Hydraulic engineering informatization integrated management system and method

Abstract

The invention belongs to the technical field of water conservancy projects, and particularly relates to a water conservancy project informatization integrated management system and method, and the system comprises a water conservancy project data collection module and a water conservancy project data processing module. A water conservancy project data analysis module and a water conservancy project scheduling optimization module; the water conservancy project data acquisition module is used for acquiring multi-source data of water conservancy project operation; the water conservancy project data processing module is used for preprocessing the collected multi-source data and transmitting the preprocessed data to the water conservancy project data analysis module; the water conservancy project data analysis module carries out analysis on the basis of the preprocessed multi-source data to obtain fault prediction and water volume change trend information, and the water conservancy project scheduling optimization module carries out water conservancy project scheduling optimization on the basis of the analysis result of the fault prediction and water volume change trend information. And optimizing the scheduling operation scheme of the water conservancy project.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water conservancy engineering, and particularly relates to a water conservancy engineering informatization integrated management system and method. BACKGROUND

[0002] Water conservancy engineering informatization integrated management is a mode of intelligent and collaborative management realized by integrating the whole-process data and management system of planning, construction, operation and maintenance of water conservancy engineering by means of modern information technology. The core of the mode is to improve the overall efficiency and safety of water conservancy engineering through technology fusion, data intercommunication and process optimization.

[0003] In traditional water conservancy engineering management, there are problems of scattered multi-source data, single collection means and poor real-time performance, which leads to delayed decision-making. The traditional method relies on manual experience, and the efficiency of fault prediction and disaster response is low. For example, the accuracy of flood forecasting is insufficient, the risk of reservoir dam break is difficult to discover in time, and the traditional scheduling relies on manual experience and lacks real-time optimization capability, which leads to low water resource utilization efficiency and slow emergency response.

[0004] Therefore, the application provides a water conservancy engineering informatization integrated management system and method. SUMMARY

[0005] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background art.

[0006] The technical scheme adopted by the application to solve the technical problem is that the water conservancy engineering informatization integrated management system comprises a water conservancy engineering data collection module, a water conservancy engineering data processing module, a water conservancy engineering data analysis module and a water conservancy engineering scheduling optimization module. The water conservancy engineering data collection module is used for collecting multi-source data of water conservancy engineering operation, and the multi-source data includes hydrological, water quality, meteorological and working condition multidimensional data, and the collected multi-source data is transmitted to the water conservancy engineering data processing module. The Internet of Things sensor network, satellite remote sensing, unmanned aerial vehicle inspection and other technologies are adopted to collect hydrological (water level), water quality (pH value), meteorological (rainfall) and working condition (gate state or equipment operation parameter) in real time. The water conservancy engineering data processing module is used for preprocessing the collected multi-source data, and transmitting the preprocessed data to the water conservancy engineering data analysis module. The water conservancy engineering data analysis module is used for analyzing the preprocessed multi-source data, and analyzing fault prediction and water quantity change trend information. The water conservancy project scheduling optimization module optimizes the water conservancy project scheduling operation scheme based on fault prediction and analysis results of water quantity change trend information.

[0007] Preferably, the water conservancy project data processing module is used for pre-processing the collected multi-source data, including: Step S1: data cleaning and exception processing: acquiring multi-source data of water conservancy project operation, using interpolation method to complete time series data, using KNN algorithm to predict missing parameters of equipment state data; Outlier detection: identifying abnormal points based on Z-score method, and detecting nonlinear abnormal patterns using isolated forest algorithm; Step S2: data transformation and standardization: converting remote sensing images, sensor time series data, etc. into a unified time stamp coordinate system, and using maximum-minimum scaling to process water level and flow data, and using Z-score standardization for meteorological data; Step S3: multi-source data fusion: satellite remote sensing data and ground monitoring station data are matched in time axis through dynamic time warping, and three-dimensional laser scanning point cloud and BIM model are registered; Step S4: feature engineering optimization: extracting trend items, periodic items and residual items of water level data through STL decomposition, and capturing transient impact features of gate vibration signals through wavelet change; Based on Kriging interpolation, a rainfall distribution map of the basin is generated, and a spatial aggregation pattern of the reservoir area leakage points is analyzed using a spatial autocorrelation index library.

[0008] Preferably, the water conservancy project data analysis module analyzes the pre-processed multi-source data to generate a fault prediction, which specifically includes: Step one: collecting the operating parameters of water pumps and water gate equipment such as flow, vibration frequency, temperature, etc. through a sensor network, constructing a parameter correlation network between devices, and using ranking correlation coefficients to determine the linkage between parameters (for example, a continuous expansion of the ranking difference between two sensor parameters may indicate an anomaly); Step two: using the random maximum graph algorithm to screen out the strongest device cluster (such as a sensor group that simultaneously monitors water gate opening, water level and flow rate) as the core observation object of fault prediction; Step three: constructing a device operation state transition model based on Markov chain, calculating the transition probability between different states (normal / early warning / fault) through historical data, for example, when the vibration frequency of a water turbine generator exceeds the baseline value for three consecutive times, triggering state transition to the early warning level; Step four: combining the prior fault model of the device (such as the life decay curve of the water pump bearing) with the real-time state transition probability to comprehensively calculate the fault risk value, and if the risk value exceeds the threshold, it is marked as a high-risk device; Step five: classify the abnormal data using a pre-trained machine learning model to distinguish between mechanical wear, electrical failure, environmental interference, etc., and match the historical case library to generate maintenance recommendations.

[0009] Preferably, a device operation state transition model is constructed based on a Markov chain, and different states are calculated through historical data: The device operation state is divided into three categories: normal, warning, and failure. Collect historical operation parameters (such as vibration frequency, temperature, and current, etc.), and eliminate abnormal sensor data, such as using a sliding window method to count the state sequence of adjacent time points, and record the device state once an hour; A 3x3 transition frequency matrix is constructed to count the number of transitions from the normal state to the warning state, and the transition probability is calculated based on the number of times, and then a 3x3 device operation state transition model is generated according to the transition probability.

[0010] Preferably, the step four comprehensive calculation of failure risk value is specifically: According to the device historical failure data or physical degradation law, a device friend failure probability model is established, for example, the life attenuation curve of the water pump bearing can quantify the functional relationship between the remaining life and the running time; Based on the Markov chain model, the transition path probability of the future state of the device is dynamically updated through the current state of the device and the state transition matrix; The prior failure probability and real-time transition probability are weighted and fused to obtain a comprehensive failure risk value; According to the importance of the device, the historical failure loss, and the industry standard, set the risk threshold, if it is high risk, stop immediately for repair, and trigger emergency response, if it is medium risk, strengthen monitoring frequency, pre-prepare spare parts, if it is low risk, regular operation and maintenance; Traditional threshold alarm is easily disturbed by single parameter fluctuation, such as temporary water flow impact leading to sensor false alarm, while the fusion model can reduce the false alarm rate by weighted calculation, such as prior model weight 40%, real-time state weight 60%, such as pump station triggering threshold alarm due to water level mutation during heavy rain, but the comprehensive model identifies that the bearing life is still in a safe range, avoiding false shutdown.

[0011] Preferably, the step five of using a pre-trained machine learning model to classify abnormal data is specifically: Step S5-1.1: Fast Fourier transform and wavelet packet decomposition are performed on time series signals such as vibration, current, etc., to extract fundamental frequency, multiple frequency and characteristic frequency (such as bearing failure frequency), and early mechanical wear characteristics are identified through envelope analysis, and current signals need to be decomposed into active / reactive components to monitor electrical characteristics such as three-phase imbalance; Step S5-1.2: Construct an environmental parameter disturbance coefficient matrix for temperature, humidity, air pressure, etc. Use Pearson correlation coefficient to analyze the correlation between vibration amplitude and temperature change, eliminate the interference of environmental noise values on the classification model, for example, in heavy rain weather, the abnormal sensor reading needs to be corrected by environmental factor weighting; Step S5-1.3: Use conditional generative adversarial network to generate abnormal samples under specific working conditions, and solve the problem of sparse mechanical wear data; Step S5-2.1: Abnormal type identification: use pre-trained ResNet-50 model for transfer learning of vibration spectrum, and combine random forest classifier to realize coarse-grained classification of mechanical wear, electrical fault and environmental disturbance; Electrical fault monitoring introduces LSTM network to analyze current harmonic distortion rate time series characteristics, and determines short circuit fault when odd harmonic proportion increases by 5%; Mechanical wear positioning: use Grad-CAM visualization technology to accurately locate the fault components (such as bearings / gears), and combine ISO10816 standard to determine the wear grade; Electrical interference classification: construct Bayesian network to quantify common mode / differential mode interference weight, and identify conductive (power line) and radiated (electromagnetic wave) interference.

[0012] Through the above design: through Fourier envelope analysis (physics) and ResNet feature extraction (data) cooperation, the problem of traditional threshold method not sensitive to early fault is solved; edge-cloud collaborative computing: LSTM model lightweight deployment (parameter compression to 1 / 10) realizes real-time inference, which promotes the transformation of water conservancy engineering equipment management from passive maintenance to predictive maintenance through multi-source data fusion and hybrid model architecture.

[0013] Preferably, the water conservancy dispatching optimization module optimizes the water conservancy dispatching operation scheme based on the analysis results of fault prediction, water quantity change trend, etc. Step I: Time series decomposition is performed on water level, precipitation and other data to separate long-term trend (such as water level decrease in dry season), periodic fluctuation (such as daily water level fluctuation in rainy season) and random noise, and identify potential abnormal points; Step II: Integrate meteorological data (rainfall, evaporation), engineering dispatching data (gate opening, water release) and hydrological data to establish a multivariate regression model, for example, the correlation analysis of the increase of upstream inflow after heavy rain and the opening amplitude of the gate; Step III: Set graded warning thresholds according to historical extreme values, and dynamically adjust them combined with real-time data, for example, when the water level exceeds the flood control water level for 2 hours and the rainfall continues to increase, the red warning is triggered; Step IV: Use ARIMA or LSTM model to predict water volume change in the next 72 hours, generate multiple sets of scheduling schemes, and evaluate the risk probability of different schemes through Monte Carlo simulation; Preferably, the water conservancy project scheduling optimization module optimizes the water conservancy project scheduling operation scheme based on the analysis results of fault prediction, water volume change trend and the like, and the optimization further comprises: Through multi-source data fusion and feature extraction: heterogeneous data collection: integrate multi-dimensional real-time monitoring data such as water level, rainfall, gate opening, pump station current, and fuse external data sources such as weather forecast and historical flood records: Then clean and decompose the data; Subsequently, a multi-objective optimization model is constructed: a thinking target system is constructed by combining flood control safety, power generation benefit, ecological flow and operation and maintenance cost, a mixed integer programming is used to process discrete variables, and a genetic algorithm or particle swarm optimization is used to realize global optimization; Finally: through the ARIMA-LSTM model, the water volume change in the next 72 hours is predicted, multiple sets of preselected schemes are generated, such as early flood discharge and water intake restriction, and through Monte Carlo simulation, the schemes are randomly sampled n times, the dam break probability and economic loss expectation value are calculated, the Pareto frontier optimal solution is screened, and the flood limit water level and scheduling threshold are adaptively adjusted according to the real-time rainfall and equipment health state; Preferably, the adaptive adjustment of the flood limit water level and the scheduling threshold comprises: A reservoir-river hydraulic model is established, and the PPO algorithm is used to optimize the upstream and downstream flood discharge timing; Based on the NURBS curved surface reconstruction Q-H efficiency characteristic, the Monte Carlo number search is used to select the optimal pump station start-stop combination; the NSGA-III algorithm is used to balance power generation and ecological demand, and ensure the minimum ecological flow of the downstream river channel; The optimized scheme is mapped to the digital twin model, the scheme feasibility is verified n times per day, each scheduling record triggers model updating, cross-basin data sharing is realized through federated learning, and the flood limit water level and the scheduling threshold are adjusted based on the scheme feasibility; Preferably, a water conservancy project information integration management method comprises the following steps: S1: Collect multi-source data of water conservancy project operation, and pre-process the multi-source data through a water conservancy project data processing module; S2: The pre-processed multi-source data is transmitted to a water conservancy project data analysis module, and fault prediction and water volume change trend information are analyzed; S3: The water conservancy project scheduling optimization module optimizes the water conservancy project scheduling operation scheme based on the fault prediction and water volume change trend information.

[0014] The beneficial effects of the present application are as follows: The water conservancy project informatization integrated management system and method provided by the application solves the problems of data island, response lag and single-target decision-making in traditional water conservancy projects through a technical closed loop of data fusion-intelligent analysis-dynamic optimization, and promotes the transformation of the industry to a closed loop management mode of perception-prediction-decision-verification. By driving the threshold value and strategy adjustment in real time, extreme weather and complex working conditions are responded to; the objective function is quantitatively optimized to balance resource utilization and environmental protection, and the safety resilience, management efficiency and sustainability of the water conservancy project are improved, thereby providing a standardized technical path for the construction of smart water conservancy. BRIEF DESCRIPTION OF DRAWINGS

[0015] The application will be further described below with reference to the drawings.

[0016] Figure 1 The system flowchart of the application; Figure 2 The method flowchart of the application. DETAILED DESCRIPTION

[0017] In order to make the technical means, creative features, purposes and effects of the application easy to understand, the application will be further described below with reference to the specific embodiments.

[0018] A water conservancy project informatization integrated management system comprises a water conservancy project data acquisition module, a water conservancy project data processing module, a water conservancy project data analysis module and a water conservancy project dispatching optimization module. The water conservancy project data acquisition module is used to acquire multi-source data of water conservancy project operation, and the multi-source data comprises hydrological, water quality, meteorological and working condition multidimensional data, and the acquired multi-source data is transmitted to the water conservancy project data processing module. The hydrological (water level), water quality (pH value), meteorological (rainfall) and working condition (gate state or equipment operation parameter) are acquired in real time by using the Internet of Things sensor network (water level meter, flow meter, water quality monitor and the like), satellite remote sensing, unmanned aerial vehicle inspection and the like. The water conservancy project data processing module is used to pre-process the acquired multi-source data, and the pre-processed data is transmitted to the water conservancy project data analysis module. The water conservancy project data analysis module is used to analyze the pre-processed multi-source data, analyze fault prediction and water quantity change trend information. The water conservancy project dispatching optimization module is used to optimize the water conservancy project dispatching operation scheme based on the analysis results of the fault prediction and water quantity change trend information.

[0019] The system realizes the transformation of water conservancy projects from "passive response" to "active control" through full-sensing data collection, intelligent analysis and decision-making, and dynamic scheduling optimization. Through technology empowerment and system integration, not only the engineering efficiency is improved, but also innovative solutions are provided to cope with climate change and ensure water safety.

[0020] The water conservancy project data processing module is used for pre-processing the collected multi-source data, including: Step S1: Data cleaning and exception handling: acquire multi-source data of water conservancy project operation, use interpolation method to complete time series data, use KNN algorithm to predict missing parameters for equipment state data; Outlier detection: identify outliers based on Z-score method, and use isolated forest algorithm to detect nonlinear anomaly patterns; Step S2: Data transformation and standardization: convert remote sensing images, sensor time series data, etc. into a unified timestamp coordinate system, and use maximum-minimum scaling to process water level and flow data, and use Z-score standardization for meteorological data; Step S3: Multi-source data fusion: satellite remote sensing data and ground monitoring station data are matched in time axis through dynamic time warping, and three-dimensional laser scanning point cloud and BIM model are registered; Step S4: Feature engineering optimization: extract trend items, periodic items and residual items of water level data through STL decomposition, and capture transient impact features of gate vibration signals through wavelet change; Generate a rainfall distribution map of the basin based on Kriging interpolation, and analyze the spatial aggregation pattern of the seepage point in the reservoir area using the spatial autocorrelation index.

[0021] The water conservancy project data analysis module analyzes the pre-processed multi-source data and generates a fault prediction, including: Step one: Collect the operating parameters of water pumps and water gate equipment such as flow, vibration frequency, temperature, etc. through sensor network, construct the parameter correlation network between devices, and use ranking correlation coefficient to judge the linkage relationship between parameters (for example, the continuous expansion of the ranking difference between two sensor parameters may indicate an anomaly); Step two: Use the random maximum graph algorithm to select the strongest device cluster (such as a sensor group that monitors water gate opening, water level and flow rate at the same time) as the core observation object for fault prediction; Step three: Construct a device operation state transition model based on Markov chain, calculate the transition probability between different states (normal / early warning / fault) through historical data, for example, when the vibration frequency of the water turbine generator exceeds the baseline value for three consecutive times, trigger state transition to early warning level; Step four: combine the device prior failure model (such as the bearing life attenuation curve of the water pump) with the real-time state transition probability, and comprehensively calculate the failure risk value. If the risk value exceeds the threshold value, it is marked as a high-risk device; Step five: use a pre-trained machine learning model to classify abnormal data, distinguish mechanical wear, electrical failure, environmental interference, etc., and match the historical case library to generate maintenance recommendations.

[0022] Based on Markov chain, the device operation state transition model is constructed, and the specific state is calculated by historical data: The device operation state is divided into normal, warning and failure three categories; Collect historical operation parameters (such as vibration frequency, temperature and current, etc.), eliminate sensor abnormal data, such as using sliding window method to count the state sequence of adjacent time points, record the device state once an hour; Construct a 3x3 transition frequency matrix to count the number of transitions from normal state to warning state; and calculate the transition probability based on the number of times, then generate a 3x3 device operation state transition model according to the transition probability; Current state / next state Normal Warning Fault Normal 85 12 3 Warning 20 65 15 Fault 0 5 95 And calculate the transition probability, that is, the probability from normal to warning is calculated by row,

[0023] Finally, the probability matrix is generated:

[0024]

[0025]

[0026] If the device is continuously from normal to warning for two times, the dynamic threshold adjustment is triggered, that is, the new threshold value is equal to ; Through the above design: by constructing the device parameter correlation network (such as the Spearman rank correlation coefficient matrix of water level-gate opening-flow rate), hidden failure modes can be identified; the dynamic risk value quantification model combines the Markov state transition probability (such as the probability of triggering warning when vibration exceeds 62%) and the device life attenuation curve (bearing wear rate prediction error <5%), to realize the dynamic update of risk value.

[0027] The comprehensive calculation of failure risk value in step four is: According to the historical failure data of the device or the physical degradation law, the device failure probability model is established, for example, the life attenuation curve of the water pump bearing can quantify the functional relationship between the remaining life and the running time; Based on the Markov chain model, the transition path probability of the future state of the equipment is dynamically updated through the current state of the equipment and the state transition matrix. The prior failure probability and the real-time transition probability are weighted and fused to obtain a comprehensive failure risk value. According to the importance of the equipment, the historical failure loss and the industry standard, the risk threshold is set. If it is high risk, it will be immediately shut down for maintenance and trigger emergency response. If it is medium risk, the monitoring frequency will be increased and spare parts will be preinstalled. If it is low risk, it will be maintained regularly. Traditional threshold alarm is easily disturbed by single parameter fluctuation, such as temporary water impact leading to sensor false alarm. The fusion model can reduce the false alarm rate by weighted calculation, such as prior model weight 40% and real-time state weight 60%. For example, during heavy rain, the threshold alarm of the pump is triggered due to sudden change of water level, but the comprehensive model identifies that the bearing life is still in the safe range, avoiding false shutdown.

[0028] The step five utilizes a pre-trained machine learning model to classify abnormal data, specifically: Step S5-1.1: Fast Fourier transform and wavelet packet decomposition are performed on time series signals such as vibration and current to extract fundamental frequency, multiple frequency, and characteristic frequency (such as bearing fault frequency). Early mechanical wear characteristics are identified through envelope analysis. Current signals need to be decomposed into active / reactive components to monitor three-phase imbalance and electrical characteristics. Step S5-1.2: Construct an environmental parameter disturbance coefficient matrix of temperature, humidity, and air pressure. Use Pearson correlation coefficient to analyze the correlation between vibration amplitude and temperature change to eliminate the interference of environmental noise on the classification model. For example, in heavy rain, the sensor reading is abnormal and needs to be corrected by environmental factors. Step S5-1.3: Use conditional generative adversarial network to generate abnormal samples under specific working conditions to solve the problem of sparse mechanical wear data. Step S5-2.1: Abnormal type identification: use pre-trained ResNet-50 model for transfer learning of vibration spectrum, and combine random forest classifier to realize coarse-grained classification of mechanical wear, electrical fault and environmental interference. Electrical fault monitoring introduces LSTM network to analyze current harmonic distortion rate time series characteristics. When the proportion of odd harmonics increases by 5%, it is determined as a short circuit fault. Mechanical wear positioning: use Grad-CAM visualization technology to accurately locate the fault components (such as bearings / gears) and determine the wear grade according to ISO10816 standard. Electrical interference classification: construct Bayesian network to quantify common mode / differential mode interference weight and identify conductive (power line) and radiated (electromagnetic wave) interference.

[0029] Through the above design: through Fourier envelope analysis (physics) and ResNet feature extraction (data) cooperation, the problem of traditional threshold method not sensitive to early fault is solved; edge-cloud collaborative computing: LSTM model lightweight deployment (parameter compression to 1 / 10) realizes real-time inference, which promotes the transformation of water conservancy engineering equipment management from passive maintenance to predictive maintenance through multi-source data fusion and hybrid model architecture.

[0030] The water conservancy dispatching optimization module optimizes the water conservancy dispatching operation scheme based on the analysis results of fault prediction, water quantity change trend and the like, including: Step I: Time series decomposition is performed on water level, precipitation and the like data, long-term trend (such as water level drop in dry season), periodic fluctuation (such as daily water level fluctuation in rainy season) and random noise are separated, and potential abnormal points are identified; Step II: Meteorological data (rainfall, evaporation), engineering dispatching data (gate opening, water release) and hydrological data are fused, and a multivariate regression model is established, for example, the correlation analysis of the increase of upstream inflow after heavy rain and the opening amplitude of the gate; Step III: The hierarchical warning threshold is set according to the historical extreme value, and is dynamically adjusted combined with real-time data, for example, when the water level exceeds the flood control water level for 2 hours continuously and the rainfall continues to increase, the red warning is triggered; Step IV: ARIMA or LSTM model is used to predict the water quantity change in the next 72 hours, to generate multiple sets of dispatching schemes, and the risk probability of different schemes is evaluated through Monte Carlo simulation; The water conservancy dispatching optimization module optimizes the water conservancy dispatching operation scheme based on the analysis results of fault prediction, water quantity change trend and the like, further including: Through multi-source data fusion and feature extraction: heterogeneous data collection: integrate water level, rainfall, gate opening, pump station current and other multi-dimensional real-time monitoring data, and fuse meteorological forecast, historical flood record and other external data sources: then clean and decompose the data; Then build a multi-objective optimization model: build a thinking target system combined with flood control safety, power generation benefit, ecological flow and operation and maintenance cost, use mixed integer programming to process discrete variables, and use genetic algorithm or particle swarm optimization to realize global optimization; Finally: through ARIMA-LSTM model to predict the water quantity change in the next 72 hours, to generate multiple sets of preselected schemes, such as early flood discharge and water intake restriction, and through Monte Carlo simulation, n times of random sampling is performed on the schemes, the dam break probability, economic loss expectation value and Pareto frontier optimal solution are calculated, and the flood control water level and dispatching threshold are adaptively adjusted according to the real-time rainfall and equipment health status; The adaptive adjustment of the flood control water level and the dispatching threshold includes: A reservoir-river hydraulic model is established, and the PPO algorithm is used to optimize the flood discharge time sequence of the upstream and downstream; Based on the Q-H efficiency characteristics of NURBS surface reconstruction, the optimal pump station start-stop combination is selected by Monte Carlo number search; the NSGA-III algorithm is used to balance the power generation and ecological demand to ensure the minimum ecological flow of the downstream river; The optimization scheme is mapped to the digital twin model, and the feasibility of the scheme is verified n times per day. Each time the scheduling record triggers model updating, cross-basin data sharing is achieved through federated learning, and the flood control water level and scheduling threshold are adjusted based on the feasibility of the scheme; A water conservancy project information integration management method, comprising the following steps: S1: Collecting multi-source data of water conservancy project operation, and preprocessing the multi-source data through a water conservancy project data processing module; S2: The preprocessed multi-source data is transmitted to the water conservancy project data analysis module, and the fault prediction and water quantity trend information are analyzed; S3: Based on the fault prediction and water quantity trend information, the water conservancy project dispatching optimization module is used to optimize the water conservancy project dispatching operation scheme.

[0031] The size of the threshold is set for easy comparison. The size of the threshold depends on the amount of sample data and the base number set by the person skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantized values.

[0032] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A water conservancy project information integrated management system, characterized in that: It includes water conservancy project data acquisition module, water conservancy project data processing module, water conservancy project data analysis module and water conservancy project scheduling optimization module: Water conservancy project data acquisition module: The water conservancy project data acquisition module is used to collect multi-source data on water conservancy project operation, including hydrological, water quality, meteorological, and construction conditions data, and transmit the collected multi-source data to the water conservancy project data processing module; Water conservancy project data processing module: The water conservancy project data processing module is used to pre-process the collected multi-source data and transmit the pre-processed data to the water conservancy project data analysis module; Water conservancy project data analysis module: The water conservancy project data analysis module analyzes pre-processed multi-source data to analyze fault prediction and water volume change trend information; Water conservancy project scheduling optimization module: The water conservancy project scheduling optimization module optimizes the water conservancy project scheduling operation plan based on the analysis results of fault prediction and water volume change trend information.

2. The water conservancy project information integrated management system according to claim 1, characterized in that: The water conservancy project data processing module is used to pre-process the collected multi-source data, including: Step S1: Data cleaning and exception handling: Obtain multi-source data on water conservancy project operation, use interpolation to complete time series data, and use the KNN algorithm to predict missing parameters for equipment status data; Outlier detection: Identify outliers based on the Z-score method and use the isolation forest algorithm to detect nonlinear abnormal patterns; Step S2: Data conversion and standardization: convert remote sensing images, sensor time series data, etc. into a unified timestamp coordinate system, use maximum-minimum scaling to process water level and flow data, and use Z-score standardization for meteorological data; Step S3: Multi-source data fusion: satellite remote sensing data and ground monitoring station data are matched to the time axis through dynamic time warping, and the 3D laser scanning point cloud is aligned with the BIM model; Step S4: Feature engineering optimization: extract the trend term, period term, and residual term of the water level data through STL decomposition, and capture the transient impact characteristics of the gate vibration signal through wavelet transformation; The basin area rainfall distribution map was generated based on Kriging interpolation, and the spatial autocorrelation index was used to analyze the spatial aggregation pattern of leakage points in the reservoir area.

3. The water conservancy project information integrated management system according to claim 2, characterized in that: The water conservancy project data analysis module analyzes the pre-processed multi-source data and generates analysis results including fault prediction, specifically: Step 1: Collect the operating parameters of water pumps and sluice gates through the sensor network, build a parameter correlation network between the devices, and use the ranking correlation coefficient to determine the linkage relationship between the parameters; Step 2: Use the random maximum graph algorithm to select the device cluster with the strongest correlation as the core observation object for fault prediction; Step 3: Build a device operation state transition model based on the Markov chain and calculate different states through historical data; Step 4: Combine the device's prior fault model with the real-time state transition probability to comprehensively calculate the fault risk value. If the risk value exceeds the threshold, the device is marked as high-risk. Step 5: Use the pre-trained machine learning model to classify abnormal data, distinguish types such as mechanical wear, electrical failure, and environmental interference, and match the historical case library to generate maintenance recommendations.

4. The water conservancy project information integrated management system according to claim 3 is characterized in that: The equipment operation state transition model is built based on the Markov chain, and different states are calculated through historical data: Divide the equipment operation status into three categories: normal, warning and fault; Collect historical operating parameters and eliminate abnormal sensor data; Construct a 3x3 transition frequency matrix to count the number of times the state transitions from normal to warning. The transition probability is calculated based on the number of times, and a 3x3 device operation state transition model is generated according to the transition probability.

5. The water conservancy project information integrated management system according to claim 4, characterized in that: The comprehensive calculation of the fault risk value in step 4 is specifically as follows: Establish equipment failure probability model based on equipment historical failure data or physical degradation laws; Based on the Markov chain model, the device's current state and state transition matrix are used to dynamically update the device's future state transition path probability. The prior failure probability and the real-time transfer probability are weighted and fused to obtain the comprehensive failure risk value.

6. The water conservancy project information integrated management system according to claim 5, characterized in that: In step 5, the abnormal data is classified using the pre-trained machine learning model as follows: Step S5-1.1: Perform fast Fourier transform and wavelet packet decomposition on time series signals such as vibration and current to extract the fundamental frequency, harmonics, and characteristic frequencies, and identify early mechanical wear characteristics through envelope analysis; Step S5-1.2: Construct an interference coefficient matrix of environmental parameters such as temperature, humidity, and air pressure, and use the Pearson correlation coefficient to analyze the correlation between vibration amplitude and temperature change to eliminate the interference of environmental noise values ​​on the classification model; Step S5-1.3: Use a conditional generative adversarial network to generate abnormal samples under specific working conditions to solve the problem of sparse mechanical wear data; Step S5-2.1: Abnormality type identification: Use the pre-trained ResNet-50 model to perform transfer learning on the vibration spectrum graph, combined with the random forest classifier to achieve coarse-grained classification of mechanical wear, electrical faults, and environmental interference; Electrical fault monitoring uses an LSTM network to analyze the time series characteristics of current harmonic distortion. When the proportion of odd harmonics suddenly increases by 5%, it is determined to be a short circuit fault. Mechanical wear location: Grad-CAM visualization technology is used to accurately locate faulty components, and the wear level is determined in accordance with ISO10816 standards; Electrical interference classification: Construct a Bayesian network to quantify the weights of common-mode and differential-mode interference and identify conducted (power lines) and radiated (electromagnetic waves) interference.

7. The water conservancy project information integrated management system according to claim 6, characterized in that: The water conservancy project scheduling optimization module optimizes the water conservancy project scheduling operation plan based on the analysis results of fault prediction, water volume change trend and other information, including: Step I: Decompose the time series of water level, precipitation and other data to separate long-term trends, periodic fluctuations and random noise, and identify potential anomalies; Step II: Integrate meteorological data, engineering scheduling data and hydrological data to establish a multivariate regression model; Step III: Set graded warning thresholds based on historical extreme values ​​and adjust them dynamically based on real-time data.

8. The water conservancy project information integrated management system according to claim 7, characterized in that: The water conservancy project scheduling optimization module optimizes the water conservancy project scheduling operation plan based on the analysis results of fault prediction, water volume change trend and other information, and also includes: Through multi-source data fusion and feature extraction: Heterogeneous data collection: integrating multi-dimensional real-time monitoring data such as water level, rainfall, gate opening, pump station current, and integrating external data sources such as weather forecasts and historical flood records: The data is then cleaned and decomposed; a multi-objective optimization model is then constructed: a thinking objective system is constructed by combining flood control safety, power generation efficiency, ecological flow, and operation and maintenance costs. Mixed integer programming is used to process discrete variables, and genetic algorithms or particle swarm optimization are combined to achieve global optimization. Finally, the ARIMA-LSTM model was used to predict water volume changes over the next 72 hours, generating multiple pre-selected scenarios. Monte Carlo simulation was then used to randomly sample the scenarios n times, calculate the probability of dam failure and the economic loss disappointment value, and screen the Pareto frontier optimal solution. The flood control water level and scheduling threshold were then adaptively adjusted based on real-time rainfall and equipment health.

9. The water conservancy project information integrated management system according to claim 8, characterized in that: The adaptive adjustment of flood control water level and scheduling threshold includes: establishing a reservoir-river hydraulic model and optimizing upstream and downstream flood discharge timing by using the PPO algorithm; Based on the reconstruction of QH efficiency characteristics based on NURBS surfaces, the Monte Carlo search was used to select the optimal pump station start-stop combination; the NSGA-III algorithm was used to balance power generation and ecological needs to ensure the minimum ecological flow in the downstream river. The optimization plan is mapped to the digital twin model, and the feasibility of the plan is verified n times per day. Each scheduling record triggers a model update, and cross-basin data sharing is achieved through federated learning. The flood limit water level and scheduling threshold are adjusted based on the feasibility of the plan.

10. A water conservancy project information integrated management method, characterized in that: The following steps are involved: S1: Collect multi-source data of water conservancy project operation and pre-process the multi-source data through the water conservancy project data processing module; S2: The pre-processed multi-source data is transmitted to the water conservancy project data analysis module, and the fault prediction and water volume change trend information are analyzed; S3: Based on fault prediction and water volume change trend information, the water conservancy project scheduling and operation plan is optimized through the water conservancy project scheduling optimization module.

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