Ecological hydrological data processing method and system

Abstract

The invention relates to the technical field of hydrological simulation, in particular to an ecological hydrological data processing method and system, and the method comprises the steps: integrating ecological hydrological data of differentiated sources, including surface water, underground water and rainfall measurement, carrying out the format unification and range alignment of the data, and carrying out the data unification of timestamps and space coordinates, and obtaining a unified data model. According to the method, hydrological subsets are identified by executing network analysis, advantages are provided for water resource management and prediction, random simulation is carried out by utilizing a climate scene data set, the influence of climate change on water flow is analyzed, probability is distributed for water flow change in each scene, a climate influence probability model is created, and the future state of the water resource is allowed to be predicted. Especially in extreme climate events, hydrological areas sensitive to climate changes are identified, adaptive management measures are designed for the hydrological areas, water resource distribution and protection are optimized, and adaptability and flexibility of water resource management strategies are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological simulation, and in particular to an eco-hydrological data processing method and system. Background Art

[0002] The field of hydrological simulation technology involves the application of mathematical models and computer simulation to simulate the water cycle and its components, including precipitation, evaporation, surface runoff and groundwater flow. The core of the technology lies in predicting and managing the distribution, flow and quality of water resources through precise data processing and simulation methods. Hydrological simulation technology is essential for developing effective water resource management strategies, predicting the risk of natural disasters such as floods and droughts, and assessing the impact of climate change on water resources.

[0003] Among them, ecohydrological data processing methods refer to a set of technologies and procedures for collecting, analyzing and processing data related to the water cycle and its impact on the ecosystem, with the aim of supporting the sustainable management and protection of water resources through a deep understanding of ecosystems and hydrological processes. This includes predicting the availability of water resources, assessing pollution loads, monitoring the health of the ecological environment, and formulating restoration and protection measures. Through precise processing of data, the aim is to achieve the effect of effectively predicting and managing water resources while ensuring the health of the ecosystem and the protection of biodiversity.

[0004] Traditional ecohydrological data processing methods have certain limitations in dealing with complex hydrological systems and predicting the impact of extreme climate events on water resources. This is mainly because traditional methods rely on relatively simplified models and assumptions, making it difficult to fully capture the dynamic complexity of hydrological systems and the integrated processing of multi-source heterogeneous data. In addition, the limitations of traditional technologies in spatial and temporal resolution, as well as the general assumptions used in simulating the impact of climate change, lead to inaccurate predictions of water resource dynamics, making it difficult to provide sufficient information support for water resource management and decision-making. The inadequacy of the methods leads to inefficient water resource allocation and insufficient response to extreme events, affecting the sustainable management of water resources and the health of ecosystems. Summary of the invention

[0005] This application provides an eco-hydrological data processing method and system to solve the limitations of traditional eco-hydrological data processing methods in processing complex hydrological systems and predicting the impact of extreme climate events on water resources. This is mainly because traditional methods rely on relatively simplified models and assumptions, and it is difficult to fully capture the dynamic complexity of hydrological systems and the integrated processing of multi-source heterogeneous data. In addition, the limitations of traditional technologies in spatial and temporal resolution, as well as the general assumptions used in simulating the impact of climate change, lead to inaccurate predictions of water resource dynamics, making it difficult to provide sufficient information support for water resource management and decision-making. The inadequacy of the method leads to inefficient water resource allocation and insufficient response capabilities to extreme events, affecting the sustainable management of water resources and the health of ecosystems.

[0006] In view of the above problems, the present application provides an eco-hydrological data processing method and system.

[0007] The present application provides an ecohydrological data processing method, wherein the method comprises: S1: Integrate ecohydrological data from different sources, including surface water, groundwater and rainfall measurements, unify the data format and align the range, and obtain a unified data model by unifying the timestamps and spatial coordinates; S2: Based on the unified data model, a network diagram of the connection between hydrological sites and rivers is constructed, with the hydrological sites as nodes in the diagram, and the river flow direction defining the edges between the nodes, and weights are assigned to the network diagram to reflect the river flow rate, thereby obtaining a hydrological network diagram; S3: performing network analysis on the hydrological network graph, identifying hydrological subsets in the graph, capturing differentiated connected components in the graph, assigning identifiers to the connected components, distinguishing differentiated hydrological management areas, and obtaining a subset identifier set; S4: using the subset identifier set, analyzing the water flow dynamics within the subset, recording the water inflow and outflow of the subset, evaluating the response of multiple subsets to rainfall and dry seasons, and establishing a water flow dynamic analysis record; S5: combining the water flow dynamic analysis records and climate scenario data in the future time period, by stochastically simulating the impact of differentiated climate change on water flow dynamics, assigning probabilities to water flow changes under each climate scenario, and generating a climate impact probability model; S6: Analyze the climate impact probability model, calculate the sensitivity of multiple subsets to climate change, evaluate the impact of the subsets under the changing climate, mark the subsets with enhanced sensitivity, and construct a sensitive area map; S7: Based on the sensitive area mapping, design adaptive management measures, refer to the needs and priorities of differentiated water resource users, simulate the water resource allocation benefits of various management measures, determine the management strategies for sensitive subsets, and form an adaptive management strategy set.

[0008] Preferably, the unified data model includes a unified time format, spatial coordinate system and data type, the hydrological network diagram includes the geographical location of the nodes, the flow direction relationship between the nodes and the edge weight of the flow velocity, the subset identification set includes the spatial distribution of differentiated connected components, the identification of multiple components and the clustering of hydrological sites within the components, the water flow dynamic analysis record includes the flow change record of the hydrological subset, the water flow exchange volume between subsets and the response pattern to climate events, the climate impact probability model includes the probability value assigned to the water flow change under the climate scenario, the predicted water flow dynamic change range and the impact assessment under the differentiated scenario, the sensitive area mapping includes the identified sensitive hydrological subsets, the spatial distribution of the subsets and the sensitivity level, and the adaptive management strategy set includes the water resource allocation plan for sensitive areas, the priority ranking of management measures and the expected benefit analysis.

[0009] Preferably, the step of integrating ecohydrological data from different sources, including surface water, groundwater and rainfall measurements, unifying the data format and aligning the range, and obtaining a unified data model by unifying the data by timestamps and spatial coordinates, also includes: S101: Integrate eco-hydrological data from differentiated sources, collect raw data on surface water, groundwater and rainfall from differentiated sources, preliminarily screen the data, remove erroneous records, and obtain preliminary screened hydrological data; S102: Formatting the pre-screened hydrological data, including formatting the date and time uniformly, and unifying the geographic coordinates into the GPS standard format, so as to maintain the consistency of the data in subsequent processing, and obtain formatted hydrological data; S103: Based on the formatted hydrological data, data fusion is performed to merge the surface water, groundwater and rainfall data into a data set, and time and space matching is performed to obtain a unified data model by matching the time and location of each record.

[0010] Preferably, based on the unified data model, a network diagram of hydrological sites and river connections is constructed, the hydrological sites are used as nodes in the diagram, the river flow direction defines the edges between the nodes, weights are assigned to the network diagram to reflect the river flow rate, and the step of obtaining the hydrological network diagram also includes: S201: using the unified data model, identifying hydrological monitoring sites and river connections, taking the sites as nodes and the river connections as edges, initializing an unweighted network graph, and constructing a preliminary network graph; S202: measuring the flow velocity of the edges in the preliminary network graph, assigning numerical weights to the edges according to the measurement data, reflecting the flow velocity of the real-time river, optimizing the information content of the network graph, and obtaining a flow velocity weighted network graph; S203: Comprehensively refer to the node relationships and edge weight information in the velocity weight network diagram, adjust the network diagram to reflect the real-time flow conditions and characteristics of the water system, refine the structure of the network diagram, and obtain a hydrological network diagram.

[0011] Preferably, the step of performing network analysis on the hydrological network graph, identifying hydrological subsets in the graph, capturing differentiated connected components in the graph, assigning identifiers to the connected components, distinguishing differentiated hydrological management areas, and obtaining a subset identifier set also includes: S301: Based on the hydrological network graph, an empty list is created to record the connected components encountered, unmarked nodes in the graph are analyzed, nodes and components are marked as visited, and the connected components to which the nodes and components belong are recorded, and an analysis record list is generated; S302: using the analysis record list and a connected component marking algorithm, assigning identifiers to the connected components identified in the graph, and obtaining a connected component identifier mapping by combining the number of nodes in the connected component with the hash value of the node identifier; The connected component labeling algorithm uses the following formula: ; in, A unique identifier representing a connected component, is the index variable, is the hash value of the node identifier, is the degree of the node, is the node attribute weight, is the average degree of nodes in the graph, is the total number of connected components in the graph, is the number of nodes in the connected component; S303: According to the connected component identification mapping, traverse the nodes and connected edges in the graph, classify the nodes and edges according to the identification of the connected components to which they belong, and obtain a subset identification set by classifying parts of the graph and assigning them to the connected components corresponding to the nodes and components.

[0012] Preferably, the steps of analyzing the flow dynamics in the subsets using the subset identifier set, recording the water inflow and outflow of the subsets, evaluating the responses of multiple subsets to rainfall and dry seasons, and establishing the flow dynamics analysis record also include: S401: for the hydrological subset determined by the subset identifier set, set monitoring points to collect water flow data at the inlet and outlet, including river flow velocity measured by a flow meter and rainfall recorded by a rain gauge, establish a preliminary data framework for the water flow dynamics of the subset, and generate a water flow monitoring framework; S402: According to the water flow monitoring framework, the water inflow and outflow of the subset within the target time period are calculated, the water flow increase caused by the reference rainfall, the water flow reduction caused by evaporation and vegetation absorption are calculated, and the subset water flow change record is obtained by accumulating the water flow changes at the inlet and outlet; S403: Using the subset water volume change records, evaluate the response of the hydrological subset to water volume changes in differentiated seasons, identify the subset's regulation capacity under extreme weather conditions by comparing water volume changes between differentiated subsets, and obtain a water flow dynamic analysis record.

[0013] Preferably, the step of combining the water flow dynamic analysis records and the climate scenario data in the future time period, randomly simulating the impact of differentiated climate change on water flow dynamics, assigning probabilities to water flow changes under each climate scenario, and generating a climate impact probability model also includes:

[0014] S501: Based on the water flow dynamic analysis record, collect climate scenario prediction data in the future time period, including expected rainfall changes, temperature fluctuations and climate factors, preliminarily screen and classify the climate factors, and obtain a climate scenario data set through climate variables that affect water flow dynamics; S502: Based on the climate scenario data set, a Monte Carlo simulation method is used to set the simulation range and parameters, including setting the temperature and rainfall variation range under the differentiated climate scenarios, and simulating the water flow dynamics of the climate scenarios. The simulation uses a method of randomly selecting climate parameters to reflect the uncertainty of climate change in the future time period, and generates a simulated water flow dynamics set; The Monte Carlo simulation method uses the following formula: ; in, represents the runoff volume, is the rainfall, is the evaporation amount, is the basin area, is the rainfall-runoff conversion rate, is the terrain slope, is the vegetation coverage rate, is the land use type; S503: Analyze the frequency and amplitude of water flow changes under multiple climate scenarios using the simulated water flow dynamics set, calculate the probability of water flow changes under multiple scenarios by comparing the simulation results under differentiated scenarios, evaluate the water flow response under extreme rainfall and drought conditions, and obtain a climate impact probability model.

[0015] Preferably, the step of analyzing the climate impact probability model, calculating the sensitivity of multiple subsets to climate change, evaluating the impact of the subsets under the changing climate, marking the subsets with enhanced sensitivity, and constructing the sensitive area map also includes: S601: Based on the climate impact probability model, collect climate model prediction data for the future time period, including rainfall, temperature, and humidity, extract factors that affect water flow dynamics from the data, including the increase or decrease of rainfall and the rise or fall of temperature, and obtain basic data on climate change; S602: combining the climate change basic data with the existing water flow dynamic analysis records, simulating the impact of the changes on the water flow dynamics of the hydrological subset by setting differentiated climate change parameters, and generating a water flow dynamic impact simulation record; S603: Analyze the data in the water flow dynamic impact simulation record, calculate the probability of water flow change of the hydrological subset under differentiated climate scenarios, including the probability of increased water flow due to increased rainfall, assign probability values ​​to water flow changes under differentiated climate change scenarios, and obtain sensitive area mapping.

[0016] Preferably, based on the sensitive area mapping, designing adaptive management measures, referring to the needs and priorities of differentiated water resource users, simulating the water resource allocation benefits of multiple management measures, determining management strategies for sensitive subsets, and forming an adaptive management strategy set, the step also includes: S701: Analyze the sensitive area map to identify hydrological subsets that are sensitive to water flow changes under the predicted climate change scenario, including identifying subsets of water flow change probability, marking the subsets as sensitive areas, and generating a list of sensitive hydrological subsets; S702: Based on the sensitivity hydrological subset list, collect the demands of multiple water resource users, including agricultural, industrial and residential water use, evaluate the priorities of the demands in the differentiated hydrological subsets, and generate demand priority analysis results; S703: Based on the results of the demand priority analysis, an adaptive management strategy set is formed by adjusting reservoir management strategies, improving water quality monitoring and promoting water-saving technologies, and evaluating the effectiveness of multiple measures by simulating the impact of measures on sensitive subsets.

[0017] An eco-hydrological data processing system, the system comprising a data source standardization module, a hydrological relationship mapping module, a subset identification and classification module, a flow direction dynamic analysis module, a climate change response module, a hydrological sensitivity analysis module, a strategy formulation and evaluation module, and a management strategy implementation module; The data source standardization module collects eco-hydrological data from differentiated sources, performs time series matching and spatial coordinate alignment on the data, adjusts the data resolution and matches the common multiples, and generates a standard hydrological data set; The hydrological relationship mapping module uses a standard hydrological dataset to identify the physical connectivity between multiple hydrological sites, including river flow direction and groundwater flow, delineates basin boundaries based on topographic features, establishes spatial associations of hydrological elements, and obtains a hydrological element relationship map; The subset identification and classification module analyzes the interaction intensity between hydrological stations based on the hydrological element relationship diagram, uses the basin division principle to distinguish key and secondary hydrological cycle structures, marks the structures according to their functions and geographical locations, and constructs a hydrological function area map; The flow direction dynamic analysis module determines the flow direction and speed within multiple regions based on the hydrological functional area mapping, records the changes within the hydrological cycle, including seasonal flow fluctuations, and establishes a regional flow direction dynamic library; The climate change response module uses the regional flow dynamic library, combines historical climate data and climate forecasts for future time periods, analyzes the response patterns of hydrological cycles under differentiated climate scenarios, identifies the risks of extreme events faced by regions, and generates climate response analysis results; The hydrological sensitivity analysis module determines the sensitivity of multiple hydrological function regions to climate change in the future time period through the results of climate response analysis, identifies sensitive areas to changes in precipitation patterns and temperature increases, and obtains a list of sensitive areas; The strategy formulation and evaluation module formulates hydrological management strategies for climate change in the future period based on the list of sensitive areas, evaluates the potential benefits of differentiated strategies for improving water resource sustainability, designs customized management plans for sensitive areas, and constructs management strategy programs; The management strategy implementation module plans the execution steps according to the management strategy scheme, including resource allocation, monitoring point setting and time planning, determines the requirements of the implementation plan, and forms an adaptive management strategy set.

[0018] One or more technical solutions provided in this application have at least the following technical effects or advantages: By integrating ecohydrological data from different sources and constructing a hydrological network diagram, and by performing network analysis on the hydrological network diagram, the method of identifying hydrological subsets provides significant advantages for water resource management and prediction. When the climate scenario data set is used to stochastically simulate the impact of climate change on water flow dynamics, it is possible to assign probabilities to water flow changes under each climate scenario and generate a climate impact probability model, allowing managers to predict the future state of water resources, especially under extreme climate events. Through this refined prediction capability, it is possible to effectively identify and mark hydrological areas that are particularly sensitive to climate change, and design adaptive management measures for sensitive areas to achieve optimal allocation and protection of water resources. Through detailed water flow dynamic analysis, it supports a deep understanding of water resource distribution and flow changes under different climate scenarios, and enhances the adaptability and flexibility of water resource management strategies.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention proposes an overall flow chart of an ecohydrological data processing method; Figure 2 The present invention proposes a specific flow chart of S1 of an ecohydrological data processing method; Figure 3 The present invention proposes a specific flow chart of S2 of an ecohydrological data processing method; Figure 4 The present invention proposes a specific flow chart of S3 of an ecohydrological data processing method; Figure 5 The present invention proposes a specific flow chart of S4 of an ecohydrological data processing method; Figure 6 The present invention proposes a specific flow chart of S5 of an ecohydrological data processing method; Figure 7 The present invention proposes a specific flow chart of S6 of an ecohydrological data processing method; Figure 8 The present invention proposes a specific flow chart of S7 of an ecohydrological data processing method; Fig. 9 A module diagram of an ecohydrological data processing system is proposed for the present invention. DETAILED DESCRIPTION

[0021] This application provides an eco-hydrological data processing method and system to solve the limitations of traditional eco-hydrological data processing methods in processing complex hydrological systems and predicting the impact of extreme climate events on water resources. This is mainly because traditional methods rely on relatively simplified models and assumptions, and it is difficult to fully capture the dynamic complexity of hydrological systems and the integrated processing of multi-source heterogeneous data. In addition, the limitations of traditional technologies in spatial and temporal resolution, as well as the general assumptions used in simulating the impact of climate change, lead to inaccurate predictions of water resource dynamics, making it difficult to provide sufficient information support for water resource management and decision-making. The inadequacy of the method leads to inefficient water resource allocation and insufficient response capabilities to extreme events, affecting the sustainable management of water resources and the health of ecosystems.

[0022] Application Overview

[0023] The existing traditional eco-hydrological data processing methods have certain limitations in dealing with complex hydrological systems and predicting the impact of extreme climate events on water resources. This is mainly because traditional methods rely on relatively simplified models and assumptions, and it is difficult to fully capture the dynamic complexity of the hydrological system and the integrated processing of multi-source heterogeneous data. In addition, the limitations of traditional technologies in spatial and temporal resolution, as well as the general assumptions used in simulating the impact of climate change, lead to inaccurate predictions of water resource dynamics, making it difficult to provide sufficient information support for water resource management and decision-making. The inadequacy of the methods leads to inefficient water resource allocation and insufficient response capabilities to extreme events, affecting the sustainable management of water resources and the health of ecosystems.

[0024] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application includes the following: include Figure 1 As shown, the present application provides an eco-hydrological data processing method, wherein the method comprises: S1: Integrate ecohydrological data from different sources, including surface water, groundwater and rainfall measurements, unify the data format and align the range, and obtain a unified data model by unifying the timestamps and spatial coordinates; S2: Based on the unified data model, a network diagram of the connection between hydrological stations and rivers is constructed. The hydrological stations are used as nodes in the diagram, and the river flow direction defines the edges between the nodes. Weights are assigned to the network diagram to reflect the river flow rate, and the hydrological network diagram is obtained; S3: Perform network analysis on the hydrological network graph, identify hydrological subsets in the graph, capture differentiated connected components in the graph, assign identifiers to the connected components, distinguish differentiated hydrological management areas, and obtain a subset identifier set; S4: Using the subset identifier set, analyze the flow dynamics within the subset, record the water inflow and outflow of the subset, evaluate the response of multiple subsets to rainfall and dry seasons, and establish a record of water flow dynamic analysis; S5: Combine the water flow dynamics analysis records and climate scenario data in the future time period, and randomly simulate the impact of differentiated climate change on water flow dynamics, assign probabilities to water flow changes under each climate scenario, and generate a climate impact probability model; S6: Analyze the climate impact probability model, calculate the sensitivity of multiple subsets to climate change, evaluate the impact of subsets under the changing climate, mark subsets with increased sensitivity, and construct sensitive area maps; S7: Based on the sensitive area mapping, design adaptive management measures, refer to the needs and priorities of differentiated water resource users, simulate the water resource allocation benefits of various management measures, determine the management strategies for sensitive subsets, and form an adaptive management strategy set.

[0025] The unified data model includes a unified time format, spatial coordinate system and data type. The hydrological network diagram includes the geographical location of the nodes, the flow direction relationship between the nodes and the edge weight of the flow velocity. The subset identification set includes the spatial distribution of differentiated connected components, the identification of multiple components and the clustering of hydrological sites within the components. The water flow dynamic analysis record includes the flow change record of the hydrological subset, the water flow exchange volume between subsets and the response pattern to climate events. The climate impact probability model includes the probability value assigned to the water flow change under the climate scenario, the predicted range of water flow dynamic changes and the impact assessment under differentiated scenarios. The sensitive area mapping includes the identified sensitive hydrological subsets, the spatial distribution of the subsets and the sensitivity level. The adaptive management strategy set includes water resource allocation plans for sensitive areas, priority ranking of management measures and expected benefit analysis.

[0026] Specifically, it includes Figure 2 As shown, the steps of integrating ecohydrological data from different sources, including surface water, groundwater and rainfall measurements, unifying the data format and aligning the range, and obtaining a unified data model by unifying the data by timestamps and spatial coordinates also include: S101: Integrate eco-hydrological data from differentiated sources, collect raw data on surface water, groundwater and rainfall from differentiated sources, preliminarily screen the data, remove erroneous records, and obtain the execution process of preliminary screening hydrological data as follows; The S101 sub-step adopts a data validation algorithm, using the Python programming language combined with the Pandas library to perform a preliminary screening of the raw data of surface water, groundwater and rainfall collected from differentiated sources, and applies a custom function through the DataFrame.apply method to verify the rationality of the values ​​of each record. Regular expression matching is used to check the accuracy of the date and time format, and a format check is performed on the geographic coordinates to ensure that they follow the preset GPS standard format. The DataFrame.dropna method is used to eliminate rows containing erroneous records or missing data to generate preliminary screening hydrological data.

[0027] S102: Formatting the pre-screened hydrological data, including formatting the date and time uniformly, and unifying the geographic coordinates into the GPS standard format, so as to maintain the consistency of the data in subsequent processing. The execution process of obtaining the formatted hydrological data is as follows;

[0028] Sub-step S102 adopts a formatting processing method, using the Python programming language combined with the Pandas library to process the preliminary screened hydrological data, converting the date and time columns into a string format through the DataFrame.astype method, and then using the pd.to_datetime function to convert the string into a unified datetime format, applying a custom conversion function to the geographic coordinate column, and converting coordinates in various formats into GPS standard longitude and latitude representations to ensure the consistency of the data in subsequent processing and generate formatted hydrological data.

[0029] S103: Based on the formatted hydrological data, data fusion is performed to merge the surface water, groundwater and rainfall data into a data set, and time and space matching is performed. The time and location of each record are matched to obtain a unified data model. The execution process is as follows; Sub-step S103 uses a data fusion algorithm and uses the Python programming language combined with the Pandas library and the Numpy library to process the formatted hydrological data. The DataFrame.merge method is used to match the surface water, groundwater, and rainfall data in time and space. The logical function of Numpy is used to ensure that the time and location of each record are accurately matched. The groupby and aggregate functions are used to aggregate the matched data, using timestamps and locations as aggregation keys to generate a unified data model.

[0030] Specifically, it includes Figure 3 As shown, based on the unified data model, a network diagram of hydrological stations and river connections is constructed, the hydrological stations are used as nodes in the diagram, the river flow direction defines the edges between the nodes, and weights are assigned to the network diagram to reflect the river flow rate. The steps of obtaining the hydrological network diagram also include: S201: Using a unified data model, identify hydrological monitoring sites and river connections, use sites as nodes and river connections as edges, initialize an unweighted network graph, and construct a preliminary network graph. The execution process is as follows; The S201 sub-step is based on hydrological monitoring data and adopts graph theory algorithms. It uses Python's NetworkX library to initialize an unweighted network graph, number the sites, use a unique identifier to represent each site node, use river connection data to establish edge connections, call the add_node and add_edge functions in NetworkX to add nodes and edges, set the node attributes to the geographic location information of the site, and leave the edge attributes blank to be filled in with subsequent flow velocity data to generate a preliminary network graph.

[0031] S202: Measure the flow velocity of the edges in the preliminary network diagram, assign numerical weights to the edges according to the measured data, reflect the flow velocity of the real-time river, optimize the information content of the network diagram, and obtain the flow velocity weighted network diagram. The execution process is as follows; Sub-step S202 adopts flow measurement technology and uses a Doppler flow meter to collect flow data of the edge, i.e. the river section. Each edge is traversed to collect real-time flow data, and a Python script is used to read the flow data file and format it into edge weight values. The set_edge_attributes function of NetworkX is used to assign a numerical weight based on the flow measurement to each edge to generate a flow weight network diagram.

[0032] S203: Comprehensively refer to the node relationship and edge weight information in the velocity weight network diagram, adjust the network diagram to reflect the real-time flow situation and characteristics of the water system, refine the structure of the network diagram, and obtain the execution process of the hydrological network diagram as follows; Sub-step S203 uses a dynamic graph adjustment algorithm, based on a velocity weighted network graph, using Python's NetworkX library and velocity weight data, to evaluate velocity differences between nodes, refine the structure of the network graph, call adjust_node and adjust_edge functions, adjust node positions and edge thicknesses according to velocity weights, reflect the real-time flow conditions and characteristics of the water system, and generate a hydrological network graph.

[0033] Specifically, it includes Figure 4 As shown, the steps of performing network analysis on the hydrological network graph, identifying hydrological subsets in the graph, capturing differentiated connected components in the graph, assigning identifiers to the connected components, distinguishing differentiated hydrological management areas, and obtaining a subset identifier set also include: S301: Based on the hydrological network graph, an empty list is created to record the connected components encountered, the unmarked nodes in the graph are analyzed, the nodes and components are marked as visited, and the connected components to which the nodes and components belong are recorded. The execution process of generating the analysis record list is as follows; Sub-step S301 is based on the hydrological network graph and adopts the depth-first search (DFS) algorithm. The Python programming language is combined with the NetworkX library to create an empty list to record the connected components encountered. A DFS traversal is performed on the unmarked nodes in the graph. During the process, the traversed nodes and their connected components are marked as visited, and the connected component information to which the nodes and components belong is recorded. A dictionary is used to store the mapping from nodes to connected components. Each time a new node is successfully visited, the connected component information to which the node belongs is checked and updated to ensure that each node is correctly classified into its connected component and generate a list of analysis records.

[0034] S302: using the analysis record list and the connected component marking algorithm, assigning identifiers to the connected components identified in the graph, and combining the number of nodes in the connected component with the hash value of the node identifier to obtain the connected component identifier mapping, the execution process is as follows; In sub-step S302, the record list is analyzed, a hash mapping method is used, and the Python programming language is combined with a hash function to adopt a connected component marking algorithm to assign identifiers to the connected components identified in the graph. The unique identifier of each connected component is calculated by combining the number of nodes in the connected component with the hash value of each node identifier. The node identifier is hashed using Python's hash function, and the hash value is adjusted using the number of nodes in the connected component as a weight to ensure the uniqueness and consistency of the connected component identifier, thereby generating a connected component identifier mapping.

[0035] The connected component labeling algorithm uses the following formula: ; in, A unique identifier representing a connected component, is the index variable, is the hash value of the node identifier, is the degree of the node, is the node attribute weight, is the average degree of nodes in the graph, is the total number of connected components in the graph, is the number of nodes in the connected component.

[0036] The execution process is as follows; The first step involves calculating the hash value of each node and degree , then determine the attribute weight of each node , based on a specific attribute of the node or its role in the graph. Compare the degree of each node to the global average degree The ratio of is taken into account to adjust the contribution of each node to the total hash value, and finally multiply the result by the total number of nodes N and the number of connected components of the graph. , get the unique identifier of the connected component. Weight coefficient The determination of the weight coefficient can be done by analyzing the properties of the nodes such as their importance, node type or the functional role of the node in the network to score or classify them, ensuring that the specific method and steps of the weight coefficient value are comprehensively considered based on the node characteristics and the structural characteristics of the graph.

[0037] According to the formula , calculate the unique identifier of each connected component and obtain the connected component identification mapping.

[0038] S303: According to the connected component identification mapping, the nodes and connected edges in the graph are traversed, and the nodes and edges are classified according to the identifications of the connected components to which they belong. By classifying the parts of the graph and assigning them to the connected components corresponding to the nodes and components, the execution flow of obtaining the subset identification set is as follows; In sub-step S303, according to the connected component identification mapping, a graph traversal algorithm is adopted, and the Python programming language is combined with the NetworkX library to traverse the nodes and connected edges in the graph, and each node and edge is classified according to the identification of the connected component to which it belongs. The DFS or BFS algorithm is used to ensure that all elements in the graph are thoroughly traversed. By checking the connected component identification of each element, it is assigned to the corresponding subset. This operation ensures that each part of the graph is correctly classified and classified into the corresponding connected component, and a subset identification set is generated.

[0039] Specifically, it includes Figure 5 As shown, using the subset identification set, analyzing the water flow dynamics in the subset, recording the water inflow and outflow of the subset, evaluating the response of multiple subsets to rainfall and dry seasons, and establishing the water flow dynamic analysis record, also includes: S401: For the hydrological subset determined by the subset identifier set, set monitoring points to collect water flow data at the inlet and outlet, including river flow velocity measured by a current meter and rainfall recorded by a rain gauge, and establish a preliminary data framework for the water flow dynamics of the subset. The execution process of generating a water flow monitoring framework is as follows; In sub-step S401, for the hydrological subset determined by the subset identifier set, monitoring points are set to collect water flow data at the inlet and outlet, including using a flow meter to measure river flow velocity and a rain gauge to record rainfall. Data integration technology is used to integrate the collected flow velocity and rainfall data using the Python programming language combined with the Pandas library. The monitoring point data is stored in the DataFrame structure, a unique identifier is assigned to each monitoring point and its geographic location is recorded. The flow velocity data and rainfall data are aligned according to the timestamp to ensure data consistency and integrity, and a water flow monitoring framework is generated.

[0040] S402: According to the water flow monitoring framework, the water inflow and outflow of the subset in the target time period are calculated, the water flow increase caused by the reference rainfall, the water flow reduction caused by evaporation and vegetation absorption are calculated, and the execution process of the subset water volume change record is obtained by accumulating the water flow changes at the inlet and outlet as follows; In sub-step S402, based on the water flow monitoring framework, the water inflow and outflow of the subset within the target time period are calculated. The water balance algorithm is used, and the Python programming language is combined with the Numpy library to calculate the increase in water flow caused by rainfall and the decrease in water flow caused by evaporation and vegetation absorption. The changes in water flow at the inlet and outlet are calculated cumulatively, and the evaporation rate and vegetation absorption rate are considered as adjustment coefficients in the calculation formula to ensure that the calculation of water volume changes reflects the actual situation and generate a record of the subset water volume changes.

[0041] S403: Using the subset water volume change records, evaluate the response of the hydrological subset to the water volume change in the differentiated seasons, and identify the adjustment capacity of the subset under extreme weather conditions by comparing the water volume changes between the differentiated subsets. The execution process of obtaining the water flow dynamic analysis record is as follows; Sub-step S403 uses the subset water volume change records to evaluate the response of the hydrological subset to water volume changes in differentiated seasons. A seasonal analysis method is used to visualize the water volume changes between differentiated subsets using the Python programming language combined with the Matplotlib library. By drawing a water volume change curve, the adjustment capacity of the subset under extreme weather conditions is identified, and the water volume change pattern of each subset in different seasons is analyzed. The seasonal change factors and the impact of extreme weather events are considered separately to ensure the accuracy of the analysis and generate a water flow dynamic analysis record.

[0042] Specifically, it includes Figure 6 As shown, combining the water flow dynamic analysis records and the climate scenario data in the future time period, by stochastically simulating the impact of differentiated climate change on water flow dynamics, assigning probabilities to water flow changes under each climate scenario, and generating the climate impact probability model, the steps also include: S501: Based on the water flow dynamic analysis records, collect climate scenario prediction data for the future time period, including expected rainfall changes, temperature fluctuations and climate factors, preliminarily screen and classify the climate factors, and obtain the climate scenario data set through the climate variables that affect the water flow dynamics. The execution process is as follows; Based on the water flow dynamics analysis records, sub-step S501 collects climate scenario forecast data for future time periods, including expected rainfall changes, temperature fluctuations, and climate factors. A data screening algorithm is used to preliminarily screen and classify climate factors using the Python programming language combined with the Pandas library. The query method of DataFrame is used to screen climate variables related to the impact of water flow dynamics. The conditional screening function is used to select climate factors that have a significant impact on hydrology, such as rainfall changes and temperature fluctuations, to ensure that the data set only includes variables that have a substantial impact on the research and generate a climate scenario data set.

[0043] S502: Based on the climate scenario data set, the Monte Carlo simulation method is used to set the simulation range and parameters, including setting the temperature and rainfall change range under the differentiated climate scenarios, and simulating the water flow dynamics of the climate scenarios. The simulation adopts the method of randomly selecting climate parameters to reflect the uncertainty of climate change in the future time period. The execution process of generating the simulated water flow dynamics set is as follows; Sub-step S502 uses the Monte Carlo simulation method based on the climate scenario data set to set the simulation range and parameters, including setting the temperature and rainfall variation range under differentiated climate scenarios. The Monte Carlo simulation method is used to simulate the water flow dynamics of the climate scenarios using the Python programming language combined with the Numpy library. The uncertainty of climate change in the future time period is reflected by randomly generating temperature and rainfall parameters that meet the predetermined range. The simulation is repeated, and different climate parameters are randomly selected each time to cover a wide range of situations and generate a set of simulated water flow dynamics.

[0044] The Monte Carlo simulation method uses the following formula: ; in, represents the runoff volume, is the rainfall, is the evaporation amount, is the basin area, is the rainfall-runoff conversion rate, is the terrain slope, is the vegetation coverage rate, For land use type.

[0045] The execution process is as follows; The implementation process includes using the Monte Carlo method and Python programming language combined with the Numpy library. First, the temperature and rainfall variation range under the climate scenario is set, and then the temperature and rainfall parameters that meet the range are randomly generated. For the newly introduced parameters in the improved formula, the following method steps are used to confirm their coefficient values: Evaporation The terrain slope is estimated by using meteorological data and evaporation models. Calculated based on terrain data, vegetation coverage Through remote sensing data, land use type Determined according to the classification of land use maps, the weight coefficient of each parameter can be determined through regression analysis of historical hydrological data to reflect the uncertainty of climate change in future time periods and generate a set of simulated water flow dynamics.

[0046] According to the formula , calculate the runoff and generate a flow dynamics set.

[0047] S503: Using the simulated flow dynamics set, the frequency and amplitude of flow changes under multiple climate scenarios are analyzed. By comparing the simulation results under different scenarios, the probability of flow changes under multiple scenarios is calculated, and the flow response under extreme rainfall and drought conditions is evaluated. The execution process of the climate impact probability model is as follows;

[0048] Sub-step S503 uses a set of simulated water flow dynamics to analyze the frequency and amplitude of water flow changes under multiple climate scenarios. It uses probabilistic statistical methods and the Python programming language combined with the SciPy library to compare the simulation results under differentiated scenarios. It calculates the statistical probability of water flow changes under each scenario and evaluates the water flow response under extreme rainfall and drought conditions. It uses probability density functions and cumulative distribution functions to ensure that the probability of water flow changes under different climate scenarios can be accurately calculated to generate a climate impact probability model.

[0049] Specifically, it includes Figure 7 As shown, the steps of analyzing the climate impact probability model, calculating the sensitivity of multiple subsets to climate change, evaluating the impact of subsets under the changing climate, marking subsets with enhanced sensitivity, and constructing sensitive area maps also include: S601: Based on the climate impact probability model, collect climate model prediction data for the future time period, including rainfall, temperature, and humidity, extract factors that affect water flow dynamics from the data, including the increase or decrease of rainfall and the rise or fall of temperature, and obtain the basic data of climate change. The execution process is as follows; Based on the climate impact probability model, sub-step S601 collects climate model prediction data for future time periods, including rainfall, temperature, and humidity. It uses data preprocessing technology and the Python programming language combined with the Pandas library to extract factors that affect water flow dynamics from the data. Through the filtering function of DataFrame, the increase or decrease in rainfall and the rise or fall in temperature are filtered out according to preset conditions to ensure that the data set includes climate factors that have a significant impact on the hydrological dynamics of the study area, and to generate basic climate change data.

[0050] S602: Combine the basic climate change data with the existing water flow dynamic analysis records, simulate the impact of the changes on the water flow dynamics of the hydrological subset by setting differentiated climate change parameters, and generate the water flow dynamic impact simulation record. The execution process is as follows; Sub-step S602 combines the basic climate change data with the existing water flow dynamics analysis records. By setting differentiated climate change parameters, a dynamic system simulation method is adopted, and the Python programming language is combined with the NumPy library to simulate the impact of changes on the water flow dynamics of the hydrological subset. The temperature and rainfall change parameters under differentiated climate scenarios are simulated through numerical integration methods such as the Euler method or the Runge-Kutta method. In this process, the specific impact of each parameter on the hydrological subset is simulated to ensure that a wide range of climate scenarios can be covered and a simulation record of the impact of water flow dynamics can be generated.

[0051] S603: Analyze the data in the simulation record of water flow dynamic impact, calculate the probability of water flow change of the hydrological subset under the differentiated climate scenario, including the probability of increased water flow caused by increased rainfall, assign probability values ​​to water flow changes under differentiated climate change scenarios, and obtain the execution process of sensitive area mapping as follows; Sub-step S603 analyzes the data in the simulation records of the impact of water flow dynamics, calculates the probability of changes in water flow in the hydrological subset under differentiated climate scenarios, and adopts statistical analysis methods. The Python programming language is combined with the SciPy library. Through probability distribution functions and statistical models, including Bayesian models and frequency distribution analysis, the probability of increased water flow caused by increased rainfall is evaluated, and the changes in the simulated data set are converted into probability values ​​to ensure that the uncertainty of water flow changes under differentiated climate change scenarios can be quantified and sensitive area mapping can be generated.

[0052] Specifically, it includes Figure 8 As shown, based on the sensitive area mapping, adaptive management measures are designed, the water resource allocation benefits of various management measures are simulated with reference to the needs and priorities of differentiated water resource users, and management strategies for sensitive subsets are determined to form an adaptive management strategy set, which also includes: S701: Analyze the sensitive area mapping, identify the hydrological subsets that are sensitive to water flow changes under the predicted climate change scenario, including identifying the probability subsets of water flow changes, marking the subsets as sensitive areas, and generating the sensitive hydrological subset list. The execution process is as follows; Sub-step S701 analyzes the sensitive area mapping and identifies the hydrological subsets that are sensitive to water flow changes under the predicted climate change scenario. The probability threshold judgment method is adopted, and the Python programming language is combined with the SciPy library. By analyzing the probability subsets of water flow changes, the sensitivity judgment threshold is set, and the subsets exceeding this threshold are marked as sensitive areas. The sensitivity threshold is determined based on historical data analysis and expert opinions to ensure that the hydrological subsets that are most sensitive to future climate change can be accurately identified and a list of sensitive hydrological subsets is generated.

[0053] S702: Based on the list of sensitive hydrological subsets, collect the demands of multiple water resource users, including agricultural, industrial and residential water use, evaluate the priority of the demands in the differentiated hydrological subsets, and generate the demand priority analysis results. The execution process is as follows; Based on the list of sensitive hydrological subsets, sub-step S702 collects the demands of multiple water resource users, including agricultural, industrial and residential water use, and adopts a demand analysis method. The Python programming language is combined with the Pandas library to evaluate the priority of demands in differentiated hydrological subsets through data screening and sorting algorithms. The demand data is compiled based on the water consumption requests submitted by water resource users and historical water consumption data. Then, according to the characteristics of the sensitive hydrological subsets and the predicted changes in water resources, each demand is prioritized to generate a demand priority analysis result.

[0054] S703: Based on the results of the demand priority analysis, the execution process of the adaptive management strategy set is formed by adjusting the reservoir management strategy, improving water quality monitoring and promoting water-saving technologies, and evaluating the effectiveness of multiple measures by simulating the impact of measures on sensitive subsets. Based on the results of demand priority analysis, sub-step S703 adjusts reservoir management strategies, improves water quality monitoring and promotes water-saving technologies. It also simulates the impact of measures on sensitive subsets using a strategy simulation evaluation method, using the Python programming language combined with the NumPy library to simulate the potential impact of measures on sensitive subsets. The evaluation method includes optimization simulation of reservoir scheduling plans, predictive models for water quality improvement effects, and efficiency analysis of water-saving technologies, to ensure that the effectiveness of the proposed adaptive management strategies can be evaluated from multiple aspects to form an adaptive management strategy set.

[0055] Specifically, it includes Fig. 9 As shown, an eco-hydrological data processing system includes a data source standardization module, a hydrological relationship mapping module, a subset identification and classification module, a flow direction dynamic analysis module, a climate change response module, a hydrological sensitivity analysis module, a strategy formulation and evaluation module, and a management strategy implementation module; The data source standardization module collects eco-hydrological data from different sources, performs time series matching and spatial coordinate alignment on the data, adjusts the data resolution and matches the common multiples, and generates a standard hydrological data set; The hydrological relationship mapping module uses standard hydrological datasets to identify the physical connectivity between multiple hydrological sites, including river flow direction and groundwater flow, delineates basin boundaries based on topographic features, establishes spatial associations of hydrological elements, and obtains a hydrological element relationship map; The subset identification and classification module analyzes the interaction intensity between hydrological stations based on the hydrological element relationship diagram, uses the basin division principle to distinguish key and secondary hydrological cycle structures, labels the structures according to their functions and geographical locations, and constructs a hydrological function area map; The flow direction dynamic analysis module determines the flow direction and speed within multiple regions based on the hydrological functional area mapping, records the changes within the hydrological cycle, including seasonal flow fluctuations, and establishes a regional flow direction dynamic library; The climate change response module uses the regional flow dynamic library, combines historical climate data and climate forecasts for future time periods, analyzes the response patterns of the hydrological cycle under differentiated climate scenarios, identifies the risk of extreme events in the region, and generates climate response analysis results; The hydrological sensitivity analysis module uses the results of climate response analysis to determine the sensitivity of multiple hydrological function regions to climate change in the future time period, identify areas sensitive to changes in precipitation patterns and temperature increases, and obtain a list of sensitive areas; The strategy formulation and evaluation module formulates hydrological management strategies for climate change in the future period based on the list of sensitive areas, evaluates the potential benefits of differentiated strategies for improving water resource sustainability, designs customized management plans for sensitive areas, and constructs management strategy programs; The management strategy implementation module plans the execution steps according to the management strategy plan, including resource allocation, monitoring point setting and time planning, determines the requirements of the implementation plan, and forms an adaptive management strategy set.

[0056] Although the present application has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. An ecohydrological data processing method, characterized in that: The method comprises: Integrate ecohydrological data from different sources, including surface water, groundwater and rainfall measurements, unify the data format and align the data range, and obtain a unified data model by unifying the data by timestamps and spatial coordinates; Based on the unified data model, a network diagram of hydrological stations and river connections is constructed, with the hydrological stations serving as nodes in the diagram, and the river flow directions defining the edges between the nodes, and weights are assigned to the network diagram to reflect the river flow rate, thereby obtaining a hydrological network diagram; Performing network analysis on the hydrological network graph, identifying hydrological subsets in the graph, capturing differentiated connected components in the graph, assigning identifiers to the connected components, distinguishing differentiated hydrological management areas, and obtaining a subset identifier set; Analyze the flow dynamics within the subsets using the subset identifier set, record the water inflow and outflow of the subsets, evaluate the response of multiple subsets to rainfall and dry seasons, and establish a flow dynamics analysis record; Combining the water flow dynamics analysis records with climate scenario data in the future time period, by stochastically simulating the impact of differentiated climate change on water flow dynamics, assigning probabilities to water flow changes under each climate scenario, and generating a climate impact probability model; Analyze the climate impact probability model, calculate the sensitivity of multiple subsets to climate change, evaluate the impact of the subsets under the changing climate, mark the subsets with increased sensitivity, and construct a sensitive area map; Based on the sensitive area mapping, adaptive management measures are designed, and the water resource allocation benefits of various management measures are simulated with reference to the needs and priorities of differentiated water resource users, and management strategies for sensitive subsets are determined to form an adaptive management strategy set.

2. The ecohydrological data processing method according to claim 1, characterized in that: The unified data model includes a unified time format, spatial coordinate system and data type; the hydrological network diagram includes the geographical location of the nodes, the flow direction relationship between the nodes and the edge weight of the flow rate; the subset identification set includes the spatial distribution of differentiated connected components, the identification of multiple components and the clustering of hydrological sites within the components; the water flow dynamic analysis record includes the flow change record of the hydrological subset, the water flow exchange volume between subsets and the response pattern to climate events; the climate impact probability model includes the probability value assigned to the water flow change under the climate scenario, the predicted water flow dynamic change range and the impact assessment under the differentiated scenario; the sensitive area mapping includes the identified sensitive hydrological subsets, the spatial distribution of the subsets and the sensitivity level; the adaptive management strategy set includes the water resource allocation plan for sensitive areas, the priority ranking of management measures and the expected benefit analysis.

3. The ecohydrological data processing method according to claim 1, characterized in that: The steps of integrating ecohydrological data from different sources, including surface water, groundwater and rainfall measurements, unifying the data format and aligning the range, and obtaining a unified data model by unifying the data by timestamps and spatial coordinates, also include: Integrate ecohydrological data from differentiated sources, collect raw data on surface water, groundwater and rainfall from differentiated sources, preliminarily screen the data, remove erroneous records, and obtain preliminary screened hydrological data; Formatting the initially screened hydrological data, including formatting the date and time uniformly, and unifying the geographic coordinates into the GPS standard format, so as to maintain the consistency of the data in subsequent processing and obtain formatted hydrological data; Based on the formatted hydrological data, data fusion is performed to merge the surface water, groundwater and rainfall data into a data set, and time and space matching is performed to obtain a unified data model through matching the time and location of each record.

4. The ecohydrological data processing method according to claim 1, characterized in that: Based on the unified data model, a network diagram of hydrological sites and river connections is constructed, with the hydrological sites serving as nodes in the diagram, and the river flow directions defining the edges between the nodes, and weights are assigned to the network diagram to reflect the river flow rate, thereby obtaining a hydrological network diagram, further comprising: Using the unified data model, hydrological monitoring sites and river connections are identified, sites are used as nodes, river connections are used as edges, an unweighted network graph is initialized, and a preliminary network graph is constructed; Measuring the flow velocity of the edges in the preliminary network diagram, assigning numerical weights to the edges according to the measured data, reflecting the flow velocity of the real-time river, optimizing the information content of the network diagram, and obtaining a flow velocity weighted network diagram; By comprehensively referring to the node relationships and edge weight information in the velocity weight network diagram, the network diagram is adjusted to reflect the real-time flow conditions and characteristics of the water system, and the structure of the network diagram is refined to obtain a hydrological network diagram.

5. The ecohydrological data processing method according to claim 1, characterized in that: The steps of performing network analysis on the hydrological network graph, identifying hydrological subsets in the graph, capturing differentiated connected components in the graph, assigning identifiers to the connected components, distinguishing differentiated hydrological management areas, and obtaining subset identifier sets also include: Based on the hydrological network graph, an empty list is created to record the connected components encountered, unmarked nodes in the graph are analyzed, nodes and components are marked as visited, and the connected components to which the nodes and components belong are recorded, and an analysis record list is generated; Using the analysis record list and a connected component labeling algorithm, the connected components identified in the graph are assigned identifiers, and a connected component identifier mapping is obtained by combining the number of nodes in the connected component with the hash value of the node identifier; The connected component labeling algorithm uses the following formula ； in, A unique identifier representing a connected component, is the index variable, is the hash value of the node identifier, is the degree of the node, is the node attribute weight, is the average degree of nodes in the graph, is the total number of connected components in the graph, is the number of nodes in the connected component; According to the connected component identification mapping, the nodes and connected edges in the graph are traversed, and the nodes and edges are classified according to the identifications of the connected components to which they belong. By classifying parts of the graph and assigning them to the connected components corresponding to the nodes and components, a subset identification set is obtained.

6. The ecohydrological data processing method according to claim 1, characterized in that: The steps of analyzing the flow dynamics in the subsets using the subset identifier set, recording the water inflow and outflow of the subsets, evaluating the response of multiple subsets to rainfall and dry seasons, and establishing the flow dynamics analysis record also include: For the hydrological subset determined by the subset identifier set, monitoring points are set to collect water flow data at the inlet and outlet, including river flow velocity measured by a current meter and rainfall recorded by a rain gauge, to establish a preliminary data framework for the water flow dynamics of the subset and generate a water flow monitoring framework; According to the water flow monitoring framework, the water inflow and outflow of the subset within the target time period are calculated, the water flow increase caused by the reference rainfall, the water flow reduction caused by evaporation and vegetation absorption are calculated, and the subset water volume change record is obtained by accumulating the water flow changes at the inlet and outlet; The subset water volume change records are used to evaluate the response of the hydrological subset to water volume changes in differentiated seasons. By comparing the water volume changes between differentiated subsets, the regulation capacity of the subset under extreme weather conditions is identified, and the water flow dynamic analysis records are obtained.

7. The ecohydrological data processing method according to claim 1, characterized in that: Combining the water flow dynamic analysis records and climate scenario data in the future time period, by randomly simulating the impact of differentiated climate change on water flow dynamics, assigning probabilities to water flow changes under each climate scenario, and generating a climate impact probability model, the step also includes: Based on the water flow dynamics analysis records, collect climate scenario forecast data for future time periods, including expected rainfall changes, temperature fluctuations and climate factors, preliminarily screen and classify climate factors, and obtain a climate scenario data set through climate variables that affect water flow dynamics; Based on the climate scenario data set, a Monte Carlo simulation method is used to set the simulation range and parameters, including setting the temperature and rainfall variation range under differentiated climate scenarios, and simulating the water flow dynamics of the climate scenarios. The simulation uses a method of randomly selecting climate parameters to reflect the uncertainty of climate change in the future time period and generate a simulated water flow dynamics set; The Monte Carlo simulation method uses the following formula ； in, represents the runoff volume, is the rainfall, is the evaporation amount, is the basin area, is the rainfall-runoff conversion rate, is the terrain slope, is the vegetation coverage rate, is the land use type; The simulated water flow dynamics set is used to analyze the frequency and amplitude of water flow changes under multiple climate scenarios. By comparing the simulation results under differentiated scenarios, the probability of water flow changes under multiple scenarios is calculated, the water flow response under extreme rainfall and drought conditions is evaluated, and a climate impact probability model is obtained.

8. The ecohydrological data processing method according to claim 1, characterized in that: The steps of analyzing the climate impact probability model, calculating the sensitivity of multiple subsets to climate change, evaluating the impact of the subsets under the changing climate, marking the subsets with enhanced sensitivity, and constructing a sensitive area map also include: Based on the climate impact probability model, collect climate model prediction data for future time periods, including rainfall, temperature, and humidity, extract factors that affect water flow dynamics from the data, including the increase or decrease of rainfall and the rise or fall of temperature, and obtain basic data on climate change; Combining the basic climate change data with existing water flow dynamic analysis records, simulating the impact of the changes on the water flow dynamics of the hydrological subset by setting differentiated climate change parameters, and generating a water flow dynamic impact simulation record; The data in the simulation record of the water flow dynamic impact is analyzed to calculate the probability of water flow change in the hydrological subset under differentiated climate scenarios, including the probability of increased water flow due to increased rainfall, assign probability values ​​to water flow changes under differentiated climate change scenarios, and obtain sensitive area mapping.

9. The ecohydrological data processing method according to claim 1, characterized in that: Based on the sensitive area mapping, designing adaptive management measures, referring to the needs and priorities of differentiated water resource users, simulating the water resource allocation benefits of multiple management measures, determining management strategies for sensitive subsets, and forming an adaptive management strategy set, further comprising: Analyzing the sensitive area map to identify hydrological subsets that are sensitive to water flow changes under predicted climate change scenarios, including identifying subsets of water flow change probabilities, marking the subsets as sensitive areas, and generating a list of sensitive hydrological subsets; Based on the list of sensitive hydrological subsets, collect the demands of multiple water resource users, including agricultural, industrial and residential water use, evaluate the priorities of the demands in the differentiated hydrological subsets, and generate demand priority analysis results; Based on the results of the demand priority analysis, an adaptive management strategy set is formed by adjusting reservoir management strategies, improving water quality monitoring and promoting water-saving technologies, and evaluating the effectiveness of multiple measures by simulating the impact of measures on sensitive subsets.

10. An ecohydrological data processing system, characterized in that: According to an eco-hydrological data processing method according to any one of claims 1 to 9, the system comprises a data source standardization module, a hydrological relationship mapping module, a subset identification and classification module, a flow direction dynamic analysis module, a climate change response module, a hydrological sensitivity analysis module, a strategy formulation and evaluation module, and a management strategy implementation module; The data source standardization module collects eco-hydrological data from differentiated sources, performs time series matching and spatial coordinate alignment on the data, adjusts the data resolution and matches the common multiples, and generates a standard hydrological data set; The hydrological relationship mapping module uses a standard hydrological dataset to identify the physical connectivity between multiple hydrological sites, including river flow direction and groundwater flow, delineates basin boundaries based on topographic features, establishes spatial associations of hydrological elements, and obtains a hydrological element relationship map; The subset identification and classification module analyzes the interaction intensity between hydrological stations based on the hydrological element relationship diagram, uses the basin division principle to distinguish key and secondary hydrological cycle structures, marks the structures according to their functions and geographical locations, and constructs a hydrological function area map; The flow direction dynamic analysis module determines the flow direction and speed within multiple regions based on the hydrological functional area mapping, records the changes within the hydrological cycle, including seasonal flow fluctuations, and establishes a regional flow direction dynamic library; The climate change response module uses the regional flow dynamic library, combines historical climate data and climate forecasts for future time periods, analyzes the response patterns of hydrological cycles under differentiated climate scenarios, identifies the risks of extreme events faced by regions, and generates climate response analysis results; The hydrological sensitivity analysis module determines the sensitivity of multiple hydrological function regions to climate change in the future time period through the results of climate response analysis, identifies sensitive areas to changes in precipitation patterns and temperature increases, and obtains a list of sensitive areas; The strategy formulation and evaluation module formulates hydrological management strategies for climate change in the future period based on the list of sensitive areas, evaluates the potential benefits of differentiated strategies for improving water resource sustainability, designs customized management plans for sensitive areas, and constructs management strategy programs; The management strategy implementation module plans the execution steps according to the management strategy scheme, including resource allocation, monitoring point setting and time planning, determines the requirements of the implementation plan, and forms an adaptive management strategy set.

Images