3432 results about "Hydropower" patented technology

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention discloses a hydropower station equipment fault analysis method based on state data mining, and relates to the technical field of hydropower station equipment intelligent fault diagnosis, and the method comprises the steps: collecting key operation parameters through deploying multiple types of sensors, and constructing a unified state time series data set; carrying out supervised training by adopting an LSTM network, extracting a dynamic feature vector, and constructing an AI state analysis model; introducing a micro-fluctuation abnormal coefficient WBYX, and evaluating the operation stability of the equipment; a coupling disturbance collaboration coefficient OHRD is calculated, and a fault conduction relation among multiple devices is identified; and calculating a trend evolution coefficient QSYH based on the state vector included angle offset, and analyzing whether the equipment operation trend is abnormal or not. By setting a multi-level threshold value, generation of a hierarchical early warning mechanism and a response strategy is realized, and the operation safety and the fault prediction capability of hydropower station equipment are effectively improved. The method is suitable for hydropower station key equipment state monitoring and intelligent operation and maintenance management in a complex environment.

The invention discloses a hydropower station unit state online monitoring system, relates to the technical field of hydropower station unit monitoring control, and adopts a hybrid digital twin modeling technology combining a physical mechanism main model and a liquid neural network residual compensation model to construct a high-fidelity unit operation state model. The system comprises a data acquisition module, a digital twin modeling module, a fault evolution prediction module, a multi-target optimization module and an adaptive control generation module. Multi-source heterogeneous data fusion is realized through a space-time adaptive weight distribution algorithm, and residual compensation modeling is performed by using dynamic time constant characteristics of a liquid neural network. Virtual fault injection and fault evolution trajectory prediction are realized, and passive fault response is converted into active fault prediction. A two-stage optimization strategy is adopted to realize'safety-efficiency-life 'three-dimensional target collaborative optimization, and a continuous and smooth adaptive control parameter trajectory is generated by controlling a liquid neural network. The modeling precision is improved, and the fault early warning time is advanced.

The invention discloses a hydropower station workshop deformation monitoring method, which comprises the following steps of collecting multi-dimensional monitoring data and point cloud data of a hydropower station workshop, performing adaptive filtering and noise reduction on the multi-dimensional monitoring data, performing spatial registration on the multi-dimensional monitoring data and the point cloud data in a unified three-dimensional coordinate system, constructing a multi-channel time sequence deep learning model, and performing multi-channel time sequence deep learning on the multi-channel time sequence deep learning model; a dynamic early warning threshold model is constructed based on material characteristics, equipment operation parameters and historical monitoring data, risk assessment is carried out, a three-level response mechanism is triggered according to a risk assessment result, a sensor reliability evaluation index is established based on deviation analysis of monitoring data and an early warning result, and a data fusion weight is dynamically optimized. Adjusting the decision boundary of the classification model, and carrying out visualization and traceability analysis on the multi-dimensional monitoring data; the invention further discloses a hydropower station plant deformation monitoring system. According to the invention, a multi-dimensional and three-dimensional risk assessment system is established through data monitoring and real-time analysis, and the structural safety of the hydropower house is guaranteed to the maximum extent.

The invention provides a hydropower station generator set excitation system fault prediction method and device, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the continuous sampling of a generalized state observation vector through a sliding time window, so as to form an operation parameter observation tensor; fusing the operation parameter observation tensor and the boundary constraint type operation parameters of the generator, and constructing a fault observation tensor fused by multi-source operation parameters; introducing a structural causal atlas in a modeling layer, establishing a causal path network embedded based on causal reasoning and topological time sequence for a fault observation tensor, and dividing system interaction faults encountered by excitation system prediction into a plurality of typical modes; and performing graph attention modeling on the evolution trend and the instability boundary of each key parameter in the fault causal chains of different typical modes contained in the causal path network, and dynamically identifying the path characteristics of the excitation system entering the fault critical state. According to the method, the interaction type fault of the excitation system of the hydropower station generator set can be predicted.

An optical storage system designed to compensate for the water hammer effect in hydropower turbines and its cooperative frequency modulation method are provided. The method involves detecting the water hammer effect and utilizing a pre-established system model integrating a hydropower unit, photovoltaic system, and hybrid energy storage system. Through model predictive control combined with a whale optimization algorithm, the governor parameters of the hydropower unit are optimized. The system then obtains the photovoltaic active power output and the battery's state of charge (SOC) within the hybrid energy storage system. Based on these parameters, a collaborative control strategy for the photovoltaic and hybrid energy storage systems is determined and executed. This strategy enables effective control of the photovoltaic system and / or hybrid energy storage to compensate for reverse power adjustments in the hydropower unit, addressing the water hammer effect and ensuring stable power grid operation.

The invention discloses a hydroelectric oil filter fault diagnosis system and method based on blind source separation, and relates to the technical field of fault diagnosis, and the system comprises a data acquisition module, a self-adaptive preprocessing module, a diagnosis engine module, a digital twin model library and an application module. The data acquisition module synchronously acquires multi-source heterogeneous observation signals; the self-adaptive preprocessing module carries out preprocessing by adopting self-adaptive variational mode decomposition based on an intelligent optimization algorithm; the diagnosis engine module comprises a multi-physical-quantity deep fusion unit, a dynamic source number estimation unit and an online blind source separation unit, the multi-physical-quantity deep fusion unit performs deep fusion on heterogeneous data through a physical information self-encoder to generate a high-dimensional feature matrix, and the dynamic source number estimation unit adopts a three-layer layered structure to perform online estimation on the number of source signals; an independent component analysis algorithm driven by the running state of the on-line blind source separation unit; and a complete diagnosis process is realized. The problem that a traditional method is low in diagnosis precision under strong noise, multi-source coupling and dynamic working conditions is solved.

The invention relates to the technical field of layered surrounding rock inversion analysis, and discloses a layered surrounding rock three-dimensional crustal stress field inversion intelligent analysis system and method, and the system comprises a dimensional geological value module, an intelligent inversion analysis module and an inversion result verification module. The method comprises the steps of collecting geological data in target layered surrounding rock; carrying out normalization processing on the geological data, and carrying out sensitivity analysis; constructing a three-dimensional geologic model, and performing numerical simulation; a crustal stress field inversion intelligent analysis model is generated based on the three-dimensional geologic model; training the crustal stress field inversion intelligent analysis model by using the training sample; inputting the real-time geological data into the crustal stress field inversion intelligent analysis model to obtain a crustal stress inversion analysis result; carrying out visual output on the ground stress analysis result; and adjusting the crustal stress field inversion intelligent analysis model according to the actually measured data. Scientific ground stress field analysis basis can be provided for complex projects such as tunnel engineering, underground engineering and hydropower stations.

The invention provides an intelligent speed regulation control method and system for hydroelectric generating set multi-source data fusion, and relates to the technical field of hydropower station automation control, and the method comprises the steps: S1, collecting multi-source heterogeneous data, including mechanical quantity, hydraulic parameters and electrical quantity, in the operation process of a hydroelectric generating set; s2, performing standardized preprocessing on the multi-source heterogeneous data to obtain preprocessed data; s3, on the basis of the preprocessed data, global deep state feature information and operation trend prediction information of the hydroelectric generating set are extracted through a multi-layer data fusion algorithm, and a fused speed regulation decision basis is obtained; s4, generating a speed regulation control instruction for regulating the rotating speed of the hydroelectric generating set based on the fused speed regulation decision basis; and S5, performing speed regulation control on the hydroelectric generating set according to the speed regulation control instruction. Through multi-layer data fusion and a dynamic self-adaptive control strategy, comprehensive perception and accurate regulation and control of the operation state of the hydroelectric generating set are realized.

The invention relates to the field of water conservancy and hydropower engineering, in particular to a multi-objective optimized hydropower ecological scheduling decision-making system and method, and the system comprises a data collection module, an ecological model module, an intelligent decision-making engine module, a scheduling execution module, an effect evaluation module and a knowledge base module. The data acquisition module collects multi-source data, the ecological model module generates a training data set, the intelligent decision engine carries out ecological process modeling and probability prediction based on a deep learning architecture and spatial-temporal feature extraction, and generates a scheduling decision, the scheduling execution module controls hydropower engineering operation, and the effect evaluation module monitors ecological and economic effects. The knowledge base module stores historical experience and provides optimization suggestions, and the system improves the simulation accuracy of the ecological system, especially when the flow changes suddenly; and through uncertainty quantification, the system reliability and the ecological safety guarantee rate are enhanced, and high efficiency, accuracy and sustainability of ecological scheduling of the hydropower engineering are realized.

The invention belongs to the technical field of automatic assembly, and discloses an automatic assembly system and method for a large nut of a water turbine ball valve, and the system comprises a mechanical arm, an end effector and an intelligent nut tightening control system. The end effector integrates an electromagnet adsorption mechanism, a servo drive rotating mechanism, a floating compensation mechanism and a visual recognition mechanism, nuts of different specifications are grabbed in a self-adaptive mode through an electromagnet, submillimeter-level accurate positioning is achieved through a laser galvanometer stereo camera, and the assembling posture is automatically adjusted in combination with the floating compensation mechanism. The intelligent control system adopts a staged tightening strategy, performs real-time monitoring through a torque sensor, and has a dual protection mechanism. The hydraulic turbine ball valve large nut assembling machine achieves full-automatic assembling of hydraulic turbine ball valve large nuts, has the advantages of being high in assembling efficiency, high in positioning precision, high in adaptability, reliable in quality and the like, solves the problems that manual assembling is large in labor intensity, many in potential safety hazards, poor in quality consistency and the like, and remarkably improves the assembling quality and efficiency of key parts of a hydropower station.

The invention relates to a runoff hydropower station generating capacity prediction method based on depth data fusion, and the method comprises the following steps: S1, carrying out data layer fusion, integrating multi-source hydro meteorological data, obtaining multi-source data from a meteorological station, a hydrological station, a satellite remote sensing platform and a numerical weather forecasting system, carrying out the cleaning, interpolation and normalization processing of the data, and carrying out the prediction of the generating capacity of a runoff hydropower station; performing data space-time alignment; s2, carrying out feature layer fusion, firstly inputting a multi-source data set of a unified space-time reference, carrying out feature extraction, carrying out feature fusion based on a self-attention mechanism, embedding a water balance equation in a deep learning model, and obtaining a comprehensive feature vector; and S3, performing prediction, inputting the comprehensive feature vector in the step S2, and performing power generation prediction by using a deep neural network.

The invention provides a hydropower station natural disaster intelligent early warning and emergency decision-making method and system based on multi-source data fusion and dynamic threshold optimization, and the method comprises the steps: collecting the water regimen data of a hydropower station and a drainage basin in real time through the deployment of a water level gauge, a rain gauge, a wind speed sensor and a thunder and lightning monitoring device, and combining weather forecast data and historical hydrological data, thereby achieving the intelligent early warning and emergency decision-making of the hydropower station. And dynamically calculating a disaster threshold value, and generating a graded early warning signal. The system comprises a data acquisition module, a dynamic threshold matching module, an early warning generation module, a case retrieval module, an information issuing module and a response tracking module. The early warning threshold is dynamically adjusted through multi-source data fusion and a machine learning technology, and the early warning accuracy is improved; a historical case library is constructed by using a knowledge graph technology, and intelligent recommendation of a disaster disposal scheme is realized; and early warning information and disposal suggestions are directionally pushed through multiple channels, so that the emergency response efficiency is improved. The conversion from passive response to active early warning is realized, and the influence of natural disasters on the operation of the hydropower station is obviously reduced.

The invention discloses a hydroelectric generating set operation fault data enhancement method and device, and the method comprises the steps: firstly collecting a set vibration signal, selecting a time-frequency transformation method to convert a one-dimensional vibration signal into a two-dimensional time-frequency image, enhancing the feature dimension of the signal, constructing a diffusion feature migration model, gradually disturbing the data distribution to Gaussian noise through forward diffusion, and carrying out the recognition of the Gaussian noise. The method comprises the following steps of: performing inverse denoising to generate simulation data highly similar to a real fault sample, realizing relevance learning and migration sharing of fault features among different working conditions in combination with an adversarial feature migration architecture, and finally evaluating an enhancement effect by calculating similarity among samples, and inputting enhanced data into a fault diagnosis model to verify precision improvement. Through the combination of time-frequency transformation and a diffusion model, sample scarcity and working condition barriers are broken through, a remarkable effect is shown in the aspects of expanding the fault sample scale and enriching the sample dimension, the similarity of generated data and a real sample is improved, the diagnosis precision is improved, and the model generalization ability is remarkably enhanced.

The invention provides an intelligent safety and stability assessment method, system and device for an underground cavern and a medium, and the method comprises the following steps: S1, building a three-dimensional geological model of the underground cavern according to initial geological exploration data; s2, multiple monitoring instruments are pre-buried in key parts of the underground cavern, and monitoring data are collected in real time; s3, constructing an underground cavern safety and stability evaluation model based on a deep learning algorithm; s4, inputting real-time monitoring data into an evaluation model, combining historical monitoring data and geological model information, analyzing and comparing with a three-dimensional numerical calculation result, adjusting geological parameters, performing reconstruction evaluation, and outputting a safety and stability level; and S5, according to an evaluation result, automatically generating early warning information and processing suggestions. According to the method, the problems of poor real-time performance and low data utilization rate of traditional evaluation under complex geological conditions are solved, and the intelligent level of construction safety monitoring of underground cavern groups such as hydropower stations and pumped storage projects can be effectively improved.

The invention provides a hydropower station AI supervision system and method based on a multi-modal large model, and relates to the technical field of intelligent hydropower. The system comprises a multi-modal data acquisition module, a cross-modal space-time alignment module, a multi-modal feature extraction module, a multi-modal large model processing module and an intelligent reasoning and decision module. A neural differential equation model is introduced to carry out space-time alignment on asynchronous sensing data, networks such as Vision Transformer, MelCNN, TCN and the like are utilized to extract multi-modal features, cross-modal fusion analysis is realized by combining a local and global attention mechanism and dynamic weight distribution, and equipment abnormality is further reasoned based on a reconstruction error, a mahalanobis distance and a knowledge graph and a maintenance strategy is generated. According to the method, high-precision anomaly detection, fault root cause positioning and dynamic maintenance optimization of key equipment of the hydropower station are realized, diagnosis errors caused by traditional manual inspection and data splitting are avoided, and the operation and maintenance intelligence level and the equipment operation reliability are improved.

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The invention discloses a system climbing demand evaluation and calling method for high-proportion new energy consumption. The method comprises the following steps: step 1, determining a thermal power generating unit start-stop model based on a fan, photovoltaic and load prediction curve of a time period scale; 2, evaluating the output condition of the fire motor start-stop model in the sub-period based on the short-time scale prediction fan and photovoltaic data; 3, evaluating the climbing power vacancy of the system by calculating the climbing capacity and the climbing capacity based on the output data of the thermal power generating unit; 4, constructing an energy storage sequential constraint model based on the system climbing power vacancy; and step 5, evaluating the system power supply shortage, the wind and light discarding result and the calling cost based on the system climbing power vacancy. The multi-source cooperative climbing capacity evaluation model including thermal power, hydroelectric power and energy storage is constructed, and the deep peak regulation capability of the thermal power generating unit and the cooperative optimization mechanism of the energy storage rapid response characteristic are combined; and the extreme climbing event coping capability is obviously improved.

The invention provides an automatic generation control (AGC) collaborative optimization method based on time sequence deep reinforcement learning PID (Proportion Integration Differentiation) control. Comprising the following steps: establishing a three-area multi-source interconnected power system with participation of hydroelectric power, thermal power, wind power, photovoltaic power and energy storage, and bringing wind and light storage resources into an automatic power generation control loop. The invention provides a novel AGC adaptive PID controller based on time sequence deep reinforcement learning by utilizing the time sequence perception capability of a long short-term memory neural network and combining a double-delay depth deterministic strategy gradient algorithm, and provides an efficient time sequence deep reinforcement learning solution for intelligent power grid frequency control.

The invention discloses a hydropower station expansion tank water level prediction alarm method and system, belongs to the technical field of hydropower station automatic control, and aims to solve the technical problems of limited monitoring points, high false alarm rate, response delay and the like of a traditional hydropower station expansion tank water level alarm system. According to the invention, the multi-mode sensing network is constructed, and data of the pressure sensor array, the radar level meter and the float switch group are fused, so that space continuous monitoring is realized; a three-level sensing architecture is adopted, heterogeneous data is subjected to normalization processing, and dimensional differences are eliminated; the LSTM network is used for liquid level prediction, and a self-adaptive window adjustment mechanism is combined, so that the prediction flexibility and accuracy are improved; a DQN algorithm is adopted to optimize a control strategy, fine adjustment of the liquid level is achieved by adjusting the opening degree of the valve, the accuracy, timeliness and intelligent level of water level monitoring and alarming of the expansion water tank of the hydropower station are remarkably improved, the stability of the liquid level of the water tank is ensured, the system operation efficiency is optimized, the manual intervention requirement is reduced, and safety is improved.

The invention discloses a hydropower station safety management method and device based on a sensor network, and the method comprises the steps: constructing a digital twin model of a hydropower station, simulating different directed sensor node layout schemes, and determining an optimal putting strategy; randomly putting a plurality of directed sensor nodes in the to-be-monitored area of the hydropower station according to the optimal putting strategy to obtain an initial arrangement scheme; optimizing the initial arrangement scheme based on a genetic algorithm to obtain a target arrangement scheme; activating directed sensor nodes in the target arrangement scheme; acquiring current operation information through the activated directed sensor node; constructing a multi-dimensional equipment knowledge graph and predicting the operation trend of the hydropower station equipment in combination with the current operation information; and identifying a potential fault chain based on the operation trend and carrying out hydropower station safety early warning and safety decision. According to the invention, the sensor network is introduced to acquire the current operation information of the equipment for safety early warning and decision making, so that the safety management efficiency and accuracy of the hydropower station are effectively improved.

The invention discloses a hydropower station unit fire alarm auxiliary decision-making management system, which relates to the technical field of power equipment safety and comprises a fire data acquisition module, a fire data analysis module, a fire alarm module, a linkage control module and a system management module. And the fire data acquisition module is used for acquiring fire related parameter data in a hydropower station unit area in real time, wherein the fire related parameter data comprises smoke concentration, temperature change rate, flame characteristics and carbon monoxide concentration. By adopting multiple types of sensor units, a smoke sensor, a temperature sensor, a flame sensor, a carbon monoxide sensor and the like are covered for combined acquisition, compared with the condition that the types of sensors of an existing system are limited, multi-dimensional fire related data can be comprehensively and accurately acquired, deep analysis is performed on the acquired data by means of an intelligent algorithm, and the accuracy of the system is improved. The historical data and the real-time data are compared, the false alarm probability is reduced, and the alarm accuracy is improved.

The invention discloses a hydropower station diversion tunnel longitudinal crack depth detection method, which comprises the following steps: arranging ultrasonic measuring points above and below a diversion tunnel longitudinal crack to obtain a reinforced concrete sound velocity representative value; arranging ultrasonic measuring points within the length range of the crack; performing ultrasonic detection, and judging the depth of the crack through ultrasonic waves; adjusting the position of the ultrasonic measuring point according to the depth of the fracture until the ultrasonic shape is in a suitable state; and calculating the depth of each detection point of the longitudinal crack. According to the technical scheme, by means of the method for detecting the longitudinal crack depth of the reinforced concrete in the diversion tunnel of the hydropower station through the ultrasonic waves, crack depth detection and calculation of the circular tunnel are completed, and in order to judge the development condition and the stability state of the crack, the crack depth can be regularly detected in the overhaul period of a hydropower station generator set; therefore, monitoring of longitudinal crack expansion and extension is realized.

The invention belongs to the technical field of hydroelectric generating set monitoring, and relates to a water turbine bearing temperature and lubricating oil state monitoring and fault diagnosis method and system. According to the method, triple-redundancy temperature measurement values of a water turbine bearing are adopted, multiple parameters are fused, redundancy design is adopted, sensor failure diagnosis is executed, a temperature-vibration-oil pressure combined feature space is constructed according to the triple-redundancy temperature measurement values and online oil quality parameters, a fault propagation map is established, and the fault propagation map is analyzed. And finally, based on the multi-level diagnosis model and in combination with the fault propagation atlas, composite fault mode recognition is realized, a fault classification result is obtained, and the composite fault mode recognition is realized based on the multi-level diagnosis model, so that the problems of low reliability, single monitoring dimension and insufficient fault recognition capability of a sensor in the prior art are effectively solved. And the equipment reliability and the operation safety are obviously improved.

The invention provides a hydropower station reservoir water level prediction analysis method, a hydropower station reservoir water level prediction analysis system and a storage medium, and relates to the technical field of reservoir water level prediction. A unified modeling path from hydrological element collection to water level prediction is realized, a directed weighted graph reflecting a real hydrological conduction path is formed by fusing a digital elevation model and hydrological data, constructing a space node and a water flow connection relation thereof and giving edge weights, so that the water level prediction is not limited to point data analysis any more, and the water level prediction accuracy is improved. According to the method, modeling of collaborative change of hydrological states in a whole influence basin is changed, a unified framework fusing a terrain structure, a hydrological process and machine learning capability is constructed, coupling modeling of spatial distribution characteristics and time dynamic characteristics is achieved, and the precision and reliability of water level prediction are improved.

The invention discloses a hydropower station microcomputer operation and maintenance system and method based on deep learning. The method comprises the following steps: S1, preprocessing to obtain standardized operation data; s2, extracting a hyper-parameter initial value of the improved liquid time constant network framework; s3, extracting a candidate feature set of the standardized operation data to construct a mayfly naiad algorithm search environment, and operating a mayfly naiad algorithm to obtain an optimal feature subset and an optimal hyper-parameter set; s4, training the improved liquid state time constant network model by adopting the standardized operation data to obtain a trained improved liquid state time constant network model; and S5, deploying the trained improved liquid state time constant network model to an online reasoning environment, executing anomaly detection on real-time operation data of the microcomputer protection system of the hydropower station updated in real time, and outputting an anomaly detection result. According to the system, the severity of the fault can be determined while the fault is identified, so that an operator on duty or a system background can quickly make a response strategy.

The invention discloses a BIM-based hydropower station full life cycle design, construction, operation and maintenance integrated control system, and the system comprises an intelligent sensing layer which integrates 5G + Beidou positioning, a LoRa gateway and a sensor, collects multi-source heterogeneous data in real time, and transmits the multi-source heterogeneous data to a digital twinborn layer after the multi-source heterogeneous data is filtered by an edge node; a digital twinborn layer: constructing a parameterized model library through laser point cloud and BIM automatic registration, integrating a geological parameter dynamic correction algorithm, mapping a construction period stress field in real time, and updating model parameters based on sensing data self-evolution; the intelligent decision-making layer performs equipment fault prediction by using an LSTM neural network, optimizes multi-machine load distribution in combination with an improved PSO algorithm, and automatically adjusts a start-stop strategy when the load fluctuates; and the security execution layer is used for triggering equipment operation after virtual twinborn deduction verification through a block chain evidence storage instruction, realizing virtual-real dual verification in combination with an industrial firewall, and finally feeding back a running state to the sensing layer to calibrate and update a model, and supporting intelligent decision.

The invention relates to the technical field of hydropower station equipment monitoring, in particular to a deep learning modeling and analysis method for hydropower station equipment operation trend early warning. Comprising the following steps: collecting multi-source parameters and dividing dynamic working conditions; mechanism-data driven fusion feature construction is carried out; training a physical informed deep learning model; carrying out meta-learning migration optimization; performing dynamic threshold early warning judgment; and performing mechanism closed-loop verification. The model is built based on a physical informed neural network framework, a differentiable mechanism constraint loss function is introduced, and dual verification is carried out through an equipment simplified simulation model and a historical fault case, so that model output can be ensured to accord with an equipment operation physical rule, and the situation that a pure data driven model possibly deviates from physical common knowledge is avoided; the reliability of the early warning model is improved; according to the method, the basic model is trained by adopting the meta-learning algorithm guided by the fault type label, so that the problems of model over-fitting and high adaptation cost in a small sample scene in the traditional technology are solved.

The invention, which relates to the technical field of hydropower station dam safety monitoring, discloses a hydropower station dam safety monitoring data acquisition and transmission system comprising a cloud twin brain module and edge neurons. The cloud twin brain module comprises a sequence neural network engine and a reflection kernel generation module, the sequence neural network engine adopts a neural network architecture with parallel and cyclic dual representation, comprises a time mixing module and a channel mixing module, and can learn a normal operation mode of the dam from historical monitoring data; and the reflection nuclear generation module compresses the reference twin model into a lightweight reflection nuclear model and issues the lightweight reflection nuclear model to the edge device. The edge neuron comprises a micro-twinborn prediction module and a hierarchical transmission control module, and the micro-twinborn prediction module predicts a theoretical expected value of a dam state in real time and calculates a reflection deviation with an actual observation value; the hierarchical transmission control module implements a three-level response strategy according to the magnitude of the reflection deviation, transmits abstract information according to an abnormal trend, and uploads an emergency abnormality in time.

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.