Ecological irrigation area hydrological connectivity assessment method, device, equipment and medium

Abstract

The invention relates to an ecological irrigation area hydrological connectivity assessment method, device and equipment and a medium. According to the method, a monitoring data set is generated by collecting time sequence data of parameters such as flow; analyzing a dynamic water quantity exchange relation between water bodies based on the time sequence data, and generating a water quantity exchange relation matrix; constructing an irrigation area hydrological network topology model by using the matrix; key connection nodes are identified through a node centrality calculation and threshold screening mechanism, and a hierarchical node importance list is generated; performing connectivity quantitative analysis in combination with the topological model and the node list, and outputting a three-dimensional evaluation vector; generating a regulation and control strategy based on an evaluation result, and outputting a priority regulation and control sequence of the key nodes; and the monitoring network and the model are dynamically optimized by regulating and controlling feedback data. Therefore, the dynamic characteristics of the hydrological network are captured, the accuracy of key node identification is improved, a scientific basis is provided for optimization design and risk early warning of an irrigation area network, and meanwhile, the reliability and adaptability of overall evaluation are improved through a closed-loop feedback mechanism.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological water delivery engineering, and in particular relates to a method, device, equipment and medium for evaluating hydrological connectivity in ecological irrigation areas. Background Art

[0002] As a crucial water conservancy system integrating agricultural production with ecological protection, the hydrological connectivity of ecological irrigation districts is directly related to the efficiency of regional water resource allocation and the health of the ecological environment. Hydrological connectivity assessment has become an indispensable and key technical tool in modern irrigation district management, playing a decisive role in maintaining ecological balance and improving water resource utilization efficiency.

[0003] Current assessments of irrigation district hydrological connectivity primarily rely on traditional qualitative analysis and single-point monitoring methods, which are significantly limited in terms of spatial coverage and temporal continuity. Existing assessment methods often lack systematic data support, making it difficult to accurately reflect the dynamic relationships between water bodies within complex irrigation district systems. This severely limits the reliability and practicality of assessment results.

[0004] Ecological irrigation districts encompass a variety of water bodies, including rivers, canals, and lakes. Complex water exchange processes occur between these water bodies, but a lack of effective water monitoring methods makes these processes difficult to accurately capture and quantify. This lack of clarity regarding water exchange relationships further hinders the identification of key hydrological connection nodes within the district. The functional status of these connection points directly impacts the hydrological connectivity of the entire district. The inability to accurately locate and assess key connection points makes it difficult for irrigation district managers to develop targeted regulatory strategies, leading to a continued exacerbation of hydrological connectivity issues, ultimately impacting the district's overall ecological function and water resource allocation efficiency.

[0005] How to establish a complete water volume monitoring network to achieve accurate analysis of the water exchange relationship between different water bodies in the irrigation area, and on this basis identify key hydrological connection points to accurately assess the hydrological connectivity of the irrigation area has become a key issue that needs to be urgently addressed in the field of hydrological connectivity assessment in ecological irrigation areas. Summary of the Invention

[0006] Based on this, it is necessary to provide a method, device, equipment and medium for evaluating hydrological connectivity in ecological irrigation areas in response to the above technical problems.

[0007] In the first aspect, the present application provides a method for assessing hydrological connectivity in ecological irrigation areas, including:

[0008] S1. Through a monitoring network consisting of multiple types of sensors deployed in the hydrological units of the irrigation area, time series data including flow, water level and water quality parameters are collected to generate a rasterized monitoring dataset with a unified temporal and spatial reference;

[0009] S2. Analyze the dynamic water exchange relationship between water bodies based on the monitoring data set and generate a water exchange relationship matrix including the exchange direction, intensity, and period;

[0010] S3. Construct a topological model of the irrigation district hydrological network based on the water exchange relationship matrix. The topological model includes node connection relationships and edge weights.

[0011] S4. Identify key connection nodes of the topology model through node centrality calculation and threshold screening mechanism, and generate a hierarchical node importance list;

[0012] S5. Combining the topological model with the hierarchical node importance list to quantify multi-dimensional connectivity, outputting a three-dimensional evaluation vector including path length, network density, and clustering coefficient;

[0013] S6. Generate a control strategy based on the abnormal results of the evaluation vector to obtain a priority control sequence of key connection nodes;

[0014] S7. Obtain control feedback data of the priority control sequence, and dynamically optimize the configuration of the monitoring network and the parameters of the topology model based on the control feedback data.

[0015] In a second aspect, the present application also provides a device for evaluating hydrological connectivity in an ecological irrigation area, comprising:

[0016] The hydrological monitoring module is used to collect time series data including flow, water level and water quality parameters through a monitoring network composed of multiple types of sensors deployed in the hydrological units of the irrigation area, and generate a rasterized monitoring dataset with a unified time and space reference;

[0017] The water exchange relationship analysis module is used to analyze the dynamic water exchange relationship between water bodies based on the monitoring data set and generate a water exchange relationship matrix including the exchange direction, intensity and period;

[0018] A topological model construction module is used to construct a topological model of the irrigation area hydrological network based on the water exchange relationship matrix. The topological model includes node connection relationships and edge weights.

[0019] The key node identification module is used to identify the key connection nodes of the topological model through node centrality calculation and threshold screening mechanism, and generate a hierarchical node importance list;

[0020] Multi-dimensional connectivity quantification module, which combines the topological model with the hierarchical node importance list to quantify multi-dimensional connectivity and outputs a three-dimensional evaluation vector containing path length, network density and clustering coefficient;

[0021] A control strategy generation module is used to generate a control strategy based on the abnormal results of the evaluation vector and obtain the priority control sequence of the key connection nodes;

[0022] The dynamic optimization module is used to obtain the control feedback data of the priority control sequence and dynamically optimize the configuration of the monitoring network and the parameters of the topology model based on the control feedback data.

[0023] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements a method for evaluating hydrological connectivity of an ecological irrigation area as described in the first aspect.

[0024] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for evaluating hydrological connectivity of an ecological irrigation area as described in the first aspect.

[0025] The aforementioned method, device, equipment, and medium for assessing hydrological connectivity in ecological irrigation areas deploy multiple sensors within hydrological units within the irrigation area to collect time-series data on flow, water level, and water quality parameters, generating a unified monitoring dataset with a temporal and spatial basis. Based on this data, the dynamic water exchange relationships between water bodies are analyzed, generating a water exchange relationship matrix that includes exchange direction, intensity, and period. This matrix is ​​then used to construct a topological model of the irrigation area's hydrological network, clarifying node connectivity and weights. Key connected nodes are identified and a hierarchical node importance list is generated through node centrality calculation and a threshold screening mechanism. Multidimensional connectivity quantitative analysis is performed, combining the topological model and node list, outputting a three-dimensional evaluation vector containing path length, network density, and clustering coefficient. Based on the evaluation results, a multi-objective control strategy is generated, outputting a priority control sequence for key nodes. Finally, the control feedback data is used to dynamically optimize the monitoring network configuration and model parameters, forming a closed-loop evaluation system. This solution comprehensively captures the dynamic characteristics of the hydrological network, improves the accuracy of key node identification, and provides a scientific basis for optimized design and risk warning of the irrigation area network. Furthermore, the closed-loop feedback mechanism enhances the reliability and adaptability of the overall assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A schematic diagram of a flow chart of a method for evaluating hydrological connectivity in ecological irrigation areas provided by the present invention;

[0028] Figure 2 A schematic diagram of a process for generating a three-dimensional evaluation vector in an optional embodiment of the present invention;

[0029] Figure 3 This is a structural schematic diagram of a device for evaluating hydrological connectivity in ecological irrigation areas provided by the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0031] refer to Figure 1 , which presents a flow chart of a method for evaluating hydrological connectivity in ecological irrigation areas provided by this application, the method comprising the following steps:

[0032] S1. Through the monitoring network containing multiple types of sensors deployed in the hydrological units of the irrigation area, time series data including flow, water level and water quality parameters are collected to generate a raster monitoring dataset with a unified time and space benchmark.

[0033] Specifically, hydrological units encompass all water bodies within the irrigation district, including rivers, canals, and lakes, as well as the connecting areas between them. Multiple sensor types include electromagnetic and ultrasonic flowmeters for flow measurement, which sense water velocity and calculate real-time flow based on the cross-sectional area of ​​the waterway. Float and pressure gauges for water level monitoring provide a visual representation of water level fluctuations. Finally, water quality analyzers measure water quality parameters, such as dissolved oxygen, chemical oxygen demand, and ammonia nitrogen. These sensors are evenly and rationally distributed throughout the irrigation district according to a pre-planned spatial layout.

[0034] Sensors collect data such as flow, water level, and water quality parameters, storing and transmitting them at a certain frequency and format to form time series data. Subsequently, these time series data are processed using a unified spatiotemporal reference. This involves converting data collected at different times and locations into a unified spatiotemporal reference system, such as a unified time step (e.g., hourly or daily) and spatial coordinate system (e.g., geographic coordinate system). Data interpolation and format conversion are then performed to generate a gridded monitoring dataset. This dataset divides the irrigation area into regular grid cells, each of which corresponds to a corresponding flow, water level, and water quality parameter value.

[0035] S2. Analyze the dynamic water exchange relationship between water bodies based on the monitoring data set and generate a water exchange relationship matrix including the exchange direction, intensity and period.

[0036] Specifically, through time series analysis, flow and water level data for the same water body at different time points are analyzed to identify characteristics such as trends, cycles, and mutation points in water volume changes. Then, by combining data from adjacent water bodies and using correlation analysis and hydrodynamic models, the direction of water exchange between water bodies is calculated. This determines whether the water flows from one water body to another or vice versa. This can be determined by analyzing factors such as water level differences, flow differences, and the phase relationship between the two. At the same time, based on the principle of water balance and the law of conservation of mass, the intensity of water exchange is quantified. This means calculating the amount of water flowing into or out of one water body per unit time. This can be obtained by establishing and solving a set of water balance equations. In addition, with the help of time-frequency analysis methods such as Fourier transform, the periodic laws of water exchange, such as daily changes, monthly changes or annual changes, are identified, and then a water exchange relationship matrix containing the exchange direction, intensity and period is generated. The matrix has water bodies as rows and columns, and the matrix elements represent the water exchange direction (such as expressed by positive and negative signs), intensity (specific numerical values) and period (such as parameters such as cycle duration) between the corresponding two water bodies.

[0037] S3. Construct a topological model of the irrigation district hydrological network based on the water exchange relationship matrix. The topological model includes node connection relationships and edge weights.

[0038] Specifically, each water body within the irrigation area is abstracted as a node in the topological model, including river nodes, canal nodes, lake nodes, etc. Each node represents an independent water body unit and is assigned corresponding attribute information, such as water body type, area, volume, etc. The connection relationship between water bodies is represented by edges based on the water exchange relationship matrix. If there is water exchange between two water bodies, an edge is established between the corresponding two nodes, and the direction of the edge is determined according to the direction of the water exchange, that is, a directed edge is formed. At the same time, the weight of the edge is determined. The calculation of the weight can comprehensively consider factors such as the intensity of water exchange, distance, and connection stability. For example, the intensity of water exchange is used as the main component of the weight, and the impact of connection distance on water flow resistance and the correction of weight by connection stability in historical data are appropriately considered. The weight value of each edge is obtained through a specific weight calculation formula (such as a linear weighted combination formula). In this way, the topological model includes the node connection relationship (represented by directed edges) and edge weights (reflecting the importance and strength of the connection), thereby constructing a complete irrigation district hydrological network topology structure and intuitively showing the mutual connection and interaction between the various water bodies in the irrigation district.

[0039] S4. Identify the key connection nodes of the topology model through node centrality calculation and threshold screening mechanism, and generate a hierarchical node importance list.

[0040] Specifically, node centrality is a metric system that measures the importance of nodes in a network. Node centrality calculation methods include degree centrality, closeness centrality, and betweenness centrality. Degree centrality reflects the number of direct connections a node has with other nodes. Specifically, the more edges a node has, the higher its degree centrality, indicating more direct water exchange relationships with other water bodies. Closeness centrality represents the sum of the reciprocals of the shortest path lengths between a node and all other nodes. A larger value indicates a closer connection with other nodes, enabling faster water exchange and information transfer within the entire hydrological network. Betweenness centrality refers to the number of times a node is on the shortest path between other pairs of nodes in the network. A higher number indicates a stronger control over information dissemination within the network and a more critical role for maintaining hydrological connectivity within the irrigation district. By calculating these centrality metrics for each node and then weighting them together according to preset weights (each metric is assigned a weight based on the assessment focus; for example, if the number of direct connections to a node is emphasized, degree centrality is given a higher weight), a comprehensive centrality score is obtained for each node. Next, a reasonable threshold is set to screen nodes based on the distribution of node centrality scores (e.g., using the percentile method to select the top 20% or 30% of nodes as candidate key nodes) and the actual requirements of irrigation district hydrological connectivity. Finally, the selected key nodes are ranked from high to low according to their comprehensive centrality scores to generate a hierarchical node importance list, clarifying the nodes that play a key role in hydrological connectivity in the irrigation district and their importance levels.

[0041] S5. Combine the topological model with the hierarchical node importance list to quantify multi-dimensional connectivity and output a three-dimensional evaluation vector containing path length, network density, and clustering coefficient.

[0042] Specifically, the path length of the network can be calculated by measuring the average shortest path length. That is, for each pair of nodes in the topological model, find the shortest path between them (pay attention to the path direction in a directed graph) and calculate the average of all shortest path lengths. This reflects the average number of nodes and edges that water needs to pass through in the irrigation area hydrological network to propagate from one water body to another. The shorter the path length, the better the connectivity of the hydrological network and the higher the water flow transmission efficiency. Secondly, evaluate the network density. The network density is the ratio of the actual number of edges to the possible number of edges in the network (that is, the square of the number of nodes minus the number of nodes). It is used to indicate the closeness of the connection between water bodies in the hydrological network. The higher the network density, the more water exchange relationships there are between water bodies and the stronger the connectivity. Next, the clustering coefficient is calculated. This measures the degree of clustering of nodes in the network—that is, the degree of interconnectedness between a given node and its neighboring nodes. It reflects the connectivity of local water bodies within the irrigation district's hydrological network. A high clustering coefficient indicates strong interrelationships between local water bodies, forming a relatively stable hydrological subsystem and helping to maintain the stability of hydrological connectivity across the entire irrigation district. By combining these three indicators, a three-dimensional evaluation vector is formed, encompassing path length, network density, and the clustering coefficient. This vector comprehensively quantifies the irrigation district's hydrological connectivity from three different perspectives: overall connectivity efficiency, connectivity density, and local clustering characteristics, providing a strong basis for subsequent assessment and decision-making.

[0043] S6. Generate a control strategy based on the abnormal results of the evaluation vector to obtain the priority control sequence of the key connection nodes.

[0044] Specifically, according to the abnormal results in the evaluation vector obtained in step S5, that is, when the indicators of certain dimensions in the evaluation vector exceed the normal threshold range (such as a sudden increase in path length, a sharp drop in network density, or abnormal fluctuations in clustering coefficient, etc.), it indicates that there is a problem or potential risk in the hydrological connectivity of the irrigation area, and a corresponding control strategy is generated at this time. In response to the abnormal situation of the evaluation vector, combined with the hierarchical node importance list, key connection nodes are prioritized for control. For example, if the assessment finds that the flow of key connection nodes has decreased abnormally, resulting in a decrease in network density, then the control strategy may include desilting the river, canal or lake where these key nodes are located, increasing water replenishment, optimizing gate scheduling, etc., to restore their normal water exchange function. At the same time, according to the importance level of the node, the control priority is determined in order from high to low, forming a priority control sequence for the key connection nodes. This means that when the actual control operation is carried out, control measures are first implemented on the most important key nodes to ensure that their hydrological connectivity returns to normal, and then the less important nodes are processed in turn, so as to make the most effective use of limited control resources and quickly improve the overall hydrological connectivity of the irrigation area.

[0045] S7. Obtain control feedback data of the priority control sequence, and dynamically optimize the configuration of the monitoring network and the parameters of the topology model based on the control feedback data.

[0046] Specifically, feedback data can be derived from the monitoring network, including changes in key connection nodes after regulation, as well as data on flow, water level, and water quality parameters of relevant water bodies. By analyzing this feedback data, the actual effectiveness of regulatory measures can be evaluated to verify whether the intended goal of improving hydrological connectivity has been achieved. Simultaneously, the configuration of the monitoring network can be dynamically optimized based on new developments and emerging issues revealed by the feedback data. For example, if significant fluctuations or anomalies in monitoring data in certain areas are observed after regulation, the number of sensors in these areas can be increased or their layout adjusted to improve monitoring accuracy and data reliability. Furthermore, feedback data can be used to dynamically adjust parameters of the topological model, such as revising the exchange direction, intensity, and periodicity parameters in the water exchange relationship matrix, and updating node connectivity and edge weights. This allows the topological model to more accurately reflect the actual state of hydrological connectivity in the irrigation district, providing a more precise basis for subsequent assessment and regulation, and ultimately achieving continuous improvement and optimization of hydrological connectivity assessment and regulation in ecological irrigation districts.

[0047] The aforementioned method for assessing hydrological connectivity in ecological irrigation districts deploys multiple sensors within hydrological units within the district to collect time-series data on flow, water level, and water quality parameters, generating a unified monitoring dataset with a temporal and spatial basis. Based on this data, the dynamic water exchange relationships between water bodies are analyzed, generating a water exchange relationship matrix that includes exchange direction, intensity, and period. This matrix is ​​then used to construct a topological model of the irrigation district's hydrological network, clarifying node connectivity and weights. Key connected nodes are identified and a hierarchical node importance list is generated through node centrality calculation and a threshold screening mechanism. Combining the topological model with the node list, a multi-dimensional connectivity quantitative analysis is performed, outputting a three-dimensional evaluation vector containing path length, network density, and clustering coefficient. Based on the evaluation results, a multi-objective control strategy is generated, outputting a priority control sequence for key nodes. Finally, the control feedback data is used to dynamically optimize the monitoring network configuration and model parameters, forming a closed-loop evaluation system. This approach comprehensively captures the dynamic characteristics of the hydrological network, improves the accuracy of key node identification, and provides a scientific basis for optimized design and risk warning of irrigation district networks. Furthermore, the closed-loop feedback mechanism enhances the reliability and adaptability of the overall assessment.

[0048] In an optional embodiment, S2 includes the following steps:

[0049] S21. Extract the flow gradient time series data and water level difference time series data of adjacent hydrological units from the rasterized monitoring data set.

[0050] Specifically, the rasterized monitoring dataset divides the irrigation area into regular raster units, and each unit stores time series data such as flow, water level and water quality parameters. Adjacent hydrological units refer to two hydrological units that are adjacent to each other in space, and there may be water exchange between them. The flow gradient time series data reflects the rate of change of flow between adjacent hydrological units over time, that is, the difference in flow change per unit time, which can be obtained by performing differential calculations on the flow time series data of two adjacent units, that is, calculating the ratio of the flow difference between two adjacent time points to the time interval. The water level difference time series data is a sequence of changes in the difference between the water level values ​​of two adjacent units over time, which is obtained by directly extracting the water level data of the corresponding time points from the rasterized monitoring dataset and subtracting them. By extracting these two time series data, we can preliminarily understand the dynamic relationship between adjacent hydrological units in flow changes and water level differences, and provide basic data support for the subsequent accurate analysis of water exchange relationships.

[0051] S22. Apply the dynamic compensation water balance model. Based on the synchronous change relationship between the flow gradient time series data and the water level difference time series data, correct the current section flow difference by superimposing the historical average flow error, and calculate the net exchange between units; where the current section is the monitoring point between adjacent hydrological units.

[0052] Specifically, the dynamic compensation water balance model is a water balance calculation model that takes into account the dynamic characteristics of the hydrological system and the error compensation mechanism. The model is based on the principle of conservation of mass, that is, in any time period, the difference between the water income and expenditure of a hydrological unit is equal to the change in its storage capacity. When calculating the net exchange volume between units, the synchronous change relationship between the flow gradient time series data and the water level difference time series data is first analyzed, that is, when the flow gradient and the water level difference increase or decrease at the same time, it may indicate a change in the direction and intensity of water exchange. Then, the historical average flow error is introduced as a correction term. This is because the actual monitoring data may have systematic errors or random errors. By calculating the average error of the flow in the long-term monitoring data and superimposing it on the current section flow difference, the flow difference is dynamically corrected to more accurately reflect the actual water exchange situation. Finally, based on the corrected flow difference and water level difference, combined with the geometric characteristics (such as water-passing cross-sectional area, connection length, etc.) and hydraulic characteristics (such as roughness, etc.) between units, the net exchange volume between adjacent hydrological units is calculated through the formula of the water balance model, that is, the net amount of water flowing from one unit to another per unit time.

[0053] S23. When the net exchange volume exceeds the fluctuation threshold of the historical average flow rate during the continuous monitoring period, the exchange relationship mark is triggered, and the net exchange volume time series data with the exchange relationship mark is generated.

[0054] Specifically, the fluctuation threshold of the historical average flow is derived from statistical analysis of long-term monitoring data. It serves as a reference value for measuring the normal fluctuation range of net exchange volume. It can be set as a certain percentage of the historical average flow (e.g., ±10% or ±20%) or a multiple of the standard deviation determined through statistical methods. During a continuous monitoring period, i.e., multiple consecutive time steps (e.g., hourly, daily, etc.), the net exchange volume is calculated in real time and compared with the fluctuation threshold. When the net exchange volume exceeds this fluctuation threshold, it indicates an abnormal or significant change in the water exchange between adjacent hydrological units, triggering an exchange relationship marker. The exchange relationship marker is information used to identify the state of water exchange. It can be a simple symbol (e.g., a positive sign indicates inflow, a negative sign indicates outflow) or a more detailed status code containing information such as the start time, end time, direction, and intensity level of the exchange. The exchange relationship marker is attached to the corresponding net exchange volume time series data to generate labeled net exchange volume time series data, allowing for clearer identification and analysis of key periods and events in water exchange, providing important time series information for accurately constructing a water exchange relationship matrix.

[0055] S24. Based on the time series data of net exchange volume, the fluctuation periodic characteristics of exchange intensity are extracted through sliding window analysis method, and the directional association is determined in combination with the exchange relationship mark to generate a water volume exchange relationship matrix containing direction, intensity and period.

[0056] Specifically, the sliding window analysis method is a method for extracting local features of time series data. It sets a window of fixed length and slides point by point on the time series data to perform statistical analysis on the data within the window. In this step, the length of the sliding window can be pre-set according to the typical periodic characteristics of water exchange, such as a daily cycle, a weekly cycle, or a monthly cycle as the window length. In each sliding window, the statistical characteristics of the net exchange volume, such as the maximum value, the minimum value, the average value, the standard deviation, etc., are calculated to identify the fluctuation of the exchange intensity. By analyzing the results of multiple sliding windows, the fluctuation period characteristics of the exchange intensity are extracted, that is, the repetitive change pattern of the exchange intensity over time is determined, such as the daily morning and evening peaks, specific time periods of the week, or seasonal change cycles. At the same time, combined with the exchange relationship marker, the directional correlation of water exchange in different fluctuation cycles is clarified, that is, in each cycle stage, whether the exchange direction between water bodies is fixed or changing, and the relative intensity of exchanges in different directions. Based on these analysis results, a water exchange relationship matrix is ​​constructed, which includes direction (such as represented by directional arrows between water bodies), intensity (represented by the numerical value or relative size of the net exchange volume), and period (represented by the specific duration or time unit of the fluctuation period). This matrix can intuitively and comprehensively display the complex dynamic relationship of water exchange between adjacent hydrological units in the irrigation area, providing an accurate quantitative basis for the subsequent construction of hydrological network topology models and connectivity assessment.

[0057] In an optional embodiment, S3 includes the following steps:

[0058] S31. Use the exchange amount in the water exchange relationship matrix as the network edge weight, and construct an initial directed weighted network with the monitoring points as nodes.

[0059] Specifically, the water exchange relationship matrix contains information on the direction, intensity, and periodicity of water exchange between adjacent hydrological units in an irrigation area. The exchange volume is a key metric for measuring the magnitude of water exchange between two hydrological units. When constructing the initial directed weighted network, monitoring points are first abstracted as nodes in the network. Each node represents a specific monitoring location, which can be located at a possible water exchange section between hydrological units, such as a river confluence, a canal water diversion, or the junction between a lake and a river. Then, based on the exchange volume recorded in the water exchange relationship matrix, a weight is assigned to each connection between two monitoring points where water exchange occurs, representing the absolute value of the exchange volume. Furthermore, because water exchange is directional—for example, flow from monitoring point A to monitoring point B is different from flow from B to A—the constructed network is directed, and the direction of the water exchange is reflected by the direction of the directed edges. In this way, the initial directed weighted network preliminarily forms the connection framework between monitoring points in the irrigation district hydrological system. The edge weight reflects the strength of water exchange, and the direction of the edge indicates the direction of water flow, providing the basic structure for the subsequent construction of the hydrological network topology model.

[0060] S32. Use the path backtracking algorithm to verify the logical consistency of the water flow path and topological connection of the initial directed weighted network, perform spatial topological correction on the contradictory connection edges, and obtain the correction results.

[0061] Specifically, the path backtracking algorithm starts from a certain endpoint in the network and traces back the source path of water flow along the network edges. Its purpose is to verify whether the water flow path constructed based on the initial network is consistent with the actual hydrological topological connections. For example, suppose there is a directed edge from monitoring point C to monitoring point D in the initial network. However, in the actual hydrological system, due to factors such as terrain and hydraulic structures, water flow cannot flow from C to D, resulting in a logical contradiction. In this case, the path backtracking algorithm will detect such contradictory connecting edges. When a contradiction is found, spatial topology correction is performed. Correction methods may include field survey verification, re-examination of the geographic location and hydrological characteristics of the monitoring points, adjustment of edge direction or weight, deletion of unreasonable edges, and addition of missing edges. After a series of correction measures, the correction result is obtained. This correction result is more consistent with the actual situation, ensuring the logical consistency and correctness of the topological connections between water flow paths and hydrological units, and providing a reliable data foundation for generating an accurate hydrological network topology model.

[0062] S33. Generate a hydrological network topology model including river channels, canal systems, lake nodes, and dynamic connection relationships based on the correction results as a topology model.

[0063] Specifically, the correction results can include the connection relationship between the verified and corrected monitoring points, as well as the corresponding edge weights and direction information. On this basis, the hydrological unit types represented by the monitoring points (such as rivers, canals, lakes, etc.) are further clarified as different node types in the topological model. For example, the monitoring points located on the river are classified as river nodes, the monitoring points on the canal system are classified as canal nodes, and the monitoring points in the lake are lake nodes, etc. At the same time, taking into account the dynamic nature of the water flow connection relationship in the hydrological system, for example, in different seasons and different water use conditions, some connections may be strengthened, weakened or even interrupted, so the concept of dynamic connection relationship is introduced into the topological model. Dynamic connection relationship can be reflected by adding time dimension attributes, such as recording the weight change and direction change of the connection edge in different time periods. In addition, it can also be combined with the hydrological dynamics model to predict the changing trend of water flow under different circumstances and further improve the description of dynamic connection relationship. The resulting hydrological network topology model not only includes different types of water body nodes, such as rivers, canals, and lakes, but also reflects the dynamic connection relationships between them under different times and conditions, providing a comprehensive, accurate, and dynamic network structure for subsequent hydrological connectivity assessment and analysis. This helps to deeply understand the operating mechanism and connectivity characteristics of the irrigation area's hydrological system, and provides strong support for the formulation of scientific and reasonable hydrological regulation strategies.

[0064] In an optional embodiment, S4 includes the following steps:

[0065] S41. Calculate the degree centrality and betweenness centrality of each node in parallel based on the topological model.

[0066] Specifically, when calculating degree centrality and betweenness centrality, degree centrality is calculated first. Degree centrality essentially measures the closeness of a node's direct connections with other nodes. For a directed hydrological network, out-degree centrality reflects the node's external influence, i.e., the amount of traffic flowing from that node to other nodes; in-degree centrality reflects the node's popularity, i.e., the amount of traffic flowing from other nodes to that node.

[0067] When calculating outdegree centrality, assuming there are n nodes in the topological model, for a specific node v, we count the number of edges pointing from node v to other nodes, which is the outdegree of node v. This outdegree value is then divided by (n-1), since a node can have direct connections to at most n-1 other nodes. The result is the outdegree centrality of the node, which represents the node's ability to actively output water flow to other water bodies in the hydrological network. Similarly, when calculating indegree centrality, the focus is on the number of edges pointing from other nodes to node v, which is the indegree. Similarly, dividing the indegree by (n-1) yields the indegree centrality of node v, which reflects the node's ability to receive water flow from other water bodies. Total degree centrality is simply the sum of the outdegree centrality and the indegree centrality, which represents the overall activeness of the connections between node v and other nodes.

[0068] Next, calculate the betweenness centrality. This indicator can better reflect the strategic position of a node in the entire hydrological network. It takes into account the frequency of the node's appearance on the shortest path between other nodes. When calculating, first find the shortest path between all pairs of nodes in the network, which can be achieved through the breadth-first search (BFS) algorithm. In this process, traverse the entire network, starting from a starting node and gradually expanding outward until the shortest path to the target node is found. Count the number of times each node appears on these shortest paths. This number reflects the intermediary ability of the node in the network. The higher the number, the more critical the bridge role played by the node in the water flow propagation in the hydrological network.

[0069] Finally, to obtain the standardized betweenness centrality, the number of times node v appears on the shortest path is divided by the total number of shortest paths between all pairs of nodes. This step aims to eliminate the influence of network size on the results and make the node centrality comparable across different networks. The final result of betweenness centrality is a value between 0 and 1, with larger values ​​indicating greater control of the node's influence on the flow of water in the network.

[0070] Through the above process, the degree centrality and betweenness centrality of each node can be calculated, providing a basis for the subsequent screening of key connection nodes.

[0071] S42. Filter nodes whose degree centrality is higher than the mean degree centrality of all nodes in the topological model to obtain the primary filtered nodes, and summarize the primary filtered nodes to obtain a node candidate set.

[0072] Specifically, the average degree centrality of all nodes in the topological model is calculated as a baseline threshold for measuring node importance. The degree centrality of each node is then compared to this average, and nodes with higher degree centrality than the average are selected. These nodes have relatively more direct connections in the hydrological network and play a role in maintaining network connectivity. These initially screened nodes are aggregated to form a candidate set of nodes, focusing on more active and critical nodes in the network, paving the way for further screening.

[0073] S43. Nodes whose betweenness centrality exceeds a set multiple are extracted from the node candidate set to obtain secondary screening nodes.

[0074] Specifically, the multiplier is determined based on the criticality of network connectivity and can be set based on experience or actual needs, such as 1.5 or 2. For each node in the candidate set, its betweenness centrality is compared with the set multiplier multiplied by the average betweenness centrality of all nodes. Nodes exceeding this value are extracted. These nodes are not only directly connected to many nodes, but also play a key intermediary role in the water flow propagation path. Nodes after secondary screening are both active and key control nodes in the hydrological network, and their impact on the hydrological connectivity of the irrigation area is more significant.

[0075] S44. Mark the secondary screening nodes as key connection points of multiple levels according to the grading threshold, and generate a node importance list with priority sorting as a graded node importance list.

[0076] Specifically, the grading threshold is set according to business needs and network characteristics. For example, the betweenness centrality is sorted from high to low, and the levels are divided according to a certain proportion or interval, such as the top 10% are first-level key nodes, 10%-30% are second-level key nodes, etc. According to the grading threshold, the secondary screening nodes are marked as key connection points of different levels. The higher the level, the more important the node is in hydrological connectivity. The nodes are sorted from high to low by level to form a node importance list with priority sorting. This list clearly shows the importance and priority of each key connection node, providing precise guidance for subsequent targeted hydrological connectivity regulation and optimization, giving priority to ensuring the normal connectivity and functioning of high-level nodes, and realizing efficient management and maintenance of irrigation area hydrological connectivity.

[0077] refer to Figure 2 In an optional embodiment, S5 includes the following steps:

[0078] S51. Run the Floyd algorithm based on the topology model to calculate the shortest path length between each node, and count the distribution characteristics of the reachable path lengths between nodes to obtain a path length distribution data set.

[0079] Specifically, the Floyd algorithm is a classic dynamic programming method used to find the shortest path length between all pairs of nodes in a graph. In the hydrological network topology model, each node represents a water unit, and the edge weight represents the water exchange intensity or other related indicators between units. The algorithm gradually optimizes the path length by introducing intermediate nodes, and finally obtains a matrix in which the elements represent the shortest path length from node i to node j. Based on the calculated shortest path length matrix, the distribution characteristics of the reachable path length between nodes are statistically analyzed. Specifically, the number of node pairs within different path length ranges is counted to form a path length distribution data set. This data set can reflect the efficiency and coverage of water flow propagation in the hydrological network. For example, a higher proportion of shorter path lengths indicates that the hydrological network has good connectivity and water flow can propagate quickly between nodes.

[0080] S52. Count the number of valid connection edges in the topology model, calculate the ratio of the number of valid connection edges to the theoretical maximum number of edges, and obtain a network density index; wherein, valid connection edges refer to edges whose edge weights are higher than a set threshold, and the theoretical maximum number of edges is the number of edges in the complete graph of the topology model.

[0081] Specifically, to calculate the network density index, the number of valid connection edges in the topological model is first counted. The valid connection edges here refer to edges with edge weights higher than the set threshold, because edges with high edge weights are more practical in hydrological connectivity, such as indicating a stronger water exchange relationship. The threshold can be determined according to the actual application scenario. For example, by analyzing the edge weight distribution, the top 30% of weight values ​​are selected as the threshold for valid connection. Then, the theoretical maximum number of edges is calculated, that is, the number of edges in the complete graph corresponding to the topological model. For a topological model with n nodes, the number of edges in the complete graph is n(n-1) (assuming it is a directed graph). The network density index is the ratio of the number of valid connection edges to the theoretical maximum number of edges. This index can reflect the degree of connection density of the hydrological network. The higher the network density, the stronger the connectivity between water units, and vice versa.

[0082] S53. Use the Gaussian kernel function to perform nonlinear mapping on the edge weights of each edge in the topological model to obtain mapping values; based on the mapping values, convert the edge weight differences between adjacent nodes into probabilistic connection strengths to generate a weighted adjacency matrix that reflects the dynamic characteristics of the hydrological network; among which, the bandwidth parameter of the Gaussian kernel function is dynamically adjusted according to the degree of discreteness of the overall network weight distribution of the topological model.

[0083] Specifically, in order to generate a weighted adjacency matrix that reflects the dynamic characteristics of the hydrological network, the Gaussian kernel function is used to perform nonlinear mapping on the edge weights of each edge in the topological model. The Gaussian kernel function can map the edge weights to a probability interval, and the formula is: Where x is the edge weight, and σ is the bandwidth parameter. The bandwidth parameter is dynamically adjusted based on the degree of discreteness of the overall network weight distribution in the topological model. If the weight distribution is relatively discrete, the bandwidth can be appropriately increased to smooth the mapping results. Through this nonlinear mapping, the edge weight differences between adjacent nodes are converted into probabilistic connection strengths. The elements in the resulting weighted adjacency matrix represent the probability of the connection strength from node i to node j. This can more subtly reflect the dynamic characteristics of each connection in the hydrological network, such as the stability of the connection under different water conditions.

[0084] S54. Calculate the theoretical maximum possible number of connected edges for each node based on the number of neighboring nodes directly connected to each node in the topology model; and take the edges in the weighted adjacency matrix whose connection strength exceeds a preset threshold as the actual number of valid connected edges.

[0085] Specifically, the theoretical maximum possible number of connected edges for each node is first calculated, which depends on the node's position in the topology model and the network structure. For example, in a directed graph that allows arbitrary connections, the theoretical maximum possible number of connected edges for each node is n-1 (n is the total number of nodes). Then, based on the generated weighted adjacency matrix, the edges whose connection strength exceeds a preset threshold are taken as the actual number of valid connected edges. This preset threshold can be determined based on the actual application scenario, such as determining a reasonable connection strength threshold through expert experience or data analysis to distinguish between valid connections and invalid connections.

[0086] S55. Calculate the ratio of the actual number of effective connection edges of each node to the theoretical maximum possible number of connection edges, as the clustering coefficient corresponding to each node.

[0087] Specifically, when calculating the clustering coefficient of each node, the actual number of effective connection edges and the theoretical maximum possible number of connection edges of each node are first determined. The theoretical maximum possible number of connection edges refers to the maximum possible number of edges that exist between the node and all other nodes under ideal circumstances. The actual number of effective connection edges is determined based on the number of edges in the weighted adjacency matrix whose connection strength exceeds the preset threshold. The clustering coefficient is the ratio of the actual number of effective connection edges to the theoretical maximum possible number of connection edges, which reflects the degree of connection between the neighboring nodes around the node. For example, a node with a high clustering coefficient means that there are also more effective connections between the neighboring nodes around it, forming a relatively tight local hydrological network structure.

[0088] S56. Based on the path length distribution dataset, network density index and clustering coefficient, a three-dimensional evaluation vector is generated after assigning weights using the entropy method.

[0089] Specifically, the entropy method is an objective weighting method that can determine the weight of each indicator based on its degree of dispersion. Specifically, we first normalize each indicator and then calculate the entropy value of each indicator. The formula is: Where k is a constant, p ij is the normalized indicator value. The smaller the entropy value of an indicator, the greater its weight, indicating that the indicator has a greater impact on the assessment results. Based on the calculated weights, the path length distribution dataset (for example, the average shortest path length is used as a representative value), the network density index, and the clustering coefficient are weighted and summed to generate a three-dimensional assessment vector. This vector comprehensively reflects the connectivity status of the hydrological network, providing a comprehensive quantitative basis for subsequent assessment and decision-making.

[0090] The aforementioned method for assessing hydrological connectivity in ecological irrigation districts deploys multiple sensors within hydrological units within the district to collect time-series data on flow, water level, and water quality parameters, generating a unified monitoring dataset with a temporal and spatial basis. Based on this data, the dynamic water exchange relationships between water bodies are analyzed, generating a water exchange relationship matrix that includes exchange direction, intensity, and period. This matrix is ​​then used to construct a topological model of the irrigation district's hydrological network, clarifying node connectivity and weights. Key connected nodes are identified and a hierarchical node importance list is generated through node centrality calculation and a threshold screening mechanism. Combining the topological model with the node list, a multi-dimensional connectivity quantitative analysis is performed, outputting a three-dimensional evaluation vector containing path length, network density, and clustering coefficient. Based on the evaluation results, a multi-objective control strategy is generated, outputting a priority control sequence for key nodes. Finally, the control feedback data is used to dynamically optimize the monitoring network configuration and model parameters, forming a closed-loop evaluation system. This approach comprehensively captures the dynamic characteristics of the hydrological network, improves the accuracy of key node identification, and provides a scientific basis for optimized design and risk warning of irrigation district networks. Furthermore, the closed-loop feedback mechanism enhances the reliability and adaptability of the overall assessment.

[0091] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0092] Based on the same inventive concept, embodiments of the present application also provide a device for implementing the aforementioned method for assessing hydrological connectivity in ecological irrigation areas. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for assessing hydrological connectivity in ecological irrigation areas provided below can be found in the aforementioned limitations of the method for assessing hydrological connectivity in ecological irrigation areas, and will not be further elaborated here.

[0093] In an exemplary embodiment, Figure 3 As shown, an ecological irrigation area hydrological connectivity assessment device 30 is provided, comprising:

[0094] The hydrological monitoring module 31 is used to collect time series data including flow, water level and water quality parameters through a monitoring network containing multiple types of sensors deployed in the hydrological units of the irrigation area, and generate a rasterized monitoring data set with a unified time and space reference.

[0095] The water exchange relationship analysis module 32 is used to analyze the dynamic water exchange relationship between water bodies based on the monitoring data set and generate a water exchange relationship matrix including the exchange direction, intensity and period.

[0096] The topology model construction module 33 is used to construct a topology model of the irrigation area hydrological network according to the water exchange relationship matrix. The topology model includes node connection relationships and edge weights.

[0097] The key node identification module 34 is used to identify the key connection nodes of the topology model through node centrality calculation and threshold screening mechanism, and generate a hierarchical node importance list.

[0098] The multi-dimensional connectivity quantification module 35 is used to combine the topological model with the hierarchical node importance list to perform multi-dimensional connectivity quantification and output a three-dimensional evaluation vector including path length, network density and clustering coefficient.

[0099] The control strategy generation module 36 is used to generate a control strategy according to the abnormal result of the evaluation vector and obtain a priority control sequence of the key connection nodes.

[0100] The dynamic optimization module 37 is used to obtain the control feedback data of the priority control sequence and dynamically optimize the configuration of the monitoring network and the parameters of the topology model based on the control feedback data.

[0101] Optional water exchange relationship analysis module includes:

[0102] The time series data extraction unit is used to extract the flow gradient time series data and water level difference time series data of adjacent hydrological units from the rasterized monitoring data set.

[0103] The net exchange volume calculation unit is used to apply the dynamic compensation water balance model. Based on the synchronous change relationship between the flow gradient time series data and the water level difference time series data, the current section flow difference is corrected by superimposing the historical average flow error to calculate the net exchange volume between units; among them, the current section is the monitoring point between adjacent hydrological units.

[0104] The exchange relationship mark triggering unit is used to trigger the exchange relationship mark when the net exchange volume exceeds the fluctuation threshold of the historical average flow within a continuous monitoring period, and generate the net exchange volume time series data with the exchange relationship mark.

[0105] The water exchange relationship matrix generation unit is used to extract the fluctuation period characteristics of the exchange intensity based on the net exchange volume time series data through the sliding window analysis method, and determine the directional association in combination with the exchange relationship mark to generate a water exchange relationship matrix containing direction, intensity and period.

[0106] Optionally, the topology model building module includes:

[0107] The initial network construction unit is used to use the exchange amount in the water exchange relationship matrix as the network edge weight and to construct an initial directed weighted network with monitoring points as nodes.

[0108] The network correction unit is used to verify the logical consistency of the water flow path and topological connection of the initial directed weighted network by using a path backtracking algorithm, and to perform spatial topological correction on the contradictory connection edges to obtain the correction results.

[0109] The topology model generation unit is used to generate a hydrological network topology model including river channels, canal systems, lake nodes and dynamic connection relationships according to the correction results as a topology model.

[0110] Optionally, the key node identification module includes:

[0111] The node centrality calculation unit is used to parallelly calculate the degree centrality and betweenness centrality of each node based on the topological model.

[0112] The primary screening unit is used to screen nodes whose degree centrality is higher than the mean degree centrality of all nodes in the topological model to obtain the primary screened nodes, and summarize the primary screened nodes to obtain the node candidate set.

[0113] The secondary screening unit is used to extract nodes whose betweenness centrality exceeds a set multiple from the node candidate set to obtain secondary screening nodes.

[0114] The node importance grading unit is used to mark the secondary screening nodes as key connection points of multiple levels according to the grading threshold, and generate a node importance list with priority sorting as a graded node importance list.

[0115] Optional, multi-dimensional connectivity quantification module includes:

[0116] The path length calculation unit is used to run the Floyd algorithm based on the topology model to calculate the shortest path length between each node, and to count the distribution characteristics of the reachable path lengths between nodes to obtain a path length distribution data set.

[0117] The network density index calculation unit is used to count the number of valid connection edges in the topology model, calculate the ratio of the number of valid connection edges to the theoretical maximum number of edges, and obtain the network density index; among them, the valid connection edge refers to the edge with an edge weight higher than the set threshold, and the theoretical maximum number of edges is the number of edges in the complete graph of the topology model.

[0118] The weighted adjacency matrix generation unit is used to use the Gaussian kernel function to perform nonlinear mapping on the edge weights of each edge in the topological model to obtain mapping values; based on the mapping values, the edge weight differences between adjacent nodes are converted into probabilistic connection strengths to generate a weighted adjacency matrix that reflects the dynamic characteristics of the hydrological network; among them, the bandwidth parameter of the Gaussian kernel function is dynamically adjusted according to the degree of discreteness of the overall network weight distribution of the topological model.

[0119] The actual effective connection edge number determination unit is used to calculate the theoretical maximum possible number of connection edges of each node based on the number of neighboring nodes directly connected to each node in the topology model; and the edges in the weighted adjacency matrix whose connection strength exceeds a preset threshold are regarded as the actual effective connection edge number.

[0120] The clustering coefficient calculation unit is used to calculate the ratio of the actual number of effective connection edges of each node to the theoretical maximum possible number of connection edges, as the clustering coefficient corresponding to each node.

[0121] The three-dimensional evaluation vector generation unit is used to generate a three-dimensional evaluation vector after allocating weights through the entropy method based on the path length distribution data set, the network density index and the clustering coefficient.

[0122] An embodiment of the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0123] An embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0124] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0125] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A method for evaluating hydrological connectivity in ecological irrigation areas, characterized in that: The method comprises: S1. Through a monitoring network consisting of multiple types of sensors deployed in the hydrological units of the irrigation area, time series data including flow, water level and water quality parameters are collected to generate a rasterized monitoring dataset with a unified temporal and spatial reference; S2. Analyze the dynamic water exchange relationship between water bodies based on the monitoring data set, and generate a water exchange relationship matrix including exchange direction, intensity, and period; S3. Constructing a topological model of the irrigation district hydrological network based on the water exchange relationship matrix, wherein the topological model includes node connection relationships and edge weights; S4. Identifying key connection nodes of the topological model through node centrality calculation and threshold screening mechanism, and generating a hierarchical node importance list; S5. Combining the topological model with the hierarchical node importance list to perform multi-dimensional connectivity quantification, and outputting a three-dimensional evaluation vector including path length, network density, and clustering coefficient; S6. Generate a control strategy based on the abnormal result of the evaluation vector to obtain a priority control sequence of the key connection node; S7. Obtain control feedback data of the priority control sequence, and dynamically optimize the configuration of the monitoring network and the parameters of the topology model based on the control feedback data.

2. The method according to claim 1, characterized in that The S2 includes: S21, extracting the flow gradient time series data and the water level difference time series data of adjacent hydrological units from the gridded monitoring data set; S22. Applying a dynamic compensation water balance model, based on the synchronous change relationship between the flow gradient time series data and the water level difference time series data, by superimposing the historical average flow error to correct the current section flow difference, and calculating the net exchange between units; wherein the current section is the monitoring point between the adjacent hydrological units; S23. When the net exchange volume exceeds the fluctuation threshold of the historical average flow rate during a continuous monitoring period, triggering an exchange relationship mark, and generating net exchange volume time series data with the exchange relationship mark; S24. Based on the net exchange volume time series data, the fluctuation period characteristics of the exchange intensity are extracted by a sliding window analysis method, and the directional association is determined in combination with the exchange relationship mark to generate the water volume exchange relationship matrix including direction, intensity and period.

3. The method according to claim 2, characterized in that The S3 includes: S31, using the exchange amount in the water exchange relationship matrix as the network edge weight, and constructing an initial directed weighted network with the monitoring points as nodes; S32, using a path backtracking algorithm to verify the logical consistency of the water flow path and the topological connection of the initial directed weighted network, and performing spatial topological correction on the conflicting connection edges to obtain a correction result; S33. Generate a hydrological network topology model including river channels, canal systems, lake nodes and dynamic connection relationships according to the correction results as the topology model.

4. The method according to claim 3, characterized in that The S4 includes: S41, calculating the degree centrality and betweenness centrality of each node in parallel based on the topological model; S42, screening nodes whose degree centrality is higher than the mean degree centrality of all nodes in the topological model to obtain initially screened nodes, and summarizing the initially screened nodes to obtain a node candidate set; S43, extracting nodes whose betweenness centrality exceeds a set multiple from the node candidate set to obtain secondary screening nodes; S44. Mark the secondary screening nodes as key connection points of multiple levels according to the grading threshold, and generate a node importance list with priority sorting as the graded node importance list.

5. The method according to any one of claims 1 to 4, characterized in that The S5 includes: S51. Run the Floyd algorithm based on the topology model to calculate the shortest path length between nodes, and collect statistics on the distribution characteristics of the reachable path lengths between nodes to obtain a path length distribution data set; S52. Count the number of valid connection edges in the topology model, calculate the ratio of the number of valid connection edges to the theoretical maximum number of edges, and obtain a network density index; wherein the valid connection edges refer to edges whose edge weights are higher than a set threshold, and the theoretical maximum number of edges is the number of edges in the complete graph of the topology model; S53, using a Gaussian kernel function to perform nonlinear mapping on the edge weights of each edge in the topological model to obtain a mapping value; based on the mapping value, converting the edge weight differences between adjacent nodes into probabilistic connection strengths to generate a weighted adjacency matrix reflecting the dynamic characteristics of the hydrological network; wherein the bandwidth parameter of the Gaussian kernel function is dynamically adjusted according to the degree of discreteness of the overall network weight distribution of the topological model; S54, calculating the theoretical maximum possible number of connected edges of each node based on the number of neighboring nodes directly connected to each node in the topology model; and taking the edges in the weighted adjacency matrix whose connection strength exceeds a preset threshold as the actual number of valid connected edges; S55. Calculate the ratio of the actual number of valid connection edges of each node to the theoretical maximum possible number of connection edges, as the clustering coefficient corresponding to each node; S56. Based on the path length distribution dataset, the network density index, and the clustering coefficient, the three-dimensional evaluation vector is generated after assigning weights using an entropy method.

6. An ecological irrigation area hydrological connectivity assessment device, characterized in that: The device comprises: The hydrological monitoring module is used to collect time series data including flow, water level and water quality parameters through a monitoring network composed of multiple types of sensors deployed in the hydrological units of the irrigation area, and generate a rasterized monitoring dataset with a unified time and space reference; A water exchange relationship analysis module is used to analyze the dynamic water exchange relationship between water bodies based on the monitoring data set and generate a water exchange relationship matrix including exchange direction, intensity and period; A topology model construction module is used to construct a topology model of the irrigation area hydrological network according to the water exchange relationship matrix, wherein the topology model includes node connection relationships and edge weights; A key node identification module is used to identify key connection nodes of the topological model through node centrality calculation and threshold screening mechanism, and generate a hierarchical node importance list; A multi-dimensional connectivity quantification module, configured to combine the topological model with the hierarchical node importance list to perform multi-dimensional connectivity quantification and output a three-dimensional evaluation vector including path length, network density, and clustering coefficient; A control strategy generation module, configured to generate a control strategy according to the abnormal result of the evaluation vector, and obtain a priority control sequence of the key connection node; A dynamic optimization module is used to obtain the control feedback data of the priority control sequence and dynamically optimize the configuration of the monitoring network and the parameters of the topology model based on the control feedback data.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

Images