Water ecological digital and intelligent monitoring data comprehensive processing system

Abstract

This invention relates to the field of water ecological monitoring system technology, and discloses a comprehensive data processing system for intelligent digital monitoring of water ecology. The system's monitoring data acquisition unit is responsible for acquiring real-time monitoring data from water body monitoring points; the feature analysis unit processes the real-time monitoring data, extracting the temporal sequence and abnormal fluctuation characteristics of water quality parameters; the event identification unit identifies aquatic ecological anomalies based on these characteristics and generates an event response tag set; the affected area delineation unit calls upon this tag set to determine the affected water body sections and their time ranges; the parameter adjustment unit generates a monitoring parameter adjustment scheme based on the affected water body sections; and the control command generation unit uses this scheme to generate water ecological monitoring control commands. This system can effectively improve the efficiency of water ecological monitoring and the ability to respond to abnormal events, providing strong technical support for water environment protection.

Description

Technical Field

[0001] This invention relates to the field of water ecological monitoring system technology, specifically a comprehensive data processing system for intelligent digital monitoring of water ecology. Background Technology

[0002] Aquatic ecosystems are among the most important ecosystems on Earth, playing a crucial role in maintaining ecological balance and ensuring human survival and development. However, with the acceleration of global industrialization and urbanization, human activities are increasingly disrupting aquatic ecosystems, leading to more prominent problems such as water pollution, water scarcity, and aquatic ecosystem degradation.

[0003] In terms of water pollution, the large-scale discharge of industrial wastewater is a significant source. Many industrial enterprises, in an effort to reduce costs, discharge wastewater containing heavy metals, chemicals, and other harmful substances into rivers, lakes, and seas without effective treatment. Taking the electroplating industry as an example, wastewater often contains heavy metals such as chromium, nickel, and copper. These heavy metals are difficult to degrade naturally and accumulate in water bodies, not only poisoning aquatic life but also potentially entering the human body through the food chain, endangering human health. Agricultural non-point source pollution is equally serious. The overuse of pesticides and fertilizers results in large amounts of unused chemicals being washed into water bodies by rainwater, causing eutrophication.

[0004] The problem of water scarcity is becoming increasingly severe. With population growth and economic development, human demand for water resources continues to rise. Global climate change has further exacerbated the uneven spatial and temporal distribution of water resources, and the frequent occurrence of extreme weather events, with alternating droughts and floods, has made the water scarcity problem even more serious.

[0005] The degradation of aquatic ecosystems is becoming increasingly apparent. Human activities such as river channelization and lake reclamation have damaged the natural structure and function of aquatic ecosystems. Rivers have been artificially transformed into straight channels, destroying their meandering forms and natural habitats, leading to a reduction in aquatic biodiversity. Lake reclamation has shrunk lake areas, reduced their flood control capacity, and weakened their ecosystem service functions. Wetlands, as an important component of aquatic ecosystems, have also been severely damaged by human activities, with their area continuously decreasing, and many rare waterbirds and wetland plants losing their habitats and survival environments.

[0006] Faced with the increasingly severe water ecological crisis, traditional water environment monitoring methods have revealed many limitations. Traditional monitoring mainly adopts the "point sampling" method, that is, collecting water samples for analysis at a limited number of points. This method has an extremely limited monitoring range and is difficult to comprehensively reflect the true water quality of large areas of water. In vast rivers and lakes, relying on only a few monitoring points is like looking for a needle in a haystack, and many potentially polluted areas are easily overlooked.

[0007] Manual sampling is typically infrequent, making it impossible to capture instantaneous changes in water quality and sudden pollution events in a timely manner. In rivers near some chemical industrial parks, in the event of a sudden pollution incident such as a chemical spill, the inability to conduct manual sampling in real time may lead to the pollution being discovered only after it has spread, missing the optimal time for emergency response. Moreover, manual sampling is limited by manpower, resources, and time, making it difficult to achieve high-frequency, long-term continuous monitoring. This results in monitoring data that cannot accurately reflect the dynamic changes in water quality.

[0008] Traditional monitoring methods also have shortcomings in data processing and analysis. While laboratory analysis can provide relatively accurate water quality parameters, the analysis cycle is long, often taking hours or even days from sampling to obtaining results. During this time, water quality may have already changed, making it impossible to provide timely guidance for water environment management decisions. Traditional data analysis methods struggle to deeply mine and comprehensively analyze large amounts of monitoring data, making it difficult to grasp the overall trends and patterns of water quality changes from complex data, and hindering the accurate diagnosis of the root causes of water environment problems. Summary of the Invention

[0009] The purpose of this invention is to provide a comprehensive data processing system for intelligent digital monitoring of water ecology to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, the present invention provides a comprehensive data processing system for intelligent digital monitoring of aquatic ecosystems, the system comprising: The monitoring data acquisition unit acquires real-time monitoring data from water body monitoring points. The feature analysis unit processes the real-time monitoring data to extract the temporal variation and abnormal fluctuation characteristics of water quality parameters; The event identification unit identifies aquatic ecological abnormal events based on the change time sequence and abnormal fluctuation characteristics and generates a set of event response tags; The affected section delineation unit calls the event response tag set to determine the affected water body section and its time range; The parameter adjustment unit generates a monitoring parameter adjustment scheme based on the affected water body section; The control command generation unit generates water ecological monitoring and control commands using the monitoring parameter adjustment scheme.

[0011] Preferably, the generation of the event response tag set includes: The monitoring data acquisition unit acquires multi-dimensional parameter sequences from water monitoring points in real time. The feature analysis unit performs sliding window segmentation on the parameter sequences, extracts parameter mutation points and trend offsets within each window, calculates the deviation between mutation points and baseline parameters, and marks potential abnormal events when the deviation exceeds a dynamic threshold. Combining the event's timestamp and geographic coordinates, the unit assigns event type codes and impact levels, generates spatial location identifiers, and finally integrates them into an event response tag set.

[0012] Preferably, the specific implementation steps of the event identifier unit are as follows: Real-time water quality parameter streams, including dissolved oxygen, turbidity, and pH, are received from the monitoring data acquisition unit. A wavelet transform algorithm is used to decompose the multi-scale components of the parameter stream, extracting abnormal fluctuation patterns from high-frequency components. The energy spectral density of the fluctuation patterns is calculated, and the onset point of the abnormal event corresponding to the energy peak is identified. Based on the onset point of the abnormal event, a sliding time window is used to calculate the variance and autocorrelation function of the parameter values ​​within the window, determining the duration and decay characteristics of the abnormal event. The abnormal fluctuation patterns are matched with predefined event templates, and event type codes are assigned. The impact level is then corrected using monitoring point elevation data. Finally, a set of event response labels is output, where each label contains an event type code, an impact level, and a spatial location identifier.

[0013] Preferably, the specific implementation steps of the influence section delineation unit are as follows: The system retrieves the spatial location identifiers and timestamps from the event response tag set to obtain the center coordinates and occurrence time of the abnormal event clusters. Using Kriging spatial interpolation, it generates a continuous distribution map of water parameters based on monitoring point data, identifying spatial clusters of abnormal parameter values. It calculates the centroid location and boundary radius of each cluster, and, combined with water flow velocity vector data, predicts the diffusion path and arrival time of the abnormal segments. Based on the diffusion path, it divides the affected water segments and marks the segment start time, segment duration, and water quality anomaly intensity value. Through a time series clustering algorithm, it merges continuous abnormal events into traffic density offset time periods and generates a set of affected water segments.

[0014] Preferably, the specific implementation steps of the parameter adjustment unit are as follows: Based on the monitoring point numbers in the set of affected water body sections, the log data of the last abnormal event within two consecutive monitoring periods is queried, and the time difference between the end time of the abnormality and the end time of monitoring is extracted. A time series prediction model is used to predict the remaining duration of the last abnormality and compare it with a preset threshold to filter monitoring points that have not completed abnormality processing. Based on the waiting time of the uncompleted monitoring points, a priority sequence of abnormality waiting for monitoring points is generated. According to the priority sequence, monitoring resources are dynamically reallocated, an additional monitoring time period is calculated for each monitoring point, the monitoring frequency and parameter acquisition depth are adjusted, and the monitoring point number and the adjusted configuration are recorded to form a monitoring parameter adjustment plan.

[0015] Preferably, the specific implementation steps of the control command generation unit are as follows: The system analyzes the initial configuration time value of monitoring points in the monitoring parameter adjustment scheme to obtain the water quality parameter flow of the monitoring point group in real time; it applies edge computing nodes to process the parameter flow, detects the synchronicity of parameter changes of adjacent monitoring points, and calculates the cross-correlation function of the change time points; if the cross-correlation value is higher than the synchronization threshold, it is marked as a group synchronous change event; it compares the change time point with the monitoring preparation switch time point, and generates a synchronous change status identifier when all change points are ahead of the switch point; it binds the monitoring point number and status identifier, and outputs water ecological monitoring and control instructions, including instruction trigger conditions and control action types.

[0016] Preferably, the system further includes a data verification unit, which performs the following steps: The system captures the cycle number in the water ecological monitoring and control instructions and retrieves the complete trajectory data of the last abnormal event in the corresponding cycle. It then uses a Kalman filter algorithm to smooth the trajectory data and extracts the derivative sequence of the parameter change rate. The monotonicity of the derivative sequence is analyzed; if the change rate continues to increase positively and has not reached a steady state, the time integral from the current point to the preset stable point is calculated. Based on the time integral result, the end-of-cycle time setting is updated, and the results of the subsequent monitoring and control are output, including the duration of the remaining release path and the recommended time window for signal maintenance.

[0017] Preferably, the steps for obtaining the results of the subsequent monitoring and control are detailed as follows: The data is input from the data verification unit to align the time series of different monitoring points using a time warping algorithm. The timestamp sequence and parameter value sequence of the period following the last abnormal event are extracted, and the exponential smoothing method is used to predict the parameter change trend. The deviation between the predicted trend and the actual duration is compared. If the deviation exceeds the tolerance range, the supplementary time required to reach the parameter equilibrium point is recalculated. The supplementary time is called to adjust the monitoring and control cycle, and the end time point is corrected using a dynamic time warping algorithm to generate a simulated output of the continuous green light control result in the tail end.

[0018] Preferably, the implementation steps of the monitoring data acquisition unit include: A multi-source sensor network is deployed, covering chemical, biological, and physical sensors, to simultaneously collect parameters such as water temperature, conductivity, and chlorophyll concentration. Sensor data is aggregated through an IoT gateway for preliminary data cleaning and outlier removal. The cleaned data stream is stored in a time-series database, and the collection time points and geographical coordinates are marked.

[0019] Preferably, the implementation steps of the feature analysis unit include: The system receives data streams from monitoring data acquisition units, performs multi-resolution analysis to separate long-term trends from short-term fluctuations, uses a machine learning classifier to identify abnormal patterns in the fluctuations, calculates the entropy value of parameter changes as a measure of uncertainty based on the pattern recognition results, and reconstructs the parameter phase space by combining entropy value and time delay embedding techniques, extracting feature vectors to generate change time series and abnormal fluctuation features.

[0020] Compared with the prior art, the beneficial effects of the present invention are: This intelligent water ecology monitoring data processing system utilizes sensors, IoT technology, and artificial intelligence algorithms to achieve "three-dimensional monitoring" from local points to the entire watershed. Sensors are deployed at various locations along the banks and in the center of rivers and lakes to monitor multiple indicators such as water quality, water level, and flow rate in real time, enabling multi-dimensional analysis and digital display of the water body's health status. Through this comprehensive and multi-layered monitoring approach, regulatory authorities can fully perceive water environmental quality, accurately grasp the water quality status and changing patterns in different areas, and provide a comprehensive and accurate data foundation for water ecological environment management. This allows managers to move beyond simply understanding a few monitoring points and truly achieve comprehensive control over the water environment.

[0021] The system can analyze water quality data in a timely manner and is extremely sensitive to abnormal fluctuations. It utilizes advanced data analysis algorithms to perform real-time analysis of the collected water quality data. Once water quality parameters become abnormal, such as a sudden increase in ammonia nitrogen levels or a sharp decrease in dissolved oxygen levels, the system can immediately issue an alarm. Taking a river near a chemical industrial park as an example, in the event of a sudden pollution incident such as a chemical spill, the system can detect abnormal changes in water quality within a short period and quickly issue a warning to the management department, buying them valuable time for remediation and enabling them to take timely countermeasures to prevent the spread of pollution and minimize the losses caused by pollution.

[0022] Utilizing high-precision monitoring equipment and advanced analytical technologies, this system can accurately track and locate pollution sources. In the event of a water pollution incident, it uses high-tech terminal devices such as intelligent water quality monitors to achieve comprehensive, 24 / 7 monitoring and sensing of river and lake water quality. These devices are deployed at various key points in rivers and lakes. Once an abnormality in water quality is detected, the system, through data analysis and intelligent algorithms, can quickly and accurately locate the pollution source in the first instance, accurately identifying whether it is industrial wastewater discharge, agricultural non-point source pollution, or domestic sewage discharge. This greatly improves the efficiency and accuracy of pollution investigation, helping management departments quickly pinpoint the root cause of the problem, providing strong support for subsequent pollution control, making the control work more targeted, and improving the efficiency of pollution control.

[0023] The system collects and analyzes a large amount of monitoring data, providing a solid data foundation for the formulation of environmental policies. It conducts in-depth mining and correlation analysis of historical water quality data, meteorological data, and socio-economic data to reveal the inherent patterns and influencing factors of water quality changes. For example, by analyzing water quality data from different seasons and regions, as well as factors such as surrounding industrial production and agricultural activities, it provides a basis for formulating scientific and rational water resource management policies. Regarding water resource allocation, it uses monitoring data to understand the water resource needs and water quality conditions of different regions, achieving rational allocation and efficient utilization of water resources, avoiding ecological degradation caused by unreasonable water resource allocation, making the decision-making process more scientific and rational, and promoting the development of water ecological environment management towards refinement and scientification. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the working principle of the water ecological digital intelligent monitoring data integrated processing system described in this invention; Figure 2 A flowchart generated for the event response tag set; Figure 3 This is a flowchart illustrating the steps involved in defining the affected section units. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figure 1 This invention provides a comprehensive data processing system for intelligent digital monitoring of aquatic ecosystems. The system includes a monitoring data acquisition unit, a feature analysis unit, an event identification unit, an impact zone delineation unit, a parameter adjustment unit, and a control command generation unit. Specific implementation details are as follows: The monitoring data acquisition unit is responsible for acquiring real-time monitoring data from water body monitoring points. This data originates from a sensor network deployed in various aquatic environments and is transmitted in real-time to the central processing platform. The feature analysis unit receives the output from the monitoring data acquisition unit, performs time-series analysis and feature extraction on the real-time monitoring data, and identifies patterns and abnormal fluctuations in water quality parameters. Based on the results of the feature analysis unit, the event identification unit detects aquatic ecological anomalies and generates a set of event response tags containing event type and location information. The impact zone delineation unit uses the event response tag set to determine the water body segments affected by the anomaly and their time range, accurately delineating them through spatial interpolation and diffusion models. The parameter adjustment unit dynamically adjusts monitoring parameters, such as sampling frequency and depth, based on the output of the impact zone delineation unit, generating an optimized scheme. The control command generation unit converts the parameter adjustment scheme into executable control commands, enabling adaptive control of the monitoring system.

[0027] Example 1: See Figure 2The monitoring data acquisition unit is responsible for acquiring real-time multi-dimensional parameter sequences from water monitoring points. These sequences include continuous readings of various water quality indicators such as dissolved oxygen, turbidity, and pH. This data is continuously collected at a set sampling frequency by a sensor network deployed in the water body and transmitted to the data receiving platform via an IoT communication module. The output of the monitoring data acquisition unit constitutes the input source for the feature analysis unit. The feature analysis unit performs sliding window segmentation on the input parameter sequences. The size of the sliding window is dynamically configured according to the actual needs of the monitoring task and the characteristics of the data flow. Each window covers data points within a specific time span. Within each sliding window, the feature analysis unit performs parameter abrupt change point extraction. Abrupt change point identification uses a numerical difference-based method to detect sharp jumps in parameter values. Simultaneously, a linear regression model is used to fit the overall trend of the data within the window to calculate the trend offset. The feature analysis unit compares the calculated abrupt change points with a dynamically updated benchmark parameter derived from the moving average of historical monitoring data and calculates the deviation between the two. When the deviation calculated by the feature analysis unit exceeds a dynamically adjusted threshold, which is adaptively calculated based on the background fluctuation level of the aquatic environment, the feature analysis unit marks the event within this window as a potential anomalous event. The feature analysis unit integrates the precise timestamp information of the potential anomalous event and the geographic coordinates of the sensor nodes. The timestamp accuracy reaches the millisecond level, and the geographic coordinates are derived from the integrated GPS positioning module. Based on a predefined event classification rule base, the feature analysis unit assigns a unique event type code and a preliminary impact level to the potential anomalous event. The event type code corresponds to typical aquatic ecological events such as chemical pollutant leaks or abnormal algal proliferation. The feature analysis unit calls a geographic information system service to generate a spatial location identifier for the event, associating the event with a specific location on the map. Finally, the feature analysis unit packages and integrates the event type code, impact level, spatial location identifier, timestamp, and other relevant attributes to form a structured event response tag set. This event response tag set is temporarily stored in the system cache in a specific data format for subsequent unit calls.

[0028] The event identification unit receives a continuous real-time water quality parameter stream from the monitoring data acquisition unit interface. This stream contains time-series data from sensor readings of dissolved oxygen, turbidity, pH, etc. The data stream is transmitted asynchronously via a message middleware to ensure system responsiveness. The wavelet transform algorithm integrated within the event identification unit begins multi-scale decomposition of the input parameter stream. The wavelet transform uses Daubechies wavelet basis functions to decompose the original signal into sub-signals with different frequency components. The event identification unit focuses on analyzing the high-frequency component sequence obtained after the wavelet transform. These high-frequency components carry short-term fluctuation information of the parameters. The event identification unit applies a peak detection algorithm to identify abnormal fluctuation patterns from these high-frequency components. The event identification unit calculates the energy spectral density of these abnormal fluctuation patterns. The energy spectral density reveals the distribution characteristics of the fluctuation energy in the frequency domain. By finding peak points on the energy spectral density curve that are significantly higher than the background noise level, the event identification unit determines the start time point of the abnormal event. Based on the identified abnormal event start point, the event identification unit slides a preset-length time window forward along the time axis. The window length is set according to the typical duration of the identified event type. The event identification unit calculates the statistical variance of parameter values ​​within a sliding window to quantify fluctuation amplitude and calculates the autocorrelation function of the parameter sequence to assess its time dependence. Based on the persistently high range of variance values ​​and the decay pattern of the autocorrelation function, the event identification unit determines the duration and decay characteristics of abnormal events. The duration refers to the length of time the abnormal state persists, and the decay characteristics describe the rate at which parameters return to normal. The event identification unit has a built-in predefined event template library containing waveform feature patterns of various typical aquatic ecological anomalies. The unit performs similarity matching between the currently extracted abnormal fluctuation pattern and the templates in the event template library. When the matching degree exceeds a set threshold, the unit assigns the corresponding event type code. The event identification unit also accesses the elevation database of monitoring points, with elevation data sourced from the Digital Elevation Model (DEM). The unit uses elevation information to correct the preliminary assessment of the event's impact level; for example, anomalies located in low-lying areas may have a wider spread. The event identification unit ultimately outputs a complete set of event response tags. Each tag entry in the set explicitly includes the event type code, the corrected impact level, the spatial location identifier, and relevant time information. The entire event response tag set is serialized and output in JSON format to ensure that downstream units can accurately parse and use it.

[0029] The implementation of the monitoring data acquisition unit relies on the deployment of a multi-layered sensor network. This network includes various types of sensing devices, such as chemical sensors, biological sensors, and physical sensors. Chemical sensors measure the concentration of various ions and compounds in the water, biological sensors detect microbial community activity or specific biomarkers, and physical sensors collect physical parameters such as water temperature, conductivity, and chlorophyll concentration. All these sensor nodes aggregate data through a unified IoT gateway device. The IoT gateway supports multiple communication protocols to adapt to the interface standards of different sensors. The IoT gateway performs preliminary data cleaning on the received raw sensor data. Cleaning rules include range rationality checks and statistical outlier removal algorithms to remove obviously erroneous readings. The cleaned, valid data stream is stored in a time-series database optimized for time-series data. The database management system adds a precise acquisition timestamp and corresponding geographic coordinate label to each data point. The timestamp synchronization uses a high-precision clock source, and the geographic coordinates are provided by the sensor's built-in GPS module or an external positioning system.

[0030] The feature analysis unit continuously receives data streams from the monitoring data acquisition unit. Internally, the feature analysis unit executes a multi-resolution analysis algorithm, employing methods such as wavelet transform or empirical mode decomposition to decompose the original data sequence into low-frequency components reflecting long-term trends and detailed components reflecting short-term fluctuations. The feature analysis unit employs a machine learning classifier to perform pattern recognition on the decomposed short-term fluctuation components. The machine learning classifier, such as a support vector machine or convolutional neural network, is trained on anomaly fluctuation cases labeled in a large amount of historical monitoring data. Based on the pattern recognition results, the feature analysis unit calculates the information entropy value of the parameter change sequence. Information entropy serves as a quantitative indicator of the uncertainty of parameter changes. The feature analysis unit further combines the calculated entropy value with time-delay embedding technology from time-series analysis. Time-delay embedding technology is used to reconstruct the phase space of parameter changes, extracting a set of feature vectors that characterize the system dynamics from the reconstructed phase space. This set of feature vectors is ultimately used to generate a time-series describing the evolution of water quality parameters and characterizing abnormal fluctuation features of anomalous states.

[0031] Example 2: See Figure 3The impact zone delineation unit calls upon the event response tag set generated by the event identification unit. This set contains spatial location identifiers and timestamps. The spatial location identifiers are geographic coordinate pairs, and the timestamps record the exact time of occurrence of the anomalous event. The impact zone delineation unit performs spatial clustering analysis on the events in the event response tag set, using the DBSCAN clustering algorithm to identify geographically adjacent anomalous event clusters. It calculates the center coordinates of each event cluster, taking the arithmetic mean of the coordinates of all points within the cluster. The occurrence time of the event cluster is the timestamp of the earliest event within the cluster. The impact zone delineation unit uses the Kriging spatial interpolation method, which, based on the water quality parameter values ​​of discrete monitoring points, calculates the parameter estimates for unsampled locations within the water body area, generating a continuous parameter spatial distribution map. The impact zone delineation unit identifies spatial clusters of parameter anomalies on the generated parameter spatial distribution map. Parameter anomalies are determined by calculating the Z-score of each grid point and comparing it with a threshold. Spatial clusters are divided using a connected component labeling algorithm in image processing. The influence zone delineation unit calculates the geometric centroid position of each identified cluster area and the boundary radius representing the area's extent. The geometric centroid is obtained through the polygon centroid calculation formula, and the boundary radius is defined as the distance from the centroid to the farthest boundary point of the area.

[0032] The affected section delineation unit accesses an external hydrological database to obtain real-time water flow velocity vector data. This data includes information on flow velocity magnitude and direction. Combined with the flow data, a particle tracking model is used to simulate the diffusion path of anomalous substances. The particle tracking model, based on the Eulerian-Lagrange method, calculates the trajectory of virtual particles under the influence of water flow, thereby predicting the dynamic diffusion process of the anomalous section and the time it takes for them to reach downstream points. Based on the simulated diffusion path, the affected section delineation unit divides the affected water body sections. These sections are represented as polygonal areas on an electronic map. Each water body section is marked with its start time, duration, and water quality anomaly intensity value. The start time is calculated from the time the source event occurs, the duration is determined by the duration of the anomalous state simulated by the diffusion model, and the water quality anomaly intensity value is the average of the anomaly parameter values ​​of all grid points within the section. The affected area delineation unit applies a time series clustering algorithm to analyze continuously occurring anomalous events. The time series clustering algorithm uses the K-means algorithm to cluster the timestamp vectors of the events, merging temporally consecutive events into a single traffic density offset time period, which represents the peak period of anomalous activity. The affected area delineation unit ultimately generates a set containing information on all affected water body segments. Each water body segment entry in the set includes its spatial geometry, temporal attributes, and anomaly intensity information. The set of information on all affected water body segments is stored and output in a spatial database format.

[0033] The parameter adjustment unit receives the set of affected water body segments output by the affected segment delineation unit. It then parses the monitoring point numbers recorded in the set, ensuring a one-to-one correspondence between each monitoring point number and a physical sensor device identifier. The unit accesses the system database, querying historical log data for the specified monitoring points over the two most recent complete monitoring cycles. It focuses on extracting detailed records of the last occurring anomaly, extracting the end time of the anomaly and the official end time of the monitoring cycle from the log records, and calculating the difference between the two time points. The unit uses a time series prediction model to predict the remaining duration of the last anomaly. This model employs an autoregressive integral moving average model, using the historical parameter value sequence of the last anomaly as input for fitting and extrapolation. Finally, the unit compares the predicted remaining duration with a preset time threshold. This threshold is set based on the water body's self-purification rate and the urgency of the monitoring task. Monitoring points whose predicted remaining duration exceeds the threshold are considered as sites where anomaly handling has not been completed and require continued close monitoring.

[0034] The parameter adjustment unit sorts the monitoring points according to their predicted remaining duration. Monitoring points with longer predicted remaining durations have higher urgency for processing. A priority sequence of monitoring point anomalies is generated, consisting of a list of monitoring point numbers arranged from highest to lowest priority. The parameter adjustment unit dynamically adjusts the allocation of system monitoring resources based on this priority sequence. Monitoring resources include available sampling time, data transmission bandwidth, and sensor power consumption budget. It calculates the additional monitoring time period required for each monitoring point in the priority sequence, with the length of the additional monitoring time period proportional to the predicted remaining duration and severity of the anomaly. Specifically, the parameter adjustment unit adjusts the monitoring frequency and parameter acquisition depth. Monitoring frequency refers to the number of samples taken per unit time, while parameter acquisition depth may involve adjusting the sensor measurement range or changing the sampling water depth. The parameter adjustment unit records each monitoring point number and its corresponding adjusted configuration parameters, forming a complete monitoring parameter adjustment plan. This plan exists in the form of a structured configuration file, which explicitly lists the new sampling strategy for each monitoring point.

[0035] The internal processing flow of the influence zone delineation unit begins by reading the data structure of the event response label set. This data structure includes multiple fields such as event ID, latitude and longitude, timestamp, and event type. The spatial clustering module of the influence zone delineation unit loads the latitude and longitude coordinates of all events, constructing a spatial point dataset. When applying the DBSCAN clustering algorithm, the neighborhood radius and minimum number of points need to be set. The algorithm automatically groups spatially connected event points into the same cluster. The influence zone delineation unit calculates representative coordinates for each identified event cluster, typically using the average latitude and longitude of all points within the cluster as the center coordinates. It also records the timestamp of the earliest event occurring within the cluster as the cluster's start time. The influence zone delineation unit calls the spatial interpolation engine. The engine reads the latest parameter measurements and locations of all monitoring points. During execution, the Kriging spatial interpolation method calculates a variogram to quantify spatial correlation, thereby generating a smooth, continuous spatial distribution surface. The spatial analysis module of the impact zone delineation unit scans the interpolated raster map and uses a thresholding method to identify anomalous rasters. The Z-score threshold is typically set to 2 or 3, grouping anomalous rasters into different clustering regions. The impact zone delineation unit calculates the geometric properties of each clustering region. The centroid coordinates are calculated using the average of the polygon vertex coordinates, and the boundary radius is determined by calculating the maximum distance from the particle to each vertex of the polygon. The hydrological analysis module of the impact zone delineation unit calls upon the velocity vector field data stored in the hydrological database. The particle tracking model releases a large number of virtual particles starting from the center of the event cluster and iteratively calculates the new positions of the particles according to the velocity field at time steps, thereby depicting the diffusion path. The impact zone delineation unit determines the boundary of the affected water body segment based on the distribution range of the particle cloud. The start time of the water body segment is synchronized with the start time of the event cluster, and the duration is determined by analyzing the time it takes for the parameter concentration in the particle trajectory to decay to the background level.

[0036] The time series analysis module of the affected segment delineation unit clusters event timestamps, merging events that are close in time into larger time windows, and finally outputs a set of water body segments containing spatial polygon and time window information. The parameter adjustment unit's workflow begins with parsing the set of affected water body segments, extracting all monitoring point numbers mentioned in the set. The parameter adjustment unit initiates a query request to the system log database, with the query conditions being these monitoring point numbers and the time range of the two most recent monitoring periods. From the returned log data, it locates the record of the last abnormal event for each monitoring point, parses the abnormal end time field and the period end time field in the record, and directly calculates the difference. The prediction module of the parameter adjustment unit loads the parameters of the time series prediction model. The autoregressive integral moving average model needs to determine the autoregression order, differencing times, and moving average order. The model is trained using historical parameter data during the abnormal event and predicts the time required for parameters to return to normal in the future. The parameter adjustment unit compares the prediction results with a preset threshold, which is a fixed value, such as 4 hours or 8 hours, and marks monitoring points whose predicted duration exceeds the threshold. The parameter adjustment unit sorts these monitoring points in descending order based on their timeout duration, generating a priority list. The resource allocation algorithm of the parameter adjustment unit calculates the additional monitoring time allocated to each monitoring point based on the priority list and the total available system resources, adjusting the monitoring frequency (e.g., increasing from once per hour to once every 15 minutes) and the parameter acquisition depth (e.g., adjusting from 0.5 meters underwater to 1.0 meter). The parameter adjustment unit binds the adjustment commands to the monitoring point numbers and writes them into a structured configuration file, completing the formulation of the monitoring parameter adjustment plan.

[0037] Example 3: The control command generation unit receives a monitoring parameter adjustment plan from the parameter adjustment unit. This plan is a structured document containing a list of monitoring point numbers requiring configuration adjustments and a new starting configuration time value for each monitoring point. The starting configuration time value defines the start time of the new sampling period for each monitoring point. The control command generation unit receives real-time water quality parameter stream data from the monitoring point group. This group consists of spatially adjacent and functionally related monitoring points. The water quality parameter stream includes continuous time-series readings of parameters such as dissolved oxygen, turbidity, and pH. The data is transmitted to the data receiving buffer of the control command generation unit via a dedicated network. The control command generation unit invokes a processing program deployed on an edge computing node. Edge computing nodes are lightweight computing devices located close to the monitoring points. The processing program performs real-time analysis tasks on the input parameter stream, including detecting the synchronicity characteristics of parameter changes at adjacent monitoring points. Synchronicity characteristics are evaluated by comparing the change patterns of parameter sequences at different monitoring points along the time axis. The control command generation unit calculates the cross-correlation function value between each pair of adjacent monitoring point parameter change time points. The cross-correlation function quantifies the similarity between two time series at different time lags. Cross-correlation function The calculation formula is:

[0038] in: Represents the cross-correlation function, symbol The time series of parameters representing the first monitoring point at time 1 The value of , sign The time series of parameters representing the second monitoring point at time 1 The value of , sign Represents the time lag between two sequences, with the sign... Representative in lag The control command generation unit calculates the cross-correlation function value. It compares the calculated maximum cross-correlation function value with a preset synchronization threshold, an empirical value derived from historical normal data to distinguish between random fluctuations and genuine synchronization events. When the cross-correlation function value exceeds the synchronization threshold, the control command generation unit identifies the parameter changes at this group of monitoring points as a group synchronization event. A group synchronization event means that multiple monitoring points are affected by the same abnormal factor almost simultaneously. The control command generation unit further compares the identified parameter change time points with the preset monitoring preparation switching time points in the monitoring parameter adjustment plan. The monitoring preparation switching time point refers to the planned moment for adjusting the monitoring point parameters. The control command generation unit checks whether the change time point of each monitoring point is earlier than its corresponding monitoring preparation switching time point. If all change time points meet the advance condition, the control command generation unit generates a synchronization change status flag, a Boolean flag with a value of true. The control command generation unit binds the list of monitoring point numbers with the synchronous change status identifier to generate the final water ecological monitoring control command. The water ecological monitoring control command is a data packet containing a command header, a trigger condition list, and an execution action list. The command header contains the command ID and timestamp, the trigger condition list specifies the conditions under which the command takes effect, and the execution action list describes in detail the type of control operation to be performed, such as increasing the sampling frequency, switching the sensor range, or activating the emergency communication link.

[0039] The data verification unit within the system architecture begins operation. This unit captures the monitoring cycle number carried in the water ecological monitoring and control instructions. The monitoring cycle number is a unique identifier used to associate monitoring task instances within a specific time period. The data verification unit retrieves the complete trajectory data of the last anomalous event occurring within the monitoring cycle from the monitoring cycle number. This complete trajectory data is a multi-dimensional time series, recording the history of changes in all relevant water quality parameters from the occurrence, development, and dissipation of the anomalous event. The data verification unit applies a Kalman filter algorithm to smooth the acquired trajectory data. The Kalman filter algorithm is a recursive filtering technique that effectively suppresses measurement noise and restores the true trend of parameter changes. The data verification unit calculates the first derivative of the smoothed trajectory data, obtaining a derivative sequence of the parameter change rate over time. This derivative sequence reflects the speed and direction of parameter change. The data verification unit analyzes the monotonicity of the derivative sequence, checking whether the sign of the derivative sequence values ​​remains consistent. If the analysis finds that the parameter change rate continues to show a positive growth trend and the parameter value has not yet reached a preset stable state range (determined by the water environment background value), the data verification unit initiates time integration calculation. The goal of time integration calculation is to estimate the time required from the current time point until the parameter value enters a steady-state range. Integration is performed on the parameter rate of change curve, integrating from the current point to the intersection of the curve and the steady-state threshold boundary. Based on the results of the time integration calculation, the data validation unit updates the tail-end time setting of the current monitoring cycle. The tail-end time refers to the period reserved before the end of the monitoring cycle for observing the subsequent development of abnormal events. The data validation unit outputs the follow-up monitoring and control results, which is a data structure containing two key fields: the remaining release path duration field, whose value is derived from the time integration result and predicts how long it will take for the abnormal impact to completely dissipate; and the recommended signal maintenance time window field, whose value is calculated based on historical patterns and current trends, suggesting an additional time range for the system to maintain the current monitoring intensity.

[0040] The internal logic of the control command generation unit begins by parsing the configuration file of the monitoring parameter adjustment scheme. The configuration file is in JSON or XML format, and the control command generation unit uses a parser to read the monitoring point number and the starting configuration time value fields. The control command generation unit subscribes to message queue topics to obtain the water quality parameter stream of the monitoring point group in real time. The parameter stream data packet contains a timestamp and the monitoring point ID. The control command generation unit distributes the parameter stream data to the corresponding edge computing nodes. The service program running on the edge computing nodes receives the data and stores it in a local cache. The synchronization detection module on the edge computing nodes selects spatially adjacent monitoring point pairs and extracts their parameter sequences within the most recent time window from the cache. The synchronization detection module performs cross-correlation calculations on each pair of sequences, performing the cross-correlation calculation within a certain lag range to find the maximum correlation value. The edge computing nodes return the calculated maximum cross-correlation value and its corresponding lag value to the main logic of the control command generation unit. The main logic of the control command generation unit compares the received cross-correlation value with the synchronization threshold stored in the configuration library. The synchronization threshold may be configured according to different water areas and different seasons. When the conditions are met, the main logic creates a group synchronization change event object, which records the monitoring point IDs and change times involved. The time comparison module of the control command generation unit reads the monitoring preparation switching time points in the monitoring parameter adjustment plan and compares them sequentially with the change times in the event object. The comparison results drive the status identifier generation module to generate synchronization change status identifiers. Finally, the command assembly module of the control command generation unit combines the monitoring point number list, synchronization change status identifiers, trigger condition logic, and preset control action templates into a water ecological monitoring control command data packet, which is then sent to the actuator through the communication interface.

[0041] The execution thread of the data verification unit is triggered upon receiving a water ecological monitoring and control command. The data verification unit parses the command header to obtain the monitoring cycle number. The data verification unit sends a query request to the data warehouse, retrieving the trajectory data of the last abnormal event based on the monitoring cycle number and event type. The trajectory data may be distributed across multiple data tables, requiring a join query. The data preprocessing module of the data verification unit aligns and interpolates the query results to ensure uniform timestamp intervals. The preprocessed data is then fed into the input interface of a Kalman filter. The Kalman filter recursively filters using a preset process noise and observation noise covariance matrix, outputting a smoothed trajectory. The differential calculation module of the data verification unit numerically differentiates the smoothed trajectory, using the central difference method to calculate the rate of change at each time point. The monotonicity analysis module of the data verification unit scans the rate of change sequence, checks the continuity of its sign, and calls the state judgment module to check whether the current parameter value is within a stable range. If the condition of continuous positive growth without reaching stability is met, the integral calculation module of the data verification unit begins operation. The integral calculation adopts a numerical integration method, such as the trapezoidal rule, integrating from the current time point to the intersection of the rate of change curve and the zero line. The intersection time minus the current time is the integral result. The periodic update module of the data verification unit adjusts the end time parameter of the monitoring period according to the integral result and generates a post-monitoring continuous control result message containing the remaining release path duration and the recommended signal maintenance time window, which is then sent to the system control center.

[0042] Example 4: The trajectory data output by the data verification unit serves as the starting point for processing. The trajectory data includes a timestamp sequence of the final anomaly event's decline phase and a corresponding water quality parameter value sequence. The timestamp intervals are uniform, and the parameter values ​​include multiple indicators such as dissolved oxygen and turbidity. A time warping algorithm is applied to align the time series sequences from different monitoring points. The time warping algorithm employs a dynamic time warping method, which overcomes the differences in sequence length and phase shifts caused by slight asynchrony in data acquisition times at different monitoring points. It aligns the two sequences on the time axis by finding an optimal curved path. The timestamp sequence and parameter value sequence for the latter part of the final anomaly event are extracted. The latter part refers to the period from the occurrence of the anomaly peak until the end of data recording. The timestamp sequence is used to locate the time point, and the parameter value sequence characterizes the water quality recovery process. An exponential smoothing method is used to predict the parameter change trend. The exponential smoothing method assigns greater weight to recent observations and adjusts the dependence on historical data through a smoothing coefficient, generating a predicted value sequence of parameter values ​​for future time points. The deviation between the predicted trend and the actual duration is compared. The predicted trend refers to the parameter change path extrapolated by the smoothing method, while the actual duration is the time span from the current moment to the last recorded parameter. The deviation is the absolute difference between the predicted value and the actual observed value at the corresponding time point. If the deviation exceeds the tolerance range, which is a preset allowable error threshold, the supplementary time required to reach the parameter equilibrium point is recalculated. The parameter equilibrium point is the stable parameter value determined based on the historical normal state of the water body. The supplementary time is estimated using an iterative algorithm to estimate the additional time required for the evolution from the current state to the equilibrium point. The supplementary time is then used to adjust the monitoring and control cycle. The monitoring and control cycle defines the time interval for the system to perform monitoring tasks. A dynamic time warping algorithm is used to correct the end time point, matching the ideal supplementary time with system resource constraints. A simulated output of the tail-end green light continuous control result is generated. The simulated output is a data object containing the adjusted monitoring end time suggestion and the expected parameter change curve within the time period.

[0043] The implementation steps of the monitoring data acquisition unit include deploying a multi-source sensor network, which consists of several sensor nodes distributed across the target water body area. Each node integrates multiple sensing modules, including chemical sensors, biological sensors, and physical sensors. Chemical sensors detect the concentrations of chemical substances such as dissolved oxygen, ammonia nitrogen, and total phosphorus in the water. Biological sensors monitor biological activity indicators such as chlorophyll a concentration and algae density. Physical sensors collect physical parameters such as water temperature, conductivity, and turbidity. Sensor nodes transmit the collected raw data to an IoT gateway device via wired or wireless means. The IoT gateway is responsible for protocol conversion and initial data aggregation. The IoT gateway performs preliminary data cleaning on the received sensor data, including removing outliers that are significantly outside the physical limits and correcting data loss caused by temporary sensor malfunctions. A time-series database is used to store the cleaned data stream. This database is optimized for data points arriving in chronological order, supporting efficient writing and querying by time range. Each data point successfully stored in the database is marked with its precise acquisition time and corresponding geographic coordinates. The acquisition time is synchronized by a high-precision clock source, and the geographic coordinates come from the GPS module built into the sensor node.

[0044] The process of obtaining the results of continuous monitoring and control in the later stages relies on in-depth processing of the output data from the data verification unit. The processing system reads the trajectory data file, typically in CSV or binary format, containing timestamps, monitoring point IDs, and multiple parameter numerical fields. The time warping module loads the time-series data of two or more monitoring points to be compared. The dynamic time warping algorithm calculates the Euclidean distance between each point in the sequence and finds the warping path with the minimum cumulative cost through dynamic programming. The aligned sequence is then sent to the feature extraction module, which extracts a subsequence representing the later stage of the anomalous event from each sequence. The starting point of the subsequence is defined as the moment when the parameter drops from its peak to a certain percentage. The prediction module calls the exponential smoothing algorithm, which uses a smoothing coefficient α and iteratively predicts based on the principle of weighted averaging to generate parameter prediction values ​​for several future time steps. The deviation analysis module compares the predicted sequence with the actual subsequent observation sequence or with the expected recovery curve, calculating the mean absolute deviation. The decision module determines whether the mean absolute deviation exceeds the set fault tolerance limit. If it does, it initiates the supplementary time calculation routine, which uses numerical integration to estimate the time from the current parameter value to the equilibrium point along the predicted trend. The cycle adjustment module receives the supplementary time value and, combined with the system's current load and task priorities, adjusts the end time of the original monitoring and control cycle using the idea of ​​dynamic time warping algorithm, generating a new cycle configuration. The result simulation module packages the new configuration and the predicted curve into a tail-end green light continuous control result message and outputs it. Refer to Table 1, which shows a parameter sequence segment of a monitoring point and its predicted value to illustrate the data format.

[0045] Table 1: Dissolved oxygen sequence and prediction for monitoring point A (subsequent segment)

[0046] The deployment of multi-source sensor networks requires detailed site surveys and network planning. Site surveys determine the optimal placement of sensor nodes to cover key water areas, while network planning ensures reliable communication links. Chemical sensors require regular on-site calibration using standard solutions to guarantee measurement accuracy. Biosensors may involve optical or electrochemical detection principles, necessitating the maintenance of clean optical windows or active electrodes. Physical sensors, such as water temperature sensors, typically employ platinum resistance thermometers or thermistors, while conductivity sensors utilize electrode methods. The IoT gateway is selected from industrial-grade devices supporting multiple communication protocols, including 4G, 5G, and LoRa. The IoT gateway's built-in preliminary data cleaning program operates based on a configurable rule base. The time-series database is deployed on a server cluster, providing high availability and scalability. The database schema design includes fields such as timestamps, measurement point IDs, parameter types, parameter values, and quality identifiers. Synchronization of data acquisition time points is achieved through a network time protocol, and geographic coordinate information is accurately measured using a professional GPS receiver and written into the configuration file during sensor node deployment.

[0047] Example 5: The feature analysis unit receives a continuous data stream from the monitoring data acquisition unit. The data stream contains time-series readings of parameters such as dissolved oxygen, turbidity, and pH value uploaded from water monitoring points. This data is transmitted to the feature analysis unit's data input buffer at fixed time intervals. The feature analysis unit performs multi-resolution analysis to separate long-term trends and short-term fluctuations. Multi-resolution analysis employs a wavelet transform algorithm, which uses a specific mother wavelet function to decompose the input signal, obtaining approximation coefficients and detail coefficients at different scales. The approximation coefficients correspond to the macroscopic trend components of the signal, while the detail coefficients correspond to the microscopic fluctuation components. For example, for a continuous 24-hour dissolved oxygen concentration sequence, the wavelet transform decomposes it into a low-frequency trend line reflecting slow intraday changes and a high-frequency fluctuation signal superimposed on it, reflecting instantaneous disturbances. A machine learning classifier is used to identify abnormal patterns in the fluctuations. The machine learning classifier is a pre-trained support vector machine model. The training data for the support vector machine model comes from historically accumulated, labeled normal fluctuations and various abnormal fluctuation samples. The feature analysis unit inputs the high-frequency detail coefficient sequence obtained from wavelet decomposition into the support vector machine model. The support vector machine model extracts the statistical and morphological features of the sequence and compares them with the trained decision boundary to determine whether the current fluctuation belongs to an abnormal pattern. For example, when the high-frequency detail coefficient sequence shows an amplitude far exceeding the historical average and a violent oscillation pattern in a short period of time, the support vector machine model may classify it as an abnormal fluctuation caused by a suspected pollution event.

[0048] The entropy value of parameter changes, calculated based on pattern recognition results, serves as a measure of uncertainty. The entropy calculation employs the information entropy formula, which quantifies the degree of uncertainty or randomness in the parameter value sequence. The feature analysis unit selects a parameter value sequence within a sliding time window, calculates the probability of each unique value appearing in the sequence, and then sums them according to the information entropy formula. When the sequence values ​​change smoothly and predictably, the information entropy value is low; when the sequence values ​​fluctuate drastically and disorderly, the information entropy value increases significantly. For example, after identifying an abnormal fluctuation pattern, the feature analysis unit calculates the information entropy of the parameter sequence within a time window before and after the abnormal event, quantifying the increased uncertainty brought about by the abnormal event through abrupt changes in entropy value. The parameter phase space is reconstructed by combining entropy and time-delay embedding techniques. Time-delay embedding is a method for reconstructing the dynamic characteristics of a system from a univariate time series. This method requires determining two key parameters: time delay and embedding dimension. The feature analysis unit first uses the mutual information method or autocorrelation function method to determine the optimal time delay, and then uses the spurious nearest neighbor method to determine the appropriate embedding dimension. After determining the time delay and embedding dimension, the feature analysis unit maps the original one-dimensional parameter time series into a high-dimensional phase space, where each point represents the system's state at a given moment. The calculated entropy value can be used as a basis for filtering or weighting phase space points. For example, time periods with excessively high entropy values ​​may correspond to abnormally chaotic states of the system, requiring extra caution or different weights when reconstructing the phase space. Feature vectors are extracted to generate variation time series and abnormal fluctuation features. These feature vectors are a set of quantitative indicators extracted from the reconstructed phase space that characterize the system's dynamics. The feature analysis unit calculates the geometric and statistical features of the phase space trajectory. These feature vectors collectively constitute a mathematical description of the parameter variation time series pattern and abnormal fluctuation features. For example, a positive Lyapunov exponent indicates that the system is sensitive to initial conditions and exhibits chaotic characteristics, which may correspond to a specific abnormal fluctuation pattern; the correlation dimension reflects the complexity of the system's dynamics.

[0049] The specific operation of the feature analysis unit begins with the data interface module receiving data packets pushed by the monitoring data acquisition unit. The data interface module parses and verifies the data packets to ensure data integrity and correct format. Verified data is sent to the preprocessing module, which performs necessary data normalization or smoothing to eliminate dimensional differences and some noise. The preprocessed data stream enters the core analysis engine, which first activates the wavelet transform module. The wavelet transform module performs multi-resolution decomposition of the signal according to the preset wavelet basis functions and decomposition level. The resulting sequence of detail coefficients is sent to the pattern recognition module, which calls the loaded support vector machine model for classification. The output of the pattern recognition module triggers the entropy calculation module, which calculates the information entropy of the parameter sequence within a configurable sliding window. The phase space reconstruction module reconstructs the original parameter sequence using time-delay embedding technology. The phase space reconstruction module receives the entropy calculation results and uses them as auxiliary information to optimize the reconstruction process or subsequent analysis. Finally, the feature extraction module calculates a predefined set of feature vectors from the reconstructed phase space. The feature extraction module packages the feature vectors with information such as pattern recognition results and entropy values ​​to generate the final change time series and abnormal fluctuation feature descriptors.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive data processing system for intelligent digital monitoring of water ecology, characterized in that, The system includes: The monitoring data acquisition unit acquires real-time monitoring data from water body monitoring points. The feature analysis unit processes the real-time monitoring data to extract the temporal variation and abnormal fluctuation characteristics of water quality parameters; The event identification unit identifies aquatic ecological abnormal events based on the change time sequence and abnormal fluctuation characteristics and generates a set of event response tags; The affected section delineation unit calls the event response tag set to determine the affected water body section and its time range; The parameter adjustment unit generates a monitoring parameter adjustment scheme based on the affected water body section; The control command generation unit generates water ecological monitoring and control commands using the monitoring parameter adjustment scheme.

2. The water ecological intelligent monitoring data integrated processing system according to claim 1, characterized in that, The generation of the event response tag set includes: The monitoring data acquisition unit acquires multi-dimensional parameter sequences from water monitoring points in real time. The feature analysis unit performs sliding window segmentation on the parameter sequences, extracts parameter mutation points and trend offsets within each window, calculates the deviation between mutation points and baseline parameters, and marks potential abnormal events when the deviation exceeds a dynamic threshold. Combining the event's timestamp and geographic coordinates, the unit assigns event type codes and impact levels, generates spatial location identifiers, and finally integrates them into an event response tag set.

3. The water ecological digital monitoring data integrated processing system according to claim 1, characterized in that, The specific implementation steps of the event identifier unit are as follows: Real-time water quality parameter streams, including dissolved oxygen, turbidity, and pH, are received from the monitoring data acquisition unit. Wavelet transform algorithm is used to decompose the multi-scale components of the parameter stream, extract abnormal fluctuation patterns in high-frequency components, calculate the energy spectral density of the fluctuation patterns, and identify the starting point of abnormal events corresponding to energy peaks. Based on the starting point of the abnormal event, a sliding time window is used to calculate the variance and autocorrelation function of the parameter values ​​within the window, thereby determining the duration and decay characteristics of the abnormal event. The abnormal fluctuation pattern is matched with a predefined event template, an event type code is assigned, and the impact level is corrected by combining the elevation data of the monitoring points. Finally, an event response label set is output, where each label contains an event type code, an impact level, and a spatial location identifier.

4. The water ecological intelligent monitoring data integrated processing system according to claim 3, characterized in that, The specific implementation steps for the affected section delineation unit are as follows: The spatial location identifiers and timestamps in the event response tag set are used to obtain the center coordinates and occurrence time of the abnormal event clusters; the Kriging spatial interpolation method is used to generate a continuous distribution map of water parameters based on the monitoring point data to identify the spatial clustering areas of abnormal parameter values. Calculate the centroid location and boundary radius of each agglomeration region, and combine it with water flow velocity vector data to predict the diffusion path and arrival time of the anomalous section; Based on the diffusion path, the affected water body segments are divided, and the segment start time, segment duration, and water quality anomaly intensity value are marked. Through time series clustering algorithm, continuous abnormal events are merged into traffic density offset time periods, and a set of affected water body segments is generated.

5. The water ecological digital monitoring data integrated processing system according to claim 4, characterized in that, The specific implementation steps of the parameter adjustment unit are as follows: Based on the monitoring point numbers in the set of affected water body sections, the log data of the last abnormal event within two consecutive monitoring periods is queried, and the time difference between the end time of the abnormality and the end time of monitoring is extracted. A time series prediction model is used to predict the remaining duration of the last abnormality and compare it with a preset threshold to filter monitoring points that have not completed abnormality processing. Based on the waiting time of the uncompleted monitoring points, a priority sequence of abnormality waiting for monitoring points is generated. According to the priority sequence, monitoring resources are dynamically reallocated, an additional monitoring time period is calculated for each monitoring point, the monitoring frequency and parameter acquisition depth are adjusted, and the monitoring point number and the adjusted configuration are recorded to form a monitoring parameter adjustment plan.

6. The water ecological digital monitoring data integrated processing system according to claim 5, characterized in that, The specific implementation steps of the control command generation unit are as follows: The system analyzes the initial configuration time value of monitoring points in the monitoring parameter adjustment scheme to obtain the water quality parameter flow of the monitoring point group in real time; it applies edge computing nodes to process the parameter flow, detects the synchronicity of parameter changes of adjacent monitoring points, and calculates the cross-correlation function of the change time points; if the cross-correlation value is higher than the synchronization threshold, it is marked as a group synchronous change event; it compares the change time point with the monitoring preparation switch time point, and generates a synchronous change status identifier when all change points are ahead of the switch point; it binds the monitoring point number and status identifier, and outputs water ecological monitoring and control instructions, including instruction trigger conditions and control action types.

7. The water ecological intelligent monitoring data integrated processing system according to claim 6, characterized in that, The system also includes a data verification unit, which performs the following steps: The system captures the cycle number in the water ecological monitoring and control instructions and retrieves the complete trajectory data of the last abnormal event in the corresponding cycle. It then uses a Kalman filter algorithm to smooth the trajectory data and extracts the derivative sequence of the parameter change rate. The monotonicity of the derivative sequence is analyzed; if the change rate continues to increase positively and has not reached a steady state, the time integral from the current point to the preset stable point is calculated. Based on the time integral result, the end-of-cycle time setting is updated, and the results of the subsequent monitoring and control are output, including the duration of the remaining release path and the recommended time window for signal maintenance.

8. The water ecological intelligent monitoring data integrated processing system according to claim 7, characterized in that, The steps for obtaining the results of the subsequent monitoring and control are detailed as follows: The data is input from the data verification unit to align the time series of different monitoring points using a time warping algorithm. The timestamp sequence and parameter value sequence of the period following the last abnormal event are extracted, and the exponential smoothing method is used to predict the parameter change trend. The deviation between the predicted trend and the actual duration is compared. If the deviation exceeds the tolerance range, the supplementary time required to reach the parameter equilibrium point is recalculated. The supplementary time is called to adjust the monitoring and control cycle, and the end time point is corrected using a dynamic time warping algorithm to generate a simulated output of the continuous green light control result in the tail end.

9. The water ecological digital monitoring data integrated processing system according to claim 1, characterized in that, The implementation steps of the monitoring data acquisition unit include: A multi-source sensor network is deployed, covering chemical, biological, and physical sensors, to simultaneously collect parameters such as water temperature, conductivity, and chlorophyll concentration. Sensor data is aggregated through an IoT gateway for preliminary data cleaning and outlier removal. The cleaned data stream is stored in a time-series database, and the collection time points and geographical coordinates are marked.

10. The water ecological intelligent monitoring data integrated processing system according to claim 9, characterized in that, The implementation steps of the feature analysis unit include: The system receives data streams from monitoring data acquisition units, performs multi-resolution analysis to separate long-term trends from short-term fluctuations, uses a machine learning classifier to identify abnormal patterns in the fluctuations, calculates the entropy value of parameter changes as a measure of uncertainty based on the pattern recognition results, and reconstructs the parameter phase space by combining entropy value and time delay embedding techniques, extracting feature vectors to generate change time series and abnormal fluctuation features.

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