Water ecological environment monitoring method and system

Abstract

The invention relates to the technical field of data processing, in particular to a water ecological environment monitoring method and system.The monitoring method comprises the steps that firstly, a monitoring data set containing multiple indexes of each monitoring point is collected, and a weight correction coefficient is calculated by evaluating the fluctuation degree of the indexes; then, the aggregation degree of each monitoring point is calculated according to the distance between the monitoring points, and the weight of each monitoring point is determined according to the aggregation degree; then, weighted summation is carried out on the numerical values of the indexes at different monitoring points by using the weights of the monitoring points, so that the comprehensive numerical value of each index is obtained; calculating a comprehensive evaluation index of the water ecological environment quality by combining the initial weight of the index with the weight correction coefficient; finally, the water ecological environment is monitored according to the evaluation index, and errors caused by fixed weight and discontinuous sampling in a traditional monitoring method are avoided, so that the accuracy and scientificity of water ecological environment monitoring are improved.

Description

Technical Field

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

[0002] As an important part of the ecosystem, the water ecological environment plays a key role in maintaining ecological balance and ensuring the sustainable development of humanity. Therefore, the monitoring of water ecological environment has become an extremely complex and crucial task, which covers the monitoring of indicators in multiple dimensions 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 regions. At each monitoring point, a non - continuous sampling method is used to collect multiple indicator data, and then in - depth analysis is carried out according to the established classification standard of the water ecosystem state. The core of this classification standard is to assign a fixed weight to each monitoring indicator, and then by comprehensively considering the value of each indicator and its corresponding weight, the quantitative evaluation of the water ecological environment quality is finally realized, so as to complete the monitoring task of the water ecological environment quality. For example, the Chinese patent application document with the publication number CN114493285A discloses a method for investigating and evaluating the ecological quality of river water environment, which includes: determining the evaluation index system and the scoring values of each index; using the subjective assignment method and the objective entropy value method to assign weights to each index, multiplying the scoring values of each index by the corresponding weights, and adding up 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, biological indicators have unique complexity. Organisms have aggregation characteristics and the habit of moving irregularly, which makes it extremely easy for biological indicators to show non - pollution anomalies or data missing at some monitoring points. Once the fixed weight and non - continuous sampling method relied on by traditional monitoring methods encounter anomalies or missing of biological indicators, they cannot accurately reflect the real situation of the water ecological environment, ultimately leading to deviations in the evaluation of water ecological environment quality, seriously interfering with the accurate monitoring of the water ecological environment, and making it difficult to effectively provide a reliable basis for subsequent ecological protection and governance. Summary of the Invention

[0005] To solve the problem that the traditional water ecological environment monitoring method leads to deviations in the evaluation of water ecological environment quality and interferes with the accurate monitoring of the water ecological environment, the present invention proposes a method and system for monitoring water ecological environment.

[0006] In the first aspect, the present invention provides a method for monitoring water ecological environment, including: Collect the monitoring data sets of each monitoring point in the water ecological environment. The monitoring data sets are composed of multiple indicators. Evaluate the fluctuation degree of each indicator, and calculate the weight correction coefficient of each indicator based on the fluctuation degree of each indicator: , is the weight correction coefficient of the th indicator, is the natural constant, is the th indicator's fluctuation degree; Calculate the aggregation degree of each monitoring point according to the distance between the monitoring points: , is the aggregation degree of the th monitoring point, is the total number of monitoring points, is the th monitoring point and the th monitoring point's distance, is the natural exponential function; Determine the weight of each monitoring point according to the aggregation degree of each monitoring point. Based on the weight of each monitoring point, perform weighted summation on the values of each indicator at each monitoring point to obtain the comprehensive value of each indicator; Calculate the comprehensive evaluation index of the water ecological environment quality: , is the total number of indicators, is the initial weight of the th indicator, is the th indicator's comprehensive value; Monitor the water ecological environment according to the size of the comprehensive evaluation index of the water ecological environment quality.

[0007] Through evaluating the degree of fluctuation and calculating the weight correction coefficient, the above technical solution can dynamically adjust the weights of each index to more accurately reflect the importance of each index in the environmental quality assessment, solve the problem that the traditional fixed weights cannot adapt to the dynamic changes of the indexes, and make the evaluation results more in line with the actual situation. Furthermore, considering the aggregation characteristics of organisms, by calculating the aggregation degree of monitoring points based on the relative position relationship between monitoring points, the aggregation situation 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 monitoring data is more comprehensive. Furthermore, higher weights are assigned to the monitoring points with high aggregation degree, and the comprehensive value of each index is obtained through weighted summation, which can better integrate the data of each monitoring point and eliminate the data deviation caused by biological aggregation and uneven distribution of monitoring points. Furthermore, by comprehensively integrating the information of multiple indexes, a scientific and comprehensive comprehensive evaluation index of the water ecological environment is obtained. This comprehensive evaluation index can accurately evaluate the quality of the water ecological environment, realize the precise monitoring of the water ecological environment, and help to detect water ecological environment problems in time.

[0008] Preferably, the method for evaluating the degree of fluctuation of each index is as follows: Dividing the historical numerical sequence of each index at each monitoring point by using sliding windows of different lengths, and determining the volatility of each index at each monitoring point according to the division results; Calculating the degree of fluctuation of each index based on the volatility of each index at different monitoring points:

[0009] Wherein, is the degree of fluctuation of the th index, is the normalization function, is the total number of monitoring points, is the th index at the th monitoring point, is the serial number of the monitoring point, takes values of all integers within the range of , is the absolute value symbol.

[0010] The above technical solution conducts a comprehensive evaluation from both the time and space dimensions. In the time dimension, different-length sliding windows are used to take into account both the short-term fluctuations and long-term trends of the historical data of each monitoring point. In the space dimension, the differences between different monitoring points are considered, effectively avoiding the one-sidedness caused by evaluating only from a single monitoring point or a certain time period, and being able to more accurately and comprehensively grasp the true fluctuation situation of the index in the entire water ecological system.

[0011] Preferably, determining the volatility of each indicator at each monitoring point according to the partitioning result is based on the following formula: , is the volatility of the th indicator at the th monitoring point, is the normalization function, is the total number of partitions, is the th indicator at the th monitoring point, and is the standard deviation of the th partition result of the historical numerical sequence of the th indicator at the th monitoring point. is the absolute value symbol.

[0012] The above technical solution comprehensively considers the standard deviation and the average difference of the standard deviation of different partitioning results of the historical numerical sequence of the indicator, so that the calculated volatility can more accurately reflect the true volatility degree of the indicator at each monitoring point, providing a reliable basis for the subsequent comprehensive evaluation of the overall volatility of the indicator.

[0013] Preferably, the method of partitioning the historical numerical sequence of each indicator at each monitoring point using sliding windows of different lengths is as follows: Partition the historical numerical sequence of each indicator at each monitoring point using sliding windows of different lengths included from L1 to L2. Each sliding window of a certain length corresponds to a partitioning result. L1 is the preset minimum length of the sliding window, and L2 is the length of the historical numerical sequence.

[0014] The above technical solution partitions the historical numerical sequence using sliding windows of different lengths, which can take into account both the short-term changes and long-term trends of the data. Shorter sliding windows can capture the high-frequency fluctuations of the data and reflect the recent local change situations; longer sliding windows are helpful for observing the low-frequency trends of the data and grasping the evolution trend of the indicator over a longer time span. This comprehensive capture method can uncover the characteristics of the historical values of the indicator at different time scales, providing richer information for subsequent analysis.

[0015] Preferably, the weight of each monitoring point is determined based on the following formula: , is the weight of the th monitoring point, is the aggregation degree of the th monitoring point, is the total number of monitoring points.

[0016] The aggregation degree of each monitoring point in the above technical solution is compared with that of other monitoring points to determine the weight, which helps to highlight those monitoring points with special spatial positions or close relationships with the surrounding environment. This way of determining the weight can capture the local characteristics of the water ecological environment more comprehensively.

[0017] Preferably, the method for monitoring the water ecological environment according to the size of the comprehensive evaluation index of the water ecological environment quality is as follows: Preset the first threshold, the second threshold, the third threshold and the fourth threshold; When the comprehensive evaluation index of the water ecological environment quality is greater than or equal to the first threshold, the water ecological environment quality is excellent; when the comprehensive evaluation index of the water ecological environment quality is less than the first threshold and greater than or equal to the second threshold, the water ecological environment quality is good; when the comprehensive evaluation index of the water ecological environment quality is less than the second threshold and greater than or equal to the third threshold, the water ecological environment quality is medium; when the comprehensive evaluation index of the water ecological environment quality is less than the third threshold and greater than or equal to the fourth threshold, the water ecological environment quality is poor; when the comprehensive evaluation index of the water ecological environment quality is less than the fourth threshold, the water ecological environment quality is inferior.

[0018] The above technical solution compares the comprehensive evaluation index of the water ecological environment quality with the preset thresholds, divides different levels, and realizes the quantitative evaluation and hierarchical management of the water ecological environment quality. This way transforms the complex ecological environment situation into intuitive and clear level information, which is beneficial for relevant personnel to quickly understand the quality status of the water ecological environment.

[0019] Preferably, the method for obtaining the initial weight of the index is: taking the weight set for each index in the pre-obtained classification standard of the water ecosystem state as the initial weight of the index.

[0020] In a second aspect, the present invention provides a water ecological environment monitoring system. The environment monitoring system includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, it can implement the steps in any one of the above environment monitoring methods.

[0021] The present invention has the following effects: With the help of data processing technology, the present invention realizes the effective monitoring of the water ecological environment. By dividing the historical numerical sequence of indicators through sliding windows of different lengths, the long-term and short-term variation characteristics of the indicators are comprehensively captured, and the volatility of the indicators is obtained. Based on the volatility, the degree of fluctuation of the indicators is calculated, which is used as a weight correction coefficient to adjust the weights of the indicators according to the actual changes and improve the scientificity of evaluation. At the same time, the aggregation degree is determined according to the distance of the monitoring points, fully considering the spatial factors and reducing the errors caused by biological aggregation and uneven distribution. Finally, the comprehensive evaluation index is calculated, which realizes the accurate quantitative evaluation of the water ecological environment quality, overcomes the limitations of fixed weights and non-continuous sampling in traditional monitoring methods, and provides a scientific and reliable basis for the decision-making of water ecological environment protection and governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a schematic flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Referring to Figure 1 , a method and system for monitoring the water ecological environment provided by the present invention include steps S1 - S7: S1: Construct a water ecological environment monitoring data set.

[0024] To accurately evaluate the health status of the water ecological environment, it is crucial to construct a comprehensive and scientific water ecological environment monitoring data set. The present invention takes into account that the health of the water ecological environment is closely related to water quality, environmental 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.

[0025] 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: Monitoring point setting: Reasonably set multiple monitoring points at different positions in the water ecological environment to ensure that various water area conditions can be comprehensively covered, laying a foundation for obtaining accurate data.

[0026] Indicator selection: Different indicators need to be considered when evaluating the ecological quality of different water bodies. Therefore, the "Technical Specification for Monitoring and Evaluation of Water Ecological Environment Quality of Rivers and Lakes" (DB11 / T2320 - 2024) is pre-obtained as the classification standard for the state of the water ecosystem. According to the content of this standard, different indicators (i.e., secondary indicators in the classification table) are considered for different water bodies. For example, for the river water ecological environment, the indicators to be considered include water quality category, water quality stability index, benthic macroinvertebrate biotic integrity index, benthic macroinvertebrate biological index, indigenous fish index, habitat index, proportion of flowing river length, and flow process maintenance time.

[0027] Data collection: Use sensors or monitoring instruments to collect data from multiple dimensions. Use water quality sensors to measure water quality monitoring data such as dissolved oxygen, pH value, chemical oxygen demand, turbidity, etc. in water; use environmental and meteorological sensors to obtain environmental monitoring data such as air temperature, water temperature, flow velocity, flow rate, humidity, etc.; use biological monitoring sensors to measure the types and quantities of phytoplankton, zooplankton, and benthic organisms in water. For any sensor, the collection frequency is set to once a week, and the collection duration is one year. Ensure the timeliness and comprehensiveness of monitoring data through continuous sampling.

[0028] Construct a water ecological environment monitoring data set: Continuously collect the selected indicators at any monitoring point to form a water ecological environment monitoring data set for that monitoring point. The water ecological environment monitoring data set is represented in matrix format:

[0029] Among them, is the number of collections at this 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 this monitoring point, obtaining indicators at this monitoring point. Each column of the matrix represents the historical value sequence of each indicator at this monitoring point after multiple collections. For example, is the value of the first indicator obtained at the first collection of this monitoring point, is the value of the th indicator obtained at the first collection of this monitoring point. is the value of the first indicator obtained at the th collection of this monitoring point, is the value of the th indicator obtained at the th collection of this monitoring point. The first column of the matrix represents the historical value sequence of the first indicator at this monitoring point, and the th column of the matrix represents the historical value sequence of the th indicator at this monitoring point.

[0030] In summary, through the method of this step, each monitoring point can obtain a water ecological environment monitoring data set.

[0031] S2: Evaluate the fluctuation degree of each indicator.

[0032] Specifically, in this step, the historical numerical sequence of each indicator at each monitoring point is divided according to sliding windows of different lengths. Each sliding window of a certain length will obtain a division result. Based on the division results, the volatility of each indicator at each monitoring point is analyzed. Furthermore, by synthesizing the long-term volatility and short-term volatility of each indicator at different monitoring points, the degree of volatility of the indicator is calculated.

[0033] In one embodiment, the method of dividing the historical numerical sequence of each indicator at each monitoring point by using sliding windows of different lengths is as follows: Obtain the historical numerical sequence of the th indicator at the th monitoring point through step S1; 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 divide the historical numerical sequence of the th indicator at the th monitoring point. For example, if the historical numerical sequence of the th indicator at the th monitoring point is , and at this time L2 is 5. First, divide it according to the sliding window with a length of 3, and the obtained division results are , , . Then divide it according to the sliding window with a length of 4, and the obtained division results are , . Finally, divide it according to the sliding window with a length of 5, and the obtained division result is . It can be seen that a total of 3 divisions are performed, and 3 division results are obtained.

[0034] This kind of operation takes into account that in the water ecological environment, the changes of various indicators are complex, and different time-scale fluctuations can be captured by sliding windows of different lengths. The window with a minimum length of 3 can focus on the local fluctuations (short-term fluctuations) of the indicator, such as the short-term impact of sudden pollution on water quality indicators. The window with the maximum length covers all historical numerical changes and can reflect the long-term change trend of the indicator, such as the slow changes of the ecosystem over seasons or years.

[0035] In one embodiment, by analyzing the volatility of the historical numerical sequence of each indicator at each monitoring point within sliding windows of different lengths, the volatility of each indicator at each monitoring point is obtained. Specifically, the volatility of each indicator at each monitoring point is calculated based on the following formula:

[0036] In this formula, is the The indicator in The volatility of each monitoring point provides a unified and quantifiable indicator fluctuation measurement standard, which helps to quickly understand the degree of change of various indicators in the water ecological environment at different locations and provide basic data support for subsequent evaluation and decision-making. It is a normalization function, which maps the calculated value to a specific range, eliminates the differences in dimension and order of magnitude of different indicator data, and makes the volatility of different indicators comparable. After normalization, different types of water ecological indicators, such as temperature, pH, dissolved oxygen, etc., can be compared under the same standard regardless of the range and unit of their original data. is the total number of divisions (the total number of division results).

[0037] In this formula, For the The indicator in The first of the historical numerical series of the monitoring point The standard deviation of the partition results. For the The indicator in The average standard deviation of all partition results of the historical numerical series of monitoring points, is the absolute value symbol.

[0038] Using sliding windows of varying lengths to partition a historical numerical series yields multiple partitioning results, each with a corresponding standard deviation. The standard deviation is a statistic used to measure the degree of dispersion in a set of data. Greater dispersion indicates greater volatility, while smaller dispersion indicates less volatility and a more concentrated data set. These varying standard deviations provide data support for multi-scale analysis. The fluctuation characteristics of an indicator can be observed across different time scales or data ranges. For example, the standard deviation at a short time scale can reflect rapid, short-term fluctuations in the indicator, while the standard deviation at a longer time scale can reveal the indicator's long-term trend and overall level of fluctuation. Comprehensive analysis at different scales allows for a more comprehensive and detailed understanding of the indicator's fluctuation patterns, helping to identify potential cyclical changes in the aquatic ecosystem, seasonal influences, or the impact of unexpected events on the indicator.

[0039] In this formula, The part is used to measure the degree to which the fluctuation of each division result deviates from the average fluctuation, which can highlight the difference 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 indicators at different time scales.

[0040] In this formula, By taking the average, it can integrate multi-scale information, avoiding the one-sided impact of a single partitioning result on volatility assessment. It integrates the index volatility at different time scales, making the calculated volatility better reflect the overall change characteristics of the index, reducing the interference of accidental factors or local fluctuations on the result, and improving the stability and accuracy of volatility assessment.

[0041] In one embodiment, the method for evaluating the volatility degree of each index is as follows: First, obtain the volatility of each index at each monitoring point, and then calculate the volatility degree of each index based on the volatility of each index at different monitoring points:

[0042] In this formula, is the volatility degree of the th index, is the normalization function, is the total number of monitoring points, is the th index at the th monitoring point, is the serial number of the monitoring point, takes values for all integers within the range of , is the absolute value symbol.

[0043] In this formula, the part is the average value of the volatility of the th index at the th monitoring point, which represents the average volatility level of the th index within the entire monitoring range and reflects the overall volatility trend of this index at all monitoring points.

[0044] In this formula, the part represents the absolute difference between the volatility of the th index at the th monitoring point and the average volatility of this index at all monitoring points. It quantifies the deviation amplitude of the volatility of each monitoring point relative to the overall average volatility and reflects the difference degree between the volatility situation of the

[0045] th monitoring point and the overall average situation. By calculating the absolute difference, the particularity of each monitoring point can be highlighted, identifying those monitoring points with a large difference in volatility from the overall average level, which helps to further analyze the possible special influencing factors or abnormal situations existing in these special monitoring points. the part in this formula is for the The sum of the absolute differences between the volatility of an indicator at all monitoring points and the average volatility is calculated. This comprehensive indicator takes into account the fluctuation deviation of the indicator at all monitoring points and measures the degree of dispersion of the indicator's fluctuations across the entire monitoring area. A larger sum indicates a greater difference in the volatility of the indicator across different monitoring points, meaning that the more uneven the spatial distribution of the indicator's fluctuations, the greater the degree of fluctuation. Conversely, a smaller sum indicates a relatively consistent volatility across all monitoring points, a more even spatial distribution of fluctuations, and a lower degree of fluctuation.

[0046] The degree of fluctuation for each indicator obtained through this operation is a normalized value that comprehensively considers the volatility of that indicator across all monitoring points and its deviation from the average volatility. This value serves as a comprehensive measure of the fluctuation characteristics of each indicator within the entire monitoring system. The degree of fluctuation for each indicator provides an intuitive understanding of its relative level of volatility, facilitating comparison and analysis of fluctuations across different indicators. Indicators with significant fluctuations receive special attention and further research into the causes and influencing factors, allowing for the implementation of appropriate monitoring and regulatory measures.

[0047] S3: Calculate the weight correction coefficient of each indicator based on the volatility of each indicator.

[0048] Aquatic ecosystems are complex and diverse, and environmental conditions vary greatly across regions and seasons, making fluctuations in indicators even more complex. For example, during periods of heavy rain in summer, indicators such as river flow rate and turbidity can fluctuate significantly, and dissolved oxygen content may also change due to water agitation and changes in microbial activity. Traditional fixed-weight monitoring methods cannot fully account for these differences. Calculating weight correction coefficients can personalize weight adjustments based on indicator fluctuations at different monitoring points and at different times, so that the assessment results can truly reflect the current state of the water ecological environment. For example, in waters where monitoring points are unevenly distributed, certain indicator data may fluctuate significantly due to the influence of local special environments. The weight correction coefficient can reduce the weight of these unstable indicators, avoid their interference with the overall assessment, and make the assessment more reflective of the true state of the water ecosystem.

[0049] In one embodiment, the weight correction coefficient of each indicator satisfies the following relationship:

[0050] In this formula, For the The weight correction coefficient of each indicator, is a natural constant, For the The degree of fluctuation of an indicator. This formula establishes an exponential relationship between the degree of fluctuation of an indicator and the weight correction coefficient. When the degree of fluctuation of the indicator changes, the weight correction coefficient will change accordingly. Due to the properties of the exponential function, this change is non-linear, and it can more flexibly and sensitively reflect the impact of the degree of fluctuation on weight correction.

[0051] If the degree of fluctuation of an indicator is greater, the weight correction coefficient of this indicator will be smaller, that is, the relative importance of this indicator in the entire evaluation system will decrease. This is because an indicator with large fluctuations may be greatly affected by accidental factors or short-term changes, its stability is poor, and its contribution to the reliability of the overall evaluation result is relatively small, so its weight should be reduced. On the contrary, if the degree of fluctuation of an indicator is smaller, the weight correction coefficient of this indicator will be larger, indicating that the stability of this indicator is better. A stable indicator has a higher weight in the evaluation and can better reflect its important contribution to the overall result, so its weight should be increased, which conforms to the principle that stable factors are usually expected to play a greater role in the evaluation.

[0052] S4: Calculate the aggregation degree of each monitoring point based on the distance between different monitoring points, and determine the weight of each monitoring point according to the aggregation degree of each monitoring point.

[0053] In traditional water ecological environment monitoring, generally based on expert experience, the indicators of multiple monitoring points are averaged to calculate the comprehensive evaluation index of water ecological environment quality, and then the water ecological environment quality is evaluated. However, due to the uneven distribution of monitoring points in traditional monitoring methods, the comprehensive evaluation index of water ecological environment quality of some monitoring points with dense distribution dominates in the evaluation of water ecological environment quality, and finally there is a phenomenon of local deviation.

[0054] Therefore, this step proposes a solution: calculate the aggregation degree of monitoring points based on the distance between different monitoring points, increase the index weight of monitoring points with small aggregation degree, and reduce the index weight of monitoring points with large aggregation degree, so as to increase the confidence of the comprehensive evaluation index of water ecological environment quality.

[0055] In one embodiment, the aggregation degree of each monitoring point is calculated according to the following formula:

[0056] In this formula, is the aggregation degree of the th monitoring point, representing the aggregation degree of the th monitoring point relative to other monitoring points, and its value range is , The closer it is to 1, the higher the aggregation degree of the th monitoring point, and the denser the surrounding monitoring points; The closer to 0, the The lower the concentration of a monitoring point, the sparser the surrounding monitoring points. The smaller the concentration of the monitoring points, the The more indicators collected at each monitoring point, the more effective it is in evaluating the quality of the water ecological environment. is the total number of monitoring points, For the Monitoring points and The distance between two monitoring points is an indicator to measure the spatial position relationship between two monitoring points, which accurately reflects the actual distance between any two monitoring points. is the natural exponential function.

[0057] In this formula, Part represents other monitoring points and The average distance between monitoring points, the larger the average is, the The farther the average distance between a monitoring point and other monitoring points is, the The sparser the distribution of monitoring points around the first monitoring point, the The smaller the concentration of the monitoring points, the smaller the concentration of the monitoring points. The denser the distribution of monitoring points around a monitoring point, the The greater the concentration of monitoring points.

[0058] This operation allows monitoring points that are farther away from other monitoring points and have lower concentrations to receive more attention in subsequent analysis and evaluation, because these monitoring points often cover a larger area of water, and the indicators they collect play an important role in evaluating the water ecological environment of the entire water area, avoiding evaluation bias caused by uneven distribution of monitoring points.

[0059] In one embodiment, the weight of each monitoring point is determined based on the following formula:

[0060] In this formula, For the The weight of each monitoring point, For the The concentration of monitoring points, is the total number of monitoring points.

[0061] In this formula, the weight It is based on The concentration of monitoring points It is certain that this weight distribution method based on aggregation degree meets the actual needs of water ecological environment monitoring. Small monitoring points mean that their distribution is relatively scattered, which can cover a larger range of waters and has certain value for comprehensively evaluating the water ecological environment. Therefore, their weights are appropriately increased; while Large monitoring points are relatively concentrated, and there may be certain similarities and limitations in the data. Therefore, their weights are appropriately reduced. Through such weight allocation, when calculating the comprehensive value, more representative monitoring points can play a greater role, making the result more in line with the actual water ecological environment status.

[0062] Taking the water ecological environment monitoring of a large lake as an example, the environmental conditions in different regions of the lake vary greatly. In the area near the inflowing river, due to the influence of factors such as water flow and pollutant input, the changes in the water ecological environment are relatively complex. To monitor more meticulously, relatively dense monitoring points may be set, and the aggregation degree of these monitoring points is relatively large. However, to a certain extent, there are similarities in the indicators collected by these densely distributed monitoring points because the local environments they are in are relatively similar. For example, they are all affected by the same pollutants in the inflowing river, and the data such as water quality indicators and biological species monitored may be relatively similar, which makes these data have limitations in reflecting the overall ecological status of the lake. In contrast, in the open area or some relatively remote corners of the lake, the monitoring points are relatively sparse, and their aggregation degree is small. But these scattered monitoring points have unique values. They can cover a larger range of waters and collect more diverse data. For example, in the open area, the monitoring points can reflect information such as the overall fluidity of the lake water and the impact of light on the water body; while the monitoring points in the remote corners may monitor some special aquatic biological communities, and these data are crucial for comprehensively understanding the integrity of the lake's ecosystem.

[0063] In this case, if all monitoring points are treated equally and the same weight is used to calculate the comprehensive value of each indicator, then the monitoring points in the area with a large aggregation degree will dominate the comprehensive evaluation due to the large number and similar data, resulting in the evaluation result being overly biased towards these local areas and unable to accurately reflect the overall ecological status of the lake.

[0064] Therefore, the above method of determining weights reduces the weights of monitoring points with a large aggregation degree, which can prevent local data from overly affecting the overall evaluation result. The weights of monitoring points with a small aggregation degree are relatively increased, which can cover a larger range of waters. Increasing their weights can more comprehensively and reasonably consider the actual situations of different monitoring points, thereby increasing the confidence level in calculating the comprehensive evaluation index of the water ecological environment quality, avoiding evaluation biases caused by uneven distribution of monitoring points, and making the evaluation result more accurate.

[0065] 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.

[0066] In the monitoring of the aquatic ecological environment, monitoring points at different locations can reflect the conditions of different regions of the water area. The data of a single monitoring point can only represent the situation of the small area where the monitoring point is located, which has limitations. Therefore, by integrating the indicators of these monitoring points at different locations and calculating the comprehensive value of each indicator, the one-sided evaluation caused by relying only on the data of a few monitoring points is avoided, and the true state of the aquatic ecological environment can be accurately grasped.

[0067] In one embodiment, the comprehensive value of each indicator satisfies the following relational expression:

[0068] In this formula, represents the comprehensive value of the th indicator, represents the total number of monitoring points, represents the aggregation degree of the th monitoring point, represents the weight of the th monitoring point, represents the th indicator at the th monitoring point.

[0069] By performing weighted summation on the values of each indicator at all monitoring points and the weights of all monitoring points, the comprehensive value of each indicator can be comprehensively utilized, and then the comprehensive value of the indicator can be obtained.

[0070] S6: Obtain the comprehensive evaluation index of the aquatic ecological environment quality according to the comprehensive values of each indicator, the initial weights of each indicator, and the weight correction coefficients of each indicator.

[0071] The aquatic ecological environment is a complex system, and a single indicator cannot comprehensively reflect its quality status. Therefore, in this step, through comprehensive analysis of the comprehensive values of each indicator, the quality of the aquatic ecological environment is evaluated from multiple dimensions, and the comprehensive evaluation index of the aquatic ecological environment quality is obtained, providing a more comprehensive and accurate evaluation result.

[0072] In one embodiment, the calculation formula of the comprehensive evaluation index of the aquatic ecological environment quality is:

[0073] In this formula, is 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 th comprehensive value of the indicator, is the The weight correction coefficient of each index.

[0074] This formula is achieved by realizing the correction of the initial weight of the th index. The larger the correction coefficient of an index, the more effective the index is in reflecting the water ecological environment quality, and the larger its corrected weight will be.

[0075] The traditional fixed-weight evaluation method has obvious limitations when facing the complex and changeable water ecological environment. The water ecological environment is affected by both natural factors (such as seasonal changes, climate changes, etc.) and human factors (such as industrial pollution, agricultural activities, etc.), and is constantly in a dynamic change. Fixed weights cannot adapt to this change, which easily leads to a deviation between the evaluation result and the actual situation. In contrast, this solution improves the accuracy of the comprehensive evaluation index of water ecological environment quality by introducing a weight correction coefficient and dynamically adjusting the initial weights of each index based on actual monitoring data.

[0076] During the actual monitoring process, the degrees to which different indexes are affected by environmental factors are different. For example, some indexes are more sensitive to environmental changes and have a larger fluctuation range. Such indexes may be interfered by accidental factors, resulting in abnormal fluctuations in data. If the traditional fixed-weight method is used, these abnormally fluctuating data may have a greater impact on the evaluation result and interfere with the judgment of the true quality of the water ecological environment. In this solution, through weight correction, the role of indexes with large fluctuations and poor stability in the evaluation can be reduced, avoiding the interference of abnormal information and making the evaluation index more accurately reflect the true quality of the water ecological environment.

[0077] In addition, this way of comprehensive calculation and weight correction also fully considers the relative importance of each index under different monitoring conditions. In different water ecological environment scenarios, the impacts of each index on the overall environmental quality are not constant. This solution can flexibly adjust the weights according to the actual situation, making the evaluation result more in line with the specific water ecological environment conditions and enhancing the scientificity and adaptability of the evaluation.

[0078] In one embodiment, the method for obtaining the initial weight of each index is: Take the weight set for each indicator in the pre - obtained water ecosystem status classification standard as the initial weight of the indicator. The water ecosystem status classification standard here is specifically the "Technical Specification for Monitoring and Evaluation of Water Ecological Environment Quality of Rivers and Lakes" (DB11 / T 2320 - 2024). Set the initial weights of each indicator according to the content of this classification standard. For example, the primary indicators and their initial weights are: 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 resources index is 0.1. The secondary indicators included in the primary indicator and their initial weights are: for the water environment index, the secondary indicators are the water quality category and the water quality stability index, and the initial weights of both the water quality category and the water quality stability index are 0.5.

[0079] S7: Monitor and give early warnings about the water ecological environment quality according to the comprehensive evaluation index of water ecological environment quality.

[0080] By monitoring the comprehensive evaluation index of water ecological environment quality and making grade divisions, problems existing in the water ecosystem can be discovered in a timely manner. For example, when it is monitored that a certain water area is in the poor or inferior quality grade, it indicates that the water ecosystem in this water area may have been severely damaged, and the survival and reproduction of organisms are threatened. At this time, giving an early warning can prompt relevant personnel to take targeted measures, such as treating pollution and restoring habitats, to restore and ensure the health of the water ecosystem and protect biodiversity.

[0081] In one embodiment, the method for monitoring and giving early warnings about the water ecological environment according to the size of the comprehensive evaluation index of water ecological environment quality is as follows: Preset the first threshold as 4.5, the second threshold as 3.5, the third threshold as 2.5, and the fourth threshold as 1.5. When the comprehensive evaluation index of water ecological environment quality is, the water ecological environment quality is excellent; when is, the water ecological environment quality is good; when is, the water ecological environment quality is medium; when is, the water ecological environment quality is poor; when is, the water ecological environment quality is inferior.

[0082] The scoring and grade - division rules here can refer to the "Technical Specification for Monitoring and Evaluation of Water Ecological Environment Quality of Rivers and Lakes" (DB11 / T 2320 - 2024).

[0083] Furthermore, when the water ecological environment quality is poor and inferior, give an early warning to notify relevant staff to focus on carrying out environmental governance measures for the poor and inferior water bodies.

[0084] The present invention also provides a water ecological environment monitoring system. The environment monitoring system includes a processor and a memory, and a computer program is stored on the memory. When the computer program is executed by the processor, the steps in any one of the environment monitoring methods can be implemented.

[0085] Although this specification has shown and described multiple embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. In the process of practicing the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for monitoring water ecological environment, characterized in that, Including: Collect the monitoring data sets of each monitoring point in the water ecological environment. The monitoring data sets are composed of multiple indicators. Evaluate the fluctuation degree of each indicator, and calculate the weight correction coefficient of each indicator based on the fluctuation degree of each indicator: , is the weight correction coefficient of the th indicator, is the natural constant, is the th indicator's fluctuation degree; Calculating the aggregation degree of each monitoring point according to the distance between monitoring points: , is the aggregation degree of the th monitoring point, is the total number of monitoring points, is the th monitoring point and the th monitoring point, is the natural exponential function; Determining the weight of each monitoring point according to the aggregation degree of each monitoring point, and based on the weight of each monitoring point, performing weighted summation on the values of each indicator at each monitoring point to obtain the comprehensive value of each indicator; Calculating the comprehensive evaluation index of water ecological environment quality: , is the total number of indicators, is the initial weight of the th indicator, is the th comprehensive value of the indicator; Monitoring the water ecological environment according to the size of the comprehensive evaluation index of water ecological environment quality.

2. The water ecological environment monitoring method according to claim 1, characterized in that The method for evaluating the fluctuation degree of each indicator is: Dividing the historical value sequence of each indicator at each monitoring point by using sliding windows of different lengths, and determining the volatility of each indicator at each monitoring point according to the division result; Calculating the fluctuation degree of each indicator based on the volatility of each indicator at different monitoring points: ； Among them, is the fluctuation degree of the th index, is the normalization function, is the total number of monitoring points, is the th index at the th monitoring point, is the serial number of the monitoring point, takes values over all integers within the range of , is the absolute value symbol.

3. The water ecological environment monitoring method according to claim 2, wherein, Determining the volatility of each indicator at each monitoring point according to the division result is based on the following formula: , is the volatility of the th indicator at the th monitoring point, is the normalization function, is the total number of divisions, is the th indicator at the th monitoring point for the th division result of the historical value sequence, is the th indicator at the th monitoring point for the mean of the standard deviations of all division results of the historical value sequence, is the absolute value symbol.

4. The water ecological environment monitoring method according to claim 2, characterized in that, The method for dividing the historical value sequence of each indicator at each monitoring point by using sliding windows of different lengths is: Dividing the historical value sequence of each indicator at each monitoring point by using sliding windows of different lengths included from L1 to L2. Each length of the sliding window corresponds to a division result. L1 is the preset minimum length of the sliding window, and L2 is the length of the historical value sequence.

5. The water ecological environment monitoring method according to claim 1, characterized in that, The weight of each monitoring point is determined based on the following formula: , is the weight of the th monitoring point, is the aggregation degree of the th monitoring point, is the total number of monitoring points.

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

7. The water ecological environment monitoring method according to claim 1, characterized in that, The method for obtaining the initial weight of an indicator is: using the weight set for each indicator in the pre-obtained classification standard of the water ecosystem state as the initial weight of the indicator.

8. An aquatic ecological environment monitoring system, characterized in that, The environmental monitoring system includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, it can implement the steps in the environmental monitoring method according to any one of claims 1-7.

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