on a subset of complex environmental features. The grid cells marked as environmentally complex are traversed sequentially by grid number, and the feature vectors in each time window are scanned at multiple time granularities. By combining and analyzing relevant indicators, when all relevant indicators exceed the threshold set based on long-term statistical distribution and industry standards within a certain time window, that time window is determined to be the interval corresponding to the environmental variation pattern. The system records the variation patterns of each environment in vector form and constructs a high-variability feature library based on this.

5. The reservoir emergency prediction and management method based on cloud data according to claim 1, characterized in that, Highly variable samples were selected from extreme event index data and combined with the initial dataset, including: Sample augmentation is performed on a subset of extreme event indicators. First, the number distribution of normal scenario samples and extreme scenario samples is statistically analyzed, and then highly variable samples are selected from them. The key evolution segments of extreme events are replicated, random perturbations are applied to preset driving factors to generate synthetic samples, the synthetic samples are aligned with the original events on the time axis and spatial grid index, and an enhanced set of extreme events is constructed. The complex environment high-variability feature library and extreme event augmentation set are aligned and vertically stitched with the initial dataset according to grid number and time order to form an expanded training set. The extended training set is stored in a columnar format in a cloud distributed file system, partitioned according to time dimension and scene type, and feature statistics and sample proportion metadata are recorded for each partition.

6. The reservoir emergency prediction and management method based on cloud data according to claim 1, characterized in that, Hierarchical validation is implemented on the expanded training set by dividing the dataset into a normal environment subset and a complex extreme environment subset, and cross-validation is performed within each subset, including: The extended training set is divided into training set, validation set and test set according to a preset ratio, and the proportion of the three types of samples is balanced by adding or removing normal scene samples, complex environment samples and extreme event samples. A preliminary prediction framework is constructed using the training set, the sample sequences are organized by batch, the correlation between features is extracted, and the correlation is solidified into a hierarchical prediction structure. The validation set is subdivided into a normal environment subset, a complex environment subset, and a complex extreme mixed subset, and cross-validation is used in each subset, while maintaining temporal order and spatial continuity during the cross-partitioning process; Calculate metrics such as time series matching degree and spatial grid accuracy, summarize the metric vectors of each fold for fluctuation analysis, and generate a structured report.

7. The reservoir emergency prediction and management method based on cloud data according to claim 1, characterized in that, Based on the results of hierarchical validation, the prediction logic is adjusted to identify deviation patterns under complex environments and extreme events. These patterns are then integrated into the main prediction process through iterative feedback, and the overall logical structure is gradually optimized, including: Based on the index distribution in the stratified verification report, an error threshold is set, and samples with scores below the threshold in complex environments and extreme subsets are marked as high-error samples. Tracing the high-error sample identifiers and environmental characteristics, extracting the deviation vector to generate structured entries, and writing them into the key-value database after deduplication and merging; The knowledge base is loaded at the prediction front end, and multi-dimensional matching of real-time feature vectors is performed. When the bias pattern is hit, the correction path is used for compensation, and when the pattern is not hit, the general path is used. Periodically trigger iterative tasks, compare samples with observations to update deviation entries and decay expired records, atomically release new versions and record audit logs.

8. The reservoir emergency prediction and management method based on cloud data according to claim 1, characterized in that, The adjusted prediction logic is applied to generate management outputs. Real-time input data is imported into the optimized logic flow, and emergency scenario parameters are calculated according to the flow sequence to form a reservoir management instruction sequence, including: Real-time data is subjected to protocol identification and integrity verification, and meteorological, water level, gate status and remote sensing data are parsed into standard event streams and feature vectors are generated according to standardization. The feature vector is fed into the adaptive kernel, and the bias pattern is matched according to the grid and time. When a match is found, a correction offset is applied; otherwise, prediction is made along the general path, and a risk distribution matching plan is calculated. The system generates instruction sequences, standardizes the arrangement of various scheduling and early warning instructions, and attaches a decision basis chain and digital signature; The push service distributes instructions to terminals based on geofencing and permission matrix, records delivery status and writes feedback back to the database, triggers a second push when timeout, and periodically archives instruction sequences, test data and feedback information.

9. A cloud-based reservoir emergency prediction and management system, used to implement the cloud-based reservoir emergency prediction and management method according to any one of claims 1-8, characterized in that, include: Data acquisition module: responsible for acquiring meteorological, water level and remote sensing data from the cloud platform in real time, and integrating historical disaster records and geological parameters to form an initial dataset for subsequent processing; Data classification and organization module: Groups the initial dataset by time series and spatial grid, marks complex regions and extreme event periods according to environmental complexity index, performs standardization processing, and outputs structured subsets; Extended training set construction module: Extract environmental variation patterns based on the cleaned data subset, generate synthetic samples based on this, and vertically concatenate the synthetic samples with the initial dataset to form an extended training set covering normal, complex and extreme scenarios; The stratified validation module performs subset partitioning of the extended training set and conducts stratified cross-validation, calculates various performance indicators and records their fluctuations, and generates a validation report. Prediction logic adjustment module: Extract deviation patterns based on the error distribution recorded in the hierarchical verification report, build and update the deviation pattern knowledge base, and dynamically correct the main prediction logic through online feedback loop and periodic iterative tasks; Management output generation module: It is used to process real-time data based on the optimized prediction logic, and sequentially complete feature matching, deviation correction, risk calculation and emergency simulation, generate management instruction sequence and push it to various terminals.

10. A cloud-based reservoir emergency prediction electronic device, used to implement the cloud-based reservoir emergency prediction and management method according to any one of claims 1-8 and the cloud-based reservoir emergency prediction and management system according to claim 9, characterized in that, include: Processor: The core computing unit, used to perform data acquisition, classification and organization, training set construction, hierarchical validation, prediction logic adjustment and output generation management steps; Temporary storage space: used to cache the initial dataset, various subsets, and validation reports; Storage devices: used for persistent storage of the initial dataset, expanded training set, and bias pattern knowledge base; Communication interface: Used to support MQTT, HTTPS, 5G, and BeiDou short message communication methods to realize multi-source data access, management command push and multi-terminal distribution; Input / output interface: Used for data exchange with water level monitoring sensors, meteorological data acquisition equipment and management terminals.

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