A water conservancy monitoring method and system for automatically warning water conservancy hazards

Abstract

The application discloses a kind of automatic early warning water conservancy danger water conservancy monitoring method and system, belong to water conservancy engineering safety monitoring and intelligent early warning technical field, including the real-time acquisition of target water conservancy area's basic hydrology, engineering structure and environmental monitoring original data by multi-source perception terminal;The application builds multi-source global data acquisition system, combines standardized data preprocessing, multidimensional hydrological risk factor fusion evaluation, historical sample iterative correction risk model, hierarchical precision early warning mechanism and deep learning equipment extreme force prediction technology, constructs the whole process, stereoscopic, intelligent water conservancy danger automatic early warning monitoring system, substantially improves the authenticity and effectiveness of water conservancy and hydrology monitoring data, strengthens the comprehensiveness and accuracy of water conservancy danger risk assessment, realizes danger hierarchical precision early warning and closed-loop control, significantly improves the global safety control ability and intelligent operation level of water conservancy project.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy project safety monitoring and intelligent early warning technology, specifically referring to a water conservancy monitoring method and system for automatic early warning of water conservancy risks. Background Technology

[0002] Water conservancy projects are core infrastructure for flood control and drainage, water resource allocation, and soil and water conservation in river basins. Their safe and stable operation is directly related to the safety of people's lives and property and the stable development of the social economy in the region. With the continuous advancement of smart water conservancy construction, intelligent monitoring, automated risk assessment, and precise early warning of emergencies have become the core development direction of modern water conservancy project operation and maintenance management. At present, various water conservancy facilities such as reservoirs, rivers, dikes, dams, and sluice gates are equipped with corresponding monitoring methods to keep track of hydrological changes in water areas and the operating status of engineering structures in real time, so as to avoid various water conservancy emergencies such as floods, seepage, and structural deformation in advance.

[0003] However, existing automatic early warning systems for water conservancy risks still have certain shortcomings. Current technologies suffer from limited data collection dimensions and rudimentary data preprocessing mechanisms, making it difficult to avoid monitoring noise and data gaps in complex water conservancy environments. This results in insufficient data accuracy and continuity, failing to comprehensively reflect the actual operational conditions of the water conservancy area. Furthermore, traditional monitoring technologies often rely on single hydrological indicators for risk assessment, lacking multi-dimensional risk factor fusion assessment and historical data iterative correction mechanisms. Risk assessments are often one-sided and prone to subjective errors, making it difficult to accurately adapt to the complex and ever-changing dynamic characteristics of watershed hydrology. Consequently, risk prediction is delayed and inaccurate. The traditional water conservancy early warning system also suffers from hierarchical limitations. The current situation is characterized by vague classifications, simplistic push methods, and a lack of closed-loop management and control mechanisms that precisely match risk levels. This results in poor targeting and implementation of early warnings, an inability to establish standardized hazard tracing procedures, and a focus on monitoring only the main water conservancy project's hazards. It neglects secondary water conservancy risks such as equipment failures and power outages caused by extreme stress conditions of wave energy power generation equipment in water areas. Furthermore, it lacks proactive intelligent forecasting capabilities for extreme equipment conditions. Overall, the level of intelligence in water conservancy hazard prevention and control is low, and risk prevention coverage is incomplete, failing to meet the actual operation and maintenance needs of modern water conservancy projects for comprehensive safety, accurate early warning, and proactive prevention. Therefore, this paper proposes an automatic early warning method and system for water conservancy hazards. Summary of the Invention

[0004] The purpose of this invention is to provide a water conservancy monitoring method and system for automatic early warning of water conservancy risks, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a water conservancy monitoring method for automatic early warning of water conservancy risks, comprising the following steps:

[0006] S1. Real-time collection of basic hydrological, engineering structure and environmental monitoring raw data of the target water conservancy area through multi-source sensing terminals;

[0007] S2. Clean, denoise, fill in gaps and normalize the collected multi-source raw monitoring data to obtain a standardized monitoring dataset;

[0008] S3. Calculate real-time hydrological risk characteristic parameters of water conservancy areas based on standardized monitoring datasets and construct dynamic hydrological risk assessment factors;

[0009] S4. Combine historical hazard sample data with real-time risk assessment factors to iteratively calculate the comprehensive hazard risk value of the water conservancy area;

[0010] S5. Compare the comprehensive risk value of the water conservancy emergency with the preset multi-level risk thresholds to determine the risk level of the water conservancy emergency.

[0011] S6. Match the corresponding early warning strategy according to the risk level of the emergency, generate graded water conservancy emergency early warning instructions and push them out;

[0012] S7. Real-time collection of wave energy power generation equipment operation data in water conservancy areas, and completion of extreme stress condition forecasting through deep learning models to assist in the prevention and control of emergencies.

[0013] Preferably, in step S1, the multi-source sensing terminal collects raw data on basic hydrology, engineering structure, and environmental monitoring of the target water conservancy area in real time, specifically including:

[0014] The system deploys various types of IoT sensing terminals covering rivers, reservoirs, dams, and sluice gates, including water level sensors, flow velocity and flow rate sensors, rainfall sensors, dam seepage pressure sensors, structural displacement sensors, wave sensors, and temperature and humidity environmental sensors. Through a dual acquisition mode of high-frequency timed acquisition and abnormal trigger-triggered acquisition, it periodically acquires real-time monitoring raw data of the target water conservancy area. The system has a preset timed acquisition frequency, and when the data fluctuation of a single sensor exceeds the historical preset average, it automatically triggers a preset number of high-frequency supplementary acquisitions. All raw data is accompanied by a collection timestamp, terminal number, and geographical location tag, and is uniformly packaged into raw monitoring data packets and uploaded to the cloud monitoring platform.

[0015] Preferably, in step S2, an improved Kalman filter is used to denoise the original monitoring data, eliminating abnormal and sudden data caused by sensor failure, electromagnetic interference, and environmental disturbances. At the same time, short-term missing data is filled and repaired based on adjacent time-series data interpolation. For monitoring data of different dimensions and scales, extreme value normalization is used to complete the unified scale processing, resulting in standardized monitoring data with a unified value range, and a standardized monitoring dataset is constructed.

[0016] The preprocessing process incorporates filter optimization calculations, implemented as follows:

[0017] ,

[0018] In the formula, This represents the denoised and standardized monitoring data at time t. This represents the Kalman filter gain coefficient at time t. This represents the raw monitoring data collected by the sensor at time t. This represents the historical standardized data that was preprocessed at time t-1.

[0019] Preferably, in step S3, the preprocessed standardized monitoring dataset is retrieved, and risk characteristic parameters such as water level deviation coefficient, flow overload coefficient, rainfall stress coefficient, and dam seepage anomaly coefficient are extracted. These parameters are then weighted and fused to construct a dynamic hydrological risk assessment factor, accurately quantifying the real-time risk of hazard in the water conservancy area. The fusion of these characteristic parameters achieves the following:

[0020] ,

[0021] In the formula, Indicates dynamic hydrological risk assessment factors, This is the water level deviation coefficient, representing the ratio of the difference between the real-time water level and the warning water level. The flow overload factor is the ratio of real-time flow rate to rated flood discharge flow rate. The rainfall stress coefficient is the ratio of real-time cumulative rainfall to the critical rainfall threshold. The seepage anomaly coefficient is the ratio of the real-time seepage pressure value to the normal seepage pressure threshold. These are the weighting coefficients corresponding to each parameter. These weighting coefficients were pre-calibrated using the analytic hierarchy process (AHP) combined with the water conservancy project level and regional hydrological characteristics, and satisfy the following conditions: .

[0022] Preferably, in step S4, a historical sample database of water conservancy risks is established. This database stores risk occurrence samples, risk-free normal operating condition samples, and critical risk samples from different hydrological and environmental conditions over the years. Risk assessment factors and actual risk levels are labeled for each type of sample. Real-time dynamic hydrological risk assessment factors are used as input to an iterative risk calculation model. Combining the risk mapping relationship of historical sample data, real-time risk deviations are iteratively corrected to calculate an accurate comprehensive risk value. The comprehensive risk iteration is implemented as follows:

[0023] ,

[0024] In the formula, This represents the overall risk value of the current iteration. This is a historical sample correction factor, dynamically adjusted based on regional hydrological stability. Values ​​are set higher for areas with drastic hydrological fluctuations and lower for areas with stable hydrology. The benchmark risk value under the same historical period and working conditions.

[0025] Preferably, in step S5, based on water conservancy engineering design specifications, river basin flood control standards, and historical hazard statistics, four risk threshold ranges are pre-defined, corresponding to four levels: no risk, general risk, significant risk, and major risk, respectively, and a comprehensive hazard risk value is retrieved. It compares each of the four preset threshold ranges to accurately match the corresponding risk level, and the risk level determination is quantified as follows:

[0026] ,

[0027] in, The risk level is coded as follows: 0 corresponds to no risk, 1 to moderate risk, 2 to significant risk, and 3 to major risk. This is a pre-set multi-level risk threshold that increases progressively.

[0028] Preferably, in step S6, a tiered early warning strategy library is pre-built. When the risk level is determined to be no risk, the system maintains normal monitoring status, outputs monitoring reports periodically, and does not trigger an early warning. When the risk level is determined to be moderate, a normal early warning instruction is generated and pushed to grassroots maintenance personnel through platform pop-ups and work group messages, prompting them to strengthen regional patrols and monitoring. When the risk level is determined to be relatively high, a medium-level early warning instruction is generated, linking sound and light early warning equipment and pushing it to water conservancy management department staff via SMS and APP, initiating routine prevention and control duty. When the risk level is determined to be severe, a high-level emergency early warning instruction is generated, linking all-area early warning equipment and pushing it to flood control leaders and emergency departments at all levels through multiple channels. Simultaneously, regional flood control plans, personnel and material allocation plans, and emergency response procedures are automatically retrieved. The entire early warning push process is logged, recording the early warning time, risk level, push recipients, and response progress, achieving closed-loop control of early warnings.

[0029] Preferably, in step S7, the operating parameters of the wave energy generators supporting the water conservancy area are collected in real time, and the real-time wave parameters and water level and flow velocity data of the water area are synchronously associated to construct a generator operation sequence dataset. A deep learning extreme force prediction model based on CNN-LSTM fusion is built, and the generator force data and hydrological environment data under historical extreme wind and wave conditions are used as training samples to complete the model iterative training and parameter optimization.

[0030] Preferably, in step S7, the real-time time series dataset is input into the trained deep learning model. The model automatically extracts wave impact features and equipment stress evolution patterns, accurately predicts the extreme peak stress, duration of continuous stress, and deformation risk of the wave energy generator, and outputs equipment failure warning information in advance.

[0031] Preferably, a water conservancy monitoring system for automatic early warning of water conservancy risks includes a multi-source data acquisition module, a data preprocessing module, a risk factor construction module, a comprehensive risk calculation module, a risk level determination module, a graded early warning push module, a deep learning extreme force forecasting module, and a cloud management and control module.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] 1. To address industry pain points and technical deficiencies such as distorted and mixed multi-source monitoring data, single-dimensional risk assessment, insufficient accuracy of hazard warning classification, delayed prediction of extreme operating conditions of water-related power generation equipment, and the potential for secondary water conservancy risks, this invention effectively solves the problems of traditional water conservancy monitoring relying on single monitoring indicators, poor data preprocessing, strong subjectivity in risk assessment, lack of closed-loop early warning and control, and inability to predict the operational risks of wave energy power generation equipment in advance. This invention constructs a full-process, three-dimensional, and intelligent automatic early warning and monitoring system for water conservancy hazards by building a multi-source, full-domain data acquisition system, combined with standardized data preprocessing, multi-dimensional hydrological risk factor fusion assessment, iterative correction of risk models using historical samples, a graded and precise early warning mechanism, and deep learning equipment extreme force prediction technology. This significantly improves the authenticity and effectiveness of water conservancy and hydrological monitoring data, strengthens the comprehensiveness and accuracy of water conservancy hazard risk assessment, realizes graded and precise early warning and closed-loop control of hazards, and significantly improves the overall safety prevention and control capabilities and intelligent operation and maintenance level of water conservancy projects.

[0034] 2. This invention uses multiple types of sensing terminals to collect multi-dimensional data on hydrology, engineering structures, and the environment across the entire domain. It combines improved filtering algorithms and time-series interpolation algorithms to complete data denoising, missing data filling, and normalization preprocessing. Through a standardized data preprocessing mechanism, invalid and abnormal data are effectively removed, missing monitoring information is repaired, and the dimensions of multi-source monitoring data are unified. This ensures the continuity, authenticity, and effectiveness of water conservancy monitoring time-series data, providing an accurate and reliable data foundation for hydrological risk feature extraction, risk value calculation, and hazard level determination.

[0035] 3. This invention integrates four core risk characteristics—water level, flow rate, rainfall, and dam seepage—to construct a dynamic hydrological risk assessment factor. Simultaneously, it iteratively corrects real-time risk values ​​using massive historical hazard sample data. Multi-factor weighted fusion achieves comprehensive quantification of risks induced by water conservancy hazards. It corrects real-time assessment biases based on historical sample data, weakens the influence of subjective human judgment, and realizes dynamic, accurate, and objective assessment of water conservancy hazard risks. It can accurately identify routine operating conditions and critical risk conditions, significantly improving the accuracy of water conservancy hazard prediction in complex hydrological environments.

[0036] 4. This invention combines a hierarchical closed-loop early warning system with deep learning forecasting technology for extreme stress on wave energy generators to construct a dual safety control system that includes early warning of primary hazards and prediction of secondary equipment risks. It matches specific early warning strategies, push channels, and response plans to different risk levels, achieving precise hierarchical early warning of hazards and closed-loop traceability management throughout the entire process. Simultaneously, relying on a deep learning model, it autonomously learns the evolution of wind, waves, and equipment stress, predicting generator failure risks under extreme stress in advance and proactively avoiding secondary water conservancy risks caused by equipment failure. This achieves integrated, proactive, and intelligent prevention and control of main hazards and associated equipment risks in water conservancy projects, comprehensively improving the overall safe operation and emergency response capabilities of water conservancy projects. Attached Figure Description

[0037] Figure 1 The present invention provides the operational flow of a water conservancy monitoring method for automatic early warning of water conservancy risks. Figure 1 ;

[0038] Figure 2 The present invention provides the operational flow of a water conservancy monitoring method for automatic early warning of water conservancy risks. Figure 2 ;

[0039] Figure 3 The present invention provides the operational flow of a water conservancy monitoring method for automatic early warning of water conservancy risks. Figure 3 ;

[0040] Figure 4 The present invention provides the operational flow of a water conservancy monitoring method for automatic early warning of water conservancy risks. Figure 4 ;

[0041] Figure 5 This is a schematic diagram of the structure of a water conservancy monitoring system for automatic early warning of water conservancy risks according to the present invention. Detailed Implementation

[0042] 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.

[0043] Example

[0044] Please see Figures 1-5 As shown, the present invention provides a technical solution comprising the following steps:

[0045] S1. Real-time collection of basic hydrological, engineering structure and environmental monitoring raw data of the target water conservancy area through multi-source sensing terminals;

[0046] S2. Clean, denoise, fill in gaps and normalize the collected multi-source raw monitoring data to obtain a standardized monitoring dataset;

[0047] S3. Calculate real-time hydrological risk characteristic parameters of water conservancy areas based on standardized monitoring datasets and construct dynamic hydrological risk assessment factors;

[0048] S4. Combine historical hazard sample data with real-time risk assessment factors to iteratively calculate the comprehensive hazard risk value of the water conservancy area;

[0049] S5. Compare the comprehensive risk value of the water conservancy emergency with the preset multi-level risk thresholds to determine the risk level of the water conservancy emergency.

[0050] S6. Match the corresponding early warning strategy according to the risk level of the emergency, generate graded water conservancy emergency early warning instructions and push them out;

[0051] S7. Real-time collection of wave energy power generation equipment operation data in water conservancy areas, and completion of extreme stress condition forecasting through deep learning models to assist in the prevention and control of emergencies.

[0052] In this embodiment, step S1 involves the multi-source sensing terminal collecting raw data on basic hydrology, engineering structure, and environmental monitoring of the target water conservancy area in real time, specifically including:

[0053] The system deploys various types of IoT sensing terminals covering rivers, reservoirs, dams, and sluice gates, including water level sensors, flow velocity and flow rate sensors, rainfall sensors, dam seepage pressure sensors, structural displacement sensors, wave sensors, and temperature and humidity environmental sensors. It periodically acquires real-time monitoring raw data of the target water conservancy area through a dual acquisition mode of high-frequency timed acquisition and abnormal trigger-triggered acquisition. A preset timed acquisition frequency is established; when the fluctuation range of a single sensor's data exceeds the historical preset average, a preset number of high-frequency supplementary acquisitions are automatically triggered to ensure data integrity under extreme conditions. The acquired raw data specifically includes real-time water level data, cross-sectional flow velocity and flow rate data, cumulative rainfall data, dam seepage pressure data, vertical / horizontal displacement data of engineering structures, water wave parameter data, and regional environmental meteorological data. All raw data is accompanied by a acquisition timestamp, terminal number, and geographic location tag, and is uniformly packaged into raw monitoring data packages and uploaded to the cloud monitoring platform.

[0054] In this embodiment, in step S2, an improved Kalman filter is used to denoise the original monitoring data, eliminating abnormal and sudden data caused by sensor failure, electromagnetic interference, and environmental disturbances. At the same time, short-term missing data is filled and repaired based on the adjacent time series data interpolation method. For monitoring data of different dimensions and units, extreme value normalization is used to complete the unified unit processing, resulting in standardized monitoring data with a unified value range, and a standardized monitoring dataset is constructed.

[0055] The preprocessing process incorporates filter optimization calculations, implemented as follows:

[0056] ,

[0057] In the formula, This represents the denoised and standardized monitoring data at time t. Let represent the Kalman filter gain coefficient at time t, with a value range of (0,1). This represents the raw monitoring data collected by the sensor at time t. This represents the historical standardized data that has been preprocessed at time t-1; the current raw data is corrected based on the standardized data from the previous time, random noise interference is removed, and the continuity and authenticity of the data time sequence are ensured.

[0058] In this embodiment, in step S3, the preprocessed standardized monitoring dataset is retrieved, and risk characteristic parameters such as water level deviation coefficient, flow overload coefficient, rainfall stress coefficient, and dam seepage anomaly coefficient are extracted. Dynamic hydrological risk assessment factors are constructed through weighted fusion to accurately quantify the real-time risk of hazard-induced events in the water conservancy area. The fusion of characteristic parameters is then implemented as follows:

[0059] ,

[0060] In the formula, Indicates dynamic hydrological risk assessment factors, This is the water level deviation coefficient, representing the ratio of the difference between the real-time water level and the warning water level. The flow overload factor is the ratio of real-time flow rate to rated flood discharge flow rate. The rainfall stress coefficient is the ratio of real-time cumulative rainfall to the critical rainfall threshold. The seepage anomaly coefficient is the ratio of the real-time seepage pressure value to the normal seepage pressure threshold. These are the weighting coefficients corresponding to each parameter. These weighting coefficients were pre-calibrated using the analytic hierarchy process (AHP) combined with the water conservancy project level and regional hydrological characteristics, and satisfy the following conditions: .

[0061] In this embodiment, in step S4, a historical sample database of water conservancy risks is established. This database stores risk occurrence samples, risk-free normal operating condition samples, and critical risk samples from different hydrological and environmental conditions over the years. Risk assessment factors and actual risk levels are labeled for each type of sample. Real-time dynamic hydrological risk assessment factors are used as input to a risk iteration calculation model. Combined with the risk mapping relationship of historical sample data, real-time risk deviations are iteratively corrected to calculate an accurate comprehensive risk value. The comprehensive risk iteration is implemented as follows:

[0062] ,

[0063] In the formula, This represents the overall risk value of the current iteration. This is a historical sample correction factor, dynamically adjusted based on regional hydrological stability. Values ​​are set higher for areas with drastic hydrological fluctuations and lower for areas with stable hydrology. It serves as the benchmark risk value under the same historical conditions and operating conditions; by correcting real-time risk deviations using historical sample data, it solves the problem of large errors in assessment of a single real-time parameter, and significantly improves the accuracy of risk assessment.

[0064] In this embodiment, in step S5, based on water conservancy engineering design specifications, river basin flood control standards, and historical hazard statistics, four risk threshold intervals are pre-defined, corresponding to four levels: no risk, general risk, significant risk, and major risk. These threshold intervals are trained and calibrated using massive samples to adapt to the risk assessment needs of different water conservancy scenarios, and then the comprehensive hazard risk value is retrieved. It compares each of the four preset threshold ranges to accurately match the corresponding risk level, and the risk level determination is quantified as follows:

[0065] ,

[0066] in, The risk level is coded as follows: 0 corresponds to no risk, 1 to moderate risk, 2 to significant risk, and 3 to major risk. The system uses a multi-level preset risk threshold that increases progressively. The threshold values ​​are set differently based on the scale of the target water conservancy project, the flood control level, and geographical environmental parameters. The system uses a comprehensive risk value as the basis for judgment, which realizes the standardized conversion of risk value to risk level. The judgment logic is clear and the accuracy is controllable.

[0067] In this embodiment, in step S6, a tiered early warning strategy library is pre-built to match specific early warning response plans, early warning push scope, early warning push methods, and response time limits for different risk levels. When the risk level is determined to be no risk, the system maintains normal monitoring status, outputs monitoring reports periodically, and does not trigger an early warning. When the risk level is determined to be general, a normal early warning instruction is generated and pushed to grassroots maintenance personnel through platform pop-ups and work group messages, prompting them to strengthen regional patrols and monitoring. When the risk level is determined to be relatively high, a medium-level early warning instruction is generated, linking sound and light early warning equipment and pushing it to water conservancy management department staff via SMS and APP, initiating routine prevention and control duty. When the risk level is determined to be severe, a high-level emergency early warning instruction is generated, linking all-area early warning equipment and pushing it to flood control leaders and emergency departments at all levels through multiple channels. Simultaneously, regional flood control plans, personnel and material allocation plans, and emergency response procedures are automatically retrieved. The entire early warning push process is logged, recording the early warning time, risk level, push recipients, and response progress, achieving closed-loop control of early warning.

[0068] In this embodiment, in step S7, the operating parameters of the wave energy generators supporting the water conservancy area are collected in real time, including the main shaft stress, support deformation, wave impact load, equipment vibration frequency, real-time power generation, and other operating data. Simultaneously, real-time wave parameters and water level and flow velocity data of the water area are correlated to construct a generator operating sequence dataset. A deep learning extreme force prediction model based on CNN-LSTM fusion is built, and the generator stress data and hydrological environment data under historical extreme wind and wave conditions are used as training samples to complete the model iterative training and parameter optimization.

[0069] In this embodiment, in step S7, the real-time time series dataset is input into the trained deep learning model. The model automatically extracts wave impact characteristics and equipment stress evolution patterns, accurately predicts the extreme peak stress, duration of continuous stress, and deformation risk of the wave energy generator, predicts extreme working conditions such as equipment overload and structural damage, and outputs equipment failure warning information in advance to avoid secondary risks such as power outages and equipment failures caused by power generation equipment failures, thus assisting in the comprehensive prevention and control of water conservancy risks across the entire region.

[0070] In this embodiment, a water conservancy monitoring system for automatic early warning of water conservancy risks includes a multi-source data acquisition module, a data preprocessing module, a risk factor construction module, a comprehensive risk calculation module, a risk level determination module, a graded early warning push module, a deep learning extreme force prediction module, and a cloud management and control module.

[0071] Working Principle: A three-dimensional water conservancy monitoring and sensing network is constructed through a globally deployed network of IoT sensing devices, covering core water conservancy areas such as rivers, reservoirs, dams, and sluice gates. Multiple types of sensors collect three core monitoring information categories: hydrology, engineering structure, and environment. The system employs a dual-mode acquisition mechanism combining regular timed acquisition with abnormal trigger-based supplementary acquisition. Under normal conditions, it maintains a stable data acquisition frequency to ensure monitoring continuity. When abnormal fluctuations occur in the monitoring data, a high-frequency supplementary acquisition mechanism is automatically activated to compensate for data blind spots under extreme conditions. All collected raw monitoring information is accompanied by complete spatiotemporal and device identification information. After unified packaging, the data is uploaded to the cloud platform to achieve comprehensive, real-time, and seamless multi-dimensional monitoring of the water conservancy area's operational status. Standardized processing is implemented to address common issues in raw monitoring data such as noise interference, data gaps, and inconsistent units. Optimized filtering techniques are used to filter out anomalous data changes caused by sensor malfunctions, electromagnetic interference, and disturbances in complex aquatic environments, eliminating invalid interference information. Simultaneously, the correlation characteristics of time-series data are utilized to interpolate adjacent data to fill in short-term missing monitoring data, repairing data gaps. For various monitoring data with different dimensions and inconsistent units of measurement, normalization processing is used to complete the full-domain data dimensionality assessment. To ensure uniformity and eliminate the influence of dimensional differences between various parameters, a standardized dataset with continuous time series, accurate numerical values, and unified dimensions is formed after a complete cleaning, denoising, gap filling, and normalization process. Based on the preprocessed standardized monitoring dataset, the risk factors behind various hydrological and engineering monitoring parameters are deeply explored, and core characteristic parameters that can intuitively reflect the operational risks of water conservancy areas are selected. Combining four key risk dimensions—water level changes, discharge status, rainfall stress, and seepage status of dam structures—each individual risk characteristic is quantitatively analyzed, and then multi-dimensional single risk characteristics are integrated into a unified whole through weighted fusion. The system employs dynamic hydrological risk assessment factors; it rationally allocates risk weights for each dimension based on the actual level of water conservancy projects and the hydrological characteristics of the basin, accurately distinguishing the impact of different factors on water conservancy risks; it establishes a massive historical sample database of water conservancy operating conditions, creating a correlation mapping mechanism between real-time risks and historical operating condition data. The database covers various sample types, including normal operating conditions, critical operating conditions, and dangerous operating conditions, forming a complete risk reference system; it uses the previously constructed dynamic hydrological risk assessment factors as real-time risk input parameters, combining the risk evolution patterns under similar historical hydrological environments and similar engineering operating conditions to iteratively correct the real-time assessment results.

[0072] Based on industry water conservancy design specifications, basin flood control standards, and historical statistical patterns of emergencies, a scientific and standardized multi-level hazard risk assessment standard is established, dividing risk level intervals into gradients to adapt to the risk assessment needs of different water conservancy project scales and basin environments. The comprehensive hazard risk results obtained through iterative calculation are matched and compared one by one with the preset multi-level risk standards. Standardized risk level classification is completed based on the interval of the risk value, clearly distinguishing four operational states: no risk, general risk, significant risk, and major risk. A graded early warning strategy system corresponding to the risk level is pre-established, configuring differentiated early warning methods, push scope, and handling requirements for different risk levels, forming standardized early warning and handling specifications. After completing the risk level assessment, the system automatically matches the corresponding early warning strategy. In the low-risk state, routine monitoring is maintained and monitoring reports are periodically output without triggering early warning actions. In the low-to-medium-risk state, targeted early warning information is pushed to prompt manual inspection and control. In the medium-to-high-risk state, on-site early warning equipment is linked to push warnings to management and maintenance personnel. Sending alerts for handling; in cases of major risks, initiating a comprehensive emergency warning, and simultaneously retrieving flood control and response plans and resource allocation schemes; recording the warning trigger time, risk level, target audience, and handling progress throughout the entire process, forming a closed-loop management mechanism from risk identification and warning delivery to handling tracing; simultaneously collecting structural stress, operating status, and wave hydrological information of the wave power generation equipment in the water conservancy area, as well as surrounding waters, to construct a dataset of equipment operating sequence characteristics, fully recording the changing patterns of equipment operating status under different wind and wave conditions; relying on a fusion deep learning architecture combining convolutional neural networks and long short-term memory networks, building an extreme stress forecasting model, and using massive historical wind and wave conditions and equipment stress samples to complete model training and parameter optimization, enabling the model to autonomously learn wave evolution characteristics and equipment stress change patterns; inputting real-time equipment and hydrological data into the trained model, intelligently inferring the future operating status of the equipment, predicting potential risks such as extreme stress, structural deformation, and equipment overload damage in advance, and outputting equipment failure warnings in advance.

[0073] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

[0074] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A water conservancy monitoring method for automatic early warning of water conservancy risks, characterized in that, Includes the following steps: S1. Real-time collection of basic hydrological, engineering structure and environmental monitoring raw data of the target water conservancy area through multi-source sensing terminals; S2. Clean, denoise, fill in gaps and normalize the collected multi-source raw monitoring data to obtain a standardized monitoring dataset; S3. Calculate real-time hydrological risk characteristic parameters of water conservancy areas based on standardized monitoring datasets and construct dynamic hydrological risk assessment factors; S4. Combine historical hazard sample data with real-time risk assessment factors to iteratively calculate the comprehensive hazard risk value of the water conservancy area; S5. Compare the comprehensive risk value of the water conservancy emergency with the preset multi-level risk thresholds to determine the risk level of the water conservancy emergency. S6. Match the corresponding early warning strategy according to the risk level of the emergency, generate graded water conservancy emergency early warning instructions and push them out; S7. Real-time collection of wave energy power generation equipment operation data in water conservancy areas, and completion of extreme stress condition forecasting through deep learning models to assist in the prevention and control of emergencies.

2. The water conservancy monitoring method for automatic early warning of water conservancy risks according to claim 1, characterized in that: In S1, various types of IoT sensing terminals are deployed to cover rivers, reservoirs, dams, and sluice gates, including water level sensors, flow velocity and flow rate sensors, rainfall sensors, dam seepage pressure sensors, structural displacement sensors, wave sensors, and temperature and humidity environmental sensors. Through a dual acquisition mode of high-frequency timed acquisition and abnormal trigger supplementary acquisition, real-time monitoring raw data of the target water conservancy area is periodically acquired. The timed acquisition frequency is preset, and when the fluctuation amplitude of a single sensor data exceeds the historical preset average value, a preset number of high-frequency supplementary acquisitions are automatically triggered. All raw data is accompanied by acquisition timestamps, terminal numbers, and geographical location tags, and is uniformly packaged into raw monitoring data packets and uploaded to the cloud monitoring platform.

3. The water conservancy monitoring method for automatic early warning of water conservancy risks according to claim 1, characterized in that: In step S2, an improved Kalman filter is used to denoise the original monitoring data, eliminating abnormal and sudden data caused by sensor failure, electromagnetic interference, and environmental disturbances. At the same time, short-term missing data is filled and repaired based on the adjacent time series data interpolation method. For monitoring data of different dimensions and scales, extreme value normalization is used to complete the unified scale processing, resulting in standardized monitoring data with a unified value range, and a standardized monitoring dataset is constructed. The preprocessing process incorporates filter optimization calculations, implemented as follows: ， In the formula, This represents the denoised and standardized monitoring data at time t. This represents the Kalman filter gain coefficient at time t. This represents the raw monitoring data collected by the sensor at time t. This represents the historical standardized data that was preprocessed at time t-1.

4. The water conservancy monitoring method for automatic early warning of water conservancy risks according to claim 1, characterized in that: In step S3, the preprocessed standardized monitoring dataset is retrieved, risk characteristic parameters are extracted, and a dynamic hydrological risk assessment factor is constructed through weighted fusion to accurately quantify the real-time risk of water conservancy area emergencies. The feature parameter fusion is implemented as follows: ， In the formula, Indicates dynamic hydrological risk assessment factors, This is the water level deviation coefficient. This is the flow overload factor. This is the rainfall stress coefficient. This is the seepage anomaly coefficient. These are the weighting coefficients for each parameter.

5. The water conservancy monitoring method for automatic early warning of water conservancy risks according to claim 1, characterized in that: In step S4, a historical sample database of water conservancy risks is established. This database stores risk occurrence samples, risk-free normal operating condition samples, and critical risk samples from different hydrological and environmental conditions over the years. Risk assessment factors and actual risk levels are labeled for each type of sample. Real-time dynamic hydrological risk assessment factors are used as input to an iterative risk calculation model. Combining the risk mapping relationship of historical sample data, real-time risk deviations are iteratively corrected to calculate an accurate comprehensive risk value. The comprehensive risk iteration is implemented as follows: ， In the formula, This represents the overall risk value of the current iteration. The historical sample correction coefficient. This is the benchmark risk value under the same historical period and working conditions.

6. The water conservancy monitoring method for automatic early warning of water conservancy risks according to claim 1, characterized in that: In S5, based on water conservancy engineering design specifications, river basin flood control standards, and historical hazard statistics, four levels of hazard risk threshold intervals are pre-defined, corresponding to four levels: no risk, general risk, relatively high risk, and major risk, respectively. The comprehensive hazard risk value is then retrieved. It compares each of the four preset threshold ranges to accurately match the corresponding risk level, and the risk level determination is quantified as follows: ， in, The risk level is coded as follows: 0 corresponds to no risk, 1 to moderate risk, 2 to significant risk, and 3 to major risk. This is a pre-set multi-level risk threshold that increases progressively.

7. The water conservancy monitoring method for automatic early warning of water conservancy risks according to claim 1, characterized in that: In S6, a hierarchical early warning strategy library is pre-built. When the risk level is determined to be no risk, the system maintains the normal monitoring state, outputs monitoring reports on a regular basis, and does not trigger an early warning. When the risk level is determined to be general, a routine early warning instruction is generated and pushed to the grassroots operation and maintenance personnel through platform pop-ups and work group messages, prompting them to strengthen regional patrol and monitoring. When the risk level is determined to be relatively high, a medium-level early warning instruction is generated, which is linked to the sound and light early warning equipment and pushed to the staff of the water conservancy management department via SMS and APP, and the routine prevention and control duty is initiated. When a major risk level is determined, a high-level emergency warning instruction is generated, which is linked to warning equipment across the entire region and pushed to flood control leaders and emergency departments at all levels through multiple channels. At the same time, regional flood control plans, personnel and material allocation plans, and emergency response procedures are automatically retrieved. The entire warning push process is logged, recording the warning time, risk level, target audience, and response progress, thus achieving closed-loop management of the warning system.

8. The water conservancy monitoring method for automatic early warning of water conservancy risks according to claim 1, characterized in that: In S7, the operating parameters of the wave energy generators in the water conservancy area are collected in real time, and the real-time wave parameters and water level and flow velocity data of the water area are synchronously associated to construct the generator operation sequence dataset. A deep learning extreme force prediction model based on CNN-LSTM fusion is built, and the generator force data and hydrological environment data under historical extreme wind and wave conditions are used as training samples to complete the model iterative training and parameter optimization.

9. A water conservancy monitoring method for automatic early warning of water conservancy risks according to claim 8, characterized in that: In S7, the real-time time series dataset is input into the trained deep learning model. The model automatically extracts wave impact features and equipment stress evolution patterns, accurately predicts the extreme peak stress, duration of continuous stress, and deformation risk of the wave energy generator, and outputs equipment failure warning information in advance.

10. A water conservancy monitoring system for automatic early warning of water conservancy risks, implemented according to the water conservancy monitoring method for automatic early warning of water conservancy risks as claimed in claim 1, is characterized in that: It includes a multi-source data acquisition module, a data preprocessing module, a risk factor construction module, a comprehensive risk calculation module, a risk level determination module, a graded early warning push module, a deep learning extreme force prediction module, and a cloud management module.

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