A water conservancy river ecological management whole-process monitoring system

Abstract

The application discloses a kind of water conservancy river course ecological management whole-process monitoring system, it is related to water conservancy engineering and water ecological monitoring technical field.System includes data space-time alignment module, dynamic characteristic extraction module, ecological degradation index calculation module, trajectory positioning module, critical approximation early warning module, intervention suggestion generation module and adaptive update module.Through extracting state type, rate type and fluctuation type dynamic characteristics from water quality, hydrology, meteorological multi-source time series data, generating three types of stress components of dissolved oxygen stress, eutrophication stress and hydrological physical disturbance by segmented linear activation and multiplication amplification aggregation, obtaining ecological degradation index by weighted recursion, and constructing state-rate two-dimensional functional area space to track degradation trajectory, generating hierarchical early warning when detecting that trajectory accelerates approaching critical threshold, identifying dominant stress type to provide intervention suggestion, and updating parameters using feedback data.The application realizes the prospective early warning and management decision support of river course ecological degradation process.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering and water ecology monitoring technology, and more specifically, to a full-process monitoring system for ecological management of water conservancy rivers. Background Technology

[0002] In the ecological management of water conservancy and river channels, full-process monitoring is the foundation for achieving scientific management and precise intervention. Currently, river monitoring generally relies on multi-source sensing systems, using facilities such as water quality buoys, hydrological stations, and meteorological stations to collect real-time physicochemical parameters such as dissolved oxygen, ammonia nitrogen, total phosphorus, flow rate, water level, temperature, and precipitation. After the monitoring data is transmitted and aggregated to the central platform, the system makes alarm judgments based on preset single-parameter fixed thresholds. For example, when the dissolved oxygen concentration is below a certain limit, an oxygenation reminder is triggered; when the ammonia nitrogen concentration is above a certain limit, a pollution alarm is issued. Some systems also integrate multi-source data into a visualization interface, displaying the real-time changes of each monitoring indicator in chart form to help managers understand the river's water conditions.

[0003] However, existing monitoring systems have significant shortcomings in their analytical logic. First, alarm triggering relies on comparing a single parameter with a fixed threshold, failing to integrate the temporal correlation characteristics between multiple parameters. This makes it difficult to distinguish between normal water quality fluctuations caused by natural rhythms and early signs of ecosystem degradation, leading to frequent false alarms or missed alarms. Second, the system can only reflect whether parameters exceed limits at the current moment, lacking characterization of the direction and rate of degradation trends. It cannot predict the remaining time before the river ecosystem reaches an irreversible state, resulting in insufficient forward-looking early warning. Third, the monitoring and governance systems operate independently. Data on the effects of governance measures are not fed back to the monitoring system to calibrate judgment rules and threshold parameters, resulting in a lack of the system's ability to learn and optimize from governance practices, making it difficult to accumulate decision-making experience. Therefore, a full-process monitoring system for ecological governance of water conservancy and river channels is proposed to address the above problems. Summary of the Invention

[0004] To overcome the aforementioned shortcomings of the prior art, embodiments of the present invention provide a full-process monitoring system for ecological governance of water conservancy channels, the problem of which is: Existing monitoring systems rely on single-parameter fixed threshold alarms, which cannot integrate and extract dynamic features of ecological degradation from multi-source time-series data to identify early signs of degradation; The existing monitoring system lacks quantitative assessment methods for the rate of ecological degradation and the critical transition time, and cannot predict the remaining time before the system approaches an irreversible state. The existing monitoring system and the treatment system are independent of each other, and the treatment effect data cannot be fed back to calibrate the monitoring parameters, resulting in the system lacking adaptive optimization capabilities.

[0005] To achieve the above objectives, the present invention provides a full-process monitoring system for ecological governance of water conservancy and river channels, including a data spatiotemporal alignment module, a dynamic feature extraction module, an ecological degradation index calculation module, a trajectory positioning module, a critical approach early warning module, an intervention suggestion generation module, and an adaptive update module.

[0006] The data spatiotemporal alignment module acquires time-series data of multiple water quality parameters, hydrological elements, and meteorological elements from multiple monitoring sections within the water conservancy channel. It performs time benchmark unification and spatial coordinate mapping on the multi-source time-series data, outputting a standardized multivariate time-series dataset. Through this processing, monitoring data from different sources, sampling frequencies, and spatial locations are integrated into spatiotemporally consistent structured data, providing a unified data foundation for subsequent feature extraction.

[0007] The dynamic feature extraction module extracts state-type features, rate-type features, and fluctuation-type features from the standardized multivariate time-series dataset according to a preset time window, and outputs a dynamic feature vector. Among them, state-type features reflect the cumulative stress level of river water quality, rate-type features reflect the changing trend of water quality indicators and changes in system resilience, and fluctuation-type features reflect the stability of the aquatic environment. The three types of features together constitute a comprehensive characterization of the ecological stress state of the river.

[0008] Furthermore, the dynamic feature extraction module extracts state-type features including dissolved oxygen cumulative deficit index, ammonia nitrogen exposure dose index, and total phosphorus load index; rate-type features including dissolved oxygen trend slope, ammonia nitrogen accumulation rate, total phosphorus accumulation rate, and recovery rate decay index; and fluctuation-type features including dissolved oxygen diurnal amplitude anomaly index and turbidity pulse event frequency. These features characterize river ecological stress from three dimensions: cumulative exposure, trend change, and fluctuation anomaly, providing multidimensional input for degradation index calculation.

[0009] Furthermore, the dynamic feature extraction module obtains the recovery rate decay index by: identifying each dissolved oxygen decrease event within a preset time window, calculating the recovery time required for each dissolved oxygen decrease event to recover to the pre-decline level, and then determining the recovery rate decay index. ,in This is the average recovery duration within the current time window. The recovery time is based on historical benchmarks. The recovery rate decay index reflects the degree of decline in the system's recovery capability from small disturbances and is an important indicator for early warning of critical transitions.

[0010] The ecological degradation index calculation module calls a preset ecological stress activation function library to perform feature-by-feature activation processing on each feature in the dynamic feature vector. The activated features are then aggregated according to dissolved oxygen stress type, eutrophication stress type, and hydrophysical disturbance stress type to generate dissolved oxygen stress component, eutrophication stress component, and hydrophysical disturbance stress component. Each stress component is then assigned a weighting coefficient. We obtain by weighted summation And calculate the ecological degradation index of the current monitoring section according to the following formula. : ; in For ecological inertia coefficient and , For the first The weighting coefficients of each stress component. For the first The current value of each stress component. This module represents the ecological degradation index for the previous cycle. It uses a piecewise linear activation function to uniformly map characteristic values ​​of different dimensions to stress intensity, amplifies and aggregates them through multiplication to reflect the nonlinear synergistic effect of multiple stress factors, and obtains a comprehensive degradation index through weighted summation and recursion with the ecological inertia coefficient, thereby achieving a quantitative assessment of the degree of river ecological degradation.

[0011] Furthermore, the ecological degradation index calculation module calls an ecological stress activation function library containing multiple piecewise linear activation functions, each of which applies a different feature value to the input feature value. Mapped to stress intensity value using the following formula : , ; , ; , ; in and These are segmented thresholds set based on the tolerance threshold range of dissolved oxygen to river benthic organisms, the toxicity threshold range of ammonia nitrogen to river aquatic organisms, or the correspondence between total phosphorus and river eutrophication status. A piecewise linear activation function is used to normalize eigenvalues ​​with different dimensions and numerical ranges. to The stress intensity values ​​between them are used to facilitate subsequent aggregation and comparison.

[0012] Furthermore, the ecological degradation index calculation module performs similar feature aggregation in the following way: Let the basic stress activation value be... The activation values ​​corresponding to multiple derived stress features under the same stress type are The preset magnification factor is Then the stress component after aggregation of similar features ,and The aggregation is truncated to the interval [0, 1]. This aggregation method reflects the nonlinear synergistic amplification effect on the ecosystem when multiple stress factors coexist, making the stress component closer to the actual ecological response.

[0013] The trajectory positioning module calculates the rate of ecological degradation based on the ecological degradation index over multiple consecutive assessment periods. This will be determined by the current ecological degradation index. and the current rate of ecological degradation The constructed two-dimensional trajectory points are mapped to a pre-constructed state-rate two-dimensional ecological functional zone space, outputting the functional zone type and movement trend of the two-dimensional trajectory points. Two-dimensional trajectory point positioning can simultaneously reflect the current degradation state of the system and its dynamic changes, while the functional zone division clarifies the ecological degradation stage of the system.

[0014] Furthermore, the state-rate two-dimensional ecological functional zone space in the trajectory positioning module is based on the ecological degradation index. The horizontal axis represents the rate of ecological degradation. Constructed for the vertical axis, based on a preset critical degradation threshold. Attention rate threshold and critical rate threshold The two-dimensional space is divided into four functional areas: Healthy and stable zone, meeting the requirements and ; Accelerate the degradation warning zone to meet the requirements and ; Critical transition region, satisfying and ; Degenerate locked region, satisfying and .

[0015] The above functional zoning divides the ecosystem degradation process into four stages with different management implications, facilitating the adoption of corresponding monitoring or intervention strategies based on the stage the system is in.

[0016] Furthermore, the attention rate threshold The value is taken as the 95th percentile of the rate sequence of ecological degradation changes during a historically favorable period for the river channel; the critical rate threshold is... and satisfy ,in For preset multiplier coefficient and Rate thresholds are determined by statistically analyzing historical data from periods of good performance, ensuring that the functional zone zoning criteria align with the actual fluctuation characteristics of the river channel.

[0017] The critical approach warning module is based on the functional area type, movement trend, and trajectory acceleration of the two-dimensional trajectory point. Critical approach detection is performed, and graded ecological early warning information for the corresponding monitoring sections is output. The trajectory acceleration reflects the changing trend of the degradation rate. When accelerated degradation is detected and the estimated time to cross the critical threshold is lower than the early warning threshold, a critical approach warning is issued in advance.

[0018] Furthermore, the critical approach early warning module performs the critical approach detection including: when Furthermore, when the two-dimensional trajectory point is located in the healthy and stable zone or the accelerated degradation warning zone, the traversal time is calculated. ; when When the time frame is less than the preset warning time threshold, a critical approach warning is generated as the tiered ecological warning information. The calculation of the time travel provides managers with an estimated time window when the system may undergo a critical transition, which helps to deploy governance measures in advance.

[0019] When the tiered ecological early warning information reaches a preset intervention level, the intervention suggestion generation module generates a river management intervention suggestion scheme based on the dominant stress component causing the increase in the ecological degradation index. By identifying the stress type that contributes the most to degradation, it provides targeted management measures and suggestions, thus providing a basis for management decisions.

[0020] Furthermore, the intervention suggestion generation module generates river management intervention suggestion schemes in the following way: the contribution of each stress component that causes the current increase in the ecological degradation index is ranked, and the stress component with the largest contribution is identified as the dominant stress type. When the dominant stress type is dissolved oxygen stress, generate a recommended scheme that includes aeration and reoxygenation measures or oxygen-consuming pollution source control measures. When the dominant stress type is eutrophication stress, generate a proposed solution that includes nutrient interception measures or ecological water replenishment measures. When the dominant stress type is hydrophysical disturbance stress, generate a proposed solution that includes dam and gate scheduling optimization measures or measures to reduce physical disturbance.

[0021] Identifying dominant stress types by ranking contributions enables interventions to target the root causes of degradation, thus improving the targeting and effectiveness of governance.

[0022] The adaptive update module uses monitoring data fed back after river management intervention to adjust the threshold parameters and weight coefficients of each stress component in the ecological stress activation function library. Adaptive updates are performed. The weighting coefficients are incrementally adjusted based on the actual changes in the ecological degradation index before and after intervention, and the critical degradation threshold is corrected based on the early warning effect, so that the system parameters gradually conform to the actual ecological response pattern of the river.

[0023] Furthermore, the adaptive update performed by the adaptive update module includes: setting the pre-intervention ecological degradation index as... The ecological degradation index after intervention was Change The weighting coefficients for each stress component are calculated using the following formula. Perform incremental adjustments: ; in The learning rate is preset; if no actual ecological degradation event occurs within the preset observation period after the system issues a critical approach warning, the critical degradation threshold is increased. When the system detects a sudden ecological degradation event without issuing a warning, the critical degradation threshold is lowered. The incremental adjustment of the weighting coefficients enhances the weight of stress components that contribute significantly when governance measures effectively reduce the degradation index, making the system more sensitive to such stresses. The event-driven correction of the critical degradation threshold gradually brings the system's early warning threshold closer to the actual critical transition point of the river, thereby improving the accuracy and personalization of early warnings.

[0024] The technical effects and advantages of this invention are as follows: I. This invention extracts state-type features, rate-type features, and fluctuation-type features from standardized multivariate time-series data according to a preset time window using a dynamic feature extraction module. State-type features include dissolved oxygen cumulative deficit index, ammonia nitrogen exposure dose index, and total phosphorus load index; rate-type features include dissolved oxygen trend slope, ammonia nitrogen accumulation rate, total phosphorus accumulation rate, and recovery rate decay index; fluctuation-type features include dissolved oxygen diurnal amplitude anomaly index and turbidity pulse event frequency. By extracting these three types of dynamic features, the raw monitoring data is transformed into a multidimensional feature vector reflecting the degree of ecological stress accumulation, its changing trend, and system stability. This helps identify early signs of ecosystem degradation from multi-source time-series data and improves the limitations of single-threshold alarms.

[0025] II. This invention utilizes an ecological degradation index calculation module to activate various features using a piecewise linear activation function. Then, it performs multiplicative amplification and aggregation based on three categories: dissolved oxygen stress, eutrophication stress, and hydrophysical disturbance stress, generating corresponding stress components. These components are then weighted and summed, and recursively calculated using the ecological inertia coefficient to obtain the ecological degradation index. Based on this, a trajectory positioning module calculates the rate of ecological degradation change, mapping the two-dimensional trajectory points formed by the ecological degradation index and the rate of change to a state-rate two-dimensional ecological functional zone space, dividing the area into healthy stable zones, accelerated degradation warning zones, critical transition zones, and degradation-locked zones. A critical approach warning module calculates the trajectory acceleration, estimating the time required to cross the critical threshold when accelerated degradation is detected, and generating a critical approach warning when the crossing time is less than the warning threshold. This process achieves a quantitative assessment of the direction and speed of degradation trends, helping to predict the risk of river ecosystems approaching critical transitions in advance.

[0026] Third, this invention, through an intervention suggestion generation module, ranks the incremental values ​​of each stress component by contribution when the early warning reaches the intervention level, identifies the dominant stress type, and provides specific intervention suggestions such as aeration and reoxygenation, nutrient interception, ecological water replenishment, or dam scheduling optimization for dissolved oxygen stress, eutrophication stress, and hydrophysical disturbance stress, respectively. The adaptive update module uses the actual changes in the ecological degradation index before and after the intervention to incrementally adjust the weight coefficients of the stress components and perform event-driven corrections to the critical degradation threshold. This mechanism feeds back the treatment effect data to the monitoring system to calibrate parameters, enabling the system to gradually adapt to the actual ecological response characteristics of the river channel, improving early warning accuracy and decision support capabilities. Attached Figure Description

[0027] Figure 1 This is a system module framework diagram of the present invention. Detailed Implementation

[0028] 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.

[0029] Example 1 As attached Figure 1 The present invention relates to a full-process monitoring system for ecological governance of water conservancy and river channels. This system comprises a data spatiotemporal alignment module, a dynamic feature extraction module, an ecological degradation index calculation module, a trajectory positioning module, a critical approach early warning module, an intervention suggestion generation module, and an adaptive update module. The implementation methods of each part of this system are described in detail below.

[0030] First, the data spatiotemporal alignment module acquires time-series data of multiple water quality parameters, hydrological elements, and meteorological elements from multiple monitoring sections within the water conservancy channel. Then, it performs time benchmark unification and spatial coordinate mapping processing on the multi-source time-series data to output a standardized multivariate time-series dataset.

[0031] In terms of data acquisition, the time-series data of multiple water quality parameters are obtained from automatic water quality monitoring stations deployed at various monitoring sections of the river. These automatic water quality monitoring stations are equipped with dissolved oxygen electrodes, ammonia nitrogen analyzers, total phosphorus analyzers, pH electrodes, and turbidity meters.

[0032] As one implementation method, the sampling interval of each sensor is 60 minutes, and the time series data of multiple water quality parameters include dissolved oxygen concentration, ammonia nitrogen concentration, total phosphorus concentration, pH value and turbidity value, with the concentration unit being milligrams per liter.

[0033] As another implementation method, the sampling interval for each sensor is 15 minutes. The aforementioned automatic water quality monitoring stations can be connected to the river chief system management platform or the watershed water environment monitoring network, and upload monitoring data in real time through a standard data interface.

[0034] The time-series data of hydrological elements are obtained from hydrological monitoring stations along the river. The hydrological monitoring stations are equipped with flow meters, current meters, and water level gauges.

[0035] As one implementation method, the sampling interval is 15 minutes. The time-series hydrological data includes flow rate, flow velocity, and water level. The unit for flow rate is cubic meters per second, the unit for flow velocity is meters per second, and the unit for water level is meters. Flow rate data can be used to determine the degree to which the river's ecological base flow is satisfied, while water level data can reflect the impact of dam and gate operations on the river's hydrodynamics.

[0036] The time-series data of meteorological elements are sourced from the shared interface of the meteorological department.

[0037] In one implementation, the sampling interval is once daily, and the time-series meteorological data includes temperature and precipitation, with temperature in degrees Celsius and precipitation in millimeters. In another implementation, the sampling interval is once per hour.

[0038] During the process of unifying the time base, the data spatiotemporal alignment module uses the hour as the unified time base.

[0039] As one implementation method, for water quality and hydrological data where there is a discrepancy between the sampling time and the hourly time, the data value of the nearest hourly time is used as the value of that hourly time; for cases where hourly time data is missing, the data value of the previous valid hourly time is used to fill in the gaps. For daily meteorological data, the daily temperature and precipitation values ​​are mapped to all 24 hourly times of the day, with each hourly time using the daily average temperature and daily cumulative precipitation.

[0040] As another implementation method, linear time-weighted interpolation is used to handle sampling time deviations. This interpolation method uses the values ​​of two adjacent sampling times and the time interval as known quantities to calculate the interpolation result for the hour. The weight is inversely proportional to the time interval. Missing data is supplemented using linear interpolation. This interpolation method uses the values ​​of two valid hourly times before and after the missing period as known quantities to calculate the interpolation result for the missing hourly time. Daily meteorological data is extended to hourly data through linear interpolation. The average daily temperature or cumulative daily precipitation of two adjacent days is used as known quantities to calculate the interpolation result for each hour in between.

[0041] During the spatial coordinate mapping process, the data spatiotemporal alignment module establishes a mapping relationship between monitoring sections and spatial locations based on river mileage markers. Each water quality monitoring section has a unique mileage marker, each hydrological monitoring station has a corresponding river mileage interval, and each meteorological station has a spatial coverage area. The module spatially locates the water quality data of each monitoring section according to its mileage marker, spatially correlates the hydrological data according to the river mileage interval it controls, and maps the meteorological data to the corresponding river segment according to its coverage area.

[0042] As one implementation method, for river sections between two monitoring sections, if direct water quality monitoring data is lacking, linear interpolation is performed using water quality data from adjacent upstream and downstream sections, weighted by distance. This linear interpolation uses the water quality data values ​​of the upstream and downstream sections, as well as the distances from the interpolation point to the upstream and downstream sections, as known quantities to calculate the water quality data value at the interpolation point. The distance weight is the normalized reciprocal of the distances from the upstream and downstream sections to the interpolation point. As another implementation method, spatial interpolation is performed using inverse distance-weighted interpolation. This interpolation method uses the water quality data values ​​of each known section and the distances to the interpolation point as known quantities to calculate the water quality data value at the interpolation point. The weight is the reciprocal of the square of the distance.

[0043] After the above processing, the data spatiotemporal alignment module outputs a standardized multivariate time-series dataset. This dataset is stored in a relational database as a structured data table, containing fields such as timestamp, cross-section number, dissolved oxygen concentration, ammonia nitrogen concentration, total phosphorus concentration, pH value, turbidity value, flow rate, flow velocity, water level, temperature, and precipitation. The dataset is updated daily, adding 24 data records at specific times each day and removing the oldest data record.

[0044] Secondly, the dynamic feature extraction module extracts state-type features, rate-type features, and fluctuation-type features from the standardized multivariate time series dataset according to a preset time window, and outputs a dynamic feature vector.

[0045] As one implementation method, a preset time window length of 30 days is used, with the window sliding forward once daily. This module reads standardized multivariate time-series data from the database for the most recent 30 days daily as the processing object and performs feature extraction. The selection of the time window length balances the lag in the river ecosystem's response to changes in water quality and hydrology with the timeliness of feature extraction.

[0046] State-related characteristics include the dissolved oxygen cumulative deficit index, ammonia nitrogen exposure dose index, and total phosphorus load index, which are used to characterize the degree of cumulative stress on river water quality.

[0047] The method for extracting the cumulative dissolved oxygen deficit index is as follows: set a dissolved oxygen baseline value. For the dissolved oxygen concentration value at each hour within the time window, when the dissolved oxygen concentration is greater than or equal to Time Deficit Value When the dissolved oxygen concentration is less than Time Deficit Value Subtract the concentration value; sum up the deficit values ​​at all times within the window to obtain the cumulative dissolved oxygen deficit index. The value is determined based on the dissolved oxygen standard limit in the "Surface Water Environmental Quality Standard". As one implementation method, The value is 5.0 mg / L. This index reflects the cumulative effect of dissolved oxygen stress on aquatic organisms over a period of time.

[0048] The ammonia nitrogen exposure dose index was extracted by taking the 90th percentile of all hourly ammonia nitrogen concentrations within the time window. The percentile was calculated using linear interpolation, with the concentration values ​​arranged in ascending order. The position of the 90th percentile was... ,in The number of concentration value samples, taken at the following locations. The ammonia nitrogen exposure dose index is obtained by proportionally interpolating the two concentration values ​​corresponding to rounding down and rounding up. Using high percentiles can effectively reflect the stage-specific high concentration stress of ammonia nitrogen on the aquatic ecosystem.

[0049] The total phosphorus load index was extracted by taking the 90th percentile of all hourly total phosphorus concentrations within the time window, using the same method as the ammonia nitrogen exposure dose index. Total phosphorus is a key limiting factor for river eutrophication, and high exposure levels are closely related to the risk of algal blooms.

[0050] Rate-type characteristics include dissolved oxygen trend slope, ammonia nitrogen accumulation rate, total phosphorus accumulation rate, and recovery rate decay index, which are used to characterize the changing trends of river water quality indicators and changes in system recovery capacity.

[0051] The dissolved oxygen trend slope is extracted as follows: The daily average dissolved oxygen value within the calculated time window is the arithmetic mean of the dissolved oxygen concentration values ​​at 24 specific times of the day. A univariate linear regression using the least squares method is then performed on the obtained daily average value sequence. This regression method uses the day-numbered x-axis sequence and the daily average dissolved oxygen value y-axis sequence as known quantities to calculate the slope of the regression line, i.e., the dissolved oxygen trend slope, in milligrams per liter per day. A negative slope indicates a decreasing trend in dissolved oxygen; the larger the absolute value of the negative value, the more significant the oxygen consumption process.

[0052] The ammonia nitrogen accumulation rate was extracted by calculating the daily average ammonia nitrogen value within the time window, performing a univariate linear regression on the daily average value sequence using the least squares method, and calculating the slope of the regression line, which is the ammonia nitrogen accumulation rate, in milligrams per liter per day.

[0053] The total phosphorus accumulation rate was extracted by calculating the daily average total phosphorus value within the time window, performing a univariate linear regression on the daily average value series using the least squares method, and calculating the slope of the regression line, which is the total phosphorus accumulation rate, in milligrams per liter per day.

[0054] The recovery rate decay index is extracted by identifying each dissolved oxygen decrease event within a time window, where a decrease event is defined as a continuous event. The dissolved oxygen concentration continuously decreases at each full hour. For each decrease event, the time from the start of the decrease to when the dissolved oxygen concentration returns to its pre-decline level is recorded. The duration mentioned above is taken as the recovery time. The arithmetic mean of the recovery times for all descent events within the window is calculated and denoted as... Historical baseline recovery time The river channel was in a period of historically excellent water quality. The arithmetic mean of recovery time over a month; historically excellent water quality periods are defined as consecutive periods where water quality category II or above has not experienced any ecological degradation events. Recovery rate decay index. This index reflects the degree of decline in the resilience of river ecosystems and is an important indicator for early warning of critical transitions.

[0055] Fluctuation characteristics include the diurnal amplitude anomaly index of dissolved oxygen and the frequency of turbidity pulse events, which are used to characterize changes in the stability of the river water environment.

[0056] The method for extracting the dissolved oxygen diurnal amplitude anomaly index is as follows: For each day within the time window, the difference between the maximum and minimum hourly dissolved oxygen concentrations is calculated as the diurnal amplitude; the coefficient of variation of the diurnal amplitude values ​​for all days within the window is calculated, i.e., the standard deviation of the diurnal amplitude sequence divided by the arithmetic mean, to obtain the dissolved oxygen diurnal amplitude anomaly index. An abnormally large increase in amplitude is usually related to an imbalance between photosynthesis and respiration caused by excessive algal proliferation.

[0057] The frequency of turbidity pulse events is extracted as follows: For each hour within a time window, the moving average of turbidity over the preceding 24 hours is calculated; when the turbidity value at a given hour exceeds 2.5 times its corresponding 24-hour moving average, it is counted as a turbidity pulse event; the total number of turbidity pulse events within the window is counted to obtain the frequency of turbidity pulse events. Turbidity pulse events may originate from storm runoff carrying sediment, resuspension of bottom sediment, or anthropogenic disturbances.

[0058] The dynamic feature extraction module performs the above operation once a day, outputting a dynamic feature vector containing all the above feature values, which is stored in the feature library for the ecological degradation index calculation module to read.

[0059] Next, the ecological degradation index calculation module calls the preset ecological stress activation function library to perform feature-by-feature activation processing on each feature in the dynamic feature vector. Then, the activated features are aggregated into similar features according to dissolved oxygen stress type, eutrophication stress type, and hydrophysical disturbance stress type to generate the dissolved oxygen stress component. Eutrophication stress component and hydrophysical disturbance stress components The ecological degradation index of the current monitoring section is calculated by weighted summation of each stress component and recursively. .

[0060] This module reads the latest dynamic feature vectors from the feature library daily as the processing target.

[0061] The ecological stress activation function library contains multiple piecewise linear activation functions, each function responding to input feature values. Mapped to stress intensity value using the following formula : , ; , ; , ; in To activate the initial threshold, To activate the saturation threshold, each has a value similar to the input feature value. Same units. Different characteristics correspond to different... and The values ​​are selected based on research findings in aquatic ecological thresholds. The function of the activation function is to uniformly map characteristic values ​​of different dimensions and numerical ranges to a unified value. arrive The stress intensity values ​​between them are used to facilitate subsequent aggregation calculations.

[0062] After feature-by-feature activation processing, each activation value is categorized according to stress type. Dissolved oxygen stress types include activated values ​​for dissolved oxygen cumulative deficit index, dissolved oxygen trend slope, and dissolved oxygen diurnal amplitude anomaly index. Eutrophication stress types include activated values ​​for ammonia nitrogen exposure dose index, ammonia nitrogen accumulation rate, total phosphorus load index, and total phosphorus accumulation rate. Hydrophysical disturbance stress types include activated values ​​for recovery rate decay index and turbidity pulse event frequency.

[0063] Similar features are aggregated using a multiplicative amplification and superposition method. Let the base stress activation value be... The activation values ​​corresponding to multiple derived stress features under the same stress type are denoted as... subscript Indicates the first Each derived stress feature has a preset amplification factor denoted as . Stress components resulting from the aggregation of similar features Calculate using the following formula: ; If the calculation result is greater than ,but Cut off as If the calculation result is less than ,but Cut off as The multiplicative amplification superposition method reflects the nonlinear synergistic amplification effect on the ecosystem when multiple stress factors coexist.

[0064] For dissolved oxygen stress types Taking the activation value of the cumulative dissolved oxygen deficit index, the derived stress characteristics include the dissolved oxygen trend slope and the dissolved oxygen diurnal amplitude anomaly index, with corresponding amplification factors of [missing value]. and , .

[0065] For eutrophication stress type The larger of the ammonia nitrogen exposure dose index activation value and the total phosphorus load index activation value is used to derive stress characteristics including the ammonia nitrogen accumulation rate and the total phosphorus accumulation rate, with a corresponding amplification factor of [missing value]. and , .

[0066] For hydrophysical disturbance stress types Taking the activation value of the recovery rate decay exponent, the derived stress characteristics include the activation value of the turbidity pulse event frequency, with a corresponding amplification factor of... , .

[0067] The weighted summation of each stress component uses weighting coefficients. , , ,satisfy The initial values ​​of the weighting coefficients are preset based on the dominant stress type of the river channel. As one implementation method, eutrophication stress in plain river channels is used as the main stress factor, and the following settings are applied: , , The hydrophysical disturbance stress is more prominent in mountainous rivers, and the following measures are taken: , , Dissolved oxygen stress is the main stress factor in tidal river channels. , , .

[0068] Ecological degradation index Calculate recursively using the following formula: ; in For the ecological inertia coefficient, satisfying This is used to characterize the degree of lag in the response of river ecosystems to environmental changes, i.e., the inertia of the ecosystem. As one implementation method, Take 0.3. To determine the ecological degradation index for the previous cycle, an ecological inertia coefficient is introduced. The change curve is smoother, which is consistent with the actual lag pattern of ecosystem response to external stress.

[0069] The ecological degradation index calculation module calculates it once a day. The calculation results are stored in the degradation index time series library. The closer the value is This indicates that the more severe the degradation of the river ecosystem, the higher the risk of a steady-state transition.

[0070] Subsequently, the trajectory positioning module calculates the rate of ecological degradation based on the ecological degradation index over multiple consecutive assessment periods. And will be determined by the current ecological degradation index. and the current rate of ecological degradation The constructed two-dimensional trajectory points are mapped to a pre-constructed state-rate two-dimensional ecological functional zone space, and the functional zone type and movement trend of the two-dimensional trajectory points are output.

[0071] This module reads multiple consecutive periods from the degradation index time series library daily. The value is used as the processing object.

[0072] rate of ecological degradation Calculate using the following formula: ; in The length of the time window used to calculate the rate of change, in days. A positive value indicates that the ecological degradation index is on the rise, and the larger the value, the faster the degradation.

[0073] State-rate two-dimensional ecological functional zone space with ecological degradation index The horizontal axis represents the rate of ecological degradation. Constructed for the vertical axis, horizontal axis The range of values ​​is to This two-dimensional space can simultaneously reflect the current degradation state and dynamic changes of the system.

[0074] Based on the preset critical degradation threshold Attention rate threshold and critical rate threshold The two-dimensional space is divided into four functional areas.

[0075] Health and stability zone meet and ; Accelerated degradation warning zone meets and ; Critical transition region satisfies and ; Degeneracy lock region satisfies and .

[0076] Critical degradation threshold The initial values ​​are determined based on retrospective analysis of historical ecological degradation events in the river channel. As one implementation method, if the river channel has experienced a large-scale disappearance of submerged plants or a mass fish die-off, the values ​​from the week preceding that event are taken. Average value as If there is no record of historical degradation events in the river channel, then Take 0.60. The setting references the critical threshold for the transition from "sub-healthy" to "unhealthy" in river and lake health assessment.

[0077] Focus rate threshold The value is taken as the river channel is in a historically favorable period. absolute value sequence Quantiles. Historically favorable water quality periods are defined as consecutive periods where water quality reached Class II or above and no ecological degradation events occurred, with a duration of no less than [number missing]. Months. Calculate all within this period. Take the absolute value sequence of values, and then take the sequence. quantiles as .

[0078] Critical rate threshold and satisfy ,in For preset multiplier coefficient and As one implementation method, Take 2.5.

[0079] The trajectory localization module calculates the current time each time. and Then, the two-dimensional trajectory point is marked in the aforementioned functional area space, and the position sequence of the trajectory point for 30 consecutive cycles is recorded. By observing the coordinate changes of trajectory points in adjacent cycles within the position sequence, the direction and rate of movement of the trajectory point are determined, serving as the output of the movement trend. The movement trend reflects the evolution direction of the system in the state-velocity phase space.

[0080] Then, the critical approach warning module determines the location of the two-dimensional trajectory point based on the functional area type, movement trend, and trajectory acceleration. Perform critical approximation detection and output hierarchical ecological early warning information for the corresponding monitoring section.

[0081] This module obtains the function area type, movement trend, and trajectory acceleration from the trajectory positioning module daily. As the object to be processed.

[0082] trajectory acceleration Calculate using the following formula: ; in With calculation The time windows used are of the same length, and the unit is squared days. A positive value indicates that the degradation rate is accelerating, which is an important kinetic characteristic of approaching the critical transition.

[0083] The execution process of critical approximation detection is as follows: when Furthermore, when the two-dimensional trajectory point is located in the healthy and stable zone or the accelerated degradation warning zone, the traversal time is calculated. : ; in The critical degradation threshold, This represents the current ecological degradation index. This represents the current rate of ecological degradation. The unit is day. It indicates the number of days it is expected that the ecological degradation index will reach the critical degradation threshold if the current degradation rate remains unchanged.

[0084] when When the time frame is less than the preset warning time threshold, a critical approach warning is generated as a Level III warning in the tiered ecological warning information system. As one implementation method, the preset warning time threshold is set to 30 days, a value that comprehensively considers the lead time required for river management departments to initiate emergency response.

[0085] The tiered ecological early warning information includes four levels: Level I warning is indicated when the two-dimensional trajectory point is located in the healthy and stable zone and This indicates that the river ecosystem is in a healthy and stable state, and routine monitoring is sufficient. A Level II warning indicates that a two-dimensional trajectory point has entered an accelerated degradation alert zone, or, although located in a healthy and stable zone, meets the following criteria: and ,in A coefficient between 0.7 and 1.0 is used as one implementation method. A score of 0.8 indicates that the system is showing an accelerated degradation trend, requiring more frequent monitoring but no engineering intervention will be initiated at this time. A Level III warning is triggered when a two-dimensional trajectory point is located within an accelerated degradation warning zone, meeting the following criteria: and If the time is less than the preset warning time threshold, it means that the system is approaching the critical point at a quantifiable rate and preparations for governance intervention need to be initiated. A Level IV warning indicates that the two-dimensional trajectory point has entered the critical transition zone, signifying that the system has crossed the steady-state transition critical point and an emergency response plan must be activated immediately.

[0086] The critical approach early warning module generates a tiered ecological early warning message daily and pushes the message to the river management platform. The early warning message includes fields such as the early warning level, the location of the affected section, and key stress factors.

[0087] In addition, when the graded ecological early warning information reaches the preset intervention level, the intervention suggestion generation module generates river management intervention suggestions based on the dominant stress component that causes the ecological degradation index to rise.

[0088] The preset intervention levels are Level III and Level IV warning. When the warning level reaches one of these levels, the intervention suggestion generation module automatically starts, based on the current stress component value. , , and The value of coercion in the past , , For processing objects.

[0089] The intervention suggestion generation module ranks the contributions of each stress component causing the current increase in the ecological degradation index. It then calculates the increment of each stress component. , , If a certain increment is less than Then take The contribution of each stress component is the proportion of its increment to the sum of the three increments. The stress component with the largest contribution is identified as the dominant stress type.

[0090] When the dominant stress type is dissolved oxygen stress, a proposed intervention plan for river management is generated. This plan includes aeration and reoxygenation measures or oxygen-consuming pollution source control measures. Aeration and reoxygenation measures include activating river aeration devices, increasing the operating power of cascade aeration facilities, or extending their operating time. Aeration devices include microporous aerators, jet aerators, or impeller aerators. Oxygen-consuming pollution source control measures include investigating upstream point source emissions and strengthening the interception and control of non-point source pollution along the riverbank.

[0091] When the dominant stress type is eutrophication, a proposed intervention plan for river management is generated, which includes nutrient interception measures or ecological water replenishment measures. Nutrient interception measures include activating the purification function of ecological buffer zones, adding floating wetlands or artificial aquatic plants, and adding lanthanum-modified bentonite or aluminum salt phosphorus-locking agents. Ecological water replenishment measures include diverting water from nearby water bodies with better water quality for dilution, optimizing dam and gate scheduling to increase the proportion of clean water from upstream, and improving water exchange capacity through ecological scheduling.

[0092] When the dominant stress type is hydrophysical disturbance stress, a proposed river management intervention plan is generated. This plan includes measures to optimize dam and gate scheduling or measures to reduce physical disturbance. Optimization measures include adjusting the downstream flow process of upstream dams and gates, reducing the frequency of flow pulses, ensuring the stability of the ecological baseflow, and reducing the degree of hydrological variability. Measures to reduce physical disturbance include suspending river dredging projects, restricting navigation disturbances, and controlling bank slope construction activities.

[0093] The intervention suggestion generation module pushes the generated suggestion solutions to the smart water management platform in a structured data format for managers to use as a reference for decision-making.

[0094] Finally, the adaptive update module uses the monitoring data fed back after river management intervention to adjust the threshold parameters and weight coefficients of each stress component in the ecological stress activation function library. , , Perform adaptive updates.

[0095] This module is triggered after each governance intervention is completed, and it processes monitoring data before and after the intervention.

[0096] The weighting coefficients are updated as follows: Let the pre-intervention ecological degradation index be... Before implementing intervention measures, continuous Japanese Arithmetic mean; let the ecological degradation index after intervention be . Take data from the 14th to the 16th day after the intervention measures are implemented. Arithmetic mean. Amount of change. .

[0097] The weighting coefficients for each stress component are given by the following formula. Perform incremental adjustments: ; in As one implementation method, a preset learning rate is used. Take 0.03. For the first intervention The value of each stress component The sum of the three stress components is the sum of their values. This update rule increases the weight of the stress components that contribute more to the ecological degradation index when interventions effectively reduce the index, thereby enhancing the system's sensitivity to such stresses.

[0098] After incremental adjustment, for , , Normalization is performed to ensure that the sum of the updated weight coefficients remains constant. .

[0099] The threshold parameter update includes updating the critical degradation threshold. Adjustments are made. If no actual ecological degradation event occurs within the preset observation period after the system issues a critical approach warning, it indicates that the current situation is... The settings may be too low; the system will adjust them. The adjustment method is to adjust by a preset fixed step size. Upward adjustment, as one implementation method, The value is set to 0.02, and the preset observation period is 60 days. When the system detects a sudden ecological degradation event without issuing a warning, it indicates that the current situation... The settings may be too high; the system will adjust them downwards. The method of adjustment is to reduce Updated to one week prior to the occurrence of the degradation event. 0.9 times the average. Sudden ecological degradation events include mass fish deaths, large-scale disappearance of submerged plants, and abnormal algal blooms.

[0100] The adaptive update module will update the weight coefficients. , , and critical degradation threshold The data is written back to the system configuration library for use in the next calculation cycle. Through this adaptive mechanism, the system can continuously optimize model parameters based on the actual ecological response of the river channel, improving its adaptability to the ecological degradation patterns of specific rivers and the accuracy of its early warnings.

[0101] In the overall workflow of this system, the data spatiotemporal alignment module receives monitoring data from each sensing node daily. After time benchmark unification and spatial coordinate mapping, it outputs a standardized multivariate time series dataset, which is then stored in the system database.

[0102] The dynamic feature extraction module reads standardized multivariate time-series data from the database for the most recent time window every day, performs extraction operations for state-type features, rate-type features, and fluctuation-type features, outputs dynamic feature vectors, and stores them in the feature library.

[0103] The ecological degradation index calculation module reads the latest dynamic feature vectors from the feature library daily, calls the ecological stress activation function library and the current weight coefficients, and outputs the ecological degradation index through feature-by-feature activation, aggregation of similar features, and weighted recursive calculation. Stored in the degradation index time series library.

[0104] The trajectory positioning module reads multiple consecutive periods of data from the degradation index time series library daily. Values ​​used to calculate the rate of ecological degradation. It maps two-dimensional trajectory points to a state-velocity two-dimensional ecological functional zone space, and outputs the functional zone type and movement trend.

[0105] The critical approach warning module analyzes the functional area type, movement trend, and trajectory acceleration daily. Perform critical approach detection, output graded ecological early warning information and push it to the river management platform.

[0106] The intervention suggestion generation module is triggered when the early warning level reaches Level III or IV. It identifies the dominant stress type based on the contribution of each stress component, generates river management intervention suggestions, and pushes them to the smart water management platform.

[0107] The adaptive update module is triggered after each governance intervention, and adjusts the weighting coefficients based on the changes in the ecological degradation index before and after the intervention. , , and critical degradation threshold Perform an adaptive update and write the updated parameters back to the system configuration library.

[0108] The above modules work together to form a dynamic monitoring system covering the entire process of ecological governance of water conservancy and river channels, from monitoring data collection, feature extraction, degradation assessment, trajectory tracking, critical early warning, intervention suggestions to parameter adaptation.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A full-process monitoring system for ecological governance of water conservancy and river channels, characterized in that, include: The data spatiotemporal alignment module acquires time-series data of multiple water quality parameters, hydrological elements, and meteorological elements from multiple monitoring sections within the water conservancy channel. It performs time benchmark unification and spatial coordinate mapping on the multi-source time-series data and outputs a standardized multivariate time-series dataset. The dynamic feature extraction module extracts state-type features, rate-type features, and fluctuation features from the standardized multivariate time-series dataset according to a preset time window, and outputs a dynamic feature vector; The ecological degradation index calculation module calls a preset ecological stress activation function library to perform feature-by-feature activation processing on each feature in the dynamic feature vector. The activated features are then aggregated according to dissolved oxygen stress type, eutrophication stress type, and hydrophysical disturbance stress type to generate dissolved oxygen stress component, eutrophication stress component, and hydrophysical disturbance stress component. Each stress component is then assigned a weighting coefficient. We obtain by weighted summation And calculate the ecological degradation index of the current monitoring section according to the following formula. : ； in For ecological inertia coefficient and , For the first The weighting coefficients of each stress component. For the first The current value of each stress component. This represents the ecological degradation index for the previous cycle. The trajectory positioning module calculates the rate of ecological degradation change based on the ecological degradation index over multiple consecutive assessment periods. This will be determined by the current ecological degradation index. and the current rate of ecological degradation The constructed two-dimensional trajectory points are mapped to a pre-constructed state-rate two-dimensional ecological functional zone space, and the functional zone type and movement trend of the two-dimensional trajectory points are output. The critical approach warning module is based on the functional area type, movement trend, and trajectory acceleration of the two-dimensional trajectory point. Perform critical approach detection and output hierarchical ecological early warning information for the corresponding monitoring section; The intervention suggestion generation module generates a river management intervention suggestion scheme based on the dominant stress component that causes the ecological degradation index to rise when the graded ecological early warning information reaches the preset intervention level. The adaptive update module uses monitoring data fed back after river management intervention to adjust the threshold parameters and weight coefficients of each stress component in the ecological stress activation function library. Perform adaptive updates.

2. The whole-process monitoring system for ecological governance of water conservancy rivers according to claim 1, characterized in that, The state-type features extracted by the dynamic feature extraction module include dissolved oxygen cumulative deficit index, ammonia nitrogen exposure dose index, and total phosphorus load index; the rate-type features include dissolved oxygen trend slope, ammonia nitrogen accumulation rate, total phosphorus accumulation rate, and recovery rate decay index. The fluctuation characteristics include the diurnal amplitude anomaly index of dissolved oxygen and the frequency of turbidity pulse events.

3. The whole-process monitoring system for ecological governance of water conservancy rivers according to claim 2, characterized in that, The dynamic feature extraction module obtains the recovery rate decay index by: identifying each dissolved oxygen decrease event within a preset time window, calculating the recovery time required for each dissolved oxygen decrease event to recover to the pre-decline level, and then obtaining the recovery rate decay index. ,in This is the average recovery duration within the current time window. The recovery time is based on historical benchmarks.

4. The whole-process monitoring system for ecological governance of water conservancy and river channels according to claim 1, characterized in that, The ecological degradation index calculation module calls an ecological stress activation function library containing multiple piecewise linear activation functions, each of which applies a specific feature value to the input feature value. Mapped to stress intensity value using the following formula : ， ； ， ； ， ； in and These are segmented thresholds set based on the tolerance threshold range of dissolved oxygen to river benthic organisms, the toxicity threshold range of ammonia nitrogen to river aquatic organisms, or the correspondence between total phosphorus and the eutrophication status of the river.

5. The whole-process monitoring system for ecological governance of water conservancy rivers according to claim 1, characterized in that, The ecological degradation index calculation module performs feature aggregation in the following way: Let the basic stress activation value be... The activation values ​​corresponding to multiple derived stress features under the same stress type are The preset magnification factor is Then the stress component after aggregation of similar features ,and Cut off to the interval [0, 1].

6. The whole-process monitoring system for ecological governance of water conservancy and river channels according to claim 1, characterized in that, The state-rate two-dimensional ecological functional zone space in the trajectory positioning module is based on the ecological degradation index. The horizontal axis represents the rate of ecological degradation. Constructed for the vertical axis, based on a preset critical degradation threshold. Attention rate threshold and critical rate threshold The two-dimensional space is divided into four functional areas: Healthy and stable zone, meeting the requirements and ; Accelerate the degradation warning zone to meet the requirements and ; Critical transition region, satisfying and ; Degenerate locked region, satisfying and .

7. The whole-process monitoring system for ecological governance of water conservancy rivers according to claim 6, characterized in that, The critical approach early warning module performs the critical approach detection including: when Furthermore, when the two-dimensional trajectory point is located in the healthy and stable zone or the accelerated degradation warning zone, the traversal time is calculated. ;when When the time is less than the preset warning time threshold, a critical approach warning is generated as the graded ecological warning information.

8. The whole-process monitoring system for ecological governance of water conservancy and river channels according to claim 1, characterized in that, The intervention suggestion generation module generates river management intervention suggestion schemes by ranking the contribution of each stress component that causes the current increase in the ecological degradation index, and identifying the stress component with the largest contribution as the dominant stress type. When the dominant stress type is dissolved oxygen stress, generate a recommended scheme that includes aeration and reoxygenation measures or oxygen-consuming pollution source control measures. When the dominant stress type is eutrophication stress, generate a proposed solution that includes nutrient interception measures or ecological water replenishment measures. When the dominant stress type is hydrophysical disturbance stress, generate a proposed solution that includes dam and gate scheduling optimization measures or measures to reduce physical disturbance.

9. The whole-process monitoring system for ecological governance of water conservancy and river channels according to claim 1, characterized in that, The adaptive update performed by the adaptive update module includes: Let the pre-intervention ecological degradation index be... The ecological degradation index after intervention was Change The weighting coefficients for each stress component are calculated using the following formula. Perform incremental adjustments: ； in Set the learning rate; If no actual ecological degradation event occurs within the preset observation period after the system issues a critical approach warning, the critical degradation threshold will be increased. ; When the system detects a sudden ecological degradation event without issuing a warning, the critical degradation threshold is lowered. .

10. The whole-process monitoring system for ecological governance of water conservancy channels according to claim 6, characterized in that, The attention rate threshold The value is taken as the 95th percentile of the rate sequence of ecological degradation changes in a river channel during a historically favorable period; the critical rate threshold is... and satisfy ,in For preset multiplier coefficient and .

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