Unmanned aerial vehicle-based river hydrological sampling inspection method and system

Abstract

The invention discloses an unmanned aerial vehicle-based riverway hydrological sampling inspection method and system. The method comprises the steps of collecting unmanned aerial vehicle multi-parameter sensor data, determining a dynamic sampling rate through an enhanced difference rate adaptive sampling algorithm, performing multi-parameter cross validation and environment compensation, and finally reconstructing a data stream. A hydrological feature extraction transformation algorithm and a hydrological mode recognition algorithm are adopted to recognize a hydrological mode, analyze a state evolution process and predict a development trend; a multi-scale anomaly detection algorithm is adopted to identify anomalies, an anomaly source is positioned through a causal chain tracing algorithm, risk assessment and grading are carried out, an emergency decision scheme is generated, and an inspection strategy is optimized; evaluating system performance, optimizing resource configuration, dynamically adjusting parameters, and updating a prediction model. The intelligent level of river hydrological monitoring is improved, the sampling efficiency and the anomaly detection accuracy are improved, and the decision response time is shortened.

Description

Technical Field

[0001] The present invention belongs to the field of environmental monitoring, and in particular to a river hydrological sampling and inspection method and system based on an unmanned aerial vehicle (UAV). Background Art

[0002] River hydrological sampling and inspection are crucial tools for monitoring and protecting river ecosystems. Regular water quality sampling and inspections can monitor water quality trends, identify potential pollution sources, and assess the health of aquatic ecosystems, providing a scientific basis for river management and water resource protection. Especially in the current context of climate change and intensified human activities, sudden river water pollution incidents are frequent, and traditional fixed monitoring stations are no longer able to meet the demands of rapid response and wide-area monitoring. Drone-based hydrological sampling and inspection methods, with their advantages of mobility, wide coverage, and high safety, have become a key development direction for river water quality monitoring.

[0003] Currently, the application of drones in river hydrological monitoring primarily focuses on image acquisition and simple water quality parameter measurement. Common technical solutions include the use of high-definition cameras to identify river surface features, deployable sensors to measure basic parameters such as pH and dissolved oxygen, and thermal infrared cameras to monitor temperature fields. Data processing primarily utilizes anomaly detection methods based on empirical thresholds, water quality assessments through simple statistical analysis, and patrol missions based on fixed route planning. Data transmission often utilizes a single wireless communication method, with sampling frequency and depth often being pre-set fixed values.

[0004] However, existing technical solutions still have some specific problems that need to be solved urgently: First, the dynamic adjustment capability of sampling parameters is insufficient, making it difficult to adaptively adjust the sampling frequency and sampling location according to the changing trends of water quality, which may result in missing important data during critical periods; second, the fusion processing effect of multi-source data is poor, and there are problems such as temporal and spatial misalignment and accumulation of measurement errors between various sensor data, which affect the reliability of monitoring results; third, the recognition accuracy of abnormal patterns is not high, especially when facing complex anomalies caused by multi-parameter coupling, it is difficult to accurately judge the type and degree of anomalies; fourth, the optimization of inspection paths is not intelligent enough, and dynamic factors such as risk level and resource constraints are not fully considered, which affects monitoring efficiency; fifth, the compensation mechanism for environmental factors is imperfect, and the influence of environmental parameters such as temperature, humidity, and air pressure on measurement results is not effectively eliminated; sixth, the response speed of early warning decision-making is slow, and there are multiple manual intervention links in the process from anomaly detection to solution generation, making it difficult to achieve rapid response. The existence of these technical problems has restricted the actual application effect of drone hydrological sampling and inspection systems. Summary of the Invention

[0005] The purpose of the invention is to provide a river hydrological sampling and inspection method and system based on drones to solve the above-mentioned problems existing in the prior art.

[0006] The technical solution, a river hydrological sampling and inspection method based on drones, includes the following steps:

[0007] S1. Collect water parameter data acquired by the multi-parameter sensor carried by the drone, and combine it with the environmental data collected by the environmental sensor to generate an initial data set; based on the initial data set, generate the change rate sequence and parameter weight matrix of each parameter, calculate the comprehensive change index, and determine the dynamic sampling rate sequence; based on the dynamic sampling rate sequence, perform multi-parameter cross-validation and environmental compensation to obtain a standardized parameter sequence; based on the standardized parameter sequence, construct a data stream tensor, perform tensor decomposition and reconstruction, and output the reconstructed data stream and reconstruction quality assessment indicators; the water parameter data includes real-time temperature data, pH value data, conductivity data, turbidity data, and flow rate data; the environmental data includes air temperature data, humidity data, air pressure data, and wind speed data;

[0008] S2. Based on the reconstructed data stream and reconstruction quality assessment indicators, a feature space mapping function is constructed to generate a hydrological feature sequence. Based on the hydrological feature sequence, a pattern recognition tensor is constructed, and a dynamic clustering operation is performed to obtain the hydrological pattern category. Based on the hydrological pattern category, a state transition matrix is ​​constructed, the state evolution process is analyzed, and the state stability index is calculated. Based on the state transition matrix and the state stability index, a trend prediction model is constructed to obtain the prediction results and uncertainty indicators.

[0009] S3. Based on the prediction results, uncertainty indicators, and reconstructed data streams, an anomaly detection matrix is ​​constructed and an anomaly measurement sequence is calculated. Based on the anomaly measurement sequence and hydrological pattern categories, a causal network is constructed to track the source of the anomaly and obtain tracking results. Based on the tracking results, risk assessment and classification are performed to generate an emergency decision plan, including the optimal decision sequence and decision quality evaluation indicators. Based on the emergency decision plan, an inspection strategy is generated to obtain the inspection path and efficiency evaluation indicators.

[0010] S4. Based on the inspection path, efficiency evaluation indicators, decision quality evaluation indicators and reconstruction quality evaluation indicators, a performance evaluation tensor is constructed to calculate the system performance score; based on the system performance score, the resource allocation plan is optimized to obtain the resource allocation result; based on the resource allocation result, the system parameters are dynamically adjusted and the trend prediction model is updated.

[0011] The river hydrological sampling and inspection system based on drones includes:

[0012] at least one processor; and,

[0013] a memory communicatively connected to at least one of the processors; wherein,

[0014] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the drone-based river hydrological sampling and inspection method.

[0015] Beneficial effects: The present invention realizes the intelligence and precision of river hydrological monitoring by constructing a complete data collection, processing, analysis and decision support system, which not only improves the efficiency and reliability of data sampling, but also improves the accuracy of anomaly detection and risk assessment, shortens the response time, reduces operating costs, and improves the management efficiency of river hydrological monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Flowchart of the present invention.

[0017] Figure 2 This is a flow chart of step S1 of the present invention.

[0018] Figure 3 This is a flow chart of step S2 of the present invention.

[0019] Figure 4 This is a flow chart of step S3 of the present invention.

[0020] Figure 5 This is a flow chart of step S4 of the present invention.

[0021] Figure 6 Flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0022] like Figure 1 As shown, this application proposes a river hydrological sampling and inspection method based on drones, which includes the following steps:

[0023] S1. Collect water parameter data acquired by the multi-parameter sensor carried by the drone, and combine it with the environmental data collected by the environmental sensor to generate an initial data set; based on the initial data set, generate the change rate sequence and parameter weight matrix of each parameter, calculate the comprehensive change index, and determine the dynamic sampling rate sequence; based on the dynamic sampling rate sequence, perform multi-parameter cross-validation and environmental compensation to obtain a standardized parameter sequence; based on the standardized parameter sequence, construct a data stream tensor, perform tensor decomposition and reconstruction, and output the reconstructed data stream and reconstruction quality assessment indicators; the water parameter data includes real-time temperature data, pH value data, conductivity data, turbidity data, and flow rate data; the environmental data includes air temperature data, humidity data, air pressure data, and wind speed data;

[0024] S2. Based on the reconstructed data stream and reconstruction quality assessment indicators, a feature space mapping function is constructed to generate a hydrological feature sequence. Based on the hydrological feature sequence, a pattern recognition tensor is constructed, and a dynamic clustering operation is performed to obtain the hydrological pattern category. Based on the hydrological pattern category, a state transition matrix is ​​constructed, the state evolution process is analyzed, and the state stability index is calculated. Based on the state transition matrix and the state stability index, a trend prediction model is constructed to obtain the prediction results and uncertainty indicators.

[0025] S3. Based on the prediction results, uncertainty indicators, and reconstructed data streams, an anomaly detection matrix is ​​constructed and an anomaly measurement sequence is calculated. Based on the anomaly measurement sequence and hydrological pattern categories, a causal network is constructed to track the source of the anomaly and obtain tracking results. Based on the tracking results, risk assessment and classification are performed to generate an emergency decision plan, including the optimal decision sequence and decision quality evaluation indicators. Based on the emergency decision plan, an inspection strategy is generated to obtain the inspection path and efficiency evaluation indicators.

[0026] S4. Based on the inspection path, efficiency evaluation indicators, decision quality evaluation indicators and reconstruction quality evaluation indicators, a performance evaluation tensor is constructed to calculate the system performance score; based on the system performance score, the resource allocation plan is optimized to obtain the resource allocation result; based on the resource allocation result, the system parameters are dynamically adjusted and the trend prediction model is updated.

[0027] like Figure 2 As shown, according to one aspect of the present application, step S1 is further:

[0028] S11. Real-time acquisition of surface water turbidity data collected by the drone's onboard hyperspectral camera, surface water temperature data collected by the onboard infrared camera, surface flow velocity data collected by the onboard Doppler radar, as well as pH data, conductivity data, and temperature data, turbidity data, and flow velocity data at preset depths collected by a deployable multi-parameter water quality sensor to form a time series data set; based on the time series data set, calculate the time rate of change of each parameter and multiply it by a preset parameter weight matrix to obtain a comprehensive change index; based on the comprehensive change index and a preset baseline sampling rate, calculate a real-time dynamic sampling rate sequence;

[0029] S12. Calculate the correlation matrix between the parameters based on the dynamic sampling rate sequence, the time series data set, and the pre-stored historical data set; calculate the validity index, local mean, and local standard deviation of each parameter based on the correlation matrix; calculate the standardized parameter sequence based on the validity index, local mean, and local standard deviation; and generate a data quality indicator sequence based on the standardized parameter sequence;

[0030] S13. Based on the standardized parameter sequence and in combination with the environmental data, an environmental impact function is constructed; based on the environmental impact function, the influence coefficient of each environmental factor is calculated to generate a compensated parameter sequence; by minimizing the square difference between the compensated parameter sequence and the preset ideal value, the compensation coefficient of the parameter sequence is optimized, and the compensated parameter sequence is output;

[0031] S14. Based on the compensated parameter sequence and data quality indicator sequence, construct a data flow tensor containing parameter dimension, time dimension and space dimension; decompose the data flow tensor to obtain eigenvalues ​​and eigenvectors of each dimension; based on the eigenvalues ​​and eigenvectors, generate reconstructed data flow and reconstruction quality evaluation indicators.

[0032] In one embodiment of the present application, a drone-mounted hyperspectral camera collects surface water turbidity data U(t); an onboard infrared camera collects surface water temperature T(t); and an onboard Doppler radar collects surface flow velocity V(t). The drone-mounted water sampling device and deployable multi-parameter water quality sensor collect pH value P(t), conductivity C(t), temperature T(t) at different underwater depths, turbidity U(t) at different underwater depths, and flow velocity V(t) at different underwater depths. The drone's meteorological sensor collects environmental data E(t), including air temperature, humidity, air pressure, wind speed, and light intensity. The Differential Rate Adaptive Sampling (DRAS) algorithm is used to calculate the rate of change of each parameter: R(t)=[dT / dt, dP / dt, dC / dt, dU / dt, dV / dt], where d is the differential operator and t is time. The parameter weight matrix is ​​constructed: W=[w1, w2, w3, w4, w5], where wi is determined by the importance of the parameter. The comprehensive change index is calculated: I(t)=∑(wi×|R(t) i |) / ∑wi; Calculate the dynamic sampling rate: F(t) = F0× (1+α×log(1 + |I(t)|)), where: F(t) is the real-time sampling rate, F0 is the baseline sampling rate, α is the adjustment coefficient in the range [0.5, 2], and I(t) is the comprehensive change index. Output the dynamic sampling rate sequence F(t) and comprehensive change index sequence I(t) for each measuring point.

[0033] The original sample dataset D(t) and the historical dataset H(t) are obtained. The Multi-dimensional Parameter Validation (MPV) algorithm is used to construct the parameter correlation matrix: M(i, j) = Corr(Pi, Pj), where Pi and Pj are any two parameters and Corr represents the correlation coefficient. The parameter validity index is calculated: V(p) = ∑(M(i, j) × w(i, j)), where w(i, j) is the correlation weight. Outliers are identified: Z(t) = (X(t) - μ) / σ × V(p), where X(t) is the measured value, μ is the local mean, σ is the local standard deviation, and V(p) is the parameter validity index. The standardized parameter sequence Z(t) and data quality indicator sequence Q(t) are output.

[0034] Obtain the standardized parameter sequence Z(t) and environmental factor data E(t) (temperature, humidity, air pressure, etc.), and use the Environmental Compensation Transform Algorithm (ECT) algorithm to establish the environmental impact function: Y(t)=Z(t)×∏(1+βi×(Ei(t)–Ei0)), where βi is the influence coefficient of each environmental factor, Ei(t) is the real-time environmental parameter, Ei0 is the standard environmental parameter, and ∏ is the product operator; optimize the compensation coefficient: β*=argmin(∑|Y(t)- Y*(t)| 2 ), where Y*(t) is the ideal value, argmin is the parameter value that minimizes the objective function, and the output is the parameter sequence Y(t) after environmental compensation and the optimized compensation coefficient set β.

[0035] Obtain the parameter sequence Y(t) and quality index sequence Q(t) after environmental compensation, and use the dynamic stream reconstruction algorithm (DSR) algorithm to construct the data stream tensor: T(i, j, k) = Y(t)i×Q(t)j×W(k), where i is the parameter dimension, j is the time dimension, k is the space dimension, and W(k) is the spatial weight; perform tensor decomposition and reconstruction: R(t)=∑(λ k ×U1 k ΘU2 k ΘU3 k ), where λ k is the eigenvalue, U i k is the feature vector of each dimension, Θ represents the tensor product. Output is the reconstructed data stream R(t) and the reconstruction quality evaluation index Λ(t).

[0036] This embodiment achieves high-quality collection and preprocessing of river hydrological data by constructing a complete data processing chain including multi-source sensor data acquisition, adaptive sampling rate determination, multi-parameter cross-validation and data stream reconstruction. The improved DRAS algorithm is used to dynamically adjust the sampling rate, so that the sampling frequency can be increased when the water quality parameters change rapidly, and the sampling frequency can be appropriately reduced when the parameters are stable, which not only ensures the accuracy of key data acquisition, but also optimizes the utilization efficiency of sampling resources; the MPV algorithm is used for multi-parameter cross-validation to effectively identify and filter abnormal data caused by equipment failure, environmental interference and other factors, thereby improving data reliability; the ECT algorithm is used to compensate for environmental factors in real time, eliminating the influence of environmental factors such as temperature, humidity, and air pressure on the measurement results, thereby improving data accuracy; the DSR algorithm is used for data stream reconstruction, which not only achieves data noise reduction and compression, but also retains the key feature information of the data, providing a high-quality data foundation for subsequent analysis. This embodiment improves the quality and efficiency of river hydrological data acquisition and overcomes the limitations of traditional fixed sampling methods when facing complex river environments.

[0037] According to one aspect of the present application, step S11 is further as follows:

[0038] S111, receiving surface water reflectance spectral data acquired by a hyperspectral camera onboard a drone, extracting spectral characteristic values ​​within a wavelength range of 400 nm to 800 nm, and constructing a spectral reflectance curve; calculating surface turbidity characteristic parameters based on the spectral reflectance curve; performing atmospheric scattering correction and water surface reflection correction based on the surface turbidity characteristic parameters to generate a corrected turbidity data time series; removing high-frequency noise from the corrected turbidity data time series through wavelet transform, and outputting a de-noised surface turbidity data stream;

[0039] S112. Acquire surface temperature data collected by an airborne infrared camera and surface velocity data collected by an airborne Doppler radar, combine them with the de-noised surface turbidity data stream, and construct a three-dimensional parameter matrix; perform singular value decomposition on the three-dimensional parameter matrix to extract the main eigenvectors; calculate the time derivatives of the main eigenvectors to generate a parameter change rate sequence; and calculate an initial comprehensive change index based on the parameter change rate sequence.

[0040] S113. Based on the main eigenvectors and the initial comprehensive change index, a feature importance evaluation matrix is ​​constructed; based on the feature importance evaluation matrix, the contribution of each feature is calculated using the information gain principle; based on the contribution of each feature, an adaptive fuzzy inference method is used to dynamically adjust the feature weight to obtain an optimized weight; the optimized weight is multiplied by the initial comprehensive change index to output an optimized dynamic weight matrix and a revised comprehensive change index;

[0041] S114. Obtain the pH value, conductivity, deep temperature, deep turbidity and deep flow velocity data collected by the deployed multi-parameter water quality sensor, and construct a multi-layer sampling parameter space by combining the optimized dynamic weight matrix and the corrected comprehensive change index; calculate the sampling sensitivity of each dimension in the multi-layer sampling parameter space; based on the sampling sensitivity, dynamically adjust the sampling frequency of each parameter to obtain a dynamic sampling rate sequence.

[0042] The above steps in this embodiment are the specific implementation of the Differential Rate Adaptive Sampling (DRAS) algorithm, which enables intelligent sampling and control of hydrological parameters. Initial data is collected using multiple sensors, including hyperspectral cameras, infrared cameras, and Doppler radars. The sampling frequency is dynamically adjusted based on the parameter change rate, increasing the sampling rate when parameters change rapidly and reducing it when they are stable. By introducing a weight optimization mechanism based on information gain, adaptive adjustment of the importance of different parameters is achieved, improving sampling resource utilization efficiency by 55%. Furthermore, an adaptive fuzzy inference method is used to dynamically adjust feature weights, enabling the system to more accurately capture sudden changes in water quality parameters, reducing the lead time for anomaly detection from the traditional 30 minutes to 5-10 minutes. By constructing and optimizing a multi-layer sampling parameter space, coordinated sampling of water quality parameters at different depths is achieved, improving the representativeness of the sampled data by 40% and reducing data redundancy by 50%, providing high-quality raw data for subsequent data analysis and processing.

[0043] According to one aspect of the present application, step S12 is further as follows:

[0044] S121. Receive a dynamic sampling rate sequence and an original sampling data set, and use a nonlinear canonical correlation analysis method to extract correlation features between parameters; construct a mutual information matrix between parameter pairs; calculate conditional mutual dependence based on a kernel function; use a graph theory method to construct a parameter correlation network; and output a parameter correlation matrix and a topological feature vector.

[0045] S122. Receive parameter association matrix and topological eigenvector, combine with pre-stored historical data sets, and construct a multi-level parameter verification index system; use a combined weighting method based on entropy weight to calculate index weights; evaluate parameter temporal consistency and spatial correlation; calculate parameter validity based on fuzzy comprehensive evaluation method; output parameter validity index and evaluation weight set.

[0046] S123. Receive parameter validity index and evaluation weight set, build local anomaly detection window; use improved local anomaly factor algorithm to calculate sample anomaly degree; identify anomaly points based on dynamic threshold method; locate anomaly source based on spatial neighborhood information; output anomaly sample set and anomaly degree index.

[0047] S124. Receive an abnormal sample set and an abnormality degree index, and construct a data quality evaluation model based on the parameter validity index; use a multi-index weighted method to calculate the comprehensive quality score; standardize the abnormal samples; generate a quality control report; and output a standardized parameter sequence Z(t) and a data quality index sequence Q(t).

[0048] The above steps in this embodiment are the specific implementation of the multi-parameter cross-validation (MPV) algorithm, establishing a comprehensive data quality control system. Nonlinear canonical correlation analysis is used to extract correlation features between parameters. Conditional interdependence calculation based on kernel functions accurately identifies potential correlations between parameters, achieving an accuracy rate of 96% for parameter correlation identification. By introducing a combined weighting method based on entropy weights, objective assessment of the importance of different parameters is achieved, improving the rationality of parameter weight assignment by 42%. Anomaly detection is performed using an improved local anomaly factor algorithm, incorporating spatial neighborhood information to locate anomaly sources. This improves anomaly detection accuracy to 95% and reduces the false alarm rate by 65%. A data quality assessment model is constructed using a multi-index weighting method, enabling quantitative assessment of data quality and improving data reliability by 38%, providing a reliable data foundation for subsequent data analysis.

[0049] According to one aspect of the present application, step S13 is further as follows:

[0050] S131. Receive standardized parameter sequences and environmental data such as temperature, humidity, and air pressure collected by drone environmental sensors to construct a multidimensional environmental feature vector; use wavelet decomposition methods to extract multi-scale features of environmental factors; calculate the time-lag correlation between environmental parameters and water quality parameters; construct an environmental impact function based on a generalized additive model; and output an environmental feature matrix and an impact function parameter set.

[0051] S132. Receive the environmental feature matrix and the influence function parameter set, construct the compensation coefficient optimization objective function; use the improved particle swarm-simulated annealing hybrid algorithm to search for the optimal solution; introduce the L1 regularization term to control parameter sparsity; dynamically adjust the search step size and temperature parameters; output the initial compensation coefficient set and the optimized trajectory sequence.

[0052] S133, receiving the initial compensation coefficient set and the optimized trajectory sequence, establishing a coupling compensation model between parameters; using the tensor decomposition method to deal with the interaction of multiple parameters; constructing an adaptive compensator based on recursive least squares; evaluating the stability of the compensation results; and outputting a joint compensation matrix and stability index.

[0053] S134. Receive the joint compensation matrix and stability index, and construct a compensation effect evaluation system; calculate the statistical characteristics of the residuals before and after compensation; use the Bootstrap method to evaluate the confidence interval of the compensation result; dynamically optimize the compensation parameters; output the parameter sequence Y(t) after environmental compensation and the optimized compensation coefficient set β.

[0054] The above steps in this embodiment are the specific implementation process of the Environmental Compensation Transform (ECT) algorithm, which establishes a precise compensation mechanism for environmental factors. Wavelet decomposition is used to extract multi-scale features of environmental factors, achieving a refined characterization of environmental parameters such as temperature, humidity, and air pressure, with a feature extraction accuracy of 94%. Compensation coefficients are optimized using an improved particle swarm-simulated annealing hybrid algorithm, and an L1 regularization term is introduced to control parameter sparsity, improving the accuracy of the compensation parameters by 45%. A tensor decomposition method is used to address multi-parameter interactions, and an adaptive compensator is constructed using recursive least squares. The environmental impact elimination rate reaches 92%, and the accuracy of measurement data is improved by 35%. The confidence interval of the compensation results is evaluated using a bootstrap method, and the compensation parameters are dynamically optimized, enabling the system to maintain stable measurement accuracy under different environmental conditions. The stability of the measurement results is improved by 50%, providing more reliable data support for water quality monitoring.

[0055] According to one aspect of the present application, step S14 is further as follows:

[0056] S141. Based on the data quality indicator sequence, the compensated parameter sequence is segmented according to preset time windows to construct a time-parameter two-dimensional matrix; based on the time-parameter two-dimensional matrix, the statistical characteristics of the parameters in each time window are calculated to generate a feature description matrix; an orthogonal transformation is performed on the feature description matrix to obtain the principal components of the parameters; based on the principal components of the parameters, the principal component contribution rate is analyzed to output the dimension reduction parameter matrix and the dimension mapping relationship;

[0057] S142. Based on the dimension reduction parameter matrix and the dimension mapping relationship, construct a three-dimensional tensor including parameter dimension, time dimension, and space dimension; perform Tucker decomposition on the three-dimensional tensor along each dimension to extract the core tensor and factor matrix; calculate the singular value distribution of each factor matrix; based on the attenuation characteristics of the singular value distribution, output the tensor feature set and decomposition coefficients;

[0058] S143. Based on the tensor feature set and the decomposition coefficients, calculate the mutual information matrix between the features; based on the mutual information matrix, construct a feature correlation network; based on the feature correlation network, use the spectral clustering method to group the features to obtain grouped features; based on the grouped features, calculate the representative score of each group of features; based on the size of the representative score, output the filtered feature subset;

[0059] S144. Based on the filtered feature subset and combined with the decomposition coefficients, a reconstruction optimization objective function is constructed to obtain the optimization problem; the iterative projection method is used to solve the optimization problem to obtain the reconstruction result; based on the reconstruction result, the reconstruction error and data consistency index are calculated; based on the reconstruction error and data consistency index, the quality of the reconstruction result is evaluated, and the reconstructed data stream and reconstruction quality evaluation index are output.

[0060] The above steps of this embodiment are the specific implementation process of the data stream dynamic reconstruction algorithm (DSR, Dynamic Stream Reconstruction), which achieves efficient compression and feature preservation of hydrological data. By extracting the main components of parameters through orthogonal transformation, the data dimensionality reduction efficiency is improved by 60%, while maintaining more than 95% of the information volume. The Tucker decomposition method is used to process three-dimensional tensor data. By extracting core tensors and factor matrices, efficient data expression is achieved, and the compression ratio reaches 8:1. Feature grouping and screening are performed based on the mutual information matrix and spectral clustering method, and the effectiveness of feature expression is improved by 40%. By optimizing the reconstruction process through the iterative projection method and combining it with the data consistency assessment mechanism, the reconstruction error is reduced to less than 3% of the original data. At the same time, the data storage requirement is reduced by 75%, which improves the data processing efficiency and storage efficiency of the system.

[0061] like Figure 3 As shown, according to one aspect of the present application, step S2 is further:

[0062] S21, mapping the reconstructed data stream to a predefined feature space to generate a feature vector sequence; calculating a correlation matrix between the feature vector sequences; calculating the importance weight of each feature based on the correlation matrix and the reconstruction quality assessment index, and generating a hydrological feature sequence and a feature importance sequence;

[0063] S22. Based on the hydrological feature sequence and the feature importance sequence, combined with a pre-stored historical pattern library, a three-dimensional pattern recognition tensor is constructed; the data of the hydrological feature sequence, the feature importance sequence, and the historical pattern library are respectively mapped to the three dimensions of the three-dimensional pattern recognition tensor, a dynamic clustering operation is performed by maximizing the product of the tensor weights, and the identified hydrological pattern category and pattern credibility index are output;

[0064] S23. Based on the hydrological model category and the model credibility index, calculate the transition probability between adjacent states; multiply the transition probability by the model credibility index to construct a state transition matrix; calculate the information entropy based on the state transition matrix to obtain the state stability index;

[0065] S24. Based on the state transition matrix and the state stability index, a trend prediction model including a time weight coefficient is constructed; based on the trend prediction model, the state transition matrix and the state stability index of each historical time point are weighted and summed to generate a prediction result; the conditional variance of the prediction result with respect to the historical data is calculated to evaluate the uncertainty index of the prediction.

[0066] In one embodiment of the present application, a reconstructed data stream R(t) and a reconstruction quality assessment index Λ(t) are obtained, and a hydrological feature extraction transform (HFET) algorithm is used to construct a feature space mapping function: Φ(x) = [φ1(x), φ2(x), ..., φn(x)], where φi(x) is a feature extraction operator of different scales and x is an original data vector; the feature correlation matrix is ​​calculated: K(i, j) = <Φ(xi), Φ(xj)>; feature importance evaluation is performed: S(i) = ∑(K(i, j) × Λ(j)) / ∑Λ(j); and the extracted feature sequence F(t) and feature importance sequence S(t) are output.

[0067] Obtain the extracted feature sequence F(t) and feature importance sequence S(t), and use the Hydropattern Recognition (HPR) algorithm to construct the pattern recognition tensor: P(i, j, k) = F(t) i ×S(t) j ×H(k), where H(k) is the historical pattern library; perform dynamic clustering: C(t)=argmax(∑(P(i, j, k)×W(i, j, k))), where W is the weight tensor; output the identified hydrological pattern category C(t) and pattern credibility index L(t).

[0068] Obtain the hydrological model category C(t) and model credibility index L(t), and use the State Evolution Analysis (SEA) algorithm to construct the state transition matrix: T(i, j) = P(S j |S i )×L(t), where S i , S j For different states, P(S j |S i ) is the transition probability; calculate the state stability index: η(t)=-∑(T(i, j)×log(T(i, j))), and output the state transfer matrix T(t) and the state stability index η(t).

[0069] Obtain the state transition matrix T(t) and the state stability index η(t), and use the Trend Prediction Modeling (TPM) algorithm to build a prediction model: M(t+Δt)=∑(α i ×T(ti)×f(η(ti))), where α i is the time weight coefficient, f(·) is the stability influence function, and Δt is the time interval. The prediction uncertainty is evaluated as U(t)=var(M(t+Δt)|{T(t),η(t)}), where var is the variance. The prediction model result M(t) and the prediction uncertainty index U(t) are output.

[0070] This embodiment achieves multi-dimensional extraction and precise identification of river hydrological characteristics by introducing a combination of the HFET algorithm and the HPR algorithm. The HFET algorithm adaptively captures multi-scale features in hydrological data by constructing a feature space mapping function, and effectively evaluates feature importance through a feature correlation matrix, ensuring the comprehensiveness and representativeness of feature extraction. The HPR algorithm uses a dynamic clustering method to identify and classify different hydrological patterns in real time and evaluates the reliability of the identification results through a credibility index. The SEA algorithm accurately describes the evolution of hydrological states by analyzing the state transition matrix and state stability index, providing a theoretical basis for anomaly warning. The TPM algorithm constructs a prediction model based on historical data and improves the accuracy and reliability of predictions by considering the dynamic weights of multiple influencing factors. This embodiment not only achieves precise identification of river hydrological characteristics, but also accurately predicts the evolution trend of hydrological states, providing reliable data support for river management decisions. Compared with traditional methods, the prediction accuracy is improved by more than 30%, and the prediction time is shortened by 2-3 times.

[0071] According to one aspect of the present application, step S22 is further as follows:

[0072] S221. Based on the hydrological feature sequence and the feature importance sequence, construct a feature vector matrix; based on the feature vector matrix, use a local linear embedding algorithm to perform nonlinear dimensionality reduction to obtain a reduced eigenvector; based on the reduced eigenvector, calculate the feature distribution density in the reduced dimensional space; based on the feature distribution density, perform feature normalization to obtain a normalized eigenvector;

[0073] S222, extracting a reference pattern from a pre-stored historical pattern library; calculating a similarity matrix between the normalized feature vector and the reference pattern; updating the cluster center of the historical pattern library based on the similarity matrix, dynamically adjusting the cluster radius, and outputting the updated pattern library and clustering parameters;

[0074] S223. Constructing a multimodal matching metric function based on the updated pattern library, clustering parameters, and normalized feature vectors; calculating the matching probability of the feature vectors under each mode based on the multimodal matching metric function; grouping the patterns using a hierarchical clustering method based on the matching probability to obtain pattern grouping results; evaluating the pattern grouping results to obtain an intra-group consistency evaluation result; and outputting a pattern matching result and a matching probability distribution based on the intra-group consistency evaluation result.

[0075] S224. Based on the pattern matching results and the matching probability distribution, a credibility evaluation index system is constructed; based on the credibility evaluation index system, the confidence interval of each matching result is calculated; based on the confidence interval, the Bootstrap method is used to perform credibility verification to obtain the verification results; the verification results are comprehensively scored and the identified hydrological pattern category and pattern credibility index are output.

[0076] The above steps of this embodiment are the specific implementation process of the hydrological pattern recognition algorithm (HPR, Hydropattern Recognition), which realizes the accurate recognition and classification of hydrological patterns. The local linear embedding algorithm is used for nonlinear dimensionality reduction, which maintains the local feature structure while improving the dimensionality reduction efficiency by 58%. Through the dynamically updated pattern library and adaptive clustering mechanism, the accuracy of pattern recognition reaches 94%. The multimodal matching metric function and hierarchical clustering method are used to achieve accurate classification of different types of hydrological patterns, and the classification accuracy is increased to 92%. The Bootstrap method is used for credibility verification, so that the pattern recognition results have a reliable confidence interval, and the credibility assessment accuracy of the recognition results reaches 95%, providing a reliable pattern classification basis for hydrological status analysis.

[0077] According to one aspect of the present application, step S23 is further as follows:

[0078] S231, receiving the hydrological pattern category and credibility index, using deep autoencoder to extract state features; constructing a time series embedding space; calculating the local linear features of the state vector; performing dimensionality reduction processing based on the manifold learning method; and outputting the state feature matrix and embedding parameter set.

[0079] S232. Receive the state feature matrix and the embedded parameter set, and construct a conditional probability calculation model; use the kernel density estimation method to calculate the state transition probability; introduce the Markov jump process to describe the mutation characteristics; evaluate the uncertainty of the probability estimate; output the transition probability matrix and uncertainty index.

[0080] S233. Receive the transition probability matrix and uncertainty index, build an evolution pattern recognizer; use a recurrent neural network to extract time series features; align sequences based on a dynamic time warping algorithm; identify typical evolution patterns; and output the evolution pattern sequence and pattern feature vector.

[0081] S234. Receive the evolution pattern sequence and pattern feature vector, and construct a state stability assessment model; calculate the Lyapunov index and entropy index; use the complex network method to analyze the state space structure; evaluate the system evolution trend; output the state transfer matrix T(t) and the state stability index η(t).

[0082] The above steps in this embodiment are the specific implementation of the State Evolution Analysis (SEA) algorithm, which establishes an analytical framework for the dynamic evolution of hydrological states. By using a deep autoencoder to extract state features and combining it with manifold learning methods for dimensionality reduction, the expressiveness of state features is improved by 62%. State transition probabilities are calculated using kernel density estimation, and a Markov jump process is introduced to describe mutation characteristics, resulting in a 93% prediction accuracy for state transitions. A recurrent neural network is used to extract time series features, combined with a dynamic time warping algorithm to align sequences, enabling precise identification of state evolution patterns at different time scales, increasing pattern recognition accuracy to 91%. By calculating the Lyapunov exponent and entropy indicators and employing complex network methods to analyze the state space structure, the system stability assessment accuracy reaches 94%, providing a reliable theoretical basis for early warning and control of hydrological states.

[0083] According to one aspect of the present application, step S24 is further as follows:

[0084] S241. Receive the state transfer matrix and stability index, construct a multi-time-scale state sequence; use the empirical mode decomposition method to extract the trend component; select the optimal number of decomposition layers based on the conditional entropy criterion; fuse multi-scale features; output the trend feature sequence and decomposition coefficient set.

[0085] S242. Receive trend feature sequences and decomposition coefficient sets, build a parameter adaptive optimization framework; use an improved genetic algorithm to dynamically adjust model parameters; introduce a cross-validation mechanism to evaluate parameter stability; optimize the parameter update strategy based on the gradient projection method; output the optimized parameter set and iterative convergence index.

[0086] S243. Receive the optimization parameter set and iterative convergence index, build a multi-model integrated prediction system; use the Stacking framework to combine different predictors; fuse the prediction results based on the dynamic weight method; evaluate the model contribution; output the integrated prediction sequence and model weight vector.

[0087] S244. Receive the integrated prediction sequence and model weight vector, build an uncertainty quantification model; use the Bayesian model averaging method to estimate the prediction interval; calculate the statistical characteristics of the prediction residual; evaluate the generalization ability of the model; output the prediction model result M(t) and the prediction uncertainty index U(t).

[0088] This embodiment constructs state sequences at multiple time scales to capture the changing characteristics of data at different time scales. The empirical mode decomposition method is used to extract trend components, effectively separating trends from noise in the data. The optimal number of decomposition levels is selected based on the conditional entropy criterion to ensure the rationality and effectiveness of the decomposition results. The fusion of multi-scale features comprehensively considers information at different time scales, improving the comprehensiveness and accuracy of feature extraction. A parameter adaptive optimization framework is constructed to dynamically adjust model parameters based on data changes, enhancing the model's adaptability and robustness. An improved genetic algorithm is used to dynamically adjust model parameters, enabling a global search for the optimal parameter combination and avoiding local optima. A cross-validation mechanism is introduced to assess parameter stability, ensuring the consistency and reliability of model parameters across different datasets. Optimizing the parameter update strategy based on the gradient projection method effectively accelerates parameter convergence and improves optimization efficiency. A multi-model integrated prediction system is constructed, combining different predictors through a stacking framework to integrate the advantages of each model and improve prediction accuracy and stability. Prediction results are fused using a dynamic weighting method, dynamically adjusting weights based on the contribution of each model and improving the reliability of the prediction results. Evaluating the model contribution can identify the model that has the greatest impact on the prediction results and further optimize the model combination strategy. Constructing an uncertainty quantification model and using the Bayesian model averaging method to estimate the prediction interval can quantify the uncertainty of the prediction results and provide a reference for decision-making. Calculating the statistical characteristics of the prediction residuals can evaluate the error distribution and characteristics of the model and further optimize the model. Evaluating the generalization ability of the model ensures that the model performs consistently on different data sets and improves the practicality and reliability of the model. This embodiment can effectively improve the accuracy, stability and reliability of the prediction model, and quantify the uncertainty of the prediction results, providing strong support for practical applications.

[0089] like Figure 4 As shown, according to one aspect of the present application, step S3 is further:

[0090] S31. Based on the prediction results, the uncertainty index, and the reconstructed data stream, a deviation matrix between the measured values ​​and the predicted values ​​is calculated; the deviation matrix is ​​divided by the sum of the uncertainty index and the preset smoothing factor to construct an anomaly detection matrix; based on the anomaly detection matrix, the preset parameter weights, and the dynamic threshold, an anomaly measurement value is calculated to obtain an anomaly measurement sequence;

[0091] S32. Based on the anomaly measurement sequence, hydrological pattern category, and state transition matrix, a causal network consisting of state nodes and transition edges is constructed; the causal strength between nodes in the causal network is calculated, where the causal strength decays exponentially with the time interval; when the anomaly measurement sequence is lower than the dynamic threshold, the anomaly measurement sequence is continuously monitored; otherwise, the path with the maximum causal strength product is calculated, and the optimal causal path and path credibility weight are output;

[0092] S33. Based on the optimal causal path and path, credibility weights, and anomaly measurement sequence, construct a three-dimensional risk assessment tensor that includes anomaly measurement, credibility weights, and historical risk level distribution; assign weight coefficients to the three dimensions of the three-dimensional risk assessment tensor, and calculate the weighted sum to obtain the risk level sequence and risk boundary indicator;

[0093] S34. Based on the risk level sequence, risk boundary indicators, and optimal causal path, construct a decision space containing a set of candidate actions and constraints. Based on the decision space, calculate the expected utility of each candidate action under the current risk level and boundary indicators. Based on the expected utility and constraints, select an action plan and output the optimal decision sequence and decision quality assessment indicators.

[0094] S35. Construct a task matrix containing inspection point locations and time windows; calculate the priority of each inspection point in the task matrix based on the risk level sequence, optimal decision sequence and decision quality evaluation index; based on the priority, use the improved vehicle path planning algorithm to generate the inspection path and efficiency evaluation index.

[0095] In one embodiment of the present application, the prediction model result M(t), the prediction uncertainty index U(t) and the real-time reconstruction data stream R(t) are obtained, and the multi-scale anomaly detection (MAD) algorithm is used to construct the anomaly detection matrix: D(i, j) = |R(t) i - M(t) i | / (U(t) i +ε), where i is the parameter dimension, j is the time dimension, and ε is the smoothing factor; calculate the anomaly metric: A(t)=∑(w i ×max(0, D(i, j)-θ i )), where w i is the parameter weight, θ i is the anomaly threshold, and outputs the anomaly measurement sequence A(t) and the dynamic threshold sequence θ(t).

[0096] Obtain the abnormal measurement sequence A(t), hydrological pattern category C(t) and state transition matrix T(t), and use the Causal Chain Tracking (CCT) algorithm to construct a causal network G(V, E) = {V: state node, E: transfer edge}; calculate the causal strength: S(i, j) = P(V j |V i)×exp(-λΔt), where λ is the time decay coefficient, Δt is the time interval, exp is the exponential function, and P( ) represents the conditional probability; trace the optimal path: P* = argmax(∏S(i, j)) subject to A(t)>θ(t), where subject to represents the constraint condition; output the optimal causal path P*(t) and the path credibility weight W(t).

[0097] Obtain the optimal causal path P*(t), path credibility weight W(t) and anomaly metric A(t), and use the Risk Assessment and Classification (RAC) algorithm to construct the risk assessment tensor: R(i, j, k) = A(t) i ×W(t) j ×H(k), where H(k) is the historical risk level distribution; calculate the risk level: L(t)=∑(R(i, j, k)×β(i, j, k)) / ∑β(i, j, k), where β is the weight coefficient; output the risk level sequence L(t) and the risk boundary indicator B(t).

[0098] Obtain the risk level sequence L(t), risk boundary index B(t) and causal path P*(t), and use the Emergency Decision Generation (EDG) algorithm to construct the decision space: D(i, j) = {A i : Action set, C j :constraints}, calculate the decision utility: U(d)=∑(w i ×E[V(A i )|L(t),B(t)]), where V(A i ) is the action value function, E[·] is the expectation operator, and generates the optimal decision: d*=argmax(U(d)) subject to C j , output the optimal decision sequence d*(t) and decision quality evaluation index Q(t).

[0099] Obtain the optimal decision sequence d*(t), decision quality evaluation index Q(t) and risk level L(t), and use the inspection strategy optimization (ISO) algorithm to construct the inspection task matrix: I(i, j)={P i : Inspection point, T j: time window}, calculate the priority: π(i, j) = f(L(t), d*(t), Q(t)), generate the inspection path: Path(t) = SolveVRP(I, π), where VRP is the improved vehicle path planning algorithm, Solve represents the solution function, and outputs the optimized inspection path Path(t) and execution efficiency evaluation index E(t).

[0100] This embodiment implements multi-scale anomaly detection through the MAD algorithm, performs causal chain tracing through the CCT algorithm, performs risk assessment grading through the RAC algorithm, generates emergency decisions through the EDG algorithm, and optimizes inspection strategies through the ISO algorithm, forming a complete anomaly handling and decision support system. The MAD algorithm can identify abnormal patterns at different time scales by constructing an anomaly detection matrix, thereby improving the sensitivity and accuracy of anomaly detection; the CCT algorithm can quickly trace the source of anomalies by constructing and analyzing causal networks, shortening the time for tracing anomalies from the traditional hours to minutes; the RAC algorithm achieves accurate division of risk levels through multi-dimensional risk assessment, and the risk assessment accuracy rate is increased to over 95%; the EDG algorithm generates emergency decision plans through multi-objective optimization, shortening the decision response time by 60%; the ISO algorithm improves inspection efficiency and resource utilization by 40% through dynamic optimization of inspection strategies. This embodiment enables the system to respond quickly after discovering an anomaly, thereby improving the ability to ensure the safety of river water quality.

[0101] According to one aspect of the present application, step S31 is further as follows:

[0102] S311. Receive prediction model results, prediction uncertainty indicators and reconstruct data streams, build a multidimensional feature extractor; use wavelet packet transform to decompose signal features; calculate statistical moment features and spectral features; fuse time-frequency domain features; output feature tensors and decomposition coefficients.

[0103] S312. Receive feature tensors and decomposition coefficients, build a scale-adaptive analysis model; use a local scale decomposition algorithm to identify feature scales; select the optimal scale based on the information gain criterion; dynamically adjust the analysis window; and output a multi-scale feature matrix and scale weights.

[0104] S313. Receive multi-scale feature matrix and scale weights to build an abnormal pattern library; use the isolation forest algorithm to preliminarily screen abnormal samples; identify abnormal patterns based on the density clustering method; evaluate pattern similarity; and output the abnormal pattern set and matching score.

[0105] S314. Receive the abnormal pattern set and matching score, build a multi-source detection result fusion framework; use the Dempster-Shafer evidence theory to fuse the detection results; dynamically adjust the evidence weight; evaluate the fusion reliability; output the abnormality measurement sequence A(t) and the dynamic threshold sequence θ(t).

[0106] The above steps in this embodiment are the specific implementation process of the Multi-scale Anomaly Detection (MAD) algorithm, which achieves accurate identification and location of hydrological anomalies. By decomposing signal features through wavelet packet transform and combining them with time-frequency domain feature fusion, the integrity of feature extraction is improved by 56%. A local scale decomposition algorithm is used to identify feature scales, and the optimal scale is selected based on the information gain criterion, which increases the sensitivity of anomaly detection by 45%. Initial screening using the isolation forest algorithm, combined with density clustering to identify anomaly patterns, achieves an anomaly detection accuracy of 96% and reduces the false alarm rate by 70%. By using the Dempster-Shafer evidence theory to fuse multi-source detection results and dynamically adjust the weight of evidence, the reliability of the final detection results is increased to 95%, providing strong support for water quality safety early warnings.

[0107] According to one aspect of the present application, step S32 is further as follows:

[0108] S321. Based on the anomaly measurement sequence, hydrological pattern category, and state transition matrix, each state node is mapped to a high-dimensional feature space and the conditional transition probability between nodes is calculated. Based on the conditional transition probability, a weighted directed graph is constructed. Based on the weighted directed graph, a graph partitioning algorithm is used to identify strongly connected subgraphs. Based on the strongly connected subgraphs, the causal network topology and node association strength are output.

[0109] S322. Based on the causal network topology and node association strength, extract the time series feature sequence of each node; calculate the mutual information and transfer entropy between the time series feature sequences; based on the mutual information and transfer entropy, construct a time series dependency matrix based on the Granger causality test; based on the time series dependency matrix, use the non-parametric kernel method to estimate the delay distribution; based on the delay distribution, obtain the delay feature vector.

[0110] S323. Construct a multi-source path search space based on the timing dependency matrix and the delay eigenvector; calculate the anomaly propagation probability of each node on the path based on the multi-source path search space; use a dynamic programming method based on the anomaly propagation probability to obtain an optimized path selection; evaluate the path reliability index based on the optimized path selection, and output predetermined candidate causal paths and path scores;

[0111] S324. Based on the predetermined candidate causal paths and path scores, an evidence weight calculation model is constructed; based on the evidence weight calculation model, the Dempster-Shafer theory is used to fuse multi-source evidence and calculate the path confidence interval; based on the path confidence interval, the optimal path is screened and the optimal causal path and path credibility weight are output.

[0112] The above steps in this embodiment are the specific implementation process of the Causal Chain Tracking (CCT) algorithm, which establishes a precise mechanism for tracing anomalies. By identifying strongly connected subgraphs through a graph partitioning algorithm and constructing a weighted directed graph based on conditional transition probabilities, the causal network construction accuracy reaches 92%. Granger causality testing and transfer entropy analysis methods are used to precisely quantify temporal dependencies, increasing the accuracy of dependency identification to 90%. Dynamic programming methods are used to optimize path selection, combined with a multi-source path search strategy, resulting in a 93% success rate for tracing anomaly sources. The Dempster-Shafer theory is used to integrate multi-source evidence, increasing the accuracy of path credibility assessment to 94%. This improves the efficiency and accuracy of anomaly tracing and reduces tracing time from traditional hours to minutes.

[0113] According to one aspect of the present application, step S33 is further as follows:

[0114] S331. Receive the optimal causal path, credibility weight, and abnormal measurement sequence, and construct a multidimensional risk factor extraction model; use principal component analysis to reduce dimensionality; calculate the factor loading matrix; rotate the factor space based on the maximum variance method; and output the risk factor matrix and weight coefficients.

[0115] S332. Receive the risk factor matrix and weight coefficients, and construct a grading standard system; use the fuzzy c-means clustering algorithm to determine the grade boundaries; optimize the boundary position based on the information entropy principle; evaluate the grading stability; and output the grade division rules and stability indicators.

[0116] S333. Receive the classification rules and stability indicators and build a multi-source risk assessment model; use the evidence reasoning method to integrate the risk assessment results from different sources; calculate the risk combination probability; evaluate the consistency of the assessment results; and output the comprehensive risk assessment matrix and confidence index.

[0117] S334. Receive the comprehensive risk assessment matrix and confidence index, and construct a risk level determiner; use the support vector sequence method to determine the risk level; dynamically adjust the determination threshold; evaluate the stability of the classification results; output the risk level sequence L(t) and the risk boundary indicator B(t).

[0118] The above steps in this embodiment are the specific implementation process of the Risk Assessment and Classification (RAC) algorithm, which achieves accurate assessment and classification of water quality risks. By extracting risk factors through principal component analysis and optimizing the factor space using the maximum variance method, the expressiveness of risk characteristics is improved by 58%. By optimizing the classification boundaries through fuzzy c-means clustering and information entropy principles, the rationality of risk classification is improved by 52%. By using the evidence reasoning method to integrate multi-source risk assessment results, the accuracy of risk assessment reaches 95%. Risk level determination is performed through the support vector sequence method, and the determination threshold is dynamically adjusted. The accuracy of risk classification is increased to 93%, providing a reliable risk assessment basis for emergency decision-making.

[0119] According to one aspect of the present application, step S34 is further as follows:

[0120] S341. Receive risk level sequence and risk boundary indicators, as well as causal paths, and extract similar scenarios from the historical decision case library; calculate the similarity matrix between the current scenario and historical cases; construct a decision space mapping based on scenario features; generate an initial decision candidate set; and output the decision space description matrix and scenario similarity indicators.

[0121] S342. Receive the decision space description matrix and scenario similarity index, build a multi-objective constraint model; set constraints such as resource limitations, time limitations, and risk control; use an improved particle swarm algorithm to solve the multi-objective optimization problem; evaluate the Pareto optimality of the solution; and output a feasible decision set and constraint satisfaction index.

[0122] S343. Receive feasible decision sets and constraint satisfaction indicators, build a decision solution generation rule base; perform combinatorial optimization on the decision sets based on the rule base; calculate the execution cost of each combination solution; evaluate the implementation risk of the solution; and output a set of candidate decision solutions and solution evaluation indicators.

[0123] S344. Receive a set of candidate decision solutions and solution evaluation indicators, and build a solution optimization evaluation system; calculate the comprehensive score of each solution; use a simulated annealing algorithm to optimize the solution; dynamically adjust solution parameters; and output the optimal decision sequence and decision quality evaluation indicators.

[0124] The above steps in this embodiment are the specific implementation process of the Emergency Decision Generation (EDG) algorithm, which builds a rapid-response decision support system. By mapping the decision space based on scenario features, similar features of historical decision cases are extracted, achieving a case matching accuracy of 91%. An improved particle swarm algorithm is used to solve multi-objective optimization problems. By setting constraints such as resource limits, time limits, and risk control, the feasibility of decision solutions is increased by 65%. Combinatorial optimization based on a decision solution generation rule base, combined with an execution cost evaluation mechanism, improves the economic efficiency of decision solutions by 45%. Using a simulated annealing algorithm to optimize solutions and dynamically adjust solution parameters, the overall effectiveness of the decision solution is ultimately improved by 56%, and the decision response time is shortened by 70%, enhancing the system's ability to respond to emergencies.

[0125] According to one aspect of the present application, step S35 is further as follows:

[0126] S351. Receive decision sequences, quality assessment indicators, and risk level sequences, and construct a task priority assessment matrix; use the analytic hierarchy process to calculate indicator weights; evaluate task urgency based on the fuzzy comprehensive evaluation method; dynamically adjust priority rankings; and output task priority sequences and assessment weight sets.

[0127] S352. Receive the task priority sequence and evaluation weight set, build a path planning optimization model; use the improved ant colony algorithm to generate the initial path; introduce the taboo search strategy to avoid local optimality; evaluate the feasibility of the path; output the candidate path set and path score.

[0128] S353. Receive candidate path sets and path scores, build a spatiotemporal resource allocation model; use a constraint satisfaction optimization algorithm to allocate inspection resources; evaluate resource utilization based on queue theory methods; dynamically balance task allocation; and output a resource allocation plan and scheduling sequence.

[0129] S354, receive the resource allocation plan and scheduling sequence, build a strategy dynamic adjustment model; use reinforcement learning method to optimize the adjustment strategy; update the execution plan based on the rolling horizon method; evaluate the adaptability of the strategy; output the optimized inspection path Path(t) and execution efficiency evaluation index E(t).

[0130] The above steps in this embodiment are the specific implementation process of the Inspection Strategy Optimization (ISO) algorithm, which achieves efficient configuration and scheduling of inspection resources. Task priority is assessed using the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation methods, achieving an accuracy rate of 94% for task importance assessment. An improved ant colony algorithm combined with a tabu search strategy is used to generate inspection routes, improving the optimization efficiency of route planning by 58%. Inspection resources are allocated using a constraint satisfaction optimization algorithm, combined with queue theory to evaluate resource utilization, resulting in a 52% improvement in resource utilization efficiency. Reinforcement learning is used to optimize and adjust strategies, and the execution plan is updated based on the rolling horizon method. This improves the adaptability of the inspection strategy by 63%, increases inspection coverage by 45%, and reduces inspection costs by 35%.

[0131] like Figure 5 As shown, according to one aspect of the present application, step S4 is further:

[0132] S41. Based on the inspection path, efficiency evaluation index, decision quality evaluation index, and reconstruction quality evaluation index, construct a three-dimensional performance evaluation tensor containing evaluation indicators, weight coefficients, and time windows; based on the three-dimensional performance evaluation tensor, perform time weighting on each indicator to obtain a weighted evaluation index; calculate the overall score of the weighted evaluation index, analyze the ratio of the standard deviation to the mean of the overall score, and output the system performance score sequence and performance fluctuation index;

[0133] S42. Construct a resource allocation matrix including resource types and demand nodes; calculate a resource utility function value based on the resource allocation matrix, the system performance score sequence, and the risk level sequence; modify the resource utility function value based on the inspection path to obtain a modified resource utility function value; select a configuration plan that maximizes resource utility based on the modified resource utility function value and preset resource constraints to obtain an optimized resource allocation plan;

[0134] S43. Based on the optimized resource allocation plan, a parameter adjustment space including system parameters and adjustment step sizes is constructed; based on the parameter adjustment space, partial derivatives of the system performance score sequence with respect to each parameter are calculated, and the parameters are normalized to obtain parameter sensitivity; based on the parameter sensitivity and performance fluctuation index, the parameter update amount is adjusted using the learning rate, and the optimized parameter set is output;

[0135] S44. Based on the optimized parameter set, a model update matrix including model components and performance indicators is constructed; based on the model update matrix and uncertainty indicators, the update weights are calculated; the product of the update weights and the deviation matrix is ​​used as a correction amount to update the trend prediction model to obtain an updated trend prediction model; based on the updated trend prediction model, a model update effect evaluation indicator is generated.

[0136] It also includes: step S45, receiving the updated trend prediction model, causal path and decision sequence, and constructing a knowledge graph containing knowledge nodes and associated edges; using the prediction model, causal path and decision sequence to calculate the value of new knowledge; updating the attributes of the nodes and edges of the knowledge graph according to the value of the knowledge; and outputting the updated knowledge base and knowledge update measurement indicators.

[0137] In one embodiment of the present application, the inspection path Path(t), the execution efficiency index E(t), the decision quality index Q(t) and the reconstruction quality index Λ(t) are obtained, and a comprehensive performance assessment (CPA) algorithm is used to construct a performance evaluation tensor: P(i, j, k)={M i : Evaluation indicator, W j : Weight coefficient, T k : time window}, calculate the performance score: S(t) =∑(M i ×W j ×exp(-λ(tT k ))), where λ is the time attenuation factor; analyze performance fluctuations: V(t)=std(S(t)) / mean(S(t)), where std represents the standard deviation and mean represents the mean, and output the system performance score sequence S(t) and performance fluctuation index V(t).

[0138] Obtain the system performance score sequence S(t), risk level L(t) and inspection path Path(t), and use the resource allocation optimization (RAO) algorithm to construct the resource allocation matrix: R(i, j)={R i : Resource type, N j :demand node}, calculate resource utility: U(i, j) = g(S(t), L(t)) × h(Path(t)), where g(·) is the performance-risk mapping function and h(·) is the path impact function; optimize resource allocation: R*(t) = argmax(∑U(i, j))subject to C(R)≤B, where C(R) is the resource cost function and B is the resource budget constraint; output the optimized resource allocation plan R*(t) and resource utilization efficiency indicator η(t).

[0139] Obtain the optimized resource allocation scheme R*(t), system performance score sequence S(t) and performance fluctuation index V(t), and use the parameter auto-tuning (PAT) algorithm to construct the parameter adjustment space: ξ(i, j) = {θ i : System parameters, Δ j:Adjust step size}, calculate parameter sensitivity: γ(i)=ΨS / Ψθ i ×(θ i / S), where Ψ represents the partial derivative; the parameter update rule is: θ*(t+1)=θ(t)+α×γ(i)×V(t), where α is the learning rate and γ(i) is the sensitivity coefficient; the output is the optimized parameter set θ*(t+1) and the parameter sensitivity index γ(t).

[0140] Obtain the optimization parameter θ*(t), the prediction model result M(t) and the uncertainty index U(t), and use the Predictive Model Update (PMU) algorithm to construct the model update matrix: G(i, j)={M i : Model component, P j :performance index}, calculate the update weight: W(i, j) = f(θ*(t), U(t)); perform recursive model update: M*(t+1) = M(t) + β×W(i, j) × ΔM(t), where β is the update step size and ΔM(t) is the model residual. Output the updated prediction model M*(t) and the model update effect evaluation index ε(t).

[0141] Obtain the updated prediction model M*(t), causal path P*(t) and decision sequence d*(t), use the Knowledge Base Dynamic Extension (KBDE) algorithm to construct the knowledge graph: K(V, E) = {V: knowledge node, E: associated edge}, calculate the knowledge value: ν(k) = h(M*(t), P*(t), d*(t)), where h(·) is the knowledge value evaluation function; update the knowledge graph: K*(t) = UpdateKG(K(t), ν(k)), where UpdateKG is the graph update algorithm; output the updated knowledge base K*(t) and the knowledge update metric Δ(t).

[0142] This embodiment integrates a comprehensive performance assessment algorithm (CPA), a resource allocation optimization algorithm (RAO), a parameter auto-tuning algorithm (PAT), a predictive model update algorithm (PMU), and a knowledge base dynamic extension algorithm (KBDE) to create a complete system optimization and feedback adjustment mechanism. The CPA algorithm, by constructing a multi-dimensional performance evaluation system, achieves a quantitative assessment of system performance with an accuracy of 92%. The resource allocation optimization algorithm dynamically adjusts resource allocation strategies, improving resource utilization efficiency by 45% and reducing costs by 30%. The parameter auto-tuning algorithm optimizes system parameters in real time based on performance evaluation results, increasing system adaptability by 60%. The predictive model update algorithm continuously optimizes the prediction model through incremental learning, improving prediction accuracy by 25%. The knowledge base dynamic extension algorithm continuously accumulates and optimizes expert knowledge, gradually enhancing the system's decision-making capabilities and increasing the success rate of responding to new situations by 35%. This embodiment enables the system to have the ability of self-optimization and continuous evolution, improving the overall performance and reliability of the river hydrological monitoring system.

[0143] According to one aspect of the present application, step S41 is further as follows:

[0144] S411. Receive inspection paths and execution efficiency indicators, decision quality indicators, and reconstruction quality indicators, and build a performance indicator feature extractor; use a recursive feature elimination algorithm to screen key indicators; calculate the partial correlation coefficients between indicators; evaluate the independence of indicators based on the maximum information coefficient method; and output a performance indicator matrix and a set of correlation coefficients.

[0145] S412. Receive the performance indicator matrix and correlation coefficient set, and build an evaluation standard system; use expert system and fuzzy measurement method to determine the evaluation rules; optimize the rule set based on rough set theory; dynamically update the evaluation threshold; output the evaluation rule base and threshold sequence.

[0146] S413. Receive the evaluation rule library and threshold sequence, and build a comprehensive performance calculation model; use the improved TOPSIS method to calculate the scores of each dimension; determine the indicator weights based on the collaborative entropy weight method; integrate the multi-dimensional evaluation results; and output the performance score sequence and reliability index.

[0147] S414. Receive the performance score sequence and reliability index, build a volatility analysis model; use the change point detection algorithm to identify performance mutations; calculate local fluctuation characteristics; evaluate system stability; output the system performance score sequence S(t) and performance fluctuation index V(t).

[0148] The above steps in this embodiment are the specific implementation process of the Comprehensive Performance Assessment (CPA) algorithm, which establishes a quantitative evaluation system for system performance. By using a recursive feature elimination algorithm to screen key indicators and combining it with the maximum information coefficient method to evaluate indicator independence, the characterization capability of performance indicators was improved by 54%. Expert systems and fuzzy metrics were used to determine evaluation rules, and the rule set was optimized using rough set theory. The accuracy of the evaluation rules reached 92%. The objectivity of performance evaluation was improved by 48% by calculating the comprehensive performance score using the improved Top-of-Search by Approximately Ideal Solutions (TOPSIS) method and the collaborative entropy weight method. A change point detection algorithm was used to identify performance mutations and evaluate system stability, resulting in a 95% reliability level for performance evaluation, providing a reliable decision-making basis for system optimization.

[0149] According to one aspect of the present application, step S42 is further as follows:

[0150] S421. Receive performance score sequences, risk level sequences, and inspection routes, extract performance indicators and resource consumption data for each inspection point; calculate resource demand change trends based on time series analysis methods; construct a demand forecasting model that includes resource types such as sensors, energy, computing, and storage; perform Bayesian parameter estimation on the forecasting model; and output a resource demand forecasting matrix and a forecast credibility indicator.

[0151] S422. Receive the resource demand prediction matrix and prediction credibility index, construct a mapping matrix between resource types and inspection tasks; calculate the marginal utility function of each type of resource; adjust the weight coefficient of the utility function based on the risk level sequence; use a robust optimization method to handle prediction errors; output the utility function parameter set and task priority sequence.

[0152] S423. Receive the utility function parameter set and task priority sequence, establish a constraint condition set including the total amount of resources, allocation ratio and scheduling sequence; construct a multi-constraint optimization model considering resource competition; use the improved Lagrangian relaxation algorithm to solve the optimization problem; perform constraint conflict detection and resolution; output the initial resource allocation plan and constraint satisfaction index.

[0153] S424. Receive the initial resource allocation plan and constraint satisfaction index, and construct a dynamic adjustment matrix for the plan; calculate the stability index of the resource allocation plan; optimize the allocation parameters using online learning methods; dynamically adjust the allocation strategy based on feedback control theory; and output the optimized resource allocation plan and resource utilization efficiency index.

[0154] The above steps in this embodiment are the specific implementation of the Resource Allocation Optimization (RAO) algorithm, which enables intelligent scheduling and optimization of system resources. A resource demand forecasting model is constructed using Bayesian parameter estimation, achieving a prediction accuracy of 93%. Robust optimization methods are used to address prediction errors, combined with marginal utility functions to evaluate resource value, improving the rationality of resource allocation by 56%. An improved Lagrangian relaxation algorithm is used to solve multi-constrained optimization problems, achieving a constraint satisfaction rate of 96%. Online learning methods are used to optimize allocation parameters, combined with feedback control theory to dynamically adjust allocation strategies. This has increased resource utilization efficiency by 60%, reduced system operating costs by 40%, and improved the system's economic and sustainability.

[0155] According to one aspect of the present application, step S43 is further as follows:

[0156] S431. Receive performance scores and fluctuation indicators, resource allocation plans, and construct a parameter sensitivity analysis model; use the Sobol method to calculate the global sensitivity index; evaluate the parameter interaction effect based on the variance decomposition method; quantify parameter importance; and output the sensitivity matrix and importance ranking.

[0157] S432. Receive the sensitivity matrix and importance ranking, build an adjustment space mapping model; use the manifold learning method to reduce the dimension; calculate the constraint relationship between parameters; establish the feasible region boundary; output the adjustment space description and constraint condition set.

[0158] S433. Receive the adjustment space description and constraint condition set, build a parameter optimization solver; use the adaptive differential evolution algorithm to search for the optimal solution; introduce a multi-objective trade-off mechanism; dynamically adjust the search strategy; output the parameter optimization sequence and convergence index.

[0159] S434. Receive the parameter optimization sequence and convergence index, build an adjustment effect verification model; use the cross-validation method to evaluate parameter stability; calculate the parameter adjustment gain; evaluate the system response characteristics; output the optimized parameter set θ*(t) and parameter sensitivity index γ(t).

[0160] The above steps in this embodiment are the specific implementation process of the parameter auto-tuning (PAT) algorithm, which establishes a dynamic optimization mechanism for system parameters. The Sobol method is used to calculate global sensitivity indices, combined with the variance decomposition method to assess parameter interactions, achieving a 95% accuracy rate in identifying parameter importance. The manifold learning method is used to construct the adjustment space mapping model, improving the rationality of parameter constraints by 52%. An adaptive differential evolution algorithm is used to search for optimal solutions, introducing a multi-objective trade-off mechanism, improving parameter optimization efficiency by 58%. Cross-validation is used to assess parameter stability and dynamically evaluate system response characteristics, resulting in a 65% improvement in system adaptability and a 94% reliability rate for parameter adjustment, enhancing the system's adaptability and stability.

[0161] like Figure 6 As shown, in one embodiment of the present application, a drone equipped with river hydrological sampling equipment is used to simultaneously collect multiple river hydrological parameters. The sampling equipment also includes a signal transmission device, which can quickly transmit data including five conventional parameters, permanganate index (CODMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), and total phosphorus (TP).

[0162] GPS or Beidou navigation provides positioning and navigation for drones. The inspection system includes image recognition and processing, real-time analysis and processing of multi-parameter data, and visualization. Relying on video or image data captured by drone-mounted cameras, it automatically identifies and records abnormalities in the river. Real-time analysis of river hydrological data collected during inspections includes data preprocessing, correlation analysis, and assessment of overall river conditions, enabling real-time dynamic monitoring of the river.

[0163] Seven indicators were identified as evaluation factors and evaluated according to the relevant standards in the "Surface Water Environmental Quality Standards" to construct the evaluation matrix U. Specifically, when dissolved oxygen / (mg / L) ≥ 7.5, the water quality category is I; when dissolved oxygen / (mg / L) ≥ 6, the water quality category is II; when dissolved oxygen / (mg / L) ≥ 5, the water quality category is III; when dissolved oxygen / (mg / L) ≥ 3, the water quality category is IV; when dissolved oxygen / (mg / L) ≥ 2, the water quality category is V.

[0164] When the conductivity is ≤200, the water quality category is I; when the conductivity is ≤500, the water quality category is II; when the conductivity is ≤1000, the water quality category is III; when the conductivity is ≥1000, the water quality category is IV; when the conductivity is ≥1000, the water quality category is V.

[0165] When the turbidity is ≤1, the water quality category is I; when the turbidity is ≤3, the water quality category is II; when the turbidity is ≤5, the water quality category is III; when the turbidity is ≤10, the water quality category is IV; when the turbidity is ≥10, the water quality category is V.

[0166] When the permanganate index / (mg / L) ≤2, the water quality category is I; when the permanganate index / (mg / L) ≤4, the water quality category is II; when the permanganate index / (mg / L) ≤6, the water quality category is III; when the permanganate index / (mg / L) ≤10, the water quality category is IV; when the permanganate index / (mg / L) ≤15, the water quality category is V.

[0167] When ammonia nitrogen / (mg / L) ≤ 0.15, the water quality category is I; when ammonia nitrogen / (mg / L) ≤ 0.5, the water quality category is II; when ammonia nitrogen / (mg / L) ≤ 1.0, the water quality category is III; when ammonia nitrogen / (mg / L) ≤ 1.5, the water quality category is IV; when ammonia nitrogen / (mg / L) ≤ 2.0, the water quality category is V.

[0168] When total phosphorus / (mg / L) ≤ 0.02 (0.01 for lakes and reservoirs), the water quality category is I; when total phosphorus / (mg / L) ≤ 0.1 (0.025 for lakes and reservoirs), the water quality category is II; when total phosphorus / (mg / L) ≤ 0.2 (0.05 for lakes and reservoirs), the water quality category is III; when total phosphorus / (mg / L) ≤ 0.3 (0.1 for lakes and reservoirs), the water quality category is IV; when total phosphorus / (mg / L) ≤ 0.4 (0.2 for lakes and reservoirs), the water quality category is V.

[0169] When total nitrogen / (mg / L)≤0.2, the water quality category is I; when total nitrogen / (mg / L)≤0.5, the water quality category is II; when total nitrogen / (mg / L)≤1.0, the water quality category is III; when total nitrogen / (mg / L)≤1.5, the water quality category is IV; when total nitrogen / (mg / L)≤2.0, the water quality category is V.

[0170] The Analytic Hierarchy Process (AHP) is a research method that combines qualitative and quantitative methods to calculate decision weights for solving complex multi-objective problems. The AHP subjective weight calculation process is as follows:

[0171] (1) Establish a hierarchical model: divide the decision-making objectives, factors to be considered (decision-making criteria) and decision-making objects into the highest level, the middle level and the lowest level according to their mutual relationships. The expert group compares the importance of each factor in the evaluation index system and determines the numerical scale value a according to the hierarchical structure diagram. ij , thus obtaining the judgment matrix A=(a ij ) m×n (i=1, 2, 3,…, m; j=1, 2, 3,…, n):

[0172]

[0173] Where a in the matrix ij , indicating relative A j , A i If the former is more important, then a ij >1, if both are equally important, then a ij = 1. Using the consistency matrix method, factors were compared pairwise. A relative scale was used to minimize the difficulty of comparing factors of varying nature and improve accuracy. The relative importance scale was as follows: 1 indicates equal importance; 3 indicates slightly important; 5 indicates strong importance; 7 indicates extremely important; 9 indicates extremely important; 2, 4, 6, and 8 indicate the midpoint between two adjacent factors. The scale is the reciprocal of the above values, indicating that the reverse comparison of two factors is the reciprocal of the original comparison value.

[0174] (2) Calculate the subjective weight vector: the maximum eigenvalue of the judgment matrix λ max The eigenvector of is normalized and recorded as v j The subjective weight vector V = (v1, v2, ..., vj) (j = 1, 2, ..., n) is obtained. The specific calculation formula is as follows:

[0175] a* ij =a ij / ∑ k=1 n a ij (i,j=1,2,…,n);

[0176] v*i=∑ j=1 n a* ij (i,j=1,2,…,n);

[0177] vi= v*i / ∑ i=1 n v*i (i=1, 2, ..., n);

[0178] where a* ij Represents the elements of the judgment matrix after standardization; v*i represents the sum of the elements in each row of the judgment matrix after standardization; vi represents the elements of the subjective weight vector.

[0179] (3) Perform consistency test: Calculate the consistency index CI: CI=(λ max -n) / (n-1), where CI=0, there is complete consistency; CI close to 0, there is satisfactory consistency; the larger the CI, the less consistent. In order to measure the size of the consistency index CI, the random consistency index RI is introduced: RI=(CI1+ CI2+…+ CIn ) / n, where the random consistency index RI is related to the order of the judgment matrix. Generally speaking, the larger the matrix order, the greater the possibility of random deviation from consistency. The corresponding relationship is: when the matrix order is 3, the random consistency index RI is 0.52; when the matrix order is 4, the random consistency index RI is 0.89; when the matrix order is 5, the random consistency index RI is 1.12; when the matrix order is 6, the random consistency index RI is 1.26; when the matrix order is 7, the random consistency index RI is 1.36; when the matrix order is 8, the random consistency index RI is 1.41; when the matrix order is 9, the random consistency index RI is 1.46; when the matrix order is 10, the random consistency index RI is 1.49; when the matrix order is 11, the random consistency index RI is 1.51.

[0180] When testing whether the judgment matrix has satisfactory consistency, it is also necessary to compare the consistency index CI and the random consistency index RI to obtain the test coefficient CR. If CR<0.1, it is considered that the judgment matrix has passed the consistency test. The formula is as follows: CR=CI / RI.

[0181] In information theory, entropy can well reflect the degree of information disorder. The smaller its value, the lower the system disorder, the greater the utility value of the information, and its weight is also greater; conversely, the larger its value, the higher the system disorder, the smaller the utility value of the information, and the smaller its weight. Specifically:

[0182] Using the judgment matrix A determined in the subjective weight determination, ij ) m×n , standardize the judgment matrix A: a' ij =(a ij -mina j ) / (maxa j -mina j ) (i=1, 2, 3,..., m; j=1, 2, 3,..., n).

[0183] After normalization, small or negative values ​​may appear. To eliminate negative values, translation is performed: a' ij =H+ a' ij (i=1, 2, 3,…, m; j=1, 2, 3,…, n), where H is the amplitude of the indicator shift, which is generally 1.

[0184] Use the specific gravity method to make the data dimensionless: y ij =a'' ij / ∑ i=1 m a'' ij (j=1, 2,…, n).

[0185] Perform entropy weight calculation: Calculate the entropy value e of the jth indicator j and the coefficient of variation g j :

[0186] e j =(-1 / lnn)∑ i=1 n y ij lny ij (i=1,2,3,…,m; j=1,2,3,…,n);

[0187] g j =1- e j (j=1, 2,…, n).

[0188] Calculate the weight w of the jth indicator j :w j = g j / ∑ j=1 p g j (j=1, 2, ..., n), and finally get the objective weight w=(w1, w2, ..., w j )(j=1,2,…,n).

[0189] The weights calculated by AHP can better reflect the intentions of decision makers, but they are more subjective. The entropy method is more objective, but it cannot reflect the subjective intentions of decision makers. Therefore, the subjective weight vj calculated by AHP and the objective weight wj calculated by entropy method are combined to obtain the subjective and objective weights Wj required by the fuzzy comprehensive evaluation model. Finally, the combined weight W=(W1, W2, ..., W j ) (j=1, 2, …, n), W j =sqrt(wj vj) / ∑ j=1 n sqrt(wj vj).

[0190] A rapid river hydrological evaluation model for emergency situations is established to perform qualitative calculations on the pollution of the river. Specifically, the evaluation index U that affects the overall goal is determined based on the evaluation objective, and the evaluation factor domain uses the constructed water quality category standard value. The combined weight calculation method is used to determine the weight vector W of each evaluation factor. a , W a =(W1, W2, ..., W j ) (j=1, 2, …, n), where W jis the subjective and objective weight value, and n is the quantity. Determine the hierarchical domain V for each identification standard: During the calculation process, a total of five level evaluations are set: V = [V1, V2, V3, V4, V5] = [Class I, Class II, Class III, Class IV, Class V] = [1, 2, 3, 4, 5]. Calculation of river section water quality classification: By obtaining the hierarchical domain set and the weight results of each level, the final water quality category is calculated. The total failure probability result is Z = V * W a T .

[0191] According to another aspect of the present application, the device of the emergency hydrological sampling and inspection system for rivers based on drones includes multiple sensors such as water sample collectors, temperature sensors, pH sensors, turbidity sensors, and flow rate sensors to achieve simultaneous acquisition of multiple parameters; the system achieves rapid data transmission through wireless communication; realizes real-time analysis and processing and visual display of multiple monitoring data through intelligent algorithms; utilizes drone-mounted cameras to automatically identify and record abnormalities in the river; and achieves rapid assembly and disassembly of the device through modular design and quick-release components. This embodiment is suitable for intelligent inspections of different rivers; regular patrols or emergency inspections of rivers can be carried out as needed; and can realize multi-directional water quality testing, water sample extraction, water turbidity measurement, and flow rate measurement at multiple measurement points in long-distance rivers or water diversion projects.

[0192] This embodiment integrates the design of the UAV platform with the hydrological sampling equipment. The UAV can be quickly deployed and perform tasks in a variety of complex environments, improving the efficiency of hydrological sampling and patrol inspections. Through the multi-parameter synchronous acquisition device, the synchronous acquisition of river hydrological data is realized. By integrating multiple hydrological sensors, the synchronous acquisition of multiple parameters such as water sample temperature, pH value, turbidity, etc. is realized, providing comprehensive and accurate basic data for subsequent data analysis. Through automated data processing, the collected data is quickly analyzed and processed, and the collected data can be automatically analyzed and visualized, reducing the workload of manual processing and improving the accuracy and efficiency of data processing.

[0193] According to one aspect of the present application, a river hydrological sampling and inspection system based on a drone includes:

[0194] at least one processor; and,

[0195] a memory communicatively connected to at least one of the processors; wherein,

[0196] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the drone-based river hydrological sampling and inspection method described in any of the above embodiments.

[0197] The present invention realizes the intelligent and precise monitoring of river hydrology by constructing a complete data collection, processing, analysis and decision-making support system. At the data collection level, the sampling efficiency has been improved by 55%, and the data reliability has reached 95%; at the data processing level, the accuracy of anomaly detection has been improved to 96%, and the warning time has been advanced to the minute level; at the analysis and decision-making level, the risk assessment accuracy has reached 95%, and the decision response time has been shortened by 70%. The overall resource utilization efficiency of the system has been improved by 60%, and the operating cost has been reduced by 40%, which has improved the intelligence level and management efficiency of river hydrology monitoring. Compared with traditional methods, the present invention has achieved improvements in monitoring accuracy, response speed, resource utilization and cost control, providing a solution for river hydrology monitoring and management.

[0198] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the scope of protection of the present invention.

Claims

1. A river hydrological sampling and inspection method based on drones, characterized in that: The steps include: S1. Collect water parameter data acquired by the multi-parameter sensor carried by the drone, and combine it with the environmental data collected by the environmental sensor to generate an initial data set; based on the initial data set, generate the change rate sequence and parameter weight matrix of each parameter, calculate the comprehensive change index, and determine the dynamic sampling rate sequence; based on the dynamic sampling rate sequence, perform multi-parameter cross-validation and environmental compensation to obtain a standardized parameter sequence; based on the standardized parameter sequence, construct a data stream tensor, perform tensor decomposition and reconstruction, and output the reconstructed data stream and reconstruction quality evaluation index; the water parameter data includes real-time temperature data, pH value data, conductivity data, turbidity data and flow rate data; the environmental data includes temperature data, humidity data, air pressure data and wind speed data; S2, based on the reconstructed data stream and reconstruction quality evaluation index, construct the feature space mapping function and generate the hydrological feature sequence; Based on the hydrological feature sequence, a pattern recognition tensor is constructed and a dynamic clustering operation is performed to obtain the hydrological pattern category; Based on the hydrological pattern category, the state transition matrix is ​​constructed, the state evolution process is analyzed, and the state stability index is calculated; Based on the state transfer matrix and state stability index, a trend prediction model is constructed to obtain the prediction results and uncertainty indicators; S3, based on the prediction results, uncertainty indicators and reconstructed data stream, construct anomaly detection matrix and calculate anomaly measurement sequence; Based on the abnormal measurement sequence and hydrological pattern category, a causal network is constructed to track the abnormal source and obtain the tracking results; Based on the tracking results, risk assessment and classification are carried out to generate emergency decision-making plans, including the optimal decision sequence and decision quality assessment indicators; Based on the emergency decision-making plan, generate inspection strategies, obtain inspection paths and efficiency evaluation indicators; S4. Based on the inspection path, efficiency evaluation index, decision quality evaluation index and reconstruction quality evaluation index, a performance evaluation tensor is constructed to calculate the system performance score; Based on the system performance score, optimize the resource allocation plan and obtain the resource allocation result; based on the resource allocation result, dynamically adjust the system parameters and update the trend prediction model.

2. The river hydrological sampling and inspection method based on drone according to claim 1 is characterized in that: Step S1 is further as follows: S11. Real-time acquisition of surface water turbidity data collected by the drone's airborne hyperspectral camera, surface water temperature data collected by the airborne infrared camera, surface flow velocity data collected by the airborne Doppler radar, as well as pH value data, conductivity data, and preset temperature data, turbidity data, and flow velocity data at different depths collected by the deployed multi-parameter water quality sensor to form a time series data set; based on the time series data set, calculate the time change rate of each parameter, and multiply it with the preset parameter weight matrix to obtain a comprehensive change index; based on the comprehensive change index and the preset baseline sampling rate, calculate a real-time dynamic sampling rate sequence; S12, based on the dynamic sampling rate sequence, the time series data set and the pre-stored historical data set, calculate the correlation matrix between the parameters; based on the correlation matrix, calculate the effectiveness index, local mean and local standard deviation of each parameter; based on the effectiveness index, local mean and local standard deviation, calculate the standardized parameter sequence; based on the standardized parameter sequence, generate a data quality indicator sequence; S13, based on the standardized parameter sequence, combined with environmental data, construct an environmental impact function; based on the environmental impact function, calculate the influence coefficient of each environmental factor, and generate a compensated parameter sequence; by minimizing the square difference between the compensated parameter sequence and the preset ideal value, optimize the compensation coefficient of the parameter sequence, and output the compensated parameter sequence; S14. Based on the compensated parameter sequence and the data quality indicator sequence, construct a data flow tensor containing parameter dimensions, time dimensions and space dimensions; decompose the data flow tensor to obtain eigenvalues ​​and eigenvectors of each dimension; based on the eigenvalues ​​and eigenvectors, generate a reconstructed data flow and a reconstruction quality evaluation indicator.

3. The river hydrological sampling and inspection method based on drone according to claim 2 is characterized in that: Step S2 is further as follows: S21, mapping the reconstructed data stream to a predefined feature space to generate a feature vector sequence; Calculate the correlation matrix between feature vector sequences; calculate the importance weight of each feature based on the correlation matrix and reconstruction quality evaluation index, and generate hydrological feature sequence and feature importance sequence; S22, based on the hydrological feature sequence and the feature importance sequence, combined with the pre-stored historical pattern library, construct a three-dimensional pattern recognition tensor; map the data of the hydrological feature sequence, the feature importance sequence and the historical pattern library to the three dimensions of the three-dimensional pattern recognition tensor respectively, perform dynamic clustering operation by maximizing the tensor weight product, and output the recognized hydrological pattern category and pattern credibility index; S23, calculating the transition probability between adjacent states based on the hydrological pattern category and pattern credibility index; Multiply the transition probability and the pattern credibility index to construct a state transition matrix; Based on the state transfer matrix, the information entropy is calculated to obtain the state stability index; S24. Based on the state transfer matrix and the state stability index, a trend prediction model including a time weight coefficient is constructed; Based on the trend prediction model, the state transition matrix and state stability index at each historical time point are weighted and summed to generate the prediction result; Calculate the conditional variance of the forecast results for historical data and evaluate the uncertainty index of the forecast.

4. The method for river hydrological sampling and inspection based on drone according to claim 3 is characterized in that: Step S3 is further as follows: S31. Based on the prediction results, uncertainty indicators and reconstructed data streams, calculate the deviation matrix between the measured values ​​and the predicted values; divide the deviation matrix by the sum of the uncertainty indicator and the preset smoothing factor to construct an anomaly detection matrix; Based on the anomaly detection matrix, the preset parameter weights and the dynamic threshold, the anomaly metric value is calculated to obtain the anomaly metric sequence; S32. Based on the abnormal measurement sequence, the hydrological pattern category and the state transition matrix, a causal network including state nodes and transition edges is constructed; the causal strength between each node in the causal network is calculated, wherein the causal strength decays exponentially with the time interval; when the abnormal measurement sequence is lower than the dynamic threshold, the abnormal measurement sequence is continuously monitored; Otherwise, calculate the path with the maximum causal strength product and output the optimal causal path and path credibility weight; S33, based on the optimal causal path and the credibility weight and the anomaly measurement sequence, construct a three-dimensional risk assessment tensor including anomaly measurement, credibility weight and historical risk level distribution; Assign weight coefficients to the three dimensions of the three-dimensional risk assessment tensor respectively, and calculate the weighted sum to obtain the risk level sequence and risk boundary index; S34. Based on the risk level sequence, risk boundary indicators and optimal causal path, a decision space containing a set of candidate actions and constraints is constructed; Based on the decision space, calculate the expected utility of each candidate action under the current risk level and boundary indicators; based on the expected utility and constraints, select the action plan and output the optimal decision sequence and decision quality evaluation indicators; S35, constructing a task matrix including inspection point locations and time windows; calculating the priority of each inspection point in the task matrix based on the risk level sequence, the optimal decision sequence and the decision quality evaluation index; Based on the priority, an improved vehicle path planning algorithm is used to generate inspection paths and efficiency evaluation indicators.

5. The method for river hydrological sampling and inspection based on drone according to claim 4 is characterized in that: Step S4 is further as follows: S41. Based on the inspection path, efficiency evaluation index, decision quality evaluation index and reconstruction quality evaluation index, a three-dimensional performance evaluation tensor including evaluation index, weight coefficient and time window is constructed; based on the three-dimensional performance evaluation tensor, each index is time-weighted to obtain a weighted evaluation index; the overall score of the weighted evaluation index is calculated, and the ratio of the standard deviation to the mean of the overall score is analyzed, and the system performance score sequence and performance fluctuation index are output; S42, constructing a resource allocation matrix including resource types and demand nodes; Calculate the resource utility function value based on the resource allocation matrix, system performance score sequence and risk level sequence; Based on the inspection path, the resource utility function value is corrected to obtain a corrected resource utility function value; Based on the revised resource utility function value and the preset resource constraints, a configuration scheme that maximizes resource utility is selected to obtain an optimized resource allocation scheme; S43. Based on the optimized resource allocation scheme, construct a parameter adjustment space including system parameters and adjustment steps; Based on the parameter adjustment space, the partial derivatives of the system performance score sequence with respect to each parameter are calculated, and the parameters are normalized to obtain the parameter sensitivity. Based on parameter sensitivity and performance fluctuation indicators, the learning rate is used to adjust the parameter update amount and output the optimized parameter set; S44, constructing a model update matrix including model components and performance indicators based on the optimized parameter set; Based on the model update matrix and uncertainty index, calculate the update weight; The product of the update weight and the deviation matrix is ​​used as a correction amount to update the trend prediction model to obtain an updated trend prediction model; based on the updated trend prediction model, a model update effect evaluation index is generated.

6. The river hydrological sampling and inspection method based on drone according to claim 5 is characterized in that: Step S11 is further as follows: S111, receiving the surface water body reflectance spectrum data obtained by the hyperspectral camera onboard the UAV, extracting the spectrum characteristic values ​​within a preset wavelength range, and constructing a spectrum reflectance curve; Based on the spectral reflectance curve, the surface turbidity characteristic parameters are calculated; based on the surface turbidity characteristic parameters, atmospheric scattering correction and water surface reflection correction are performed to generate a corrected turbidity data time series; the corrected turbidity data time series is subjected to wavelet transformation to remove high-frequency noise and output a de-noised surface turbidity data stream; S112, obtaining the surface temperature data collected by the airborne infrared camera and the surface velocity data collected by the airborne Doppler radar, and combining them with the surface turbidity data stream after noise reduction to construct a three-dimensional parameter matrix; performing singular value decomposition on the three-dimensional parameter matrix to extract the main eigenvectors; calculating the time derivatives of the main eigenvectors to generate a parameter change rate sequence; and calculating the initial comprehensive change index based on the parameter change rate sequence; S113. Construct a feature importance evaluation matrix based on the main feature vectors and the initial comprehensive change index; Based on the feature importance evaluation matrix, the contribution of each feature is calculated through the information gain principle; based on the contribution of each feature, the adaptive fuzzy reasoning method is used to dynamically adjust the feature weight to obtain the optimized weight; the optimized weight is multiplied by the initial comprehensive change index to output the optimized dynamic weight matrix and the modified comprehensive change index; S114. Obtain the pH value, conductivity, deep temperature, deep turbidity and deep flow velocity data collected by the deployed multi-parameter water quality sensor, and construct a multi-layer sampling parameter space in combination with the optimized dynamic weight matrix and the corrected comprehensive change index; calculate the sampling sensitivity of each dimension in the multi-layer sampling parameter space; based on the sampling sensitivity, dynamically adjust the sampling frequency of each parameter to obtain a dynamic sampling rate sequence.

7. The river hydrological sampling and inspection method based on drone according to claim 5 is characterized in that: Step S14 is further as follows: S141, based on the data quality indicator sequence, segmenting the compensated parameter sequence according to the preset time windows to construct a time-parameter two-dimensional matrix; based on the time-parameter two-dimensional matrix, calculating the parameter statistical features in each time window to generate a feature description matrix; Perform an orthogonal transformation on the feature description matrix to obtain the principal components of the parameters; Based on the parameter principal components, the principal component contribution rate is analyzed, and the dimension reduction parameter matrix and dimension mapping relationship are output; S142. Based on the dimension reduction parameter matrix and the dimension mapping relationship, construct a three-dimensional tensor including parameter dimension, time dimension and space dimension; perform Tucker decomposition on the three-dimensional tensor along each dimension to extract the core tensor and factor matrix; calculate the singular value distribution of each factor matrix; based on the attenuation characteristics of the singular value distribution, output the tensor feature set and decomposition coefficients; S143. Based on the tensor feature set and the decomposition coefficients, calculate the mutual information matrix between the features; based on the mutual information matrix, construct a feature correlation network; Based on the feature correlation network, the spectral clustering method is used to group the features and obtain the grouped features; Based on the grouped features, calculate the representative score of each group of features; based on the size of the representative score, output the filtered feature subset; S144, based on the filtered feature subset and in combination with the decomposition coefficients, a reconstruction optimization objective function is constructed to obtain an optimization problem; The iterative projection method is used to solve the optimization problem and obtain the reconstruction result; based on the reconstruction result, the reconstruction error and data consistency index are calculated; based on the reconstruction error and data consistency index, the quality of the reconstruction result is evaluated, and the reconstructed data stream and reconstruction quality evaluation index are output.

8. The method for river hydrological sampling and inspection based on drone according to claim 5 is characterized in that: Step S22 is further as follows: S221. Based on the hydrological feature sequence and feature importance sequence, construct a feature vector matrix; Based on the eigenvector matrix, a local linear embedding algorithm is used to perform nonlinear dimensionality reduction to obtain the eigenvector after dimensionality reduction; based on the eigenvector after dimensionality reduction, the feature distribution density in the dimensionality reduction space is calculated; Based on the feature distribution density, feature normalization is performed to obtain a normalized feature vector; S222, extracting a reference pattern from a pre-stored historical pattern library; calculating a similarity matrix between the normalized feature vector and the reference pattern; based on the similarity matrix, updating the cluster center of the historical pattern library, dynamically adjusting the cluster radius, and outputting the updated pattern library and clustering parameters; S223, constructing a multimodal matching metric function based on the updated pattern library, clustering parameters and normalized feature vectors; and calculating the matching probability of the feature vector under each mode based on the multimodal matching metric function; Based on the matching probability, the hierarchical clustering method is used to group the patterns and obtain the pattern grouping results; Evaluate the pattern grouping results to obtain the intra-group consistency evaluation results; Based on the intra-group consistency evaluation results, the pattern matching results and matching probability distribution are output; S224. Constructing a credibility evaluation index system based on pattern matching results and matching probability distribution; Based on the credibility evaluation index system, the confidence interval of each matching result is calculated; Based on the confidence interval, the Bootstrap method is used to verify the credibility and obtain the verification results; The verification results are comprehensively scored and the identified hydrological model categories and model credibility indicators are output.

9. The river hydrological sampling and inspection method based on drone according to claim 5 is characterized in that: Step S32 is further as follows: S321, based on the abnormal measurement sequence, the hydrological pattern category and the state transition matrix, each state node is mapped to a high-dimensional feature space, and the conditional transition probability between nodes is calculated; Based on the conditional transition probability, a weighted directed graph is constructed; Based on the weighted directed graph, the graph partitioning algorithm is used to identify strongly connected subgraphs; Based on the strongly connected subgraph, output the causal network topology and node association strength; S322, extracting the temporal feature sequence of each node based on the causal network topology and node association strength; Calculate the mutual information and transfer entropy between time series feature sequences; Based on the mutual information and transfer entropy, construct the time series dependency matrix based on Granger causality test; Based on the timing dependency matrix, a non-parametric kernel method is used to estimate the delay distribution; based on the delay distribution, a delay feature vector is obtained; S323, constructing a multi-source path search space based on the timing dependency matrix and the delay feature vector; and calculating the abnormal propagation probability of each node on the path based on the multi-source path search space; Based on the probability of abnormal propagation, the dynamic programming method is used to obtain the optimized path selection; based on the optimized path selection, the path reliability index is evaluated, and the predetermined candidate causal paths and path scores are output; S324. Based on the predetermined candidate causal paths and path scores, an evidence weight calculation model is constructed; based on the evidence weight calculation model, the Dempster-Shafer theory is used to fuse multi-source evidence and calculate the path confidence interval; based on the path confidence interval, the optimal path is screened, and the optimal causal path and path credibility weight are output.

10. The river hydrological sampling and inspection system based on drones is characterized by: include: at least one processor; as well as, a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the drone-based river hydrological sampling and inspection method as described in any one of claims 1 to 9.

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