Method for evaluating water ecological condition and carrying out protection priority grading on water body

Abstract

The invention relates to the field of water ecological protection, in particular to a method for evaluating the water ecological condition and carrying out protection priority grading on a water body. The method comprises the following steps: acquiring water area characteristic data and water body soil data by using a sensor; performing image acquisition on the target water area through an ecological remote sensing technology to obtain a target water area environment image; performing water quality characteristic analysis on the water area characteristic data according to the water body soil data to generate water area water quality characteristic data; performing water ecosystem analysis on the target water area environment image according to the water area water quality characteristic data to generate water ecosystem data; carrying out biocenosis structure analysis on the water ecosystem data, and constructing a community structure relation network; and performing water body information flow quantitative analysis on the water area water quality characteristic data through the community structure relation network to generate a biological community health index. According to the invention, efficient and accurate water body protection priority grading is realized.

Description

Technical Field

[0001] The present invention relates to the field of water ecological protection, and in particular to a method for evaluating water ecological conditions and grading water body protection priorities. Background Art

[0002] Aquatic ecosystems are one of the most important ecosystems on Earth and are essential for maintaining biodiversity, providing ecological services, and ensuring human well-being. However, due to the increasing human activities, including industrial pollution, agricultural emissions, land development, etc., the ecological status of many water bodies has been severely damaged and threatened. Therefore, evaluating the status of water ecology and determining protection priorities have become key tasks in water resources management and environmental protection. Traditionally, the evaluation of water ecological status mainly relies on manual sampling and field surveys. However, this method usually requires a lot of time, manpower and material resources, and has certain limitations in large-scale and long-term monitoring. There are problems such as inaccurate priority judgment and low efficiency. Therefore, in order to improve the efficiency and accuracy of the evaluation, it is necessary to combine modern technology and data analysis methods to develop an intelligent evaluation method. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a method for evaluating the water ecological status and grading the protection priority of water bodies to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides a method for evaluating water ecological conditions and grading water protection priorities, comprising the following steps:

[0005] Step S1: using sensors to obtain water area characteristic data and water body soil data; using ecological remote sensing technology to collect images of the target water area to obtain the target water area environment image; performing water quality characteristic analysis on the water area characteristic data according to the water body soil data to generate water quality characteristic data of the water area;

[0006] Step S2: performing water ecosystem analysis on the target water environment image according to the water quality characteristic data of the water area to generate water ecosystem data; performing biological community structure analysis on the water ecosystem data to construct a community structure relationship network;

[0007] Step S3: Quantitatively analyze the water body information flow of water quality characteristic data of the water area through the community structure relationship network to generate a biological community health index;

[0008] Step S4: performing water body niche trend analysis on the community structure relationship network through the biome health index to generate water body niche trend data; performing water body evolution prediction on the water body niche trend data to generate water body evolution prediction data;

[0009] Step S5: Calculate the evolution probability through the water area evolution prediction data to construct a water area evolution probability field model; predict the health of the water area ecosystem based on the water quality characteristic data and community structure relationship network through the water area evolution probability field model to generate a water ecological health prediction index;

[0010] Step S6: Conduct protection priority analysis on water ecosystem data based on the water ecological health prediction index to generate protection priority data; optimize protection decisions for target waters through the protection priority data to construct a water ecological priority protection strategy.

[0011] The present invention obtains water area characteristic data and water body soil data through sensors, and can monitor water quality indicators, water temperature, dissolved oxygen and other key parameters in real time, providing basic data for water ecosystem evaluation. At the same time, by obtaining the target water area environment image through ecological remote sensing technology, the spatial distribution information of the water area can be obtained. Through water quality characteristic analysis, the water quality status can be quantitatively evaluated, laying the foundation for subsequent water ecosystem analysis. Through water quality characteristic data, the water ecosystem analysis of the target water area environment image can be carried out to understand the ecological status of different areas in the water area, including the distribution of ecological elements such as wetlands, aquatic plants, and aquatic animals. Through biological community structure analysis and construction of community structure relationship network, the interdependence between different organisms can be revealed, providing basic data for subsequent water ecosystem health evaluation. Through the community structure relationship network, the water quality characteristic data is quantitatively analyzed, which can reveal the interaction and information flow of biological communities in the water body, which is helpful to evaluate the health status of biological communities, including indicators such as species diversity, population size and ecological function. The community structure relationship network is analyzed by the biological community health index. Through the analysis of the network, the ecological niche trend of the water body can be evaluated, that is, the adaptability and stability of the biological community in the water body. Through the prediction of water area evolution, the future evolution trend of the water body can be inferred, including the changes in the biological community and the stability of the water ecosystem, providing a predictive basis for water area management and protection. Through the calculation of evolution probability and the construction of the water area evolution probability field model, the evolution probability of different areas of the water area can be quantitatively evaluated, that is, the evolution trend of the ecosystem in different areas can be predicted. Through the water area evolution probability field model, the water quality characteristic data and the community structure relationship network can be analyzed to predict the health of the water ecosystem and generate a water ecological health prediction index to provide a prediction and evaluation of the future development of the water ecosystem. Through the analysis of the water ecosystem data through the water ecological health prediction index, the protection priorities of different areas can be determined, which is helpful to identify the vulnerable areas and key protection areas in the water ecosystem, formulate targeted protection measures, optimize the protection decision of the target water area through the protection priority data, and construct a water ecological priority protection strategy to maximize the protection of the health and sustainable development of the water ecosystem.

[0012] Preferably, step S1 comprises the following steps:

[0013] Step S11: using sensors to obtain water area characteristic data and water body soil data;

[0014] Step S12: collecting images of the target water area through ecological remote sensing technology to obtain an environmental image of the target water area;

[0015] Step S13: analyzing the soil humus content of the water body soil data to generate water body soil nutrient data;

[0016] Step S14: performing water nutrient detection on the water body characteristic data according to the water body soil nutrient data to obtain water body nutrient data;

[0017] Step S15: identifying water turbidity of water body by using water body eutrophication data to water body characteristic data, and generating water body turbidity data;

[0018] Step S16: performing water body organic matter analysis on the water area characteristic data based on the water body turbidity data to generate water body organic matter data;

[0019] Step S17: Perform water quality characteristic analysis on the water body eutrophication data, water body turbidity data and water body organic matter data to generate water body water quality characteristic data.

[0020] The present invention can measure key parameters in water bodies, such as water temperature, pH value, dissolved oxygen, conductivity, etc., through sensors, and provide data for real-time monitoring of water quality conditions, which helps to promptly discover abnormal conditions in water bodies, such as excessively high temperature or too low dissolved oxygen, so as to take corresponding measures for regulation and management. The sensors can automatically collect data without manual sampling and analysis, saving time and labor costs. At the same time, the sensors can realize remote monitoring and can obtain water body characteristic data over a larger range, improving the comprehensiveness and coverage of the data. Ecological remote sensing technology can obtain high-resolution images of water bodies and provide spatial distribution information of water bodies, which helps to understand different areas in the water body. Vegetation distribution, wetland distribution and human activities provide detailed spatial information for water ecosystem analysis and management. By analyzing water environment images, targets in water ecosystems can be identified and classified, such as wetland identification and aquatic grass classification, which helps to understand the distribution of different habitat types in water areas and provide basic data for ecosystem evaluation and protection. By analyzing the humus content, nitrogen, phosphorus and other nutrient contents in water body soil data, the nutrient status of water body soil can be evaluated, which helps to understand the nutrient supply capacity and potential eutrophication risks of water areas, and provides a basis for water quality management and ecosystem protection. Through water nutrient testing, the nitrogen, phosphorus and other nutrients in water areas can be understood. The turbidity of water can be measured to assess the eutrophication of water bodies. High eutrophication of water bodies may lead to excessive algae growth and algal bloom events, which will have an adverse impact on the aquatic ecosystem. By monitoring and evaluating the eutrophication of water bodies, corresponding measures can be taken to regulate and manage the water to maintain good water quality. Turbidity is an important indicator for evaluating the transparency of water bodies. By measuring the turbidity of water bodies, the content and distribution of suspended particles in the water can be understood. High turbidity water bodies usually indicate the presence of more suspended matter, which may come from soil erosion, sediment suspension, etc. By measuring the turbidity data of water bodies, the transparency of water bodies can be evaluated, the clarity of water bodies can be judged, and a basis for water quality management and ecological protection can be provided. Organic matter has an important impact on water quality and ecosystems. By analyzing the organic matter data of water bodies, we can understand the type, concentration and distribution of organic matter in water bodies, which helps to assess the degree of organic pollution in water bodies, understand the potential risks of organic matter to aquatic ecosystems, and take corresponding measures to control and protect them. By comprehensively analyzing water eutrophication data, turbidity data and organic matter data, water quality characteristics can be evaluated and monitored. Water quality characteristic data can provide comprehensive information on eutrophication, transparency, organic pollution, etc., to help understand the overall water quality status of water bodies, which helps to formulate water quality improvement and protection strategies, and provide a basis for water health and ecosystem protection.

[0021] Preferably, step S2 comprises the following steps:

[0022] Step S21: segmenting the target water area environment image into aquatic ecological zones according to the water quality characteristic data of the water area to generate an aquatic ecological zone area map;

[0023] Step S22: performing species diversity analysis on the aquatic ecological zone regional map to generate regional species diversity data;

[0024] Step S23: performing water ecosystem analysis on the regional species diversity data to generate water ecosystem data;

[0025] Step S24: performing aquatic plant distribution analysis on the water ecosystem data to generate aquatic plant distribution data;

[0026] Step S25: performing aquatic animal characteristic analysis on the water ecosystem data to generate aquatic animal characteristic data;

[0027] Step S26: Performing biological community structure analysis on the aquatic plant distribution data and the aquatic animal characteristic data to construct a community structure relationship network.

[0028] The present invention can segment and extract aquatic ecological zones by combining water quality characteristic data with target water environment images. Ecological zones refer to the spatial distribution areas of different biological communities and habitat types in aquatic ecosystems. By segmenting ecological zones, the distribution of different habitat areas in aquatic ecosystems can be understood, providing basic data for subsequent biodiversity analysis and aquatic ecosystem assessment. By performing species diversity analysis on aquatic ecological zone regional maps, the species diversity status in aquatic ecosystems can be assessed. Species diversity refers to the number and relative abundance of different species in an ecosystem. By assessing the species diversity in a region, the diversity of organisms in aquatic ecosystems and the stability of ecosystems can be understood, which helps to judge the health status of aquatic ecosystems and the stability of biological communities, providing a basis for the protection and management of aquatic ecosystems. By analyzing regional species diversity data, the structural characteristics of aquatic ecosystems can be understood. The ecosystem structure includes information on biological community composition, species richness, species relative abundance, etc. By analyzing the structure of aquatic ecosystems, the ecosystem characteristics and ecological processes of different habitat areas can be understood, providing a basis for the management and protection of aquatic ecosystems. By performing aquatic plant distribution analysis on aquatic ecosystem data, the distribution of aquatic plants can be understood. Distribution in different habitat areas. Aquatic plants are important biological components in aquatic ecosystems and play an important role in water purification, oxygen generation, and habitat provision. By understanding the distribution of aquatic plants, we can evaluate the richness, diversity, and ecological functions of aquatic plants, and provide a basis for wetland protection and aquatic ecosystem management. By analyzing the characteristics of aquatic animals in aquatic ecosystem data, we can understand the distribution and characteristics of different animal groups in the water. Aquatic animals are an important part of aquatic ecosystems and play a key role in water quality, food chains, and energy flow. By analyzing the characteristics of aquatic animals, such as richness, relative abundance, and relative abundance, we can understand the distribution and characteristics of different animal groups in the water. Abundance, characteristic species, etc. can be used to evaluate the health status and biodiversity of aquatic animal groups, and provide a basis for the protection and management of water ecosystems. By analyzing the distribution data of aquatic plants and the characteristic data of aquatic animals, we can understand the relationship and interaction between different biological communities. A biological community refers to a group of species that live together and influence each other under specific environmental conditions. By constructing a community structure relationship network, we can reveal ecological processes such as the relationship between different species, food chains and energy flow, which helps to deeply understand the composition, structure and function of biological communities in water ecosystems, and provide more refined guidance for ecosystem management and protection.

[0029] Preferably, step S26 comprises the following steps:

[0030] Step S261: Calculating the niche width of the aquatic animal characteristic data to generate a niche width index;

[0031] Step S262: Calculating the species overlap index of the water animal characteristic data according to the niche breadth index to generate the species overlap index;

[0032] Step S263: performing regional niche association analysis on the aquatic plant distribution data based on the species overlap index to generate species niche association data;

[0033] Step S264: performing biocommunity structure compatibility analysis on the species overlap index based on the species niche association data to generate biocommunity structure compatibility data;

[0034] Step S265: Perform ecological network fitting on aquatic plant distribution data and aquatic animal characteristic data through biological community structure compatibility data to construct a community structure relationship network.

[0035] The present invention defines niche breadth as the range of resource utilization or the breadth of lifestyle occupied by a species in an ecosystem. By calculating the niche breadth of aquatic animal characteristic data, the diversity and adaptability of lifestyles of different species in aquatic ecosystems can be evaluated. The niche breadth index provides a quantitative measure, reflecting the differences in resource utilization by species and the diversity of niche allocation. The species overlap index measures the degree of overlap in resource utilization between different species. By calculating the species overlap index based on the niche breadth data, the resource competition and interaction relationship between species in the aquatic animal characteristic data can be evaluated. The species overlap index provides a measure of species coexistence and niche allocation, which helps to understand the relationship between species in aquatic ecosystems and the degree of niche overlap. By performing regional niche association analysis on aquatic plant distribution data based on the species overlap index, the relationship between different aquatic plants and their niches can be revealed. The data on niche association reflect the degree of niche overlap and interdependence between aquatic plants. By analyzing the species overlap index based on the species niche association data, the compatibility of biological community structure between different species can be evaluated. The data on biological community structure compatibility reflect the interaction and relative stability between species, and reveal the competition, coexistence and synergy between species. By fitting the ecological network of aquatic plant distribution data and aquatic animal characteristic data based on the biological community structure compatibility data, a community structure relationship network can be constructed. This network reveals the interaction and interdependence between different species in the aquatic ecosystem, including resource competition, predation relationship, symbiotic relationship, etc. Ecological network fitting helps to understand the ecological process and biodiversity maintenance mechanism of aquatic ecosystems, and provides an important tool and framework for the priority analysis of water ecological environment protection.

[0036] Preferably, step S265 includes the following steps:

[0037] Step S2651: marking species nodes of water ecosystem data through biome structure compatibility data to generate water ecosystem species nodes;

[0038] Step S2652: Perform species association connection on the water ecological species nodes based on the species niche association data, thereby obtaining species node connection edges;

[0039] Step S2653: performing species association stability analysis on water ecological species nodes based on species node connection edges, and constructing species association stability data;

[0040] Step S2654: performing community structure balance analysis on aquatic plant distribution data and aquatic animal characteristic data through species association stability data to generate community structure balance data;

[0041] Step S2655: evaluating the complexity of the species relationship network based on the community structure balance data, thereby obtaining a community structure relationship density index;

[0042] Step S2656: Use the community structure relationship density index to perform ecological network fitting on the aquatic plant distribution data and the water animal characteristic data to construct a community structure relationship network.

[0043] The present invention can mark the nodes of species in the water ecosystem through the biocommunity structure compatibility data. The species node marking can represent different species in the water ecosystem as nodes in the network, providing a basis for subsequent species association connection and analysis. By performing species association connection on the water ecological species nodes based on the species niche association data, connection edges between species nodes can be established. These connection edges represent the interaction relationship between different species, including resource competition, predation relationship, symbiotic relationship, etc. The species node connection edges provide important information for describing the relationship between species, providing a basis for subsequent analysis and evaluation. By performing species association stability analysis on the water ecological species nodes based on the species node connection edges, the stability and reliability of the relationship between species can be evaluated. The species association stability data reflects the intensity and continuity of the interaction between species, which helps to understand the dynamics of species relationships and the stability of the ecosystem. The species association stability data provides a basis for the subsequent community structure balance analysis. By analyzing the distribution of aquatic plants based on the species association stability data, The balance analysis of community structure based on the data and aquatic animal characteristic data can evaluate the balance and stability of community structure. The community structure balance data reflects the relative abundance and relative stability of different species in the community, which helps to understand the composition and relationship of the community. The community structure balance data provides a basis for the subsequent evaluation of relationship network complexity. By evaluating the relationship network complexity of the species relationship network based on the community structure balance data, the relationship density in the community structure can be quantified. The relationship network complexity index reflects the complexity and connection density of the species relationship network, which helps to evaluate the complexity and stability of the community structure. The relationship network complexity evaluation provides a basis for the subsequent ecological network fitting. By using the community structure relationship density index to fit the ecological network of aquatic plant distribution data and aquatic animal characteristic data, a community structure relationship network can be constructed. The ecological network is a network model that describes the interaction relationship between species in an ecosystem. By fitting the ecological network, the connection relationship between species can be established and the ecological interaction pattern between them can be revealed.

[0044] Preferably, step S36 includes the following steps:

[0045] Step S31: identifying key species nodes in the community structure relationship network to obtain key species node data;

[0046] Step S32: performing energy flow analysis on water quality characteristic data of the water area through key species node data to generate energy flow data;

[0047] Step S33: simulating the information diffusion efficiency of the community structure relationship network through energy flow data to generate information diffusion efficiency data;

[0048] Step S34: performing ecological environment change analysis on the energy flow data through the information diffusion efficiency data to generate ecological environment change data;

[0049] Step S35: Quantitatively analyzing the water body information flow on the information diffusion efficiency data and the ecological environment change data to generate water body ecological flux characteristic data;

[0050] Step S36: Perform community health index evaluation processing on the community structure relationship network through water body ecological flux characteristic data to generate a biological community health index.

[0051] The present invention can determine the key species nodes in the community structure relationship network by analyzing it. Key species nodes are species with important ecological functions and relevance in the community structure, and they play a key role in the stability and function of the entire ecosystem. Identifying key species nodes helps to deeply understand the interdependence between species and provide important guidance for subsequent analysis. Using key species node data, energy flow analysis is performed on water quality characteristic data of water areas. Energy flow analysis reveals the energy transfer and transformation process between different species in the ecosystem, and helps to understand the distribution and utilization of energy in the water ecosystem. The community structure relationship network is simulated by using energy flow data for information diffusion efficiency. Information diffusion efficiency simulation is used to evaluate the efficiency of information transmission and communication between species, and reveals the characteristics and patterns of information transmission in community structure. The generated information diffusion efficiency data provides quantitative indicators of information transmission between species, which helps to evaluate the information transmission capacity and stability in the ecosystem. The energy flow data is analyzed for ecological environmental changes using information diffusion efficiency data. Ecological environmental change analysis is used to evaluate the impact of environmental factors on information transmission and energy flow in the ecosystem. By analyzing the ecological and environmental change data, we can understand the impact of environmental changes on community structure and interspecies relationships in the ecosystem, and provide an important reference for water ecological and environmental protection. By analyzing the information diffusion efficiency data and ecological and environmental change data, we can conduct a quantitative analysis of water information flow. The quantitative analysis of water information flow is used to quantify the degree of information exchange and interaction between biological communities in water bodies. The community health index evaluation is used to evaluate the health status and functional status of biological communities in water bodies using the water body ecological flux characteristic data. By analyzing the community structure relationship network and combining the water body ecological flux characteristic data, the stability, diversity and ecological function of the community can be evaluated, and the biological community health index can be generated. These data can provide important information on the health status of water ecosystems, and provide scientific basis and decision-making support for water ecological and environmental protection.

[0052] Preferably, step S4 comprises the following steps:

[0053] Step S41: performing community structure transfer analysis on the community structure relationship network through the biological community health index to generate community structure transfer data;

[0054] Step S42: identifying community succession characteristics of the community structure relationship network based on the community structure transfer data, and generating community succession change rules;

[0055] Step S43: performing water body niche trend analysis on the community structure relationship network according to the community succession change law to generate water body niche trend data;

[0056] Step S44: performing time series evolution analysis on the water body ecological niche trend data to generate time series evolution trend data;

[0057] Step S45: Predicting the species niche width based on the water body niche trend data based on the time series evolution trend data to generate niche width prediction data;

[0058] Step S46: Predicting the ecological spatial distribution trend of the water quality characteristic data of the water area based on the niche width prediction data to generate ecological spatial distribution trend data;

[0059] Step S47: Perform water area evolution prediction on the niche width prediction data and the ecological space distribution trend data based on the time series evolution trend data to generate water area evolution prediction data.

[0060] The present invention analyzes the community structure relationship network through the biological community health index to understand the transfer process of the community structure. The community structure transfer analysis reveals the relative abundance and composition changes of different species in the community, which helps to understand the competition, cooperation and succession relationship between species. The community structure relationship network is analyzed using the water quality characteristic data of the water area to identify the characteristics and laws of community succession. The community succession characteristic identification reveals the changing trend and response mechanism of the community structure under different water quality conditions, which helps to understand the impact of water quality on community succession. The community structure relationship network is analyzed for water body niche trend according to the law of community succession change. The water body niche trend analysis is used to evaluate the niche change trend of different species in the water body, that is, the changes in their roles and resource utilization methods in the ecosystem. The water body niche trend data is analyzed for time series evolution to understand the changing trend of the water body niche over time. The time series evolution analysis reveals the history of the water body niche. Evolution and future trends help understand the dynamic changes of aquatic ecosystems. Species niche width prediction is performed based on time-series evolution trend data, that is, the changes in niche width of different species in the future succession process are predicted. Species niche width prediction provides predictive information on species adaptability and niche diversity, which helps to evaluate the adaptability and niche stability of species in aquatic ecosystems. Based on species niche width prediction data, water quality characteristic data of water bodies are analyzed to predict the spatial distribution trend of species. Ecological spatial distribution trend prediction provides predictive information on the distribution of species in different water areas, which helps to evaluate the geographical diffusion and migration capabilities of species in aquatic ecosystems, as well as the adaptability of species to different water quality conditions. Water area evolution prediction is performed based on time-series evolution trend data, species niche width prediction data and ecological spatial distribution trend data, that is, changes in aquatic ecosystems in the future evolution process are predicted.

[0061] Preferably, step S5 comprises the following steps:

[0062] Step S51: Calculating the evolution rate of the water area evolution prediction data to generate an evolution rate index;

[0063] Step S52: performing evolution curve fitting on the water area evolution prediction data through the evolution rate index to generate a water area evolution curve;

[0064] Step S53: Calculate the evolution probability of the community structure relationship network using the water area evolution probability calculation formula according to the water area evolution curve to generate a water area evolution probability index;

[0065] Step S54: constructing an evolution model for the water area evolution probability index using the water area evolution prediction data to construct a water area evolution probability field model;

[0066] Step S55: predicting the health of the water ecosystem by using the water quality characteristic data and the biome health index of the water area through the water area evolution probability field model to generate a water ecological health prediction index.

[0067] The present invention determines the rate of water area evolution by analyzing and calculating the water area evolution prediction data. The evolution rate index provides a quantitative indicator of the degree and speed of water area evolution, which helps to evaluate the evolutionary state and change trend of the water ecosystem. The water area evolution prediction data is fitted using the evolution rate index to generate a curve describing the water area evolution process. The water area evolution curve provides a visual representation of the trend and change law of water area evolution, which helps to understand the evolutionary trajectory and future development trend of the water ecosystem. According to the water area evolution curve and the water area evolution probability calculation formula, the community structure relationship network is calculated for evolution probability. The water area evolution probability index provides a quantitative indicator of the probability of community structure evolution, which helps to evaluate the stability and vulnerability of the water ecosystem under different evolution scenarios. Based on the water area evolution prediction data and the water area evolution probability index, a model describing the evolution of water area is constructed. The water area evolution probability field model provides spatial distribution information on the evolution probability of different regions in the water ecosystem, which helps to understand the spatial pattern and regional differences of water area evolution. Using the water area evolution probability field model, the water quality characteristic data and the biome health index of the water area are correlated with the water area evolution probability to predict the health status of the water ecosystem. The water ecological health prediction index provides quantitative indicators of the health status and evolution trend of the water ecosystem, which helps to evaluate the quality and sustainability of the water ecological environment.

[0068] Preferably, the water area evolution probability calculation formula in step S53 is specifically:

[0069]

[0070] Among them, P is the probability index of water area evolution, α is the number of species in the biological community, β is the spatial distribution index of plants, γ is the dissolved oxygen concentration in the water area, a is the water temperature in the water area, b is the ammonia nitrogen concentration, δ is the turbidity of water quality in the water area, ε is the annual rainfall in the regional water area, ζ is the suspended matter concentration, η is the watershed area, θ is the ecological state index, κ is the predicted time variable, c is the evolution rate index, d is the predicted value of niche width, e is the information diffusion efficiency value, f is the species overlap value, λ is the complexity of the food chain in the community structure, μ is the stability of the environment, and v is the fluctuation index of the regional environment.

[0071] The present invention is achieved by The combined effects of the number of biome species (α), plant spatial distribution index (β) and dissolved oxygen concentration in the water area (γ) were calculated. The influence of the number of biome species, plant spatial distribution and dissolved oxygen concentration on the evolution of the water area was comprehensively considered, and a probability value was provided to indicate the possibility of water area evolution. The combined effects of water quality turbidity (δ), annual rainfall in the regional waters (ε), suspended matter concentration (ζ) and watershed area (η) were calculated, and the power of the ratio of water quality turbidity to rainfall was taken as the result. The effects of water quality turbidity, rainfall and suspended matter concentration on the evolution of the waters were considered, and an index value was provided to indicate the importance of water quality and rainfall on the evolution of the waters. The influence of ecological status index and time variables on water area evolution is comprehensively considered, indicating the importance of ecological status and time on water area evolution. The combined effects of water temperature (a) and ammonia nitrogen concentration (b) in the water area were calculated. It takes the square root of the sum of water temperature and ammonia nitrogen concentration as the result, indicating the importance of water temperature and ammonia nitrogen concentration on the evolution of the water area. The influence values ​​of the evolution rate index (c) and the predicted value of niche width (d) were calculated. The square root of the natural logarithm of the ratio of the evolution rate index to the predicted value of niche width was calculated to express the influence of the evolution rate index and the predicted value of niche width on the evolution of the water area. The importance of the evolution rate and niche width on the evolution of the water area was obtained. The square root of the sum of the information diffusion efficiency value and the species overlap value is taken to remind the information diffusion efficiency and species overlap of the degree of water area evolution. The formula comprehensively considers the influence of multiple factors on water area evolution, making the calculation results more comprehensive and accurate, and quantifies the importance of each factor, helping to understand and explain the probability of water area evolution.

[0072] Preferably, step S6 comprises the following steps:

[0073] Step S61: performing ecological vulnerability analysis on the community structure relationship network through the water area evolution probability field model, thereby obtaining ecological vulnerability data;

[0074] Step S62: simulating the natural resilience of the water area evolution probability field model based on the ecological vulnerability data to generate an ecological resilience index;

[0075] Step S63: Calculating the community health weight of the ecological resilience index based on the water ecological health prediction index to generate community health weight data;

[0076] Step S64: performing a protection priority analysis on the water ecosystem data according to the community health weight data to generate protection priority data;

[0077] Step S65: Optimize the protection decision of the target water area through the protection priority data and construct a water ecological priority protection strategy.

[0078] The present invention evaluates the vulnerability of each community in the water ecosystem by using the water evolution probability field model and the community structure relationship network. The ecological vulnerability data provides vulnerability indicators for different communities, which helps to identify and understand the key areas and communities in the water ecosystem that are susceptible to disturbance and damage. According to the ecological vulnerability data, the recovery capacity of the water ecosystem under natural conditions is simulated. The ecological resilience index provides quantitative indicators of the natural recovery potential and capacity of the water ecosystem, which helps to evaluate the recovery process and time after the evolution of the water area. Based on the water ecological health prediction index, the ecological resilience index is weighted to reflect the importance of different communities to the health of the water ecosystem. The community health weight data provides quantitative indicators of the contribution of different communities to the function and stability of the water ecosystem, which helps to determine the key areas and communities of the protection and restoration strategy. Based on the community health weight data, the water ecosystem data is analyzed to determine the protection priorities of different areas and communities. The protection priority data provides quantitative indicators of the importance and urgency of the protection of different areas and communities in the water ecosystem, which helps to formulate protection strategies and resource allocation plans. Through the protection priority data, the protection decision of the target water area is optimized and the water ecological priority protection strategy is constructed. In this way, specific protection measures and management recommendations can be formulated to maximize the protection and restoration of the health and sustainability of the water ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A schematic flow chart of the steps of a method for evaluating water ecological conditions and grading water protection priorities according to the present invention;

[0080] Figure 2 Detailed implementation flow chart of step S1;

[0081] Figure 3 Detailed implementation flow chart of step S2;

[0082] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0083] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0084] This application example provides a method for evaluating water ecological conditions and grading water protection priorities. The execution subject of the method for evaluating water ecological conditions and grading water protection priorities includes but is not limited to the following: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of this application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0085] See also Figures 1 to 4 The present invention provides a method for evaluating water ecological conditions and grading water body protection priorities, the method comprising the following steps:

[0086] Step S1: using sensors to obtain water area characteristic data and water body soil data; using ecological remote sensing technology to collect images of the target water area to obtain the target water area environment image; performing water quality characteristic analysis on the water area characteristic data according to the water body soil data to generate water quality characteristic data of the water area;

[0087] Step S2: performing water ecosystem analysis on the target water environment image according to the water quality characteristic data of the water area to generate water ecosystem data; performing biological community structure analysis on the water ecosystem data to construct a community structure relationship network;

[0088] Step S3: Quantitatively analyze the water body information flow of water quality characteristic data of the water area through the community structure relationship network to generate a biological community health index;

[0089] Step S4: performing water body niche trend analysis on the community structure relationship network through the biome health index to generate water body niche trend data; performing water body evolution prediction on the water body niche trend data to generate water body evolution prediction data;

[0090] Step S5: Calculate the evolution probability through the water area evolution prediction data to construct a water area evolution probability field model; predict the health of the water area ecosystem based on the water quality characteristic data and community structure relationship network through the water area evolution probability field model to generate a water ecological health prediction index;

[0091] Step S6: Conduct protection priority analysis on water ecosystem data based on the water ecological health prediction index to generate protection priority data; optimize protection decisions for target waters through the protection priority data to construct a water ecological priority protection strategy.

[0092] The present invention obtains water area characteristic data and water body soil data through sensors, and can monitor water quality indicators, water temperature, dissolved oxygen and other key parameters in real time, providing basic data for water ecosystem evaluation. At the same time, by obtaining the target water area environment image through ecological remote sensing technology, the spatial distribution information of the water area can be obtained. Through water quality characteristic analysis, the water quality status can be quantitatively evaluated, laying the foundation for subsequent water ecosystem analysis. Through water quality characteristic data, the water ecosystem analysis of the target water area environment image can be carried out to understand the ecological status of different areas in the water area, including the distribution of ecological elements such as wetlands, aquatic plants, and aquatic animals. Through biological community structure analysis and construction of community structure relationship network, the interdependence between different organisms can be revealed, providing basic data for subsequent water ecosystem health evaluation. Through the community structure relationship network, the water quality characteristic data is quantitatively analyzed, which can reveal the interaction and information flow of biological communities in the water body, which is helpful to evaluate the health status of biological communities, including indicators such as species diversity, population size and ecological function. The community structure relationship network is analyzed by the biological community health index. Through the analysis of the network, the ecological niche trend of the water body can be evaluated, that is, the adaptability and stability of the biological community in the water body. Through the prediction of water area evolution, the future evolution trend of the water body can be inferred, including the changes in the biological community and the stability of the water ecosystem, providing a predictive basis for water area management and protection. Through the calculation of evolution probability and the construction of the water area evolution probability field model, the evolution probability of different areas of the water area can be quantitatively evaluated, that is, the evolution trend of the ecosystem in different areas can be predicted. Through the water area evolution probability field model, the water quality characteristic data and the community structure relationship network can be analyzed to predict the health of the water ecosystem and generate a water ecological health prediction index to provide a prediction and evaluation of the future development of the water ecosystem. Through the analysis of the water ecosystem data through the water ecological health prediction index, the protection priorities of different areas can be determined, which is helpful to identify the vulnerable areas and key protection areas in the water ecosystem, formulate targeted protection measures, optimize the protection decision of the target water area through the protection priority data, and construct a water ecological priority protection strategy to maximize the protection of the health and sustainable development of the water ecosystem.

[0093] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for evaluating water ecological conditions and grading water protection priorities of the present invention. In this example, the steps of the method for evaluating water ecological conditions and grading water protection priorities include:

[0094] Step S1: using sensors to obtain water area characteristic data and water body soil data; using ecological remote sensing technology to collect images of the target water area to obtain the target water area environment image; performing water quality characteristic analysis on the water area characteristic data according to the water body soil data to generate water quality characteristic data of the water area;

[0095] In this embodiment, suitable sensors and equipment are selected to obtain water area characteristic data and water body soil data. The sensors may include water quality sensors, temperature sensors, ammonia nitrogen sensors, etc., which are used to collect water quality and environmental parameter data. The selected sensors are installed at appropriate locations in the target water area. The sensors should be able to accurately measure various characteristics of the water body, such as water temperature, dissolved oxygen concentration, suspended solids concentration, etc. The location of the sensors should representatively cover different areas of the water area. The water area characteristic data and water body soil data are collected through sensors. Ecological remote sensing technology is used to use aerial or satellite remote sensing image acquisition equipment to collect images of the target water area. These images can provide spatial distribution information of the water area, such as the size, shape and location of the water body. The collected ecological remote sensing images are processed and analyzed to extract the environmental characteristics of the target water area. The water body soil data and the water area characteristic data are analyzed for water quality characteristics. Statistics and analysis are performed based on the parameters in the water body soil data and the water quality indicators in the water area characteristic data. Based on the results of the data analysis, water quality characteristic data are generated. These data may include water quality indicators of the water area, such as pH value, dissolved oxygen concentration, ammonia nitrogen concentration, turbidity, etc.

[0096] Step S2: performing water ecosystem analysis on the target water environment image according to the water quality characteristic data of the water area to generate water ecosystem data; performing biological community structure analysis on the water ecosystem data to construct a community structure relationship network;

[0097] In this embodiment, the physical environment of the water area, such as water temperature, dissolved oxygen, turbidity, etc., is analyzed based on the water quality characteristic data. These parameters can be used to evaluate the transparency, nutrient status and hydrodynamic characteristics of the water body. The water quality status of the water area is evaluated based on the water quality characteristic data, and the water quality indicators are compared with the standards or reference values ​​to determine whether the water body is polluted or healthy. The plant ecological characteristics in the water area are analyzed based on the water quality characteristic data and ecological remote sensing images, which may include vegetation types, vegetation coverage, vegetation diversity, etc. The animal ecological characteristics in the water area are analyzed based on the water quality characteristic data and ecological remote sensing images, which may include the species composition, quantity and distribution of fish, plankton, benthic organisms, etc. The collected samples are species identified to determine the existence and abundance of different species. Based on the species identification results, the relative abundance and diversity index of different species are calculated. Based on the interactions between different species, a community structure relationship network is constructed, which can describe the relationship between species through species coexistence relationships, food chain relationships, symbiotic relationships, etc.

[0098] Step S3: Quantitatively analyze the water body information flow of water quality characteristic data of the water area through the community structure relationship network to generate a biological community health index;

[0099] In this embodiment, by analyzing the community structure relationship network, species that play a key role in information flow are determined. These species may be important species, key links or species with important ecological functions. Based on the community structure relationship network, the information flow between different species is quantified. This can use network analysis methods, such as centrality indicators (such as degree centrality, betweenness centrality, etc.) to measure the importance and contribution of species in information transmission. According to the community structure relationship network and water quality characteristic data, the path and direction of information flow are determined, and which species are the source and receiver of information, as well as the transmission path between them, are identified. Based on the information flow path, the information flow intensity between different species is calculated. The intensity can be measured according to the amount of information, frequency or other appropriate indicators. According to the information flow intensity and water quality characteristic data of the water area, a biological community health index is generated. This can include evaluating indicators such as the stability of the ecosystem, the interdependence between species, and the efficiency of information transmission. The information flow intensity between different species is analyzed, and which species play a key role in information transmission is identified. According to the results of the quantitative analysis of water body information flow, the potential ecological risks of the water area are evaluated. This can be accomplished by comparing the differences with the reference ecosystem, evaluating the efficiency and stability of information transmission, etc.

[0100] Step S4: performing water body niche trend analysis on the community structure relationship network through the biome health index to generate water body niche trend data; performing water body evolution prediction on the water body niche trend data to generate water body evolution prediction data;

[0101] In this embodiment, the biome health index is standardized to ensure comparability between different indicators. Common standardization methods include zero mean standardization, maximum and minimum standardization, etc. The biome health index is calculated based on the selected evaluation index and the standardized data. This can be done by weighted summation or other appropriate calculation methods. According to the research objectives and the biome health index, the niche trend of the water body is defined. The niche trend can reflect the health status, stability and sustainability of the water body ecosystem. Based on the defined niche trend and the biome health index, the niche trend of the water body is calculated. This can be done by analyzing and evaluating the changing trend of the health index. Analyze the niche trend data of the water body and identify the trends and patterns therein. This can include trend analysis, time series analysis and other methods. Based on the analysis of the niche trend data, predict the evolution trend of the water area. This can be done by establishing a trend model, regression analysis or other appropriate methods. According to the evolution trend prediction, the evolution prediction data of the water area is generated. This can be a prediction of the health status of the water body, a prediction of species composition and abundance, etc.

[0102] Step S5: Calculate the evolution probability through the water area evolution prediction data to construct a water area evolution probability field model; predict the health of the water area ecosystem based on the water quality characteristic data and community structure relationship network through the water area evolution probability field model to generate a water ecological health prediction index;

[0103] In this embodiment, based on the evolution prediction data, the probability of water area evolution is calculated, which can be accomplished through statistical methods (such as frequency statistics, probability distribution fitting) or machine learning methods (such as Markov chain, hidden Markov model), and the evolution probability calculation results are interpolated with the water area spatial data to generate a water area evolution probability field, which can be accomplished using interpolation methods (such as Kriging interpolation, inverse distance weighted interpolation), and based on the water area evolution probability field generated by interpolation, a probability field model is constructed, which can be a statistical model based on raster data (such as Bayesian model, random forest) or a spatial regression model based on vector data, and based on the water area evolution probability field model, the ecosystem health probability of each spatial location of the water area is predicted, which can be obtained by combining and calculating the water quality characteristic data and community structure relationship network data of the water area with the probability field model, and generating a water ecological health prediction index based on the probability field prediction results, which can be a health index based on the spatial location or a comprehensive health index based on the entire water area.

[0104] Step S6: Conduct protection priority analysis on water ecosystem data based on the water ecological health prediction index to generate protection priority data; optimize protection decisions for target waters through the protection priority data to construct a water ecological priority protection strategy.

[0105] In this embodiment, the protection priority of each spatial location or area is calculated based on the water ecological health prediction index data and other relevant data. This can be a priority division based on the range of index values, or a comprehensive evaluation based on multiple indicators. Based on the protection priority calculation results, the protection priority data of the target water area is generated. This can be to associate the protection priority with the water area spatial data to generate a corresponding protection priority layer or data table. Based on the target setting and protection priority data, a water ecological priority protection strategy is formulated. This can include determining the scope of the protected area, formulating protection measures, planning protection management strategies, etc. The protection strategy is optimized and adjusted to maximize the protection benefits of the water ecosystem. This can be accomplished through methods such as model simulation, evaluation and optimization algorithms.

[0106] In this embodiment, reference Figure 2 The above is a schematic flow chart of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0107] Step S11: using sensors to obtain water area characteristic data and water body soil data;

[0108] Step S12: collecting images of the target water area through ecological remote sensing technology to obtain an environmental image of the target water area;

[0109] Step S13: analyzing the soil humus content of the water body soil data to generate water body soil nutrient data;

[0110] Step S14: performing water nutrient detection on the water body characteristic data according to the water body soil nutrient data to obtain water body nutrient data;

[0111] Step S15: identifying water turbidity of water body by using water body eutrophication data to water body characteristic data, and generating water body turbidity data;

[0112] Step S16: performing water body organic matter analysis on the water area characteristic data based on the water body turbidity data to generate water body organic matter data;

[0113] Step S17: Perform water quality characteristic analysis on the water body eutrophication data, water body turbidity data and water body organic matter data to generate water body water quality characteristic data.

[0114] The present invention can measure key parameters in water bodies, such as water temperature, pH value, dissolved oxygen, conductivity, etc., through sensors, and provide data for real-time monitoring of water quality conditions, which helps to promptly discover abnormal conditions in water bodies, such as excessively high temperature or too low dissolved oxygen, so as to take corresponding measures for regulation and management. The sensors can automatically collect data without manual sampling and analysis, saving time and labor costs. At the same time, the sensors can realize remote monitoring and can obtain water body characteristic data over a larger range, improving the comprehensiveness and coverage of the data. Ecological remote sensing technology can obtain high-resolution images of water bodies and provide spatial distribution information of water bodies, which helps to understand different areas in the water body. Vegetation distribution, wetland distribution and human activities provide detailed spatial information for water ecosystem analysis and management. By analyzing water environment images, targets in water ecosystems can be identified and classified, such as wetland identification and aquatic grass classification, which helps to understand the distribution of different habitat types in water areas and provide basic data for ecosystem evaluation and protection. By analyzing the humus content, nitrogen, phosphorus and other nutrient contents in water body soil data, the nutrient status of water body soil can be evaluated, which helps to understand the nutrient supply capacity and potential eutrophication risks of water areas, and provides a basis for water quality management and ecosystem protection. Through water nutrient testing, the nitrogen, phosphorus and other nutrients in water areas can be understood. The turbidity of water can be measured to assess the eutrophication of water bodies. High eutrophication of water bodies may lead to excessive algae growth and algal bloom events, which will have an adverse impact on the aquatic ecosystem. By monitoring and evaluating the eutrophication of water bodies, corresponding measures can be taken to regulate and manage the water to maintain good water quality. Turbidity is an important indicator for evaluating the transparency of water bodies. By measuring the turbidity of water bodies, the content and distribution of suspended particles in the water can be understood. High turbidity water bodies usually indicate the presence of more suspended matter, which may come from soil erosion, sediment suspension, etc. By measuring the turbidity data of water bodies, the transparency of water bodies can be evaluated, the clarity of water bodies can be judged, and a basis for water quality management and ecological protection can be provided. Organic matter has an important impact on water quality and ecosystems. By analyzing the organic matter data of water bodies, we can understand the type, concentration and distribution of organic matter in water bodies, which helps to assess the degree of organic pollution in water bodies, understand the potential risks of organic matter to aquatic ecosystems, and take corresponding measures to control and protect them. By comprehensively analyzing water eutrophication data, turbidity data and organic matter data, water quality characteristics can be evaluated and monitored. Water quality characteristic data can provide comprehensive information on eutrophication, transparency, organic pollution, etc., to help understand the overall water quality status of water bodies, which helps to formulate water quality improvement and protection strategies, and provide a basis for water health and ecosystem protection.

[0115] In this embodiment, according to the water characteristic data to be obtained, a suitable sensor is selected. For example, a water quality sensor, a temperature sensor, a dissolved oxygen sensor, etc. can be selected. The selected sensor is installed in an appropriate position to ensure that it can accurately measure the water characteristic data. According to the requirements of the sensor, the corresponding configuration and calibration are performed. The water characteristic data is collected in real time or regularly by the sensor. This can be continuous or intermittent collection, depending on the research needs and the capabilities of the sensor. An image acquisition plan is formulated, including acquisition time, acquisition area, acquisition resolution, etc. Considering the dynamics and seasonal changes of the target water area, the best time for acquisition is selected. The target water area is imaged using the selected ecological remote sensing technology. According to the acquisition plan, the water environment image data is obtained. Representative water body soil sample points are selected in the target water area, and soil samples are collected using appropriate tools and methods. The collected water body soil samples are sent to the laboratory for humus content analysis. This can use chemical methods, such as dissolved organic carbon (DOC) determination or combustion determination. Water body nutrient detection is performed using water body soil nutrient data and water characteristic data. This can use appropriate water quality analysis methods, such as the determination of nutrients such as nitrogen, phosphorus, and ammonium nitrogen. Turbidity in water characteristic data is measured using a turbidity sensor or turbidity meter. Turbidity is an indication of the content and concentration of suspended particles in water. Water turbidity data and water characteristic data are used to analyze water organic matter. This can use appropriate analytical methods such as chemical analysis, spectral analysis or colorimetric analysis. Use the integrated data to analyze water quality characteristics. This can include calculating indicators such as the degree of eutrophication and turbidity of water bodies, and evaluating the degree of eutrophication and organic load of water bodies.

[0116] In this embodiment, reference Figure 3 The above is a schematic flow chart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0117] Step S21: segmenting the target water area environment image into aquatic ecological zones according to the water quality characteristic data of the water area to generate an aquatic ecological zone area map;

[0118] Step S22: performing species diversity analysis on the aquatic ecological zone regional map to generate regional species diversity data;

[0119] Step S23: performing water ecosystem analysis on the regional species diversity data to generate water ecosystem data;

[0120] Step S24: performing aquatic plant distribution analysis on the water ecosystem data to generate aquatic plant distribution data;

[0121] Step S25: performing aquatic animal characteristic analysis on the water ecosystem data to generate aquatic animal characteristic data;

[0122] Step S26: Performing biological community structure analysis on the aquatic plant distribution data and the aquatic animal characteristic data to construct a community structure relationship network.

[0123] The present invention can segment and extract aquatic ecological zones by combining water quality characteristic data with target water environment images. Ecological zones refer to the spatial distribution areas of different biological communities and habitat types in aquatic ecosystems. By segmenting ecological zones, the distribution of different habitat areas in aquatic ecosystems can be understood, providing basic data for subsequent biodiversity analysis and aquatic ecosystem assessment. By performing species diversity analysis on aquatic ecological zone regional maps, the species diversity status in aquatic ecosystems can be assessed. Species diversity refers to the number and relative abundance of different species in an ecosystem. By assessing the species diversity in a region, the diversity of organisms in aquatic ecosystems and the stability of ecosystems can be understood, which helps to judge the health status of aquatic ecosystems and the stability of biological communities, providing a basis for the protection and management of aquatic ecosystems. By analyzing regional species diversity data, the structural characteristics of aquatic ecosystems can be understood. The ecosystem structure includes information on biological community composition, species richness, species relative abundance, etc. By analyzing the structure of aquatic ecosystems, the ecosystem characteristics and ecological processes of different habitat areas can be understood, providing a basis for the management and protection of aquatic ecosystems. By performing aquatic plant distribution analysis on aquatic ecosystem data, the distribution of aquatic plants can be understood. Distribution in different habitat areas. Aquatic plants are important biological components in aquatic ecosystems and play an important role in water purification, oxygen generation, and habitat provision. By understanding the distribution of aquatic plants, we can evaluate the richness, diversity, and ecological functions of aquatic plants, and provide a basis for wetland protection and aquatic ecosystem management. By analyzing the characteristics of aquatic animals in aquatic ecosystem data, we can understand the distribution and characteristics of different animal groups in the water. Aquatic animals are an important part of aquatic ecosystems and play a key role in water quality, food chains, and energy flow. By analyzing the characteristics of aquatic animals, such as richness, relative abundance, and relative abundance, we can understand the distribution and characteristics of different animal groups in the water. Abundance, characteristic species, etc. can be used to evaluate the health status and biodiversity of aquatic animal groups, and provide a basis for the protection and management of water ecosystems. By analyzing the distribution data of aquatic plants and the characteristic data of aquatic animals, we can understand the relationship and interaction between different biological communities. A biological community refers to a group of species that live together and influence each other under specific environmental conditions. By constructing a community structure relationship network, we can reveal ecological processes such as the relationship between different species, food chains and energy flow, which helps to deeply understand the composition, structure and function of biological communities in water ecosystems, and provide more refined guidance for ecosystem management and protection.

[0124] In this embodiment, according to the different water quality characteristic data of the water area, a suitable aquatic ecological zone segmentation algorithm is selected. Common algorithms include threshold segmentation, cluster segmentation, segmentation algorithms based on machine learning, etc. The selected algorithm and parameters are used to segment the target water environment image into aquatic ecological zones. This process assigns pixels in the image to different aquatic ecological zone categories to generate an aquatic ecological zone regional map. According to the research purpose and data availability, appropriate water ecosystem analysis indicators are selected. Common indicators include biomass, productivity, niche width, etc. According to the aquatic ecological zone regional map and the selected analysis indicators, the water ecosystem indicators of each area are calculated. This can be achieved by measuring parameters such as biomass and productivity. According to the aquatic ecological zone regional map, the types and quantities of aquatic plants appearing in each area are recorded. This can be obtained by field surveys, sample collection, and image analysis. According to the water quality characteristic data of the water area and the aquatic ecological zone regional map, the types and quantities of aquatic animals appearing in each area are recorded. This can be obtained by field surveys, sample collection, and image analysis. Based on the sorted aquatic plant distribution data and aquatic animal characteristic data, a community structure analysis is performed. Statistical methods, network analysis and other technical means can be used to explore the interactions, coexistence patterns, community composition, etc. between species. Based on the analysis results, a biological community structure relationship network is constructed. The network can be represented using graph theory, where nodes represent species and edges represent relationships between species (such as coexistence relationships, interaction relationships, etc.). The attributes of nodes and edges can be assigned based on the analysis results.

[0125] In this embodiment, step S26 includes the following steps:

[0126] Step S261: Calculating the niche width of the aquatic animal characteristic data to generate a niche width index;

[0127] Step S262: Calculating the species overlap index of the water animal characteristic data according to the niche breadth index to generate the species overlap index;

[0128] Step S263: performing regional niche association analysis on the aquatic plant distribution data based on the species overlap index to generate species niche association data;

[0129] Step S264: performing biocommunity structure compatibility analysis on the species overlap index based on the species niche association data to generate biocommunity structure compatibility data;

[0130] Step S265: Perform ecological network fitting on aquatic plant distribution data and aquatic animal characteristic data through biological community structure compatibility data to construct a community structure relationship network.

[0131] The present invention defines niche breadth as the range of resource utilization or the breadth of lifestyle occupied by a species in an ecosystem. By calculating the niche breadth of aquatic animal characteristic data, the diversity and adaptability of lifestyles of different species in aquatic ecosystems can be evaluated. The niche breadth index provides a quantitative measure, reflecting the differences in resource utilization by species and the diversity of niche allocation. The species overlap index measures the degree of overlap in resource utilization between different species. By calculating the species overlap index based on the niche breadth data, the resource competition and interaction relationship between species in the aquatic animal characteristic data can be evaluated. The species overlap index provides a measure of species coexistence and niche allocation, which helps to understand the relationship between species in aquatic ecosystems and the degree of niche overlap. By performing regional niche association analysis on aquatic plant distribution data based on the species overlap index, the relationship between different aquatic plants and their niches can be revealed. The data on niche association reflect the degree of niche overlap and interdependence between aquatic plants. By analyzing the species overlap index based on the species niche association data, the compatibility of biological community structure between different species can be evaluated. The data on biological community structure compatibility reflect the interaction and relative stability between species, and reveal the competition, coexistence and synergy between species. By fitting the ecological network of aquatic plant distribution data and aquatic animal characteristic data based on the biological community structure compatibility data, a community structure relationship network can be constructed. This network reveals the interaction and interdependence between different species in the aquatic ecosystem, including resource competition, predation relationship, symbiotic relationship, etc. Ecological network fitting helps to understand the ecological process and biodiversity maintenance mechanism of aquatic ecosystems, and provides an important tool and framework for the priority analysis of water ecological environment protection.

[0132] In this embodiment, based on the characteristic data of aquatic animals and the selected calculation index, the niche width of each aquatic animal is calculated. Niche width reflects the scope of resource utilization and adaptability of species. The calculated niche width is converted into a niche width index. The indexed niche width data can be used for subsequent analysis and comparison. According to the research purpose and data availability, select an appropriate species overlap index calculation index. Common indicators include species relative abundance, species coexistence relationship, etc. Based on the characteristic data of aquatic animals and the selected calculation index, calculate the species overlap index between each two species. The species overlap index reflects the competition or coexistence relationship between species. According to the research purpose and data availability, select an appropriate regional niche association analysis index. Common indicators include niche center of gravity, niche overlap, etc. Based on the species overlap index and aquatic plant distribution data, a regional niche association analysis is performed. This analysis can explore the niche relationship between different aquatic plants, such as competition, coexistence, etc. Based on the species overlap index and species niche association data, a biome structure compatibility analysis is performed. This analysis can evaluate the degree of compatibility between different species, such as whether there is the possibility of competition or coexistence. According to the research purpose and data availability, select appropriate ecological network fitting indicators. Common indicators include degree centrality, community structure, etc., to fit the ecological network and construct a community structure relationship network.

[0133] In this embodiment, step S265 includes the following steps:

[0134] Step S2651: marking species nodes of water ecosystem data through biome structure compatibility data to generate water ecosystem species nodes;

[0135] Step S2652: Perform species association connection on the water ecological species nodes based on the species niche association data, thereby obtaining species node connection edges;

[0136] Step S2653: performing species association stability analysis on water ecological species nodes based on species node connection edges, and constructing species association stability data;

[0137] Step S2654: performing community structure balance analysis on aquatic plant distribution data and aquatic animal characteristic data through species association stability data to generate community structure balance data;

[0138] Step S2655: evaluating the complexity of the species relationship network based on the community structure balance data, thereby obtaining a community structure relationship density index;

[0139] Step S2656: Use the community structure relationship density index to perform ecological network fitting on the aquatic plant distribution data and the water animal characteristic data to construct a community structure relationship network.

[0140] The present invention can mark the nodes of species in the water ecosystem through the biocommunity structure compatibility data. The species node marking can represent different species in the water ecosystem as nodes in the network, providing a basis for subsequent species association connection and analysis. By performing species association connection on the water ecological species nodes based on the species niche association data, connection edges between species nodes can be established. These connection edges represent the interaction relationship between different species, including resource competition, predation relationship, symbiotic relationship, etc. The species node connection edges provide important information for describing the relationship between species, providing a basis for subsequent analysis and evaluation. By performing species association stability analysis on the water ecological species nodes based on the species node connection edges, the stability and reliability of the relationship between species can be evaluated. The species association stability data reflects the intensity and continuity of the interaction between species, which helps to understand the dynamics of species relationships and the stability of the ecosystem. The species association stability data provides a basis for the subsequent community structure balance analysis. By analyzing the distribution of aquatic plants based on the species association stability data, The balance analysis of community structure based on the data and aquatic animal characteristic data can evaluate the balance and stability of community structure. The community structure balance data reflects the relative abundance and relative stability of different species in the community, which helps to understand the composition and relationship of the community. The community structure balance data provides a basis for the subsequent evaluation of relationship network complexity. By evaluating the relationship network complexity of the species relationship network based on the community structure balance data, the relationship density in the community structure can be quantified. The relationship network complexity index reflects the complexity and connection density of the species relationship network, which helps to evaluate the complexity and stability of the community structure. The relationship network complexity evaluation provides a basis for the subsequent ecological network fitting. By using the community structure relationship density index to fit the ecological network of aquatic plant distribution data and aquatic animal characteristic data, a community structure relationship network can be constructed. The ecological network is a network model that describes the interaction relationship between species in an ecosystem. By fitting the ecological network, the connection relationship between species can be established and the ecological interaction pattern between them can be revealed.

[0141] In this embodiment, based on the biome structure compatibility data, the species in the water ecosystem are marked as nodes. Each species is marked as a node and associated with its corresponding species characteristic data. The marked species nodes are used as water ecological species nodes for subsequent species association analysis and network construction. According to the research purpose and data availability, appropriate species association connection indicators are selected. Common indicators include species overlap index, species relative abundance, etc. Based on the species niche association data and water ecological species nodes, association connections between species nodes are established. Each connection indicates that there is a certain association relationship between two species. According to the research purpose and data availability, appropriate species association stability analysis indicators are selected. Common indicators include association strength, association persistence, etc. Based on the species association connection edges and the selected analysis indicators, the association stability between water ecological species nodes is analyzed. This analysis can evaluate the stability of species associations. According to the research purpose and data availability, appropriate community structure balance analysis indicators are selected. Common indicators include species diversity index, community stability index, etc. Based on species association stability data, aquatic plant distribution data and aquatic animal characteristic data, the structural balance of the community is analyzed. This analysis can evaluate the balance state between species in the community. According to the research purpose and data availability, appropriate relationship network complexity evaluation indicators are selected. Common indicators include network density, average path length, etc. Based on community structure balance data and species association network, the complexity of species relationship network is evaluated. This evaluation can reveal the relationship density of community structure. The evaluated relationship network complexity data is converted into community structure relationship density index. The indexed data can be used for subsequent analysis and comparison. Ecological network fitting is performed on aquatic plant distribution data and aquatic animal characteristic data to construct a community structure relationship network.

[0142] In this embodiment, reference Figure 4 The above is a schematic flow chart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0143] Step S31: identifying key species nodes in the community structure relationship network to obtain key species node data;

[0144] Step S32: performing energy flow analysis on water quality characteristic data of the water area through key species node data to generate energy flow data;

[0145] Step S33: simulating the information diffusion efficiency of the community structure relationship network through energy flow data to generate information diffusion efficiency data;

[0146] Step S34: performing ecological environment change analysis on the energy flow data through the information diffusion efficiency data to generate ecological environment change data;

[0147] Step S35: Quantitatively analyzing the water body information flow on the information diffusion efficiency data and the ecological environment change data to generate water body ecological flux characteristic data;

[0148] Step S36: Perform community health index evaluation processing on the community structure relationship network through water body ecological flux characteristic data to generate a biological community health index.

[0149] The present invention can determine the key species nodes in the community structure relationship network by analyzing it. Key species nodes are species with important ecological functions and relevance in the community structure, and they play a key role in the stability and function of the entire ecosystem. Identifying key species nodes helps to deeply understand the interdependence between species and provide important guidance for subsequent analysis. Using key species node data, energy flow analysis is performed on water quality characteristic data of water areas. Energy flow analysis reveals the energy transfer and transformation process between different species in the ecosystem, and helps to understand the distribution and utilization of energy in the water ecosystem. The community structure relationship network is simulated by using energy flow data for information diffusion efficiency. Information diffusion efficiency simulation is used to evaluate the efficiency of information transmission and communication between species, and reveals the characteristics and patterns of information transmission in community structure. The generated information diffusion efficiency data provides quantitative indicators of information transmission between species, which helps to evaluate the information transmission capacity and stability in the ecosystem. The energy flow data is analyzed for ecological environmental changes using information diffusion efficiency data. Ecological environmental change analysis is used to evaluate the impact of environmental factors on information transmission and energy flow in the ecosystem. By analyzing the ecological and environmental change data, we can understand the impact of environmental changes on community structure and interspecies relationships in the ecosystem, and provide an important reference for water ecological and environmental protection. By analyzing the information diffusion efficiency data and ecological and environmental change data, we can conduct a quantitative analysis of water information flow. The quantitative analysis of water information flow is used to quantify the degree of information exchange and interaction between biological communities in water bodies. The community health index evaluation is used to evaluate the health status and functional status of biological communities in water bodies using the water body ecological flux characteristic data. By analyzing the community structure relationship network and combining the water body ecological flux characteristic data, the stability, diversity and ecological function of the community can be evaluated, and the biological community health index can be generated. These data can provide important information on the health status of water ecosystems, and provide scientific basis and decision-making support for water ecological and environmental protection.

[0150] In this embodiment, according to the research purpose and data availability, appropriate key species node identification indicators are selected. Common indicators include centrality indicators (such as degree centrality, closeness centrality), importance indicators (such as betweenness centrality, eigenvector centrality), etc. Based on the community structure relationship network, the selected key species node identification indicators are used to identify species nodes with importance and key influence. These nodes usually play an important role in community structure and species relationships. According to the research purpose and data availability, appropriate energy flow analysis indicators are selected. Common indicators include energy transfer and conversion between producers, consumers and decomposers. Based on key species node data and water quality characteristic data of water bodies, energy flow analysis is performed, and the energy flow data obtained from the analysis are sorted and recorded for subsequent analysis and research. According to the research purpose and data availability, appropriate information diffusion efficiency simulation indicators are selected. Common indicators include information propagation speed, information loss degree, etc. Based on energy flow data And community structure relationship network, simulate the information diffusion efficiency, evaluate the information dissemination and diffusion effect in the community, organize and record the simulated information diffusion efficiency data, select appropriate ecological environment change analysis indicators according to the research purpose and data availability, common indicators include ecological environment stability, habitat suitability, etc., based on information diffusion efficiency data and energy flow data, conduct ecological environment change analysis, is to evaluate the changes in the ecological environment in the community structure relationship network, according to the research purpose and data availability, select appropriate water body information flow quantitative analysis indicators, common indicators include information transmission intensity, information flow speed, etc., according to the research purpose and data availability, select appropriate community health index evaluation indicators, common indicators include species diversity index, stability index, ecosystem function index, etc., based on water body ecological flux characteristic data and community health index evaluation indicators, conduct community health index evaluation processing on the community structure relationship network, and evaluate the overall health status of the community.

[0151] In this embodiment, step S4 includes the following steps:

[0152] Step S41: performing community structure transfer analysis on the community structure relationship network through the biological community health index to generate community structure transfer data;

[0153] Step S42: identifying community succession characteristics of the community structure relationship network based on the community structure transfer data, and generating community succession change rules;

[0154] Step S43: performing water body niche trend analysis on the community structure relationship network according to the community succession change law to generate water body niche trend data;

[0155] Step S44: performing time series evolution analysis on the water body ecological niche trend data to generate time series evolution trend data;

[0156] Step S45: Predicting the species niche width based on the water body niche trend data based on the time series evolution trend data to generate niche width prediction data;

[0157] Step S46: Predicting the ecological spatial distribution trend of the water quality characteristic data of the water area based on the niche width prediction data to generate ecological spatial distribution trend data;

[0158] Step S47: Perform water area evolution prediction on the niche width prediction data and the ecological space distribution trend data based on the time series evolution trend data to generate water area evolution prediction data.

[0159] The present invention analyzes the community structure relationship network through the biological community health index to understand the transfer process of the community structure. The community structure transfer analysis reveals the relative abundance and composition changes of different species in the community, which helps to understand the competition, cooperation and succession relationship between species. The community structure relationship network is analyzed using the water quality characteristic data of the water area to identify the characteristics and laws of community succession. The community succession characteristic identification reveals the changing trend and response mechanism of the community structure under different water quality conditions, which helps to understand the impact of water quality on community succession. The community structure relationship network is analyzed for water body niche trend according to the law of community succession change. The water body niche trend analysis is used to evaluate the niche change trend of different species in the water body, that is, the changes in their roles and resource utilization methods in the ecosystem. The water body niche trend data is analyzed for time series evolution to understand the changing trend of the water body niche over time. The time series evolution analysis reveals the history of the water body niche. Evolution and future trends help understand the dynamic changes of aquatic ecosystems. Species niche width prediction is performed based on time-series evolution trend data, that is, the changes in niche width of different species in the future succession process are predicted. Species niche width prediction provides predictive information on species adaptability and niche diversity, which helps to evaluate the adaptability and niche stability of species in aquatic ecosystems. Based on species niche width prediction data, water quality characteristic data of water bodies are analyzed to predict the spatial distribution trend of species. Ecological spatial distribution trend prediction provides predictive information on the distribution of species in different water areas, which helps to evaluate the geographical diffusion and migration capabilities of species in aquatic ecosystems, as well as the adaptability of species to different water quality conditions. Water area evolution prediction is performed based on time-series evolution trend data, species niche width prediction data and ecological spatial distribution trend data, that is, changes in aquatic ecosystems in the future evolution process are predicted.

[0160] In the present embodiment, the data of the community structure relationship network are collected and sorted, including biological species, their richness, diversity and the mutual relationship between them (such as food chain, competitive relationship, etc.). Use appropriate ecological indicators (such as Shannon-Wiener diversity index, Simpson index, etc.) to analyze the data of the community structure relationship network to calculate the health index of the biological community. Through the analysis of the community structure relationship network and the health index, the changes in the structure of the biological community in different time periods or different regions are identified. Water quality data of the water area are obtained, including water temperature, dissolved oxygen, pH value, nutrient content, organic matter content, etc. Water quality data are associated with the community structure relationship network established before to explore the relationship between them. Using water quality characteristic data analysis, the community succession characteristics related to water quality changes are identified, which may be the sensitivity of some species to water quality changes, quantitative changes or other ecological parameters. Using water quality characteristic data analysis, the community succession characteristics related to water quality changes are identified, which may be the sensitivity of some species to water quality changes, quantitative changes or other ecological parameters. Using comprehensive data, we analyze trends in water body ecological niches, that is, the impact of changes in community structure and water quality characteristics on the overall trends of aquatic ecosystems.

[0161] In this embodiment, step S5 includes the following steps:

[0162] Step S51: Calculating the evolution rate of the water area evolution prediction data to generate an evolution rate index;

[0163] Step S52: performing evolution curve fitting on the water area evolution prediction data through the evolution rate index to generate a water area evolution curve;

[0164] Step S53: Calculate the evolution probability of the community structure relationship network using the water area evolution probability calculation formula according to the water area evolution curve to generate a water area evolution probability index;

[0165] Step S54: constructing an evolution model for the water area evolution probability index using the water area evolution prediction data to construct a water area evolution probability field model;

[0166] Step S55: predicting the health of the water ecosystem by using the water quality characteristic data and the biome health index of the water area through the water area evolution probability field model to generate a water ecological health prediction index.

[0167] The present invention determines the rate of water area evolution by analyzing and calculating the water area evolution prediction data. The evolution rate index provides a quantitative indicator of the degree and speed of water area evolution, which helps to evaluate the evolutionary state and change trend of the water ecosystem. The water area evolution prediction data is fitted using the evolution rate index to generate a curve describing the water area evolution process. The water area evolution curve provides a visual representation of the trend and change law of water area evolution, which helps to understand the evolutionary trajectory and future development trend of the water ecosystem. According to the water area evolution curve and the water area evolution probability calculation formula, the community structure relationship network is calculated for evolution probability. The water area evolution probability index provides a quantitative indicator of the probability of community structure evolution, which helps to evaluate the stability and vulnerability of the water ecosystem under different evolution scenarios. Based on the water area evolution prediction data and the water area evolution probability index, a model describing the evolution of water area is constructed. The water area evolution probability field model provides spatial distribution information on the evolution probability of different regions in the water ecosystem, which helps to understand the spatial pattern and regional differences of water area evolution. Using the water area evolution probability field model, the water quality characteristic data and the biome health index of the water area are correlated with the water area evolution probability to predict the health status of the water ecosystem. The water ecological health prediction index provides quantitative indicators of the health status and evolution trend of the water ecosystem, which helps to evaluate the quality and sustainability of the water ecological environment.

[0168] In this embodiment, an appropriate algorithm or method is used to analyze the water area evolution prediction data to determine the rate of water area evolution, which may involve comparing data at different time points or locations to find the evolution trend, and expressing the calculated evolution rate result as an index or a numerical value for subsequent analysis. The calculated evolution rate result is expressed as an index or a numerical value for subsequent analysis, and the fitted evolution curve is expressed as a set of equations or curves. An appropriate evolution probability calculation formula is used to calculate the probability of water area evolution at each time point or space point, taking into account the information of the water area evolution curve. This can be done based on historical data and model assumptions. The result of the evolution probability calculation is expressed as an index or a numerical value to reflect the evolution possibility of the water area, and the water area generated in the previous step is obtained. Evolution prediction data, including evolution rate index, evolution curve and evolution probability index. Based on these data, an evolution model is constructed, which can be used to simulate the evolution process of water bodies. This may include the use of mathematical equations, computational models or machine learning methods. Based on these data, an evolution model is constructed, which can be used to simulate the evolution process of water bodies. This may include the use of mathematical equations, computational models or machine learning methods. The constructed water body evolution probability field model is combined with the current water quality characteristics and the biological community health index to predict the future health status of the water body. According to the output and analysis results of the model, an index or value describing the health status of the water ecosystem is generated. This index can be used to monitor the health changes of the water body and formulate corresponding management measures.

[0169] In this embodiment, the water area evolution probability calculation formula in step S53 is specifically:

[0170]

[0171] Among them, P is the probability index of water area evolution, α is the number of species in the biological community, β is the spatial distribution index of plants, γ is the dissolved oxygen concentration in the water area, a is the water temperature in the water area, b is the ammonia nitrogen concentration, δ is the turbidity of water quality in the water area, ε is the annual rainfall in the regional water area, ζ is the suspended matter concentration, η is the watershed area, θ is the ecological state index, κ is the predicted time variable, c is the evolution rate index, d is the predicted value of niche width, e is the information diffusion efficiency value, f is the species overlap value, λ is the complexity of the food chain in the community structure, μ is the stability of the environment, and ν is the fluctuation index of the regional environment.

[0172] The present invention is achieved by The combined effects of the number of biome species (α), plant spatial distribution index (β) and dissolved oxygen concentration in the water area (γ) were calculated. The influence of the number of biome species, plant spatial distribution and dissolved oxygen concentration on the evolution of the water area was comprehensively considered, and a probability value was provided to indicate the possibility of water area evolution. The combined effects of water quality turbidity (δ), annual rainfall in the regional waters (ε), suspended matter concentration (ζ) and watershed area (η) were calculated, and the power of the ratio of water quality turbidity to rainfall was taken as the result. The effects of water quality turbidity, rainfall and suspended matter concentration on the evolution of the waters were considered, and an index value was provided to indicate the importance of water quality and rainfall on the evolution of the waters. The influence of ecological status index and time variables on water area evolution is comprehensively considered, indicating the importance of ecological status and time on water area evolution. The combined effects of water temperature (a) and ammonia nitrogen concentration (b) in the water area were calculated. It takes the square root of the sum of water temperature and ammonia nitrogen concentration as the result, indicating the importance of water temperature and ammonia nitrogen concentration on the evolution of the water area. The influence values ​​of the evolution rate index (c) and the predicted value of niche width (d) were calculated. The square root of the natural logarithm of the ratio of the evolution rate index to the predicted value of niche width was calculated to express the influence of the evolution rate index and the predicted value of niche width on the evolution of the water area. The importance of the evolution rate and niche width on the evolution of the water area was obtained. The square root of the sum of the information diffusion efficiency value and the species overlap value is taken to remind the information diffusion efficiency and species overlap of the degree of water area evolution. The formula comprehensively considers the influence of multiple factors on water area evolution, making the calculation results more comprehensive and accurate, and quantifies the importance of each factor, helping to understand and explain the probability of water area evolution.

[0173] In this embodiment, step S6 includes the following steps:

[0174] Step S61: performing ecological vulnerability analysis on the community structure relationship network through the water area evolution probability field model, thereby obtaining ecological vulnerability data;

[0175] Step S62: simulating the natural resilience of the water area evolution probability field model based on the ecological vulnerability data to generate an ecological resilience index;

[0176] Step S63: Calculating the community health weight of the ecological resilience index based on the water ecological health prediction index to generate community health weight data;

[0177] Step S64: performing a protection priority analysis on the water ecosystem data according to the community health weight data to generate protection priority data;

[0178] Step S65: Optimize the protection decision of the target water area through the protection priority data and construct a water ecological priority protection strategy.

[0179] The present invention evaluates the vulnerability of each community in the water ecosystem by using the water evolution probability field model and the community structure relationship network. The ecological vulnerability data provides vulnerability indicators for different communities, which helps to identify and understand the key areas and communities in the water ecosystem that are susceptible to disturbance and damage. According to the ecological vulnerability data, the recovery capacity of the water ecosystem under natural conditions is simulated. The ecological resilience index provides quantitative indicators of the natural recovery potential and capacity of the water ecosystem, which helps to evaluate the recovery process and time after the evolution of the water area. Based on the water ecological health prediction index, the ecological resilience index is weighted to reflect the importance of different communities to the health of the water ecosystem. The community health weight data provides quantitative indicators of the contribution of different communities to the function and stability of the water ecosystem, which helps to determine the key areas and communities of the protection and restoration strategy. Based on the community health weight data, the water ecosystem data is analyzed to determine the protection priorities of different areas and communities. The protection priority data provides quantitative indicators of the importance and urgency of the protection of different areas and communities in the water ecosystem, which helps to formulate protection strategies and resource allocation plans. Through the protection priority data, the protection decision of the target water area is optimized and the water ecological priority protection strategy is constructed. In this way, specific protection measures and management recommendations can be formulated to maximize the protection and restoration of the health and sustainability of the water ecosystem.

[0180] In this embodiment, according to the research purpose and data availability, a suitable water area evolution probability field model is selected. This model can be used to describe the evolution probability of different areas in the water area and reflect the dynamic changes of the ecosystem. According to the research purpose and data availability, appropriate ecological vulnerability analysis indicators are selected. Common indicators include species diversity index, ecosystem stability index, habitat suitability index, etc. Based on the community structure relationship network and the selected water area evolution probability field model, ecological vulnerability analysis is performed. By evaluating the ecological vulnerability of different areas, the fragile and sensitive areas in the ecosystem can be revealed. According to the research purpose and data availability, appropriate natural resilience simulation indicators are selected. Common indicators include species adaptability, ecosystem recovery speed, etc. Based on the ecological vulnerability data and the selected water area evolution probability field model, natural resilience simulation is performed. The goal of this step is to evaluate the natural recovery capacity of each area in the water area. According to the research purpose and data availability, appropriate water ecological health prediction indexes are selected. Common indexes include water quality indicators, biological indicators, etc. Based on the water ecological health prediction index The goal of this step is to determine the community health weight of each area based on the importance of the water ecological health prediction index and the capacity of natural resilience. According to the research purpose and data availability, appropriate protection priority analysis indicators are selected. Common indicators include ecological sensitivity, ecological importance, etc. Based on the community health weight data and the selected protection priority analysis indicators, the water ecosystem data is analyzed for protection priority. The goal of this step is to determine the protection priorities of different areas to guide the formulation and implementation of protection measures. According to research needs and protection goals, the target waters that need to be optimized for protection decisions are determined. Based on the protection priority data and the characteristics of the target waters, protection decisions are optimized. The goal of this step is to develop reasonable protection strategies to maximize the ecological health and resilience of the target waters. According to the results of the protection decision optimization, a water ecological priority protection strategy is constructed. This strategy can include specific protection measures, management plans, and monitoring programs to ensure that the ecosystems of the target waters are effectively protected and restored.

[0181] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0182] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0183] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for evaluating water ecological conditions and grading water protection priorities, characterized in that: The following steps are involved: Step S1: using sensors to obtain water area characteristic data and water body soil data; using ecological remote sensing technology to collect images of the target water area to obtain the target water area environment image; performing water quality characteristic analysis on the water area characteristic data according to the water body soil data to generate water quality characteristic data of the water area; Step S2: performing water ecosystem analysis on the target water environment image according to the water quality characteristic data of the water area to generate water ecosystem data; performing biological community structure analysis on the water ecosystem data to construct a community structure relationship network; Step S3: Quantitatively analyze the water body information flow of water quality characteristic data of the water area through the community structure relationship network to generate a biological community health index; Step S4: performing water body niche trend analysis on the community structure relationship network through the biome health index to generate water body niche trend data; performing water body evolution prediction on the water body niche trend data to generate water body evolution prediction data; Step S5: Calculate the evolution probability through the water area evolution prediction data to construct a water area evolution probability field model; predict the health of the water area ecosystem based on the water quality characteristic data and community structure relationship network through the water area evolution probability field model to generate a water ecological health prediction index; Step S6: Conduct protection priority analysis on water ecosystem data based on the water ecological health prediction index to generate protection priority data; optimize protection decisions for target waters through the protection priority data to construct a water ecological priority protection strategy.

2. The method according to claim 1, characterized in that The specific steps of step S1 are: Step S11: using sensors to obtain water area characteristic data and water body soil data; Step S12: collecting images of the target water area through ecological remote sensing technology to obtain an environmental image of the target water area; Step S13: analyzing the soil humus content of the water body soil data to generate water body soil nutrient data; Step S14: performing water nutrient detection on the water body characteristic data according to the water body soil nutrient data to obtain water body nutrient data; Step S15: identifying water turbidity of water body by using water body eutrophication data to water body characteristic data, and generating water body turbidity data; Step S16: performing water body organic matter analysis on the water area characteristic data based on the water body turbidity data to generate water body organic matter data; Step S17: Perform water quality characteristic analysis on the water body eutrophication data, water body turbidity data and water body organic matter data to generate water body water quality characteristic data.

3. The method according to claim 1, characterized in that The specific steps of step S2 are: Step S21: segmenting the target water area environment image into aquatic ecological zones according to the water quality characteristic data of the water area to generate an aquatic ecological zone area map; Step S22: performing species diversity analysis on the aquatic ecological zone regional map to generate regional species diversity data; Step S23: performing water ecosystem analysis on the regional species diversity data to generate water ecosystem data; Step S24: performing aquatic plant distribution analysis on the water ecosystem data to generate aquatic plant distribution data; Step S25: performing aquatic animal characteristic analysis on the water ecosystem data to generate aquatic animal characteristic data; Step S26: Performing biological community structure analysis on the aquatic plant distribution data and the aquatic animal characteristic data to construct a community structure relationship network.

4. The method according to claim 3, characterized in that The specific steps of step S26 are: Step S261: Calculating the niche width of the aquatic animal characteristic data to generate a niche width index; Step S262: Calculating the species overlap index of the water animal characteristic data according to the niche breadth index to generate the species overlap index; Step S263: performing regional niche association analysis on the aquatic plant distribution data based on the species overlap index to generate species niche association data; Step S264: performing biocommunity structure compatibility analysis on the species overlap index based on the species niche association data to generate biocommunity structure compatibility data; Step S265: Perform ecological network fitting on the aquatic plant distribution data and the aquatic animal characteristic data through the biological community structure compatibility data to construct a community structure relationship network.

5. The method according to claim 4, characterized in that The specific steps of step S265 are: Step S2651: marking species nodes of water ecosystem data through biome structure compatibility data to generate water ecosystem species nodes; Step S2652: performing species association connection on the water ecological species nodes based on the species niche association data, thereby obtaining species node connection edges; Step S2653: performing species association stability analysis on water ecological species nodes based on species node connection edges, and constructing species association stability data; Step S2654: performing community structure balance analysis on aquatic plant distribution data and aquatic animal characteristic data through species association stability data to generate community structure balance data; Step S2655: evaluating the species relationship network complexity based on the community structure balance data, thereby obtaining a community structure relationship density index; Step S2656: Use the community structure relationship density index to perform ecological network fitting on the aquatic plant distribution data and the aquatic animal characteristic data to construct a community structure relationship network.

6. The method according to claim 1, characterized in that The specific steps of step S3 are: Step S31: identifying key species nodes in the community structure relationship network to obtain key species node data; Step S32: performing energy flow analysis on water quality characteristic data of the water area through key species node data to generate energy flow data; Step S33: simulating the information diffusion efficiency of the community structure relationship network through energy flow data to generate information diffusion efficiency data; Step S34: performing ecological environment change analysis on the energy flow data through the information diffusion efficiency data to generate ecological environment change data; Step S35: Quantitatively analyzing the water body information flow on the information diffusion efficiency data and the ecological environment change data to generate water body ecological flux characteristic data; Step S36: Perform community health index evaluation processing on the community structure relationship network through water body ecological flux characteristic data to generate a biological community health index.

7. The method according to claim 1, characterized in that The specific steps of step S4 are: Step S41: performing community structure transfer analysis on the community structure relationship network through the biological community health index to generate community structure transfer data; Step S42: identifying community succession characteristics of the community structure relationship network based on the community structure transfer data, and generating community succession change rules; Step S43: performing water body niche trend analysis on the community structure relationship network according to the community succession change law to generate water body niche trend data; Step S44: performing time series evolution analysis on the water body ecological niche trend data to generate time series evolution trend data; Step S45: Predicting the species niche width based on the water body niche trend data based on the time series evolution trend data to generate niche width prediction data; Step S46: Predicting the ecological spatial distribution trend of the water quality characteristic data of the water area based on the niche width prediction data to generate ecological spatial distribution trend data; Step S47: Perform water area evolution prediction on the niche width prediction data and the ecological space distribution trend data based on the time series evolution trend data to generate water area evolution prediction data.

8. The method according to claim 1, characterized in that The specific steps of step S5 are: Step S51: Calculating the evolution rate of the water area evolution prediction data to generate an evolution rate index; Step S52: performing evolution curve fitting on the water area evolution prediction data through the evolution rate index to generate a water area evolution curve; Step S53: Calculate the evolution probability of the community structure relationship network using the water area evolution probability calculation formula according to the water area evolution curve to generate a water area evolution probability index; Step S54: constructing an evolution model for the water area evolution probability index using the water area evolution prediction data to construct a water area evolution probability field model; Step S55: The water ecosystem health is predicted by using the water area evolution probability field model to predict the water quality characteristic data and the biome health index of the water area to generate a water ecological health prediction index.

9. The method according to claim 8, characterized in that The specific calculation formula of the water area evolution probability in step S53 is: Among them, P is the probability index of water area evolution, α is the number of species of biological communities, β is the spatial distribution index of plants, γ is the dissolved oxygen concentration in the water area, a is the water temperature in the water area, b is the ammonia nitrogen concentration, δ is the turbidity of water quality in the water area, and ε is the annual rainfall in the regional water area. is the suspended matter concentration, η is the watershed area, θ is the ecological state index, κ is the predicted time variable, c is the evolution rate index, d is the predicted value of niche width, e is the information diffusion efficiency value, f is the species overlap value, λ is the food chain complexity in the community structure, μ is the stability of the environment, and v is the fluctuation index of the regional environment.

10. The method according to claim 1, characterized in that The specific steps of step S6 are: Step S61: performing ecological vulnerability analysis on the community structure relationship network through the water area evolution probability field model, thereby obtaining ecological vulnerability data; Step S62: simulating the natural resilience of the water area evolution probability field model based on the ecological vulnerability data to generate an ecological resilience index; Step S63: Calculating the community health weight of the ecological resilience index based on the water ecological health prediction index to generate community health weight data; Step S64: performing a protection priority analysis on the water ecosystem data according to the community health weight data to generate protection priority data; Step S65: Optimize the protection decision of the target water area through the protection priority data and construct a water ecological priority protection strategy.

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