Water conservancy engineering global situation awareness and collaborative early warning system

Abstract

The application discloses a water conservancy project global situation awareness and collaborative early warning system and relates to the technical field of water conservancy project management, comprising: a multi-source perception acquisition module: collecting multiple types of original monitoring data generated in the operation process of the water conservancy project, and performing a preprocessing operation on the collected original monitoring data to generate a global perception dataset; a global digital twin deduction module: used for receiving the global perception dataset, extracting a target node historical output sequence, and constructing an exogenous input matrix including upstream outflow, water level, discharge, tributary flow, pump station state, gate opening and scheduling instruction according to a water network topology, performing multi-step prediction by using an NARX time series prediction algorithm, and outputting a virtual-real fusion situation sequence. In the application, the multi-source perception acquisition module is used to preprocess the multiple types of original monitoring data, the data island problem is solved, the comprehensive gathering and standardization of the water conservancy project operation data are realized, and a foundation is laid for subsequent analysis.

Description

Water Conservancy Project Comprehensive Situational Awareness and Collaborative Early Warning System Technical Field

[0001] This invention relates to the field of water conservancy project management technology, and in particular to a water conservancy project situational awareness and collaborative early warning system. Background Technology

[0002] Water conservancy projects are important infrastructure for ensuring flood control safety, water resource allocation, and the stable operation of the national economy. Their operation objects typically include various engineering units such as reservoirs, sluice gates, pumping stations, canals, and dikes, and they exhibit cross-regional, multi-node, and strongly coupled system characteristics. With the continuous expansion of regional and national water networks, the types of data generated during the operation of water conservancy projects are becoming increasingly diverse, and the relationship between the operation status of projects and scheduling behavior is becoming increasingly complex, which places higher demands on the ability to perceive the operation situation and provide early warning of risks.

[0003] Existing water conservancy monitoring and early warning systems have the following typical shortcomings: most systems focus on single projects or local areas, lacking cross-basin and cross-project group situational integration; traditional early warning relies on threshold judgment and single-variable analysis, which cannot characterize the complex dynamic relationships of engineering systems; when faced with complex hydrology, structural status and scheduling behavior, prediction deviations cannot be used as decision-making basis for early warning; the working status of the entire life cycle of the project lacks a unified expression system; monitoring systems usually only focus on operational risks and cannot identify the coupling risks between network anomalies and operational anomalies; early warning levels still rely on manual judgment and cannot form the ability to automatically generate instructions and coordinate scheduling. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a comprehensive situational awareness and collaborative early warning system for water conservancy projects.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a comprehensive situational awareness and collaborative early warning system for water conservancy projects, comprising: a multi-source sensing and acquisition module: collecting various types of raw monitoring data generated during the operation of water conservancy projects, and performing preprocessing operations on the collected raw monitoring data to generate a comprehensive sensing dataset; a comprehensive digital twin inference module: receiving the comprehensive sensing dataset, extracting historical output sequences of target nodes, and constructing an exogenous input matrix including upstream outflow, water level, discharge, tributary flow, pumping station status, gate opening, and scheduling instructions according to the water network topology, performing multi-step prediction using the NARX time series prediction algorithm, and outputting a virtual-real fusion situational sequence; an early warning uncertainty quantification module: receiving the virtual-real fusion situational sequence, generating multiple reconstructed prediction trajectories based on the residual sample library using the risk-sensitive time series block Bootstrap resampling uncertainty assessment algorithm, and calculating the sample mean, standard deviation, confidence interval, model consistency, and data fluctuation characteristics to form a confidence matrix; and a full life cycle operating condition scenario library module: receiving the confidence matrix and using DBSCAN... The density clustering algorithm identifies operating condition categories and their evolution trends, generating a set of operating condition scenario states. The business security joint situational awareness early warning module receives the operating condition scenario state set and uses the Independent Component Analysis (ICA) algorithm to generate a joint situational awareness feature set. The joint early warning judgment module receives the joint situational awareness feature set and uses the Support Vector Machine (SVM) classification algorithm to generate a joint early warning result set. The collaborative early warning control module receives the joint early warning result set and uses the decision tree algorithm to generate a collaborative early warning control instruction set, including gate control instructions, pump station start / stop instructions, inspection and scheduling instructions, and safety isolation instructions.

[0006] As a further description of the above technical solution:

[0007] The monitoring data includes water level, flow rate, gate commands, pumping station power, dam structural status, river hydrology, meteorological observations, and network security monitoring data. Preprocessing operations are performed on the monitoring data sequentially, including data time alignment, missing value imputation, sliding window segmentation, data format normalization, and feature value standardization. Monitoring data from different sources, sampling frequencies, and data formats are unified in terms of time reference and dimensional system, and constructed into multi-dimensional structured records, forming a comprehensive sensing dataset. The comprehensive sensing dataset includes water level sequences, flow rate sequences, structural status sequences, and operational response sequences, divided according to engineering node numbers.

[0008] As a further description of the above technical solution:

[0009] The full-domain digital twin simulation module extracts the historical water level sequence, historical flow sequence, historical structural state sequence, and historical operational response sequence of the target node within the past time window from the full-domain perception dataset, forming a real situation sample set;

[0010] Based on the real situation sample set, a corresponding exogenous input sequence set is constructed according to the water network topology. The real situation sample set and the exogenous input sequence set are then input into the NARX time series prediction algorithm to predict the water level, flow rate, structural status and operational response of the target node in multiple consecutive time steps in the future, thus obtaining a digital twin prediction situation set.

[0011] The real situation sample set and the digital twin predicted situation set are time-aligned and sequence-stitched according to a unified time axis and engineering node number to form a fusion situation sample set that simultaneously contains historical real situation data and future predicted situation data. The historical real situation data is the observed value, and the future predicted situation data is the predicted value.

[0012] The fusion situation sample set is arranged into a continuous time-series structure according to the time sequence, and then arranged according to the engineering node dimension to generate a virtual-real fusion situation sequence covering multiple engineering nodes and multiple prediction periods.

[0013] As a further description of the above technical solution:

[0014] The early warning uncertainty quantification module extracts predicted and observed values ​​from the virtual-real fusion situation sequence and obtains historical observation data corresponding to the sequence. It calculates the difference between the predicted and observed values ​​and constructs a historical residual sample set arranged chronologically based on this difference. A risk threshold is set according to engineering design specifications, operation management requirements, and statistical historical operation results. This risk threshold is a fixed configuration parameter used to determine the risk level of the predicted values. The historical residual sample set is segmented chronologically, dividing residual samples from multiple consecutive time steps into multiple residual blocks, forming a residual block sample library that maintains time correlation. Based on the relationship between the predicted value of the virtual-real fusion situation sequence at the predicted time step and the preset risk threshold, the risk level is obtained, the risk weight of the corresponding residual block is calculated, and the risk weight is normalized. A weighted sampling probability distribution is formed for the residual block sample library. Following this distribution, a bootstrap resampling operation with replacement is performed on the residual blocks as the basic unit to generate multiple reconstructed residual sequences. These reconstructed residual sequences are then superimposed onto the predicted values ​​in the virtual-real fusion situation sequence to obtain multiple reconstructed prediction trajectories, forming a prediction sample set. Based on this set, the sample mean, sample standard deviation, upper bound of the confidence interval, lower bound of the confidence interval, model consistency index, and data fluctuation characteristics are calculated for each engineering node, physical quantity, and prediction time step. The risk intensity is comprehensively represented by the sample mean and sample standard deviation. The calculation results are arranged according to the engineering node dimension, physical quantity dimension, and time dimension to construct a confidence matrix containing risk intensity, upper bound of confidence, lower bound of confidence, model consistency index, and data fluctuation characteristics.

[0015] As a further description of the above technical solution:

[0016] The full lifecycle operating condition scenario library module extracts the risk intensity, upper confidence bound, lower confidence bound, model consistency index, and data fluctuation characteristics of each engineering node within the current time window from the confidence matrix, forming a set of operating condition feature samples. Based on preset neighborhood radius parameters and minimum sample number parameters, DBSCAN is used. Density clustering algorithm performs density clustering on the set of working condition feature samples, calculates the neighborhood density of each working condition feature sample in the feature space, and marks the working condition feature samples with a number of samples in their neighborhood that is not less than the minimum number of samples as core samples. If a core sample is located within the neighborhood of a second working condition feature sample, and the second working condition feature sample meets the density condition, then the core sample is determined to have a density reachability relationship with the second working condition feature sample. If a core sample forms a continuous neighborhood connection path through one or more working condition feature samples that meet the density condition, then the core sample is determined to have a density reachability relationship with the working condition feature sample at the end of the path, and the core sample and its neighboring samples are divided into the same working condition cluster according to the density reachability relationship. Samples that cannot be assigned to any cluster are marked as isolated working condition samples, resulting in a working condition clustering result set. The neighborhood radius parameter and the minimum number of samples parameter are preset based on the confidence matrix output by the early warning uncertainty quantification module. The neighborhood radius parameter is determined based on the numerical distribution range of the working condition feature samples in the dimensions of risk intensity, upper confidence bound, lower confidence bound, model consistency index, and data fluctuation characteristics. The sample size parameter is determined based on the number of engineering nodes included in the built-in confidence matrix of a single time window. Each engineering node is matched with a pre-defined full-lifecycle engineering condition template to determine its engineering condition category within the current time window, generating the current engineering condition identification result. The full-lifecycle engineering condition template is pre-defined based on the set of engineering condition scenarios formed during the system's historical operation phases. It is formed by summarizing recurring and stable engineering condition categories within different time windows, serving as a reference for subsequent engineering condition identification and correspondence. The identification results of the engineering condition categories corresponding to the same engineering node within consecutive time windows are compared over time to form a series of engineering condition category changes. This series identifies the changes in engineering condition categories, determines the evolution trend of engineering conditions, and judges stable and abrupt states of engineering conditions based on the engineering condition category change series, generating an engineering condition evolution judgment result. The current engineering condition identification result and the engineering condition evolution judgment result are then combined to form a set of engineering condition scenario states containing engineering node identifiers, current engineering condition categories, engineering condition cluster features, and engineering condition evolution state identifiers.

[0017] As a further description of the above technical solution:

[0018] The business security joint situational awareness module extracts business condition category features, risk intensity features, and business condition evolution trend features representing the operational status of engineering business from the business condition scenario status set. It processes network security monitoring data to obtain the network security status. The business status feature set is then concatenated with the network security status. A business situational awareness feature vector is constructed using the current business condition category, business condition cluster features, and business condition evolution status identifier corresponding to each engineering node in the business condition scenario status set. This vector is then concatenated with the control command fluctuation features, communication integrity features, and security event count features corresponding to the network security status to form an observation signal vector set. Independent component analysis (ICA) is then employed. The algorithm calculates a separation matrix on the set of observed signal vectors and derives it based on the maximization of non-Gaussianity criterion. This separation matrix maps the observed signals to several approximately statistically independent components. The separation matrix is ​​then divided into row-wise coefficient vectors corresponding to each independent component; the coefficient vector for each independent component is its independent component coefficient. The observed signal vector set is input into the separation matrix time-by-time within the current time window to obtain the independent component value sequence at each time point. For each independent component, its amplitude is calculated within the current time window, taking the mean or maximum absolute value of the value sequence. This yields the independent component coefficients corresponding to each independent component. The amplitudes of independent components are determined, and the amplitudes of each independent component are normalized to obtain the amplitude normalization result. The amplitude normalization result of each independent component is used as the contribution measure. The contribution of each independent component is modulated using the independent component coefficients to make the structure of the comprehensive situation score consistent with the structure of the observation signal vector set. The absolute value of the independent component coefficient vector is used as the contribution weight of the independent component, and the contribution weight is normalized. It is then multiplied by the corresponding independent component amplitude normalization result and summed to obtain the comprehensive situation score. The independent component coefficients, independent component amplitudes, and comprehensive situation scores of each engineering node within the current time window are collected to obtain the joint situation feature set.

[0019] As a further description of the above technical solution:

[0020] The business security joint situational awareness early warning module extracts the command value sequence of control commands within the current time window from network security monitoring data; calculates the fluctuation metric of the command value sequence to obtain the control command fluctuation characteristics, where the fluctuation metric is the standard deviation of the command value sequence within the current time window, or the difference between the maximum and minimum values ​​of the command value sequence within the current time window, and outputs the control command fluctuation characteristics; extracts the communication integrity judgment result sequence within the current time window from network security monitoring data; statistically analyzes the communication integrity judgment result sequence, taking the ratio of the number of complete communication integrity judgment results within the current time window to the total number of communication integrity judgment result records within the time window, to obtain the communication integrity characteristics; extracts security event records within the current time window from network security monitoring data; counts the security event records by time window to obtain the security event counting characteristics; outputs the security event counting characteristics; and combines the control command fluctuation characteristics, communication integrity characteristics, and security event counting characteristics to form the network security status.

[0021] As a further description of the above technical solution:

[0022] The joint early warning determination module constructs a joint situation feature vector set from the independent component coefficients, independent component amplitudes, and comprehensive situation score corresponding to the current time window in the joint situation feature set. Each joint situation feature vector corresponds to the joint situation feature representation of an engineering node within the current time window. Using the joint situation feature vectors labeled in historical early warning event data as training samples and the corresponding early warning level labels as classification outputs, a joint early warning determination model is constructed using the Support Vector Machine (SVM) classification algorithm to learn the classification boundaries between different early warning levels in the joint situation feature space. During system operation, the set of joint situation feature vectors formed within the current time window is input into the joint early warning determination model, and classification determination is performed on each joint situation feature vector to obtain the corresponding early warning level and early warning type. Based on the decision function results output by the SVM classification algorithm during the classification process, the classification confidence level corresponding to each early warning level is calculated, and the early warning level, early warning type, and classification confidence level are organized according to the engineering node dimension and the time window dimension. The early warning level, early warning type, and classification confidence level corresponding to each engineering node are collected to form a joint early warning result set, which serves as the input data for the collaborative early warning control module. Historical early warning event data refers to the set of early warning event records formed and retained within historical time windows during the system's past operation. Historical early warning event data includes: joint situation feature vectors formed by each engineering node within the corresponding historical time window, and early warning level and early warning type records that correspond one-to-one with the joint situation feature vectors.

[0023] As a further description of the above technical solution:

[0024] The collaborative early warning control module constructs a set of decision feature vectors from the joint early warning results, corresponding to the early warning level, early warning type, and classification confidence of each engineering node. Each decision feature vector corresponds to the early warning judgment result of an engineering node within the current time window. Based on control samples formed by historical handling cases and operation management procedures, a collaborative early warning control model is constructed using a decision tree algorithm. The decision tree algorithm uses decision feature vectors as input features and engineering control action categories as output nodes. During system operation, the set of decision feature vectors formed within the current time window is input into the collaborative early warning control model, and the corresponding control action leaf node is selected along the decision path of the decision tree to obtain the control action results corresponding to each engineering node. Based on the control action results, corresponding gate control instructions, pump station start / stop instructions, inspection scheduling instructions, and safety isolation instructions are generated. The generated instructions are organized and aggregated according to the engineering node dimension and the time window dimension to form a collaborative early warning control instruction set.

[0025] The present invention has the following beneficial effects:

[0026] 1. In this invention, the multi-source sensing and acquisition module preprocesses various types of raw monitoring data, solving the data silo problem and realizing the comprehensive aggregation and standardization of water conservancy project operation data, laying the foundation for subsequent analysis. The full-domain digital twin inference module uses the NARX time series prediction algorithm for multi-step prediction, generating a virtual-real fusion situation sequence, achieving accurate prediction of the future operation status of water conservancy projects, and improving the depth and breadth of situation awareness. The early warning uncertainty quantification module uses the risk-sensitive time series block Bootstrap resampling uncertainty assessment algorithm to generate multiple reconstructed prediction trajectories and calculate the confidence matrix, effectively quantifying the uncertainty of prediction and improving the reliability and credibility of early warning. The full life cycle working condition scenario library module uses the DBSCAN density clustering algorithm to identify working condition categories and evolution trends, generating a working condition scenario state set, realizing intelligent identification and dynamic tracking of complex working conditions, and providing more refined contextual information for early warning.

[0027] 2. In this invention, the joint situational awareness early warning module uses the Independent Component Analysis (ICA) algorithm to generate a joint situational awareness feature set, which jointly analyzes the business operation status and network security status, realizing a comprehensive consideration of the business security and network security of water conservancy projects, avoiding the limitations of single-dimensional early warning. The joint early warning judgment module uses the Support Vector Machine (SVM) classification algorithm to generate a joint early warning result set, which can perform intelligent classification and judgment based on multi-dimensional features, improving the accuracy and intelligence level of early warning judgment. The collaborative early warning control module uses the decision tree algorithm to generate a collaborative early warning control instruction set, realizing the intelligent generation of various control instructions such as gates, pumping stations, inspections, and safety isolation, improving the automation and collaboration level of early warning response, effectively reducing the lag and uncertainty of manual intervention, thereby comprehensively improving the safety, stability, and management efficiency of water conservancy project operation. Attached Figure Description

[0028] Figure 1 is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Referring to Figure 1, an embodiment of the present invention provides a comprehensive situational awareness and collaborative early warning system for water conservancy projects, comprising: a multi-source sensing and acquisition module: collecting various types of raw monitoring data generated during the operation of water conservancy projects, and performing preprocessing operations on the collected raw monitoring data to generate a comprehensive sensing dataset; a comprehensive digital twin inference module: receiving the comprehensive sensing dataset, extracting historical output sequences of target nodes, and constructing an exogenous input matrix including upstream outflow, water level, discharge, tributary flow, pumping station status, gate opening, and scheduling instructions according to the water network topology, performing multi-step prediction using the NARX time series prediction algorithm, and outputting a virtual-real fusion situational sequence; an early warning uncertainty quantification module: receiving the virtual-real fusion situational sequence, generating multiple reconstructed prediction trajectories based on the residual sample library using the risk-sensitive time series block Bootstrap resampling uncertainty assessment algorithm, and calculating the sample mean, standard deviation, confidence interval, model consistency, and data fluctuation characteristics to form a confidence matrix; and a full life cycle operating condition scenario library module: receiving the confidence matrix and using DBSCAN... The density clustering algorithm identifies operating condition categories and their evolution trends, generating a set of operating condition scenario states. The business security joint situational awareness early warning module receives the operating condition scenario state set and uses the Independent Component Analysis (ICA) algorithm to generate a joint situational awareness feature set. The joint early warning judgment module receives the joint situational awareness feature set and uses the Support Vector Machine (SVM) classification algorithm to generate a joint early warning result set. The collaborative early warning control module receives the joint early warning result set and uses the decision tree algorithm to generate a collaborative early warning control instruction set, including gate control instructions, pump station start / stop instructions, inspection and scheduling instructions, and safety isolation instructions.

[0031] Example 1: The multi-source sensing and acquisition module collects various types of raw monitoring data generated during the operation of water conservancy projects. The monitoring data includes water level, flow rate, gate commands, pumping station power, dam structural status, river hydrology, meteorological observations, and network security monitoring data. These various types of monitoring data originate from monitoring equipment or safety monitoring systems at corresponding project nodes, are identified according to project node numbers, and are continuously collected in time-series format, forming a raw monitoring data set covering multiple project nodes and multiple monitoring dimensions.

[0032] Because different types of monitoring data differ in sampling frequency, data format, and measurement system, the multi-source sensing acquisition module performs preprocessing operations on the acquired raw monitoring data after the raw monitoring data collection is completed. The preprocessing operations include data time alignment, missing value imputation, sliding window segmentation, data format normalization, and feature value standardization.

[0033] Among these features, data time alignment maps monitoring data collected at different sampling frequencies and time bases onto the same time axis, ensuring a correspondence between various monitoring data within the same time window. Missing value imputation completes missing monitoring data due to acquisition anomalies or communication interruptions, guaranteeing the continuity of the monitoring data time series. Sliding window segmentation divides continuous time series according to a preset time window length, creating a structured segmented sequence of monitoring data in the time dimension. Data format normalization converts monitoring data from different sources and with different structural forms into a consistent data structure expression. Eigenvalue standardization unifies the numerical scale of monitoring data with different dimensions and numerical ranges, eliminating the impact of dimensional differences on subsequent calculations.

[0034] Through the aforementioned preprocessing operations, the multi-source sensing acquisition module organizes the raw monitoring data into multi-dimensional structured records, forming a comprehensive sensing dataset. The comprehensive sensing dataset is divided according to the engineering node number, and under each engineering node dimension, it forms water level sequences, flow sequences, structural status sequences, and operational response sequences. The water level sequence characterizes the water level changes of the engineering node over continuous time; the flow sequence characterizes the water transport status of the engineering node; the structural status sequence characterizes the operational status of engineering structures such as dams; and the operational response sequence characterizes the operational response behaviors of engineering structures such as gate commands and pump station power.

[0035] Example 2: The global digital twin simulation module receives the global perception dataset. The global perception dataset includes water level sequences, flow sequences, structural status sequences, and operational response sequences, categorized by project node number. Based on the project node number, the global digital twin simulation module extracts the historical water level sequences, historical flow sequences, historical structural status sequences, and historical operational response sequences corresponding to the target node within the past time window from the global perception dataset. These historical water level sequences, historical flow sequences, historical structural status sequences, and historical operational response sequences are then combined to form the historical output sequence of the target node, used to characterize the target node's actual operational status in historical periods.

[0036] Based on the extracted historical output sequences of the target nodes, the global digital twin simulation module constructs exogenous input sequences corresponding to the target nodes according to the water network topology. These exogenous input sequences include the historical outflow, water level, and discharge sequences of upstream nodes; the historical flow sequences of tributary nodes; the historical power sequences and start / stop status sequences of pumping station nodes; the historical opening change sequences of gate nodes; and the historical control command sequences issued by the dispatch center. Following the water network topology numbering system, the global digital twin simulation module maps the exogenous input sequences from different engineering nodes to a unified exogenous input vector structure, forming a topologically consistent exogenous input matrix, ensuring consistency in the model input structure across different engineering nodes.

[0037] After constructing the historical output sequence and exogenous input matrix, the global digital twin inference module uses the NARX time-series prediction algorithm to predict the future operational status of the target node. The NARX time-series prediction algorithm uses the target node's historical output sequence and corresponding exogenous input matrix as input. Through model training and forward inference calculations, it predicts the water level, flow rate, structural state, and operational response of the target node at multiple consecutive time steps in the future, obtaining a digital twin predicted status set for the target node. This digital twin predicted status set is expressed in time-series form and is used to characterize the predicted operational state of the target node in the future stages.

[0038] After obtaining the digital twin predicted situation set for the target node, the global digital twin inference module aligns and concatenates the historical real situation data and future predicted situation data of the target node along a unified time axis. Data within the historical time period are observed values, and data within the future time period are predicted values, thus forming a fused situation sample set that simultaneously contains historical real situation data and future predicted situation data. The fused situation sample set maintains continuity in the time dimension and maintains a one-to-one correspondence with the target node number. Multi-output NARX prediction mapping:

[0039] ;

[0040] Project node number is The target node at time step The predicted output vector, : The nonlinear mapping function corresponding to the NARX time series prediction algorithm Target node At time step The historical output vector, Target node At time step The exogenous input vector, The regression order of the historical output sequence. The regression order of the exogenous input sequence. Time step index Project node number. Physical constraint NARX joint loss function:

[0041] ;

[0042] The total loss function during the training phase. Prediction error term Structural response deviation term Weighting parameters for water balance deviation term. : Weighting parameters of the hydrodynamic gradient deviation term Structural response deviation term weighting parameters. Water balance constraint deviation term:

[0043] ;

[0044] Water balance deviation item Target node At time step The predicted change in reservoir capacity, Target node At time step Predicted inflow, Target node At time step The predicted outflow. Hydrodynamic gradient constraint deviation term:

[0045] ;

[0046] Hydrodynamic gradient deviation term Target node At time step The corresponding predicted water level difference, Target node At time step Corresponding predicted hydraulic gradient. Structural response consistency constraint deviation term:

[0047] ;

[0048] Structural response deviation term Target node At time step The predicted structural response change Target node At time step The predicted load change The mapping function between structural response and load variation. After constructing the fusion situation sample set for a single engineering node, the global digital twin simulation module further arranges and reorganizes the fusion situation sample sets corresponding to multiple engineering nodes according to the engineering node number, so that different engineering nodes form a holistic expression under a unified time axis. Through the temporal arrangement of the fusion situation sample sets of multiple engineering nodes, the global digital twin simulation module generates a virtual-real fusion situation sequence covering multiple engineering nodes and multiple prediction periods. The virtual-real fusion situation sequence simultaneously includes the actual operating status of each engineering node in the historical stage and the predicted operating status in the future stage.

[0049] Example 3: The early warning uncertainty quantification module receives the virtual-real fusion situation sequence. The virtual-real fusion situation sequence includes historical real situation data and future predicted situation data of multiple engineering nodes arranged on a unified time axis. The historical real situation data are observed values, and the future predicted situation data are predicted values, including predicted water level, predicted flow rate, predicted structural status, and predicted operational response.

[0050] Upon receiving the virtual-real fusion situational awareness sequence, the early warning uncertainty quantification module extracts the predicted values ​​for the prediction period and the observed values ​​for the corresponding historical period, and obtains the historical observation data corresponding to the virtual-real fusion situational awareness sequence. By calculating the difference between the predicted values ​​and the observed values, a set of historical residual samples arranged in chronological order is obtained. The set of historical residual samples includes water level residual samples, flow rate residual samples, structural status residual samples, and operational response residual samples, with each residual sample corresponding one-to-one with the project node number and time step.

[0051] After constructing the historical residual sample set, the early warning uncertainty quantification module segments the historical residual sample set according to time sequence. Residual samples from multiple consecutive time steps are divided into a residual block, and all residual blocks are stored in the residual block sample library in chronological order. Each residual block maintains the correlation of residual samples in the time dimension for subsequent time-series block resampling operations.

[0052] After constructing the residual block sample library, the early warning uncertainty quantification module sets risk thresholds based on engineering design specifications, operation management requirements, and statistically obtained historical operation results. These risk thresholds, as fixed configuration parameters, are used to determine the risk level of predicted values ​​in the virtual-real fusion situation sequence. The risk level for each prediction time step is determined based on the relationship between the predicted value and the risk threshold at that prediction time step.

[0053] Based on this, the early warning uncertainty quantification module calculates the corresponding risk weights for the residual blocks in the residual block sample library according to the risk level of each prediction time step. The risk weights reflect the degree of risk of the prediction results within the time period covered by the residual block. The calculated risk weights are normalized to form a weighted sampling probability distribution of the residual block sample library, which is used to guide the subsequent resampling process.

[0054] Subsequently, the early warning uncertainty quantification module employs the risk-sensitive time-series block Bootstrap resampling uncertainty assessment algorithm to perform time-series block resampling with replacement on the residual block sample library. Using the residual block as the basic sampling unit, residual blocks are selected sequentially according to a weighted sampling probability distribution. While maintaining the time order within each residual block, the selected residual blocks are concatenated in the sampling order to generate a reconstructed residual sequence. By independently executing the above resampling process multiple times, multiple reconstructed residual sequences are generated, forming a set of reconstructed residual sequences.

[0055] After generating the set of reconstructed residual sequences, the early warning uncertainty quantification module superimposes each reconstructed residual sequence onto the predicted values ​​in the virtual-real fusion situation sequence, resulting in multiple reconstructed prediction trajectories. These reconstructed prediction trajectories maintain the temporal correlation structure of the residuals in the time dimension and enhance the characterization of prediction uncertainty during high-risk time periods in the risk dimension, thus forming a prediction sample set for statistical analysis.

[0056] After obtaining the prediction sample set, the early warning uncertainty quantification module calculates statistical indicators for each engineering node, each physical quantity, and each prediction time step based on the prediction sample set. It calculates the sample mean and sample standard deviation for the prediction sample set, where the sample mean characterizes the expected level of the prediction result, and the sample standard deviation characterizes the uncertainty intensity of the prediction result. Based on the distribution of the prediction samples, it calculates the upper and lower bounds of the confidence interval to characterize the range of values ​​for the prediction result at a given confidence level.

[0057] Meanwhile, the early warning uncertainty quantification module calculates a model consistency index by comparing the deviation between the benchmark predicted value and the sample mean, which characterizes the stability of the prediction model under resampling disturbance conditions. It also calculates data volatility characteristics by combining the statistical characteristics of the historical residual sample set with the dispersion of the current prediction sample set, characterizing the degree of volatility of the current prediction state relative to historical operating states. Finally, the combination of the sample mean and sample standard deviation is used as a representation of risk intensity, comprehensively reflecting the risk level of the prediction results.

[0058] Formula for constructing residual sample sequences:

[0059] ;

[0060] Project node number is At time step The residual vector, : The vector of predicted values ​​for the predicted time period in the virtual-real fusion situation sequence. : The vector of actual observed values ​​corresponding to the predicted values Time step index Project node number. Formula for constructing the residual block sample library:

[0061] ;

[0062] : No. A series of continuous residual blocks, Time step The residual vector, : No. The starting time step index of each residual block. : Preset block length Residual block number index. Time step risk sensitivity index calculation formula:

[0063] ;

[0064] Project node number is At time step Risk-sensitive indicators : Indicator function, which takes the value when the condition within the parentheses is true. Otherwise, the value is , Predicted water level The warning water level is part of the system's preset risk threshold. Predicting flow or predicting leakage. The flood discharge control line is part of the system's preset risk threshold. : Predicting structural state or predicting structural response related components Safety control lines are part of the system's preset risk thresholds. Predict the rate of change vector. The predicted rate of change threshold is a component of the system's preset risk threshold. Time step index Project node number. Residual block risk weight aggregation formula:

[0065] ;

[0066] : No. Risk weight value of each residual block Time-step risk-sensitive indicators : No. The starting time step index of each residual block. Block length, Residual block number index. Weighted sampling probability distribution normalization formula:

[0067] ;

[0068] : No. The weighted sampling probabilities of each residual block constitute the weighted sampling probability distribution of the residual block sample library. : No. Risk weight value of each residual block The number of residual blocks in the residual block sample library. Residual block number index. Formula for generating risk-sensitive time-series block resampling index:

[0069] ;

[0070] : No. In the second resampling The index of the extracted residual block. : Classification sampling distribution based on weighted sampling probability distribution : No. Weighted sampling probability of each residual block Resampling number index The number of residual blocks in each resampling assembly. The number of residual blocks in the residual block sample library. Formula for reconstructing the residual sequence:

[0071] ;

[0072] : No. The reconstructed residual sequence obtained by secondary resampling Sequence concatenation operator : No. One residual block, : No. In the second resampling The index of the extracted residual block. Resampling number index. Formula for reconstructing the predicted trajectory:

[0073] ;

[0074] : No. The reconstructed predicted trajectory at time step The predicted value vector, : Baseline predicted value vector : No. The reconstructed residual sequence obtained by secondary resampling is at time step The residual vector, : Reconstruct the predicted trajectory number index. Sample mean calculation formula:

[0075] ;

[0076] Project node number is At time step The sample mean, : No. The reconstructed predicted trajectory at time step The predicted value vector, Number of reconstructed predicted trajectories. Sample standard deviation calculation formula:

[0077] ;

[0078] Project node number is At time step The sample standard deviation The L2 norm is used to measure the difference between multiple output vectors. : No. The reconstructed predicted trajectory at time step The predicted value vector, Sample mean : Number of reconstructed predicted trajectories. Formulas for calculating the upper and lower bounds of the confidence interval:

[0079] ;

[0080] : Lower bound of the confidence interval : Upper bound of the confidence interval : Quantile operator Significance level, : No. The reconstructed predicted trajectory at time step The predicted value vector, Number of reconstructed predicted trajectories. Model consistency index calculation formula:

[0081] ;

[0082] Model consistency index : Baseline predicted value vector Sample mean Norm 2. Formula for calculating data fluctuation characteristic indicators:

[0083] ;

[0084] Data fluctuation characteristic indicators Sample standard deviation Historical residual variance, obtained from statistical analysis of historical residual sample sequences. : The residual vector in the historical residual sample sequence. Risk intensity calculation formula:

[0085] ;

[0086] Risk intensity index Sample mean : Sample standard deviation. Confidence matrix construction formula:

[0087] ;

[0088] Confidence matrix The set of risk intensity indicators across all project nodes and predicted time points. The upper bound of confidence is the set of all project nodes and predicted time points. The set of confidence lower bounds at all project nodes and prediction times. The set of model consistency metrics across all project nodes and prediction times. The set of data fluctuation characteristic indicators across all project nodes and prediction times.

[0089] After completing the above statistical calculations, the early warning uncertainty quantification module arranges and organizes the calculated risk intensity, upper confidence bound, lower confidence bound, model consistency index, and data fluctuation characteristics according to the engineering node dimension, physical quantity dimension, and time dimension to construct a confidence matrix. The confidence matrix centrally expresses the uncertainty characteristics of the virtual-real integrated situation sequence in the prediction stage in matrix form.

[0090] Example 4: The full lifecycle scenario library module receives the confidence matrix and extracts the risk intensity, upper confidence bound, lower confidence bound, model consistency index, and data fluctuation characteristics of each engineering node within the current time window from the confidence matrix. These are then arranged and organized to construct the confidence matrix. The confidence matrix centrally expresses the uncertainty characteristics of the virtual-real fusion situation sequence during the prediction phase in matrix form.

[0091] Example 4: The full lifecycle operating condition scenario library module receives the confidence matrix, extracts the risk intensity, upper confidence bound, lower confidence bound, model consistency index, and data fluctuation characteristics of each engineering node within the current time window from the confidence matrix, and combines the above features according to the engineering node number to form an operating condition feature sample set. Each operating condition feature sample corresponds to a description of the risk and uncertainty state of an engineering node within the current time window.

[0092] After obtaining the set of operating condition feature samples, the full lifecycle operating condition scenario library module uses the DBSCAN density clustering algorithm to perform density clustering on the set of operating condition feature samples based on preset neighborhood radius parameters and minimum sample number parameters. In the operating condition feature space, the neighborhood range of each operating condition feature sample is calculated, the number of samples within the neighborhood of each operating condition feature sample is counted, and operating condition feature samples with a neighborhood sample number not less than the minimum sample number parameter are marked as core samples. When a core sample is located within the neighborhood range of another operating condition feature sample, and the other operating condition feature sample meets the density condition, it is determined that the core sample has a density reachable relationship with the other operating condition feature sample. If a core sample forms a continuous neighborhood connection path through one or more operating condition feature samples that meet the density condition, it is determined that the core sample has a density reachable relationship with the operating condition feature sample at the end of the path. The neighborhood radius parameter and the minimum sample size parameter are preset based on the confidence matrix output by the early warning uncertainty quantification module. The neighborhood radius parameter is determined based on the numerical distribution range of the working condition characteristic samples in terms of risk intensity, upper confidence bound, lower confidence bound, model consistency index, and data fluctuation characteristics. The minimum sample size parameter is determined based on the number of engineering nodes included in the built-in confidence matrix of a single time window.

[0093] The full lifecycle operating condition scenario library module, based on density reachability relationships, divides core samples and their neighboring samples with mutual density reachability relationships into the same operating condition cluster. Operating condition feature samples that cannot be assigned to any operating condition cluster are marked as isolated operating condition samples, thus obtaining the operating condition clustering result set. The operating condition clustering result set reflects the distribution structure and interrelationships of each engineering node in the operating condition feature space within the current time window.

[0094] After completing the working condition clustering process, the full lifecycle working condition scenario library module matches the clusters formed by the clustering results with a pre-defined full lifecycle working condition template. The full lifecycle working condition template is pre-defined based on a set of working condition scenario states formed during the system's historical operation phases. It is formed by summarizing recurring and stable working condition categories within different time windows, serving as a reference for working condition identification and correspondence. By matching the current working condition cluster with the full lifecycle working condition template, the working condition category of each engineering node within the current time window is determined, generating the current working condition identification result.

[0095] After obtaining the current operating condition identification result, the full lifecycle operating condition scenario library module further performs time series comparison on the operating condition category identification results of the same engineering node within continuous time windows. By analyzing the sequence of operating condition category changes over time, an operating condition category change sequence is formed to characterize the evolution process of the engineering node's operating condition state. Based on the operating condition category change sequence, the module identifies whether the operating condition category changes between adjacent time windows, and determines whether the operating condition is in a stable state or a sudden change state, generating an operating condition evolution determination result.

[0096] After completing the working condition identification and working condition evolution analysis, the full life cycle working condition scenario library module summarizes the current working condition identification results and working condition evolution judgment results to form a working condition scenario state set. The working condition scenario state set includes the engineering node identifier, the current working condition category, the corresponding working condition cluster features, and the working condition evolution state identifier, which is used to comprehensively characterize the working condition status and its changing trend of each engineering node within the current time window.

[0097] Example 5: The business security joint situational awareness early warning module receives the operational scenario status set and network security monitoring data. The operational scenario status set includes the current operational category, operational cluster characteristics, and operational evolution status identifier of each engineering node within the current time window, which is used to characterize the business operation status of the engineering node.

[0098] The joint business security situational awareness module first extracts business situational features representing the operational status of engineering services from the operational scenario status set. These features include operational condition category features, risk intensity features, and operational condition evolution trend features. The operational condition category features are derived from the current operational condition category recorded in the operational scenario status set; the risk intensity features are derived from the risk intensity corresponding to the engineering node in the confidence matrix; and the operational condition evolution trend features are derived from the operational condition evolution status identifier. The module then combines these business situational features according to the engineering node number to form a set of business situational features.

[0099] While extracting business situation characteristics, the business security joint situation early warning module processes network security monitoring data to obtain the network security status. It extracts the sequence of control command values ​​within the current time window from the network security monitoring data and calculates a fluctuation metric for this sequence, obtaining the control command fluctuation characteristic. The fluctuation metric is either the standard deviation of the command value sequence within the current time window or the difference between the maximum and minimum values ​​within the current time window. It also extracts the sequence of communication integrity judgment results within the current time window from the network security monitoring data and performs statistical analysis on these results to obtain the communication integrity characteristic. This characteristic is the ratio of the number of records with complete communication integrity judgments within the current time window to the total number of such records within that time window. Finally, it extracts security event records within the current time window from the network security monitoring data and counts these records by time window, obtaining the security event count characteristic. The business security joint situation early warning module then aggregates the control command fluctuation characteristic, communication integrity characteristic, and security event count characteristic to form the network security status.

[0100] After obtaining the set of business situation characteristics and the network security status, the joint business and security situation warning module concatenates the set of business situation characteristics and the network security status to form a set of observation signal vectors. The set of observation signal vectors is organized by project node, and each observation signal vector corresponds to the joint observation representation of the business situation and network security situation of a project node within the current time window.

[0101] The joint situational awareness module for business security employs Independent Component Analysis (ICA) to calculate the observed signal vector set. Based on the maximization of non-Gaussianity criterion, a separation matrix is ​​obtained. This separation matrix maps the observed signal vectors into several approximately statistically independent components. The separation matrix is ​​then divided into coefficient vectors corresponding one-to-one with each independent component. The coefficient vector for each independent component serves as its independent component coefficient, characterizing the weighting relationship when the independent component is formed by a linear combination of the features of each dimension of the observed signal vector.

[0102] After obtaining the independent component coefficients, the joint business security situational awareness module inputs the observed signal vector set into the separation matrix time-by-time within the current time window to obtain the independent component value sequence for each time moment. For each independent component, the module calculates its amplitude within the current time window, taking the mean or maximum absolute value of the value sequence to characterize the strength of the independent component's influence within the current time window. The module then normalizes the amplitudes of each independent component to obtain the amplitude normalization result.

[0103] Based on this, the joint business security situational awareness module uses the amplitude normalization result of each independent component as a contribution metric and modulates the contribution of each independent component using independent component coefficients. The absolute value of the independent component coefficient vector is used as the contribution weight of that independent component. After normalizing the contribution weight, it is multiplied by the corresponding amplitude normalization result of the independent component. Finally, the results of each independent component are summed to obtain the comprehensive situational awareness score. The comprehensive situational awareness score is used to provide a single-valued representation of the joint strength of the engineering business situation and the network security situation within the current time window.

[0104] After completing the above calculations, the business security joint situational awareness early warning module will aggregate the independent component coefficients, independent component amplitudes, and comprehensive situational scores to form a joint situational feature set.

[0105] Example 6: The joint early warning determination module extracts the independent component coefficients, independent component amplitudes, and comprehensive situation scores corresponding to each engineering node within the current time window from the joint situation feature set. It then combines these independent component coefficients, independent component amplitudes, and comprehensive situation scores according to a preset feature arrangement order to construct a set of joint situation feature vectors. Each joint situation feature vector corresponds to the joint situation feature representation of an engineering node within the current time window. The dimension and feature order of the joint situation feature vectors remain consistent throughout system operation.

[0106] After constructing the joint situation feature vector set, the joint early warning determination module uses the Support Vector Machine (SVM) classification algorithm to determine the early warning level of the joint situation feature vector set. During the model building phase, the joint early warning determination module uses historical early warning event data generated and retained during the system's previous operation as the training basis. This historical early warning event data includes joint situation feature vectors generated by each engineering node within the corresponding historical time window, as well as records of early warning levels and types corresponding to each joint situation feature vector. Using the joint situation feature vectors from the historical early warning event data as training samples and the corresponding early warning level as the classification output, the SVM classification algorithm learns the classification boundaries between different early warning levels in the joint situation feature space, forming the joint early warning determination model.

[0107] During system online operation, the joint early warning determination module inputs the set of joint situation feature vectors formed within the current time window into the joint early warning determination model, and performs a classification determination operation on each joint situation feature vector. Using a support vector machine classification algorithm, it outputs the corresponding early warning level and early warning type for each joint situation feature vector, which characterizes the early warning status of the project node within the current time window.

[0108] After determining the warning level and type, the joint warning determination module calculates the classification confidence score for each warning level based on the decision function output by the support vector machine classification algorithm during the classification process. The classification confidence score characterizes the reliability of the joint warning determination model for the current determination result. The module organizes and aggregates the calculated warning levels, warning types, and classification confidence scores according to the project node dimension and the time window dimension.

[0109] After completing the above processing, the joint early warning judgment module will collect the early warning level, early warning type and classification confidence of each project node within the current time window to form a joint early warning result set.

[0110] Example 7: The collaborative early warning control module first extracts the early warning level, early warning type, and classification confidence level of each engineering node within the current time window from the joint early warning result set. It then combines these information according to a preset feature order to construct a set of decision feature vectors. Each decision feature vector corresponds to the early warning judgment result of an engineering node within the current time window. These decision feature vectors serve as input features for subsequent control decisions, and their dimensions and feature order remain consistent throughout system operation.

[0111] After constructing the set of decision feature vectors, the collaborative early warning control module uses a decision tree algorithm to perform control decision analysis on the set of decision feature vectors. In the control model construction phase, the collaborative early warning control module uses control samples formed from historical handling cases and operation management procedures as the training basis. The control samples include decision feature vectors corresponding to each engineering node within the historical time window, and engineering control action categories that correspond one-to-one with the decision feature vectors. Using the decision feature vectors as input features and the engineering control action categories as output nodes, the collaborative early warning control model is constructed using the decision tree algorithm, enabling the model to learn the mapping relationship between early warning judgment results and engineering control actions in the decision feature space.

[0112] During system online operation, the collaborative early warning control module inputs the set of decision feature vectors formed within the current time window into the collaborative early warning control model. Feature judgments are performed layer by layer along the decision tree's decision path, ultimately reaching the corresponding control action leaf node, thus determining the corresponding control action result for each engineering node. The control action result characterizes the specific control measures that should be implemented for that engineering node under the current early warning state.

[0113] After obtaining the control action results, the collaborative early warning control module generates corresponding collaborative early warning control instructions based on the control action results. These instructions include gate control instructions, pump station start / stop instructions, inspection scheduling instructions, and safety isolation instructions. Gate control instructions are used to adjust the gate's operating status; pump station start / stop instructions are used to control the pump station's operating status; inspection scheduling instructions are used to schedule engineering inspection tasks; and safety isolation instructions are used to isolate control links or engineering units with safety risks.

[0114] The collaborative early warning and control module organizes and aggregates the gate control commands, pump station start / stop commands, inspection and scheduling commands, and safety isolation commands generated at each project node according to the project node dimension and time window dimension, forming a collaborative early warning and control command set. This command set serves as the system's control output, guiding the actual operation, scheduling, and safety procedures of the water conservancy project.

[0115] Through the above implementation process, the collaborative early warning control module realizes a structured mapping from the joint early warning result set to the collaborative early warning control instruction set, enabling the system to automatically generate control instructions that match the engineering operation management based on the early warning judgment results, thereby forming a closed-loop collaborative mechanism between early warning analysis and engineering control.

[0116] Example 8: In this example, the system performs joint operating condition evolution analysis across engineering nodes based on the virtual-real fusion situation sequence and confidence matrix to identify the synchronous changes in operating conditions of multiple nodes and their evolution trends within a continuous time window.

[0117] The system first outputs a virtual-real fusion situational sequence covering multiple engineering nodes from the full-domain digital twin simulation module. The virtual-real fusion situational sequence includes historical real situational data and future predicted situational data of each engineering node arranged on a unified time axis. The historical real situational data are observed values, and the future predicted situational data are predicted values, including predicted water level, predicted flow rate, predicted structural status, and predicted operational response.

[0118] The early warning uncertainty quantification module receives the virtual-real fusion situation sequence, performs risk-sensitive time-series block Bootstrap resampling uncertainty assessment algorithm on the predicted values ​​of each engineering node within the prediction time period, obtains the corresponding prediction sample set, and further calculates the risk intensity, upper confidence bound, lower confidence bound, model consistency index and data fluctuation characteristics of each engineering node at each prediction time step, and constructs a confidence matrix covering multiple engineering nodes.

[0119] Based on this, the full life cycle working condition scenario library module synchronously extracts the risk intensity, upper confidence bound, lower confidence bound, model consistency index and data fluctuation characteristics of multiple engineering nodes within the same time window from the confidence matrix, and organizes the above features of each engineering node in parallel according to the engineering node number to form a working condition feature sample set across engineering nodes.

[0120] The full life cycle working condition scenario library module uses the DBSCAN density clustering algorithm to perform joint density clustering on the working condition features of multiple engineering nodes based on the working condition feature sample set. It identifies engineering node sets that exhibit similar risk feature distributions within the same time window and assigns engineering nodes with density reachability relationships to the same working condition cluster.

[0121] After completing the working condition clustering process, the full life cycle working condition scenario library module matches the working condition clusters formed across engineering nodes with the full life cycle working condition templates, determines the working condition category to which each engineering node belongs in the current time window, and further compares the working condition category changes of each engineering node in continuous time windows over time to form a working condition category change sequence across engineering nodes.

[0122] Based on the aforementioned operating condition category change sequence, the full lifecycle operating condition scenario library module identifies whether multiple engineering nodes simultaneously experience operating condition category changes within adjacent time windows, and determines the synchronous evolution status of operating conditions across engineering nodes accordingly. It then generates an operating condition scenario state set containing engineering node identifiers, current operating condition categories, and operating condition evolution status identifiers. This operating condition scenario state set serves as input data for the subsequent business security joint situational awareness warning module.

[0123] Example 9: Based on the working condition scenario state set formed in Example 8, this example executes a multi-type collaborative early warning control command generation process for different early warning states that appear in multiple engineering nodes within the same time window, thereby realizing collaborative handling across engineering nodes.

[0124] The business security joint situational awareness early warning module receives a set of operational scenario states, extracts operational category features, risk intensity features, and operational evolution trend features for each engineering node, and combines these with network security monitoring data to form a network security status. A joint situational awareness feature set is then calculated using the Independent Component Analysis (ICA) algorithm. This joint situational awareness feature set includes the independent component coefficients, independent component amplitudes, and comprehensive situational awareness scores for each engineering node within the current time window.

[0125] The joint early warning determination module receives the joint situation feature set, constructs a joint situation feature vector set from the independent component coefficients, independent component amplitudes, and comprehensive situation scores corresponding to each engineering node, and uses the Support Vector Machine (SVM) classification algorithm to determine the early warning level and type of the joint situation feature vector set, forming a joint early warning result set. The joint early warning result set includes the early warning level, early warning type, and classification confidence level of each engineering node within the current time window.

[0126] The collaborative early warning control module receives the joint early warning result set, constructs a decision feature vector set by arranging the early warning level, early warning type and classification confidence level of each engineering node according to a preset feature order, and inputs the decision feature vector set into the collaborative early warning control model based on the decision tree algorithm.

[0127] During the control decision-making process, the collaborative early warning control module performs feature judgments along different decision paths in the decision tree model based on the decision feature vectors corresponding to different engineering nodes, thereby determining the corresponding control action results for different engineering nodes. The control action results include gate control commands, pump station start / stop commands, inspection and scheduling commands, and safety isolation commands.

[0128] After obtaining the control action results corresponding to each engineering node, the collaborative early warning control module organizes and aggregates different types of control commands according to the engineering node dimension and the time window dimension, forming a collaborative early warning control command set for multiple engineering nodes. This collaborative early warning control command set is used to coordinate and regulate the operating status of multiple engineering nodes within the same time window, achieving collaborative early warning handling across engineering nodes.

[0129] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A comprehensive situational awareness and collaborative early warning system for water conservancy projects, characterized by: include: Multi-source sensing and acquisition module: Collects various types of raw monitoring data generated during the operation of water conservancy projects, and performs preprocessing operations on the collected raw monitoring data to generate a global sensing dataset; The module consists of four parts: a full-domain digital twin simulation module, a business security joint situation warning module, and a business security joint situation warning module. The module receives the full-domain perception dataset, extracts the historical output sequences of target nodes, and constructs an exogenous input matrix based on the water network topology, including upstream outflow, water level, discharge, tributary flow, pumping station status, gate opening, and dispatch instructions. It then uses the NARX time series prediction algorithm for multi-step prediction and outputs a virtual-real fusion situation sequence. The early warning uncertainty quantification module receives the virtual-real fusion situation sequence, generates multiple reconstructed prediction trajectories based on the residual sample library using the risk-sensitive time series block Bootstrap resampling uncertainty assessment algorithm, and calculates the sample mean, standard deviation, confidence interval, model consistency, and data fluctuation characteristics to form a confidence matrix. The full lifecycle operating condition scenario library module receives the confidence matrix, uses the DBSCAN density clustering algorithm to identify operating condition categories and evolution trends, and generates a set of operating condition scenario states. The business security joint situation warning module receives the set of operating condition scenario states and uses the Independent Component Analysis (ICA) algorithm to generate a joint situation feature set. The joint early warning judgment module receives the joint situation feature set and uses the support vector machine (SVM) classification algorithm to generate a joint early warning result set; the collaborative early warning control module receives the joint early warning result set and uses the decision tree algorithm to generate a collaborative early warning control instruction set, including gate control instructions, pump station start / stop instructions, inspection and scheduling instructions, and safety isolation instructions.

2. The water conservancy project situational awareness and collaborative early warning system according to claim 1, characterized in that: The monitoring data includes water level, flow rate, gate commands, pumping station power, dam structural status, river hydrology, meteorological observations, and network security monitoring data. Preprocessing operations are performed on the monitoring data sequentially, including data time alignment, missing value imputation, sliding window segmentation, data format normalization, and feature value standardization. Monitoring data from different sources, sampling frequencies, and data formats are unified in terms of time reference and dimensional system, and constructed into multi-dimensional structured records, forming a comprehensive sensing dataset. The comprehensive sensing dataset includes water level sequences, flow rate sequences, structural status sequences, and operational response sequences, divided according to engineering node numbers.

3. The water conservancy project situational awareness and collaborative early warning system according to claim 1, characterized in that: The full-domain digital twin simulation module extracts historical water level sequences, historical flow sequences, historical structural state sequences, and historical operational response sequences of target nodes within past time windows from the full-domain perception dataset, forming a real situation sample set. Based on the real situation sample set, a corresponding exogenous input sequence set is constructed according to the water network topology. The real situation sample set and the exogenous input sequence set are then input into the NARX time series prediction algorithm to predict the water level, flow, structural state, and operational response of the target node for multiple consecutive time steps in the future, resulting in a digital twin predicted situation set. The real situation sample set and the digital twin predicted situation set are time-aligned and sequence-stitched according to a unified time axis and engineering node number to form a fused situation sample set that simultaneously contains historical real situation data and future predicted situation data. The historical real situation data are the observed values, and the future predicted situation data are the predicted values. The fused situation sample set is arranged into a continuous time series structure in chronological order and arranged according to the engineering node dimension to generate a virtual-real fused situation sequence covering multiple engineering nodes and multiple prediction periods.

4. The water conservancy project situational awareness and collaborative early warning system according to claim 1, characterized in that: The early warning uncertainty quantification module extracts predicted and observed values ​​from the virtual-real fusion situation sequence and obtains historical observation data corresponding to the virtual-real fusion situation sequence. It calculates the difference between the predicted and observed values ​​and constructs a historical residual sample set arranged in chronological order based on the difference. A risk threshold is set according to engineering design specifications, operation management requirements, and statistical historical operation results. The risk threshold is a fixed configuration parameter used to determine the risk level of the predicted value. The historical residual sample set is segmented according to chronological order, dividing the residual samples within multiple consecutive time steps into multiple residual blocks, forming a residual block sample library that maintains time correlation. Based on the relationship between the predicted value of the virtual-real fusion situation sequence at the predicted time step and the preset risk threshold, the risk level is obtained. The risk weight of the corresponding residual block is calculated and normalized to form a weighted sampling probability distribution of the residual block sample library. According to the weighted sampling probability distribution, a bootstrap resampling operation with replacement is performed on the time series blocks with replacement as the basic unit to generate multiple reconstructed residual sequences. The reconstructed residual sequences are superimposed on the predicted values ​​in the virtual-real fusion situation sequence to obtain multiple reconstructed prediction trajectories, forming a prediction sample set. Based on the prediction sample set, the sample mean, sample standard deviation, upper bound of the confidence interval, lower bound of the confidence interval, model consistency index, and data fluctuation characteristics are calculated for each engineering node, physical quantity, and prediction time step. The risk intensity is comprehensively represented by the sample mean and sample standard deviation. The calculation results are arranged according to the engineering node dimension, physical quantity dimension, and time dimension to construct a confidence matrix that includes risk intensity, upper bound of confidence, lower bound of confidence, model consistency index, and data fluctuation characteristics.

5. The water conservancy project situational awareness and collaborative early warning system according to claim 1, characterized in that: The full lifecycle operating condition scenario library module extracts the risk intensity, upper confidence bound, lower confidence bound, model consistency index, and data fluctuation characteristics of each engineering node within the current time window from the confidence matrix, forming a set of operating condition feature samples. Based on preset neighborhood radius parameters and minimum sample number parameters, DBSCAN is used. Density clustering algorithm performs density clustering on the set of working condition feature samples, calculates the neighborhood density of each working condition feature sample in the feature space, and marks the working condition feature samples with a number of samples in their neighborhood not less than the minimum number of samples as core samples. If a core sample is located within the neighborhood of a second working condition feature sample, and the second working condition feature sample meets the density condition, then the core sample is determined to have a density reachability relationship with the second working condition feature sample. If a core sample forms a continuous neighborhood connection path through one or more working condition feature samples that meet the density condition, then the core sample is determined to have a density reachability relationship with the working condition feature sample at the end of the path, and the core sample and its neighboring samples are divided into the same working condition cluster according to the density reachability relationship. Samples that cannot be assigned to any cluster are marked as isolated working condition samples, resulting in a working condition clustering result set. The neighborhood radius parameter and the minimum number of samples parameter are preset based on the confidence matrix output by the early warning uncertainty quantification module. The neighborhood radius parameter is determined based on the working condition feature sample's risk intensity, confidence upper bound, and other parameters. The confidence lower bound, model consistency index, and data fluctuation characteristic dimension numerical distribution range are determined, and the minimum sample number parameter is determined based on the number of engineering nodes included in the built-in confidence matrix of a single time window. The clusters of working conditions formed by the clustering results of working conditions are matched with the pre-set full life cycle working condition template to determine the working condition category of each engineering node in the current time window and generate the current working condition identification result. The full life cycle working condition template is pre-set based on the set of working condition scenario states formed in the historical operation phase of the system. It is formed by summarizing the working condition categories that recur and have stable characteristics in different time windows and is used as a reference benchmark for subsequent working condition identification and working condition correspondence. The identification results of the working condition category corresponding to the same engineering node in continuous time windows are compared with time series to form a working condition category change sequence. The change process of working condition category is identified, the working condition evolution trend is determined, and the working condition stable and abrupt working condition states are determined based on the working condition category change sequence to generate the working condition evolution judgment result. The current working condition identification results and the working condition evolution determination results are summarized to form a working condition scenario state set that includes engineering node identifiers, current working condition categories, working condition cluster features, and working condition evolution status identifiers.

6. The water conservancy project situational awareness and collaborative early warning system according to claim 1, characterized in that: The business security joint situational awareness module extracts business condition category features, risk intensity features, and business condition evolution trend features representing the operational status of engineering business from the business condition scenario status set. It processes network security monitoring data to obtain the network security status. The business status feature set is then concatenated with the network security status. A business situational awareness feature vector is constructed using the current business condition category, business condition cluster features, and business condition evolution status identifier corresponding to each engineering node in the business condition scenario status set. This vector is then concatenated with the control command fluctuation features, communication integrity features, and security event count features corresponding to the network security status to form an observation signal vector set. Independent component analysis (ICA) is then employed. The algorithm calculates a separation matrix on the set of observed signal vectors and derives it based on the maximization of non-Gaussianity criterion. This separation matrix maps the observed signals to several approximately statistically independent components. The separation matrix is ​​then divided into row-wise coefficient vectors corresponding to each independent component; the coefficient vector for each independent component is its independent component coefficient. The observed signal vector set is input into the separation matrix time-by-time within the current time window to obtain the independent component value sequence at each time point. For each independent component, its amplitude is calculated within the current time window, taking the mean or maximum absolute value of the value sequence. This yields the independent component coefficients corresponding to each independent component. The amplitudes of independent components are determined, and the amplitudes of each independent component are normalized to obtain the amplitude normalization result. The amplitude normalization result of each independent component is used as the contribution measure. The contribution of each independent component is modulated using the independent component coefficients to make the structure of the comprehensive situation score consistent with the structure of the observation signal vector set. The absolute value of the independent component coefficient vector is used as the contribution weight of the independent component, and the contribution weight is normalized. It is then multiplied by the corresponding independent component amplitude normalization result and summed to obtain the comprehensive situation score. The independent component coefficients, independent component amplitudes, and comprehensive situation scores of each engineering node within the current time window are collected to obtain the joint situation feature set.

7. The water conservancy project situational awareness and collaborative early warning system according to claim 6, characterized in that: The business security joint situational awareness early warning module extracts the sequence of control commands within the current time window from network security monitoring data; calculates the fluctuation metric of the command value sequence to obtain the control command fluctuation characteristics, wherein the fluctuation metric is the standard deviation of the command value sequence within the current time window, or the difference between the maximum and minimum values ​​of the command value sequence within the current time window, and outputs the control command fluctuation characteristics. Extract the communication integrity assessment result sequence within the current time window from network security monitoring data; statistically analyze the communication integrity assessment result sequence, and take the ratio of the number of complete communication integrity assessment results within the current time window to the total number of communication integrity assessment result records within the time window to obtain the communication integrity characteristics; Extract security event records within the current time window from network security monitoring data; count the security event records according to the time window to obtain security event counting characteristics; Output security event count characteristics; combine control command fluctuation characteristics, communication integrity characteristics, and security event count characteristics to form a network security status.

8. The water conservancy project situational awareness and collaborative early warning system according to claim 1, characterized in that: The joint early warning determination module constructs a set of joint situation feature vectors from the independent component coefficients, independent component amplitudes, and comprehensive situation scores corresponding to the current time window in the joint situation feature set. Each joint situation feature vector corresponds to the joint situation feature representation of an engineering node within the current time window. Using the joint situation feature vectors labeled in historical early warning event data as training samples and the corresponding early warning level labels as classification outputs, a joint early warning determination model is constructed using the Support Vector Machine (SVM) classification algorithm to learn the classification boundaries between different early warning levels in the joint situation feature space. During system operation, the set of joint situation feature vectors formed within the current time window is input into the joint early warning determination model, and classification determination is performed on each joint situation feature vector to obtain the corresponding early warning level and early warning type. Based on the decision function results output by the support vector machine classification algorithm during the classification process, the classification confidence scores corresponding to each warning level are calculated, and the warning levels, warning types, and classification confidence scores are organized according to the dimensions of engineering nodes and time windows. The warning levels, warning types, and classification confidence scores corresponding to each engineering node are collected to form a joint warning result set, which serves as the input data for the collaborative warning control module. Historical early warning event data refers to the set of early warning event records formed and retained within historical time windows during the system's past operation. Historical early warning event data includes: joint situation feature vectors formed by each engineering node within the corresponding historical time window, and early warning level and early warning type records that correspond one-to-one with the joint situation feature vectors.

9. The water conservancy project situational awareness and collaborative early warning system according to claim 1, characterized in that: The collaborative early warning control module constructs a set of decision feature vectors from the joint early warning results, corresponding to the early warning level, early warning type, and classification confidence of each engineering node. Each decision feature vector corresponds to the early warning judgment result of an engineering node within the current time window. Based on control samples formed by historical handling cases and operation management procedures, a collaborative early warning control model is constructed using a decision tree algorithm. The decision tree algorithm uses decision feature vectors as input features and engineering control action categories as output nodes. During system operation, the set of decision feature vectors formed within the current time window is input into the collaborative early warning control model, and the corresponding control action leaf node is selected along the decision path of the decision tree to obtain the control action results corresponding to each engineering node. Based on the control action results, corresponding gate control instructions, pump station start / stop instructions, inspection scheduling instructions, and safety isolation instructions are generated. The generated instructions are organized and aggregated according to the engineering node dimension and the time window dimension to form a collaborative early warning control instruction set.

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