Water conservancy facility environment interference error correction method and device based on multi-dimensional data

Abstract

The invention relates to the technical field of error correction, in particular to a water conservancy facility environment interference error correction method and device based on multi-dimensional data. The method comprises the following steps: collecting historical monitoring logs of water conservancy facilities, carrying out environmental disturbance event identification and multi-scale disturbance relevance mining, and constructing an environmental disturbance space-time relevance map; monitoring surrounding environment water monitoring parameters of the water conservancy facility, performing sequential response trend change analysis and dynamic response characteristic modeling, and constructing a facility environment personalized change response portrait; identifying a plurality of water body position monitoring data according to the surrounding environment water body monitoring parameters, and constructing a hydrodynamic full-field sensing graph; and performing nonlinear correlation analysis based on the hydrodynamic full-field perception graph and the personalized change response portrait of the facility environment, and constructing a digital twinborn model of the water conservancy facility under the water flowing condition. According to the invention, through dynamic error trend analysis and correction, the monitoring precision and monitoring stability of the water conservancy facility are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of error correction, in particular to a water conservancy facility environmental disturbance error correction method and device based on multi-dimensional data. BACKGROUND

[0002] The safe operation of the facility and the stability of the regional environment. However, in the complex natural environment where the water conservancy facility is located, many environmental disturbance factors such as water flow pattern change, temperature gradient, climate fluctuation and human disturbance often cause errors in monitoring data, and further affect the scientificity of facility state evaluation and operation decision. Especially in the context of multi-source and multi-dimensional environmental data interweaving, the traditional single parameter monitoring and error correction method is difficult to fully reflect the influence of environmental disturbance on monitoring data, and cannot meet the needs of modern water conservancy facility fine management and intelligent maintenance.

[0003] The existing environmental disturbance error correction technology mainly depends on empirical formula or single factor model, mainly focuses on the simple compensation of a certain type of disturbance factor, and lacks comprehensive utilization and correlation analysis of multi-dimensional environmental data. Such method not only has limited accuracy, but also is difficult to adapt to the spatiotemporal dynamic change of environmental conditions, is easy to cause error accumulation and misjudgment, and affects the safety evaluation of water conservancy facility. In addition, the traditional method mainly depends on static parameter correction, lacks dynamic modeling of environmental disturbance evolution process, and is difficult to realize real-time response and early warning to sudden disturbance events. In view of the above shortcomings, a more intelligent and efficient environmental disturbance error correction method is needed. SUMMARY

[0004] The present application is to solve the above technical problems, and provides a water conservancy facility environmental disturbance error correction method and device based on multi-dimensional data, to solve at least one of the above technical problems.

[0005] To achieve the above purpose, the present application provides a water conservancy facility environmental disturbance error correction method based on multi-dimensional data, comprising the following steps: Step S1: collecting water conservancy facility historical monitoring log, identifying environmental disturbance event and mining multi-scale disturbance correlation, and constructing environmental disturbance spatiotemporal correlation graph; Step S2: monitoring the surrounding environmental water body monitoring parameters of the water conservancy facility, and performing time series response trend change analysis and dynamic response characteristic modeling, and constructing facility environmental individualized change response image; Step S3: identifying multiple water body location monitoring data according to the surrounding environmental water body monitoring parameters, and performing water body global dynamics perception, and constructing water dynamics global perception graph; Step S4: performing nonlinear correlation analysis based on the water dynamics global perception graph and the facility environmental individualized change response image, and constructing water conservancy facility digital twin model under water flow; Step S5: Calculate the water conservancy facility monitoring error based on the environmental disturbance spatiotemporal correlation graph, and predict the error development trend to generate a facility target error development prediction value; Step S6: Perform continuous error correction driving according to the facility target error development prediction value, and perform parameter intelligent self-tuning processing to execute water conservancy facility monitoring parameter error correction operation under environmental disturbance.

[0006] In the present specification, a water conservancy facility environmental disturbance error correction device based on multi-dimensional data is provided for executing the water conservancy facility environmental disturbance error correction method based on multi-dimensional data as described above, comprising: A multi-scale disturbance correlation module is configured to collect water conservancy facility historical monitoring logs, identify environmental disturbance events, and mine multi-scale disturbance correlation, and construct an environmental disturbance spatiotemporal correlation graph; A response trend change module is configured to monitor the surrounding environmental water body monitoring parameters of the water conservancy facility, and perform time series response trend change analysis and dynamic response characteristic modeling to construct a facility environmental individualized change response portrait; A hydrodynamics perception module is configured to identify a plurality of water body location monitoring data according to the surrounding environmental water body monitoring parameters, and perform water body global dynamics perception to construct a hydrodynamics global perception graph; A digital twin module is configured to perform nonlinear correlation analysis based on the hydrodynamics global perception graph and the facility environmental individualized change response portrait to construct a water conservancy facility digital twin model under water body flow; An error trend prediction module is configured to calculate the water conservancy facility monitoring error based on the environmental disturbance spatiotemporal correlation graph, and predict the error development trend to generate a facility target error development prediction value; An error correction self-tuning module is configured to perform continuous error correction driving according to the facility target error development prediction value, and perform parameter intelligent self-tuning processing to execute water conservancy facility monitoring parameter error correction operation under environmental disturbance.

[0007] The beneficial effects of the present application are as follows: by deeply mining the environmental disturbance events in historical monitoring data, identifying the key disturbance types and their occurrence regularity, the correlation between disturbance and different time scales (such as day, ten, month) and spatial regions can be effectively revealed, thereby constructing a spatio-temporal correlation atlas that comprehensively reflects the evolution path and influence range of disturbance, providing structured disturbance background information for subsequent modeling, and enhancing the contextual understanding ability of modeling. By real-time collection of parameters such as water temperature, water level, flow rate, PH value, combining with historical change trend, modeling the response law of the facility under specific environmental conditions, an individualized response model for different water conservancy facilities is formed, which helps to dynamically understand the sensitivity and adaptability of the facility to environmental changes, and improves the individualized accuracy and interference recognition ability of the monitoring results. By deploying water body monitoring devices at multiple spatial points, collecting the flow state at different positions, the overall perception of the dynamic flow process of the entire water body is realized; the constructed water dynamics full-field perception map can comprehensively present the state evolution characteristics of water flow rate, flow direction, energy transmission, etc., providing a basis for identifying complex water flow behavior and its influence on the facility, and improving the completeness and real-time of perception. Using nonlinear analysis method to establish the complex relationship model between facility response and water flow, combining the individualized response characteristics and global water dynamics state, a high-fidelity digital twin is formed, which can simulate the running state of the facility under various water environmental disturbance conditions, realize the dynamic simulation ability of predictable, verifiable and controllable, and provide a technical basis for intelligent operation and maintenance. Linking the disturbance atlas and the digital twin model for analysis, identifying the deviation between the monitoring data and the actual running state under different disturbance backgrounds, and predicting its future evolution trend through error modeling, so as to early warning the potential monitoring distortion risk, realize the active identification and trend grasping of monitoring error, and improve the prediction ability and stability of the system. Based on the error prediction result, the monitoring system is dynamically driven for real-time parameter correction, and the model parameters are automatically adjusted by combining intelligent algorithm, realizing the continuous optimization of monitoring accuracy; this process does not require manual intervention, has self-adaptive and self-optimizing ability, and significantly improves the monitoring robustness and system intelligence level of water conservancy facilities under complex environmental disturbance conditions. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 A step flow diagram of a water conservancy facility environmental disturbance error correction method based on multi-dimensional data according to the present application is shown in the figure; Figure 2 A detailed implementation step flow diagram of step S1 is shown in the figure; Figure 3 A detailed implementation step flow diagram of step S2 is shown in the figure; Figure 4 A detailed implementation step flow diagram of step S3 is shown in the figure. DETAILED DESCRIPTION

[0009] It is to be understood that the specific embodiments described herein are merely illustrative of the present application and should not be used to limit the present application in any manner.

[0010] The application provides a water conservancy facility environment interference error correction method and device based on multi-dimensional data. The execution subject of the water conservancy facility environment interference error correction method and device based on multi-dimensional data includes but is not limited to mechanical equipment, a data processing platform, a cloud server node, a network upload device and the like which can be regarded as a general computing node of the application.

[0011] Please refer to Figures 1 to 4 The application provides a water conservancy facility environment interference error correction method based on multi-dimensional data, which includes the following steps: Step S1: Collecting water conservancy facility historical monitoring logs, identifying environment disturbance events and mining multi-scale disturbance correlation, and constructing an environment disturbance space-time correlation graph; Step S2: Monitoring surrounding environment water body monitoring parameters of the water conservancy facility, and performing time series response trend change analysis and dynamic response characteristic modeling to construct a facility environment individualized change response image; Step S3: Identifying a plurality of water body position monitoring data according to the surrounding environment water body monitoring parameters, and performing water body global dynamics perception to construct a water dynamics global perception graph; Step S4: Performing nonlinear correlation analysis based on the water dynamics global perception graph and the facility environment individualized change response image to construct a water conservancy facility digital twin model under water flow; Step S5: Calculating a water conservancy facility digital twin model monitoring error based on the environment disturbance space-time correlation graph, and performing error development trend prediction to generate a facility target error development prediction value; Step S6: Performing continuous error correction driving according to the facility target error development prediction value, and performing parameter intelligent self-optimization processing to perform water conservancy facility monitoring parameter error correction under environment disturbance.

[0012] In the embodiment of the application, please refer to Figure 1 The application provides a water conservancy facility environment interference error correction method based on multi-dimensional data, which includes the following steps: Step S1: Collecting water conservancy facility historical monitoring logs, identifying environment disturbance events and mining multi-scale disturbance correlation, and constructing an environment disturbance space-time correlation graph; In this embodiment, historical monitoring logs of water conservancy facilities are collected. The logs contain multiple dimensions of monitoring parameters, such as water level, flow rate, temperature, pressure, ion concentration, and structural stress, etc. The sampling frequency is generally set to 1 minute to 5 minutes to ensure the timeliness and fineness of the data. The collection period covers at least half a year to cover different seasons and various environmental disturbance scenarios. Data preprocessing is the first step, mainly including outlier detection and removal (using the 3σ principle based on statistical threshold or the local outlier factor LOF algorithm), missing value interpolation (using linear interpolation or KNN interpolation based on adjacent time), and multi-sensor data synchronization to ensure the completeness and continuity of the time series data.

[0013] For the preprocessed time series data, environmental disturbance event identification is carried out. A hybrid method based on time series segmentation and anomaly detection is adopted, including sliding window analysis and machine learning-based anomaly detection algorithm. The sliding window size is set to 30 minutes, and the statistical features (such as mean, variance, skewness, etc.) are calculated within the window. When a parameter exceeds the normal range, it is marked as a potential disturbance event. Combined with support vector machine (SVM) or isolation forest algorithm, the data within the sliding window is further discriminated to filter out noise and ensure the accuracy of the identified events. In the experiment, through testing on 300 known disturbance event samples, the recall rate of anomaly detection reaches 92%, and the false positive rate is controlled within 7%. For the identified multiple disturbance events, multi-scale disturbance correlation mining is carried out. Time-space correlation rule mining and multi-resolution analysis technology are adopted. Time-space correlation rule mining constructs the time delay and spatial distance matrix between disturbance events, and combines Apriori algorithm to mine frequent association patterns to determine the propagation path and impact range of disturbance events. Multi-resolution analysis uses wavelet transform to decompose the disturbance signal into different frequency bands, revealing the multi-scale characteristics of short-term transient disturbance and long-term trend disturbance. In the experiment, seven typical disturbance events are selected, and Daubechies wavelet is used to decompose the water level and temperature data into five layers, successfully extracting low-frequency trend and high-frequency mutation signals, accurately describing the different time scales of disturbance. By integrating multi-scale disturbance correlation information, an environmental disturbance space-time correlation graph is constructed. The graph takes disturbance events as nodes, disturbance propagation path and intensity as edge weights, and reflects the space-time distribution and impact mechanism of disturbance around water conservancy facilities. The graph data structure adopts a weighted directed graph form, combined with a time series dynamic updating mechanism to realize real-time dynamic reconstruction. In the graph construction process, the calculation of edge weight is based on the comprehensive function of disturbance intensity difference, spatial distance, and time delay, and the weight parameters are obtained through historical event fitting optimization. This space-time correlation graph provides an important environmental disturbance reference framework for subsequent error propagation analysis and correction, improving the pertinence and effectiveness of error correction.

[0014] Step S2: Monitor the surrounding water body monitoring parameters of the water conservancy facility, and conduct time series response trend change analysis and dynamic response characteristic modeling to construct the facility environment individualized change response image; In this embodiment, a distributed physical sensor array is deployed near the water conservancy facility to collect key water body parameters, including water temperature gradient, flow velocity vector field, pressure distribution, and water quality ion concentration, etc. The sensor sampling frequency is usually set to 1 to 5 minutes to ensure that the dynamic changes of environmental disturbances can be captured. The sensor array layout is based on the flow characteristics of the water body and the layout of the key monitoring points. In a typical experiment, 20 sensor nodes are deployed around a water gate, covering a range of 500 meters, ensuring that the spatial resolution of the data reaches 5 meters. For the collected time series data, response trend change analysis is carried out. Time series analysis methods such as moving average filtering and seasonal decomposition (Seasonal-Trend decomposition based on Loess, STL) are used to extract trends and identify periodic changes in water temperature, flow velocity, etc. The specific process includes first denoising the original signal (using wavelet threshold denoising), then using the STL method to decompose the time series into trend, seasonality, and residual three parts, and clearly identifying the contribution of each component. The experimental results show that in the 3-month continuous sampling of the water gate monitoring, the water temperature shows a clear day-night temperature difference cycle, and the flow velocity change trend is highly correlated with rainfall events, verifying the effectiveness of the trend analysis method.

[0015] Based on the time series trend data, a dynamic response characteristic model of the facility environment is constructed. Dynamic system modeling techniques such as autoregressive moving average model (ARMA) and state space model are used to model the causal relationship between key parameters and the dynamic response process. During model training, historical monitoring data and on-site disturbance event labels are combined to estimate parameters through maximum likelihood estimation and Kalman filtering algorithm, ensuring that the model's response to environmental disturbances can accurately predict future trends. In the experiment, an ARMA(2,1) model is established using 90-day water flow velocity and pressure data, with a root mean square error (RMSE) of model prediction controlled within 5%, reflecting high modeling accuracy. The individualized change response image of the facility environment is constructed by integrating the output of the dynamic response model. This image contains the spatio-temporal distribution characteristics of multi-dimensional physical parameters of the water body, disturbance response mode and dynamic evolution law, which can reflect the unique response characteristics of the facility under different environmental conditions. The image uses a multi-level data fusion method to combine sensor data, model prediction and environmental disturbance event information, and presents it in the form of charts and indicators, supporting the individualized adjustment of the subsequent error correction module. Through experimental verification, in a typical water conservancy facility scenario, the individualized response image accurately reveals the response differences of the facility under different seasons and sudden disturbances, improving the pertinence and effectiveness of error correction.

[0016] Step S3: Identify multiple water body location monitoring data according to the surrounding water body monitoring parameters, and perform water body global dynamics perception to construct a water dynamics field perception map; In this embodiment, monitoring data of multiple water body locations is screened and identified from multi-dimensional monitoring parameters. Sensors installed at different key points of the water body are used to collect water level, flow velocity vector, pressure, and water quality indicators. Through spatial clustering analysis (such as clustering method based on DBSCAN algorithm), the monitoring point data is classified to determine the water body area with similar dynamic characteristics. In the experiment, for the data of 40 monitoring points in a certain reservoir area, DBSCAN clustering identifies 6 water body location blocks with significantly different dynamic characteristics, and the spatial scale covers from dozens of meters to hundreds of meters. Based on the identified multiple water body location monitoring data, water dynamics boundary analysis is performed to determine the flow boundary position and flow constraint. Numerical fluid mechanics (CFD) simulation based on finite volume method is used to accurately set the boundary conditions by combining the flow velocity and water level data of the field measurement points, and to calculate the boundary layer flow characteristics of the water body. In the specific implementation, the monitoring data is mapped to the grid model nodes, and the Reynolds-averaged Navier-Stokes (RANS) equation is used to solve the flow field. In the experiment, the grid division density is controlled at 25 units per square meter, ensuring the balance between simulation accuracy and calculation efficiency.

[0017] For water dynamics boundary information, turbulence characteristics analysis and boundary layer effect identification are carried out. Turbulence intensity calculation (such as turbulence kinetic energy k and dissipation rate ε model) is used to identify the turbulence structure inside and outside the water body boundary layer, and to capture the vortex generation and energy transfer characteristics. Combined with the experimental observation of flow velocity fluctuation data, power spectrum analysis and vortex identification algorithm (such as Q-criterion or λ2 method) are used to quantitatively describe the turbulence characteristics, and the spatio-temporal distribution characteristics of vortex structure in typical water body disturbance events are successfully extracted. Experimental parameters show that the typical vortex scale is 5-15 meters, and the peak value of turbulence intensity reaches more than 30% of the local flow velocity. Based on the above turbulence characteristics and boundary layer analysis results, water global dynamics perception is implemented to construct a water dynamics field perception map. This map uses the form of color vector field superimposed on contour lines to intuitively reflect the spatial distribution of flow velocity size and direction, pressure distribution, and turbulence intensity and other dynamic parameters in the entire water body. In the construction process, a fusion strategy combining data-driven and physical models is used to continuously update the dynamics perception map through data assimilation technology, realizing real-time full-field perception of water flow state. In the experiment, the perception map is updated in real time using 7 days of continuous sampling monitoring data, ensuring a spatial resolution of 1 meter and a time resolution of 10 minutes, effectively capturing key events in the water dynamics change process.

[0018] Step S4: Perform nonlinear correlation analysis based on the water dynamics field perception map and facility environment individualized change response portrait to construct a water conservancy facility digital twin model under water flow. In this embodiment, two key data sources are collected and pre-processed: one is the hydrodynamic full-field perception map obtained in step S3, including flow velocity vector field, pressure distribution, and turbulence characteristics, etc. multi-dimensional dynamic parameters; the second is the facility environment individualized change response image constructed in step S2, covering the time sequence dynamic response characteristics of the facility structure to the environmental disturbance. Data preprocessing includes normalization processing (using Min-Max scaling to ensure uniformity of different dimensions), space-time alignment and abnormal data elimination, to ensure the reliability and data consistency of subsequent analysis. Nonlinear correlation analysis is carried out. Due to the complex and nonlinear relationship between hydrodynamics and facility response, multivariate nonlinear modeling methods in machine learning are used, such as support vector regression (SVR) and long short-term memory network (LSTM) deep learning model based on kernel technique. SVR captures the nonlinear mapping relationship between input features and facility response by introducing kernel function (such as radial basis function RBF), while LSTM effectively models the long-term dependence in the time sequence dynamic response process by using its time memory capability. In the experiment, the historical sampling data is used to construct the training set, and the cross-validation method is used to adjust the parameters. The determination coefficient (R²) of the SVR model reaches 0.87, and the mean square error (MSE) of the LSTM model on the validation set is reduced by about 15% compared with the traditional regression model. Based on the trained nonlinear model, the hydrodynamic full-field perception map and the facility response image are fused to construct the digital twin model. The model is based on a digital simulation platform, combined with physical rules (such as structural mechanics equations and fluid mechanics laws) and data-driven models, to realize real-time state prediction of water conservancy facilities under different hydrodynamic conditions. The specific implementation includes establishing a multi-level digital twin framework: the bottom layer is the physical parameter database and dynamic simulation module, the middle layer is the machine learning model integration module, and the upper layer is the state monitoring and decision support interface. In the experimental configuration, the simulation time step is set to 1 minute, and the spatial resolution is 0.5 meters, which can accurately reflect the stress state of the facility structure and the influence of environmental disturbance. Model verification and optimization. By comparing with the measured data, the prediction accuracy of the digital twin model for facility structure response and hydrodynamic disturbance is verified. In the experiment, a large-scale dam is monitored for three months, and the average error between the predicted structural vibration amplitude of the digital twin model and the measured value is less than 5%, effectively capturing the structural dynamic behavior under complex hydrodynamic conditions. Based on the feedback results, the genetic algorithm is used to optimize and adjust the model parameters, further improving the adaptability and prediction accuracy of the model.

[0019] Step S5: calculating the facility monitoring error of the water conservancy digital twin model based on the environmental disturbance space-time correlation map, and predicting the error development trend to generate the facility target error development prediction value; In this embodiment, based on the previously constructed environmental disturbance spatio-temporal correlation graph, the environmental disturbance features related to facility monitoring are extracted. This graph integrates historical environmental disturbance event information and its spatio-temporal propagation rules, and can reveal the influence degree of environmental disturbance on facility monitoring at different time and spatial scales. Through the calculation of environmental disturbance propagation path and influence range, the key disturbance nodes and high-impact areas are determined, providing key environmental parameters for error calculation. In the experiment, the graph covers more than 50 disturbance events in the past two years, with a spatio-temporal resolution of hourly and meter-level, ensuring the refinement of error analysis.

[0020] The environmental disturbance features are compared and analyzed with the monitoring data output by the digital twin model to calculate the facility monitoring error. The error calculation method is based on the difference between the measured data and the model predicted value, and the root mean square error (RMSE) and the relative error percentage are used as the main indicators. For multi-dimensional monitoring parameters such as structural vibration frequency and water flow pressure, the corresponding error components are calculated respectively to form a multi-dimensional error vector. Experimental examples show that in the case of continuous monitoring for 30 days, the error caused by environmental disturbance shows the greatest difference in different parameters, with a maximum difference of 15%, and the overall error mean is about 7%, reflecting the significant impact of environmental disturbance on monitoring accuracy. The error value sequence calculated is used to predict the error development trend. First, the error sequence is preprocessed for time series, and stationary processing such as difference method is applied to eliminate the trend and seasonal effects. Then, the autoregressive integrated moving average model (ARIMA) combined with the long short-term memory network (LSTM) model is used for trend prediction. The ARIMA model is good at capturing linear trends, while the LSTM model can learn non-linear, long-term dependent error dynamics. In the training process, the rolling prediction verification method is used to ensure the model generalization ability. In the experiment, the prediction model has an average prediction accuracy of 92% in the error prediction of the next 7 days, and the root mean square error (RMSE) is significantly lower than that of traditional models. Based on the error trend prediction results, the facility target error development prediction value is generated. This prediction value not only reflects the change trend of monitoring error that may be caused by future environmental disturbance, but also provides a key reference for subsequent dynamic error correction. By setting a threshold alarm mechanism, when the predicted error exceeds the maximum acceptable error, the automatic correction process is triggered. In the experiment, combined with the actual water conservancy facility monitoring data, the error abnormality early warning 24 hours in advance is successfully realized, significantly improving the initiative and accuracy of error management.

[0021] Step S6: According to the facility target error development prediction value, continuous error correction driving is performed, and intelligent self-tuning processing of parameters is carried out to execute the water conservancy facility monitoring parameter error correction operation under environmental disturbance.

[0022] In this embodiment, according to the error development prediction value generated in step S5, a continuous error correction strategy is formulated. The strategy dynamically adjusts the correction frequency and correction strength according to the size and trend of the predicted error. Specifically, when the predicted error is close to or exceeds the set maximum acceptable error threshold, high-frequency and high-intensity correction operations are started; otherwise, low-frequency maintenance correction is adopted. In the experiment, for a certain hydropower station facility, the error threshold is set to 5% of the monitored parameter error, and when the predicted value exceeds the threshold, the correction period is shortened from 24 hours to 6 hours, improving the correction response speed. Implement error correction drive. This process is based on the error feedback closed-loop control principle, inputs the predicted error into the error correction control engine, combines the digital twin model and multi-dimensional sensor data, and calculates the corresponding correction compensation value. Adaptive filtering algorithms such as Kalman filtering and extended Kalman filtering are used to achieve dynamic error compensation of the monitored parameters. The filter continuously adjusts the weight according to the current error and historical trend to ensure the real-time accuracy of the correction effect. Experimental data show that this method reduces the water flow measurement error from an average of 10% to less than 3% in actual operation.

[0023] Intelligent parameter self-optimization processing is performed. Due to the variability of environmental disturbances and the nonlinear response characteristics of the facility itself, fixed parameter correction models often cannot adapt to complex scenarios. Therefore, a reinforcement learning (RL) algorithm in machine learning is used to design an intelligent parameter optimization agent. The agent uses correction accuracy feedback information as a reward signal, and through continuous testing and adjustment of correction parameters such as filter gain and error weight, it realizes self-optimization of the correction control engine. During the experiment, the deep Q network (DQN) algorithm is used to train the optimization strategy, and after 1000 iterations, the correction accuracy is improved by about 12%, and the response time is shortened by about 20%. Real-time collection of correction execution results and correction accuracy feedback information. This feedback mechanism analyzes the error between the corrected monitoring parameters and the actual observation data in real time to evaluate the correction effect. If the feedback error is still large, the intelligent optimization module automatically triggers a new round of parameter adjustment, forming a closed-loop optimization system. In the experiment, for gate structure vibration monitoring, the correction feedback accuracy improvement increases the abnormal error detection rate by 15%, effectively improving the monitoring reliability of the facility state. By integrating the above steps, a continuous error correction drive platform is constructed to realize intelligent correction of monitoring parameter errors of water conservancy facilities under environmental disturbances. The platform supports multi-dimensional data fusion, high-frequency error updating, and intelligent optimization, ensuring high accuracy and stability of monitoring data in complex and variable environmental conditions, providing solid data support for facility safety operation and decision-making.

[0024] In this embodiment, refer to Figure 2 For the detailed implementation step flowchart of step S1, in this embodiment, the detailed implementation steps of step S1 include: Collect historical monitoring logs of water conservancy facilities; According to the environmental disturbance event identification and extraction of the water conservancy facility historical monitoring log, a plurality of historical environmental disturbance event information is obtained; The multi-level disturbance source identification is performed on the plurality of historical environmental disturbance event information, and a historical environmental disturbance feature matrix is obtained; The disturbance propagation path analysis and influence range calculation are performed on the historical environmental disturbance feature matrix, and the environmental disturbance propagation feature of each disturbance event is obtained; The disturbance duration period of the historical environmental disturbance event information is extracted; According to the disturbance duration period, the environmental disturbance propagation feature is subjected to multi-scale disturbance correlation mining, and an environmental disturbance space-time correlation graph is constructed.

[0025] In this embodiment, the system collects and integrates the historical monitoring log data related to the water conservancy facility operation state, which lays a data foundation for subsequent disturbance identification and feature modeling. The monitoring data mainly includes water level, flow, rainfall, gate opening height, pump station operation state, water quality parameters (such as pH, dissolved oxygen, ammonia nitrogen concentration, etc.), video images, remote sensing data, etc. These data come from various sensor devices (such as ultrasonic water level meter, radar rain gauge, water quality online analyzer), automatic weather station, remote sensing satellite platform and manual operation records. In order to ensure data quality, firstly, the data is subjected to integrity detection, outlier elimination and time series alignment (the problem of inconsistent sampling frequency is solved by linear interpolation, spline interpolation, etc.). In the experiment, the operation logs of two main water conservancy hubs (A sluice and B pump station) in a city from 2015 to 2024 are selected, the data frequency is uniformly converted to once per hour, and finally the integrated data amount reaches 870,000 record entries, which provides comprehensive and fine-grained data support for subsequent environmental disturbance event identification. A composite method based on time series change rate analysis and clustering analysis is adopted. Firstly, the change rate analysis is performed on each type of sensor data by using the sliding window method, and the abnormal mutation points exceeding twice the historical standard deviation (for example, the water level changes more than 15 cm in 30 minutes, or the pH value decreases more than 0.8 units in 1 hour) are extracted. Then, the DBSCAN density clustering algorithm is used for event aggregation processing of the abnormal points, and independent disturbance events are formed by combining time, space and multi-parameter correlation. Each event contains disturbance starting time, duration, disturbance fluctuation value, involved parameter, disturbance intensity and other information. In the experimental process, 203 obvious disturbance events are identified, more than 60% of which are related to heavy rain or upstream scheduling, and some are caused by human factors (such as sudden pollution discharge, gate misoperation). After successfully extracting the disturbance events, the system enters the feature modeling stage.

[0026] The main and secondary disturbance sources behind each disturbance event were identified in depth, and the disturbance information was quantitatively integrated to form a feature matrix. The specific methods included principal component analysis (PCA), causal relationship modeling based on Bayesian networks, and hierarchical clustering. First, the extracted events were subjected to disturbance parameter dimensionality reduction processing. PCA helped identify the main control variables that caused the disturbance. Then, the causal relationships between different parameters were inferred based on Bayesian networks. For example, the causal chain of "upstream rainfall-water level rise-flood overflow" was identified by calculating the posterior probability. In addition, hierarchical clustering was used to divide the disturbance events into three categories according to their similarity: geographical origin (such as upstream and downstream, left and right bank), human operation (such as gate control), and natural external factors (such as heavy rain and landslides). Finally, a disturbance feature matrix with dimensions N×M (N is the number of disturbance events, and M is the number of disturbance variables and their impact levels) was formed. This matrix served as the input basis for subsequent path analysis. In the experiment, the disturbance feature dimension was 32 parameter dimensions, and 203 events generated a 203×32-dimensional disturbance feature matrix. The disturbance propagation path analysis aimed to uncover how the disturbance source propagated along the water system and quantify its temporal and spatial range of influence. The method combined dynamic Bayesian networks and graph theory propagation models. First, a propagation network was constructed with monitoring points as nodes and river or pipe network connection relationships as edges. After mapping the parameter values in the disturbance feature matrix to the corresponding nodes, a propagation sequence was constructed based on the time series. On this basis, the PageRank algorithm was applied to evaluate the propagation influence of the nodes, and the propagation delay was analyzed to obtain the propagation path of "disturbance starting point→path→diffusion range". For example, the propagation path of a sudden upstream flood disturbance was "upstream water level↑→midstream water flow rate↑→downstream water quality↓", with a time delay of about 4 hours. The influence range was measured by the number of covered nodes and the integral of propagation intensity. In the experiment, it was found that the average propagation path length of strong disturbance events was 4.2, the maximum number of affected nodes was 7, and the duration of influence varied from 2 to 12 hours depending on the disturbance amplitude. The final output was a propagation map for each event, which was used for error correction and prediction. The occurrence cycle and duration of the disturbance events were analyzed from the time dimension to establish a time series characteristic model of the disturbance. The duration of the disturbance was the length of time from the triggering of the event to the complete elimination of its influence, while the periodicity of the disturbance reflected whether there was a seasonal or periodic pattern. Time spectrum analysis (wavelet transform) and periodicity testing (Lomb-Scargle periodogram) methods were used to extract the features. In the implementation, 203 disturbance events were classified according to the time stamp, and the distribution density changes at daily, weekly, and monthly scales were analyzed. For example, high-frequency disturbances occurred during the summer heavy rain concentration period (June to August), showing a clear seasonality, while low-frequency disturbances showed a two-time annual cycle. It was found that about 46% of the disturbance events had periodic characteristics, with an average duration of 5.8 hours and a maximum duration of 27 hours. The coupling analysis results between the duration of the disturbance and the propagation characteristics will be used for multi-scale modeling in the next stage.Based on the aforementioned disturbance propagation and time periodicity characteristics, a graph model that comprehensively reflects the spatio-temporal evolution relationship of disturbance events is constructed. This model uses a combination of graph neural networks (GNN) and multi-scale spatio-temporal modeling methods. Disturbance events are treated as nodes in the graph, and propagation paths and time delays are treated as edge attributes. First, events are divided into three levels of daily, weekly, and seasonal scales according to the disturbance period. Then, a disturbance propagation graph is constructed, in which each edge records the disturbance propagation delay, attenuation coefficient, and influence area. The nodes record the disturbance intensity, duration, source type, and other attributes of the events. The Spatio-Temporal Graph Attention Networks (ST-GAT) model is applied to mine the propagation rules and mutual influence mechanisms of events at different scales. The graph results can be used to evaluate the possible paths and error risks of future disturbance events, especially in predictive error correction. Experiments show that the graph can effectively extract the correlation between 76% of historical events, and the prediction accuracy is improved by about 19.3% compared with the baseline model.

[0027] In this embodiment, referring to Figure 3 For the detailed implementation step flowchart of step S2, in this embodiment, the detailed implementation steps of step S2 include: Monitoring the surrounding environmental water body monitoring parameters of the water conservancy facility based on a distributed physical field sensor array; Extracting the water temperature gradient, flow velocity vector field, pressure distribution, and water quality ion concentration of the surrounding environmental water body monitoring parameters to construct a facility environment multi-dimensional physical field vector; Performing time series response trend change analysis on the facility environment multi-dimensional physical field vector to generate a surrounding environmental parameter change trend; Based on the surrounding environmental parameter change trend, a dynamic response characteristic model is constructed to generate a facility environment individualized change response portrait.

[0028] In this embodiment, the physical, hydrological and chemical states of the water body surrounding the water conservancy facilities are comprehensively perceived through the distributed deployment of multi-type sensor arrays. The monitoring network adopts a multi-level layout method: high-density monitoring units are deployed in the near-facility area (0-100 meters), medium-density observation points are deployed in the peripheral area (100 meters), and remote sensing satellite data is used for large-scale supplementation. The monitoring parameters mainly include water temperature, water flow velocity, water pressure, water level, water quality ion (calcium, magnesium, sodium, chlorine, nitrate, etc.) concentration, as well as dissolved oxygen, conductivity, turbidity, etc. The sensor array includes acoustic Doppler current profiler (ADCP), pressure sensor, electrochemical sensor, temperature probe and multi-parameter water quality analyzer, with a data sampling frequency of 1-5 minutes and a minimum spatial point spacing of 10 meters. In the experiment, the Y reservoir inlet area is taken as an example, 36 sensor nodes are deployed, and continuous operation is carried out for 90 days, with a total of more than 17 million data collected, forming a high-resolution water environment multi-source original data, providing a fine basis for subsequent gradient and vector field modeling. Through spatial interpolation, numerical gradient analysis and scene restoration modeling, the spatial variation relationship of key physical properties is extracted and expressed. First, the temperature gradient extraction adopts a bidirectional spatial difference method combined with Gaussian filtering to calculate the distribution change trend of temperature in two-dimensional space (▽T), and focuses on identifying hot spot areas and thermocline structures. The flow velocity vector field is based on the layer flow velocity data measured by ADCP, and after flow direction calibration and vector synthesis processing, the velocity field (u, v, w) on each unit area is generated. The pressure distribution relies on high-frequency pressure sensor data, and after water depth and flow velocity correction modeling (considering the kinetic energy item correction in Bernoulli's law), a two-dimensional isobaric surface is formed. The water quality ion concentration data is processed through multi-sensor fusion and time-weighted interpolation to eliminate noise and measurement drift. Finally, each monitoring area or grid point is expressed as a multi-dimensional vector P = [▽T, V → , p, C_ion], which includes the spatial field expression of 4 main physical parameters, with a dimension of 20+ (including temperature gradient direction, flow velocity component, pressure field value, and ion concentration). The experimental results show that the physical field built can clearly distinguish the water disturbance area and the relatively stable area, laying a spatial structure foundation for dynamic trend analysis.

[0029] Three strategies were employed: Principal Component Analysis (PCA + EMD), Local Sensitive Trend Detection (LSTD), and Sliding Window Correlation Analysis. First, principal component analysis was performed on the constructed multidimensional vector sequence of the facility environment to reduce the dimensionality of the high-dimensional data to representative change patterns (such as temperature master patterns and flow velocity abrupt change patterns). Then, Empirical Mode Decomposition (EMD) was used to decompose each principal component into several intrinsic mode functions (IMFs) to uncover the change rhythms at multiple scales. Simultaneously, sliding window analysis was used to extract response abrupt change points on the 30-minute to 3-hour scales and identify the spatial locations corresponding to high-frequency disturbances (such as boundary areas and facility drainage outlets). In the experiment, continuous analysis of 90 days of data revealed significant flow velocity change trends and pressure fluctuation trends around the facility before nighttime low temperatures and rainfall, which can serve as a basis for early anomaly signal identification. The change trend results were encoded into a time-parameter two-dimensional matrix structure for subsequent dynamic modeling and profile generation. By structuring the trend change data into a predictive dynamic response model, a "personalized change response profile" reflecting the environmental response characteristics of the water conservancy facility was constructed. The methodology comprises Dynamic Bayesian Network (DBN) modeling, clustered response feature extraction (K-shape), and graph generation. First, time-series modeling is performed on the trend data of each parameter class to establish conditional probabilistic relationships between them and external disturbance sources (such as rainfall, gate opening, and sudden temperature changes). In the DBN structure, nodes represent disturbance factors and multidimensional parameter states, with edge weights representing state transition probabilities. Subsequently, the K-shape clustering algorithm is used to cluster the trend shapes, extracting typical response curves such as "gradual rise-sharp fall" and "steady-state-sudden change-recovery," and linking them to the facility's operational status. Finally, based on the model output, a "change response profile" is constructed, expressing the dynamic response capability of each facility under different disturbance scenarios in a graph format, including indicators such as response delay, intensity, recovery speed, and sensitivity. Experiments using the gate area of ​​Reservoir A as a sample show that the constructed response profile is highly sensitive to upstream temperature increases and downstream flow velocity backpropagation, while exhibiting a delayed response to local small-scale rainfall. This profile structure provides personalized prior references for environmental disturbance error prediction and system-level control.

[0030] In this embodiment, reference Figure 4 The above is a detailed implementation flowchart of step S3. In this embodiment, the detailed implementation steps of step S3 include: Multiple water body location monitoring data were identified based on the surrounding environmental water body monitoring parameters. Hydrodynamic boundary analysis is performed on the monitoring data of the multiple water bodies to generate hydrodynamic boundary information; Turbulent characteristics analysis and boundary layer effect identification are performed on the dynamic boundary information of water bodies to extract the distribution characteristics of the dynamic flow field of water bodies; The water body dynamic flow field distribution characteristics are perceived for water body global dynamics, and a water dynamics field perception map is constructed.

[0031] In this embodiment, in the surrounding area of the wide-area water conservancy facility, based on the existing physical field sensor array data, a plurality of water body position monitoring points with representative and significant dynamic changes are identified to constitute a dynamically reasonable dynamics data network in space. The identification process includes two parts: one is spatial division, and the other is dynamic weight discrimination. The spatial division adopts a region segmentation method based on Voronoi diagram, divides the sensor monitoring area into a plurality of sub-domains, and each sub-domain represents a monitoring unit center point; and the dynamic weight discrimination determines the dynamic activity of the monitoring point based on the parameter change rate (such as the water flow velocity change value exceeding the threshold value of 0.3 m / s, and the temperature change rate being greater than 0.8℃ / h). At the same time, considering the boundary effect and water body structure heterogeneity, the dynamic boundary active area points near the shore area, the water inlet and outlet, the bay area and the obstacles are preferentially selected as candidates. In the experiment, among the 42 monitoring nodes deployed in the T river section, 12 water dynamic representative points are selected, covering the core mainstream area, two tributary inlet areas, and one drainage area, forming a preliminary water dynamic field sampling framework. This process ensures that the subsequent boundary analysis and flow field modeling have spatial representativeness and physical integrity. The clear water body movement boundary contour is extracted from the monitoring data to support the boundary condition modeling of the fluid field. A joint boundary discrimination method based on the consistency of the flow velocity vector direction and the change of the velocity gradient is adopted. First, the change rate of the vector angle in the neighborhood is calculated using the flow velocity vector field data (from the ADCP three-dimensional flow velocity monitoring), and if the angle changes sharply and the velocity gradient exceeds the set threshold value (such as 0.6 m / s / 10m), it is determined that there is a “dynamic boundary” in the region; secondly, the flow line continuity analysis and conservation law constraint are introduced to check the water flow through the inside and outside of the boundary, to ensure that the boundary identification conforms to the actual physical motion law. At the same time, the boundary points are smoothed by using the Bezier surface interpolation to generate a boundary vector set that can be used for CFD simulation. In the experiment, three main dynamic boundary surfaces are identified in the P lake water outlet area, respectively corresponding to the division of the main flow area and the low flow speed side bay, the pressure sudden change boundary of the water inlet side, and the near-shore thermocline induced boundary. The boundary shape is an irregular curved surface, and the longest long axis is up to 62 meters, indicating that the water dynamic partition is highly complex. The boundary analysis results lay a spatial foundation for subsequent internal characteristic identification of the flow field.

[0032] Further, the internal structure of the boundary is further studied, the turbulent flow characteristics, the development of the boundary layer, and the variation law of the flow pattern are identified. The method combines the use of Reynolds stress tensor analysis, turbulent kinetic energy spectrum analysis, and wall shear stress distribution deduction. First, the Reynolds decomposition is performed on the flow velocity vector in the boundary region, and the mean flow and fluctuation components are extracted. The turbulent kinetic energy and Reynolds stress distribution are calculated to identify the dominant direction of the turbulent flow and the energy concentration area. Then, the fast Fourier transform (FFT) is used to perform frequency spectrum decomposition on the turbulent flow signal to extract the high-frequency energy segment variation and determine whether there are Karman vortex streets, boundary layer separation, and other unstable flow characteristics. For the near-wall region, the boundary layer thickness and boundary layer range are identified by wall shear stress calculation, such as the peak wall shear stress of 0.34 Pa and the boundary layer thickness of 11 cm in the C branch outflow area, indicating a significant turbulent flow separation phenomenon. Finally, the turbulent flow indicators, shear stress, and turbulent scale parameters are encoded as "flow field distribution feature vectors". Each flow field region can be quantified as a vector group F = [τw, k, ε, δ] to describe its dynamic state and boundary interaction relationship. By integrating the local flow field feature vectors, a "water body global dynamics perception map" covering the entire facility area is constructed to achieve a comprehensive understanding and system modeling of the water dynamic state around the facility. The construction method is based on graph structure modeling and physics-informed embedding representation. The specific process includes: first, define each monitoring location as a node of the graph, and the node attribute is the flow field feature vector corresponding to the point; the connection of the edge is established according to the water continuity, topographic connectivity, and flow direction trend, such as the weight of the nodes in the main flow direction is improved, and the side bay connection edge is set as a weak edge; second, introduce the weight-based graph convolutional network (GCN) model for node embedding learning, capture the flow coupling relationship through the propagation mechanism, and form the "full-field perception coding"; third, reflect the embedding results on the two-dimensional / three-dimensional map to generate a spatial perception map, represented as G(x, y) = f(τw,k, ε, u → ), where f represents the physical field aggregation mapping function. In the experiment at the Z hub area, the final perception map shows the dominance of the main waterway flow, the characteristics of the high-speed channel, and the weak flow buffer zone of the lateral boundary, providing a spatial physical reference framework for subsequent disturbance error positioning. The full-field perception map is not only a key intermediate layer for environmental disturbance error identification, but also can be used to predict future disturbance response, assist in operation scheduling, and generate control strategies.

[0033] In this embodiment, step S4 includes the following steps: Obtain the structural acoustic response parameters of the water conservancy facility, predict the structural degradation state, and construct a facility structural degradation state prediction map. Based on the water dynamics full-field perception map, the flow pattern change, vortex generation mode, and energy dissipation law of the water body are analyzed, and the water body flow evolution logic is mined to construct a real-time portrait of water body flow evolution; Based on the water body flow evolution real-time portrait, the structure degradation-water dynamics change nonlinear correlation analysis is performed on the facility structure degradation situation prediction map to construct the structure-water nonlinear correlation law; According to the facility environment individualized change response portrait and the structure-water nonlinear correlation law, the water body disturbance is digitally processed to construct a digital twin model of the water conservancy facility under water flow.

[0034] In this embodiment, acoustic response data of water conservancy facilities is collected, and key acoustic vibration features are extracted to predict the trend of structure fatigue, aging and micro-cracking in the facility operation state, and a structure degradation situation map is formed. The acoustic response data comes from a distributed acoustic sensor array installed on the structure (such as gate, pier, apron, pump station pressure wall, etc.), which captures the tiny acoustic signals generated by the facility under the action of water flow, mechanical vibration, temperature change and impact load. In the experiment, 16 broadband piezoelectric acoustic sensitive arrays (sampling rate 48 kHz) are set on the H hub pier, and 30 days of data are continuously collected, and features such as acoustic energy spectral density (PSD), frequency band energy distribution, acoustic spectrum centroid frequency, short-time Fourier transform are extracted. Through the acoustic degradation model based on time series convolutional neural network (TCN) and Bayesian residual prediction model, the initial generation of structure micro-cracks, fatigue crack propagation and material performance decline trend can be identified. The prediction output result is presented in the form of a two-dimensional heat map, with time on the horizontal axis, structure key position identification on the vertical axis, and color representing the degree of degradation. The experiment found that in the high flow impact area (such as No. 5 inlet culvert), the acoustic spectrum changes show a continuous high-frequency drift, and the prediction map shows that its structure degradation trend is significantly stronger than that of other sections, providing a degradation space reference for subsequent water power correlation modeling. Based on the water dynamics full-field perception map, the flow pattern change characteristics of the water body in the actual operation condition are further analyzed, including the occurrence and dissipation of vortex, energy transfer path, and flow stability evolution logic. Three core technologies of spatial streamline tracking, vorticity field reconstruction and turbulent energy consumption inversion are used. First, the particle tracking method (PTV) combined with the flow velocity vector field data is used to identify the closed streamline and high rotation rate area, and the vortex core point positioning and its size and rotation direction judgment are realized; then the vorticity vector (▽×V →), and the energy dissipation rate (ε) is used to inverse the energy consumption degree of different flow regions. The long short-term memory (LSTM) model is used to model the flow state evolution data in different time windows, and output the flow state logic unit sequence (such as "laminar flow → boundary layer disturbance → Kelvin-Helmholtz instability → vortex formation → stable dissipation"). In the experiment, a typical counterclockwise vortex region is found in the downstream diversion channel of Y Reservoir, with a vortex core size of about 8 meters, a formation period of 12 minutes, and a dissipation energy peak of about 3.2×10⁻³ W / kg. Finally, the "real-time image of water flow state evolution" is constructed to dynamically display the flow pattern and its evolution trend in different regions and time scales, providing a reference for the flow field disturbance source for structure coupling analysis.

[0035] Based on the spatial alignment relationship between the flow state evolution image and the degradation trend prediction map, a corresponding mapping matrix between the structural region and the fluid characteristic field is established. Then, nonlinear Granger causality analysis, mutual information analysis (MI), and coupling factor analysis based on variational inference (VIFA) are used to extract the nonlinear dependence relationship between the structural response (such as acoustic vibration frequency spectrum change) and the flow field disturbance (such as vorticity change, energy consumption mutation) from the time series. For example, when a periodic vortex disturbance occurs in the water area where a certain structural unit (such as an upstream apron) is located, the acoustic response frequency distribution of the structural unit continuously increases by 2.8% within 4 hours, indicating a high degree of time coupling. Further, a variational autoencoder (VAE) is introduced to embed high-dimensional structure-fluid coupling parameters into a latent space, and typical correlation patterns such as "vortex-induced boundary attachment fatigue" and "flow instability-acoustic spectrum drift warning" are identified through clustering analysis. Finally, a structure-water nonlinear correlation rule atlas is constructed, presenting the multi-level and multi-scale influence mechanism between different structural components and their corresponding fluid dynamic disturbance sources in a three-dimensional graph form, providing data logic support for error tracing of facility safety state. Focusing on the integration of environmental response image, flow field evolution data, and structural degradation characteristics, the digital reconstruction of water disturbance process is conducted, and finally a water conservancy facility digital twin model with predictability, interactivity, and visualization is constructed. The digital twin modeling adopts a multi-source physical driving fusion strategy: first, taking the water flow state evolution image as the main flow driving framework, different disturbance fields are embedded into the calculation model to construct a dynamic field driving module; second, the structural response model and degradation evolution mechanism are introduced, and the state change of the structure is taken as the model feedback adjustment factor to realize the bidirectional feedback of fluid-structure; third, combined with the periodicity and hysteresis characteristics extracted from the individualized response image, a disturbance adjustment function is added to make the model have multi-situation adaptability. The twin platform is driven by high-resolution CFD simulation and graph neural network inference, simulating the disturbance propagation and structural response within the next 1 hour and providing an error warning interface. In the experimental verification, a structural stress concentration event caused by flow state mutation was successfully predicted on the J Gate Station digital twin model (30 minutes in advance), with an error prediction accuracy of 91.2%. The final model output includes a visual 3D structure-fluid collaborative animation, a real-time index panel, and an interference prediction module, realizing the whole process closed loop from data perception to disturbance prediction, and providing a digital platform foundation for error correction.

[0036] In this embodiment, the specific steps for acquiring the water conservancy facility structure acoustic response parameters, predicting the structural degradation trend, and constructing the structural degradation trend prediction map are as follows: The water conservancy facility structure acoustic response parameters are continuously monitored based on an ultrasonic detection array; Time series vibration response frequency calculation is performed based on the water conservancy facility structure acoustic response parameters, and a time series response frequency curve is generated; According to the water conservancy facility structure acoustic response parameter, a facility structure resonance amplitude, a damping coefficient and a sound wave propagation speed are calculated; A multi-dimensional structure acoustic characteristic parameter set is constructed by performing multi-dimensional structure acoustic characteristic mining on the time sequence response frequency curve, the facility structure resonance amplitude, the damping coefficient and the sound wave propagation speed. A structure resonance modal diagram is obtained by performing frequency domain analysis and resonance modal evolution analysis on the multi-dimensional structure acoustic characteristic parameter set. Based on the structure resonance modal diagram, structure integrity evaluation is performed, and abnormal resonance area analysis is performed to mark the abnormal resonance area. Based on the abnormal resonance area, structure damage area precise positioning is performed to mark the structure damage positioning point. Structure damage distribution is performed on the structure damage positioning point, and structure degradation trend prediction is performed to construct a facility structure degradation trend prediction diagram.

[0037] In this embodiment, a high-precision ultrasonic sensor array is used as the core monitoring equipment to continuously monitor the acoustic response characteristics of water conservancy facilities during operation. The ultrasonic detection array is composed of multiple piezoelectric ceramic transducers, which are arranged in key stress areas of the structure, such as piers, dam foundations, culverts, and pressure steel pipe interfaces. The array adopts a point-line-surface combined distributed layout strategy, forming a dense detection grid that can perform fine-grained perception of local area structural response. The sensor operating frequency range is set to 20 kHz~500 kHz, with a data sampling frequency of 1 MHz and a time resolution of up to 1 μs. In the application of the G hub dam section, a total of 48 ultrasonic nodes are deployed, covering the middle part of the main dam body and the upstream and downstream joint areas. The sensors are coordinated by a wireless synchronous control system for data collection, and the data is uploaded to an edge computing terminal for preprocessing (including background noise removal and amplitude normalization). The array can obtain real-time high-frequency acoustic responses of the structure under external disturbances such as hydrodynamic force, temperature change, and mechanical load, and is an important basis for identifying early structural abnormalities such as micro-cracks and fatigue deformation. Based on short-time Fourier transform (STFT) and continuous wavelet transform (CWT), high-time-resolution frequency spectrum extraction is realized. The specific processing flow is as follows: first, each acoustic signal is processed by frame (frame length set to 512 points, overlap rate 50%), then STFT is performed on each frame to extract instantaneous main frequency, bandwidth, and power spectral density information; second, the Morlet wavelet function is used to perform CWT to analyze the potential non-stationary characteristics of the signal at multiple scales, and extract the instantaneous frequency drift and short-period mutation mode. In the experimental scenario, the continuous acoustic data of the D gate station running for 5 days is processed, and the time series response frequency curve is generated, with the frequency range concentrated between 38~52 kHz. The main frequency drift reaches 4.7 kHz during the high-temperature load period (3rd day), indicating potential stress concentration or micro-structural damage. Such curves not only present time evolution characteristics, but also reveal the dynamic response of the structure under specific working conditions, which is the basis for subsequent modal analysis and damage localization.

[0038] The key parameters of the structure, including resonance amplitude, damping coefficient and propagation velocity, are obtained by signal inversion and parameter identification. The resonance amplitude is directly determined by the peak amplitude at the response frequency, and the peak position is fitted with high precision. The damping coefficient is estimated by the half-power bandwidth method (3 dB bandwidth analysis), that is, the system damping ratio is calculated by the difference between the left and right frequency points of the resonance peak. The sound wave propagation velocity is calculated by the time required for the ultrasonic pulse to propagate in the structure through the multi-point synchronous receiving method, combined with the structure size (sensor spacing) to obtain the propagation velocity. Taking the K gate body as an example, the peak amplitude is 1.32 mV at the resonance peak of 43.6 kHz, and the bandwidth is about 6.1 kHz, corresponding to a damping ratio of about 0.07. The measured ultrasonic propagation velocity is 2760 m / s, which is slightly lower than the design value of the new material (2850 m / s), indicating that there is material fatigue or interface loss in the structure. The above parameters provide multi-dimensional basic quantities for describing the physical state and response performance of the structure, and are one of the core dimensions for constructing the acoustic feature space. The frequency domain features (such as the main frequency and the frequency fluctuation range), the time domain features (such as the vibration duration and the instantaneous mutation), and the structural response parameters (such as the damping coefficient and the sound velocity) are unified and fused by using principal component analysis (PCA) and hierarchical feature embedding. Each monitoring point forms a high-dimensional feature vector V = [f0, Δf, A_max, ξ, v_s,∂A / ∂t,...]. By comparing the change trends of the feature sets in different time periods and different structural regions, abnormal structural response regions can be identified. In the experiment, 120 hours of data from 5 regions of Z reservoir spillway gate were clustered and analyzed, and three typical acoustic behavior patterns were identified: stable decay type, periodic enhancement type and sudden drift type. The periodic enhancement type mainly occurs in the high flow velocity scouring area, indicating the presence of repetitive fatigue loading characteristics. The feature set provides a stable data base for modal analysis and damage prediction. The high-dimensional acoustic features are projected in the frequency domain and decoupled to reveal the spatial distribution and evolution trend of the structure's resonance modes. By using modal frequency identification methods (such as natural frequency extraction and damping modal shape reconstruction), combined with frequency response function (FRF) and finite element inversion model, the modal frequency, shape and energy distribution of each region of the structure are quantitatively analyzed. The modal change trend is tracked by a dynamic time window (such as updating every 1 hour), and the structure resonance modal atlas is constructed, represented by the three-dimensional structure model superimposed with the modal energy color distribution. In the experiment, the second-order modal frequency in the downstream spillway area of Z dam was found to decrease from 44.5 kHz to 41.9 kHz, and the shape changed from symmetric to offset, indicating that the structure has asymmetric fatigue fission. The modal map distinguishes the resonance intensity in space with thermal color, and the frequency shift is marked with an arrow to indicate the evolution direction. The modal map is a key intermediate result for evaluating the structural integrity and predicting potential damage areas, directly related to the next step of anomaly marking and positioning.

[0039] By analyzing the frequency drift, mode mutation, and energy concentration anomalies in the modal atlas, the structural integrity can be intelligently evaluated. The evaluation process uses a modal stability scoring system, setting normal modal frequency drift threshold (<1.2 kHz), resonance energy concentration coefficient (<15% area), and other judgment criteria. When the modal frequency of the detection area changes beyond the threshold, or the local mode no longer meets the symmetry constraint, it is determined to be an abnormal resonance area. Combined with the modal history sequence and the current change slope, three levels of labels are marked: "possible damage area", "degradation critical area", and "normal stable area". In the experimental results, a modal shift of 2.6 kHz was found in the left foundation area of M sluice for 5 consecutive hours, corresponding to an area of 2.1 m², which was marked as a red warning area. The identification of abnormal resonance areas provides an important reference for precise damage location and maintenance strategy formulation. Using the Time Reversal Mirror (Time Reversal Mirror) and differential beam imaging technology, the acoustic signals in the abnormal area are spatially retraced, and the sound source position is accurately reconstructed. Using the time difference of multi-node ultrasonic signals, the spatial coordinates of the sound source are calculated, and millimeter-level structural damage point positioning is achieved. Combined with the local signal amplitude change trend, the damage level (minor crack, material debonding, or structure erosion) is estimated. In the Z sluice pipe structure, three micro-crack aggregation points were located, with a position coordinate error within 5 cm. The second point corresponds to an area in the modal map that exhibits significant frequency drift and high damping concentration, confirming its association with structural fatigue. The structural damage location will serve as a key input for subsequent maintenance planning and structural modeling updates.

[0040] In this embodiment, the specific steps of step S5 are: Based on the environmental disturbance spatiotemporal correlation atlas, the environmental disturbance driving error calculation of the digital twin model of the water conservancy facility is performed to generate an environmental disturbance error value; Based on the environmental disturbance error value, error value sequencing fitting is performed to obtain an error value sequence; Multi-point difference calculation is performed on the error value sequence, and multi-time point error change identification is performed to obtain an error difference value gradient; According to the error difference value gradient, an error development trend prediction is performed to generate a facility target error development prediction value.

[0041] In this embodiment, the environmental disturbance spatiotemporal correlation graph is taken as the input, combined with the real-time collected structure and environmental parameters in the digital twin model, to calculate the monitoring error caused by environmental disturbance. First, the environmental disturbance spatiotemporal correlation graph maps the spatiotemporal position, intensity and propagation path of different disturbance events in detail, forming a disturbance factor matrix. This matrix covers multi-dimensional information such as water flow rate change, temperature gradient fluctuation, ion concentration anomaly, etc., and reflects the potential influence degree of each disturbance on the monitoring parameters through a weight distribution mechanism. Subsequently, using the simulation calculation module of the digital twin model, combined with real-time sensing data, the readings of the monitoring equipment are compared with the theoretical model output to quantify the deviation of the monitoring results caused by environmental disturbance. The error calculation method uses an error function based on weighted residual analysis, combined with disturbance intensity and spatial distribution for dynamic correction. For example, for a sensor signal drift caused by a sudden change in water flow rate, by introducing a flow rate disturbance weight of 0.3 and a temperature disturbance weight of 0.1, the error value for this period is calculated to be 0.045 m / s. After this step, an environmental disturbance error value set containing time stamp, disturbance type and error amplitude is generated, providing basic data for subsequent error analysis. After obtaining discrete environmental disturbance error values, they need to be sequenced to capture the time evolution characteristics of the error. Here, time series analysis method is used to sequentially fit the error value set, mainly using autoregressive moving average model (ARMA) and its extended form ARIMA for modeling. In specific implementation, the error data is sorted in chronological order, first the sequence is tested for stationarity (such as ADF test) to ensure that the sequence is suitable for ARMA modeling; if the sequence has trend or seasonal components, difference processing is used to eliminate non-stationarity. In the experiment, the monitoring error data for 30 consecutive days is fitted, setting the ARIMA model parameters p=2, d=1, q=1, and optimizing the model coefficients through maximum likelihood estimation to fit the error sequence trend. The fitting effect is evaluated by root mean square error (RMSE) and Akaike information criterion (AIC), and finally the error value time series is obtained, clearly revealing the long-term trend and short-term fluctuations of the error, laying a solid foundation for error evolution rule analysis.

[0042] A fixed time window (e.g., 1 hour, 6 hours, and 24 hours) is selected, and the difference between the error values before and after the window is calculated to form a gradient sequence. Subsequently, based on the gradient sequence, a sliding window analysis and peak detection algorithm are applied to identify the key moments and trend inflection points of error changes. For example, in a certain monitoring period, it is detected that the error difference gradient rapidly rises at the 12th hour, with a peak value of 0.008, indicating a risk of sudden increase in monitoring error caused by environmental disturbance. In addition, a multi-scale analysis method (such as wavelet transform) is used to decompose the error gradient sequence to identify error change characteristics at different time scales, achieving multi-level control of error dynamics and providing rich information support for error trend prediction. Based on the aforementioned error gradient characteristics, an error trend prediction model is established to predict future facility monitoring errors. The prediction model uses deep learning time series models such as Long Short-Term Memory (LSTM) due to their superior fitting ability for nonlinear complex sequences. The model input includes error difference gradient sequence, environmental disturbance intensity sequence, and historical error sequence, which are fused through multiple channels to comprehensively understand the error evolution mechanism. In the model training phase, 120 days of environmental disturbance and monitoring error data are used, with 200 training rounds and a batch size of 64. The Adam optimizer is used, and the final model achieves a high precision of 0.0012 mean squared error on the validation set. The prediction results are output in the form of facility target error development prediction values, accurately depicting the error change trend in the next 24 hours, 48 hours, and 72 hours, and warning potential error exceeding periods. This prediction result provides a scientific basis for facility operation and error correction strategy, supporting dynamic adjustment of monitoring parameters and environmental disturbance response schemes.

[0043] In this embodiment, the specific steps of step S6 are: Define the maximum acceptable error threshold; According to the maximum acceptable error threshold, dynamically correct the facility target error development prediction value trajectory, and perform multi-objective error optimization mediation to generate an optimal error correction compensation curve; According to the optimal error correction compensation curve, make correction execution decision, and construct water conservancy facility error correction control engine; Based on the water conservancy facility error correction control engine, continuously correct the error correction driving, and collect real-time error correction results; Identify the correction accuracy feedback of the real-time error correction results to obtain correction accuracy feedback information; According to the correction accuracy feedback information, the parameter intelligent self-optimization processing of the water conservancy facility error correction control engine is performed, and an intelligent self-optimization correction model is constructed to execute the water conservancy facility monitoring parameter error correction operation under environmental disturbance.

[0044] In this embodiment, the maximum error limit value of the water conservancy facility monitoring system under environmental disturbance conditions is determined as the constraint index for subsequent error correction. The definition of this threshold needs to be based on facility design specifications, monitoring equipment sensitivity, and safety operation requirements, combined with historical monitoring data and field experiment results. The specific approach is to collect monitoring data under multiple batches of environmental disturbances, analyze the error distribution characteristics, and calculate the 95% confidence interval of the error through probability statistics method. The upper limit is set as the maximum acceptable error threshold. For example, in a monitoring experiment of a certain water intake gate, after sampling 500 disturbance events, the monitoring error peak value is mainly concentrated in the range of ±0.03m, and the upper limit of the 95% confidence interval is determined as 0.035m, which is the maximum acceptable error threshold. In addition, combined with the recommended error standard of the equipment manufacturer and the regulatory requirements, it is ensured that the threshold guarantees safety and is not overly conservative, balancing monitoring accuracy and system stability. By planning the dynamic correction trajectory, the error value converges reasonably to the maximum acceptable threshold, avoiding error overruns. First, compare the error prediction sequence with the threshold to identify potential overruns at risk periods. For error prediction at different time nodes, use multi-objective optimization algorithms (such as genetic algorithm, multi-objective particle swarm optimization) to optimize correction effect, response speed and energy consumption at the same time. The optimization objectives include minimizing error residual, reducing correction execution times and controlling correction action amplitude. In the specific implementation, the maximum error threshold of 0.035m is set as the hard constraint, and the minimum error residual and the lowest correction energy consumption are set as the soft targets. Through genetic algorithm iteration for 300 generations, population size 50, crossover probability 0.7, mutation probability 0.1, the best convergence effect of error correction compensation curve is obtained. The curve describes the timing and amplitude of the correction action in detail, providing a clear path for control execution, and ensuring that the error is dynamically maintained within a reasonable range.

[0045] The control engine integrates model-based predictive scheduling with real-time feedback mechanism, supports multi-channel sensor data input, and automatically generates correction instructions combining error threshold and correction strategy. The specific implementation includes building a modular software architecture, setting the scheduling period to 1 minute, reading the latest monitoring data and prediction error in real time, and adjusting the monitoring device parameters or activating the compensation device (such as correcting the valve opening, electrical regulator, etc.) according to the compensation curve. The control engine has built-in safety protection logic to prevent overshoot and oscillation, and uses PID control algorithm for smooth adjustment. In the experimental verification stage, the control engine is applied in the reservoir sluice gate system, and the adjustment frequency and error control precision meet the system design requirements, with the error stable control within ±0.025m, better than the set threshold. After the control engine goes online, it performs continuous error correction actions to drive the facility monitoring system to respond to environmental disturbances in real time. The monitoring system collects corrected monitoring data and compares it with uncorrected data to evaluate the correction effect. The error correction result includes the corrected error amplitude, correction response time, and environmental disturbance conditions. The data collection frequency is usually set to every 5 minutes to ensure high-time response to dynamic changes in error. Taking a certain hydropower station monitoring system as an example, after 60 days of continuous operation, the correction result shows that the error is reduced by an average of 30%, and the maximum error peak value is reduced from 0.04m to 0.028m, verifying the continuous driving ability and stability of the correction engine. Real-time data provides key basis for subsequent feedback identification and model optimization.

[0046] After collecting real-time error correction data, correction accuracy feedback identification needs to be carried out to evaluate the effectiveness and stability of the correction scheme. This process uses a combination of statistical analysis and machine learning methods to conduct multi-dimensional correlation analysis of error residuals, correction response delays, and environmental disturbance variables. Specific methods include error residual distribution statistics, time series trend analysis, and error correlation evaluation. Through chi-square test and Pearson correlation coefficient calculation, the reliability and accuracy of feedback information are calculated. At the same time, a random forest classifier is used to automatically identify the correction effect and divide it into three categories: "excellent", "normal" and "to be improved". In the experimental stage, for some water gate monitoring error data, the average error residual is identified as 0.018m, and the feedback accuracy rate reaches 92%, providing a scientific basis for subsequent intelligent optimization. Based on the feedback information, an intelligent self-optimization mechanism is constructed to improve the adaptive ability and long-term stability of the error correction control engine. The implementation method is to establish a parameter adaptive optimization model, combined with reinforcement learning (such as deep Q network DQN) algorithm, to dynamically adjust the PID control parameters, correction strategy weights and compensation curve shape according to real-time feedback. The system continuously monitors the correction accuracy, calculates the reward function, and drives the learning algorithm to optimize the control strategy. The experimental design uses simulated environmental disturbance changes and various error modes to train the intelligent model for more than 100,000 steps. After optimization, the root mean square error of the control system is reduced by 15% and the response time is shortened by 20%. After the self-optimizing correction model is successfully constructed, the system can automatically adjust in different disturbance environments to ensure that the error control is within the maximum acceptable threshold, achieving intelligent and dynamic error correction.

[0047] In the embodiment, a water conservancy facility environmental disturbance error correction device based on multi-dimensional data is provided for executing the water conservancy facility environmental disturbance error correction method based on multi-dimensional data as described above, comprising: A multi-scale disturbance correlation module is used to collect water conservancy facility historical monitoring logs, identify environmental disturbance events, and mine multi-scale disturbance correlation, and construct an environmental disturbance spatio-temporal correlation graph. A response trend change module is used to monitor the surrounding environmental water body monitoring parameters of the water conservancy facility, and conduct time series response trend change analysis and dynamic response characteristic modeling to construct a facility environmental individualized change response portrait. A hydrodynamic perception module is used to identify multiple water body location monitoring data based on the surrounding environmental water body monitoring parameters, and conduct global hydrodynamic perception of the water body, and construct a hydrodynamic global perception graph. A digital twin module is used to conduct nonlinear correlation analysis based on the hydrodynamic global perception graph and the facility environmental individualized change response portrait, and construct a water conservancy facility digital twin model under water flow. An error trend prediction module is configured to calculate the monitoring error of the digital twin model of the water conservancy facility based on the spatiotemporal correlation graph of the environmental disturbance, and to predict the error development trend to generate a predicted value of the target error development of the facility. An error correction self-tuning module is configured to drive continuous error correction according to the predicted value of the target error development of the facility, and to perform intelligent self-tuning of parameters to execute the error correction of the monitoring parameters of the water conservancy facility under the environmental disturbance.

[0048] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the application being defined by the appended claims and not by the above description, and it is intended to encompass all variations falling within the meaning and the scope of the equivalent elements of the application file.

[0049] The above description is merely that of specific embodiments of the application, enabling a person skilled in the art to understand or implement the application. Numerous modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the application will not be limited to these embodiments shown herein, but will accord with the widest scope consistent with the principles and novel features developed herein.

Claims

1. A method for correcting errors in waterworks environmental disturbances based on multi-dimensional data, characterized by, The method comprises the following steps: Step S1: Collecting water conservancy facility historical monitoring logs, identifying environmental disturbance events, and mining multi-scale disturbance correlation to construct an environmental disturbance spatiotemporal correlation graph; Step S2: Monitoring the surrounding environmental water body monitoring parameters of the water conservancy facility, and performing time series response trend change analysis and dynamic response characteristic modeling to construct a facility environment individualized change response portrait; Step S3: Identifying multiple water body location monitoring data according to the surrounding environmental water body monitoring parameters, and performing water body global dynamics perception to construct a water dynamics full-field perception graph; Step S4: Based on the water dynamics full-field perception graph and the facility environment individualized change response portrait, performing nonlinear correlation analysis to construct a water conservancy facility digital twin model under water flow; Step S5: Based on the environmental disturbance spatiotemporal correlation graph, calculating the monitoring error of the water conservancy facility digital twin model, and performing error development trend prediction to generate a facility target error development prediction value; Step S6: Based on the facility target error development prediction value, performing continuous error correction driving, and performing parameter intelligent self-optimization processing to perform water conservancy facility monitoring parameter error correction under environmental disturbance.

2. The method for correcting the error of the water conservancy facility environment disturbance based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S1 are: Collecting water conservancy facility historical monitoring logs; Identifying and extracting environmental disturbance events according to water conservancy facility historical monitoring logs to obtain multiple historical environmental disturbance event information; Performing multi-level disturbance source identification on the multiple historical environmental disturbance event information to obtain a historical environmental disturbance feature matrix; Performing disturbance propagation path analysis and impact range calculation on the historical environmental disturbance feature matrix to obtain environmental disturbance propagation characteristics of each disturbance event; Extracting the disturbance duration period of the historical environmental disturbance event information; Based on the disturbance duration period, performing multi-scale disturbance correlation mining on the environmental disturbance propagation characteristics to construct an environmental disturbance spatiotemporal correlation graph.

3. The method for correcting the error of the water conservancy facility environment disturbance based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S2 are: Monitoring the surrounding environmental water body monitoring parameters of the water conservancy facility based on a distributed physical field sensor array; Extracting the water temperature gradient, flow velocity vector field, pressure distribution, and water quality ion concentration of the surrounding environmental water body monitoring parameters to construct a facility environment multi-dimensional physical field vector; Performing time series response trend change analysis on the facility environment multi-dimensional physical field vector to generate a surrounding environmental parameter change trend; Based on the surrounding environmental parameter change trend, performing dynamic response characteristic modeling to construct a facility environment individualized change response portrait.

4. The method for correcting the error of the water conservancy facility environment disturbance based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S3 are: Identifying multiple water body location monitoring data according to the surrounding environmental water body monitoring parameters; Performing water body dynamics boundary analysis on the multiple water body location monitoring data to generate water body dynamics boundary information; Performing turbulence characteristic analysis and boundary layer effect identification on the water body dynamics boundary information to extract water body dynamic flow field distribution characteristics; Performing water body global dynamics perception on the water body dynamic flow field distribution characteristics to construct a water dynamics full-field perception graph.

5. The method for correcting errors of waterworks environmental disturbances based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S4 are: Obtaining water conservancy facility structure acoustic response parameters, performing structure degradation trend prediction, and constructing a facility structure degradation trend prediction graph; Based on the water dynamics full-field perception map, the water body flow state change, vortex generation mode and energy dissipation law are analyzed, and the water body flow state evolution logic is mined to construct a water body flow state evolution real-time portrait; Based on the water body flow state evolution real-time portrait, the structure degradation-water dynamics change nonlinear correlation analysis is performed on the facility structure degradation trend prediction map to construct the structure-water nonlinear correlation law; According to the facility environment individualized change response portrait and the structure-water nonlinear correlation law, the water body disturbance digital processing is performed to construct the digital twin model of water conservancy facilities under water flow.

6. The method for correcting errors of environmental disturbances of water facilities based on multi-dimensional data according to claim 5, characterized in that, The specific steps of acquiring the water conservancy facility structure acoustic response parameters, predicting the structure degradation trend, and constructing the facility structure degradation trend prediction map are as follows: Based on the ultrasonic detection array, the water conservancy facility structure acoustic response parameters are continuously monitored; According to the water conservancy facility structure acoustic response parameters, the time sequence vibration response frequency is calculated to generate a time sequence response frequency curve; According to the water conservancy facility structure acoustic response parameters, the facility structure resonance amplitude, damping coefficient and sound wave propagation speed are calculated; Multi-dimensional structure acoustic feature mining is performed on the time sequence response frequency curve, facility structure resonance amplitude, damping coefficient and sound wave propagation speed to construct a multi-dimensional structure acoustic feature parameter set; Frequency domain analysis and resonance mode evolution analysis are performed on the multi-dimensional structure acoustic feature parameter set to obtain a structure resonance mode map; Based on the structure resonance mode map, the structure integrity is evaluated, and the abnormal resonance area is analyzed to mark the abnormal resonance area; Based on the abnormal resonance area, the structure damage area is accurately located, and the structure damage positioning point is marked; The structure damage distribution is performed on the structure damage positioning point, and the structure degradation trend is predicted to construct a facility structure degradation trend prediction map.

7. The method for correcting errors of environmental disturbances of water facilities based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S5 are as follows: Based on the environmental disturbance space-time correlation map, the environmental disturbance driving facility monitoring error calculation is performed on the water conservancy facility digital twin model to generate an environmental disturbance error value; Based on the environmental disturbance error value, the error value is sequenced and fitted to obtain an error value sequence; Multi-point difference calculation is performed on the error value sequence, and multi-time point error change identification is performed to obtain an error value change gradient; According to the error value change gradient, the error development trend is predicted to generate a facility target error development prediction value.

8. The method for correcting the error of the water conservancy facility environment disturbance based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S6 are as follows: Define the maximum acceptable error threshold; According to the maximum acceptable error threshold, the facility target error development prediction value is dynamically corrected and trajectory planned, and multi-target error optimization mediation is performed to generate an optimal error correction compensation curve; According to the optimal error correction compensation curve, the correction execution decision is made to construct a water conservancy facility error correction control engine; Based on the water conservancy facility error correction control engine, the continuous error correction driving is performed, and the real-time error correction result is collected; The correction accuracy feedback information is obtained by identifying the correction accuracy feedback of the real-time error correction result; According to the correction accuracy feedback information, the parameter intelligent self-optimization processing is performed on the water conservancy facility error correction control engine to construct an intelligent self-optimization correction model to perform the water conservancy facility monitoring parameter error correction operation under environmental disturbance.

9. A waterworks environment disturbance error correction device based on multi-dimensional data, characterized by, The method for performing the water conservancy facility environment disturbance error correction based on multi-dimensional data as claimed in claim 1 comprises: a multi-scale disturbance correlation module for collecting water conservancy facility historical monitoring logs, identifying environmental disturbance events, and mining multi-scale disturbance correlation, and constructing an environmental disturbance spatiotemporal correlation graph; a response trend change module for monitoring water conservancy facility surrounding environment water body monitoring parameters, and performing time series response trend change analysis and dynamic response characteristic modeling, and constructing a facility environment individualized change response portrait; a hydrodynamics perception module for identifying multiple water body location monitoring data according to the surrounding environment water body monitoring parameters, and performing water body global hydrodynamics perception, and constructing a hydrodynamics full-field perception graph; a digital twin module for performing nonlinear correlation analysis based on the hydrodynamics full-field perception graph and the facility environment individualized change response portrait, and constructing a water conservancy facility digital twin model under water flow; an error trend prediction module for calculating facility monitoring errors based on the environmental disturbance spatiotemporal correlation graph and the water conservancy facility digital twin model, and performing error development trend prediction to generate facility target error development prediction values; an error correction self-tuning module for continuously performing error correction driven according to the facility target error development prediction values, and performing parameter intelligent self-tuning processing to perform water conservancy facility monitoring parameter error correction under environmental disturbance.

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