Automated monitoring method and system for ecological water conservancy projects

Abstract

The present application relates to the technical field of water conservancy project monitoring and control, and discloses an automated monitoring method and system for ecological water conservancy projects. The method comprises: collecting water parameters through sensors and preprocessing to obtain standard monitoring data; performing noise reduction alignment and correlation analysis on the monitoring data to obtain an optimized data set and feature library; evaluating the water environment status based on the data set and feature library to obtain an evaluation index and early warning signal; analyzing the treatment plan based on the index and signal to obtain control instructions; adjusting the equipment operating parameters according to the instructions to obtain operating status data; performing periodic analysis and strategy optimization based on the status data to obtain control parameters. The present application realizes multi-dimensional correlation analysis of water quality parameters, and on this basis formulates an accurate water environment control plan to improve the pertinence and efficiency of water quality improvement.

Description

Technical Field

[0001] The present application relates to the technical field of water conservancy project monitoring and control, and in particular to an automated monitoring method and system for ecological water conservancy projects. Background Art

[0002] Existing monitoring technologies for ecological water conservancy projects primarily rely on a combination of single-point water quality monitoring and manual inspections for water environment management. Traditional monitoring methods rely on deploying water quality monitoring stations at key river sections to collect basic water quality parameters such as dissolved oxygen, turbidity, and pH, while also relying on regular manual sampling for water quality analysis. Water environment regulation primarily relies on empirical judgment, improving water quality through methods such as adjusting gate openings and activating aeration systems. Some projects are beginning to utilize automated monitoring equipment, enabling real-time collection of water quality data and establishing simple water quality early warning mechanisms.

[0003] However, existing technologies have the following shortcomings: the collection and processing of monitoring data lacks systematicity, resulting in uneven data quality; the correlation analysis between water quality parameters is not in-depth enough, making it difficult to accurately determine the root causes of water quality problems; the formulation of control plans relies too much on experience and lacks scientific quantitative analysis and optimization processes; the improvement process of different water quality indicators lacks synergistic considerations, often neglecting one while focusing on another; the water quality improvement effect evaluation method is single and cannot accurately reflect the overall response characteristics of the ecosystem; the optimization of the operating parameters of the control equipment is not precise enough, resulting in energy waste and low processing efficiency. Summary of the Invention

[0004] This application provides an automated monitoring method and system for ecological water conservancy projects, which is used to realize multi-dimensional correlation analysis of water quality parameters, and on this basis, formulate accurate water environment control plans to improve the pertinence and efficiency of water quality improvement.

[0005] In the first aspect, the present application provides an automated monitoring method for ecological water conservancy projects, which includes: collecting dissolved oxygen, turbidity, water level flow and pump station gate operating status of water bodies through monitoring sensors, and obtaining standardized monitoring data of water conservancy projects through data preprocessing; performing water quality parameter noise reduction and hydrological data spatiotemporal alignment based on the standardized monitoring data of water conservancy projects, analyzing the correlation of ecological water system parameters, and obtaining a water ecological optimization data set and a water quality anomaly feature library; based on the water ecological optimization data set and the water quality anomaly feature library, establishing an ecological water system health assessment system to perform water environment dynamic analysis, and obtain a water conservancy project ecological assessment index and water quality early warning signal; based on the water conservancy project ecological assessment index and water quality early warning signal, constructing a water environment control model to analyze water system management plans and obtain river and lake water system zoning control instructions; based on the river and lake water system zoning control instructions, adjusting the operating parameters and analyzing the working conditions of hydraulic regulating devices and water purification equipment to obtain project operation status data; based on the project operation status data, obtaining water conservancy project control parameters through water ecological recovery cycle analysis and water quality improvement strategy optimization.

[0006] In a second aspect, the present application provides an automated monitoring system for an ecological water conservancy project, the automated monitoring system for an ecological water conservancy project comprising:

[0007] The acquisition module is used to collect dissolved oxygen, turbidity, water level flow and pump station gate operation status through monitoring sensors, and obtain standardized monitoring data of water conservancy projects through data preprocessing;

[0008] An alignment module is used to perform water quality parameter noise reduction and spatiotemporal alignment of hydrological data based on the standardized monitoring data of the water conservancy project, analyze the correlation between ecological water system parameters, and obtain a water ecological optimization data set and a water quality anomaly feature library;

[0009] An analysis module is used to establish an ecological water system health assessment system based on the water ecological optimization data set and the water quality abnormality feature library to conduct dynamic analysis of the water environment and obtain an ecological assessment index for water conservancy projects and a water quality early warning signal;

[0010] An early warning module is used to construct a water environment control model based on the water conservancy project ecological assessment index and water quality early warning signals to analyze water system management plans and obtain river and lake water system zoning control instructions;

[0011] An adjustment module is used to adjust the operating parameters and analyze the working conditions of the hydraulic regulating device and water purification equipment according to the river and lake water system zoning control instructions to obtain project operation status data;

[0012] The optimization module is used to obtain the water conservancy project control parameters through water ecological restoration cycle analysis and water quality improvement strategy optimization based on the project operation status data.

[0013] In the technical solution provided by this application, the problem of unstable data quality in traditional monitoring methods is effectively solved by adopting layered data collection and multi-dimensional data processing technology, and comprehensive monitoring of dissolved oxygen, turbidity, water level flow and pump station gate operation status of water bodies is realized. The standardized monitoring data of water conservancy projects obtained after data preprocessing provides a reliable data basis for subsequent analysis, and the noise reduction of water quality parameters and spatiotemporal alignment of hydrological data are effectively eliminated. Combined with the correlation analysis of ecological water system parameters, the intrinsic connection between water quality indicators is deeply explored. The formed water ecological optimization data set and water quality anomaly feature library provide an important basis for the diagnosis of water quality problems. At the same time, based on the water ecological optimization data set and water quality anomaly feature library The ecological water system health assessment system established by the database, through the water environment dynamic analysis to obtain the water project ecological assessment index and water quality early warning signal, realizes the early identification and early warning of water quality problems, and then constructs the water environment regulation model based on the assessment index and early warning signal. The river and lake water system zoning regulation instructions generated by the analysis of the water system management plan realize the precise implementation of the regulation measures. The operation parameter adjustment and working condition analysis of the hydraulic regulating device and water purification equipment ensure the effective implementation of the regulation measures. The water conservancy project regulation parameters obtained through the water ecological restoration cycle analysis and water quality improvement strategy optimization have established a complete closed-loop management system of water environment monitoring-analysis-regulation-optimization, which has significantly improved the intelligent management level and water quality improvement effect of ecological water conservancy projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A schematic diagram of an embodiment of an automated monitoring method for an ecological water conservancy project in an embodiment of the present application;

[0016] Figure 2 Schematic diagram of turbidity value measurement and principle in the examples of this application;

[0017] Figure 3 This is an introduction diagram of the grouping indicators in the embodiment of the present application;

[0018] Figure 4 This is a schematic diagram of an embodiment of an automated monitoring system for ecological water conservancy projects in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The embodiments of the present application provide an automated monitoring method and system for ecological water conservancy projects. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the automated monitoring method for ecological water conservancy projects includes:

[0021] Step S101: Collecting dissolved oxygen, turbidity, water level flow, and pump station gate operation status in water bodies through monitoring sensors, and obtaining standardized monitoring data of water conservancy projects through data preprocessing;

[0022] Step S102: Denoise water quality parameters and align hydrological data in time and space based on standardized monitoring data of water conservancy projects, analyze the correlation between ecological water system parameters, and obtain a water ecological optimization data set and a water quality anomaly feature library;

[0023] Step S103: Based on the water ecological optimization data set and the water quality abnormality feature library, an ecological water system health assessment system is established to perform dynamic analysis of the water environment, and obtain a water conservancy project ecological assessment index and a water quality early warning signal;

[0024] Step S104: Based on the water conservancy project ecological assessment index and water quality early warning signals, a water environment control model is constructed to analyze water system management plans and obtain river and lake water system zoning control instructions;

[0025] Step S105: According to the river and lake water system zoning control instructions, the operating parameters of the hydraulic regulating device and water purification equipment are adjusted and the working condition analysis is performed to obtain the project operation status data;

[0026] Step S106: According to the project operation status data, water ecological restoration cycle analysis and water quality improvement strategy optimization are performed to obtain water conservancy project control parameters.

[0027] It is understandable that the execution subject of this application can be an automated monitoring system for ecological water conservancy projects, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0028] Specifically, data collection is performed by deploying multiple types of monitoring sensors. Dissolved oxygen monitoring uses an optical dissolved oxygen sensor, which measures dissolved oxygen content based on the principle of fluorescence quenching. Turbidity monitoring uses a scattered light turbidimeter, which measures turbidity based on the intensity of scattered light at 90 degrees. Water level and flow monitoring uses ultrasonic water level meters and electromagnetic flowmeters to obtain cross-sectional water level and flow data, respectively. Pump station gate operation status monitoring uses opening sensors and speed sensors to collect operating parameters. The collected raw data undergoes standardized preprocessing, including unit unification, range normalization, and outlier removal, before being converted into a standard data format to generate standardized monitoring data for water conservancy projects.

[0029] In further processing of standardized monitoring data, water quality parameter noise reduction uses wavelet transforms to perform multi-scale decomposition on water quality data such as dissolved oxygen and turbidity, removing high-frequency noise. Spatiotemporal alignment of hydrological data uses spatial interpolation functions based on the spatial distribution of monitoring points to align data from different sampling points and times. Parameter correlation analysis uses the Pearson correlation coefficient to determine the degree of mutual influence between monitoring indicators and construct a parameter correlation matrix. Cluster analysis combines data with similar variation characteristics to form a water ecological optimization dataset. Anomalous data features are also extracted to establish a water quality anomaly signature database. During the ecological water system health assessment phase, an evaluation indicator system is established based on the water ecological optimization dataset, encompassing three levels: water quality, hydrological, and ecological. The analytic hierarchy process (AHP) is used to determine the weights of each indicator. During the dynamic analysis process, each indicator is calculated in real time to generate a water conservancy project ecological assessment index. This is combined with characteristic patterns in the water quality anomaly signature database to identify potential water quality anomalies and generate water quality warning signals.

[0030] The construction of the water environment regulation model emphasizes the holistic and systematic nature of the water system, dividing the river and lake system into multiple control units and analyzing the hydraulic connections and water quality impacts between these units. Based on inputs from ecological assessment indices and early warning signals, the required regulation volume for each control unit is calculated, taking into account upstream and downstream relationships and water quality targets, and generating zoning regulation instructions for the river and lake system. During the equipment regulation execution phase, these zoning regulation instructions are converted into specific equipment operating parameters. Hydraulic regulation devices primarily include hydraulic structures such as pumping stations and gates, which regulate water flow by adjusting parameters such as opening and flow rate. Water purification equipment, including aeration equipment and ecological floating beds, adjusts operating parameters to improve water quality. Simultaneously, equipment operating data is collected and analyzed to generate project operational status data.

[0031] The final phase focused on the effectiveness of water ecological restoration and analyzed the temporal response characteristics of water quality improvement. By statistically analyzing the water quality recovery cycles under different control measures, a corresponding relationship between control measures and water quality improvement outcomes was established. Based on actual operational data, control strategies were optimized to determine the optimal control parameters for the water conservancy project.

[0032] For example, when eutrophication occurs in a certain river section, monitoring data indicates that dissolved oxygen levels remain consistently below 4 mg / L, and turbidity increases. Data processing and analysis reveal that this phenomenon is closely related to changes in upstream water quality and gate operation. The ecological assessment system identifies a water quality warning and immediately initiates a regulatory response. Based on the zone-specific control instructions, the upstream gate discharge is increased, and aeration equipment is activated for oxygen replenishment. The dissolved oxygen recovery process is continuously monitored, and the aeration intensity and duration are dynamically adjusted based on the recovery rate over time until water quality indicators return to normal.

[0033] In the embodiment of the present application, by adopting layered data collection and multi-dimensional data processing technology, the problem of unstable data quality in traditional monitoring methods is effectively solved, and comprehensive monitoring of dissolved oxygen, turbidity, water level flow and pump station gate operation status of water bodies is achieved. The standardized monitoring data of water conservancy projects obtained after data preprocessing provides a reliable data basis for subsequent analysis, and by denoising water quality parameters and aligning hydrological data in time and space, the influence of data noise and time and space differences is effectively eliminated. Combined with the correlation analysis of ecological water system parameters, the intrinsic connection between water quality indicators is deeply explored, and the formed water ecological optimization data set and water quality anomaly feature library provide important basis for the diagnosis of water quality problems. At the same time, based on the water ecological optimization data set and water quality anomaly feature library, a reliable data base is established. An established ecological water system health assessment system was established. The ecological assessment index and water quality early warning signal of the water conservancy project obtained through dynamic analysis of the water environment realized the early identification and early warning of water quality problems. Then, a water environment regulation model was constructed based on the assessment index and early warning signal. The river and lake water system zoning regulation instructions generated through analysis of the water system management plan realized the precise implementation of regulation measures. The operating parameter adjustment and working condition analysis of the hydraulic regulating device and water purification equipment ensured the effective implementation of the regulation measures. The water conservancy project regulation parameters obtained through water ecological restoration cycle analysis and water quality improvement strategy optimization established a complete closed-loop management system of water environment monitoring-analysis-regulation-optimization, which significantly improved the intelligent management level and water quality improvement effect of ecological water conservancy projects.

[0034] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0035] (1) Collect dissolved oxygen data in water using a water sensor, group the dissolved oxygen data according to the sampling time, perform signal denoising on each group of data, calculate the average value according to the time window, and then perform value range verification, remove data points that exceed the range, and obtain standardized dissolved oxygen data;

[0036] (2) The water turbidity is collected by optical sensors, the original turbidity data is linearly calibrated, the data is compensated according to the ambient light intensity, interference fluctuations are removed by signal filtering, and the processed data is rearranged according to the time series to obtain standardized turbidity data;

[0037] (3) Pair the water level data collected by the water level station with the flow data collected by the flow station according to the sampling time, fill in the missing data points through linear interpolation, perform data verification according to the water level-flow relationship curve, eliminate outliers that do not conform to the relationship curve, and obtain standardized water level and flow data;

[0038] (4) Collect opening data and speed data from the control system of the pump station gate, convert the opening data into a percentage value, normalize the speed data, verify the validity according to the upper and lower limits of the equipment operating parameters, filter out the data that meets the operating specifications, and obtain standardized operating status data;

[0039] (5) Import the standardized dissolved oxygen data, standardized turbidity data, standardized water level flow data, and standardized operating status data into the time series database, align them according to a unified timestamp, downsample or interpolate the data at different sampling frequencies, normalize the data after unifying the time resolution, and obtain time series correlation data;

[0040] (6) Perform data integrity check on time series correlation data, calculate the correlation coefficient of each parameter, identify outliers based on the distribution characteristics of historical data, integrate all processed data into a unified data structure, and obtain standardized monitoring data for water conservancy projects.

[0041] Specifically, for the collection of dissolved oxygen data in water bodies, an optical dissolved oxygen sensor is used to measure the dissolved oxygen content based on the principle of fluorescence quenching. The sensor collects data every 10 minutes, and the data within 24 hours is grouped per hour. Each set of data is first denoised by wavelet transform, the original signal is decomposed into wavelet coefficients of different scales, a threshold is set to remove high-frequency noise, and then the signal is reconstructed to obtain smoothed data. The arithmetic mean of the reconstructed data is calculated within each hourly time window as the representative value of the period. The data is then verified for the range of measurement. The normal value range of dissolved oxygen is 0-20 mg / L. Data points outside this range are marked as abnormal and eliminated to obtain a standardized dissolved oxygen data sequence.

[0042] The water turbidity data is collected using a scattered light turbidimeter, which measures the turbidity value through 90-degree scattered light. Figure 2 Figure 2 shows a schematic diagram of the turbidity measurement and principle used in an embodiment of the present application. The light source (black rectangle on the left) emits a steady beam of light, typically an LED or tungsten filament lamp, with a wavelength within the visible light range. This beam of light is directed into the water sample in a straight line. The water sample container (center rectangle) is a transparent container used to hold the water sample to be tested. The black dots within the container represent suspended particles in the water, which may be silt, microorganisms, or other insoluble matter. The scattered light path (dashed lines) shows the horizontal dashed line representing the main path of the incident light, and the upward vertical dashed line representing the 90-degree scattered light path. When incident light strikes suspended particles in the water, some of the light is scattered. The 90-degree scattered light best reflects the water turbidity. The detector (top rectangle) receives the 90-degree scattered light and converts it into an electrical signal. Stronger scattered light indicates more suspended matter in the water and higher turbidity. The ambient light sensor (circle in the upper right corner) monitors ambient light intensity to eliminate interference from external light on the measurement results. Water turbidity is determined by measuring the 90-degree scattered light intensity of a water sample. Raw turbidity data is first linearly calibrated, converting scattered light intensity into turbidity values ​​according to the instrument calibration curve. To account for the impact of ambient light on measurement results, a built-in light intensity sensor measures ambient light intensity and a compensation function is developed to correct the measured values. The corrected data is filtered through a Butterworth low-pass filter with a cutoff frequency set to 1 / 10 of the sampling frequency to remove high-frequency interference fluctuations. The filtered data is sorted in time series to form a standardized turbidity data sequence. Water level and flow data are collected from ultrasonic level meters at the water level station and electromagnetic flow meters at the flow station. Data from the two stations are paired according to timestamps. Missing data points due to equipment failure or communication interruptions are supplemented using piecewise linear interpolation. Interpolation is based on adjacent valid data points to maintain data continuity and smoothness. After interpolation, the data is verified using an established water level-flow relationship curve, which is fitted based on long-term observation data. The measured data points are compared with the relationship curve, and a deviation threshold is set. Data points exceeding the threshold are judged as outliers and eliminated to obtain standardized water level and flow data.

[0043] Pump station gate operating status data collection includes opening and speed data. Opening data is collected using displacement sensors, and the physical displacement is converted into a percentage of opening. Fully open gates correspond to 100%, and fully closed gates correspond to 0%. Speed ​​data is collected using speed sensors, and the speed ranges of different equipment models are normalized to the 0-1 range. The converted data is validated according to the equipment operating specifications, and data points that do not meet the upper and lower limits of the equipment parameters are eliminated to obtain standardized operating status data. When importing these four types of standardized data into a time series database, it is necessary to address the issue of inconsistent sampling frequencies for different data types. The sampling interval for dissolved oxygen and turbidity data is 10 minutes, for water level and flow data, 5 minutes, and for equipment operating status data, 1 minute. All data is aligned to a unified timestamp, using the greatest common denominator principle, with a base interval of 10 minutes. Data with a sampling frequency higher than 10 minutes is downsampled using a moving average to reduce the sampling rate. Data with a sampling frequency lower than 10 minutes is increased through linear interpolation to increase the sampling points. After unifying the time resolution, all types of data are normalized, and data of different dimensions are uniformly mapped to standard intervals to form time series correlation data.

[0044] Finally, the integrity of the time series correlation data was checked, and the Pearson correlation coefficient was calculated to assess the degree of correlation between parameters. Based on historical data, the distribution characteristics of each parameter were established, including statistical features such as mean and standard deviation, and outliers were identified using the 3σ criterion. All processed data was integrated into a unified data structure, including fields such as parameter identifiers, timestamps, values, and quality tags, to form standardized monitoring data for water conservancy projects.

[0045] For example, in an actual ecological water conservancy project, a monitoring system was installed in a certain river section. The raw data from the dissolved oxygen sensor showed large fluctuations, so wavelet denoising was used to smooth out the unreasonable jumps. Turbidity data exhibited deviations during periods of direct sunlight, so light intensity compensation was used to correct the measurement errors. Water level and flow data were missing during equipment maintenance, so interpolation algorithms were used to fill in the gaps. Pump station gate operation data showed some exceeding limit values, so parameter validation was used to screen out abnormal operating conditions. After standardization, these data types were aligned in time series to establish a monitoring dataset.

[0046] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0047] (1) The water quality parameters in the standardized monitoring data of water conservancy projects are divided into different zones according to the sampling locations. The signal features are extracted by wavelet decomposition. The coefficients of each frequency band after decomposition are thresholded and the signal is reconstructed to obtain the water quality data after noise reduction.

[0048] (2) Sort the denoised water quality data by timestamp, calculate the distance matrix between sampling points based on the distribution characteristics of the spatial sampling points, generate spatiotemporal registration data points through interpolation calculation, and obtain spatiotemporal aligned hydrological data;

[0049] (3) Group and count the spatiotemporally aligned hydrological data, calculate the correlation coefficient matrix between different water quality parameters, establish the influence weight relationship between the parameters, and obtain the correlation data of water system parameters;

[0050] (4) Divide the water system parameter correlation data into levels according to the correlation strength, extract the parameter combination characteristics under each level, construct the numerical distribution characteristics of the parameter combination, and obtain the initial water ecological data;

[0051] (5) Establish a parameter change trend diagram based on the initial water ecological data, analyze the parameter change rules, extract key inflection points and mutation characteristics, and obtain a water quality abnormality feature library through data clustering;

[0052] (6) The water quality anomaly feature library is classified and optimized and reorganized based on the initial water ecological data. The parameter groups are sorted according to the water quality categories to obtain the water ecological optimization data set and the water quality anomaly feature library.

[0053] Specifically, the sampling locations are zoned, and monitoring points are divided into different water body types, such as river sections and lake areas. The water quality data for each zone is subjected to wavelet decomposition, using the db4 wavelet function to decompose the signal into four scales, obtaining approximate coefficients and detail coefficients. The detail coefficients are denoised using a soft thresholding method, with the threshold value determined based on the standard deviation of the noise at each scale, preserving the main signal features while removing high-frequency noise. The signal is reconstructed using an inverse wavelet transform to obtain de-noised water quality data. The de-noised water quality data is timestamped and arranged in chronological order according to sampling time. Based on the geographic coordinates of the monitoring points, the Euclidean distance between each sampling point is calculated to construct a distance matrix. For areas with uneven spatial distribution, inverse distance weighted interpolation is used to generate virtual sampling points to fill in monitoring blind spots. Data at different locations and times are aligned using a spatiotemporal interpolation algorithm to establish a unified spatiotemporal reference system, resulting in spatiotemporally aligned hydrological data.

[0054] The spatiotemporally aligned hydrological data are grouped according to the monitoring indicator type, with each group containing parameters such as dissolved oxygen, turbidity, and water level flow. The correlation coefficient matrix R between the parameters is calculated using the following formula:

[0055]

[0056] in, represents the correlation coefficient between the i-th water quality parameter and the j-th hydrological parameter, represents the observed value of the i-th water quality parameter at the k-th moment, represents the observed value of the jth hydrological parameter at the kth moment, and They represent the mean values ​​of the corresponding parameters, and n represents the number of samples.

[0057] Based on the correlation coefficient matrix, the parameter influence weight relationship is constructed. The entropy method is used to calculate the weight, and the weight of each parameter is determined by calculating the information entropy. The smaller the information entropy, the more effective information the parameter contains, and the larger the corresponding weight. The obtained water system parameter correlation data contains the quantitative relationship between the parameters. The water system parameter correlation data is divided into three levels according to the strength of the correlation, and three levels are set: high correlation, moderate correlation, and weak correlation. The parameter combination characteristics are extracted in each level, including the linear relationship between the parameters, the periodic change characteristics, and the mutation characteristics. The numerical distribution characteristics of the parameter combination are established, including statistical quantities such as mean, variance, skewness, and kurtosis, to form the initial water ecological data.

[0058] Based on the initial water ecological data, parameter trends were analyzed, time series curves were plotted, and key inflection points and mutation characteristics were identified. Anomaly patterns were classified using the K-means clustering algorithm, with the number of cluster centers adaptively determined based on data characteristics. The clustering results were organized into a water quality anomaly feature library. Each anomaly feature type included a description, triggering conditions, and typical cases. The features in the water quality anomaly feature library were further categorized and grouped according to water quality categories (such as eutrophication, hypoxia, and pollution). Combined with the parameter combination characteristics in the initial water ecological data, the anomaly features were optimized and reorganized, and a mapping relationship between the feature parameters and water quality issues was established. Parameter groups were organized according to water quality categories to obtain the water ecological optimization dataset and the water quality anomaly feature library.

[0059] For example, a river section had multiple water quality monitoring points. The raw data showed significant fluctuations in dissolved oxygen and turbidity. Wavelet noise reduction was used to remove noise caused by factors such as equipment jitter and electromagnetic interference. Taking into account the uneven distribution of monitoring points, spatial interpolation was used to supplement data from blind spots. Correlation analysis revealed that the dissolved oxygen content in this river section was significantly negatively correlated with water temperature and turbidity, and positively correlated with flow rate. Based on this, an influencing weight relationship between water quality parameters was established. During continuous monitoring, when a rapid decrease in dissolved oxygen content was accompanied by an increase in turbidity, typical water pollution characteristics were identified based on the anomaly feature library, and appropriate regulatory measures were promptly initiated, establishing correlations between water quality parameters.

[0060] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0061] (1) The water ecological optimization dataset is stratified according to water quality indicators, and the evaluation coefficient of each indicator is determined by weight calculation. The initial health score is obtained through stratified weighted calculation;

[0062] (2) Extract key parameter nodes based on the water quality anomaly feature library, analyze the fluctuation range by comparing with historical data, and obtain the critical value of parameter changes by time period;

[0063] (3) Water environment quality is graded based on the initial health score, and the warning level interval is divided based on the critical value of parameter changes, and water quality status assessment data is output;

[0064] (4) Based on the key indicators in the water quality status assessment data, trend prediction and dynamic compensation are carried out in sequence to generate a water environment quality change curve;

[0065] (5) According to the trend characteristics of the water environment quality change curve, abnormal change points are extracted in sections, and the warning trigger conditions are output after data verification;

[0066] (6) Conduct graded assessments of the early warning triggering conditions and conduct comprehensive analysis based on the water quality status assessment data to obtain the water conservancy project ecological assessment index and water quality early warning signal.

[0067] Specifically, during the water environment health assessment phase, the water ecological optimization dataset is stratified into physical, chemical, and biological indicator layers based on the nature of water quality indicators. The physical indicator layer includes parameters such as water temperature, turbidity, and conductivity; the chemical indicator layer includes parameters such as dissolved oxygen, pH, and ammonia nitrogen; and the biological indicator layer includes parameters such as chlorophyll a and plankton density. The weights of each indicator are calculated using the Analytic Hierarchy Process (AHP) to establish an assessment matrix, with weight coefficients determined based on the indicator's impact on water environment health. Each indicator layer is normalized to bring the different dimensions of the indicators to a comparable scale. A stratified weighted calculation is then performed to generate a health score for each layer, which is then combined to form an initial health score. Key parameter nodes are extracted from the water quality anomaly feature library, focusing on characteristic changes in sensitive indicators such as dissolved oxygen and turbidity. Historical data for each key parameter is categorized according to factors such as season and hydrological conditions, and their fluctuation ranges are analyzed. A sliding time window method is used to analyze the time series characteristics of the parameters, calculating the rate and magnitude of change at different time scales. The upper and lower thresholds of parameter changes are determined through statistical analysis, and a critical value system for parameter changes is established in combination with water quality standard requirements.

[0068] Water environment quality is graded based on the initial health score, with five levels: excellent, good, fair, poor, and poor. A correspondence between scores and levels is established. Based on parameter change thresholds, each level is further subdivided into warning intervals: safe, caution, warning, and dangerous. The specific warning interval for the water environment is determined by comprehensively considering the current values, changing trends, and critical values ​​of water quality indicators. Water quality status assessment data, including the status of each indicator, is then output. Trend prediction and dynamic compensation are performed on key indicators in the water quality status assessment data. Trend prediction utilizes time series analysis, taking into account the seasonal, cyclical, and trend characteristics of the data to establish a prediction model. A dynamic compensation mechanism uses real-time monitoring data to correct prediction results, adjust prediction parameters, and improve prediction accuracy. The prediction results are combined with measured data to generate a trend curve reflecting the dynamic changes in water environment quality.

[0069] In the water environment quality change curve, a piecewise linear fitting method is used to identify abnormal change points, focusing on nodes where the slope of the curve changes significantly. The identified abnormal points are data-checked and their validity is confirmed through multiple verifications. Different levels of warning conditions are set according to the degree and duration of the abnormal changes, forming a set of warning trigger rules. The warning trigger conditions are graded and determined, with level one, level two, and level three warnings. Level one warning corresponds to slight exceedances or short-term abnormalities in water quality indicators, level two warning corresponds to significant exceedances or persistent abnormalities in indicators, and level three warning corresponds to serious exceedances or sudden abnormalities. Combined with the status of multiple indicators in the water quality status assessment data, a comprehensive assessment is conducted to determine the warning level. The health score and warning level are normalized to obtain a standardized water conservancy project ecological assessment index, and the corresponding water quality warning signal is generated at the same time.

[0070] In a practical application, a river section experienced changes in water quality due to upstream water quality. Monitoring data was evaluated in a tiered manner, with chemical indicators such as dissolved oxygen and pH given higher weights. This weighted calculation resulted in an initial health score. Analysis of historical data revealed that water quality issues often occur when dissolved oxygen concentrations fall below 4 mg / L, leading to the establishment of a critical dissolved oxygen value. Combining the health score and critical dissolved oxygen value, the water quality of this river section was classified as "poor," placing it in the warning zone. Trend prediction revealed a continuous downward trend in dissolved oxygen concentrations, predicted to drop even lower within 24 hours. A rapid drop in dissolved oxygen concentration was identified in the water quality curve, triggering a Level 2 warning. Comprehensive analysis indicated moderate water pollution in this river section, generating a corresponding warning signal.

[0071] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0072] (1) Extract the water quality category judgment value from the water conservancy project ecological assessment index, perform difference calculation based on the regional water quality standards, and output the water quality improvement target value;

[0073] (2) Based on the water quality warning signal, the warning level range is divided, the impact propagation path is determined according to the water system connectivity, and the regional warning response sequence is generated;

[0074] (3) Conduct upstream and downstream correlation analysis of the water quality improvement target value, determine the water quality control amount according to the flow relationship, and output water allocation parameters;

[0075] (4) Match and verify the regional early warning response sequence with the water allocation parameters, and calculate the water quality control target sequence by block;

[0076] (5) Divide the control units according to the water quality control target sequence, calculate the control quantity allocation ratio based on the water system connectivity, and generate a regional control plan;

[0077] (6) Decompose the regional control plan according to the control units, determine the operation sequence according to the hydraulic connection, and obtain the regional control instructions for the river and lake water systems.

[0078] Specifically, the water quality category judgment value is extracted from the water conservancy project ecological assessment index. Specific judgment standards are set for each water quality category, including benchmark values ​​for indicators such as dissolved oxygen, turbidity, and ammonia nitrogen. The current water quality indicators are compared with the regional water quality standards, the difference between each indicator is calculated, and the target value that needs to be improved is determined. The difference calculation takes into account the requirements of water body functional zoning, and differentiated improvement targets are set for different functional areas. The warning level is graded based on the water quality warning signal, and three levels are set: first-level warning, second-level warning, and third-level warning. The water system connectivity is analyzed according to the river network structure, a water flow map is established, and the hydraulic connection between each section is marked. The propagation path of water quality impact is analyzed through graph theory methods, taking into account the water flow direction, flow velocity and water mixing characteristics, the migration law of pollutants in the water system is calculated, and an early warning response sequence is generated.

[0079] The upstream and downstream correlation analysis of the water system is carried out for the water quality improvement target value, and the water quality control amount is calculated as follows:

[0080]

[0081] in, represents the water quality control amount from section i to section j, represents the weight coefficient of the kth water quality index, represents the hydraulic transmission coefficient between sections, Indicates the target water quality value of the upstream section, Indicates the current water quality value of the downstream section, Indicates the distance between sections, and the attenuation rate of water quality indicators. represents the hydraulic loss coefficient, t represents the water flow transmission time, and m represents the number of water quality indicators.

[0082] The regional early warning response sequence is matched and verified with the water allocation parameters to establish a corresponding relationship between the early warning level and the control intensity. Different control parameter thresholds are set according to different early warning levels, and the control target of each control unit is determined through block calculation. The control target sequence includes parameters such as water volume regulation, regulation time, and expected water quality indicators. The control units are divided according to the water quality control target sequence, and the entire water system is divided into several relatively independent control blocks according to hydraulic characteristics and management needs. The mutual influence between the control units is analyzed in combination with the water system connectivity, and the control volume allocation ratio of each unit is calculated. Regional control plans are formulated based on the hydraulic conditions and the distribution characteristics of the control facilities.

[0083] Break down regional control plans into specific operational instructions, including operating parameters for hydraulic structures like pumping stations and gates. Analyze the hydraulic connections between control units, determine the sequence of control operations, and compile a control schedule. Generate regional control instructions for river and lake systems based on hydraulic response times and control objectives.

[0084] For example, the water quality management process of a river-lake interconnection project exemplifies the concrete implementation of this approach. When the water quality assessment index of an upstream river section indicated excessive ammonia nitrogen levels, the target value for improvement in ammonia nitrogen concentration was determined by calculating the difference from the water quality standard. A level 2 warning signal was issued, and analysis of the river network structure revealed that the impact of the pollution would propagate downstream along the main river channel. Through upstream and downstream correlation analysis, the required amount of clean water was calculated, and the control cycle was determined based on the hydraulic transmission time. The warning response sequence was matched with the control parameters, and a regional water quality improvement plan was developed. The outflow rate of the upstream gate and the water diversion volume of the tributary were determined according to the control unit division, forming specific control instructions including gate opening adjustment and pump station start-up and shutdown arrangements.

[0085] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0086] (1) Decompose the control quantity of the river and lake water system zoning control instructions, determine the opening sequence according to the flow range of the hydraulic regulating device, and generate the operating parameters of the regulating device;

[0087] (2) Convert the operating parameters of the regulating device into the equipment operation control quantity, calculate the regulation timing according to the equipment response characteristics, and output the regulation control data;

[0088] (3) Based on the regulation and control data, the operating parameters of the water purification equipment are collected, the changes in the purification load are analyzed according to the process flow, and the equipment operating data is obtained;

[0089] (4) Extract key operating indicators from equipment operating condition data, perform operating stability analysis based on the equipment dynamic response curve, and generate operating condition evaluation data;

[0090] (5) Group the operating condition evaluation data according to the operating parameter type, calculate the deviation value by comparing it with the standard operating condition range, and output the parameter correction data;

[0091] (6) Perform operation status statistics based on parameter correction data and conduct comprehensive analysis in combination with the equipment operation efficiency curve to obtain the project operation status data.

[0092] Specifically, the control instructions for each river and lake system are broken down into control units. For each control unit, the required flow change is determined based on the control objectives, and a specific gate opening sequence is calculated using a hydraulic model. Hydraulic control devices have specific flow regulation ranges. This flow demand needs to be converted into opening changes, while also considering the impact of water level differences on the discharge coefficient. This generates the control device operating parameters, which include opening, flow, water level, and other parameters.

[0093] The conversion of the operating parameters of the regulating device to the equipment operation control quantity involves complex hydraulic calculations. The conversion formula is:

[0094]

[0095] in, represents the equipment operation control quantity at time t, represents the device response coefficient of the nth control unit, represents the flow change function of the control unit, Indicates the water level difference, Indicates the equipment inertia attenuation coefficient, represents the dynamic adjustment coefficient, and N represents the total number of control units. This formula is used to calculate the adjustment timing of the device and form the adjustment control data.

[0096] Based on the regulation and control data, the operating parameters of the water purification equipment are collected in real time, including influent water quality, processing load, energy consumption indicators, etc. The equipment load changes are analyzed according to the process sequence, and the relationship between influent and effluent water quality, processing load, and equipment energy consumption is established. The operating data of the equipment under different load conditions is recorded and compiled into equipment operating condition data. Key operating indicators such as processing efficiency, energy consumption intensity, and operating stability are extracted from the equipment operating condition data. Using time series analysis methods, the dynamic response curve of the equipment is drawn to analyze the response characteristics of the equipment during load changes. Focus on the operating stability of the equipment during the start-up and shutdown transition phase and the load mutation phase, record the characteristic parameters of each phase, and generate operating condition evaluation data.

[0097] Condition assessment data is grouped and organized according to equipment type, operating conditions, load conditions, and other factors. Based on the standard operating range, the deviation of each operating parameter is calculated, and the degree and trend of the parameter deviation from the standard operating conditions are analyzed. The deviation analysis results are statistically summarized to generate parameter correction data, which is used to guide the adjustment of equipment operating parameters. Based on this parameter correction data, the equipment's operating status is statistically analyzed and combined with the equipment's operating efficiency curve to evaluate its operating quality. By comparing and analyzing the equipment's performance under different operating conditions, the optimal operating parameter range is determined, generating engineering operating status data that reflects the equipment's overall operating status.

[0098] For example, the control process for water purification equipment in a river water quality improvement project demonstrates the practical application of this method. First, the control instructions were converted into specific gate opening adjustment requirements. Through calculation, a control sequence was determined, increasing the gate opening from 30% to 50%. Based on the equipment's response characteristics, a step-by-step adjustment plan was developed, with each opening adjustment amplitude controlled within 5% to avoid water flow shock. During the control process, real-time monitoring of equipment operating parameters revealed that the dissolved oxygen treatment load increased with increasing water inflow, prompting timely adjustments to the aeration intensity. Analysis of equipment operating data identified fluctuations in dissolved oxygen concentration when the equipment suddenly changed load, leading to optimization of the control parameters. After operating parameter calibration and efficiency analysis, a combination of equipment operating parameters suitable for this operating condition was determined, ensuring the stability of the water purification effect.

[0099] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0100] (1) The project operation status data is divided into daily, weekly, and monthly scales according to the time span. The mean, variance, and coefficient of variation of the operation parameters in each time period are calculated respectively. Then, the water ecological response sequence is generated through cross-validation based on the parameter fluctuation pattern.

[0101] (2) Filter the water quality index change data from the water ecological response sequence, calculate the dissolved oxygen recovery time, turbidity decrease time and water quality standard time, compare the response rate under different hydrological conditions, and output the water quality recovery period data through data fitting calculation;

[0102] (3) The water quality recovery cycle data were seasonally grouped, and the differences in recovery cycles between the flood season, normal water season, and dry season were calculated. Weighted calculations were performed based on influencing factors such as temperature and water volume, and the data were classified according to the speed of the response rate to obtain the period characteristic parameters.

[0103] (4) Compare the periodic characteristic parameters with the target values ​​of various water quality indicators one by one, calculate the deviation rate between the time for reaching the water quality standard and the target time, determine the adjustment ratio coefficient based on the degree of deviation, and generate a water quality improvement plan through multiple rounds of iterative calculations;

[0104] (5) Extract the improvement amount and improvement time of water quality indicators based on the water quality improvement plan, compare the improvement effects of similar working conditions in the historical control database, sort them from high to low according to the improvement efficiency, and output strategy optimization data through data screening and parameter matching;

[0105] (6) The control parameters are calibrated based on the strategy optimization data. The flow regulation range of the hydraulic regulating device, the processing load and energy consumption indicators of the water purification equipment are calculated respectively. The feasibility is verified in combination with the equipment operation constraints. After multi-objective balance calculation, the control parameters of the water conservancy project are obtained.

[0106] Specifically, project operational data was first divided into time scales. For daily data, 24-hour data was aggregated at hourly intervals; weekly data was aggregated over a 7-day period; and monthly data was calculated over a calendar month. Within each time scale, the mean, variance, and coefficient of variation of operational parameters such as dissolved oxygen, turbidity, and flow were calculated. These statistics reflect the central tendency and dispersion of the parameters. The calculated statistical characteristics were cross-validated to analyze the consistency and correlation of parameter fluctuation patterns, generating a response sequence reflecting the dynamic changes in the aquatic ecosystem. Within this aquatic ecological response sequence, changes in water quality indicators such as dissolved oxygen and turbidity were specifically monitored. For each water quality recovery process after regulation, the time required for dissolved oxygen to recover from a low value to stabilize, the time required for turbidity to decrease from a peak value to a baseline value, and the time required for all water quality indicators to reach standard values ​​were recorded. A correlation between the rate of water quality improvement and hydrological conditions was established, considering the response process under different hydrological conditions, including the influence of factors such as flow and water level fluctuations. Data fitting methods were used to obtain water quality recovery period data for different scenarios.

[0107] Water quality recovery cycle data was seasonally grouped, corresponding to three typical hydrological periods: high water, normal water, and low water. The high water period is characterized by abundant flow and strong water self-purification capacity; the normal water period is characterized by relatively stable hydrological conditions; and the low water period faces water shortages and reduced self-purification capacity. By comparing and analyzing the differences in recovery cycles between the three periods, and incorporating the effects of temperature on water purification rates and water volume fluctuations on dilution effects, a weighted calculation model was developed. Based on the measured response rates, the recovery cycles were categorized into three levels: fast, moderate, and slow, generating characteristic parameters for the cycles. These parameters were then compared with the target values ​​for various indicators specified in the water quality standards, and the deviation between the actual and required timeframes was calculated. The magnitude of the deviation was used to determine the water quality control coefficient; the greater the deviation, the greater the control effort. Through multiple rounds of iterative calculations, the control parameters were continuously optimized until a control solution that both met the standards and was operationally feasible was found, forming a water quality improvement plan.

[0108] Based on the water quality improvement plan, extract the target improvement amount and expected improvement time for each water quality indicator. Retrieve operating records of similar hydrological conditions and water quality issues from the historical control database, and compare and analyze the improvement effects under different control strategies. Rank the control strategies based on their improvement efficiency, select the optimal control parameter combination, and generate strategy optimization data through data screening and parameter matching. Verify the control parameters of the strategy optimization data, focusing on analyzing the flow regulation capacity of the hydraulic regulating device to ensure that the control plan is within the equipment's capabilities. Simultaneously, calculate the processing load of the water purification equipment, evaluate energy consumption indicators, and determine the equipment's optimal operating conditions. Verify the feasibility of the control plan based on the operating constraints of various types of equipment, including start-up and shutdown conditions and load change rate limits. Determine the control parameters of the water conservancy project through multi-objective balance calculations, taking into account factors such as water quality improvement effects, energy consumption, and operating costs.

[0109] During a river water quality improvement project, operational data was recorded when dissolved oxygen levels first fell below the standard. Analysis of this data over a 24-hour period revealed a periodic pattern of low levels in the morning and evening, and high levels at noon. Further statistical analysis of the weekly data allowed the calculation of the daily mean and coefficient of variation for dissolved oxygen, reflecting the overall level and fluctuations. During the water quality control process, the time course of the dissolved oxygen level rising from 3 mg / L to 6 mg / L was recorded, while the downward trend in turbidity was also monitored. Comparison of recovery rates under different flow rates revealed the fastest recovery at a flow rate of 2 m³ / s. Given the dry season and high water temperatures, a larger control coefficient was determined through weighted calculation. After multiple rounds of optimization of control parameters, a comprehensive control plan, including gate control and aeration, was developed. During implementation, equipment operating status was continuously monitored to ensure that load and energy consumption remained within reasonable ranges, achieving stable improvements in water quality.

[0110] In a specific embodiment, the process of performing the step of comparing the periodic characteristic parameters with the target values ​​of the water quality indicators item by item may specifically include the following steps:

[0111] (1) Group the periodic characteristic parameters according to the indicator type, establish the water quality indicator change rate matrix, and obtain the standardized parameter set through unified dimension conversion;

[0112] (2) Match the target value of the standardized parameter set, calculate the difference sequence between the current value and the target value of each water quality indicator, and generate difference comparison data;

[0113] (3) Divide the difference comparison data into segments according to the time of reaching the standard, calculate the water quality improvement rate in each time period, establish the time-improvement relationship curve, and output the progress data of reaching the standard;

[0114] (4) Calculate the deviation rate between the target achievement time and the prescribed time for each indicator based on the target achievement progress data, divide the priority levels according to the size of the deviation, and generate the adjustment priority sequence;

[0115] (5) Conduct correlation analysis on the indicators in the adjustment priority sequence, calculate the complementary or restrictive relationship between the indicators, and determine the adjustment ratio coefficient through feedback correction;

[0116] (6) Substitute the adjustment ratio coefficient into the water quality improvement objective function, perform step-by-step iterative optimization calculations, screen the optimal control combination, and generate a water quality improvement plan.

[0117] Specifically, the periodic characteristic parameters are grouped according to the indicator type, including physical indicator group (including water temperature, turbidity, conductivity), chemical indicator group (including dissolved oxygen, pH value, ammonia nitrogen) and biological indicator group (including chlorophyll a, plankton density). Figure 3 As shown, this is an introduction diagram of the grouping indicators in the embodiment of the present application; a change rate matrix is ​​established for each group of indicators to record the rate of change of the indicators in different time periods. Since the dimensions of each indicator are different, a unified dimension conversion is required, and the maximum and minimum value standardization method is used to map all indicators to the [0,1] interval to form a standardized parameter set. A target value matching analysis is performed on the standardized parameter set, and the current monitoring value of each water quality indicator is compared with the target value specified in the water quality standard. A difference sequence is established by calculating the difference between the current value and the target value. In the difference sequence, a positive value indicates the degree of exceeding the standard, and a negative value indicates the degree of exceeding the standard. The difference sequence is arranged in chronological order to form difference comparison data reflecting the water quality status.

[0118] The difference comparison data is segmented according to the time requirements for meeting the standards. The short-term standard requirement is set to be within 24 hours, the medium-term standard requirement is within 7 days, and the long-term standard requirement is more than 30 days. In each time period, the improvement rate of the water quality index is calculated, that is, the change in the index value per unit time. By drawing a time-improvement relationship curve, the dynamic process of water quality improvement is analyzed, and the progress data for meeting the standards at each time node is determined. Based on the progress data for meeting the standards, the deviation rate between the meeting time of each indicator and the specified time is calculated. The larger the deviation rate, the more difficult it is to meet the standard for the indicator, and priority regulation is required. According to the size of the deviation rate, the indicators are divided into three priority levels: emergency regulation, key regulation, and routine regulation. The indicators in each level are arranged in descending order according to the deviation rate to generate a regulation priority sequence that reflects the urgency of regulation.

[0119] Conduct a correlation analysis on the indicators in the regulation priority sequence. By calculating the correlation coefficients between the indicators, identify the complementary and restrictive relationships between them. A complementary relationship manifests itself as an improvement in one indicator driving a similar change in another, such as an increase in dissolved oxygen often accompanied by a decrease in turbidity. A restrictive relationship manifests itself as a trade-off between indicators, such as an increase in water temperature causing a decrease in dissolved oxygen content. Based on these relationship characteristics, determine the regulation proportional coefficient for each indicator through feedback correction. Substitute the determined regulation proportional coefficient into the water quality improvement objective function, which comprehensively considers multiple objectives such as water quality improvement effect, energy consumption, and operating costs. Through step-by-step iterative optimization calculations, continuously adjust the control parameter combination until the optimal solution is found that meets both water quality improvement requirements and ensures economic efficiency. Generate a water quality improvement plan that includes specific control measures.

[0120] During the eutrophication control process in a particular river, periodic characteristic parameters revealed dissolved oxygen, turbidity, and ammonia nitrogen as the primary problem indicators. Standardizing the rate of change data for these indicators revealed that the current dissolved oxygen value was 4 mg / L, 2 mg / L below the target value of 6 mg / L; the current turbidity value was 25 NTU, 15 NTU below the target value of 10 NTU. Analysis of improvement rates based on the required timeframe for achieving the target revealed that dissolved oxygen increased by 0.2 mg / L per hour with aeration, while turbidity decreased by 2 NTU per hour with sedimentation. Prioritization determined that improved dissolved oxygen was an urgent control indicator. Correlation analysis revealed a negative correlation between dissolved oxygen and turbidity, indicating that turbidity improved with aeration. A control strategy was established, primarily emphasizing aeration and supplemented by sedimentation. Through multiple iterations of optimization, the optimal operating parameters for the aeration equipment and the sludge discharge cycle were determined.

[0121] In a specific embodiment, the process of calculating the deviation rate between the target achievement time and the specified time of each indicator based on the target achievement progress data may specifically include the following steps:

[0122] (1) Conduct time series analysis on the progress data of compliance with water quality indicators, extract the compliance time nodes of each indicator, and establish the compliance time series;

[0123] (2) Compare the time series of the target achievement with the specified time nodes, calculate the lead time and the lag time respectively, and obtain the deviation data through the time difference statistics;

[0124] (3) Quantify and grade the deviation data, set the threshold interval according to the deviation rate, calculate the deviation score of each indicator, and output the indicator score table;

[0125] (4) Arrange the data in the indicator scoring table in descending order, set the weight coefficient according to the degree of deviation, and generate the indicator priority matrix;

[0126] (5) Divide the indicator priority matrix into segments, calculate the control urgency of each priority level, and obtain priority level data through threshold classification;

[0127] (6) Establish a control order relationship based on the priority data, sort and combine the water quality indicators according to their response time, and generate a regulation priority sequence.

[0128] Specifically, the periodic characteristic parameters were grouped by indicator type into physical indicators (including water temperature, turbidity, and conductivity), chemical indicators (including dissolved oxygen, pH, and ammonia nitrogen), and biological indicators (including chlorophyll a and plankton density). A rate of change matrix was established for each indicator group, recording the rate of change over different time periods. Because the indicators had different dimensions, a unified dimensionality conversion was required. A maximum-minimum normalization method was used to map all indicators to the [0,1] interval, forming a standardized parameter set. A target value matching analysis was performed on this standardized parameter set, comparing the current monitored value of each water quality indicator with the target value specified in the water quality standard. A difference sequence was constructed by calculating the difference between the current and target values. Positive values ​​in the difference sequence indicate the degree of exceeding the standard, while negative values ​​indicate the degree of improvement over the standard. The difference sequence was arranged chronologically to form differential comparison data reflecting water quality conditions.

[0129] The difference comparison data is segmented according to the time requirements for meeting the standards. The short-term standard requirement is set to be within 24 hours, the medium-term standard requirement is within 7 days, and the long-term standard requirement is more than 30 days. In each time period, the improvement rate of the water quality index is calculated, that is, the change in the index value per unit time. By drawing a time-improvement relationship curve, the dynamic process of water quality improvement is analyzed, and the progress data for meeting the standards at each time node is determined. Based on the progress data for meeting the standards, the deviation rate between the meeting time of each indicator and the specified time is calculated. The larger the deviation rate, the more difficult it is to meet the standard for the indicator, and priority regulation is required. According to the size of the deviation rate, the indicators are divided into three priority levels: emergency regulation, key regulation, and routine regulation. The indicators in each level are arranged in descending order according to the deviation rate to generate a regulation priority sequence that reflects the urgency of regulation.

[0130] Conduct a correlation analysis on the indicators in the regulation priority sequence. By calculating the correlation coefficients between the indicators, identify the complementary and restrictive relationships between them. A complementary relationship manifests itself as an improvement in one indicator driving a similar change in another, such as an increase in dissolved oxygen often accompanied by a decrease in turbidity. A restrictive relationship manifests itself as a trade-off between indicators, such as an increase in water temperature causing a decrease in dissolved oxygen content. Based on these relationship characteristics, determine the regulation proportional coefficient for each indicator through feedback correction. Substitute the determined regulation proportional coefficient into the water quality improvement objective function, which comprehensively considers multiple objectives such as water quality improvement effect, energy consumption, and operating costs. Through step-by-step iterative optimization calculations, continuously adjust the control parameter combination until the optimal solution is found that meets both water quality improvement requirements and ensures economic efficiency. Generate a water quality improvement plan that includes specific control measures.

[0131] For example, the periodic characteristic parameters show that dissolved oxygen, turbidity, and ammonia nitrogen are the main problem indicators. After standardizing the change rate data of these indicators, it was found that the current value of dissolved oxygen is 4 mg / L, and the difference from the target value of 6 mg / L is 2 mg / L; the current value of turbidity is 25 NTU, and the difference from the target value of 10 NTU is 15 NTU. According to the improvement rate analysis based on the time requirement for reaching the standard, it was found that dissolved oxygen increased by 0.2 mg / L per hour under oxygenation control, and turbidity decreased by 2 NTU per hour through sedimentation treatment. After priority division, dissolved oxygen improvement was listed as an urgent control indicator. Through correlation analysis, it was found that dissolved oxygen and turbidity showed a negative correlation. When oxygenation measures were taken, turbidity would also improve. A control plan with oxygenation as the main method and sedimentation as the auxiliary method was determined. Through multiple iterative optimizations, the optimal operating parameters of the aeration equipment and the sludge discharge cycle were determined.

[0132] The above describes the automated monitoring method for ecological water conservancy projects in the embodiment of the present application. The following describes the automated monitoring system for ecological water conservancy projects in the embodiment of the present application. Figure 4 In one embodiment of the present application, an automated monitoring system for an ecological water conservancy project includes:

[0133] The acquisition module 201 is used to collect dissolved oxygen, turbidity, water level flow and pump station gate operation status of water bodies through monitoring sensors, and obtain standardized monitoring data of water conservancy projects through data preprocessing;

[0134] An alignment module 202 is used to perform water quality parameter noise reduction and spatiotemporal alignment of hydrological data based on the standardized monitoring data of the water conservancy project, analyze the correlation of ecological water system parameters, and obtain a water ecological optimization data set and a water quality anomaly feature library;

[0135] Analysis module 203, for establishing an ecological water system health assessment system based on the water ecological optimization data set and the water quality abnormality feature library to perform dynamic analysis of the water environment and obtain a water conservancy project ecological assessment index and a water quality early warning signal;

[0136] The early warning module 204 is used to construct a water environment control model based on the water conservancy project ecological assessment index and water quality early warning signal to analyze water system management plans and obtain river and lake water system zoning control instructions;

[0137] An adjustment module 205 is configured to adjust operating parameters and analyze operating conditions of hydraulic regulating devices and water purification equipment according to the river and lake water system zoning control instructions, thereby obtaining project operation status data;

[0138] The optimization module 206 is used to obtain the water conservancy project control parameters through water ecological restoration cycle analysis and water quality improvement strategy optimization based on the project operation status data.

[0139] Through the collaborative cooperation of the above components, by adopting layered data collection and multi-dimensional data processing technology, the problem of unstable data quality in traditional monitoring methods is effectively solved, and comprehensive monitoring of dissolved oxygen, turbidity, water level flow and pump station gate operation status of water bodies is achieved. The standardized monitoring data of water conservancy projects obtained after data preprocessing provides a reliable data basis for subsequent analysis, and by denoising water quality parameters and aligning hydrological data in time and space, the influence of data noise and time and space differences is effectively eliminated. Combined with the correlation analysis of ecological water system parameters, the intrinsic relationship between water quality indicators is deeply explored. The formed water ecological optimization data set and water quality anomaly feature library provide an important basis for the diagnosis of water quality problems. At the same time, based on the water ecological optimization data set and water quality anomaly feature library, The ecological water system health assessment system established by the feature database, the water conservancy project ecological assessment index and water quality warning signal obtained through dynamic analysis of the water environment, realize the early identification and warning of water quality problems, and then construct a water environment regulation model based on the assessment index and warning signal. The river and lake water system zoning regulation instructions generated by the analysis of the water system management plan realize the precise implementation of regulation measures. The operating parameter adjustment and working condition analysis of the hydraulic regulating device and water purification equipment ensure the effective implementation of the regulation measures. The water conservancy project regulation parameters obtained through the water ecological restoration cycle analysis and water quality improvement strategy optimization have established a complete closed-loop management system of water environment monitoring-analysis-regulation-optimization, which has significantly improved the intelligent management level and water quality improvement effect of ecological water conservancy projects.

[0140] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An automated monitoring method for ecological water conservancy projects, characterized in that: include: The monitoring sensors collect dissolved oxygen, turbidity, water level flow and pump station gate operation status in the water body, and the standardized monitoring data of the water conservancy project is obtained through data preprocessing; Based on the standardized monitoring data of water conservancy projects, water quality parameter noise reduction and hydrological data spatiotemporal alignment were carried out, and the correlation between ecological water system parameters was analyzed to obtain a water ecological optimization data set and a water quality anomaly feature library; Based on the water ecological optimization data set and water quality anomaly feature database, an ecological water system health assessment system was established to conduct dynamic analysis of the water environment, and obtain the water conservancy project ecological assessment index and water quality early warning signals; Based on the ecological assessment index of water conservancy projects and water quality early warning signals, a water environment control model is constructed to analyze water system management plans and obtain zoning control instructions for river and lake water systems; Based on the river and lake water system zoning control instructions, the operating parameters of the hydraulic regulating devices and water purification equipment are adjusted and the working condition analysis is performed to obtain the project operation status data, including: decomposing the control quantity of the river and lake water system zoning control instructions, determining the opening sequence by comparing with the flow range of the hydraulic regulating device, and generating the operating parameters of the regulating device; converting the operating parameters of the regulating device into the equipment operation control quantity, calculating the adjustment timing according to the equipment response characteristics, and outputting the adjustment control data; based on the adjustment control data, the operating parameters of the water purification equipment are collected, and the purification load changes are analyzed according to the process flow to obtain the equipment working condition data; key operating indicators are extracted from the equipment working condition data, and the equipment dynamic response curve is drawn through the time series analysis method to analyze the response characteristics of the equipment during the load change process; based on the equipment dynamic response curve, the operation stability analysis is performed to generate the working condition evaluation data; the working condition evaluation data is grouped according to the operating parameter type, and the deviation value is calculated by comparing with the standard working condition range to output the parameter correction data; the operating status statistics are performed based on the parameter correction data, and a comprehensive analysis is performed in combination with the equipment operation efficiency curve to obtain the project operation status data; According to the project operation status data, the water ecological restoration cycle analysis and water quality improvement strategy optimization are used to obtain the water conservancy project control parameters, including: dividing the project operation status data into daily, weekly and monthly scales according to the time span, calculating the mean, variance and coefficient of variation of the operation parameters in each time period, and then cross-validating the parameter fluctuation law to generate a water ecological response sequence; screening the water quality index change data from the water ecological response sequence, and statistically analyzing the dissolved oxygen recovery time, turbidity decrease time and water quality compliance time, and comparing the response rate under different hydrological conditions. Through data fitting calculation, the water quality recovery cycle data is output; the water quality recovery cycle data is seasonally grouped, and the recovery cycle differences between the flood season, normal water season and dry season are calculated. The weighted calculation is combined with the influencing factors of temperature and water volume, and the water quality recovery cycle data is output according to the water quality recovery cycle data. The response rates are graded to obtain periodic characteristic parameters; the periodic characteristic parameters are compared with the target values ​​of various water quality indicators one by one, the deviation rate between the time for water quality to reach the standard and the target time is calculated, the adjustment ratio coefficient is determined according to the degree of deviation, and a water quality improvement plan is generated through multiple rounds of iterative calculations; the water quality indicator improvement amount and improvement time are extracted based on the water quality improvement plan, and the improvement effects of similar working conditions in the historical control database are compared, and the improvement efficiency is sorted from high to low. After data screening and parameter matching, the strategy optimization data is output; the control parameters are calibrated according to the strategy optimization data, and the flow adjustment range of the hydraulic control device, the processing load and energy consumption indicators of the water purification equipment are calculated respectively. The feasibility is verified in combination with the equipment operation constraints, and the water conservancy project control parameters are obtained through multi-objective balance calculations.

2. The automated monitoring method for ecological water conservancy projects according to claim 1, characterized in that: The monitoring sensors collect dissolved oxygen, turbidity, water level flow, and pump station gate operating status in the water body. After data preprocessing, standardized monitoring data for water conservancy projects is obtained, including: The dissolved oxygen data of the water body is collected through the water sensor, and the dissolved oxygen data is grouped according to the sampling time. The signal of each group of data is denoised, and the average value is calculated according to the time window. Then, the value range is verified and the data points that exceed the range are eliminated to obtain the standardized dissolved oxygen data. The water turbidity is collected by optical sensors, the original turbidity data is linearly calibrated, the data is compensated according to the ambient light intensity, interference fluctuations are removed through signal filtering, and the processed data is rearranged according to the time series to obtain standardized turbidity data; The water level data collected by the water level station and the flow data collected by the flow station are paired according to the sampling time, the missing data points are supplemented by linear interpolation, the data are verified according to the water level-flow relationship curve, and the outliers that do not conform to the relationship curve are eliminated to obtain the standardized water level and flow data; The opening and speed data are collected from the control system of the pump station gate. The opening data is converted into a percentage value, the speed data is dimensionally normalized, and the validity is verified according to the upper and lower limits of the equipment operating parameters. The data that meets the operating specifications is screened out to obtain standardized operating status data. Import the standardized dissolved oxygen data, standardized turbidity data, standardized water level flow data, and standardized operating status data into the time series database, align them according to a unified timestamp, downsample or interpolate the data at different sampling frequencies, normalize the data after unifying the time resolution, and obtain time series correlation data; Perform data integrity check on time series correlation data, calculate the correlation coefficient of various parameters, identify outliers based on the distribution characteristics of historical data, integrate all processed data into a unified data structure, and obtain standardized monitoring data for water conservancy projects.

3. The automated monitoring method for ecological water conservancy projects according to claim 1, characterized in that: Based on the standardized monitoring data of water conservancy projects, water quality parameter noise reduction and hydrological data spatiotemporal alignment were carried out, and the correlation analysis of ecological water system parameters was carried out to obtain a water ecological optimization dataset and a water quality anomaly feature library, including: The water quality parameters in the standardized monitoring data of water conservancy projects are divided into zones according to the sampling locations. The signal features are extracted through wavelet decomposition. The coefficients of each frequency band after decomposition are threshold processed and the signal is reconstructed to obtain the water quality data after noise reduction. The denoised water quality data are sorted by timestamp, and the distance matrix between sampling points is calculated based on the distribution characteristics of the spatial sampling points. The spatiotemporal registration data points are generated through interpolation calculation to obtain the spatiotemporal aligned hydrological data. The time-space aligned hydrological data were grouped and statistically analyzed to calculate the correlation coefficient matrix between different water quality parameters, establish the influence weight relationship between the parameters, and obtain the correlation data of water system parameters; The water system parameter correlation data are divided into levels according to the correlation strength, the parameter combination characteristics under each level are extracted, the numerical distribution characteristics of the parameter combination are constructed, and the initial water ecological data are obtained; Based on the initial water ecological data, a parameter change trend diagram is established, the parameter change law is analyzed, the key inflection points and mutation characteristics are extracted, and the water quality abnormality feature library is obtained through data clustering; The water quality anomaly feature library was classified and optimized and reorganized based on the initial water ecological data. The parameter groups were sorted according to the water quality categories to obtain the water ecological optimization data set and the water quality anomaly feature library.

4. The automated monitoring method for ecological water conservancy projects according to claim 1, characterized in that: Based on the water ecological optimization data set and water quality anomaly feature database, an ecological water system health assessment system was established to conduct dynamic analysis of the water environment, and obtain the water conservancy project ecological assessment index and water quality early warning signals, including: The water ecological optimization dataset was stratified according to water quality indicators, and the evaluation coefficient of each indicator was determined by weight calculation. The initial health score was obtained through stratified weighted calculation. Extract key parameter nodes based on the water quality anomaly feature library, analyze the fluctuation range by comparing with historical data, and obtain the critical value of parameter changes by time period; Water environment quality is graded based on the initial health score, and warning level intervals are divided based on the critical values ​​of parameter changes to output water quality status assessment data; Based on the key indicators in the water quality status assessment data, trend prediction and dynamic compensation are carried out in sequence to generate a water environment quality change curve; According to the trend characteristics of the water environment quality change curve, abnormal change points are extracted segment by segment, and the warning trigger conditions are output after data verification; The early warning triggering conditions are graded and judged, and a comprehensive analysis is conducted in combination with the water quality status assessment data to obtain the water conservancy project ecological assessment index and water quality early warning signal.

5. The automated monitoring method for ecological water conservancy projects according to claim 1, characterized in that: Based on the ecological assessment index of water conservancy projects and water quality early warning signals, a water environment control model is constructed to analyze water system management plans and obtain zoning control instructions for river and lake water systems, including: Extract the water quality category judgment value from the water conservancy project ecological assessment index, calculate the difference between it and the regional water quality standards, and output the water quality improvement target value; Based on the water quality warning signal, the warning level range is divided, the impact propagation path is determined according to the water system connectivity, and a regional warning response sequence is generated; Conduct upstream and downstream correlation analysis of water quality improvement targets, determine water quality control amounts based on flow relationships, and output water allocation parameters; The regional early warning response sequence is matched and verified with the water allocation parameters, and the water quality control target sequence is obtained through block calculation; The control units are divided according to the water quality control target sequence, and the control volume allocation ratio is calculated based on the water system connectivity relationship to generate a regional control plan; The regional control plan is decomposed according to the control units, the operation sequence is determined according to the hydraulic connection, and the river and lake water system zoning control instructions are obtained.

6. The automated monitoring method for ecological water conservancy projects according to claim 5, characterized in that: Compare the periodic characteristic parameters with the target values ​​of various water quality indicators one by one, calculate the deviation rate between the time for reaching the water quality standard and the target time, determine the adjustment ratio coefficient based on the degree of deviation, and generate a water quality improvement plan through multiple rounds of iterative calculations, including: The periodic characteristic parameters are grouped according to the indicator type, and the water quality indicator change rate matrix is ​​established. The standardized parameter set is obtained through unified dimension conversion. Match the target value of the standardized parameter set, calculate the difference sequence between the current value and the target value of each water quality indicator, and generate difference comparison data; Divide the difference comparison data into segments according to the time of reaching the standard, calculate the water quality improvement rate in each time period, establish the time-improvement relationship curve, and output the progress data of reaching the standard; Based on the progress data, the deviation rate between the target time and the stipulated time for each indicator is calculated, and the priority levels are divided according to the size of the deviation to generate the adjustment priority sequence; Conduct correlation analysis on the indicators in the adjustment priority sequence, calculate the complementary or restrictive relationship between the indicators, and determine the adjustment ratio coefficient through feedback correction; Substitute the adjustment proportion coefficient into the water quality improvement objective function, perform step-by-step iterative optimization calculations, screen the optimal control combination, and generate a water quality improvement plan.

7. The automated monitoring method for ecological water conservancy projects according to claim 6, characterized in that: Based on the progress data, the deviation rate between the target time and the stipulated time for each indicator is calculated. The priority levels are divided according to the size of the deviation, and an adjustment priority sequence is generated, including: Conduct time series analysis on the progress data of compliance with water quality indicators, extract the compliance time nodes of each indicator, and establish the compliance time series; Compare the time series of reaching the target with the specified time node, calculate the lead time and lag time respectively, and obtain the deviation data through the time difference statistics; Quantify and grade the deviation data, set the threshold interval according to the deviation rate, calculate the deviation score of each indicator, and output the indicator score table; Arrange the data in the indicator scoring table in descending order, set the weight coefficient according to the degree of deviation, and generate the indicator priority matrix; Divide the indicator priority matrix into segments, calculate the control urgency of each priority level, and obtain priority level data through threshold classification; A control order relationship is established based on the priority data, and the response time between water quality indicators is sorted and combined to generate a regulation priority sequence.

8. An automated monitoring system for an ecological water conservancy project, used to implement the automated monitoring method for an ecological water conservancy project according to any one of claims 1 to 7, characterized in that: include: The acquisition module is used to collect dissolved oxygen, turbidity, water level flow and pump station gate operation status through monitoring sensors, and obtain standardized monitoring data of water conservancy projects through data preprocessing; The alignment module is used to reduce the noise of water quality parameters and align the hydrological data in time and space based on the standardized monitoring data of water conservancy projects, analyze the correlation between ecological water system parameters, and obtain a water ecological optimization data set and a water quality anomaly feature library; The analysis module is used to establish an ecological water system health assessment system based on the water ecological optimization data set and water quality abnormality feature library to conduct dynamic analysis of the water environment and obtain the water conservancy project ecological assessment index and water quality early warning signals; The early warning module is used to construct a water environment control model based on the ecological assessment index of water conservancy projects and water quality early warning signals to analyze water system management plans and obtain river and lake water system zoning control instructions; The adjustment module is used to adjust the operating parameters and analyze the working conditions of hydraulic regulating devices and water purification equipment according to the river and lake water system zoning control instructions, and obtain project operation status data; The optimization module is used to obtain the water conservancy project control parameters based on the project operation status data through water ecological restoration cycle analysis and water quality improvement strategy optimization.

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