A method for detecting the degree of stress of river water ecology

Abstract

This invention relates to the field of water pollution detection technology and discloses a method for detecting the degree of stress on river aquatic ecosystems. The method includes: acquiring river drought indicators, environmental water quality indicators, and aquatic organism status indicators and constructing a synchronous monitoring sequence; calculating the cross-correlation between indicators and determining the characteristic lag step; correcting the temporal offset of the synchronous monitoring sequence based on the characteristic lag step and constructing a time-aligned feature data matrix; calculating the correlation components of each indicator relative to the evaluation standard and establishing a correlation model; identifying the variation nodes of aquatic ecosystem state variables and fitting the probability density function of the feedback evolution sequence to determine the evaluation threshold and confidence interval. This invention eliminates the logical bias caused by the sampling synchronicity assumption, making the assessment process conform to the hydrological and biochemical evolution law and improving the physical self-consistency of aquatic ecosystem stress identification.

Description

Technical Field

[0001] This invention relates to the field of water pollution detection technology, and in particular to a method for detecting the degree of stress on river water ecosystems. Background Technology

[0002] Currently, this technology typically involves the correlation assessment of multi-source heterogeneous indicators such as precipitation evapotranspiration index, comprehensive water quality index, and terrestrial vegetation index. By constructing an evaluation model that reflects the relationship between environmental pressure and system feedback, the health status level of aquatic ecosystems can be defined. The evolution of river water ecology is objectively constrained by both hydrodynamic diffusion resistance and biochemical metabolic cycles. When subjected to environmental stress or pollutant invasion, the physical response time scale of water physicochemical factors is relatively short. However, the biological community, as the main recipient, exhibits obvious lag characteristics in its physical state feedback due to the inertia of growth and metabolism. This asymmetric time lag caused by the difference between physical transmission resistance and biochemical reaction rate makes the temporal evolution of various monitoring factors not ideally synchronous.

[0003] Besides the limitations of physical monitoring hardware in capturing data at spatial scales, existing data processing and evaluation control methods based on software algorithms also have shortcomings. For example, Chinese invention patent CN114548754B discloses a wetland buffer zone water ecological health evaluation method based on trend judgment. It collects multi-year datasets by dividing time windows and relies on machine learning to build a predictive model to analyze the evolution of water ecological health. A deeper analysis of the underlying mechanism reveals that the existing algorithm implicitly relies on the ideal premise that water quality factors and ecological biological indicators evolve synchronously within the same absolute time window. In real river stress evolution scenarios, the hydrodynamic diffusion resistance of pollutants and the metabolic inertia of benthic plankton communities lead to asymmetric time misalignment between physical stimuli and biological responses. Submitting time series with hysteresis biases to a pure data-driven model for regression mapping strips away the objectively existing biochemical metabolic hysteresis patterns, causing the causal chain during environmental abrupt changes to break in the data dimension, resulting in systemic evaluation distortion.

[0004] Therefore, how to eliminate the systematic evaluation distortion caused by the time lag of physical transmission and biochemical metabolism in the process of water pollution detection, and improve the physical self-consistency of the multi-source heterogeneous factor evaluation process, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a method for detecting the degree of stress on river aquatic ecosystems, comprising the following steps: Step 101: Obtain drought characteristic indicators, environmental water quality indicators, and aquatic biological status indicators of the river to be tested, and construct a synchronous monitoring sequence that reflects the evolution of the river's physicochemical properties over time. Step 102: Calculate the cross-correlation between drought characteristic indicators, environmental water quality indicators, and aquatic organism status indicators; extract the time domain offset when the cross-correlation reaches its maximum value; and define the time domain offset as the characteristic lag step size characterizing the diffusion delay of pollutants in water bodies. Step 103: Based on the characteristic lag step size, implement asymmetric address displacement mapping on the data points in the synchronous monitoring sequence, and reassemble the mapped index data to generate a time-aligned feature data matrix that eliminates the error of physical transmission resistance. Step 104: Establish the correlation components of each indicator in the feature data matrix relative to the preset evaluation standard level, calculate the correlation parameter used to characterize the distribution frequency of the three ecological states of health, sub-health and damage, and thus construct a correlation model that reflects the uncertain causal relationship of multi-source indicators. Step 105: Use mutation point analysis to identify the variation nodes of the state variables of the river aquatic ecosystem to determine the feedback evolution sequence, and use kernel density estimation to fit the probability density function of the feedback evolution sequence to determine the evaluation threshold and confidence interval for the aquatic ecosystem to enter the disturbance stage from the stable stage under stress.

[0006] Preferably, in step 102, the process of determining the characteristic lag step includes: within a preset sliding time window, calculating the cross-correlation function between drought characteristic indicators and various environmental water quality indicators and various aquatic organism status indicators; extracting the delay time parameter corresponding to the maximum value of the cross-correlation function; determining the ratio of the delay time parameter to the preset sampling period as the characteristic lag step, which is used to compensate for the asymmetric response time difference of the monitoring indicators in the process of aquatic ecological evolution; wherein, the drought characteristic indicators cover drought duration and drought intensity, the environmental water quality indicators cover dissolved oxygen (DO) concentration and total phosphorus (TP) content, and the aquatic organism status indicators cover biodiversity index and chlorophyll a content.

[0007] Preferably, in step 103, the process of generating the time-series aligned feature data matrix includes: physically aligning the historical sampling points in the synchronous monitoring sequence according to the feature lag step length corresponding to each indicator; and implementing synchronous logical mapping on the same time axis for the recombined indicator data to eliminate the evaluation logic deviation caused by the sampling synchronicity assumption.

[0008] Preferably, the method further includes: using the random forest algorithm to calculate the feature importance score of each indicator in the feature data matrix; and selecting the core driving factors that dominate the evolution of aquatic ecosystems from drought feature indicators, environmental water quality indicators, and aquatic organism status indicators based on the feature importance score.

[0009] Preferably, in step 105, the process of determining the feedback evolution sequence includes: locating the variation nodes of the aquatic ecosystem state variables through cumulative anomaly analysis or ordered clustering analysis; defining the time series before the variation node as the stable stage, and defining the time series after the variation node as the disturbance stage.

[0010] Preferably, the process of determining the confidence interval includes: fitting the probability distribution of the aquatic ecosystem state variables during the disturbance phase by kernel density estimation; and extracting the corresponding numerical fluctuation range from the probability distribution as the confidence interval at a significance level of 0.05.

[0011] Preferably, the method further includes: using a generalized equilibrium feedback analysis method to verify the physical accuracy of the evaluation threshold and confidence interval; and correcting the detection results by adjusting the dimension of the feature data matrix when the feedback evolution sequence deviates from the preset equilibrium state.

[0012] Preferably, the biodiversity index and chlorophyll a content are obtained through remote sensing image inversion or real-time acquisition by on-site sensors; dissolved oxygen (DO) concentration, total phosphorus (TP) content, and total nitrogen (TN) content are obtained through automatic water quality monitoring stations.

[0013] Preferably, the method further includes generating an early warning signal indicating the need for pollution load reduction when the monitored degree of water ecological stress reaches the evaluation threshold.

[0014] The beneficial effects of this invention are: 1. In the detection of the stress level of river water ecology, by extracting the physical hysteresis step length that reflects the metabolic cycle of aquatic organisms and the resistance to pollutant diffusion, the multi-source monitoring characteristics are reorganized in the time domain to restore the physical causal relationship between environmental pressure and ecological feedback. This mechanism eliminates the systematic logical deviation caused by the assumption of synchronous acquisition of monitoring data, and ensures that the assessment process conforms to the objective laws of hydrological and biochemical evolution, thereby improving the physical self-consistency of the identification of the stress state of water ecology.

[0015] 2. By synergistically applying phase space reconstruction and ternary connectivity analysis, the uncertainty characteristics of aquatic ecosystems under different stress stages are quantified. By deeply anchoring the feature vectors after hysteresis compensation to the connectivity model, the weight distribution of similarity, difference, and opposition within the model can accurately map the actual disaster-affected physical processes in the watershed. This solves the problem of distorted evaluation results during periods of severe environmental fluctuations and enhances the system's ability to capture nonlinear transitions in ecological states.

[0016] 3. By utilizing feature mapping scheduling logic based on asymmetric time delay, compensation is implemented for the response lag characteristics of biological indicators in the early stage of stress and the environmental mitigation period. This approach avoids the risk of false alarms caused by traditional statistical alignment methods at abrupt change nodes in drought evolution, and achieves accurate definition of the critical threshold and confidence boundary for the system to enter the disturbance period from the stable period, providing technical support with engineering practicality for water pollution prevention and control. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a schematic diagram of the river water ecology stress evolution feedback and threshold detection steps of the present invention; Figure 2 This is a diagram of the water ecological monitoring system architecture covering data acquisition and feedback analysis in this invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, an embodiment or embodiment referred to herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. An embodiment appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0021] This invention is described in detail with reference to the schematic diagrams. When describing the embodiments of this invention, for ease of explanation, the cross-sectional views of the device structure will be partially enlarged without adhering to the general scale. Moreover, the schematic diagrams are only examples and should not limit the scope of protection of this invention. In addition, in actual manufacturing, the three-dimensional spatial dimensions of length, width and depth should be included.

[0022] Furthermore, in the description of this invention, it should be noted that the terms such as "upper," "lower," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or component referred to has a specific orientation, or is constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] Unless otherwise explicitly specified and limited, the terms installation, connection, and linking in this invention should be interpreted broadly. For example, they can refer to fixed connection, detachable connection, or integrated connection; similarly, they can refer to mechanical connection, electrical connection, or direct connection, or indirect connection through an intermediate medium, or internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0024] A method for detecting the degree of stress on river aquatic ecosystems includes the following steps: Step 101: Obtain drought characteristic indicators, environmental water quality indicators, and aquatic biological status indicators of the river to be tested, and construct a synchronous monitoring sequence that reflects the evolution of the river's physicochemical properties over time. Step 102: Calculate the cross-correlation between drought characteristic indicators, environmental water quality indicators, and aquatic organism status indicators; extract the time domain offset when the cross-correlation reaches its maximum value; and define the time domain offset as the characteristic lag step size characterizing the diffusion delay of pollutants in water bodies. Step 103: Based on the characteristic lag step size, implement asymmetric address displacement mapping on the data points in the synchronous monitoring sequence, and reassemble the mapped index data to generate a time-aligned feature data matrix that eliminates the error of physical transmission resistance. Step 104: Establish the correlation components of each indicator in the feature data matrix relative to the preset evaluation standard level, calculate the correlation parameter used to characterize the distribution frequency of the three ecological states of health, sub-health and damage, and thus construct a correlation model that reflects the uncertain causal relationship of multi-source indicators. Step 105: Use mutation point analysis to identify the variation nodes of the state variables of the river aquatic ecosystem to determine the feedback evolution sequence, and use kernel density estimation to fit the probability density function of the feedback evolution sequence to determine the evaluation threshold and confidence interval for the aquatic ecosystem to enter the disturbance stage from the stable stage under stress.

[0025] Preferably, in step 102, the process of determining the characteristic lag step includes: within a preset sliding time window, calculating the cross-correlation function between drought characteristic indicators and various environmental water quality indicators and various aquatic organism status indicators; extracting the delay time parameter corresponding to the maximum value of the cross-correlation function; determining the ratio of the delay time parameter to the preset sampling period as the characteristic lag step, which is used to compensate for the asymmetric response time difference of the monitoring indicators in the process of aquatic ecological evolution; wherein, the drought characteristic indicators cover drought duration and drought intensity, the environmental water quality indicators cover dissolved oxygen (DO) concentration and total phosphorus (TP) content, and the aquatic organism status indicators cover biodiversity index and chlorophyll a content.

[0026] Preferably, in step 103, the process of generating the time-series aligned feature data matrix includes: physically aligning the historical sampling points in the synchronous monitoring sequence according to the feature lag step length corresponding to each indicator; and implementing synchronous logical mapping on the same time axis for the recombined indicator data to eliminate the evaluation logic deviation caused by the sampling synchronicity assumption.

[0027] Preferably, in step 104, the mathematical expression of the connection degree model is: μ=a+bi+cj, where μ is the connection degree; a, b, and c are the frequency distribution values ​​of the indicators to be evaluated falling into the health level, sub-health level, and damaged level intervals in the feature data matrix, respectively, and all are dimensionless real numbers; i is the difference coefficient, representing the difference degree logical weight; j is the opposition coefficient, representing the opposition degree logical weight.

[0028] Preferably, the method further includes: using the random forest algorithm to calculate the feature importance score of each indicator in the feature data matrix; and selecting the core driving factors that dominate the evolution of aquatic ecosystems from drought feature indicators, environmental water quality indicators, and aquatic organism status indicators based on the feature importance score.

[0029] Preferably, in step 105, the process of determining the feedback evolution sequence includes: locating the variation nodes of the aquatic ecosystem state variables through cumulative anomaly analysis or ordered clustering analysis; defining the time series before the variation node as the stable stage, and defining the time series after the variation node as the disturbance stage.

[0030] Preferably, the process of determining the confidence interval includes: fitting the probability distribution of the aquatic ecosystem state variables during the disturbance phase by kernel density estimation; and extracting the corresponding numerical fluctuation range from the probability distribution as the confidence interval at a significance level of 0.05.

[0031] Preferably, the method further includes: using a generalized equilibrium feedback analysis method to verify the physical accuracy of the evaluation threshold and confidence interval; and correcting the detection results by adjusting the dimension of the feature data matrix when the feedback evolution sequence deviates from the preset equilibrium state.

[0032] Preferably, the biodiversity index and chlorophyll a content are obtained through remote sensing image inversion or real-time acquisition by on-site sensors; dissolved oxygen (DO) concentration, total phosphorus (TP) content, and total nitrogen (TN) content are obtained through automatic water quality monitoring stations.

[0033] Preferably, the method further includes generating an early warning signal indicating the need for pollution load reduction when the monitored degree of water ecological stress reaches the evaluation threshold.

[0034] Example 1: In a watershed with continuous rainfall scarcity and upstream agricultural non-point source total phosphorus input, dissolved oxygen concentration in the water drops rapidly while total phosphorus concentration rises sharply. As the main recipient, the phytoplankton community is constrained by its biological metabolic cycle, and its chlorophyll content and biodiversity index do not show synchronous degradation within the same sampling time stamp of physical water quality deterioration. The traditional assessment logic based on rigid alignment of absolute timestamps forces data frames at different stages of physical resistance and biochemical lag to calculate correlation regression in the same calculation matrix. This results in the data processing system capturing physical environmental deterioration in the early stages of drought stress, but biological indicators maintaining inertial fluctuations, and outputting a health status judgment that contradicts the objective physical evolution. This causal time mismatch caused by the lack of physical lag time constant in the underlying data structure constitutes the problem of underlying data mapping distortion that the water pollution detection system needs to overcome under this condition.

[0035] The system acquires drought characteristic indicators covering drought duration and intensity for the river under test, and simultaneously collects environmental water quality indicators covering dissolved oxygen concentration and total phosphorus content, as well as aquatic organism status indicators covering biodiversity index and chlorophyll content. It constructs a synchronous monitoring sequence reflecting the evolution of the river's physicochemical properties over time. Within a preset sliding time window, the system calculates the cross-correlation functions between drought characteristic indicators and each environmental water quality indicator and each aquatic organism status indicator. It extracts the delay time parameter corresponding to the maximum value of the cross-correlation function, and determines the characteristic lag step size characterizing the diffusion delay of pollutants in the water body by the ratio of the delay time parameter to the preset sampling period. The system then uses the characteristic lag step size corresponding to each indicator... Asymmetric address displacement mapping is applied to data points in the synchronous monitoring sequence. Through physical time-scale alignment, the recombined index data are established with synchronous logical mapping on the same time axis, generating a time-series aligned feature data matrix that eliminates errors caused by physical transmission resistance. The system loads the misaligned time-series feature vectors into the logic operation unit and uses the random forest algorithm to calculate the feature importance score of each indicator in the feature data matrix to screen the core driving factors dominating aquatic ecological evolution. The system establishes the correlation components of the indicators to be evaluated in the feature data matrix relative to the preset evaluation standard level, calculates the correlation degree parameter representing the distribution frequency of three ecological states: healthy, sub-healthy, and damaged, and constructs a model reflecting the multi-source indicators. The deterministic causal relationship correlation model is μ = a + bi + cj, where μ is the correlation degree, a, b, and c are the frequency distribution values ​​of the evaluated indicators in the feature data matrix falling into the healthy level, sub-healthy level, and damaged level intervals, respectively, and are all dimensionless real numbers; i is the logical weight of difference, and j is the logical weight of opposition. This mechanism eliminates the evaluation logic bias caused by the sampling synchronicity assumption by calculating the correlation probability between the hysteresis-compensated feature vector and the baseline state. In specific implementation, the system has a built-in physical and biochemical calibration matrix for defining the above three evaluation levels. Taking the key combination of dissolved oxygen concentration and biodiversity index as an example, the system sets: when a certain value in the feature matrix... When the dissolved oxygen concentration corresponding to the sequence node is ≥5.0 mg / L and the biodiversity index is ≥2.5, the time series segment is determined to fall into the healthy level; when the dissolved oxygen concentration is between 3.0 mg / L and 5.0 mg / L, or the biodiversity index is between 1.5 and 2.5, it is determined to fall into the sub-healthy level; when the dissolved oxygen concentration is <3.0 mg / L or the biodiversity index is <1.5, it is determined to fall into the damaged level. The system traverses the time series alignment feature data matrix, counts the number of data points of each indicator falling into the above three intervals on the time axis, and divides them by the total number of effective time steps of the matrix, that is, calculates and generates the corresponding dimensionless real numbers a, b, and c that constitute the connection degree model.

[0036] After generating the correlation expression of uncertain causal relationships among multi-source indicators, the system uses the cumulative anomaly method to locate the variation nodes of aquatic ecosystem state variables. The time series before the variation nodes is defined as the stable stage, and the time series after the variation nodes is defined as the disturbance stage. The feedback evolution sequence is determined, and the system uses kernel density estimation to fit the probability distribution of aquatic ecosystem state variables within the disturbance stage. At a significance level of 0.05, the corresponding numerical fluctuation range is extracted from the probability distribution as the confidence interval to determine the evaluation threshold for the aquatic ecosystem stress state to enter the disturbance stage from the stable stage. The system introduces the generalized equilibrium feedback analysis method to establish a statistical regression model including external forcing terms. When the feedback evolution sequence deviates from the preset equilibrium state, the dimension of the feature data matrix is ​​adjusted. Errors are removed from the detection results and the physical state is verified. When the monitored aquatic ecosystem stress degree reaches the evaluation threshold, the system data interface outputs risk parameters that quantify the damaged state and generates an early warning signal indicating the need for pollution load reduction. The system state data shows that this time-series alignment mechanism compensates for the asymmetric response time difference between the hydrological diffusion mechanism and the bottom aquatic organism metabolic mechanism. The output ecological stress identification results are consistent with the historical records of objective physical processes in the watershed in terms of time evolution.

[0037] Example 2: For the condition of delayed pollutant diffusion due to hydrological drought, this verification relies on a physical sensor array deployed along the river to collect raw signals. The sensor array includes a dissolved oxygen electrochemical probe with a measurement accuracy of 0.1 mg / L, an optical chlorophyll sensor with a sampling rate of 1 Hz, and an acoustic Doppler current profiler. Gaussian white noise with a signal-to-noise ratio of 15 dB is actively superimposed onto the collected drought characteristic indicators, environmental water quality indicators, and aquatic organism status indicators. The Gaussian white noise is used to simulate the turbulent disturbances and sensor baseline drift deviation in the real water body on the baseline data. A sliding time window for calculating the cross-correlation function is set. The parameters affecting the value of this sliding time window include the hydrodynamic diffusion rate and the phytoplankton reproductive cycle. The window needs to establish a threshold balance between capturing transient physical disturbance characteristics and smoothing long-term biological metabolic trends. The system determines the window span based on the ratio of the velocity sensing data obtained by the acoustic Doppler current profiler to the preset basal metabolic constant. When the velocity data is below the threshold of 0.1 m / s, the water retention time... As the time interval is extended, the system command sliding time window tends to the upper limit of the calculation range, thereby covering the complete single algal cell division cycle. Based on the on-site measured flow velocity of 0.05 m / s, the system sets the sliding time window to 72 hours. The specific engineering calculation basis for this setting is as follows: Under the extreme slow flow condition of 0.05 m / s, the average macroscopic hydrodynamic residence time required for pollutant molecules to diffuse and transport through the characteristic river section (set as a benchmark span of 10 kilometers) defined by the system is 55 hours. In addition, the system extracts the primary cell doubling lag constant of the representative chlorophyll dominant species (such as green algae) in the river section at the current water temperature by calling the built-in ecological basic metabolic parameter library. The biological physiological lag dimension is determined to be 17 hours. The system linearly sums the 55 hours of physical residence of water flow with the 17 hours of biological metabolic lag to accurately obtain the threshold conclusion of 72 hours, ensuring that the selected time window can completely cover the entire cascade evolution cycle of physical diffusion and biosensory burst.

[0038] Independent test channels were used to form experimental groups. Control group 1 extracted raw monitoring data points using hard alignment based on absolute timestamps, control group 2 set the lower limit of the sliding time window to 12 hours, and control group 3 set the upper limit of the sliding time window to 168 hours. The experimental groups used a 72-hour sliding time window, extracted the time-domain offset when the cross-correlation reached its maximum value to define the feature lag step, applied an asymmetric address displacement mapping based on the feature lag step, and reconstructed to generate a time-series aligned feature data matrix. The physical consistency of using a single rigid step size extraction logic to address the complex lag in ecological feedback lies in the fact that, although the stress response of biological populations in receiving water bodies is long-term... At a scale of 1000, highly nonlinear transitions and collapses inevitably occur. However, within the local short-period sliding time window defined by this algorithm (e.g., within 72 hours), the hydrophysical transport disturbances of exogenous disaster-causing factors and the primary enzymatic reactions of biological receptors closely approximate a first-order inertial dynamic response. Therefore, within this time-domain slice, the high-dimensional nonlinear hysteresis effect of microscopic biochemical processes is physically reduced to a global translation of the macroscopic signal curve sequence on the time axis. This allows the maximum extreme step size captured by the cross-correlation function to compensate for the time difference of the physical causal response in the early stage of drought mutation without distortion, thus contributing to the output of the above-mentioned test channels. Monitoring data streams corresponding to three different deviation gradients—mild hydrological drought, moderate water quality deterioration, and severe ecological collapse—were injected into the input end. When the data stream for moderate water quality deterioration was input, the dissolved oxygen concentration in the environmental water quality index showed a non-linear decrease at 12 hours. Control group one input the dissolved oxygen data at 12 hours and the biodiversity index at the same time into a correlation model for solving. The correlation model is expressed as μ = a + bi + cj, where μ is the correlation degree, variables a, b, and c represent the frequency distribution values ​​of the evaluated index in the feature data matrix falling into the healthy, sub-healthy, and damaged levels, respectively, and are all dimensionless real numbers. Variable i is... The difference degree logical weight is used, and variable j is the opposition degree logical weight. The moment when the output connectivity of the control group exceeds the damage threshold is at 14h. The aquatic organism status index is stunted by the bottom metabolic cycle and changes abruptly at 60h. The experimental group extracts the extreme value of the cross-correlation function within a 72h sliding time window, and the output feature lag step is 48h. The system instruction memory addresses and maps the dissolved oxygen data address of 12h to the data interval of 60h, and reassembles it to generate a time-aligned feature data matrix. The connectivity model of the experimental group calculates that the node where the output connectivity reaches the damage threshold is at 62h. This output node coincides with the metabolic mutation point of the bottom biological community.

[0039] Under Gaussian white noise interference, the 12-hour window set by control group 2 failed to filter out the diurnal reoxygenation fluctuations of the water body. Its characteristic data matrix was dominated by local noise, and the output risk parameters generated 37 limit-breaking alarms within 48 hours, with the evaluation status exhibiting high-frequency oscillations. The 168-hour window set by control group 3 over-averaged the evolution characteristics, resulting in the inability to optimize the cross-correlation function. Its mutation node identification was delayed until the 156th hour, causing a lag in the generation of warning signals. The experimental group calculated the cross-correlation function based on the data distribution within the 72-hour window. The 0.05 significance level confidence interval determined by the kernel density estimation and the fitted feedback evolution sequence probability density function effectively filtered out the frequency band interference of Gaussian white noise. Its output evaluation threshold remained steady, and no limit-breaking false alarms occurred. When the data stream of ecological collapse was being processed, the drought intensity index exceeded its extreme value, and the ecosystem experienced nonlinear physical feedback. The collapse of biological metabolism caused a sharp increase in the biochemical oxygen demand at the bottom layer, and the characteristic lag step length, which was originally 48 hours, contracted exponentially to 6 hours. The experimental group used the cumulative anomaly method to locate the variation nodes of the aquatic ecosystem state variables and introduced a generalized equilibrium feedback analysis method to establish a statistical regression model containing external forcing terms. This statistical regression model detected that the feedback evolution sequence deviated from the preset equilibrium state, triggering the system to automatically reduce the dimension of the characteristic data matrix. The system removed the invalid lag step length parameters, and the quantitative damage state risk parameter finally output by the experimental group's data interface completed a hierarchical transition at the 8th hour. The early warning signal generated by it was consistent with the objective biochemical oxygen demand mutation trajectory.

[0040] Example 3: In situations where multi-source indicator fusion evaluation lacks quantitative benchmarks for weight parameters and long-sequence feature data increases the dimensionality of the computational matrix, the water pollution detection system establishes quantitative rules for evaluation parameters and dimensionality reduction benchmarks for the computational matrix before inputting real-time monitoring data. The system retrieves a feature dataset of historical major drought events containing water ecological evolution status markers. This dataset includes drought characteristic indicators, environmental water quality indicators, and aquatic biological status indicators with accompanying occurrence timestamps. The system establishes a connection degree model μ = a + bi + cj, where μ is the connection degree, variables a, b, and c represent the frequency distribution values ​​of the indicators to be evaluated falling into the healthy, sub-healthy, and damaged levels in the feature data matrix, respectively, variable i is the difference degree logical weight, and variable j is the opposition degree logical weight. The system delineates a two-dimensional parameter optimization grid for the difference degree logical weight i and the opposition degree logical weight j within a numerical range of -1 to 1 with a step size of 0.1. The system then... The dataset is substituted into a two-dimensional parameter optimization grid to calculate the theoretical predicted value of the connectivity degree under each grid node. The system calculates the root mean square error between the theoretical predicted value of the connectivity degree and the corresponding real damage record in the feature dataset. The parameter combination that minimizes the root mean square error is selected, and the logical weight of the difference degree i is determined to be 0.3 and the logical weight of the opposition degree j is -0.8. The association expression rule is established. The real damage record here is actually a quantized array sequence manually calibrated from the regional historical environmental survey archives. It is formatted in the system as a one-dimensional state comparison vector with the same time axis resolution as the time series feature dataset. The elements in this vector are assigned a normalized scalar risk value. For example, the elements corresponding to the stable data segment where the water ecology has been in the background level period for a long time are rigidly labeled as 0, while the array elements under the corresponding timestamp of the historical mutation data segment where there has been a clear algal bloom or large-scale death of benthic organisms are labeled as the absolute value 1.0.

[0041] The system receives real-time acquired time-series aligned feature data matrices and uses the cumulative anomaly method to locate the variation nodes of aquatic ecosystem state variables. It calculates the arithmetic mean of each aquatic ecosystem state variable in the time-series aligned feature data matrix within the evaluation period, and calculates the difference sequence between each time-series data point and the arithmetic mean. The system accumulates the difference sequence item by item along the time axis to generate a cumulative anomaly time series. The system locates the data coordinate point with the largest absolute value in the cumulative anomaly time series, and determines its corresponding timestamp as the variation node reflecting a structural abrupt change in the river's physicochemical properties. The time series before the variation node is defined as the stable stage, and the time series after the variation node is defined as the disturbance stage. A feedback evolution sequence is output. The system introduces a generalized equilibrium feedback analysis method to establish a system that includes external... The system employs a statistical regression model for the forced term; calculates the covariance matrix of the feedback evolution sequence, extracts the eigenvalues ​​and corresponding eigenvectors of the covariance matrix, and calculates the cumulative variance contribution rate of each eigenvector in descending order of eigenvalues; when the feedback evolution sequence deviates from the equilibrium state and the cumulative variance contribution rate reaches the truncation threshold of 85%, the system removes the low-order eigencomponents corresponding to the remaining eigenvalues ​​and retains the high-order eigenvectors to reconstruct the feature data matrix; it uses the dimensionality-reduced and reconstructed feature data matrix to extract the water ecology stress state evaluation threshold, and the data interface outputs the risk parameters of the quantified damaged state based on the established connectivity model parameters. This calibration and dimensionality reduction mechanism avoids the bias of preset parameters and the redundancy of high-dimensional data calculations, and the output ecological stress identification results are logically aligned with the watershed hydrological and biochemical evolution law.

[0042] Example 4: When the system faces the initial deployment and baseline initialization of cross-regional water bodies, the sensor stations at the control section of the target river segment are scheduled to continuously collect water quality signals during the normal water period to obtain an initial background dataset covering baseline flow velocity, dissolved oxygen background concentration, and chlorophyll baseline content. The extreme values ​​and average variances of each indicator in this initial background dataset are extracted. Combined with the baseline flow velocity parameter, the dynamic balance coefficient between the natural reoxygenation rate of the water body and the basic metabolic oxygen consumption rate of algae is calculated. The specific physical calculation path of this coefficient is as follows: the system calls the classic O'Connor-Dobins gas-liquid mass transfer equation component of fluid mechanics, and divides the 0.5th power of the extracted baseline flow velocity parameter by the 1.5th power of the water depth at the measurement section. A hemodynamic magnitude characteristic value representing the physical reoxygenation capacity of the water surface turbulence is generated. Simultaneously, the scalar product of the dissolved oxygen background concentration and the chlorophyll baseline content is calculated to generate a basic oxygen consumption magnitude characteristic value representing the biochemical metabolic intensity of phytoplankton at the bottom. Finally, the obtained physical reoxygenation characteristic value is divided by the biochemical oxygen consumption characteristic value, and the quotient is established as the dynamic equilibrium coefficient required by the system. The system uses this dynamic equilibrium coefficient as the unforced equilibrium state benchmark value in the generalized equilibrium feedback analysis method and writes it into the initial configuration register of the statistical regression model. The fluctuation extreme value and the average variance are used to set the initial frequency distribution baseline representing the health level for the correlation degree model, and establish a basic reference quantity that is suitable for the physical and biochemical characteristics of the watershed.

[0043] When faced with seasonal changes causing time drift in baseline data, the system sets a sliding update window to periodically extract stable period monitoring sequences that have not triggered warnings. It calculates the time-series covariance matrix of these stable period monitoring sequences and compares it with the parameter distribution distance of the unforced equilibrium benchmark value in the statistical regression model. When the parameter distribution distance exceeds the preset deviation control line, the system refits the probability density function of the kernel density estimate based on the newly extracted stable period monitoring sequences, extracts the latest numerical boundary at a significance level of 0.05 to update the evaluation threshold of the water ecology under stress, and simultaneously reconstructs the health level frequency interval boundary in the connectivity model. The system outputs the updated evaluation threshold and maps it to the current hydrological environmental indicators of the target river section to form an objective time-series mapping.

[0044] Example 5: In the case of the first deployment of a new watershed monitoring node and the lack of quantitative constraints on algorithm parameters, the system loads the time-series monitoring dataset covering the historical normal and drought periods of the watershed offline, establishes a gradient optimization loop for the cross-correlation function calculation window length, sets the initial window length to 12h and increases it to 168h in 12h steps, calculates the time-domain offset of drought characteristic indicators, dissolved oxygen concentration and chlorophyll content at each step length, and defines the 120h that minimizes the variance of the time-domain offset and has a condition number of the recombined feature data matrix below a preset threshold as the feature sliding update window of the watershed. Under this feature sliding update window, the system starts a two-dimensional grid search of the random forest algorithm parameters, limits the number of decision trees to 50 to 200 and the maximum depth to 5 to 20, and selects the parameter combination that takes the maximum sum of the Gini impurity decrease in cross-validation and writes it into the logic operation unit.

[0045] Faced with the situation where sensor hardware failure causes missing segments of monitoring data stream, the system deploys data integrity judgment logic at the input end of the time-series aligned feature data matrix. It calculates the effective data timestamp interval within a single sampling period. When the root mean square error of this timestamp interval is greater than three times the theoretical sampling period, the system blocks the computation link of the current physically failed data segment into the connectivity model, extracts the median value of the historical evolution sequence of the same season to fill the missing address bits, and the system synchronously calculates the first-order difference parameter of the filled time-series feature vector. When the first-order difference parameter of three consecutive sampling points crosses the kernel density probability distribution information boundary, the feature evaluation weight of that failure dimension is removed, the feature data matrix is ​​reconstructed based on the remaining effective parameters, and the fault-tolerant correction instruction for the corresponding sensing channel is output.

[0046] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A method for detecting the degree of stress on river aquatic ecosystems, characterized in that, Includes the following steps: Step 101: Obtain drought characteristic indicators, environmental water quality indicators, and aquatic biological status indicators of the river to be tested, and construct a synchronous monitoring sequence that reflects the evolution of the river's physicochemical properties over time. Step 102: Calculate the cross-correlation between drought characteristic indicators, environmental water quality indicators, and aquatic organism status indicators; extract the time domain offset when the cross-correlation reaches its maximum value; and define the time domain offset as the characteristic lag step size characterizing the diffusion delay of pollutants in water bodies. Step 103: Based on the characteristic lag step size, implement asymmetric address displacement mapping on the data points in the synchronous monitoring sequence, and reassemble the mapped index data to generate a time-aligned feature data matrix that eliminates the error of physical transmission resistance. Step 104: Establish the correlation components of each indicator in the feature data matrix relative to the preset evaluation standard level, calculate the correlation parameter used to characterize the distribution frequency of the three ecological states of health, sub-health and damage, and construct a correlation model that reflects the uncertain causal relationship of multi-source indicators. Step 105: Use mutation point analysis to identify the variation nodes of the state variables of the river aquatic ecosystem to determine the feedback evolution sequence, and use kernel density estimation to fit the probability density function of the feedback evolution sequence to determine the evaluation threshold and confidence interval for the aquatic ecosystem to enter the disturbance stage from the stable stage under stress.

2. The method for detecting the degree of stress on river aquatic ecosystems according to claim 1, characterized in that, In step 102, the process of determining the characteristic lag step includes: within a preset sliding time window, calculating the cross-correlation function between drought characteristic indicators and various environmental water quality indicators and various aquatic organism status indicators; extracting the delay time parameter corresponding to the maximum value of the cross-correlation function; determining the ratio of the delay time parameter to the preset sampling period as the characteristic lag step, which is used to compensate for the asymmetric response time difference of the monitoring indicators in the process of aquatic ecological evolution; wherein, drought characteristic indicators cover drought duration and drought intensity, environmental water quality indicators cover dissolved oxygen (DO) concentration and total phosphorus (TP) content, and aquatic organism status indicators cover biodiversity index and chlorophyll a content.

3. The method for detecting the degree of stress on river aquatic ecosystems according to claim 1, characterized in that, In step 103, the process of generating the time-series aligned feature data matrix includes: physically aligning the historical sampling points in the synchronous monitoring sequence according to the feature lag step length corresponding to each indicator; and implementing synchronous logical mapping on the same time axis for the recombined indicator data to eliminate the evaluation logic deviation caused by the sampling synchronicity assumption.

4. The method for detecting the degree of stress on river aquatic ecosystems according to claim 1, characterized in that, The method also includes: using the random forest algorithm to calculate the feature importance score of each indicator in the feature data matrix; and screening the core driving factors that dominate the evolution of aquatic ecosystems from drought feature indicators, environmental water quality indicators and aquatic organism status indicators based on the feature importance score.

5. The method for detecting the degree of stress on river aquatic ecosystems according to claim 1, characterized in that, In step 105, the process of determining the feedback evolution sequence includes: locating the variation nodes of the aquatic ecosystem state variables through cumulative anomaly analysis or ordered clustering analysis; defining the time series before the variation node as the stable stage and the time series after the variation node as the disturbance stage.

6. The method for detecting the degree of stress on river aquatic ecosystems according to claim 1, characterized in that, The process of determining the confidence interval includes: fitting the probability distribution of the aquatic ecosystem state variables during the disturbance phase using kernel density estimation; and extracting the corresponding numerical fluctuation range from the probability distribution as the confidence interval at a significance level of 0.

05.

7. The method for detecting the degree of stress on river aquatic ecosystems according to claim 1, characterized in that, The method also includes: using a generalized equilibrium feedback analysis method to verify the physical accuracy of the evaluation threshold and confidence interval; and correcting the detection results by adjusting the dimension of the feature data matrix when the feedback evolution sequence deviates from the preset equilibrium state.

8. The method for detecting the degree of stress on river aquatic ecosystems according to claim 1, characterized in that, Biodiversity index and chlorophyll a content were obtained through remote sensing image inversion or real-time acquisition by on-site sensors; dissolved oxygen (DO) concentration, total phosphorus (TP) content, and total nitrogen (TN) content were obtained through automatic water quality monitoring stations.

9. The method for detecting the degree of stress on river aquatic ecosystems according to claim 1, characterized in that, The method also includes generating an early warning signal indicating the need for pollution load reduction when the monitored degree of aquatic ecological stress reaches the evaluation threshold.

Images