Ecological restoration effect evaluation method for micro-polluted river

Abstract

The invention discloses a method for evaluating the ecological restoration effect of a micro-polluted river, particularly relates to the field of ecological restoration, and is used for solving the problem of accurate identification and evaluation of sudden pulse pollution events of the micro-polluted river. The data consistency and high resolution are ensured through a unified time and space reference axis, and the accuracy of event identification is guaranteed; a time sequence windowing comparison and deviation mapping inspection technology is adopted, random interference is effectively filtered, and an original fragment of a pulse pollution event is extracted; the impact force and diffusion characteristics of the pollution event are analyzed by applying an amplitude change and propagation dynamic quantification method, and accurate data support is provided for a risk distribution map; according to the method, a correlation mapping model of adaptive clustering and priority evaluation is combined, pollution events are scientifically classified and sorted, high-risk areas with high impact force and high diffusion potential are accurately identified, accurate calibration and deployment and control division are carried out according to a risk distribution map, and the pertinence and efficiency of treatment measures are optimized.

Description

Technical Field

[0001] The present invention relates to the field of ecological restoration, and more specifically, to a method for evaluating the ecological restoration effect of a micro-polluted river. Background Art

[0002] As the problem of water pollution becomes increasingly serious, micro-polluted rivers have gradually become the focus of ecological governance and environmental protection. Although the concentration of pollutants in micro-polluted rivers has not reached the level of serious pollution, their long-term accumulation will pose a potential threat to the aquatic ecosystem and river functions that cannot be ignored. In response to this problem, ecological restoration technology has been widely used to improve water quality and restore ecological balance through physical, chemical and biological means. However, the evaluation of ecological restoration effects still faces huge challenges. Traditional evaluation methods mainly rely on regular collection of water quality data and simple analysis of ecological indicators. This method is not only limited by the lag of low-frequency sampling, but also difficult to fully reflect dynamic pollution changes, especially sudden pollution events in a short period of time, resulting in insufficient accuracy and real-time performance of the evaluation results.

[0003] The existing evaluation methods for the ecological restoration of micro-polluted rivers lack sufficient sensitivity and analytical capabilities when facing sudden pulse pollution events, and cannot effectively extract the short-term peak characteristics and propagation dynamics of these events from long-term series data. The occurrence of pulse pollution events will cause drastic fluctuations in water quality and have far-reaching potential impacts on the ecosystem, but the existing methods are mostly based on low-frequency sampling and average analysis, which makes it difficult to capture the true characteristics of the events. In addition, the existing evaluation model fails to effectively quantify the amplitude changes and propagation characteristics of pollution events, and cannot form an accurate basis for the division of high-risk areas, thus affecting the accuracy and effectiveness of subsequent restoration measures. Therefore, there is an urgent need for an evaluation method that can identify and quantify the characteristics of sudden pulse pollution events, and based on this, conduct scientific and reasonable regional divisions to comprehensively improve the accuracy and pertinence of the evaluation of the ecological restoration effects of micro-polluted rivers. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for evaluating the ecological restoration effect of a micro-polluted river, which uses a high-frequency water quality sensor to obtain continuous dynamic data, and ensures the consistency and high resolution of the data through a unified time and space reference axis, thereby ensuring the accuracy of event identification. Through time series window comparison and deviation mapping inspection technology, random interference can be effectively filtered, and the original fragments of pulse pollution events can be accurately extracted. Furthermore, the amplitude change and propagation dynamic quantification method is adopted to deeply analyze the impact and diffusion characteristics of pollution events, providing accurate data support for the generation of risk distribution maps. The association mapping model combines adaptive clustering and priority assessment to scientifically classify and sort pollution events, accurately identify high-risk areas with high impact and high diffusion potential, and accurately calibrate and control division according to the risk distribution map, optimize the pertinence and efficiency of governance measures, so as to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] S1. Obtain the continuous dynamic parameters of a specific section of the river, mark them on a unified time and space reference axis, and construct long sequence data;

[0007] S2. Perform time-series window comparison and deviation mapping test on long sequence data, compare the steady-state baseline with the instantaneous abnormal peak, filter out random interference and invalid fluctuations, and extract the original fragments of identifiable pulse pollution events;

[0008] S3. Extract the amplitude change information and propagation dynamic information of the event from the extracted pulse pollution event, including analyzing the intensity change of the pollution peak to quantify its impact on water quality, and at the same time evaluate the propagation speed and diffusion range of the pollution event in the river;

[0009] S4. Construct an association mapping model to classify and sort the amplitude change information and propagation dynamic information of different pulse events, identify high-risk areas through cluster analysis and priority assessment, and form a clear risk distribution map;

[0010] S5. Based on the risk distribution map, calibrate and divide the high-risk pulse pollution sections.

[0011] In a preferred embodiment, step S1 includes the following contents:

[0012] First, key monitoring sections are selected, and a high-frequency water quality sensor network is deployed according to the characteristics of the river basin and the distribution of pollution sources to collect water quality parameters in the water body; secondly, time synchronization and spatial calibration are implemented; next, data preprocessing is performed to construct a complete spatiotemporal data matrix; then, the processed water quality parameters are arranged in time series and mapped on a unified time and space reference axis, integrating the multi-parameter data collected by different sensors into a single long-sequence data set.

[0013] In a preferred embodiment, step S2 includes the following contents:

[0014] First, time series windowing technology is applied to long sequence data to divide the overall data into multiple time windows; each time window covers a fixed time period;

[0015] Secondly, the deviation mapping test constructs a dynamic baseline model to conduct real-time comparative analysis of water quality parameters within each time window; the dynamic baseline model uses the autoregressive integrated moving average method. Specifically, for each time window, a prediction model is established to predict the normal fluctuation range of water quality parameters; the residual between the actual observed value and the predicted value is used to evaluate the deviation;

[0016] Next, the deviation threshold setting and mapping adopts the adaptive threshold method, which dynamically adjusts the threshold according to the residual distribution of historical data to obtain the marking threshold;

[0017] Then, pulse contamination events are extracted by comparing whether the residual in each time window exceeds the marking threshold. The time period exceeding the marking threshold is marked as the original segment of the pulse contamination event.

[0018] In a preferred embodiment, step S3 includes the following contents:

[0019] The amplitude variation information and propagation dynamic information of the event are quantified from the extracted pulse contamination event, wherein the amplitude variation information includes the pulse peak intensity index, and the propagation dynamic information includes the delay gradient index.

[0020] In a preferred embodiment, the pulse peak intensity index reflects the instantaneous impact of pollution events on water quality, which is achieved through multi-dimensional decomposition and dynamic accumulation calculation:

[0021] a. Multi-point baseline construction: The multi-point sliding window method is used to dynamically calculate the baseline value sequence before the pollution event occurs; the baseline value is determined within the window by the baseline function B(t): Among them, P(x) is the water quality parameter value, Δt is the window width, and α is the time attenuation factor;

[0022] b. Peak detection and curve fitting: In the pollution event interval, identify the maximum value and perform cubic spline interpolation fitting on the surrounding data points to calculate the peak function;

[0023] c. Instantaneous impact force integral: The pulse peak intensity index is defined as the logarithmic difference integral of the peak function and the baseline function over the event interval.

[0024] In a preferred embodiment, the delayed gradient index describes the propagation dynamics of pulse pollution events in the river channel, which is achieved by jointly modeling the time response function and the spatial propagation characteristics:

[0025] a. Time response curve modeling: For each monitoring section S o , through the response time T of the pollution event o Constructing a time response function Among them, β is the steepness parameter of the response curve;

[0026] b. Spatial gradient construction: Suppose adjacent monitoring sections S o and S o+1 The distance between them is d o , then the propagation gradient G o The calculation formula is:

[0027] c. Propagation dynamics integration: The final calculation of the delayed gradient index is obtained by accumulating the propagation gradients between all adjacent sections.

[0028] In a preferred embodiment, step S4 includes the following contents:

[0029] First, a multidimensional feature space is constructed. The amplitude variation index and propagation dynamic index of each pulse pollution event are represented as a two-dimensional feature vector F = (PI, DG) in the feature space. In order to enhance the distinguishing ability of features, principal component analysis is applied to perform linear transformation on the feature vector to obtain the principal component F′, so as to reduce the dimension and eliminate the correlation between features.

[0030] Secondly, a density-based clustering algorithm is used for clustering;

[0031] After clustering is completed, the events in each cluster are prioritized and scored according to their position and density in the feature space in the following way: S(F′ i )=tanh(α1·PI i +β1·DG i ), where α1 and β1 are adjustment parameters that control the weight of amplitude change and propagation dynamics on the score.

[0032] Subsequently, the identification and classification of high-risk areas is based on the priority scoring results, and each cluster is comprehensively evaluated and divided into high-risk areas, medium-risk areas and low-risk areas according to the distribution of score values; high-risk areas refer to clusters with a rated proportion of events with a priority score higher than the set high-risk threshold; medium-risk areas include clusters with scores between the high-risk threshold and the low-risk threshold; low-risk areas cover clusters with scores lower than the low-risk threshold;

[0033] Finally, the risk distribution map is generated; the classification results are mapped into the geographic information system to generate the risk distribution map.

[0034] Technical effects and advantages of a method for evaluating the ecological restoration effect of a micro-polluted river according to the present invention:

[0035] The present invention significantly improves the ability to identify and analyze sudden pulse pollution events through systematic data collection, accurate pulse pollution event stripping, feature quantification and association mapping model. Continuous dynamic data is obtained by high-frequency water quality sensors, and the consistency and high resolution of data are ensured through unified time and space reference axes, ensuring the accuracy of event identification. Through time series window comparison and deviation mapping inspection technology, random interference can be effectively filtered and the original fragments of pulse pollution events can be accurately extracted. Further, the amplitude change and propagation dynamic quantification method is adopted to deeply analyze the impact and diffusion characteristics of pollution events, providing accurate data support for the generation of risk distribution maps. The association mapping model combines adaptive clustering and priority evaluation, scientifically classifies and sorts pollution events, accurately identifies high-risk areas with high impact and high diffusion potential, and accurately calibrates and controls according to the risk distribution map, optimizing the pertinence and efficiency of governance measures. Overall, the present invention comprehensively improves the accuracy, real-time and scientific nature of the evaluation of the ecological restoration effect of micro-polluted rivers through data processing and analysis technology, provides reliable decision-making support for the precise implementation of ecological restoration measures and optimal resource allocation, and significantly enhances the health and sustainable development of micro-polluted river ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The present invention is a schematic flow chart of a method for evaluating the ecological restoration effect of a micro-polluted river. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Embodiment 1: Figure 1 The present invention provides a method for evaluating the ecological restoration effect of a slightly polluted river, comprising:

[0039] S1. Obtain the continuous dynamic parameters of a specific section of the river, mark them on a unified time and space reference axis, and construct long sequence data.

[0040] S2. Perform time-series window comparison and deviation mapping test on long sequence data, compare the steady-state baseline with the instantaneous abnormal peak, filter out random interference and invalid fluctuations, and extract the identifiable original fragments of pulse pollution events.

[0041] S3. Extract the amplitude change information and propagation dynamic information of the event from the refined pulse pollution event, including analyzing the intensity change of the pollution peak to quantify its impact on water quality, and at the same time evaluate the propagation speed and diffusion range of the pollution event in the river.

[0042] S4. Construct an association mapping model to classify and sort the amplitude change information and propagation dynamic information of different pulse events, identify high-risk areas through cluster analysis and priority assessment, and form a clear risk distribution map.

[0043] S5. Based on the risk distribution map, calibrate and divide the high-risk pulse pollution sections.

[0044] Although the concentration of pollutants in micro-polluted river ecosystems will not cause serious environmental crises, their long-term accumulation poses a potential threat to water quality and ecological functions, making them an important focus of current water environment management and ecological restoration. Accurate evaluation of ecological restoration effects is not only a key link in measuring the effectiveness of governance measures, but also provides a scientific basis for optimizing restoration strategies and resource allocation. Existing evaluation methods have limitations in dynamic monitoring and identification of sudden pollution events, and it is difficult to fully reflect the complex changes in micro-polluted river ecosystems. Therefore, the development of an efficient and accurate method for evaluating the ecological restoration effects of micro-polluted rivers, especially a technical means that can identify and quantify sudden pulse pollution events in real time, has important theoretical significance and application value.

[0045] Step S1 includes the following contents:

[0046] The data collection phase aims to obtain the continuous dynamic parameters of a specific section of the river, and uniformly mark them on the time and space reference axes to obtain long sequence data. The specific processing technology logic is as follows:

[0047] First, key monitoring sections are selected, and a high-frequency water quality sensor network is deployed based on the river basin characteristics and pollution source distribution. These sensors include but are not limited to dissolved oxygen sensors, conductivity sensors, and high-frequency flow meters, which can collect water quality parameters in the water body in real time. Sensor data is uploaded to the central data processing platform in real time through a wireless transmission module to ensure high temporal and spatial resolution of the data.

[0048] Secondly, strict time synchronization and spatial calibration are implemented. A high-precision GPS clock is used to synchronize the time of all sensors to ensure the consistency of data from different sensors on the time axis. Spatial positioning uses a geographic information system to accurately calibrate each monitoring section and establish a unified spatial reference frame. Specifically, a spatial coordinate system (such as WGS84) is set, and the geographic location of each sensor is accurately recorded to facilitate subsequent spatial comparison and analysis of data.

[0049] Next, data preprocessing is performed to ensure data quality and consistency. This includes data cleaning, missing value filling, and outlier detection. Adaptive filtering-based algorithms (such as Kalman filtering) are used to smooth sensor data and remove short-term noise interference. At the same time, interpolation algorithms (such as Kriging interpolation) are used to fill in the spatial data gaps between sensors and build a complete spatiotemporal data matrix.

[0050] Then, a long sequence data model is constructed. The processed high-frequency water quality parameters are arranged in time series and mapped on a unified time and space reference axis. Through multi-dimensional data fusion technology, multi-parameter data collected by different sensors are integrated into a single long sequence data set to ensure the synchronization and comparability of the data. For example, for each moment t, all key parameters {P1(t), P2(t),…, P n (t)}, forming a multidimensional time series data set {P i (t)}.

[0051] Finally, apply efficient data storage and management systems to ensure the integrity and traceability of long-sequence data. Use distributed database technology (such as NoSQL database) to achieve efficient storage and fast retrieval of large-scale data, supporting subsequent high-frequency data analysis and real-time monitoring needs.

[0052] Through the above technical logic, the data collection link can achieve the accurate acquisition and integration of high-frequency, continuous dynamic parameters of specific sections of micro-polluted rivers, construct high-quality long-sequence data, and provide a solid data foundation for the identification and evaluation of subsequent pulse pollution events.

[0053] Through rigorous data acquisition technology logic, this invention ensures the acquisition and integration of high-quality, continuous dynamic data required in the process of evaluating the ecological restoration effect of micro-polluted rivers. Unified time and space reference calibration, multi-source data fusion and efficient data management system lay a solid foundation for the accurate identification and risk distribution analysis of subsequent pulse pollution events. It not only improves the timeliness and accuracy of the evaluation, but also provides scientific and reliable data support for the ecological restoration of micro-polluted rivers.

[0054] In the process of ecological restoration of micro-polluted rivers, accurate identification and evaluation of sudden pulse pollution events are crucial for formulating effective governance strategies and optimizing resource allocation. Pulse pollution events are usually manifested as drastic fluctuations in water quality indicators in a short period of time. These instantaneous changes have a profound impact on the health and function of the ecosystem. However, traditional ecological restoration effect evaluation methods mostly rely on low-frequency data collection and simple statistical analysis, which makes it difficult to effectively separate and identify these pulse pollution events, resulting in a lack of timeliness and accuracy in the evaluation results. Therefore, developing a method that can efficiently extract pulse pollution events from long sequence data has become an important technical requirement to improve the accuracy of ecological restoration effect evaluation of micro-polluted rivers.

[0055] Step S2 includes the following contents:

[0056] The data stripping process aims to accurately identify and extract the original fragments of pulse pollution events from the acquired long sequence data. The specific processing technology logic is as follows:

[0057] First, time series windowing technology is applied to long series data, dividing the overall data into multiple overlapping or non-overlapping time windows. Each time window covers a fixed time period, such as 10 minutes or 30 minutes, to ensure that the pollution peak in a short period of time can be captured. The selection of time series windows is based on the river flow rate and the diffusion characteristics of pollutants to ensure that the window size can cover the duration of the pulse event without being too large to cover the details of the event.

[0058] Secondly, the deviation mapping test constructs a dynamic baseline model to conduct real-time comparative analysis of water quality parameters in each time window. The dynamic baseline model uses methods such as autoregressive integral moving average or wavelet packet decomposition to adapt to the nonlinear and non-stationary characteristics of the data. Specifically, for each time window W i , build a prediction model To predict the normal fluctuation range of water quality parameters. Actual observed value P(t) and predicted value The residual between Used to assess bias.

[0059] Next, the deviation threshold setting and mapping adopts the adaptive threshold method to dynamically adjust the threshold according to the residual distribution of historical data to obtain the marking threshold θi The specific formula is as follows: i =μ i +k·σ i +λ·max(R(t)); where μ i and σ i are time windows W i The mean and standard deviation of the internal residual R(t), k is the adjustment factor, and λ is the extreme value magnification factor. This formula not only takes into account the average level and dispersion of the residuals, but also strengthens the sensitivity to extreme deviations through the maximum residual max(R(t)).

[0060] Then, the pulse contamination event extraction identifies the time periods that deviate significantly from the stable baseline by comparing whether the residuals in each time window exceed the marking threshold. These time periods that exceed the marking threshold are marked as potential raw fragments of pulse contamination events. In order to reduce the false detection rate, a continuity detection mechanism is adopted, that is, only when the residuals in multiple consecutive time windows exceed the marking threshold, it is confirmed as a real pulse contamination event.

[0061] Finally, noise filtering and event verification uses anomaly detection algorithms in machine learning, such as isolation forests or support vector machines, to perform secondary verification on the extracted original fragments. By training the model to identify the characteristics of real pulse events, pseudo events caused by instrument errors or short-term environmental fluctuations are further filtered out, ensuring that the final extracted pulse pollution event fragments have high accuracy and reliability.

[0062] Through time series windowing and deviation mapping inspection technology, the data stripping link can efficiently and accurately extract the original fragments of pulse pollution events from long sequence data. The dynamic baseline model and adaptive threshold setting method are combined with the anomaly detection method of machine learning to ensure the accuracy and real-time recognition of pulse events. It not only overcomes the limitations of traditional methods in identifying sudden pollution events, but also lays a solid data foundation for subsequent feature extraction and risk analysis, significantly improving the overall accuracy and practicality of the evaluation method of the ecological restoration effect of micro-polluted rivers.

[0063] In the process of ecological restoration of micro-polluted rivers, accurately quantifying and understanding the characteristics of pulse pollution events is of key significance for evaluating restoration effects and optimizing governance strategies. Pulse pollution events are usually manifested as drastic changes in water quality parameters in a short period of time. These instantaneous pollution not only poses a direct impact on the aquatic ecosystem, but may also affect a wider range of river sections through diffusion. Existing assessment methods mostly rely on simple statistical analysis, which makes it difficult to deeply analyze the dynamic characteristics and propagation mechanisms of pulse events, resulting in limited accuracy and effectiveness of restoration measures. Therefore, developing a technical method that can systematically extract the amplitude changes and propagation dynamics of pulse pollution events is an important technical requirement to improve the accuracy of ecological restoration effect assessment for micro-polluted rivers.

[0064] Step S3 includes the following contents:

[0065] The amplitude variation information and propagation dynamic information of the event are quantified from the extracted pulse contamination event, wherein the amplitude variation information includes the pulse peak intensity index, and the propagation dynamic information includes the delay gradient index.

[0066] The pulse peak intensity index reflects the instantaneous impact of pollution events on water quality, which is achieved through multi-dimensional decomposition and dynamic accumulation calculation:

[0067] a. Multi-point baseline construction: The multi-point sliding window method is used to dynamically calculate the baseline value sequence before the pollution event occurs. The baseline value is determined within the window by the baseline function B(t): Among them, P(x) is the water quality parameter value, Δt is the window width, and α is the time decay factor, which ensures that the data at the more recent time contributes more to the baseline.

[0068] b. Peak detection and curve fitting: Identify the maximum value P within the pollution event interval. max And perform cubic spline interpolation fitting on the surrounding data points to calculate the peak function P peak (t).

[0069] c. Instantaneous impact force integral: The pulse peak intensity index PI is defined as the logarithmic difference integral of the peak function and the baseline function over the event interval.

[0070] The pulse peak intensity index is used to quantify the instantaneous impact of sudden pulse pollution events on water quality, reflecting the change in the amplitude of the pollution peak relative to the baseline. The larger the pulse peak intensity index value, the higher the peak amplitude of the pollution event and the stronger the impact on water quality, which means that the area is more seriously polluted; on the contrary, the smaller the pulse peak intensity index value, the weaker the impact of the pollution event and the smaller the impact on water quality. Areas with high pulse peak intensity index values ​​are identified as high-impact areas in subsequent steps, which provides a basis for the precise division of high-risk sections, helps to concentrate resources and take more stringent governance measures to effectively control and mitigate the negative impact of severe pollution on the ecosystem.

[0071] The delayed gradient index describes the propagation dynamics of pulse pollution events in the river channel, which is mainly achieved by jointly modeling the time response function and spatial propagation characteristics:

[0072] a. Time response curve modeling: For each monitoring section S o , through the response time T of the pollution event o Constructing a time response function Among them, β is the steepness parameter of the response curve, which is used to reflect the gradual characteristics of event propagation.

[0073] b. Spatial gradient construction: Suppose adjacent monitoring sections S o and S o+1 The distance between them is d o , then the propagation gradient G o The calculation formula is:

[0074] c. Propagation dynamics integral: The final calculation of the delayed gradient index DG is obtained by accumulating the propagation gradients between all adjacent sections.

[0075] The delayed gradient index is used to evaluate the propagation speed and diffusion range of pulse pollution events in rivers, reflecting the dynamic diffusion characteristics of pollution events. The larger the delay gradient index value, the faster the pollution event propagates and the wider the diffusion range, indicating that the pollution has a higher potential for propagation in the river; conversely, the smaller the delay gradient index value, the slower the pollution event propagates and the more limited the diffusion range. Areas with high delay gradient index values ​​are identified as areas with high diffusion potential. When combined with high pulse peak intensity index values, it can further clarify which areas are not only strongly impacted by pollution, but also have a higher risk of pollution diffusion. This identification result is used in subsequent steps to construct a risk distribution map to guide the precise calibration and control division of high-risk sections, thereby achieving key supervision and targeted governance of high-impact and high-diffusion potential areas, and improving the efficiency and effectiveness of overall ecological restoration.

[0076] Through systematic feature extraction technology, the feature extraction link can accurately quantify the amplitude changes and propagation dynamics of pulse pollution events. The peak detection and normalization methods are used to quantify the pulse peak intensity, and the delay gradient is calculated by combining time delay and propagation speed, which effectively reveals the impact of pollution events on water quality and its diffusion characteristics in the river. This not only improves the accuracy and reliability of the quantitative analysis of pulse pollution events, but also provides a solid data foundation for the subsequent construction of correlation mapping models and the generation of risk distribution maps. Through feature quantification, the ecological restoration effect evaluation method of micro-polluted rivers has achieved significant technical breakthroughs in dynamic monitoring and precise governance, greatly improving the scientificity and practicality of the evaluation.

[0077] In the process of ecological restoration of micro-polluted rivers, identifying and quantifying the amplitude changes and propagation dynamics of sudden pulse pollution events is a key link in evaluating the restoration effect. These pulse events not only have an instantaneous impact on water quality, but may also affect a wider range of river sections through diffusion, thereby affecting the health and stability of the overall ecosystem. In order to achieve scientific classification and risk assessment of these complex dynamic characteristics, it is necessary to construct an association mapping model that can comprehensively consider the event amplitude and propagation characteristics. The model should have efficient data processing capabilities and accurate classification mechanisms to generate a clear risk distribution map to guide subsequent precise governance and resource allocation. Therefore, the association mapping and classification sorting methods are of great significance for improving the scientificity and practicality of the evaluation of the ecological restoration effect of micro-polluted rivers.

[0078] Step S4 includes the following contents:

[0079] The purpose of the correlation mapping model construction phase is to scientifically classify and sort pulse pollution events based on their amplitude changes (pulse peak intensity index) and propagation dynamics (delay gradient index) to identify high-risk areas and generate risk distribution maps. The specific processing technology logic is as follows:

[0080] First, a multidimensional feature space is constructed. The amplitude change index and propagation dynamic index of each pulse pollution event are represented in the feature space as a two-dimensional feature vector F = (PI, DG). In order to enhance the distinguishing ability of features, principal component analysis is applied to perform linear transformation on the feature vector to obtain the principal component F′, so as to reduce the dimension and eliminate the correlation between features, thereby improving the efficiency and accuracy of subsequent classification.

[0081] Secondly, the application of adaptive clustering algorithm. The density-based adaptive clustering algorithm (such as adaptive DBSCAN) is used for clustering, and the clustering parameters are dynamically adjusted according to the distribution density of events in the feature space to ensure that it can adapt to the clustering needs of different density areas. Specifically, the local density is defined as and similarity threshold For each event Calculated by the following formula:

[0082]

[0083] in, is the global average density, is the adjustment coefficient, is the local density standard deviation of each event. Through this adaptive method, the clustering algorithm can effectively identify the cluster centers and boundaries in different density areas, avoiding the problem of poor clustering effect caused by fixed parameters in traditional algorithms.

[0084] Indicates the pulse pollution event index of the current local density to be calculated, Representation and Events All other event indices for which similarity is calculated, and N represents the total number of all pulse contamination events in the feature space.

[0085] After clustering is completed, the events in each cluster are prioritized according to their position and density in the feature space. For example, the priority score S can be calculated in the following way: S(F′ i )=tanh(α1·PI i +β1·DG i ) ; where α1 and β1 are adjustment parameters that control the weight of amplitude change and propagation dynamics on the score. The priority score reflects the comprehensive risk level of each event. The closer the value is to 1, the higher the risk, and vice versa.

[0086] Subsequently, the identification and classification of high-risk areas is based on the priority scoring results, and each cluster is comprehensively evaluated and divided into high-risk areas, medium-risk areas and low-risk areas according to the distribution of scoring values. High-risk areas refer to clusters where the priority score of the rated proportion of events is higher than the set high-risk threshold, indicating that these events have significant amplitude changes and large propagation dynamics, which may have a strong impact on the river ecosystem and a wide range of diffusion. Medium-risk areas include clusters with scores between the high-risk threshold and the low-risk threshold. Events in these areas show moderate amplitude changes and propagation potential, usually with moderate impact on water quality but limited diffusion capacity. Low-risk areas cover clusters with scores below the low-risk threshold, indicating that the amplitude changes and propagation dynamics of these events are weak, and the impact and diffusion capacity on water quality are relatively small. Through this classification, areas with different risk levels can be intuitively identified, providing an accurate basis for subsequent governance measures.

[0087] Finally, a risk distribution map is generated. The classification results are mapped into the geographic information system to generate a high-precision risk distribution map. The map marks various risk areas with different colors or symbols, providing an intuitive spatial reference to assist decision makers in accurately calibrating and managing high-risk areas.

[0088] By constructing an association mapping model that combines multidimensional feature space, adaptive clustering algorithm and priority assessment mechanism, it is possible to scientifically classify and sort the amplitude changes and propagation dynamics of pulse pollution events, and accurately identify high-risk areas with high impact and high diffusion potential, as well as buffer zones with medium or low risks. This not only improves the scientificity and accuracy of the risk distribution map, but also provides solid data support and decision-making basis for subsequent high-risk section calibration and governance measures, significantly enhancing the overall effectiveness and application value of the ecological restoration effect assessment method for micro-polluted rivers.

[0089] In the process of ecological restoration of micro-polluted rivers, identifying and accurately dividing high-risk pulse pollution sections is a key step to ensure the effectiveness of governance measures and optimal resource allocation. High-risk areas are not only strongly impacted by sudden pollution events, but also have a high potential for pollution diffusion, posing a major threat to the health of the overall ecosystem. By constructing an association mapping model and generating a risk distribution map, the risk levels of different areas can be intuitively displayed. However, how to accurately calibrate and control the high-risk pulse pollution sections based on the risk distribution map still requires scientific technical processing methods to ensure the pertinence and efficiency of governance measures. Therefore, developing a systematic method to perform the calibration and division of high-risk areas is of great significance for improving the scientificity and practicality of the evaluation of the ecological restoration effect of micro-polluted rivers.

[0090] Step S5 includes the following contents:

[0091] The result execution phase aims to accurately calibrate and control the high-risk pulse pollution sections based on the risk distribution map generated in the aforementioned association mapping model. The specific processing technology logic is as follows:

[0092] First, the interpretation of the risk distribution map and regional identification. Through the risk distribution map, the entire river is divided into areas with different risk levels, including high-risk areas, medium-risk areas and low-risk areas. High-risk areas refer to those areas that show significantly high values ​​in both pulse peak intensity and delay gradient. Pulse pollution events in these areas have strong impact and high diffusion potential. Medium-risk areas show moderate pollution impact and diffusion capacity, and low-risk areas show weaker pollution impact and diffusion potential.

[0093] Secondly, the high-risk areas are precisely demarcated. After identifying the high-risk areas, spatial resolution analysis techniques are used to further subdivide these areas into specific pollution-sensitive sections. By applying spatial buffer analysis in the geographic information system, combined with the physical characteristics of the river (such as flow velocity, river width, flow direction, etc.) and surrounding environmental factors (such as land use type, distribution of nearby pollution sources, etc.), the precise boundaries and impact range of each high-risk section are determined. This process ensures that the demarcation of high-risk sections is geographically accurate and ecologically relevant.

[0094] Next, control division and governance strategy formulation. According to the results of the high-risk area calibration, formulate corresponding governance strategies and management measures. Specifically include:

[0095] Key monitoring and rapid response mechanism: Deploy high-sensitivity monitoring equipment in high-risk areas to monitor water quality changes in real time, and establish a rapid response mechanism so that timely response measures can be taken when pulse pollution incidents occur.

[0096] Implementation of pollution source control and abatement measures: Implement source control and pollution abatement measures for major pollution sources around high-risk areas, such as upgrading sewage treatment facilities, restricting highly polluting industrial activities, and promoting eco-friendly agricultural practices.

[0097] Ecological restoration and buffer zone construction: Build ecological buffer zones around high-risk areas, such as artificial wetlands and vegetation-covered areas, to enhance the self-purification capacity of water bodies and reduce the spread and impact of pollutants.

[0098] Subsequently, cross-departmental coordination and resource optimization are implemented. The governance of high-risk areas usually involves multiple departments and stakeholders. Therefore, it is crucial to establish a cross-departmental coordination mechanism. By integrating the resources and information of environmental monitoring departments, river management departments, local governments and related enterprises, resource allocation is optimized to ensure the coordinated implementation of governance measures and comprehensive coverage of effect evaluation.

[0099] Finally, dynamic evaluation and feedback adjustment. After the implementation of governance measures, the water quality and ecological conditions of high-risk areas are continuously monitored, and the effect evaluation is carried out regularly. Based on the evaluation results, the governance strategies and management measures are dynamically adjusted to ensure the continuous optimization and adaptability of ecological restoration work. This closed-loop management process not only improves the effectiveness of governance measures, but also enhances the sustainability of overall ecological restoration.

[0100] Through rigorous processing technology logic, the result execution link can accurately calibrate and control the high-risk pulse pollution sections based on the risk distribution map. Combined with spatial resolution analysis, cross-departmental coordination and dynamic evaluation mechanism, the scientificity and efficiency of the governance measures are ensured. It not only improves the overall accuracy and practicality of the ecological restoration effect evaluation of micro-polluted rivers, but also provides a solid technical guarantee for the long-term health and sustainable development of river ecosystems.

[0101] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0102] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0103] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0104] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for evaluating the ecological restoration effect of a slightly polluted river, characterized in that: Includes steps: S1. Obtain the continuous dynamic parameters of a specific section of the river, mark them on a unified time and space reference axis, and construct long sequence data; S2. Perform time-series window comparison and deviation mapping test on long sequence data, compare the steady-state baseline with the instantaneous abnormal peak, filter out random interference and invalid fluctuations, and extract the original fragments of pulse pollution events that can be identified; S3. Extract the amplitude change information and propagation dynamic information of the event from the extracted pulse pollution event, including analyzing the intensity change of the pollution peak to quantify its impact on water quality, and at the same time evaluate the propagation speed and diffusion range of the pollution event in the river; S4. Construct an association mapping model to classify and sort the amplitude change information and propagation dynamic information of different pulse events, identify high-risk areas through cluster analysis and priority assessment, and form a clear risk distribution map; S5. Based on the risk distribution map, calibrate and divide the high-risk pulse pollution sections.

2. The method for evaluating the ecological restoration effect of a micro-polluted river according to claim 1, characterized in that: Step S1 includes the following contents: First, key monitoring sections are selected, and a high-frequency water quality sensor network is deployed according to the characteristics of the river basin and the distribution of pollution sources to collect water quality parameters in the water body; secondly, time synchronization and spatial calibration are implemented; next, data preprocessing is performed to construct a complete spatiotemporal data matrix; then, the processed water quality parameters are arranged in time series and mapped on a unified time and space reference axis, integrating the multi-parameter data collected by different sensors into a single long-sequence data set.

3. A method for evaluating the ecological restoration effect of a micro-polluted river according to claim 2, characterized in that: Step S2 includes the following contents: First, time series windowing technology is applied to long sequence data to divide the overall data into multiple time windows; each time window covers a fixed time period; Secondly, the deviation mapping test constructs a dynamic baseline model to conduct real-time comparative analysis of water quality parameters within each time window; the dynamic baseline model uses the autoregressive integrated moving average method. Specifically, for each time window, a prediction model is established to predict the normal fluctuation range of water quality parameters; the residual between the actual observed value and the predicted value is used to evaluate the deviation; Next, the deviation threshold setting and mapping adopts the adaptive threshold method, which dynamically adjusts the threshold according to the residual distribution of historical data to obtain the marking threshold; Then, pulse contamination events are extracted by comparing whether the residual in each time window exceeds the marking threshold. The time period exceeding the marking threshold is marked as the original segment of the pulse contamination event.

4. A method for evaluating the ecological restoration effect of a slightly polluted river according to claim 3, characterized in that: Step S3 includes the following contents: The amplitude variation information and propagation dynamic information of the event are quantified from the extracted pulse contamination event, wherein the amplitude variation information includes the pulse peak intensity index, and the propagation dynamic information includes the delay gradient index.

5. A method for evaluating the ecological restoration effect of a slightly polluted river according to claim 4, characterized in that: The pulse peak intensity index reflects the instantaneous impact of pollution events on water quality, which is achieved through multi-dimensional decomposition and dynamic accumulation calculation: a. Multi-point baseline construction: The multi-point sliding window method is used to dynamically calculate the baseline value sequence before the pollution event occurs; the baseline value is determined within the window by the baseline function B(t): Among them, P(x) is the water quality parameter value, Δt is the window width, and α is the time attenuation factor; b. Peak detection and curve fitting: In the pollution event interval, identify the maximum value and perform cubic spline interpolation fitting on the surrounding data points to calculate the peak function; c. Instantaneous impact force integral: The pulse peak intensity index is defined as the logarithmic difference integral of the peak function and the baseline function over the event interval.

6. A method for evaluating the ecological restoration effect of a slightly polluted river according to claim 4, characterized in that: The delayed gradient index describes the propagation dynamics of pulse pollution events in the river channel, which is achieved by jointly modeling the time response function and spatial propagation characteristics: a. Time response curve modeling: For each monitoring section S o , through the response time T of the pollution event o Constructing a time response function Among them, β is the steepness parameter of the response curve; b. Spatial gradient construction: Suppose adjacent monitoring sections S o and S o+1 The distance between them is d o , then the propagation gradient G o The calculation formula is: c. Propagation dynamics integration: The final calculation of the delayed gradient index is obtained by accumulating the propagation gradients between all adjacent sections.

7. A method for evaluating the ecological restoration effect of a slightly polluted river according to claim 4, characterized in that: Step S4 includes the following contents: First, a multidimensional feature space is constructed; The amplitude variation index and propagation dynamic index of each pulse pollution event are taken as a two-dimensional feature vector F = (PI, DG) and represented in the feature space. In order to enhance the distinguishing ability of the features, the principal component analysis is applied to perform linear transformation on the feature vector to obtain the principal component F′, so as to reduce the dimension and eliminate the correlation between the features. Secondly, a density-based clustering algorithm is used for clustering; After clustering is completed, the events in each cluster are prioritized and scored according to their position and density in the feature space in the following way: S(F′ i )=tanh(α1·PI i +β1·DG i ), where α1 and β1 are adjustment parameters that control the weight of amplitude change and propagation dynamics on the score.

8. A method for evaluating the ecological restoration effect of a slightly polluted river according to claim 7, characterized in that: Step S4 also includes the following contents: Subsequently, high-risk area identification and classification are based on the priority scoring results, and each cluster is comprehensively evaluated and divided into high-risk areas, medium-risk areas, and low-risk areas according to the distribution of the scoring values; The high-risk area refers to the clusters where the priority score of the rated proportion of events is higher than the set high-risk threshold; the medium-risk area includes those clusters whose score values ​​are between the high-risk threshold and the low-risk threshold; The low-risk area includes clusters with scores below the low-risk threshold; Finally, the risk distribution map is generated; The classification results are mapped into the geographic information system to generate a risk distribution map.

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