Water ecological environment quality evaluation method based on cross-domain ecological network analysis

Abstract

The invention belongs to the technical field of water ecology evaluation, and discloses a water ecology environment quality evaluation method based on cross-domain ecological network analysis, and the method comprises the following specific steps: 1, collecting data, determining a specific target of the water ecology environment quality evaluation, and defining a water area range related to the evaluation from the spatial dimension and the time dimension; according to the invention, by analyzing the association relationship between the network nodes, the complex action mechanism in the water ecosystem is deeply revealed, and the operation rule of the water ecosystem can be understood; the evolution rule and driving factors of the water ecological environment are revealed by analyzing the change of network characteristic parameters along with time and space, possibility is provided for predicting the future development trend, compared with a traditional method, the requirements for comprehensive, scientific and dynamic evaluation in current ecological protection work are met, the reference of an evaluation result is improved, and the method is suitable for popularization and application. And better data support can be provided for water ecological environment management and protection and water ecological restoration.

Description

Technical Field

[0001] This invention belongs to the field of water ecological assessment technology, specifically a method for evaluating the quality of water ecological environment based on cross-domain ecological network analysis. Background Technology

[0002] Aquatic ecological assessment is an important means of measuring whether a water body is healthy and whether an ecosystem is complete. Based on aquatic ecological survey data, it analyzes and evaluates the quality of the aquatic ecological environment from the perspectives of the complexity and evenness of the relationships among aquatic organisms in the aquatic ecosystem and the spatiotemporal evolution of key species. This provides a reasonable basis for protecting and restoring the ecological environment and making rational use of natural resources, and ultimately serves aquatic biodiversity, aquatic ecological health and human health.

[0003] Among the publicly available water ecological environment assessment systems in China, the "Technical Guidelines for Monitoring and Evaluation of Lake and Reservoir Water Ecological Environment Quality (Draft for Comments)" and the "Technical Guidelines for Monitoring and Evaluation of River Water Ecological Environment Quality (Trial)" issued by the Ministry of Ecology and Environment place greater emphasis on biological integrity in the overall assessment system. Furthermore, these systems expand upon the traditional water ecological environment quality assessment model, which primarily focuses on water quality testing, into a comprehensive system encompassing multiple elements such as hydrology, water quality, aquatic organisms, and the aquatic environment. However, the following problems exist: the evaluation results of each element are relatively independent, and even differ, leading to ambiguous assessment results; the correlation between various element indicators is unclear, especially the correlation between aquatic organisms; and the key aquatic species and environmental indicator species for maintaining the structure and function of the aquatic ecosystem are unclear, resulting in weak reference value for the water ecological environment quality assessment results. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the quality of the aquatic ecological environment based on cross-domain ecological network analysis, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the quality of the aquatic ecological environment based on cross-domain ecological network analysis, the specific steps of which are as follows:

[0006] Step 1: Data Collection

[0007] The specific objectives of this water ecological environment quality assessment are determined. The scope of the water area involved in this assessment is defined from both spatial and temporal dimensions, including the specific geographical location, water area, and time range. Then, hydrological, water quality, aquatic organism, and aquatic environment data are collected.

[0008] Step 2: Data Preprocessing

[0009] The collected data is cleaned to remove outliers, missing values, and duplicate values, and then the cleaned data is standardized.

[0010] Step 3: Construct a cross-domain ecological network model

[0011] Different elements in the aquatic ecosystem are used as network nodes. The relationships between nodes are then analyzed, and the identified nodes and relationships are visualized to construct a cross-domain ecological network model.

[0012] Step 4: Network Feature Parameter Analysis

[0013] By conducting correlation complexity and evenness analysis, key species in the aquatic ecosystem are identified;

[0014] Step 5: Spatiotemporal Evolution Analysis

[0015] Spatiotemporal evolution analysis was conducted in different regions and time series, and the results of the spatiotemporal evolution analysis were combined to screen out indicator species that are sensitive to changes in the aquatic ecological environment.

[0016] Step Six: Construct an evaluation index system

[0017] Taking into account the complexity of associations, evenness, key species and indicator species, a water ecological environment quality evaluation index system is constructed. Then, the specific indicators are determined by combining the analysis results of the cross-domain ecological network model and the actual evaluation needs. Finally, the specific indicators are weighted, calculated and adjusted.

[0018] Step 7: Calculation of Evaluation Results

[0019] Based on the constructed evaluation index system and weights, a weighted summation method is used to comprehensively evaluate and calculate the water ecological environment quality. Then, based on the comprehensive evaluation score, corresponding water ecological environment quality classification standards are formulated.

[0020] Step 8: Feedback and Optimization

[0021] Establish a long-term dynamic monitoring mechanism for the aquatic ecological environment, conduct regular surveys and evaluations of the aquatic ecosystem, grasp the changes in the quality of the aquatic ecological environment, and dynamically adjust and optimize the evaluation methods, indicator system, and weight allocation based on the monitoring results.

[0022] As a preferred technical solution of the present invention, in the data cleaning process described in step two, for outliers, the cause of their occurrence is analyzed to determine whether to correct or delete them; for missing values, interpolation and mean substitution methods are used to fill them in; for duplicate values, they are directly deleted; in the standardization process described in step two, Z-score standardization is used to process the cleaned data.

[0023] As a preferred embodiment of the present invention, the node association relationship described in step three specifically includes:

[0024] Identify network nodes:

[0025] Hydrological parameters, water quality indicators, aquatic species, and aquatic environment data in the aquatic ecosystem are used as network nodes, with each node representing a specific ecological element or indicator.

[0026] Relationships between nodes:

[0027] Ecological Principles Analysis: Based on ecological knowledge, the direct and indirect relationships between different nodes are analyzed, including food chain relationships, habitat dependence relationships, and water quality impact relationships; Data Analysis Methods Mining: Statistical methods such as correlation analysis, regression analysis, and principal component analysis, as well as association rule mining algorithms in ecological network analysis, are used to further quantify the strength and direction of relationships between nodes.

[0028] As a preferred technical solution of the present invention, the specific construction method of the cross-domain ecological network model in step three is as follows: select Gephi as the modeling tool and the NetworkX library in Python as the programming language; organize the determined node and relationship data into a format suitable for the requirements of the modeling tool or programming language; import or input the node and relationship data into the model according to the selected construction tool or programming language; and make visual adjustments to the generated network model to optimize the network layout and display effect.

[0029] As a preferred embodiment of the present invention, the network feature parameter analysis in step four specifically includes:

[0030] Association complexity analysis:

[0031] Calculate connectivity: For each node, traverse all edges in the network and count the number of edges connected to that node, which is the connectivity of that node; the calculation method is to use the NetworkX library in Python and call the degree() function to get the connectivity of each node.

[0032] Analyze the network topology:

[0033] Small World Characteristics Analysis

[0034] The average path length and clustering coefficient are calculated using the average_shortest_path_length() and clustering() functions in the NetworkX library. Then, they are compared with the corresponding indicators of random networks. When the average path length of an ecological network is close to that of a random network and the clustering coefficient is much higher than that of a random network, it indicates that the ecological network has small-world characteristics.

[0035] Analyzing scale-free properties

[0036] Using the numpy and matplotlib libraries in Python, we can count the number of nodes with different degrees in an ecological network and plot the degree distribution curve. When the degree distribution curve approximates a power-law distribution, it indicates that the ecological network has scale-free properties.

[0037] Uniformity analysis:

[0038] Traverse all nodes in the ecological network, record the connectivity of each node, and then count the number of nodes with different connectivity. Use a bar chart or line chart to visually display the distribution of node connectivity. The horizontal axis represents the node connectivity, and the vertical axis represents the number of nodes with the corresponding connectivity. By observing the graph, judge the uniformity of the node connectivity distribution.

[0039] Key species identification:

[0040] Use the degree() function in the NetworkX library to obtain the degree centrality of each node (i.e., the node's connectivity, which measures the number of direct connections a node has in the network), sort the nodes according to their degree centrality, and select the nodes with high degree centrality as key species.

[0041] As a preferred embodiment of the present invention, the spatiotemporal evolution analysis in step five specifically includes:

[0042] Comparative analysis of different regions:

[0043] Regional division

[0044] Based on the geographical characteristics and ecological environment differences of the water area, the evaluation area was divided into several sub-regions for data collection and processing.

[0045] For each sub-region, collect corresponding hydrological, water quality, aquatic organism, and aquatic environment data, ensuring that the time range and collection methods of the data are consistent. Organize the collected data and store and classify it according to a unified format.

[0046] Constructing sub-region network models

[0047] Each sub-region was constructed using a method for building cross-domain ecological network models.

[0048] Comparison of network feature parameters

[0049] Create feature parameter comparison charts: Organize the association complexity, uniformity, and key species distribution parameters of each sub-region and draw them into bar charts, line charts, or radar charts for intuitive comparison;

[0050] Analysis of the reasons for the differences: Combining the geographical environment and human activity factors of each sub-region, analyze the reasons for the differences in network characteristic parameters;

[0051] Summarize regional characteristics: Based on the comparative analysis results, summarize the characteristics and differences of the aquatic ecological environment of each sub-region;

[0052] Different time series analyses:

[0053] Determine the time node

[0054] Based on the evaluation objectives and data availability, select appropriate time points for data collection and analysis; construct network models at different time points.

[0055] Following the method of constructing cross-domain ecological network models, a corresponding ecological network model is constructed for each time point to ensure that the definition and determination methods of nodes and relationships remain consistent across different time points during the construction process.

[0056] Analyze dynamic changes

[0057] Plot the characteristic parameter variation curves: Plot the variation curves of the correlation complexity, uniformity, and number of key species at different time points, and observe their changing trends over time;

[0058] Identifying key periods of change: By analyzing change curves, identify key periods in which significant changes occur in the characteristic parameters of the aquatic ecological environment;

[0059] Indicator species identification

[0060] Based on the spatiotemporal evolution analysis results, indicator species sensitive to changes in the aquatic ecological environment were selected, and the changes in the survival status and quantity of indicator species reflected the quality changes in the aquatic ecological environment.

[0061] As a preferred technical solution of the present invention, the specific method for constructing the evaluation index system in step six is ​​as follows: A multi-level index system framework is designed based on the principles of scientific rigor, comprehensiveness, operability, and representativeness, divided into a target layer, a criterion layer, and an indicator layer. The target layer is a comprehensive evaluation of the water ecological environment quality; the criterion layer includes correlation complexity, evenness, key species, indicator species, water quality status, and biodiversity; the indicator layer consists of specific evaluation indicators, including average connectivity, Gini coefficient, key species richness, indicator species health index, dissolved oxygen content, and planktonic biodiversity index.

[0062] As a preferred embodiment of the present invention, the method for weight allocation, calculation, and adjustment in step six is ​​as follows:

[0063] The entropy weight method is chosen for weight allocation. Based on the definition of information entropy, the information entropy of each indicator is calculated, and then the weight of each indicator is calculated based on the information entropy. The weight calculation formula is as follows:

[0064]

[0065] In the formula, ω i E represents the weight of the i-th indicator. i Let be the information entropy of the i-th indicator, and n be the number of indicators, where j ranges from 1 to n, covering all indicators involved in the evaluation. 1-E for all indicators j The summation of values ​​is used to sum the 1-E values ​​of the molecule. i Normalization is performed to ensure that the calculated weights ω are accurate. i The condition that the sum of the weights of all indicators is 1 is met, so that the weights of each indicator can be compared and allocated on a uniform scale.

[0066] Finally, experts in relevant fields were invited to evaluate and consult on the calculated weights, and the weights were adjusted appropriately based on the experts' opinions and suggestions.

[0067] As a preferred embodiment of the present invention, the calculation formula for the evaluation result in step seven is as follows:

[0068]

[0069] In the formula, S is the comprehensive evaluation score, and ω i Let x be the weight of the i-th indicator. i Let be the standardized value of the i-th indicator, and n be the number of evaluation indicators;

[0070] The equidistant division method or the quantile division method are adopted, and the specific division criteria are as follows: Excellent: S≥85; Good: 70≤S<85; Average: 50≤S<70; Poor: S<50.

[0071] The beneficial effects of this invention are as follows:

[0072] This invention, by analyzing the relationships between network nodes, deeply reveals the complex mechanisms of action within aquatic ecosystems, contributing to a better understanding of their operational patterns. By analyzing the changes in network characteristic parameters over time and space, it reveals the evolutionary patterns and driving factors of the aquatic ecological environment, providing a basis for predicting future trends. Compared to traditional methods, this invention meets the current demand for comprehensive, scientific, and dynamic evaluation in ecological protection work, enhances the reference value of evaluation results, and provides better data support for aquatic ecological environment governance and protection, as well as aquatic ecological restoration. Attached Figure Description

[0073] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] like Figure 1 As shown in the figure, this invention provides a method for evaluating the quality of the water ecological environment based on cross-domain ecological network analysis. The specific steps are as follows:

[0076] Step 1: Data Collection

[0077] The specific objectives of this water ecological environment quality assessment are determined. The scope of the water area involved in this assessment is defined from both spatial and temporal dimensions, including the specific geographical location, water area, and time range. Then, hydrological, water quality, aquatic organism, and aquatic environment data are collected.

[0078] Step 2: Data Preprocessing

[0079] The collected data is cleaned to remove outliers, missing values, and duplicate values, and then the cleaned data is standardized.

[0080] Step 3: Construct a cross-domain ecological network model

[0081] Different elements in the aquatic ecosystem are used as network nodes. The relationships between nodes are then analyzed, and the identified nodes and relationships are visualized to construct a cross-domain ecological network model.

[0082] Step 4: Network Feature Parameter Analysis

[0083] By conducting correlation complexity and evenness analysis, key species in the aquatic ecosystem are identified;

[0084] Step 5: Spatiotemporal Evolution Analysis

[0085] Spatiotemporal evolution analysis was conducted in different regions and time series, and the results of the spatiotemporal evolution analysis were combined to screen out indicator species that are sensitive to changes in the aquatic ecological environment.

[0086] Step Six: Construct an evaluation index system

[0087] Taking into account the complexity of associations, evenness, key species and indicator species, a water ecological environment quality evaluation index system is constructed. Then, the specific indicators are determined by combining the analysis results of the cross-domain ecological network model and the actual evaluation needs. Finally, the specific indicators are weighted, calculated and adjusted.

[0088] Step 7: Calculation of Evaluation Results

[0089] Based on the constructed evaluation index system and weights, a weighted summation method is used to comprehensively evaluate and calculate the water ecological environment quality. Then, based on the comprehensive evaluation score, corresponding water ecological environment quality classification standards are formulated.

[0090] Step 8: Feedback and Optimization

[0091] Establish a long-term dynamic monitoring mechanism for the aquatic ecological environment, conduct regular surveys and evaluations of the aquatic ecosystem, grasp the changes in the quality of the aquatic ecological environment, and dynamically adjust and optimize the evaluation methods, indicator system, and weight allocation based on the monitoring results.

[0092] The cross-domain ecological network analysis method for evaluating the quality of aquatic ecological environment uses aquatic biological species, hydrological parameters, water quality indicators, and aquatic environmental factors as network nodes. It comprehensively considers all components of the aquatic ecosystem and their interrelationships, which can more accurately reflect the overall status of the aquatic ecological environment and avoid bias in evaluation results due to one-sided focus on a single element. At the same time, it emphasizes the interaction and synergy between different elements, thus breaking through the limitations of traditional evaluation methods and providing a new perspective and method for evaluating the quality of aquatic ecological environment.

[0093] In step two, during data cleaning, outliers are analyzed to determine whether to correct or delete them; missing values ​​are filled using interpolation and mean substitution methods; and duplicate values ​​are deleted directly. In step two, Z-score standardization is used to process the cleaned data.

[0094] Because the data dimensions and orders of magnitude of different indicators vary greatly, standardization is required to make the data comparable. Z-score standardization can transform the data into a distribution with a mean of 0 and a standard deviation of 1.

[0095] Specifically, the relationships between nodes in step three include:

[0096] Identify network nodes:

[0097] Hydrological parameters, water quality indicators, aquatic species, and aquatic environment data in the aquatic ecosystem are used as network nodes, with each node representing a specific ecological element or indicator.

[0098] Relationships between nodes:

[0099] Ecological Principles Analysis: Based on ecological knowledge, the direct and indirect relationships between different nodes are analyzed, including food chain relationships, habitat dependence relationships, and water quality impact relationships; Data Analysis Methods Mining: Statistical methods such as correlation analysis, regression analysis, and principal component analysis, as well as association rule mining algorithms in ecological network analysis, are used to further quantify the strength and direction of relationships between nodes.

[0100] For example, some fish may feed on specific plankton, thus establishing a predator-prey relationship between fish and plankton; the dissolved oxygen content in the water affects the survival and distribution of aquatic organisms, thus forming a link between water quality and organisms; correlation analysis is used to calculate the correlation coefficient between different water quality indicators and aquatic biodiversity indices to determine the degree of their association.

[0101] The specific construction method of the cross-domain ecological network model in step three is as follows: select Gephi as the modeling tool and the NetworkX library in Python as the programming language; organize the determined node and relationship data into a format suitable for the requirements of the modeling tool or programming language; import or input the node and relationship data into the model according to the selected construction tool or programming language; and make visual adjustments to the generated network model to optimize the network layout and display effect.

[0102] When modeling, for node data, a table containing information such as node name, type, and attributes can be created; for relational data, a table containing information such as source node, target node, and relation strength can be created; in professional network modeling software, the network model can be directly generated by importing data files; in programming languages, corresponding functions and methods are needed to create nodes and edges and construct network graph objects; when visually adjusting the generated network model, the color, size, thickness, etc. of nodes and edges can be set according to factors such as node type and relation strength, so that the network model can more intuitively reflect the relationships between nodes and the characteristics of the network.

[0103] Specifically, the network feature parameter analysis in step four includes:

[0104] Association complexity analysis:

[0105] Calculate connectivity: For each node, traverse all edges in the network and count the number of edges connected to that node, which is the connectivity of that node; the calculation method is to use the NetworkX library in Python and call the degree() function to get the connectivity of each node.

[0106] Analyze the network topology:

[0107] Small World Characteristics Analysis

[0108] The average path length and clustering coefficient are calculated using the average_shortest_path_length() and clustering() functions in the NetworkX library. Then, they are compared with the corresponding indicators of random networks. When the average path length of an ecological network is close to that of a random network and the clustering coefficient is much higher than that of a random network, it indicates that the ecological network has small-world characteristics.

[0109] Analyzing scale-free properties

[0110] Using the numpy and matplotlib libraries in Python, we can count the number of nodes with different degrees in an ecological network and plot the degree distribution curve. When the degree distribution curve approximates a power-law distribution, it indicates that the ecological network has scale-free properties.

[0111] Uniformity analysis:

[0112] Traverse all nodes in the ecological network, record the connectivity of each node, and then count the number of nodes with different connectivity. Use a bar chart or line chart to visually display the distribution of node connectivity. The horizontal axis represents the node connectivity, and the vertical axis represents the number of nodes with the corresponding connectivity. By observing the graph, judge the uniformity of the node connectivity distribution.

[0113] Key species identification:

[0114] Use the degree() function in the NetworkX library to obtain the degree centrality of each node (i.e., the node's connectivity, which measures the number of direct connections a node has in the network), sort the nodes according to their degree centrality, and select the nodes with high degree centrality as key species.

[0115] In ecological networks, connectivity reflects the degree of association between a node and other ecological elements. Higher connectivity indicates more frequent interactions between the node and other elements in the ecosystem, potentially having a more significant impact on the stability and function of the ecosystem. Average path length refers to the average of the shortest paths between any two nodes in the network, reflecting the efficiency of information propagation within the network. Clustering coefficient refers to the degree of interconnection between a node's neighboring nodes, reflecting the local compactness of the network. In scale-free networks, the degree distribution of nodes follows a power-law distribution, meaning that a few highly connected nodes (called hub nodes) exist, while most nodes have low connectivity. This characteristic of scale-free networks makes them robust to random failures. Nodes with high degree centrality usually hold important positions in ecological networks, interacting more with other nodes and having a significant impact on the structure and function of the ecosystem.

[0116] Specifically, the spatiotemporal evolution analysis in step five includes:

[0117] Comparative analysis of different regions:

[0118] Regional division

[0119] Based on the geographical characteristics and ecological environment differences of the water area, the evaluation area was divided into several sub-regions for data collection and processing.

[0120] For each sub-region, collect corresponding hydrological, water quality, aquatic organism, and aquatic environment data, ensuring that the time range and collection methods of the data are consistent. Organize the collected data and store and classify it according to a unified format.

[0121] Constructing sub-region network models

[0122] Each sub-region was constructed using a method for building cross-domain ecological network models.

[0123] Comparison of network feature parameters

[0124] Create feature parameter comparison charts: Organize the association complexity, uniformity, and key species distribution parameters of each sub-region and draw them into bar charts, line charts, or radar charts for intuitive comparison;

[0125] Analysis of the reasons for the differences: Combining the geographical environment and human activity factors of each sub-region, analyze the reasons for the differences in network characteristic parameters;

[0126] Summarize regional characteristics: Based on the comparative analysis results, summarize the characteristics and differences of the aquatic ecological environment of each sub-region;

[0127] Different time series analyses:

[0128] Determine the time node

[0129] Based on the evaluation objectives and data availability, select appropriate time points for data collection and analysis; construct network models at different time points.

[0130] Following the method of constructing cross-domain ecological network models, a corresponding ecological network model is constructed for each time point to ensure that the definition and determination methods of nodes and relationships remain consistent across different time points during the construction process.

[0131] Analyze dynamic changes

[0132] Plot the characteristic parameter variation curves: Plot the variation curves of the correlation complexity, uniformity, and number of key species at different time points, and observe their changing trends over time;

[0133] Identifying key periods of change: By analyzing change curves, identify key periods in which significant changes occur in the characteristic parameters of the aquatic ecological environment;

[0134] Indicator species identification

[0135] Based on the spatiotemporal evolution analysis results, indicator species sensitive to changes in the aquatic ecological environment were selected, and the changes in the survival status and quantity of indicator species reflected the quality changes in the aquatic ecological environment.

[0136] After identifying the indicator species, the response of the selected species is observed by artificially altering certain indicators of the water body (such as increasing pollutant concentration or changing water temperature). Changes in the survival rate, growth rate, and reproductive capacity of the species are recorded to verify whether they can accurately reflect changes in the aquatic ecological environment.

[0137] The specific method for constructing the evaluation index system in step six is ​​as follows: Based on the principles of scientificity, comprehensiveness, operability, and representativeness, a multi-level index system framework is designed, which is divided into a target layer, a criterion layer, and an indicator layer. The target layer is a comprehensive evaluation of the water ecological environment quality; the criterion layer includes correlation complexity, evenness, key species, indicator species, water quality status, and biodiversity; the indicator layer consists of specific evaluation indicators, including average connectivity, Gini coefficient, key species richness, indicator species health index, dissolved oxygen content, and planktonic biodiversity index.

[0138] The principle of scientific rigor means that the indicators should be based on scientific ecological theories and methods, and be able to accurately reflect the essential characteristics and changing patterns of the aquatic ecological environment. The principle of comprehensiveness means that factors such as correlation complexity, evenness, key species, and indicator species should be comprehensively considered, combined with traditional indicators for aquatic ecological environment quality assessment (such as water quality indicators and biodiversity indicators), to ensure that the evaluation indicator system can comprehensively cover all aspects of the aquatic ecological environment. The principle of operability means that the indicators should be quantifiable, accessible, and comparable, so as to facilitate the implementation of actual monitoring and evaluation work. The principle of representativeness means that representative indicators should be selected to reflect the main characteristics and key issues of the aquatic ecological environment, and to avoid too many or too complicated indicators.

[0139] The method for weight allocation, calculation, and adjustment in step six is ​​as follows:

[0140] The entropy weight method is chosen for weight allocation. Based on the definition of information entropy, the information entropy of each indicator is calculated, and then the weight of each indicator is calculated based on the information entropy. The weight calculation formula is as follows:

[0141]

[0142] In the formula, ω i E represents the weight of the i-th indicator. iLet be the information entropy of the i-th indicator, and n be the number of indicators, where j ranges from 1 to n, covering all indicators involved in the evaluation. 1-E for all indicators j The summation of values ​​is used to sum the 1-E values ​​of the molecule. i Normalization is performed to ensure that the calculated weights ω are accurate. i The condition that the sum of the weights of all indicators is 1 is met, so that the weights of each indicator can be compared and allocated on a uniform scale.

[0143] Finally, experts in relevant fields were invited to evaluate and consult on the calculated weights, and the weights were adjusted appropriately based on the experts' opinions and suggestions.

[0144] The smaller the information entropy, the greater the degree of variation of the indicator, the more information it provides, and the greater its weight. After the weight is determined, the calculated weight is applied to the actual water ecological environment quality assessment. Based on the rationality and accuracy of the assessment results, the weight is further adjusted and optimized.

[0145] The formula for calculating the evaluation result in step seven is as follows:

[0146]

[0147] In the formula, S is the comprehensive evaluation score, and ω i Let x be the weight of the i-th indicator. i Let be the standardized value of the i-th indicator, and n be the number of evaluation indicators;

[0148] The equidistant division method or the quantile division method are adopted, and the specific division criteria are as follows: Excellent: S≥85; Good: 70≤S<85; Average: 50≤S<70; Poor: S<50.

[0149] Based on the calculated comprehensive evaluation score and in accordance with the established grading standards, the water ecological environment quality level is assessed. The grading can intuitively reflect the quality of the water ecological environment and provide decision-makers with a clear reference.

[0150] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0151] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the quality of the aquatic ecological environment based on cross-domain ecological network analysis, characterized in that, The specific steps are as follows: Step 1: Data collection. Determine the specific objectives of this water ecological environment quality assessment. Define the water area involved in this assessment from both spatial and temporal dimensions, including the specific geographical location, water area, and time range. Then, collect hydrological, water quality, aquatic organism, and aquatic environment data. Step 2: Data preprocessing. The collected data is cleaned to remove outliers, missing values, and duplicates. Then, the cleaned data is standardized. Step 3: Construct a cross-domain ecological network model, taking different elements in the aquatic ecosystem as network nodes, then analyzing the relationships between nodes, visualizing the identified nodes and relationships, and constructing a cross-domain ecological network model; Step 4: Analyze network feature parameters, perform association complexity and uniformity analysis, and identify key species in the aquatic ecosystem; Step 5: Spatiotemporal evolution analysis. Spatiotemporal evolution analysis is conducted from different regions and time series. Based on the results of the spatiotemporal evolution analysis, indicator species that are sensitive to changes in the aquatic ecological environment are screened out. Step Six: Construct an evaluation index system. Taking into account the complexity of associations, evenness, key species and indicator species, construct an evaluation index system for water ecological environment quality. Then, combine the analysis results of the cross-domain ecological network model and the actual evaluation needs to determine specific indicators. Finally, assign weights, calculate and adjust the specific indicators. Step 7: Evaluation result calculation. Based on the constructed evaluation index system and weights, a weighted summation method is used to comprehensively evaluate and calculate the water ecological environment quality. Then, based on the comprehensive evaluation score, corresponding water ecological environment quality classification standards are formulated. Step 8: Feedback and optimization. Establish a long-term dynamic monitoring mechanism for the aquatic ecological environment, conduct regular surveys and evaluations of the aquatic ecosystem, grasp the changes in the quality of the aquatic ecological environment, and dynamically adjust and optimize the evaluation methods, indicator system, and weight allocation based on the monitoring results.

2. The method for evaluating the quality of aquatic ecological environment based on cross-domain ecological network analysis according to claim 1, characterized in that: In the data cleaning process described in step two, outliers are analyzed to determine whether to correct or delete them; missing values ​​are filled using interpolation and mean substitution methods; duplicate values ​​are deleted directly; and the standardization process described in step two uses Z-score standardization to process the cleaned data.

3. The method for evaluating the quality of aquatic ecological environment based on cross-domain ecological network analysis according to claim 1, characterized in that: The specific relationships between nodes mentioned in step three include: Determine network nodes: Use hydrological parameters, water quality indicators, aquatic species, and aquatic environment data in the aquatic ecosystem as network nodes, with each node representing a specific ecological element or indicator; Inter-node relationships: Ecological principle analysis: Based on ecological knowledge, analyze the direct and indirect relationships between different nodes, including food chain relationships, habitat dependence relationships, and water quality impact relationships; Data analysis methods: Utilize statistical methods such as correlation analysis, regression analysis, and principal component analysis, as well as association rule mining algorithms in ecological network analysis, to further quantify the strength and direction of relationships between nodes.

4. The method for evaluating the quality of aquatic ecological environment based on cross-domain ecological network analysis according to claim 1, characterized in that: The specific construction method of the cross-domain ecological network model described in step three is as follows: select Gephi as the modeling tool and the NetworkX library in Python as the programming language; organize the determined node and relationship data into a format suitable for the requirements of the modeling tool or programming language; import or input the node and relationship data into the model according to the selected construction tool or programming language; and make visual adjustments to the generated network model to optimize the network layout and display effect.

5. The method for evaluating the quality of aquatic ecological environment based on cross-domain ecological network analysis according to claim 1, characterized in that: The network feature parameter analysis described in step four specifically includes: Association complexity analysis: Calculating connectivity: For each node, traverse all edges in the network and count the number of edges connected to that node, which is the connectivity of that node; the calculation method is: in Python, use the NetworkX library and call the degree() function to get the connectivity of each node; Network topology analysis: Small-world property analysis: The average path length and clustering coefficient were calculated using the `average_shortest_path_length()` and `clustering()` functions from the NetworkX library. These were then compared with corresponding metrics of random networks. If the average path length of the ecological network is close to that of the random network, and the clustering coefficient is significantly higher, then the ecological network exhibits small-world properties. Scale-free property analysis: The number of nodes with different degrees in the ecological network was counted using the `numpy` and `matplotlib` libraries in Python. The node degree distribution curve was plotted. If the node degree distribution curve approximates a power-law distribution, then the ecological network exhibits scale-free properties. Uniformity analysis: Traverse all nodes in the ecological network, record the connectivity of each node, and then count the number of nodes with different connectivity. Use a bar chart or line chart to visually display the distribution of node connectivity. The horizontal axis represents the node connectivity, and the vertical axis represents the number of nodes with the corresponding connectivity. By observing the graph, judge the uniformity of the node connectivity distribution. Key species identification: Use the degree() function in the NetworkX library to obtain the degree centrality of each node (i.e., the connectivity of the node, which measures the number of direct connections of the node in the network), sort the nodes according to the degree centrality, and select the nodes with high degree centrality as key species.

6. The method for evaluating the quality of aquatic ecological environment based on cross-domain ecological network analysis according to claim 1, characterized in that: The spatiotemporal evolution analysis described in step five specifically includes: Comparative Analysis of Different Regions: The evaluation area is divided into several sub-regions based on the geographical characteristics and ecological environment differences of the water bodies. Data Collection and Processing: For each sub-region, corresponding hydrological, water quality, aquatic biological, and aquatic environmental data are collected, ensuring consistency in the time frame and collection methods. The collected data is then processed, stored, and categorized according to a unified format. Sub-regional Network Model Construction: A separate ecological network model is constructed for each sub-region using methods for building cross-domain ecological network models. Comparison of Network Characteristic Parameters and Plotting of Characteristic Parameter Comparison Charts: The correlation complexity, evenness, and key species distribution parameters of each sub-region are compiled and plotted into bar charts, line graphs, or radar charts for intuitive comparison. Analysis of Reasons for Differences: The reasons for the differences in network characteristic parameters are analyzed in conjunction with the geographical environment and human activity factors of each sub-region. Summary of Regional Characteristics: Based on the comparative analysis results, the characteristics and differences of the aquatic ecological environment of each sub-region are summarized. Different time series analyses: Determine time points; based on evaluation objectives and data availability, select appropriate time points for data collection and analysis; construct network models for different time points; following the method for constructing cross-domain ecological network models, construct corresponding ecological network models for each time point, ensuring that the definition and determination methods of nodes and relationships remain consistent across different time points during the construction process; analyze dynamic changes and plot characteristic parameter change curves: plot change curves for the association complexity, evenness, and key species quantity parameters at different time points, observing their trends over time; identify critical periods of change: by analyzing change curves, identify critical periods in which significant changes occur in aquatic ecological environment characteristic parameters; confirm indicator species: combining the spatiotemporal evolution analysis results, screen out indicator species sensitive to changes in the aquatic ecological environment, reflecting changes in the quality of the aquatic ecological environment through changes in the survival status and quantity of indicator species.

7. The method for evaluating the quality of aquatic ecological environment based on cross-domain ecological network analysis according to claim 1, characterized in that: The specific method for constructing the evaluation index system described in step six is ​​as follows: Based on the principles of scientificity, comprehensiveness, operability, and representativeness, a multi-level index system framework is designed, which is divided into the target layer, the criterion layer, and the index layer. The target layer is the comprehensive evaluation of the water ecological environment quality. The criteria layer includes correlation complexity, evenness, key species, indicator species, water quality, and biodiversity; the indicator layer consists of specific evaluation indicators, including average connectivity, Gini coefficient, key species richness, indicator species health index, dissolved oxygen content, and planktonic biodiversity index.

8. The method for evaluating the quality of aquatic ecological environment based on cross-domain ecological network analysis according to claim 1, characterized in that: The method for weight allocation, calculation, and adjustment described in step six is ​​as follows: The entropy weight method is chosen for weight allocation. Based on the definition of information entropy, the information entropy of each indicator is calculated, and then the weight of each indicator is calculated based on the information entropy. The weight calculation formula is as follows: In the formula, ω i E represents the weight of the i-th indicator. i Let be the information entropy of the i-th indicator, and n be the number of indicators, where j ranges from 1 to n, covering all indicators involved in the evaluation. 1-E for all indicators j The summation of values ​​is used to sum the 1-E values ​​of the molecule. i Normalization is performed to ensure that the calculated weights ω are accurate. i The condition that the sum of the weights of all indicators is 1 is met, so that the weights of each indicator can be compared and allocated on a uniform scale. Finally, experts in relevant fields were invited to evaluate and consult on the calculated weights, and the weights were adjusted appropriately based on the experts' opinions and suggestions.

9. The method for evaluating the quality of aquatic ecological environment based on cross-domain ecological network analysis according to claim 1, characterized in that: The formula for calculating the evaluation result mentioned in step seven is as follows: In the formula, S is the comprehensive evaluation score, and ω i Let x be the weight of the i-th indicator. i Let be the standardized value of the i-th indicator, and n be the number of evaluation indicators; The equidistant division method or the quantile division method are adopted, and the specific division criteria are as follows: Excellent: S≥85; Good: 70≤S<85; Medium: 50≤S<70; Poor: S<50.

Images