A method and system for monitoring aquatic ecological environment

Abstract

This invention relates to the field of data processing technology, specifically to a method and system for monitoring aquatic ecological environment. The method includes: first, collecting monitoring datasets containing multiple indicators from various monitoring points; then, calculating weight correction coefficients by assessing the degree of indicator fluctuation; next, calculating the clustering degree of each monitoring point based on the distance between them, and determining the weight of each monitoring point accordingly; then, using the monitoring point weights, weighted summing the values ​​of each indicator at different monitoring points to obtain a comprehensive value for each indicator; finally, calculating a comprehensive evaluation index for aquatic ecological environment quality by combining the initial indicator weights and the weight correction coefficients; and finally, monitoring the aquatic ecological environment based on the magnitude of this evaluation index. This method avoids the errors caused by fixed weights and discontinuous sampling in traditional monitoring methods, thereby improving the accuracy and scientific rigor of aquatic ecological environment monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing. Specifically, it relates to a water ecological environment monitoring method and system. BACKGROUND

[0002] As an important part of the ecological system, the water ecological environment plays a key role in maintaining ecological balance and ensuring sustainable development of human beings. Therefore, water ecological environment monitoring is a very complex and crucial task, which covers a wide range of indicators such as water quality, physics, chemistry, and biology.

[0003] Traditional water ecological environment monitoring is mainly achieved by scientifically setting multiple monitoring points in different areas. At each monitoring point, multiple index data are collected by non-continuous sampling method, and then in-depth analysis is carried out according to the established water ecological system state classification standard. The core of the classification standard is to assign a fixed weight to each monitoring index, and then to comprehensively consider the index value and its corresponding weight to finally realize the quantitative evaluation of water ecological environment quality, so as to complete the monitoring task of water ecological environment quality. For example, the Chinese patent application file with publication number CN114493285A discloses a river water environment ecological quality investigation and evaluation method, which includes: determining the evaluation index system and the score value of each index; adopting subjective valuation method and objective entropy value method to assign weights to each index, multiplying the score value of each index by the corresponding weight, and accumulating the calculation results of each index to obtain the final score and classification of the target river water environment evaluation.

[0004] However, in the comprehensive evaluation index system of water ecological environment quality, the biological indicators have unique complexity. The aggregation characteristics and irregular movement habits of organisms make it easy for biological indicators to appear non-pollution anomalies or data missing at some monitoring points. The fixed weight and non-continuous sampling method relied on by the traditional monitoring method cannot accurately reflect the true situation of the water ecological environment once the biological indicators are abnormal or missing, which eventually leads to deviation in the evaluation of water ecological environment quality, seriously interfering with the accurate monitoring of water ecological environment, and making it difficult to effectively provide reliable basis for subsequent ecological protection and management. SUMMARY

[0005] To solve the problem of deviation in the evaluation of water ecological environment quality caused by the traditional water ecological environment monitoring method, which interferes with the accurate monitoring of water ecological environment, the present application provides a water ecological environment monitoring method and system.

[0006] In a first aspect, the present application provides a water ecological environment monitoring method, comprising:

[0007] Monitoring datasets are collected from each monitoring point in the aquatic ecological environment. These datasets consist of multiple indicators. The fluctuation level of each indicator is assessed, and a weight correction coefficient for each indicator is calculated based on its fluctuation level. , For the first The weighting adjustment coefficient for each indicator. It is a natural constant. For the first The degree of fluctuation of each indicator;

[0008] The clustering degree of each monitoring point is calculated based on the distance between monitoring points:

[0009] , For the first Clustering of monitoring points The total number of monitoring points. For the first The monitoring point and the first The distance between monitoring points It is a natural exponential function;

[0010] The weight of each monitoring point is determined based on the degree of clustering of each monitoring point. Based on the weight of each monitoring point, the values ​​of each indicator at each monitoring point are weighted and summed to obtain the comprehensive value of each indicator.

[0011] Calculate the comprehensive evaluation index of water ecological environment quality:

[0012] , The total number of indicators, For the first The initial weights of each indicator, For the first The combined value of each indicator;

[0013] The aquatic ecological environment is monitored based on the magnitude of the comprehensive evaluation index of aquatic ecological environment quality.

[0014] The technical solution can dynamically adjust the weight of each index by evaluating the fluctuation degree and calculating the weight correction coefficient, so as to more accurately reflect the importance of each index in the environmental quality evaluation, solve the problem that the traditional fixed weight cannot adapt to the dynamic change of the index, and make the evaluation result more in line with the actual situation. Furthermore, considering the aggregation characteristics of the biological, the aggregation degree of the monitoring point is calculated by monitoring the relative position relationship between the monitoring points, so that the aggregation of each monitoring point in space can be accurately measured, the spatial characteristics of the water ecological environment are fully considered, and the analysis of the monitoring data is more comprehensive. Furthermore, a higher weight is given to the monitoring point with a high aggregation degree, and the comprehensive value of each index is obtained by weighted summation, so that the data of each monitoring point can be better integrated, and the data deviation caused by the aggregation of the biological and the uneven distribution of the monitoring points can be eliminated. Furthermore, the information of multiple indexes is integrated to obtain a scientific and comprehensive water ecological environment comprehensive evaluation index, which can accurately evaluate the water ecological environment quality, realize the accurate monitoring of the water ecological environment, and help to timely find the water ecological environment problems.

[0015] Preferably, the method for evaluating the fluctuation degree of each index is:

[0016] The historical value sequence of each index at each monitoring point is divided by using different length sliding windows, and the fluctuation of each index at each monitoring point is determined according to the division result;

[0017] The fluctuation degree of each index is calculated based on the fluctuation of each index at different monitoring points:

[0018]

[0019] Wherein, is the fluctuation degree of the i th index, is a normalization function, is the total number of monitoring points, is the fluctuation of the i th index at the j th monitoring point, is the serial number of the monitoring point, takes all integers in the range of , and is the absolute value symbol. The above technical solution comprehensively evaluates from the time and space dimensions, utilizes different length sliding windows in the time dimension, considers the short-term fluctuation and long-term trend of the historical data of each monitoring point, considers the difference between different monitoring points in the space dimension, effectively avoids the one-sidedness caused by the evaluation from a single monitoring point or a certain time period, and can more accurately and comprehensively grasp the real fluctuation of the index in the entire water ecological system.

[0020] The above technical solution comprehensively evaluates from the time and space dimensions, utilizes different length sliding windows in the time dimension, considers the short-term fluctuation and long-term trend of the historical data of each monitoring point, considers the difference between different monitoring points in the space dimension, effectively avoids the one-sidedness caused by the evaluation from a single monitoring point or a certain time period, and can more accurately and comprehensively grasp the real fluctuation of the index in the entire water ecological system. ​​

[0021] Preferably, determining the volatility of each index at each monitoring point according to the division result is based on the following formula:

[0022] , is the volatility of the i-th index at the j-th monitoring point, is a normalization function, is the total number of divisions, is the standard deviation of the i-th division result of the historical value sequence of the i-th index at the j-th monitoring point, is the average of the standard deviations of all division results of the historical value sequence of the i-th index at the j-th monitoring point, is an absolute value symbol. The above technical solution comprehensively considers the standard deviation of different division results of the historical value sequence of the index and the average difference of the standard deviations, making the calculated volatility more accurately reflect the true volatility of the index at each monitoring point, providing a reliable basis for subsequent comprehensive evaluation of the overall volatility of the index. Preferably, the method of dividing the historical value sequence of each index at each monitoring point using different lengths of sliding windows is as follows: The historical value sequence of each index at each monitoring point is divided using sliding windows of different lengths contained in L1 to L2, each length of sliding window corresponds to a division result, L1 is the minimum length of the preset sliding window, and L2 is the length of the historical value sequence.

[0023] The above technical solution divides the historical value sequence using sliding windows of different lengths, which can take into account both short-term changes and long-term trends in the data. Shorter sliding windows can capture high-frequency fluctuations in the data, reflecting recent local changes, while longer sliding windows can help observe low-frequency trends in the data and grasp the evolving trends of the index over a longer time span. This comprehensive capture method can uncover the characteristics of the historical values of the index at different time scales, providing more information for subsequent analysis.

[0024] Preferably, the weight of each monitoring point is determined based on the following formula:

[0025] ,

[0026] is the weight of the i-th monitoring point, is the average of the standard deviations of all division results of the historical value sequence of the i-th index at the j-th monitoring point,

[0027] is an absolute value symbol.

[0028] , is the weight of the i-th monitoring point, is the average of the standard deviations of all division results of the historical value sequence of the i-th index at the j-th monitoring point, is an absolute value symbol. ​​​aggregation of each monitoring point, total number of monitoring points.

[0029] The aggregation of each monitoring point is compared with the aggregation of other monitoring points, and the weight is determined, which helps to highlight those monitoring points with special spatial positions or close relationship with the surrounding environment, and the weight determination method can more comprehensively capture the local characteristics of the water ecological environment.

[0030] Preferably, the method for monitoring the water ecological environment according to the size of the water ecological environment quality comprehensive evaluation index is as follows:

[0031] The first threshold value, the second threshold value, the third threshold value and the fourth threshold value are preset.

[0032] When the water ecological environment quality comprehensive evaluation index is greater than or equal to the first threshold value, the water ecological environment quality is excellent; when the water ecological environment quality comprehensive evaluation index is less than the first threshold value and greater than or equal to the second threshold value, the water ecological environment quality is good; when the water ecological environment quality comprehensive evaluation index is less than the second threshold value and greater than or equal to the third threshold value, the water ecological environment quality is medium; when the water ecological environment quality comprehensive evaluation index is less than the third threshold value and greater than or equal to the fourth threshold value, the water ecological environment quality is poor; and when the water ecological environment quality comprehensive evaluation index is less than the fourth threshold value, the water ecological environment quality is inferior.

[0033] The water ecological environment quality comprehensive evaluation index is compared with the preset threshold value, different grades are divided, the quantitative evaluation and hierarchical management of the water ecological environment quality are realized, the complex ecological environment condition is converted into intuitive and clear grade information, and this is beneficial to the relevant personnel to quickly understand the quality condition of the water ecological environment.

[0034] Preferably, the initial weight of the index is obtained by taking the weight of each index in the pre-obtained water ecological system state classification standard as the initial weight of the index.

[0035] In a second aspect, the present application provides a water ecological environment monitoring system, which comprises a processor and a memory, and the memory stores a computer program, and the computer program can realize the steps in the environment monitoring method when executed by the processor.

[0036] The present application has the following effects:

[0037] The present application realizes effective monitoring of water ecological environment by means of data processing technology. The historical value sequence of the index is divided by different length sliding windows, the long-term and short-term change characteristics of the index are comprehensively captured, the volatility of the index is obtained, the volatility degree of the index is calculated based on the volatility, and the volatility degree is used as a weight correction coefficient, so that the weight of the index can be adjusted according to the actual change, and the scientificity of evaluation is improved. At the same time, the aggregation degree is determined according to the distance of the monitoring point, the spatial factors are fully considered, and the error caused by the aggregation and uneven distribution of organisms is reduced. Finally, the comprehensive evaluation index is calculated, which realizes the accurate quantitative evaluation of the water ecological environment quality, overcomes the limitations of fixed weight and non-continuous sampling in the traditional monitoring method, and provides a scientific and reliable basis for water ecological environment protection and governance decision. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a method flowchart of the present application. DETAILED DESCRIPTION

[0039] REFERENCE Figure 1 The present application provides a water ecological environment monitoring method and system, comprising steps S1-S7:

[0040] S1: Constructing a water ecological environment monitoring data set.

[0041] In order to accurately evaluate the health status of water ecological environment, it is crucial to construct a comprehensive and scientific water ecological environment monitoring data set. The present application considers that the health of water ecological environment is closely related to water quality, environment and biological factors. Therefore, multi-dimensional data monitoring is carried out by setting monitoring points in the water ecological environment, and then the water ecological environment monitoring data set of each monitoring point is obtained.

[0042] In one embodiment, the method for obtaining the water ecological environment monitoring data set of each monitoring point is carried out according to the following steps:

[0043] Monitoring point setting: A plurality of monitoring points are reasonably set at different positions of the water ecological environment to ensure that all types of water conditions are comprehensively covered, laying a foundation for obtaining accurate data.

[0044] Index selection: Different water bodies need to consider different indexes when evaluating ecological quality. Therefore, the "River and Lake Water Ecological Environment Quality Monitoring and Evaluation Technical Specification" (DB11 / T2320-2024) is obtained in advance as the water ecological system state classification standard. According to the content of the standard, different indexes (i.e. secondary indexes in the classification table) are considered for different water bodies. For example, for river water ecological environment, the indexes that need to be considered include water quality category, water quality stability index, benthic animal biological integrity index, benthic animal biological index, indigenous fish index, habitat index, proportion of water-bearing river length, and flow process maintenance time.

[0045] Data collection: using sensors or monitoring instruments, data is collected from multiple dimensions. Water quality sensors are used to measure water quality monitoring data such as dissolved oxygen, pH value, chemical oxygen demand, turbidity, etc.; environmental and meteorological sensors are used to obtain environmental monitoring data such as air temperature, water temperature, flow rate, flow, humidity, etc.; biological monitoring sensors are used to determine biological monitoring data such as phytoplankton, zooplankton, benthic species and quantity, etc. For any sensor, the collection frequency is set to once a week, and the collection time is one year, which ensures the timeliness and comprehensiveness of the monitoring data through continuous sampling.

[0046] Building water ecological environment monitoring dataset: continuously collecting the above selected indicators at any monitoring point to form the water ecological environment monitoring dataset of the monitoring point. The water ecological environment monitoring dataset is represented in matrix format:

[0047]

[0048] wherein, is the number of collections at the monitoring point, and its value is used as the number of rows of the matrix, is the total number of indicators, and its value is used as the number of columns of the matrix, then each row of the matrix represents one collection at the monitoring point, and the indicators of the monitoring point are obtained. Each column of the matrix represents the historical value sequence of each indicator at the monitoring point after multiple collections. For example, is the value of the first indicator obtained by collecting the monitoring point for the first time, is the value of the indicator obtained by collecting the monitoring point for the first time. is the value of the first indicator obtained by collecting the monitoring point for the time, is the value of the indicator obtained by collecting the monitoring point for the time. The first column of the matrix represents the historical value sequence of the first indicator at the monitoring point, and the column of the matrix represents the historical value sequence of the indicator at the monitoring point.

[0049] As described above, through the method of this step, each monitoring point can obtain a water ecological environment monitoring dataset.

[0050] S2: Evaluate the fluctuation degree of each indicator.

[0051] Specifically, this step involves dividing the historical value sequence of each indicator at each monitoring point using sliding windows of different lengths. Each sliding window of different lengths will yield a division result. Based on the division results, the volatility of each indicator at each monitoring point is analyzed. Then, by combining the long-term and short-term volatility of each indicator at different monitoring points, the degree of volatility of the indicator is calculated.

[0052] In one embodiment, the method for dividing the historical value sequence of each indicator at each monitoring point using sliding windows of different lengths is as follows:

[0053] Obtain the first step through step S1. The first indicator in the Historical numerical sequences of monitoring points;

[0054] Set the minimum length L1 of the sliding window to 3, and the maximum length L2 to the total number of historical values ​​of the indicator at this monitoring point. Use sliding windows of different lengths corresponding to L1 to L2 to analyze the data. The first indicator in the The historical data sequences of each monitoring point are divided. For example, the first... The first indicator in the The historical value sequence of each monitoring point is as follows: At this point, L2 is 5. First, the partitioning is performed according to a sliding window of length 3, and the resulting partition is: , , Then, dividing the window further according to a sliding window of length 4, the resulting division is as follows: , Finally, the partitioning is performed using a sliding window of length 5, resulting in the following partitioning outcome: As can be seen, a total of 3 partitions were performed, resulting in 3 partitioning results.

[0055] This approach takes into account the complex changes in various indicators within the aquatic ecosystem. By using sliding windows of different lengths, fluctuations at different time scales can be captured. A minimum window length of 3 can focus on local fluctuations (short-term fluctuations) of the indicator, such as the short-term impact of sudden pollution on water quality indicators. The maximum window length covers all historical value changes and can reflect the long-term trend of the indicator, such as the slow changes in the ecosystem with seasons or years.

[0056] In one embodiment, the volatility of each indicator at each monitoring point is obtained by analyzing the volatility of its historical numerical series within sliding windows of different lengths at each monitoring point. Specifically, the volatility of each indicator at each monitoring point is calculated based on the following formula:

[0057]

[0058] In the formula, is the fluctuation of the i-th index at the j-th monitoring point, providing a unified, quantifiable index fluctuation measurement standard, which helps quickly understand the degree of change of each index in the water ecological environment at different locations, providing basic data support for subsequent evaluation and decision-making. is a normalization function that maps the calculated values to a specific range, eliminating differences in dimensions, orders of magnitude, and other aspects of different index data, making the fluctuation of different indexes comparable. After normalization, different types of water ecological indexes, such as temperature, pH, dissolved oxygen, etc., can be compared under the same standard, regardless of their original data range and unit. is the total number of divisions (total number of division results). In the formula, is the standard deviation of the i-th index at the j-th monitoring point,

[0059] is the standard deviation of the i-th division result of the historical value sequence of the i-th index at the j-th monitoring point. is the average of the standard deviations of all division results of the historical value sequence of the i-th index at the j-th monitoring point, is the absolute value symbol. Dividing the historical value sequence with different lengths of sliding windows will result in multiple division results, each with a corresponding standard deviation. Standard deviation is a statistical measure of the dispersion of a group of data, the greater the dispersion, the more volatile the index, and vice versa, the data is more concentrated. These different standard deviations provide data support for multi-scale analysis. We can observe the volatility characteristics of the index from different time scales or data ranges, such as the standard deviation under short time scale can reflect the short-term rapid change of the index, and the standard deviation under long time scale can reflect the long-term change trend and overall volatility level of the index. By analyzing the index under different scales, we can more comprehensively and carefully grasp the volatility rules of the index, which helps to discover possible periodic changes, seasonal influences or sudden events in the water ecological environment that may affect the index. In the formula, part of it is used to measure the degree of deviation of each division result from the average fluctuation, which can highlight the differences between each division result and the overall average. The greater the deviation, the greater the deviation of the fluctuation of the division result from the average fluctuation, and the greater the contribution to the overall volatility. This helps to capture abnormal fluctuations of the index under different time scales.

[0060] In the formula, is the average of the standard deviations of all division results of the historical value sequence of the i-th index at the j-th monitoring point,

[0061] is the absolute value symbol.

[0062] ​​​In the formula, By averaging, the information of multiple scales can be integrated, avoiding the one-sided influence of a single division result on the volatility evaluation. It integrates the volatility of indicators at different time scales, making the calculated volatility better reflect the overall change characteristics of the indicators, reducing the interference of accidental factors or local volatility on the results, and improving the stability and accuracy of volatility evaluation.

[0063] In one embodiment, the method for evaluating the volatility degree of each indicator is:

[0064] First, the volatility of each indicator at each monitoring point is obtained, and then the volatility degree of each indicator is calculated based on the volatility of each indicator at different monitoring points:

[0065]

[0066] In the formula, is the volatility degree of the i-th indicator, is a normalization function, is the total number of monitoring points, is the volatility of the i-th indicator at the j-th monitoring point, is the sequence number of the monitoring point, the value of i takes all integers in the range is the absolute value symbol. In the formula, is the average value of the volatility of the i-th indicator at the j-th monitoring point, which represents the average volatility level of the i-th indicator in the entire monitoring range, reflecting the overall volatility trend of the indicator at all monitoring points. In the formula,

[0067] represents the absolute difference between the volatility of the i-th indicator at the j-th monitoring point and the average volatility of the indicator at all monitoring points. It quantifies the deviation amplitude of the volatility of each monitoring point relative to the overall average volatility, reflecting the difference between the volatility of the j-th monitoring point and the overall average. By calculating the absolute difference, the particularity of each monitoring point can be highlighted, and those monitoring points with large differences in volatility from the overall average can be identified, which helps to further analyze the possible special influencing factors or abnormal situations of these special monitoring points. In the formula, is the average value of the volatility of the i-th indicator at the j-th monitoring point, which represents the average volatility level of the i-th indicator in the entire monitoring range, reflecting the overall volatility trend of the indicator at all monitoring points. In the formula,

[0068] represents the absolute difference between the volatility of the i-th indicator at the j-th monitoring point and the average volatility of the indicator at all monitoring points. It quantifies the deviation amplitude of the volatility of each monitoring point relative to the overall average volatility, reflecting the difference between the volatility of the j-th monitoring point and the overall average. By calculating the absolute difference, the particularity of each monitoring point can be highlighted, and those monitoring points with large differences in volatility from the overall average can be identified, which helps to further analyze the possible special influencing factors or abnormal situations of these special monitoring points. In the formula, is the average value of the volatility of the i-th indicator at the j-th monitoring point, which represents the average volatility level of the i-th indicator in the entire monitoring range, reflecting the overall volatility trend of the indicator at all monitoring points.

[0069] In the formula, is the average value of the volatility of the i-th indicator at the j-th monitoring point, which represents the average volatility level of the i-th indicator in the entire monitoring range, reflecting the overall volatility trend of the indicator at all monitoring points. ​​​The fluctuation of each index at all monitoring points is summed with the absolute difference of the average fluctuation. It comprehensively considers the fluctuation deviation of the index at all monitoring points, and is a comprehensive index for measuring the fluctuation dispersion degree of the first index in the entire monitoring area. The larger the sum value is, the greater the difference in the fluctuation of the index between different monitoring points, that is, the more uneven the spatial distribution of the fluctuation of the index, and the greater the fluctuation degree; on the contrary, the smaller the sum value is, the more consistent the fluctuation of the index at each monitoring point, the more uniform the spatial distribution of the fluctuation, and the smaller the fluctuation degree.

[0070] The fluctuation degree of each index obtained by this operation is a value obtained by normalization after comprehensively considering the fluctuation of the index at all monitoring points and the deviation from the average fluctuation, which is used to comprehensively measure the fluctuation characteristics of each index in the entire monitoring system. Through the fluctuation degree of each index, the relative fluctuation level of each index can be intuitively understood, which facilitates the comparison and analysis of the fluctuation of different indexes. For indexes with greater fluctuation degree, attention should be paid and further research should be conducted on the change reasons and influencing factors, so as to take corresponding measures for monitoring and control.

[0071] S3: Calculate the weight correction coefficient of each index based on the fluctuation degree of each index.

[0072] The water ecosystem is complex and diverse, and the environmental conditions in different regions and different seasons differ greatly, which makes the fluctuation of the index more complex. For example, during the summer rain period, the flow rate, turbidity and other indexes of the river will fluctuate greatly, and the dissolved oxygen content may also change due to water agitation and microbial activity changes. The traditional fixed weight monitoring method cannot fully consider these differences, and the calculation of the weight correction coefficient can adjust the weight of the index for different monitoring points and different time according to the individual needs, so that the evaluation result can truly reflect the current water ecological environment status, such as in the water area where the monitoring points are unevenly distributed, some index data may be affected by the fluctuation of the local special environment, and the weight correction coefficient can reduce the weight of these unstable indexes, avoid the interference of the indexes on the overall evaluation, and make the evaluation more reflect the true status of the water ecological system.

[0073] In one embodiment, the weight correction coefficient of each index satisfies the following relationship:

[0074]

[0075] In the formula, is the weight correction coefficient of the first index, is a natural constant, is the weight correction coefficient of the first index, is a natural constant, The formula establishes an exponential relationship between the volatility of an indicator and its weight adjustment coefficient. When the volatility of an indicator changes, the weight adjustment coefficient changes accordingly. Due to the nature of the exponential function, this change is non-linear, which can more flexibly and sensitively reflect the impact of volatility on weight adjustment.

[0076] The greater the volatility of an indicator, the smaller its weight adjustment coefficient will be, meaning its relative importance in the overall evaluation system will decrease. This is because highly volatile indicators may be more susceptible to accidental factors or short-term changes, exhibiting poor stability and contributing relatively little to the reliability of the overall evaluation results; therefore, their weight should be reduced. Conversely, the smaller the volatility of an indicator, the larger its weight adjustment coefficient will be, indicating better stability. Stable indicators have higher weights in the evaluation, better reflecting their significant contribution to the overall results; therefore, their weight should be increased, aligning with the principle that evaluations typically aim to maximize the role of stable factors.

[0077] S4: Calculate the clustering degree of each monitoring point based on the distance between different monitoring points, and determine the weight of each monitoring point based on the clustering degree of each monitoring point.

[0078] Traditional aquatic ecological environment monitoring generally relies on expert experience and averages indicators from multiple monitoring points to calculate a comprehensive evaluation index for aquatic ecological environment quality, thereby assessing the quality of the aquatic ecological environment. However, due to the uneven distribution of monitoring points, the comprehensive evaluation index of aquatic ecological environment quality from some densely distributed monitoring points dominates the assessment, ultimately leading to local biases.

[0079] Therefore, this step proposes a solution: calculate the clustering degree of monitoring points based on the distance between different monitoring points, increase the index weight of monitoring points with low clustering degree and decrease the index weight of monitoring points with high clustering degree, thereby increasing the confidence of the comprehensive evaluation index of water ecological environment quality.

[0080] In one embodiment, the clustering degree of each monitoring point is calculated according to the following formula:

[0081]

[0082] In this formula, For the first The clustering degree of the monitoring point represents the clustering degree of the monitoring point. The degree of clustering of a monitoring point relative to other monitoring points, with a value range of [value missing]. , The closer it is to 1, the better. The higher the clustering of individual monitoring points, the denser the surrounding monitoring points; The closer it is to 0, the more it indicates the first... The lower the clustering of a monitoring point, the sparser the surrounding monitoring points. This indicates that... The smaller the clustering of the monitoring points, the better. The more indicators collected from each monitoring point, the more effectively the quality of the aquatic ecological environment can be assessed. The total number of monitoring points. For the first The monitoring point and the first The distance between monitoring points is an indicator that measures the spatial relationship between two monitoring points, accurately reflecting the actual distance between any two monitoring points. It is a natural exponential function.

[0083] In this formula, Partially representing other monitoring points and the first The average distance between monitoring points; the larger the average value, the greater the meaning of the distance between the monitoring points. The greater the average distance between the first monitoring point and other monitoring points, the better. The sparser the distribution of monitoring points around a given monitoring point, the more... The smaller the clustering of each monitoring point, the better; conversely, the smaller the clustering of the first monitoring point, the more likely it is to be clustered. The denser the distribution of monitoring points around a given monitoring point, the better. The greater the clustering of monitoring points.

[0084] This approach allows monitoring points that are far from other monitoring points and have low clustering to receive more attention in subsequent analysis and evaluation. This is because these monitoring points often cover a larger area of ​​water, and the indicators they collect play an indispensable role in assessing the aquatic ecological environment of the entire water area, thus avoiding evaluation bias caused by uneven distribution of monitoring points.

[0085] In one embodiment, the weight of each monitoring point is determined based on the following formula:

[0086]

[0087] In this formula, For the first The weight of each monitoring point For the first Clustering of monitoring points This represents the total number of monitoring points.

[0088] In this formula, the weights It is based on the first Clustering of monitoring points Indeed, this clustering-based weight allocation method meets the actual needs of aquatic ecological environment monitoring. Small monitoring points mean that they are scattered and can cover a larger range of water, which is valuable for overall assessment of water ecological environment, so their weights should be appropriately increased; while Large monitoring points are relatively concentrated, and the data may have certain similarity and limitations, so their weights should be appropriately reduced. Through such weight allocation, more representative monitoring points can play a greater role in calculating the comprehensive value, making the result more in line with the actual water ecological environment status.

[0089] Taking the water ecological environment monitoring of a large lake as an example, the environmental conditions of different areas of the lake are quite different. In the area close to the inflow river, the water ecological environment changes more complex due to the influence of water flow, pollutant input and other factors. In order to monitor more carefully, relatively dense monitoring points may be set in this area, and the aggregation degree of these monitoring points is larger. However, the indicators collected by these densely distributed monitoring points have certain similarity to some extent, because the local environment they are in is relatively similar. For example, they are all affected by the same pollutants from the inflow river, so the monitored water quality indicators, biological species and other data may be similar, which makes these data have limitations in reflecting the overall ecological status of the lake. On the contrary, in the open area or some remote corners of the lake, the monitoring points are relatively sparse, and their aggregation degree is smaller. However, these scattered monitoring points have unique value, as they can cover a larger range of water and collect more diverse data. For example, in the open area, the monitoring points can reflect the overall flow of the lake water, the influence of light on the water body, etc.; while the monitoring points in remote corners may monitor some special aquatic biological communities, and these data are crucial for a comprehensive understanding of the integrity of the lake's ecological system.

[0090] In this case, if all monitoring points are treated equally and the same weight is used to calculate the comprehensive value of each indicator, the monitoring points in the area with large aggregation degree will dominate the overall evaluation because of their large number and similar data, which may lead to an overemphasis on these local areas and an inaccurate reflection of the overall ecological status of the lake.

[0091] Therefore, the above method of determining the weight makes the weight of the monitoring points with large aggregation degree relatively lower, which can avoid the excessive influence of local data on the overall evaluation result, and the weight of the monitoring points with small aggregation degree is relatively higher, which can cover a larger range of water. Increasing the weight of the monitoring points can more comprehensively and reasonably consider the actual situation of different monitoring points, thereby increasing the confidence of the calculation of the comprehensive evaluation index of water ecological environment quality, avoiding the evaluation deviation caused by uneven distribution of monitoring points, and making the evaluation result more accurate.

[0092] S5: According to the weight of each monitoring point, the values of each indicator at each monitoring point are weighted and summed to obtain the comprehensive value of each indicator.

[0093] In aquatic ecological environment monitoring, monitoring points at different locations can reflect the conditions of different areas of aquatic waters. Data from a single monitoring point can only represent the situation of a small area where that point is located, which has limitations. Therefore, by integrating the indicators from these monitoring points at different locations and calculating the comprehensive value of each indicator, we can avoid one-sided assessments caused by relying on data from only a few monitoring points and accurately grasp the true state of the aquatic ecological environment.

[0094] In one embodiment, the combined value of each indicator satisfies the following relationship:

[0095]

[0096] In this formula, Indicates the first The combined value of the indicators, This indicates the total number of monitoring points. Indicates the first Clustering of monitoring points Indicates the first The weight of each monitoring point Indicates the first The first indicator in the The values ​​of each monitoring point.

[0097] By weighted summing of the values ​​of each indicator at all monitoring points and the weights of all monitoring points, the comprehensive value of the indicator obtained from each monitoring point can be fully utilized to obtain the comprehensive value of the indicator.

[0098] S6: The comprehensive evaluation index of water ecological environment quality is obtained based on the comprehensive value of each indicator, the initial weight of each indicator, and the weight correction coefficient of each indicator.

[0099] The aquatic ecological environment is a complex system, and a single indicator cannot fully reflect its quality. Therefore, this step comprehensively analyzes the combined values ​​of various indicators to assess the quality of the aquatic ecological environment from multiple dimensions, obtaining a comprehensive evaluation index for the aquatic ecological environment quality, and providing a more comprehensive and accurate evaluation result.

[0100] In one embodiment, the formula for calculating the comprehensive evaluation index of water ecological environment quality is:

[0101]

[0102] In this formula, It is a comprehensive evaluation index for the quality of the aquatic ecological environment. The total number of indicators, For the first The initial weights of each indicator, For the first The combined value of the indicators, For the first The weighting adjustment coefficient for each indicator.

[0103] The formula is passed Achieve the first The initial weights of each indicator are adjusted. The larger the adjustment coefficient of an indicator, the more effective the indicator is in reflecting the quality of the aquatic ecological environment, and the larger its adjusted weight will be.

[0104] Traditional fixed-weight evaluation methods have significant limitations when dealing with complex and ever-changing aquatic ecological environments. These environments are constantly changing due to both natural factors (such as seasonal and climatic variations) and anthropogenic factors (such as industrial pollution and agricultural activities). Fixed weights cannot adapt to this change, easily leading to discrepancies between the assessment results and the actual situation. In contrast, this scheme introduces weight correction coefficients to dynamically adjust the initial weights of each indicator based on actual monitoring data, thereby improving the accuracy of the comprehensive evaluation index of aquatic ecological environment quality.

[0105] In actual monitoring, different indicators are affected by environmental factors to varying degrees. For example, some indicators are more sensitive to environmental changes and fluctuate significantly. These indicators may be affected by accidental factors, leading to abnormal data fluctuations. If the traditional fixed-weight method is used, these abnormal fluctuations may have a significant impact on the evaluation results, interfering with the judgment of the true quality of the aquatic ecological environment. However, in this scheme, through weight adjustment, the role of such highly volatile and unstable indicators in the evaluation can be reduced, avoiding interference from abnormal information, and enabling the evaluation index to more accurately reflect the true quality of the aquatic ecological environment.

[0106] Furthermore, this comprehensive calculation and weighting adjustment method fully considers the relative importance of each indicator under different monitoring conditions. In different aquatic ecological environment scenarios, the impact of each indicator on the overall environmental quality is not constant. This scheme can flexibly adjust the weights according to the actual situation, making the evaluation results more closely reflect the specific aquatic ecological environment conditions and enhancing the scientific rigor and adaptability of the evaluation.

[0107] In one embodiment, the initial weight of each metric is obtained as follows:

[0108] The weight set in the pre-obtained water ecosystem state classification standard is taken as the initial weight of each index. The water ecosystem state classification standard here is specifically the Technical Specification for Monitoring and Evaluation of Water Ecological Environment Quality of Rivers and Lakes and Reservoirs (DB11 / T2320-2024), and the initial weights of the indexes are set according to the content of the classification standard, for example: the initial weight of the water environment index is 0.3, the initial weight of the aquatic organism index is 0.4, the initial weight of the habitat index is 0.2, and the initial weight of the water resource index is 0.1. The secondary indexes contained in the primary index and the initial weights are: for the water environment index, the secondary indexes contained are water quality category and water quality stability index, and the initial weights of the water quality category and the water quality stability index are both 0.5.

[0109] S7: Monitoring and early warning of the water ecological environment quality according to the water ecological environment quality comprehensive evaluation index.

[0110] By monitoring the water ecological environment quality comprehensive evaluation index and grading, problems existing in the water ecosystem can be found in a timely manner. For example, when it is monitored that a water area is at a poor or inferior quality level, it indicates that the water ecosystem of the water area may have been severely damaged, and the survival and reproduction of organisms are threatened. At this time, an early warning is issued, which can prompt relevant personnel to take targeted measures, such as pollution control and habitat restoration, to restore and protect the health of the water ecosystem and protect biodiversity.

[0111] In one embodiment, the method for monitoring and early warning of the water ecological environment according to the size of the water ecological environment quality comprehensive evaluation index is:

[0112] The first threshold is preset as 4.5, the second threshold is preset as 3.5, the third threshold is preset as 2.5, and the fourth threshold is preset as 1.5. When the water ecological environment quality comprehensive evaluation index is greater than the first threshold, the water ecological environment quality is excellent; when the water ecological environment quality comprehensive evaluation index is greater than the second threshold and less than or equal to the first threshold, the water ecological environment quality is good; when the water ecological environment quality comprehensive evaluation index is greater than the third threshold and less than or equal to the second threshold, the water ecological environment quality is medium; when the water ecological environment quality comprehensive evaluation index is greater than the fourth threshold and less than or equal to the third threshold, the water ecological environment quality is poor; and when the water ecological environment quality comprehensive evaluation index is less than or equal to the fourth threshold, the water ecological environment quality is inferior.

[0113] The scoring and grading rules here can refer to the Technical Specification for Monitoring and Evaluation of Water Ecological Environment Quality of Rivers and Lakes and Reservoirs (DB11 / T2320-2024).

[0114] Further, when the water ecological environment quality is poor and inferior, an early warning is issued to notify relevant personnel to focus on environmental management measures for poor and inferior water bodies.

[0115] ​​​​​The application further provides a water ecological environment monitoring system, comprising a processor and a memory, wherein the memory stores a computer program, and the computer program, when executed by the processor, can realize the steps in any one of the environment monitoring methods.

[0116] While the present specification has shown and described a number of embodiments of the application, it will be apparent to those skilled in the art that various alternatives exist that do not depart from the scope of the application described herein.

Claims

1. A water environment monitoring method characterized by, The method comprises the following steps: Collecting a monitoring data set of each monitoring point of the aquatic ecological environment, including: collecting data from multiple dimensions by using sensors or monitoring instruments, measuring water quality monitoring data such as dissolved oxygen, pH value, chemical oxygen demand, and turbidity in water by using water quality sensors; obtaining environmental monitoring data such as air temperature, water temperature, flow rate, flow, and humidity by using environmental and meteorological sensors; determining biological monitoring data such as the types and quantities of phytoplankton, zooplankton, and benthic organisms by using biological monitoring sensors; the monitoring data set is composed of multiple indexes, the historical value sequence of each index at each monitoring point is divided by using sliding windows of different lengths, and the volatility of each index at each monitoring point is determined according to the division result: ， the fluctuation of the jth index at the ith monitoring point, the fluctuation of the jth index at the ith monitoring point, the fluctuation of the jth index at the ith monitoring point, the normalization function, the total number of divisions, the standard deviation of the jth index at the ith monitoring point, the standard deviation of the jth index at the ith monitoring point, the standard deviation of the jth index at the ith monitoring point, the standard deviation of the jth index at the ith monitoring point, the average of the standard deviations of the jth index at the ith monitoring point, the average of the standard deviations of the jth index at the ith monitoring point, the average of the standard deviations of the jth index at the ith monitoring point, the absolute value symbol Calculating the volatility degree of each index based on the volatility of each index at different monitoring points: ； the degree of fluctuation of the first index, the degree of fluctuation of the second index, the total number of monitoring points, the serial number of the monitoring point, the numerical value of the first index takes all integers within the range ] The weight correction coefficient of each index is calculated based on the fluctuation degree of each index, the initial weight of each index is dynamically adjusted, the weight of each index is adjusted according to the actual change, and the role of the index with large fluctuation and poor stability in the evaluation is reduced: , is the weight correction coefficient of the first index, is a natural constant, is the fluctuation degree of the first index; Calculating the aggregation degree of each monitoring point according to the distance between the monitoring points: , For the first Clustering of monitoring points For the first The monitoring point and the first The distance between monitoring points It is a natural exponential function; Determining the weight of each monitoring point according to the aggregation degree of each monitoring point, and performing weighted summation on the values of each index at the monitoring points to obtain the comprehensive value of each index according to the weight of each monitoring point; Calculating the comprehensive evaluation index of the aquatic ecological environment quality: , is the total number of indicators, is the initial weight of the th indicator, is the comprehensive value of the th indicator; Monitoring the aquatic ecological environment according to the size of the comprehensive evaluation index of the aquatic ecological environment quality.

2. The water environment monitoring method according to claim 1, characterized by, The method for dividing the historical value sequence of each index at each monitoring point by using sliding windows of different lengths is: The historical value sequence of each index at each monitoring point is divided by using sliding windows of different lengths contained in L1 to L2, each length of the sliding window corresponds to a division result, L1 is the minimum length of the preset sliding window, and L2 is the length of the historical value sequence.

3. The water environment monitoring method according to claim 1, characterized by, The weight of each monitoring point is determined based on the following formula: , is the weight of the i-th monitoring point, is the weight of the i-th monitoring point, is the weight of the i-th monitoring point, is the weight of the i-th monitoring point, is the weight of the i-th monitoring point.

4. The water environment monitoring method according to claim 1, characterized by, The method for monitoring the aquatic ecological environment according to the size of the comprehensive evaluation index of the aquatic ecological environment quality is: Predefining a first threshold value, a second threshold value, a third threshold value, and a fourth threshold value; When the comprehensive evaluation index of the aquatic ecological environment quality is greater than or equal to the first threshold value, the aquatic ecological environment quality is excellent; when the comprehensive evaluation index of the aquatic ecological environment quality is less than the first threshold value and greater than or equal to the second threshold value, the aquatic ecological environment quality is good; when the comprehensive evaluation index of the aquatic ecological environment quality is less than the second threshold value and greater than or equal to the third threshold value, the aquatic ecological environment quality is medium; when the comprehensive evaluation index of the aquatic ecological environment quality is less than the third threshold value and greater than or equal to the fourth threshold value, the aquatic ecological environment quality is poor; and when the comprehensive evaluation index of the aquatic ecological environment quality is less than the fourth threshold value, the aquatic ecological environment quality is inferior.

5. The water environment monitoring method according to claim 1, characterized by, The method for obtaining the initial weight of the index is: taking the weight of each index set in the pre-obtained aquatic ecological system state classification standard as the initial weight of the index.

6. An aquatic environment monitoring system, characterized by, The environmental monitoring system comprises a processor and a memory, and the memory stores a computer program, and the computer program is executed by the processor to realize the steps in the environmental monitoring method in any one of claims 1-5.

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