Water network pattern planning method and device and electronic equipment

Abstract

The invention relates to the technical field of hydraulic engineering, in particular to a water network pattern planning method and device and electronic equipment. Obtaining at least one water body corresponding to the target area and water body characteristics corresponding to the water bodies; the water body features comprise water body position pattern features, water body morphological features, water body structural features and water body flow features; calculating a target water system connectivity result of a target water network corresponding to the target area based on the water body features corresponding to the water bodies; the target water network is formed based on water bodies; calculating a target water resource supply and demand matching degree corresponding to the target water network based on the target water system connectivity result; and planning and adjusting the pattern of the target water network based on the target water resource supply and demand matching degree. The water network pattern can be accurately and effectively planned. And sustainable utilization of water resources and sustainable development of regions can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and in particular to a water network pattern planning method, device and electronic equipment. Background Art

[0002] With the acceleration of global climate change and urbanization, problems such as uneven temporal and spatial distribution of water resources and ecological environmental degradation are becoming increasingly serious. Water network planning has become a key link in ensuring regional water resource security and sustainable development.

[0003] Traditional water network planning is mostly based on experience and static data, mainly to meet single functional needs such as water supply and flood control, and is difficult to adapt to the complex and changing needs of modern water network management.

[0004] Therefore, how to plan the water network pattern accurately and effectively has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a water network pattern planning method, device and electronic equipment to solve the problem of how to accurately and effectively plan the water network pattern.

[0006] In a first aspect, the present invention provides a water network pattern planning method, the method comprising:

[0007] Obtain at least one water body corresponding to the target area and water body characteristics corresponding to each water body; water body characteristics include water body location pattern characteristics, water body morphology characteristics, water body structure characteristics and water body flow characteristics;

[0008] Based on the water body characteristics corresponding to each water body, the target water system connectivity results of the target water network corresponding to the target area are calculated; the target water network is based on the composition of each water body;

[0009] Based on the target water system connectivity results, calculate the target water resource supply and demand matching degree corresponding to the target water network;

[0010] Based on the target water resource supply and demand matching degree, the pattern of the target water network is planned and adjusted.

[0011] The water network pattern planning method provided in the embodiments of the present application obtains at least one water body corresponding to a target area and the corresponding water characteristics of each water body. Based on the corresponding water characteristics of each water body, the target water system connectivity results for the target water network corresponding to the target area are calculated. This method can quantitatively assess the degree of connectivity between the various water bodies in the target water network. This helps determine the smoothness of water resource flow throughout the entire water network and the feasibility and difficulty of water resource allocation between different regions. By analyzing the water system connectivity, it is possible to identify potential weak links or obstacles in the water network. Based on the target water system connectivity results, the target water resource supply and demand matching degree for the target water network is calculated. This method can clarify the balance between water resource supply and demand in the target water network. This helps managers understand the water resource supply and demand status of different regions, thereby more scientifically allocating and scheduling water resources, achieving optimal resource allocation, and avoiding the unreasonable phenomenon of water resource surplus in some areas and severe shortages in others. Accurate calculation results of the water resource supply and demand matching degree provide an important quantitative basis for water resource management decisions. Based on the target water resource supply and demand matching degree, the pattern of the target water network is planned and adjusted. Targeted improvements to the structure and function of water networks are made. Reasonable adjustments to water network planning contribute to the sustainable use of water resources and regional sustainable development. By optimizing the water network, we can better protect the ecological environment while ensuring water needs for economic development, maintain the balance of aquatic ecosystems, achieve coordinated allocation and utilization of water resources among the economic, social, and ecological sectors, and promote the long-term, stable development of the target region.

[0012] In an optional embodiment, based on the water body characteristics corresponding to each water body, calculating the target water system connectivity result of the target water network corresponding to the target area includes:

[0013] According to the water body characteristics corresponding to each water body, each water body is regarded as a water body node, and the channels between water bodies are regarded as edges, so as to construct the target water system network model;

[0014] Based on the target water system network model, construct the target adjacency matrix corresponding to each water body;

[0015] Based on the target adjacency matrix, the target connectivity index, target shortest flow path, and target water flow efficiency corresponding to the target shortest flow path of the target water network are calculated;

[0016] Based on the target connectivity index, target shortest flow path and target water flow efficiency, the target water system connectivity result corresponding to the target water network is determined.

[0017] The water network pattern planning method provided in the embodiments of the present application constructs a target water network model based on the water characteristics corresponding to each water body, with each water body as a water node and the channels between water bodies as edges. This method can present the structure of the water network in the target area in an intuitive graphical manner. The target water network model can simplify complex water network systems into a combination of water body nodes and edges, facilitating the analysis of complex water flow relationships within the water network. Based on the target water network model, a target adjacency matrix corresponding to each water body is constructed; this can quantify the connectivity relationships between water bodies in the water network. The elements in the matrix can accurately reflect information such as whether there is a direct connection between any two water bodies and the strength of the connection, providing a standardized data structure for subsequent numerical calculations and analysis. Based on the target adjacency matrix, the target connectivity index, target shortest flow path, and target water flow efficiency corresponding to the target shortest flow path for the target water network are calculated. Calculating the target connectivity index can quantitatively measure the overall connectivity of the target water network. The target shortest flow path helps find the most efficient path for water resources to flow within the water network. This is crucial for optimizing water resource allocation plans and reducing energy loss and time delays during water flow. By selecting the shortest flow path, water resource delivery efficiency can be improved, allocation costs can be reduced, and water resources can be quickly and efficiently delivered to demand areas. The target flow efficiency further understands the flow characteristics of water within the water network. Based on the target connectivity index, target shortest flow path, and target flow efficiency, the target water system connectivity results for the target water network are determined.

[0018] In an optional embodiment, based on the water characteristics corresponding to each water body, each water body is regarded as a water body node, and the channels between the water bodies are regarded as edges, thereby constructing a target water system network model, including:

[0019] Obtain future meteorological observation data and current hydrological monitoring data corresponding to each water body;

[0020] Based on the water body characteristics, future meteorological observation data, and current hydrological monitoring data of each water body, node attribute information is added to the water body nodes corresponding to each water body, and edge attribute information is added to the edges corresponding to each channel to construct an initial water system network model.

[0021] Input the water characteristics, future meteorological observation data and current hydrological monitoring data corresponding to each water body into the preset prediction model, and output the hydrological dynamic change data corresponding to each water body within the preset time in the future;

[0022] The hydrological dynamic change data corresponding to each water body are integrated with the initial water system network model to generate the target water system network model corresponding to the target water network.

[0023] The water network pattern planning method provided in the embodiment of the present application obtains future meteorological observation data and current hydrological monitoring data corresponding to each water body. Based on the water body characteristics, future meteorological observation data and current hydrological monitoring data corresponding to each water body, node attribute information is added to the water body nodes corresponding to each water body, and edge attribute information is added to the edges corresponding to each channel to construct an initial water system network model. Different types of data (such as position pattern data reflecting the static characteristics of the water body and hydrological data reflecting dynamic changes) are organically combined through the addition of attribute information, which facilitates the analysis of the interaction and influence mechanism between various factors, laying the foundation for in-depth research on the dynamic change laws of the water system. The water body characteristics, future meteorological observation data and current hydrological monitoring data corresponding to each water body are input into a preset prediction model, and the hydrological dynamic change data corresponding to each water body within a preset time in the future are output. This helps to understand in advance the possible changes in the water system in the future and provide forward-looking information for decisions such as water resources management, flood prevention and disaster reduction, and ecological protection. Accurate prediction of hydrological dynamic changes can help rationally plan the utilization and allocation of water resources. The hydrological dynamic change data corresponding to each water body are fused with the initial water system network model to generate a target water system network model corresponding to the target water network. This enables the generated target water network model to dynamically reflect changes in the water system in real time. The target water network model integrates current and future water system information, providing a more comprehensive and accurate basis for decision-making in water resource management, water ecological protection, and water conservancy project planning. Through model analysis and simulation, the effectiveness of different decision-making options can be evaluated, and the pros and cons of various potential measures can be compared to select the optimal option. This improves the scientific and rational nature of decision-making, promoting the sustainable use of water resources and the healthy development of the water system ecosystem.

[0024] In an optional embodiment, based on the target water system network model, a target adjacency matrix corresponding to each water body is constructed, including:

[0025] According to the connection status between each water body, generate the initial adjacency matrix corresponding to each water body;

[0026] Generate a dynamic data list corresponding to each water body based on the water body characteristics, future meteorological observation data, current hydrological monitoring data, and hydrological dynamic change data corresponding to each water body in the target water system network model;

[0027] Associating each dynamic data list with the initial adjacency matrix to generate a backup adjacency matrix;

[0028] According to the water body characteristics, future meteorological observation data, current hydrological monitoring data and hydrological dynamic change data corresponding to each water body, the weight information corresponding to each edge in the backup adjacency matrix is ​​calculated to generate the target adjacency matrix.

[0029] The water network pattern planning method provided in the embodiments of the present application generates an initial adjacency matrix corresponding to each water body based on the connectivity status between each water body. This matrix intuitively and concisely describes the connectivity relationships between each water body in the target water network. Based on the water characteristics, future meteorological observation data, current hydrological monitoring data, and hydrological dynamic change data corresponding to each water body in the target water network model, a dynamic data list corresponding to each water body is generated. This achieves in-depth integration of relevant water network data and provides a rich information foundation for comprehensive analysis of water network behavior. Furthermore, the dynamic data list can accurately reflect the state changes of each water body under different time and environmental conditions in real time, providing strong support for prediction and decision-making. Each dynamic data list is associated with the initial adjacency matrix to generate a backup adjacency matrix. This gives the adjacency matrix richer connotations, allowing the adjacency matrix to not only reflect the connectivity relationships between water bodies but also the dynamic attribute information of each water body. Based on the water characteristics, future meteorological observation data, current hydrological monitoring data, and hydrological dynamic change data corresponding to each water body, the weight information corresponding to each edge in the backup adjacency matrix is ​​calculated to generate the target adjacency matrix. This achieves precise quantification of the connectivity relationships between water bodies. The weights comprehensively consider multiple factors, including flow resistance, water transmission costs, and connectivity stability, enabling the matrix to more realistically reflect the actual flow conditions within the water network. Furthermore, the target adjacency matrix provides core data support for scientific decision-making regarding the water network. In water resources planning and network optimization, the weight information based on the matrix can accurately assess the pros and cons of different connection paths and formulate optimal water resource allocation plans and water conservancy project construction plans. Furthermore, by dynamically updating the weights, changes in the water network can be reflected in real time, enabling decision-making to adapt to the ever-changing water network environment and ensuring the scientific and effective management of water resources.

[0030] In an optional embodiment, based on the target adjacency matrix, calculating the target connectivity index, the target shortest flow path, and the target water flow efficiency corresponding to the target shortest flow path corresponding to the target water network includes:

[0031] Based on the target adjacency matrix, calculate the current connectivity index corresponding to the target water network and the corresponding predicted connectivity index within a preset time period in the future;

[0032] Generate a target connectivity index based on the current connectivity index and the predicted connectivity index;

[0033] Based on the target adjacency matrix, calculate the current shortest flow path corresponding to the target water network and the predicted shortest flow path within a preset time period in the future;

[0034] Generate a target shortest flow path based on the current shortest flow path and the predicted shortest flow path;

[0035] Based on the current shortest flow path and the predicted shortest flow path, calculate the current water flow efficiency corresponding to the target water network and the predicted water flow efficiency corresponding to the preset time period in the future;

[0036] The target water flow efficiency is generated based on the current water flow efficiency and the predicted water flow efficiency.

[0037] The water network pattern planning method provided in the embodiments of the present application calculates the current connectivity index of the target water network and the corresponding predicted connectivity index within a preset future time period based on the target adjacency matrix. This method can accurately quantify the current connectivity level of the target water network. Calculating the predicted connectivity index within a preset future time period allows for an estimation of the connectivity development trend of the water network over a period of time based on existing data and models. A target connectivity index is generated based on the current connectivity index and the predicted connectivity index. This method can comprehensively assess the connectivity of the target water network from both static and dynamic perspectives. This comprehensive assessment approach avoids the limitations of only considering the current state while ignoring future changes, or relying solely on predictions while ignoring current actual conditions, providing a more accurate and comprehensive reference for long-term planning and management of water networks. Based on the target adjacency matrix, the current shortest flow path for the target water network and the predicted shortest flow path within a preset future time period are calculated. Determining the current shortest flow path helps achieve efficient allocation of water resources in the current period. Predicting the predicted shortest flow path within a preset future time period allows for consideration of the flow direction of the water network under different future scenarios. Based on the current shortest flow path and the predicted shortest flow path, a target shortest flow path is generated, providing a comprehensive water flow routing solution for water network management. This solution takes into account both the current water network conditions and possible future changes. Based on the current shortest flow path and the predicted shortest flow path, the current water flow efficiency of the target water network and the predicted water flow efficiency for a preset future time period are calculated. Calculating the current water flow efficiency helps understand the water flow status of each part of the water network, identifying areas with slow flow or excessive energy loss, and taking timely optimization measures to improve the efficiency of the entire water network. Calculating the predicted water flow efficiency provides early insight into potential efficiency issues that may arise in the water network in the future. Based on the current and predicted water flow efficiencies, a target water flow efficiency is generated. This provides an important reference indicator for the long-term operation and management of the water network. Long-term maintenance and upgrade plans based on the target water flow efficiency can ensure that the water network maintains high efficiency over time, achieving efficient use and sustainable management of water resources.

[0038] In an optional embodiment, the target water system connectivity result includes the current water system connectivity result and the future water system connectivity result within a preset time period in the future; based on the target water system connectivity result, the target water resource supply and demand matching degree corresponding to the target water network is calculated, including:

[0039] Based on the current water system connectivity results, calculate the current water resource supply and demand matching degree corresponding to the target water network;

[0040] Based on the future water system connectivity results, calculate the future water resource supply and demand matching degree corresponding to the target water network;

[0041] Based on the current water resource supply and demand matching degree and the future water resource supply and demand matching degree, the target water resource supply and demand matching degree corresponding to the target water network is determined.

[0042] The water network pattern planning method provided in the embodiment of the present application calculates the current water resource supply and demand matching degree corresponding to the target water network based on the current water system connectivity results. It can accurately reflect the balance state of water resource supply and demand of the target water network at the current moment. Based on the future water system connectivity results, the future water resource supply and demand matching degree corresponding to the target water network is calculated, which can predict in advance the water resource supply and demand change trend of the target water network within a preset time period in the future. Based on the current water resource supply and demand matching degree and the future water resource supply and demand matching degree, the target water resource supply and demand matching degree corresponding to the target water network is determined. A comprehensive evaluation of the water resource supply and demand status of the target water network from the present to the future is achieved. This evaluation method avoids the limitations of focusing only on a single stage in the present or future, comprehensively considers the dynamic changes in water network operation, and provides a more systematic and comprehensive perspective for water resource management. In addition, the target water resource supply and demand matching degree combines the current actual situation and future development trends, providing a more scientific and reliable basis for water resource management decisions.

[0043] In an optional embodiment, based on the future water system connectivity results, calculating the future water resource supply and demand matching degree corresponding to the target water network includes:

[0044] Obtaining future time characteristic data of a preset future time period corresponding to the target area and the future water resource demand corresponding to the target area;

[0045] Calculate the future water resource supply corresponding to the target water network based on the corresponding water body characteristics of each water body, future meteorological observation data and current hydrological monitoring data;

[0046] Based on future time characteristic data, future water resource demand, future water resource supply, and future water system connectivity results, calculate the future spatial supply and demand matching degree and future temporal supply and demand matching degree corresponding to the target water network;

[0047] Based on the future spatial supply and demand matching degree and the future temporal supply and demand matching degree, the future water resource supply and demand matching degree corresponding to the target water network is determined.

[0048] The water network pattern planning method provided in the embodiment of the present application obtains the future time characteristic data of the preset time period in the future corresponding to the target area, and the future water resource demand corresponding to the target area. Based on the water body characteristics corresponding to each water body, future meteorological observation data and current hydrological monitoring data, the future water resource supply corresponding to the target water network is calculated, thereby ensuring the accuracy of the obtained future water resource supply corresponding to the target water network. Based on the future time characteristic data, future water resource demand, future water resource supply and future water system connectivity results, the future spatial supply and demand matching degree and future time supply and demand matching degree corresponding to the target water network are calculated, thereby ensuring the accuracy of the calculated future spatial supply and demand matching degree and future time supply and demand matching degree corresponding to the target water network. Based on the future spatial supply and demand matching degree and the future time supply and demand matching degree, the future water resource supply and demand matching degree corresponding to the target water network is determined. The accuracy of the determined future water resource supply and demand matching degree corresponding to the target water network is ensured.

[0049] In an optional embodiment, based on future time characteristic data, future water resource demand, future water resource supply, and future water system connectivity results, the future spatial supply-demand matching degree and future temporal supply-demand matching degree corresponding to the target water network are calculated, including:

[0050] Each water-using area in the target water network is used as a regional node, and the water flow channels between the water-using areas are used as edges. Weights are assigned to each edge based on the future water system connectivity results to construct a basic graph structure.

[0051] According to the time step, based on the future time characteristic data, future water resource demand, and future water resource supply, the status of each regional node and the weight of each edge in the basic graph structure are adjusted to generate multiple spatiotemporal graphs based on time series;

[0052] Construct a multi-layer spatiotemporal graph convolutional network and use graph convolution operations to perform spatial node features corresponding to each regional node in the basic graph structure;

[0053] Use the temporal convolution layer to perform convolution operations on each spatiotemporal graph to obtain the time node features corresponding to each regional node in the basic graph structure;

[0054] Based on the characteristics of each spatial node, the future spatial supply and demand matching degree corresponding to the target water network is calculated;

[0055] Based on the characteristics of each time node, the future time supply and demand matching degree of the target water network is calculated.

[0056] The water network pattern planning method provided in the embodiments of the present application uses each water-using area in the target water network as a regional node and the water flow channels between each water-using area as an edge. Weights are assigned to each edge based on the future water system connectivity results to construct a basic graph structure. This method can intuitively present the water flow relationship and connectivity between each water-using area in the target water network. According to the time step, the status of each regional node in the basic graph structure and the weight of each edge are adjusted based on future time characteristic data, future water resource demand, and future water resource supply. Multiple spatiotemporal graphs based on time series are generated, which can fully account for the dynamic changes in water resource supply and demand over time. A multi-layer spatiotemporal graph convolutional network is constructed, and the spatial node features corresponding to each regional node in the basic graph structure are processed through graph convolution operations. This effectively mines the spatial correlation features between each water-using area. A temporal convolution layer is used to perform convolution operations on each spatiotemporal graph to obtain the time node features corresponding to each regional node in the basic graph structure. This method can capture the characteristics and patterns of water resource supply and demand in the time series. This helps understand the evolution pattern of water resource supply and demand over time and provides important temporal information for predicting future supply and demand. By analyzing the characteristics of time nodes, we can assess the degree of matching between water supply and demand at different time scales, providing support for developing appropriate time scheduling strategies. Based on the characteristics of each spatial node, we calculate the future spatial matching degree of supply and demand for the target water network. This allows us to spatially assess the matching between water supply and demand across water-using areas within the target water network, clarifying the spatial distribution of water surpluses and shortages across different regions. Based on the characteristics of each time node, we calculate the future temporal matching degree of supply and demand for the target water network. This temporal assessment of the matching degree between water supply and demand at different moments can help develop water scheduling plans that adapt to different time scales.

[0057] In a second aspect, the present invention provides a water network pattern planning device, characterized in that the device comprises:

[0058] An acquisition module is used to acquire at least one water body corresponding to the target area and water body characteristics corresponding to each water body; water body characteristics include water body location pattern characteristics, water body morphology characteristics, water body structure characteristics and water body flow characteristics;

[0059] The first calculation module is used to calculate the target water system connectivity result of the target water network corresponding to the target area based on the water body characteristics corresponding to each water body; the target water network is based on the composition of each water body;

[0060] The second calculation module is used to calculate the target water resource supply and demand matching degree corresponding to the target water network based on the target water system connectivity result;

[0061] The adjustment module is used to plan and adjust the pattern of the target water network based on the target water resource supply and demand matching degree.

[0062] The water network pattern planning device provided in an embodiment of the present application obtains at least one water body corresponding to a target area and the water characteristics corresponding to each water body; based on the water characteristics corresponding to each water body, it calculates the target water system connectivity results for the target water network corresponding to the target area. This device can quantitatively assess the degree of connectivity between the various water bodies in the target water network. This helps determine the smoothness of water resource flow throughout the entire water network, as well as the possibility and difficulty of water resource allocation between different regions. By analyzing water system connectivity, it is possible to identify potential weak links or obstacles in the water network. Based on the target water system connectivity results, it calculates the target water resource supply and demand matching degree corresponding to the target water network. This device can clarify the balance between water resource supply and demand in the target water network. This helps managers understand the water resource supply and demand status in different regions, thereby more scientifically allocating and scheduling water resources, achieving optimal resource allocation, and avoiding the unreasonable phenomenon of water resource surplus in some regions and severe shortages in others.

[0063] Accurate calculations of water resource supply-demand matching provide an important quantitative basis for water resource management decisions. Based on the target water resource supply-demand matching, adjustments are made to the target water network layout to improve its structure and functionality in a targeted manner. Reasonable adjustments to the water network layout contribute to the sustainable use of water resources and sustainable development in the region. By optimizing the water network layout, while ensuring water demand for economic development, we can better protect the ecological environment, maintain the balance of aquatic ecosystems, achieve coordinated allocation and utilization of water resources among the economic, social, and ecological dimensions, and promote the long-term and stable development of the target region.

[0064] In a third aspect, the present invention provides an electronic device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the water network pattern planning method of the first aspect or any corresponding embodiment thereof by executing the computer instructions. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0066] Figure 1 is a flow chart of a water network pattern planning method according to an embodiment of the present invention;

[0067] Figure 2 is a flow chart of another water network pattern planning method according to an embodiment of the present invention;

[0068] Figure 3 is a structural block diagram of a water network pattern planning device according to an embodiment of the present invention;

[0069] Figure 4 FIG. 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0071] It should be noted that the execution subject of the method for water network pattern planning provided in the embodiment of the present application can be a device for water network pattern planning, and the device for water network pattern planning can be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware, wherein the electronic device can be a server or a terminal, wherein the server in the embodiment of the present application can be a single server or a server cluster composed of multiple servers, and the terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, a wearable device, an intelligent robot, and other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0072] According to an embodiment of the present invention, an embodiment of a water network pattern planning method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0073] In this embodiment, a water network pattern planning method is provided, which can be used for the above-mentioned electronic equipment. Figure 1 FIG. 1 is a flow chart of a water network pattern planning method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0074] Step S101: Acquire at least one water body corresponding to a target area and water body features corresponding to each water body.

[0075] Water body characteristics include location and pattern characteristics, morphological characteristics, structural characteristics, and flow characteristics. Location and pattern characteristics can include information such as the water body's geographic location and altitude. Morphological characteristics can include planar and three-dimensional (elevational) morphological characteristics. Planar morphological characteristics include river length, curvature, shoreline development coefficient, and main river area-to-length ratio. Three-dimensional (elevational) morphological characteristics include indicators such as width-to-depth ratio, channel storage capacity, channel storage capacity per unit area, and adjustable storage capacity per unit area. Structural characteristics include structural characteristic indicators reflecting the relationships between rivers and lakes, and structural characteristic indicators reflecting the relationships between rivers and lakes. Structural characteristic indicators reflecting the relationships between rivers and lakes include river branching ratio, water network fractal dimension, river network development coefficient, river network structural stability, and water network heterogeneity coefficient. Structural characteristic indicators reflecting the relationships between rivers and lakes include the number of river network nodes, water network circularity, node connectivity, and hydrological connectivity. Flow characteristics include flow direction, velocity, flow rate, water level fluctuations, and flow paths.

[0076] The electronic device may receive at least one water body and water characteristics corresponding to the target area input by the user, or may receive at least one water body and water characteristics corresponding to the water bodies sent by other devices.

[0077] Step S102 : calculating the target water system connectivity result of the target water network corresponding to the target area based on the water body characteristics corresponding to each water body.

[0078] Among them, the target water network is based on the composition of various water bodies.

[0079] Specifically, the electronic device can input the water body characteristics corresponding to each water body into a preset model to calculate the target water system connectivity result of the target water network corresponding to the target area.

[0080] This step will be described in detail below.

[0081] Step S103: Calculate the target water resource supply and demand matching degree corresponding to the target water network based on the target water system connectivity result.

[0082] Specifically, the electronic device can calculate the target water resource supply and demand matching degree corresponding to the target water network based on the target water system connectivity result.

[0083] This step will be described in detail below.

[0084] Step S104: Based on the target water resource supply and demand matching degree, the pattern of the target water network is planned and adjusted.

[0085] Specifically, based on the target water resource supply-demand matching degree, the electronic device can analyze the distribution of various water bodies in the target water network to determine whether there is a spatial misalignment between water resource supply and demand areas. For example, some urban areas with high water demand lack stable water sources, while areas with abundant water resources are located far from water consumption centers, resulting in high water transmission costs and significant losses. Furthermore, based on the target water resource supply-demand matching degree, the electronic device can assess the connectivity of the target water network to determine whether there is poor connectivity between water bodies, resulting in some areas becoming "islands." Poor connectivity can limit the ability to allocate water resources, making it difficult to effectively replenish water when local areas are short of water. Furthermore, based on the target water resource supply-demand matching degree, the electronic device can examine the scale and function of water storage bodies such as reservoirs, ponds, and wetlands in the target water network to determine whether they have sufficient storage capacity to cope with temporal and spatial variations in water resources. For example, if excess water cannot be effectively stored during the flood season, it will be difficult to meet water demand during the dry season.

[0086] Then, based on the target water resource supply-demand match and the target water system connectivity, more rational water resource allocation routes can be planned. For water-scarce areas, water should be transferred preferentially from nearby surplus areas, choosing routes with good connectivity and high water transfer efficiency. For example, by constructing new water transfer channels or optimizing existing river channels, water flow paths can be shortened and water losses reduced. To address insufficient connectivity, engineering measures (such as building new connecting ditches and widening narrow river channels) and management measures (optimizing the operation of water conservancy facilities) can be implemented to enhance water network connectivity. Improving the overall connectivity of the water network will facilitate flexible water resource allocation and enhance the ability to respond to emergencies. Rationally plan and construct water storage facilities, increase the storage capacity of reservoirs and ponds, and restore and protect natural storage spaces such as wetlands. By optimizing the layout and operational management of water storage facilities, water storage capacity can be improved, ensuring a rational allocation of water resources during both wet and dry seasons. Based on water resource supply and demand, industrial layout and urban development plans can be rationally adjusted. Highly water-consuming industries can be encouraged to relocate to water-rich regions, and agricultural cropping structures can be optimized to reduce water demand in water-scarce areas. For example, promote the planting of drought-resistant crops in water-scarce areas and develop water-saving agriculture.

[0087] The water network pattern planning method provided in this embodiment obtains at least one water body corresponding to a target area and the corresponding water characteristics of each water body. Based on the corresponding water characteristics of each water body, the target water system connectivity results for the target water network corresponding to the target area are calculated. This method can quantitatively assess the degree of connectivity between the various water bodies in the target water network. This helps determine the smoothness of water resource flow throughout the entire water network, as well as the feasibility and difficulty of water resource allocation between different regions. By analyzing water system connectivity, it is possible to identify potential weak links or obstacles in the water network. Based on the target water system connectivity results, the target water resource supply and demand matching degree for the target water network is calculated. This method can clarify the balance between water resource supply and demand in the target water network. This helps managers understand the water resource supply and demand status in different regions, enabling more scientific allocation and scheduling of water resources, achieving optimal resource allocation, and avoiding the unreasonable phenomenon of water resource surplus in some areas and severe shortages in others.

[0088] Accurate calculations of water resource supply-demand matching provide an important quantitative basis for water resource management decisions. Based on the target water resource supply-demand matching, adjustments are made to the target water network layout to improve its structure and functionality in a targeted manner. Reasonable adjustments to the water network layout contribute to the sustainable use of water resources and sustainable development in the region. By optimizing the water network layout, while ensuring water demand for economic development, we can better protect the ecological environment, maintain the balance of aquatic ecosystems, achieve coordinated allocation and utilization of water resources among the economic, social, and ecological dimensions, and promote the long-term and stable development of the target region.

[0089] In this embodiment, a water network pattern planning method is provided, which can be used for the above-mentioned electronic equipment. Figure 2 FIG. 1 is a flow chart of a water network pattern planning method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0090] Step S201: Acquire at least one water body corresponding to a target area and water body characteristics corresponding to each water body.

[0091] Among them, water body characteristics include water body location pattern characteristics, water body morphology characteristics, water body structure characteristics and water body flow characteristics.

[0092] Regarding this step, please refer to the above description of step S101, which will not be described in detail here.

[0093] Step S202 : Calculate the target water system connectivity result of the target water network corresponding to the target area based on the water body characteristics corresponding to each water body.

[0094] Among them, the target water network is based on the composition of various water bodies.

[0095] Specifically, the above step S202 may include the following steps:

[0096] In step S2021 , based on the water body characteristics corresponding to each water body, each water body is regarded as a water body node, and the channels between the water bodies are regarded as edges, thereby constructing a target water system network model.

[0097] Specifically, the above step S2021 may include the following steps:

[0098] Step a1: Obtain future meteorological observation data and current hydrological monitoring data corresponding to each water body.

[0099] Among them, future meteorological observation data may include rainfall, evaporation, temperature, wind speed, wind direction, sunshine hours, etc. Current hydrological monitoring data may include water level, flow, water quality, etc.

[0100] Specifically, electronic devices can obtain public future weather observation data from national meteorological department websites and international climate data centers. For research with special needs, they can also purchase commercial weather data services or collaborate with meteorological research institutions to obtain customized future weather observation data.

[0101] Electronic equipment can monitor water levels using water level gauges installed near water bodies, measuring the vertical distance from the water surface to a certain reference plane to reflect changes in water volume. Velocity meters installed near water bodies can measure flow velocity, providing information on water flow speed. Flow meters installed near water bodies can also measure flow rate, i.e., the volume of water passing through a specific section per unit time. Water quality indicators such as dissolved oxygen, chemical oxygen demand, total nitrogen, and total phosphorus are also monitored, often using water quality monitors.

[0102] In step a2, based on the water body characteristics, future meteorological observation data, and current hydrological monitoring data corresponding to each water body, node attribute information is added to the water body nodes corresponding to each water body, and edge attribute information is added to the edges corresponding to each channel to construct an initial water system network model.

[0103] Specifically, the electronic device can use a preset autoencoder to extract key features from various data types, including water body characteristics, future meteorological observation data, and current hydrological monitoring data. These features are then encoded into a unified feature vector, enabling deep data fusion. For example, the location, pattern, and morphological characteristics of a water body can be integrated with future precipitation probability and current water level data to explore potential correlations between the data and provide richer and more accurate information for subsequent modeling.

[0104] Then, based on the characteristic vector corresponding to each water body, node attribute information is added to each corresponding water body node.

[0105] The electronic device calculates the velocity correction factor for the water flow in the channel at different times based on future meteorological data, combined with the channel's topography and orientation, and dynamically adjusts the coefficient over time. Furthermore, the electronic device uses current flow data from hydrological monitoring, combined with the channel's water-carrying capacity (based on its morphology and structural characteristics), to determine the flow-carrying capacity of the edge and dynamically adjusts the coefficient over time.

[0106] Then, based on the velocity correction coefficient and flow carrying capacity attributes, the electronic device adds edge attribute information to the edges corresponding to each channel to build an initial water network model.

[0107] Step a3: input the water characteristics, future meteorological observation data and current hydrological monitoring data corresponding to each water body into a preset prediction model, and output the hydrological dynamic change data corresponding to each water body within a preset time in the future.

[0108] Specifically, for each water body, the corresponding future meteorological observation data and current hydrological monitoring data are input into the first sub-prediction model of the preset prediction model, which outputs the first sub-hydrological dynamic change data corresponding to the water body within the preset future time. The corresponding future meteorological observation data and current hydrological monitoring data are input into the second sub-prediction model of the preset prediction model, which outputs the second sub-hydrological dynamic change data corresponding to the water body within the preset future time.

[0109] The water body characteristics corresponding to the water body are input into a preset spatial characteristic model in a preset prediction model, and the spatial characteristics corresponding to the water body are output.

[0110] The first sub-hydrological dynamic change data, the second sub-hydrological dynamic change data and the spatial characteristics are input into the meta-model, and finally the hydrological dynamic change data corresponding to the water body within a preset time in the future is output.

[0111] Among them, the first sub-prediction model and the second sub-prediction model can be any one of a recurrent neural network (RNN) and its variant long short-term memory network (LSTM), a gated recurrent unit (GRU), wherein the first sub-prediction model and the second sub-prediction model are different. The preset spatial feature model can be a convolutional neural network or other networks. The meta-model can be a logistic regression model or other models. The embodiment of the present application does not specifically limit the first sub-prediction model, the second sub-prediction model, the preset spatial feature model and the meta-model.

[0112] Step a4: The hydrological dynamic change data corresponding to each water body is integrated with the initial water system network model to generate a target water system network model corresponding to the target water network.

[0113] Specifically, the hydrological dynamic change data corresponding to each water body is used as new attribute information and associated with the attribute information corresponding to each water body to generate a target water system network model corresponding to the target water network.

[0114] For example, for each water body node, the predicted future water level change data is added as the water body node's dynamic attribute; for the channel edge, the water flow velocity change data is updated as the edge attribute. In this way, the model can reflect the dynamic characteristics of water bodies and water flows.

[0115] Step S2022: construct a target adjacency matrix corresponding to each water body based on the target water system network model.

[0116] Specifically, the above step S2022 may include the following steps:

[0117] Step b1: Generate an initial adjacency matrix corresponding to each water body according to the connection status between each water body.

[0118] Specifically, the electronic device counts the total number of water bodies in the target area, and assumes that the total number of water bodies is n. The size of the initial adjacency matrix is ​​determined to be n×n based on the number of water bodies. The rows and columns of the matrix correspond to each water body, so that each element in the matrix can accurately represent the connection relationship between a pair of water bodies. Then, create an n×n matrix and initialize all elements to 0. In this initial state, the matrix indicates that there is no connection between all water bodies, and the elements will be modified later based on the actual connection situation. Traverse each pair of water bodies in turn according to the order of rows and columns of the matrix. For each pair of water bodies, fill in the matrix elements according to the connection relationship determined previously. If the two water bodies currently traversed are connected to each other, the element at the intersection of the corresponding row and column in the matrix is ​​set to 1; if the two water bodies are not connected, keep the element at 0. For example, if water body A and water body B are connected, and water body A corresponds to the i-th row in the matrix, and water body B corresponds to the j-th column (i

[0119] =j), then both elements aij and aji in the matrix are set to 1 (in an undirected graph, the connectivity is symmetrical; if flow direction is considered, in a directed graph, only the elements in one direction may be set to 1). For the connectivity between a water body itself (i.e., the diagonal elements aii of the matrix), the value is usually determined based on the research purpose. In general water system connectivity analysis, it can be set to 0, indicating that there is no actual flow connectivity between the water body itself (except in special cases, such as some water bodies with internal circulation).

[0120] Step b2: Generate a dynamic data list corresponding to each water body based on the water body characteristics, future meteorological observation data, current hydrological monitoring data, and hydrological dynamic change data corresponding to each water body in the target water system network model.

[0121] Specifically, electronic devices can extract information related to the static properties of water bodies from water feature data, such as the water body's geographic coordinates, altitude, surface area, and river network density. These attributes reflect the basic characteristics of the water body and are the basis for constructing dynamic data lists, helping to determine the location and scale of the water body in the river network.

[0122] The electronic device then combines future meteorological observations with current hydrological monitoring data to extract properties closely related to the dynamic changes in the water body. For example, it calculates the changing trend of the water balance based on precipitation and evaporation data; extracts factors that may affect the surface flow of the water body from wind speed and direction data; and uses indicators such as dissolved oxygen and chemical oxygen demand from water quality monitoring data to reflect the dynamic changes in water quality.

[0123] Furthermore, electronic equipment conducts in-depth analysis of hydrological dynamic change data, selecting key dynamic change characteristics. For example, it focuses on the amplitude of water level fluctuations, the rate of change of flow, and the changing trends of water quality indicators. These key characteristics can highlight the dynamic change trends of water bodies in the future period, providing important basis for subsequent decision-making and analysis.

[0124] Finally, the electronic device determines the structure of the dynamic data list based on the extracted data features. The list is typically presented in a table format, with each row representing a time step (e.g., hourly, daily, etc.) and each column corresponding to a different attribute or characteristic of a water body. The table header clearly indicates the meaning of the data represented by each column, such as "Time," "Water Level," "Flow," "Dissolved Oxygen," etc., to ensure data clarity and readability. The processed information related to the static and dynamic properties of the water body is populated into the dynamic data list in chronological order, using dynamic change characteristics. During the populating process, data accuracy and completeness are ensured, and each attribute data for each water body at the same time step is accurately filled into the corresponding cells. For example, at a specific moment, data such as the water level, flow, water balance change calculated based on meteorological data, and predicted water quality changes for a water body are accurately entered into the corresponding rows and columns. To facilitate understanding of the data in the dynamic data list, necessary annotations and explanatory information are added. Explanations should be provided for any unique data values ​​or trends, including the data source, processing method, and possible error range. This helps users interpret the data more accurately and avoid misunderstandings and misjudgments.

[0125] Step b3: Associating each dynamic data list with the initial adjacency matrix to generate a backup adjacency matrix.

[0126] Specifically, the electronic device can assign a unique identifier (such as a water body number or geographic location code) to each water body. Both the dynamic data list and the initial adjacency matrix use this identifier to identify the corresponding water body. Using the water body identifier as a link, an association is established between the two. This allows accurate matching of a water body's dynamic data with the corresponding row and column in the initial adjacency matrix during data association.

[0127] Then, the electronic device determines the correspondence rules between the dynamic data list and the initial adjacency matrix elements. For example, certain key data in the dynamic data list (such as flow rate, water level change trend, etc.) can be used as weights and combined with the connection relationship in the initial adjacency matrix. If there is a connection between two water bodies (the corresponding element in the initial adjacency matrix is ​​1), the weight of this connection is adjusted according to the size of their flow data. The larger the flow rate, the higher the weight; or the direction of water flow is determined according to the water level change trend. If the water level of one water body rises and the water level of the other water body drops, and the two are connected, the connection direction and strength information can be further refined.

[0128] Based on the size and structure of the initial adjacency matrix, a framework for a backup adjacency matrix is ​​created. The backup adjacency matrix has the same number of rows and columns as the initial adjacency matrix, again using water bodies as row and column identifiers. Within this framework, each element carries fused information, reflecting not only the connectivity between water bodies but also incorporating dynamic data.

[0129] Finally, according to the established association method and corresponding rules, the data in the dynamic data list are filled into the backup adjacency matrix. For the position where the element in the initial adjacency matrix is ​​1 (that is, there is a connection between water bodies), new values ​​are calculated and filled based on the dynamic data. For example, if two water bodies are connected, the flow data between them is used as the weight, and the weight value is filled into the corresponding element position of the backup adjacency matrix; if the element is 0 (there is no connection between water bodies), it is decided according to the actual situation whether to fill in a special value (such as -1 means no connection and no relevant dynamic data association) or keep the value 0 to maintain the sparsity and readability of the matrix. When filling in matrix elements, an appropriate data fusion strategy is adopted. For the case where multiple dynamic data affect the same matrix element, weighted average, principal component analysis and other methods can be used for fusion. For example, when the flow and water level change trends both affect the weight of the connection between two water bodies, the final weight value is determined by calculating the weighted average, where the weight can be set according to the importance or relevance of the data.

[0130] Step b4, based on the water body characteristics corresponding to each water body, future meteorological observation data, current hydrological monitoring data and hydrological dynamic change data, calculate the weight information corresponding to each edge in the backup adjacency matrix to generate the target adjacency matrix.

[0131] Specifically, the electronic device can calculate the initial weight information corresponding to each edge in the backup adjacency matrix based on the water flow characteristics between the water bodies. For example, water flow-related factors such as flow rate, flow velocity and water level difference are considered. The weight of the connecting edge between water bodies with large flow rate can be set higher, because a large flow rate means more frequent material and energy exchange between water bodies, which has a greater impact on the water network. Flow velocity can also be used as a basis for weight adjustment. The weight of the connecting edge with a fast flow rate is appropriately increased, because a fast flow rate facilitates rapid connectivity and material transfer between water bodies. At the same time, the water level difference can reflect the potential energy difference between water bodies. The weight of the connecting edge with a large water level difference can be appropriately increased, indicating that water flows more easily between them.

[0132] Then, based on future meteorological observations, the initial weights corresponding to each edge in the backup adjacency matrix are adjusted to generate backup weights. For example, increased precipitation may lead to rising water levels and increased flow. For water body edges that may be affected by precipitation and thus have stronger connections, the weights are increased accordingly.

[0133] Finally, the backup weight information is adjusted again based on the characteristics and functions of the water bodies to generate the target weight information corresponding to each edge in the backup adjacency matrix, and the target adjacency matrix is ​​generated. For example, the morphological characteristics of the water body, such as the curvature and width of the river channel, will affect the water flow resistance. The weight of the connecting edge with small curvature and wide river channel can be appropriately increased because its water flow is smoother. The structural characteristics of the water body, such as the connectivity of the river network and the importance of the nodes, can also be used to set the weight. The weight of the connecting edge of the water body at the key connection node should be higher because these water body nodes are crucial to the overall connectivity and function of the water network. In addition, the weights of water bodies with different functions, such as irrigation water bodies and ecological water bodies, can be adjusted according to their functional importance when calculating the weight. For the connecting edges between water bodies that ensure agricultural irrigation, the weight can be appropriately increased during the peak period of agricultural water use.

[0134] Step S2023 , based on the target adjacency matrix, calculating the target connectivity index, the target shortest flow path, and the target water flow efficiency corresponding to the target shortest flow path corresponding to the target water network.

[0135] Specifically, the above step S2023 may include the following steps:

[0136] Step c1: Based on the target adjacency matrix, calculate the current connectivity index corresponding to the target water network and the corresponding predicted connectivity index within a preset time period in the future.

[0137] Specifically, based on the target adjacency matrix, each row (or column) of the matrix is ​​traversed to determine the connectivity of each water body. For a certain water body, the matrix elements in its row (or column) are checked. Non-zero elements indicate that the corresponding water body is connected to it, and the number of connected water bodies is counted. For example, for water body C, there are 5 non-zero elements in its corresponding row in the target adjacency matrix, so the number of water bodies connected to water body C, Nconnected, is 5. Then, based on the formula Calculate the current connectivity index corresponding to the target water network, where W i is the weight of water body i (such as area, ecological importance, etc.), C i is the connectivity coefficient of water body i. WCI can more comprehensively assess the overall function of the water system and highlight the connectivity of important water bodies.

[0138] The future connectivity of water networks will be affected by various factors, such as climate change and human activities. The target adjacency matrix is ​​adjusted based on future meteorological observations, water conservancy project plans, and other information. For example, if precipitation in a particular region is predicted to increase, the connection between two previously weakly connected water bodies may be strengthened, and the corresponding elements in the target adjacency matrix will be increased accordingly. If a planned new water conservancy project severes the connection between two water bodies, the corresponding elements will be set to 0. Using the same method used to calculate the current connectivity index, the connectivity index for a preset future duration is calculated based on the adjusted target adjacency matrix. Select the appropriate connectivity index calculation method (basic connectivity index or weighted connectivity index) and follow the calculation steps to determine the number of connected water bodies (re-traversing the adjusted matrix to determine Nconnected), calculate the weights (if using the weighted connectivity index), and substitute these into the formula to calculate the index value. The result is the predicted connectivity index for the target water network for the preset future duration.

[0139] Step c2: generating a target connectivity index based on the current connectivity index and the predicted connectivity index.

[0140] Specifically, the electronic device combines the current connectivity index and the predicted connectivity index to generate a target connectivity index.

[0141] Step c3: Based on the target adjacency matrix, the current shortest flow path corresponding to the target water network and the predicted shortest flow path corresponding to the future preset time period are calculated.

[0142] Specifically, the electronic device can construct a graph data structure based on the target adjacency matrix. Each water body is represented as a water node in the graph, and the connections between water bodies are edges. The weight of the edge is the value of the corresponding element in the adjacency matrix. Simultaneously, the starting node and target node are determined. The starting node can be selected based on the research objective, such as a major water source; the target node can be a water body corresponding to an area with high water demand.

[0143] Then, first mark the distance of the starting node as 0, and the distances of other nodes as infinity. Then, starting from the starting node, traverse its adjacent nodes and update the distances from the adjacent nodes to the starting node (the distance is equal to the distance from the starting node to the current node plus the edge weights from the current node to the adjacent nodes). Each time, select the undetermined node with the smallest distance and continue to expand its adjacent nodes until the target node is reached or all nodes are processed. During the traversal process, record the predecessor node of each water body node so that the shortest path can be obtained by backtracking at the end. By backtracking the predecessor node of the target node, the current shortest flow path from the starting node to the target node is obtained. This path is the optimal path for water flow from the starting point to the target point under the current water network connection status and weight settings.

[0144] In addition, the electronic equipment considers the impact of changes in precipitation patterns on water network flow based on future meteorological observation data. If precipitation in a certain area increases in the future, the flow of certain water bodies may increase. The weights of the connecting edges between these water bodies will be adjusted accordingly to make it more conducive to water flow (such as reducing the weight value); conversely, if precipitation decreases, the weights of certain edges may need to be increased. If there are plans to build new river channels, sluices, or widen existing river channels in the future, the target adjacency matrix will be adjusted according to the project plan. In addition, land use changes (such as urbanization and agricultural expansion) may affect the structure and water flow conditions of the water network.

[0145] The electronic device then selects a preset shortest path algorithm, similar to the one used to calculate the current shortest flow path, and performs calculations based on the adjusted target adjacency matrix. Similarly, the starting and target nodes are determined, and the algorithm steps are followed to calculate the predicted shortest flow path from the starting node to the target node within a preset future timeframe.

[0146] Step c4: generating a target shortest flow path based on the current shortest flow path and the predicted shortest flow path.

[0147] Specifically, the electronic device combines the current shortest flow path and the predicted shortest flow path to generate a target shortest flow path.

[0148] Step c5: Based on the current shortest flow path and the predicted shortest flow path, calculate the current water flow efficiency corresponding to the target water network and the predicted water flow efficiency corresponding to the preset time period in the future.

[0149] Specifically, the electronic device calculates the total resistance of water flow through the path based on the weight of each connecting edge on the current shortest flow path. Assuming that the current shortest flow path consists of edges e1, e2, ..., en, whose weights are w1, w2, ..., wn respectively, the total resistance The current water flow efficiency E can be expressed as the inverse of the total resistance, that is, E = 1 / R.

[0150] Similarly, the electronic device calculates the predicted water flow efficiency corresponding to the future preset time period based on the predicted shortest flow path.

[0151] Step c6: generating a target water flow efficiency based on the current water flow efficiency and the predicted water flow efficiency.

[0152] Specifically, the electronic device combines the current water flow efficiency and the predicted water flow efficiency to generate a target water flow efficiency.

[0153] Step S2024: determining a target water system connectivity result corresponding to the target water network based on the target connectivity index, the target shortest flow path, and the target water flow efficiency.

[0154] Specifically, the electronic device generates a current water system connectivity result based on the fusion of the current connectivity index in the target connectivity index, the current shortest flow path in the target shortest flow path, and the current water flow efficiency in the target water flow efficiency.

[0155] Specifically, the electronic device can compare the relative importance of the current connectivity index, the current shortest flow path, and the current water flow efficiency to the current water system connectivity to construct a judgment matrix. The comparison adopts a 1-9 scale, where 1 means that the two factors are equally important, 3 means that one factor is slightly more important than the other, 5 means that one factor is significantly more important than the other, 7 means that one factor is strongly more important than the other, 9 means that one factor is extremely important than the other, and 2, 4, 6, and 8 are the intermediate values ​​of adjacent judgments. Assume that A represents the judgment matrix, a ij Represents the ratio of the relative importance of the i-th factor to the j-th factor, then the judgment matrix A is as follows:

[0156]

[0157] For example, if the current connectivity index is considered to be significantly more important than the current shortest flow path, strongly more important than the current water flow efficiency, and the current shortest flow path is slightly more important than the current water flow efficiency, then the judgment matrix may be:

[0158]

[0159] Calculate the maximum eigenvalue λmax of the judgment matrix A and its corresponding eigenvector W. Usually, methods such as the square root method or the sum-product method can be used for calculation. Taking the square root method as an example, the calculation steps are as follows:

[0160] Calculate the product Mi of each row element of the judgment matrix A:

[0161] M1=1×5×7=35, M2=1 / 5×1×3=3 / 5, M3=1 / 7×1 / 3×1=1 / 21.

[0162] Calculate M i The nth root of (n is the order of the matrix, here n = 3):

[0163]

[0164] Will Normalize and get the weight vector W:

[0165]

[0166] Therefore, the weight vector W = (0.730, 0.188, 0.082) T , which represents the relative weights of the current connectivity index, the current shortest flow path, and the current water flow efficiency to the current water system connectivity.

[0167] Similarly, the electronic device generates a future water system connectivity result based on the fusion of the future connectivity index in the target connectivity index, the future shortest flow path in the target shortest flow path, and the future water flow efficiency in the target water flow efficiency.

[0168] Then, the current water system connectivity results are combined with the future water system connectivity results to generate the target water system connectivity results.

[0169] Step S203: Calculate the target water resource supply and demand matching degree corresponding to the target water network based on the target water system connectivity result.

[0170] Specifically, the target water system connectivity result includes the current water system connectivity result and the future water system connectivity result within a preset time period. The above step S203 may include the following steps:

[0171] Step S2031: Calculate the current water resource supply and demand matching degree corresponding to the target water network based on the current water system connectivity result.

[0172] Specifically, electronic devices can obtain current time characteristic data corresponding to the target area, which may include time information such as season, month, and day. These data will affect the demand and supply of water resources. For example, water consumption usually increases in summer, while river runoff may decrease in winter.

[0173] The electronic device calculates the current water resource demand in the target area by counting and analyzing water users such as population, industry, and agriculture. For example, the domestic water demand in densely populated urban areas and the production water demand in industrial areas. Then, based on the corresponding water characteristics of each water body (such as the width, depth, and flow rate of rivers, the water storage capacity of lakes, etc.) and current hydrological monitoring data (such as precipitation, evaporation, runoff, etc.), it calculates the water supply that the target water network can provide. For example, by monitoring river runoff and lake water level changes, combined with relevant water body characteristics, the amount of water resources available for supply can be estimated.

[0174] Electronic devices can analyze the water supply and demand situation at different locations within a target area. For example, some areas may have abundant water supply but low demand, while other areas may have high demand but insufficient supply. Specifically, the target area can be divided into several sub-areas, and the water supply-demand ratio can be calculated for each sub-area. For example, for sub-area i, its water supply is Si and its demand is Di, then the supply-demand ratio Ri = Si / Di.

[0175] Then, based on the area, population and other factors of each sub-region, these supply-demand ratios are weighted averaged to obtain the current spatial supply-demand matching degree of the entire target region. For example, if the area of ​​sub-region i accounts for the proportion of the total area of ​​the target region wi, then the current spatial supply-demand matching degree is Where n is the number of sub-regions.

[0176] Consider the impact of current temporal data on water supply and demand. For example, during high temperatures in summer, agricultural irrigation and residential water demand increase, while at this time there may be less precipitation, lower river levels, and a relatively reduced water supply.

[0177] Electronic devices can analyze water supply and demand under similar time periods throughout history and build supply-demand relationship models. For example, by analyzing water supply and demand over the past several summers, they can identify patterns in their relationship.

[0178] Substitute the current water supply and demand into the model to calculate the current supply and demand matching degree. For example, the ratio of current supply to the historical average supply during the same period, and the ratio of current demand to the historical average demand during the same period can be used for comprehensive measurement. Assuming the current supply is S now , the average supply during the same period in history is S avg , the current demand is D now The average demand during the same period in history is D avg , then the current time supply and demand matching degree

[0179] The current water resource supply and demand matching degree corresponding to the target water network is determined by comprehensively considering the current spatial supply and demand matching degree Ms and the current temporal supply and demand matching degree Mt. Different weights can be assigned to the spatial and temporal supply and demand matching degrees according to actual conditions, for example, weights α and β are set respectively (α+β=1).

[0180] The current water resource supply and demand matching degree, M, is then calculated as αMs + βMt. This approach integrates the supply and demand conditions in both spatial and temporal dimensions to comprehensively assess the current water resource supply and demand matching degree of the target water network. A value of M close to 1 indicates a good water resource supply and demand match; a value significantly greater than 1 indicates that supply exceeds demand; and a value significantly less than 1 indicates that demand exceeds supply, indicating a water shortage.

[0181] Step S2032: Calculate the future water resource supply and demand matching degree corresponding to the target water network based on the future water system connectivity result.

[0182] Specifically, the above step S2032 may include the following steps:

[0183] Step d1, obtaining future time characteristic data of a preset future time period corresponding to the target area, and the future water resource demand corresponding to the target area.

[0184] Specifically, the electronic device can collect time-related data of the target area over a long period of time in the past (for example, several years to several decades), including seasonal changes, monthly average temperature, monthly precipitation, holiday distribution and other information.

[0185] Then, based on the characteristics of the target area and the data features, a time series prediction model is determined. Examples of time series prediction models include moving averages, exponential smoothing, and ARIMA models (autoregressive integrated moving average models). Furthermore, electronic devices need to consider external factors that may affect future temporal characteristics. For example, climate change trends may lead to changes in temperature and precipitation patterns, and changes in policies and regulations may affect holiday schedules or social activity patterns. These external factors are used as input variables or adjustment factors in the time series prediction model to improve prediction accuracy. Finally, based on the time series prediction model and taking external factors into account, the electronic device predicts temporal characteristics within a preset future duration.

[0186] Electronic equipment can collect historical water usage data for different industries and sectors in the target area, including domestic, industrial, agricultural, and ecological water use. This data can be analyzed for trends, seasonality, and its relationship to factors such as population growth and economic development. Based on the target area's population and economic development plans, population and economic growth rates can be predicted over a predetermined timeframe. Furthermore, if the target area is planning industrial restructuring, such as shifting from water-intensive heavy industry to low-water-consuming high-tech industries, the impact of this restructuring on water demand needs to be analyzed. Future water demand for each industry can be re-estimated based on its water quotas and development plans.

[0187] The electronic device integrates the aforementioned factors, such as population, economy, and industrial structure, and uses various established models and analytical methods to calculate the water resource demand of each industry and sector in the target area over a predetermined period of time. This is then aggregated to form the total future water resource demand. This demand is a dynamic forecast that takes into account the changes in multiple factors and provides an important basis for water resource planning and management.

[0188] Step d2, based on the water body characteristics corresponding to each water body, future meteorological observation data and current hydrological monitoring data, calculate the future water resource supply corresponding to the target water network.

[0189] Specifically, the electronic device can input the water body characteristics, future meteorological observation data, and current hydrological monitoring data corresponding to each water body into a preset water resource supply calculation model to calculate the future water resource supply corresponding to the target water network. Among them, the preset water resource supply calculation model can be a distributed hydrological model (such as SWAT, HEC-HMS, etc.), a lumped model (such as the Xin'an River model), and a numerical model based on physical processes. The preset water resource supply calculation model is obtained by calibrating and verifying the original model parameters based on historical data. Specifically, past meteorological data and hydrological observation data are input into the original model, and the model parameters are adjusted so that the model output results match the actual observation data as much as possible. For example, by adjusting the soil permeability coefficient, vegetation interception parameters, etc., the river flow simulated by the preset water resource supply calculation model is consistent with the measured flow. The calibrated preset water resource supply calculation model can more accurately reflect the hydrological process of the target water network.

[0190] Step d3, based on the future time characteristic data, future water resource demand, future water resource supply and future water system connectivity results, calculate the future spatial supply and demand matching degree and future time supply and demand matching degree corresponding to the target water network.

[0191] Specifically, the above step d3 may include the following steps:

[0192] In step d31, each water-using area in the target water network is used as a regional node, and the water flow channels between the water-using areas are used as edges. A weight is assigned to each edge according to the future water system connectivity results to construct a basic graph structure.

[0193] Specifically, electronic devices can perform a detailed analysis of the target water network's coverage area, clarifying the boundaries and scope of each water-using area. Water-using areas can include various functional areas of a city (such as residential, industrial, and commercial areas), agricultural irrigation areas, and ecological protection areas. Based on actual water demand and management units, the target water network is divided into several relatively independent water-using areas, each of which serves as a regional node in the basic graph structure.

[0194] Next, we analyze the water flow channels between each water-using area in the target water network. These channels include natural rivers, canals, and lake connections, as well as artificially constructed pipelines and canals. These channels connect different water-using areas and serve as an important basis for constructing edges in the underlying graph structure.

[0195] Correlate each indicator from the future water connectivity results with the water channels between water-using areas. Then, weight each edge based on the future water connectivity results. The weight should reflect the importance of the channel in the future water network and the difficulty of water flow through it. Generally speaking, higher weights are assigned to channels with good future water connectivity, while lower weights are assigned to channels with poor connectivity or those with certain obstacles to water flow.

[0196] Optionally, a comprehensive score can be assigned based on indicators such as the target connectivity index, target shortest flow path length, and target water flow efficiency. For example, the target connectivity index is standardized and multiplied by a certain weight coefficient (such as 0.4), the inverse of the target shortest flow path length is standardized and multiplied by a certain coefficient (such as 0.3), and the target water flow efficiency is standardized and multiplied by a certain coefficient (such as 0.3). These three scores are then added together to obtain the weight value of the edge. Finally, the determined water use area is used as the regional node, the water flow channel is used as the edge, and each edge is assigned a corresponding weight to construct the basic graph structure.

[0197] In step d32, the state of each regional node and the weight of each edge in the basic graph structure are adjusted according to the time step, based on future time characteristic data, future water resource demand, and future water resource supply, to generate multiple spatiotemporal graphs based on time series.

[0198] Specifically, the electronic device can determine the appropriate time step based on the accuracy requirements of the research and the temporal resolution of the data. The time step can be hours, days, weeks, months, etc. Then, using the constructed basic graph structure as the initial state, the status of each regional node (water use area) in the basic graph structure is adjusted according to the future water resource demand. If the water resource demand of a water use area increases within the current time step, the attributes of the regional node are changed accordingly, such as being marked as a high demand state; conversely, if the demand decreases, it is marked as a low demand state. In addition, the water use priority of different water use areas is taken into consideration. For example, domestic water use generally takes precedence over industrial and agricultural water use. This priority difference is reflected when adjusting the status of regional nodes. For example, when water resources are in short supply, priority is given to ensuring the demand of the domestic water use area, and the status of other water use areas is adjusted accordingly.

[0199] The weights of each edge (flow channel) are adjusted based on future changes in water supply. If the water source connected to a flow channel increases supply during the current time step and is able to supply water more smoothly to the user area, the weight of the edge is increased to reflect the increased likelihood and efficiency of water flow through the channel. Conversely, if supply decreases or water flow is obstructed, the edge weight is decreased. Edge weights are then further adjusted based on future temporal data. For example, during the dry season, the weights of certain flow channels that rely on specific water sources may decrease due to reduced water supply at the source. During the rainy season, the weights of some channels with lower flow rates may increase due to replenishment from precipitation. Furthermore, special periods such as holidays may cause changes in water demand between certain regions, thus affecting edge weights.

[0200] After adjusting the regional node states and edge weights in the underlying graph structure at each time step, a new spatiotemporal graph is generated. This spatiotemporal graph reflects the state of the water network at the current time step, including demand in each water-using area and the connectivity of water channels. These spatiotemporal graphs are arranged in chronological order to form multiple spatiotemporal graphs based on time series.

[0201] Step d33: construct a multi-layer spatiotemporal graph convolutional network, and perform graph convolution operations on the spatial node features corresponding to each regional node in the basic graph structure.

[0202] Specifically, a multi-layer spatiotemporal graph convolutional network typically consists of an input layer, multiple hidden layers, and an output layer. The input layer receives the underlying graph structure, along with relevant regional node features and edge information. The hidden layer, the core of the network, extracts and learns spatial and temporal features from the graph structure by stacking multiple graph convolutional layers. The output layer outputs results based on task requirements, such as predicted values ​​for each regional node and overall water network evaluation metrics.

[0203] Specifically, each regional node in the underlying graph structure corresponds to certain spatial node features. These features can include information such as the geographic location, area, population, and water usage type of the water-using area. Before performing graph convolution operations, these features need to be encoded and vectorized for input into the network for processing. Discrete features can be converted into continuous vectors using methods such as one-hot encoding and embedding vectors. Numerical features can be normalized to ensure uniform scale.

[0204] For each region node, a graph convolution layer calculates the weighted sum of the features of its neighboring nodes, thereby updating the feature representation of the region node. Different graph convolution methods have different strategies for calculating weights. For example, in graph convolutional networks, weights are pre-calculated based on the graph's Laplacian matrix; in graph attention convolution, weights are adaptively learned through an attention mechanism, which can better reflect the importance between region nodes.

[0205] After aggregating neighborhood information, the features of neighboring region nodes are fused with the features of the current region node to update the feature representation of the current region node. This updated feature not only incorporates the region node's own information but also incorporates information from its neighborhood, better reflecting the region node's contextual relationships within the graph structure. As the number of network layers increases, the features of the region nodes are continuously propagated and updated within the graph structure, enabling each region node to access a wider range of graph structural information.

[0206] To increase the network's expressive power, nonlinear activation functions, such as ReLU (Rectified Linear Unit), are often introduced into graph convolution operations. After updating the regional node features, a nonlinear activation function is applied to them, mapping the linearly transformed features into a nonlinear space. This yields spatial node features corresponding to each regional node, enabling the learning of more complex feature relationships.

[0207] In step d34, a convolution operation is performed on each spatiotemporal graph using a temporal convolution layer to obtain the time node features corresponding to each regional node in the basic graph structure.

[0208] Specifically, the core of the temporal convolution layer is the convolution kernel, which slides the spatiotemporal graph in the time dimension. The size of the convolution kernel determines the number of time steps processed at a time in the time series. For example, a convolution kernel of size 3 will process three consecutive spatiotemporal graphs simultaneously. The depth (number of channels) of the convolution kernel can be set according to the feature dimension of the input spatiotemporal graph to ensure that features can be effectively extracted. By adjusting the size and depth of the convolution kernel, the range and accuracy of the temporal convolution layer's capture of time series information can be controlled.

[0209] When a temporal convolution layer performs convolution operations on the time dimension of a spatiotemporal graph, the convolution kernel slides along the time series, with each slide being a fixed step size (typically 1). At each position, the convolution kernel is element-wise multiplied with the corresponding spatiotemporal graph region and the sum is calculated to produce a new feature value. This process is similar to traditional image convolution, except that the graph-structured data is processed in the time dimension. In this way, the temporal convolution layer can capture the local characteristics and changing patterns of the spatiotemporal graph over time.

[0210] To increase the model's expressiveness, a nonlinear activation function is often applied to the output of the temporal convolutional layer. This activation function transforms the linear output of the convolution operation into a nonlinear one, enabling the model to learn more complex temporal feature relationships. For example, the ReLU function sets negative outputs to 0 while retaining positive outputs, introducing nonlinearity and helping the model better fit complex patterns in the data.

[0211] After the temporal convolution layer convolves the spatiotemporal graph, each regional node receives a new feature representation. This feature representation incorporates information about that regional node across multiple time steps. After the convolution operation, the original regional node features in each spatiotemporal graph are updated to new features that incorporate information about the temporal dimension. For example, the water demand and supply characteristics of a water-using regional node at different time steps are integrated into a comprehensive temporal node feature through the temporal convolution operation, reflecting the changing trends and patterns of that regional node over time.

[0212] A temporal convolution layer may contain multiple convolution kernels, each of which generates a corresponding feature map. These feature maps can be combined through aggregation operations (such as addition and concatenation) to obtain the final time node features. The aggregated time node features contain rich temporal information and can better describe the behavior and characteristics of each regional node in the underlying graph structure along the temporal dimension. These time node features can serve as input for subsequent analysis and prediction tasks, such as predicting the future state of water networks and assessing changing trends in water supply and demand.

[0213] Step d35: Calculate the future spatial supply-demand matching degree corresponding to the target water network based on the characteristics of each spatial node.

[0214] Specifically, for each regional node i, the ratio of its water resource supply Si to its demand Di is calculated: Ri=Si / Di.

[0215] Based on the spatial node characteristics corresponding to each regional node, clustering algorithms (such as K-means clustering) are used to divide the regional nodes in the target water network into different categories. For example, regional nodes can be clustered based on characteristics such as their geographic location and water use type, so that regional nodes in the same category have similar spatial characteristics and functions.

[0216] For each cluster, the weight of the cluster can be determined based on the number of regional nodes it contains, the total amount of water resources, the importance of water supply or water use, etc. For example, a cluster contains multiple large industrial water regional nodes, whose water demand accounts for a large proportion of the entire water network, then the weight of this cluster is relatively high. Assume that there are k clusters in total, and the weight of cluster j is c j For region node i, if it belongs to cluster j, its weight w i =c j ×1 / n j , where n j is the number of regional nodes in cluster j. This method takes into account the overall importance of the cluster to which the regional node belongs and its relative importance within the cluster, and can reflect the status of the node in the water network in a more detailed manner.

[0217] In order to comprehensively consider the spatial distribution of the entire water network, a spatial weight matrix W can be introduced, which reflects the spatial correlation between nodes in each region. For example, the weights between adjacent nodes can be set higher, while the weights of nodes farther away can be set lower.

[0218] Then, based on the formula: Calculate the future spatial supply-demand matching degree for the target water network, where MS is the spatial supply-demand matching degree and n is the number of regional nodes. This formula measures the degree of variation in supply-demand ratios between nodes using a spatial weight matrix. Smaller differences indicate a higher degree of spatial supply-demand matching.

[0219] Step d36, based on the characteristics of each time node, calculate the future time supply and demand matching degree corresponding to the target water network.

[0220] Specifically, the characteristics of each time node are input into the preset time series model, and the supply quantity St and demand quantity Dt of each regional node at each time point t are predicted.

[0221] Based on the formula Calculate the future supply-demand matching degree for the target water network. T is the number of predicted time periods. A smaller MT value indicates a higher future supply-demand matching degree.

[0222] Step d4: determining the future water resource supply and demand matching degree corresponding to the target water network based on the future spatial supply and demand matching degree and the future temporal supply and demand matching degree.

[0223] Specifically, the electronic device uses the future spatial supply-demand match and the future temporal supply-demand match as the evaluation factor set U = {u1, u2}, where u1 represents the future spatial supply-demand match and u2 represents the future temporal supply-demand match. Based on the actual situation, a review set V = {v1, v2, v3, v4, v5} is established, which can range from very good to fair to poor to very poor. For each evaluation factor, a membership function is determined for each level in the review set. For example, for the future spatial supply-demand match u1, the membership function is determined to be "very good," "good," "fair," "poor," and "very poor" based on its numerical range. Similarly, a corresponding membership function is determined for the future temporal supply-demand match u2. Then, based on the membership functions, the membership of the future spatial supply-demand match and the future temporal supply-demand match with each level in the review set is calculated, and a fuzzy relationship matrix R is constructed. Each row of matrix R represents the membership vector of an evaluation factor with a different review level.

[0224] Determine the weight vector A = {a1, a2} for the future spatial and temporal supply-demand matching, where a1 + a2 = 1. The weights can be determined based on the actual situation of the water network and the opinions of relevant experts. Using fuzzy matrix multiplication, multiply the weight vector A by the fuzzy relationship matrix R to obtain a comprehensive evaluation vector B = A·R. The elements of vector B represent the degree of membership of the target water network's future water resource supply-demand matching with respect to each level in the evaluation set. The level to which the future water resource supply-demand matching belongs can then be determined based on the principle of maximum membership, or a comprehensive numerical value can be calculated to represent the matching degree. For example, by assigning a corresponding numerical value to each level in the evaluation set, multiplying the values ​​by the membership, and summing the values ​​to obtain a specific future water resource supply-demand matching value.

[0225] Step S2033: determining a target water resource supply and demand matching degree corresponding to the target water network based on the current water resource supply and demand matching degree and the future water resource supply and demand matching degree.

[0226] Specifically, the current water resource supply and demand matching degree and the future water resource supply and demand matching degree are combined to determine the target water resource supply and demand matching degree corresponding to the target water network.

[0227] Step S204: Based on the target water resource supply-demand matching degree, the target water network pattern is planned and adjusted.

[0228] For details about this step, please refer to the above description of step S104 and will not be repeated here.

[0229] The water network pattern planning method provided in the embodiment of the present application obtains future meteorological observation data and current hydrological monitoring data corresponding to each water body. Based on the water body characteristics, future meteorological observation data and current hydrological monitoring data corresponding to each water body, node attribute information is added to the water body nodes corresponding to each water body, and edge attribute information is added to the edges corresponding to each channel to construct an initial water system network model. It is convenient to analyze the interaction and influence mechanism between various factors, and lay the foundation for in-depth research on the dynamic change law of the water system. The water body characteristics, future meteorological observation data and current hydrological monitoring data corresponding to each water body are input into a preset prediction model, and the hydrological dynamic change data corresponding to each water body within a preset time in the future are output. This helps to understand in advance the possible changes in the water system in the future, and provide forward-looking information for decisions such as water resources management, flood prevention and disaster reduction, and ecological protection. The hydrological dynamic change data corresponding to each water body are integrated with the initial water system network model to generate a target water system network model corresponding to the target water network. The generated target water system network model can reflect the changes in the water system in real time and dynamically. The target river network model integrates current and future river system information, providing a more comprehensive and accurate basis for decision-making on water resource management, water ecological protection, and water conservancy project planning. Through model analysis and simulation, the effectiveness of different decision-making options can be evaluated, and the pros and cons of various potential measures can be compared to select the optimal option. This improves the scientific and rational nature of decision-making, promoting the sustainable use of water resources and the healthy development of the aquatic ecosystem.

[0230] Based on the connectivity between each water body, an initial adjacency matrix is ​​generated for each water body. This matrix intuitively and concisely describes the connectivity relationships between water bodies in the target water network. Based on the water characteristics, future meteorological observation data, current hydrological monitoring data, and hydrological dynamic change data for each water body in the target water network model, a dynamic data list is generated for each water body. This achieves in-depth integration of relevant water network data and provides a rich information foundation for comprehensive analysis of water network behavior. Furthermore, the dynamic data list accurately reflects the state changes of each water body under different time and environmental conditions in real time, providing strong support for prediction and decision-making. Each dynamic data list is associated with the initial adjacency matrix to generate a backup adjacency matrix. This enriches the adjacency matrix, allowing it to not only reflect the connectivity relationships between water bodies but also associate their dynamic attribute information. Based on the water characteristics, future meteorological observation data, current hydrological monitoring data, and hydrological dynamic change data for each water body, the weights corresponding to each edge in the backup adjacency matrix are calculated to generate the target adjacency matrix. This achieves precise quantification of the connectivity relationships between water bodies. The weights comprehensively consider multiple factors, including flow resistance, water transmission costs, and connectivity stability, enabling the matrix to more realistically reflect the actual flow conditions within the water network. Furthermore, the target adjacency matrix provides core data support for scientific decision-making regarding the water network. In water resources planning and network optimization, the weight information based on the matrix can accurately assess the pros and cons of different connection paths and formulate optimal water resource allocation plans and water conservancy project construction plans. Furthermore, by dynamically updating the weights, changes in the water network can be reflected in real time, enabling decision-making to adapt to the ever-changing water network environment and ensuring the scientific and effective management of water resources.

[0231] Based on the target adjacency matrix, the current connectivity index of the target water network and the predicted connectivity index for a preset future timeframe are calculated. This accurately quantifies the current connectivity level of the target water network. Calculating the predicted connectivity index for a preset future timeframe allows for an estimation of the network's connectivity trends over the next period of time based on existing data and models. A target connectivity index is generated based on the current and predicted connectivity indices. This allows for a comprehensive assessment of the target water network's connectivity from both static and dynamic perspectives. This comprehensive assessment avoids the limitations of solely considering the current state while ignoring future changes, or relying solely on predictions while ignoring current realities, providing a more accurate and comprehensive reference for long-term water network planning and management. Based on the target adjacency matrix, the current shortest flow path and the predicted shortest flow path for a preset future timeframe are calculated for the target water network. Determining the current shortest flow path helps achieve efficient water resource allocation in the current period. Predicting the predicted shortest flow path for a preset future timeframe allows for consideration of water flow patterns under different future scenarios. Based on the current shortest flow path and the predicted shortest flow path, a target shortest flow path is generated, providing a comprehensive water flow routing solution for water network management. This solution takes into account both the current water network conditions and possible future changes. Based on the current shortest flow path and the predicted shortest flow path, the current water flow efficiency of the target water network and the predicted water flow efficiency for a preset future time period are calculated. Calculating the current water flow efficiency helps understand the water flow status of each part of the water network, identifying areas with slow flow or excessive energy loss, and taking timely optimization measures to improve the efficiency of the entire water network. Calculating the predicted water flow efficiency provides early insight into potential efficiency issues that may arise in the water network in the future. Based on the current and predicted water flow efficiencies, a target water flow efficiency is generated. This provides an important reference indicator for the long-term operation and management of the water network. Long-term maintenance and upgrade plans based on the target water flow efficiency can ensure that the water network maintains high efficiency over time, achieving efficient use and sustainable management of water resources.

[0232] Based on the current water system connectivity results, the current water resource supply and demand matching degree for the target water network is calculated. This accurately reflects the current balance between water resource supply and demand in the target water network. Future temporal characteristic data for a preset time period and the corresponding future water resource demand for the target area are obtained. The future water resource supply for the target water network is calculated based on the water body characteristics of each water body, future meteorological observation data, and current hydrological monitoring data. Using each water-using area in the target water network as a regional node and the flow channels between them as edges, weights are assigned to each edge based on the future water system connectivity results to construct a basic graph structure. This intuitively displays the water flow relationships and connectivity between each water-using area in the target water network. Based on the future temporal characteristic data, future water resource demand, and future water resource supply, the state of each regional node and the weights of each edge in the basic graph structure are adjusted according to the time step, generating multiple spatiotemporal graphs based on time series, which fully account for the dynamic changes in water resource supply and demand over time. A multi-layer spatiotemporal graph convolutional network is constructed. Using graph convolution operations, the spatial node features corresponding to each regional node in the underlying graph structure are analyzed. This effectively mines the spatial correlation features between water-using areas. Using the temporal convolution layer, each spatiotemporal graph is convolved to obtain the temporal node features corresponding to each regional node in the underlying graph structure. This captures the temporal characteristics and patterns of water supply and demand. This helps understand the temporal evolution of water supply and demand, providing important temporal information for predicting future supply and demand. By analyzing temporal node features, the degree of matching between water supply and demand at different time scales can be assessed, supporting the formulation of appropriate time-based scheduling strategies. Based on the spatial node features, the future spatial supply and demand matching degree of the target water network is calculated. This allows for a spatial assessment of the water supply and demand matching between water-using areas within the target water network, clarifying the spatial distribution of water surpluses and shortages in different regions. Based on the temporal node features, the future temporal supply and demand matching degree of the target water network is calculated. This allows for a temporal assessment of the matching between water supply and demand at different moments, assisting in the formulation of water resource scheduling plans adapted to different time scales. Based on the future spatial and temporal matching of water supply and demand, the future water resource supply and demand matching degree corresponding to the target water network is determined. Based on the current and future water resource supply and demand matching degrees, the target water resource supply and demand matching degree corresponding to the target water network is determined. This provides a comprehensive assessment of the water resource supply and demand situation of the target water network from the present to the future. This assessment method avoids the limitations of focusing on a single stage, either the present or the future, and comprehensively considers the dynamic changes in water network operations, providing a more systematic and comprehensive perspective for water resource management. Furthermore, the target water resource supply and demand matching degree integrates current actual conditions and future development trends, providing a more scientific and reliable basis for water resource management decisions.

[0233] This embodiment also provides a water network pattern planning device, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0234] This embodiment provides a water network pattern planning device, such as Figure 3 As shown, including:

[0235] An acquisition module 301 is configured to acquire at least one water body corresponding to a target area and water body characteristics corresponding to each water body; water body characteristics include water body location and pattern characteristics, water body morphology characteristics, water body structure characteristics, and water body flow characteristics;

[0236] The first calculation module 302 is used to calculate the target water system connectivity result of the target water network corresponding to the target area based on the water body characteristics corresponding to each water body; the target water network is based on the composition of each water body;

[0237] The second calculation module 303 is used to calculate the target water resource supply and demand matching degree corresponding to the target water network based on the target water system connectivity result;

[0238] The adjustment module 304 is used to plan and adjust the pattern of the target water network based on the target water resource supply and demand matching degree.

[0239] An embodiment of the present invention further provides an electronic device having the above Figure 3 The water network layout planning device shown. Figure 4 , Figure 4 It is a structural diagram of an electronic device provided by an optional embodiment of the present invention.

[0240] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A water network pattern planning method, characterized in that: The method comprises: Acquire at least one water body corresponding to the target area and water body characteristics corresponding to each of the water bodies; the water body characteristics include water body location pattern characteristics, water body morphology characteristics, water body structure characteristics, and water body flow characteristics; Calculating a target water system connectivity result of a target water network corresponding to the target area based on the water body characteristics corresponding to each of the water bodies; the target water network is composed of each of the water bodies; Calculating the target water resource supply and demand matching degree corresponding to the target water network based on the target water system connectivity result; Based on the target water resource supply and demand matching degree, the pattern of the target water network is planned and adjusted.

2. The method according to claim 1, characterized in that The calculating, based on the water body characteristics corresponding to each of the water bodies, a target water system connectivity result of the target water network corresponding to the target area, includes: According to the water body characteristics corresponding to each water body, each water body is regarded as a water body node, and the channels between the water bodies are regarded as edges, thereby constructing a target water system network model; Based on the target water system network model, construct a target adjacency matrix corresponding to each water body; Calculating a target connectivity index, a target shortest flow path, and a target water flow efficiency corresponding to the target shortest flow path corresponding to the target water network based on the target adjacency matrix; Based on the target connectivity index, the target shortest flow path, and the target water flow efficiency, a target water system connectivity result corresponding to the target water network is determined.

3. The method according to claim 2, characterized in that According to the water body characteristics corresponding to each water body, each water body is regarded as a water body node, and the channels between the water bodies are regarded as edges, so as to construct a target water system network model, including: Obtaining future meteorological observation data and current hydrological monitoring data corresponding to each of the water bodies; Based on the water body characteristics corresponding to each water body, the future meteorological observation data, and the current hydrological monitoring data, node attribute information is added to the water body nodes corresponding to each water body, and edge attribute information is added to the edges corresponding to each channel to construct an initial water system network model; Input the water body characteristics, future meteorological observation data and current hydrological monitoring data corresponding to each water body into a preset prediction model, and output the hydrological dynamic change data corresponding to each water body within a preset time in the future; The hydrological dynamic change data corresponding to each of the water bodies are integrated with the initial water network model to generate the target water network model corresponding to the target water network.

4. The method according to claim 3, characterized in that The target adjacency matrix corresponding to each water body is constructed based on the target water system network model, including: generating an initial adjacency matrix corresponding to each water body according to the connection status between each water body; Generate a dynamic data list corresponding to each water body based on the water body characteristics corresponding to each water body in the target water system network model, the future meteorological observation data, the current hydrological monitoring data, and the hydrological dynamic change data; Associating each of the dynamic data lists with the initial adjacency matrix to generate a spare adjacency matrix; According to the water body characteristics corresponding to each water body, the future meteorological observation data, the current hydrological monitoring data and the hydrological dynamic change data, the weight information corresponding to each edge in the backup adjacency matrix is ​​calculated to generate the target adjacency matrix.

5. The method according to claim 4, characterized in that The calculating, based on the target adjacency matrix, a target connectivity index corresponding to the target water network, a target shortest flow path, and a target water flow efficiency corresponding to the target shortest flow path includes: Based on the target adjacency matrix, calculating the current connectivity index corresponding to the target water network and the predicted connectivity index corresponding to the future preset time period; generating the target connectivity index based on the current connectivity index and the predicted connectivity index; Based on the target adjacency matrix, calculating the current shortest flow path corresponding to the target water network and the predicted shortest flow path corresponding to the future preset time period; generating the target shortest flow path based on the current shortest flow path and the predicted shortest flow path; Based on the current shortest flow path and the predicted shortest flow path, calculating the current water flow efficiency corresponding to the target water network and the predicted water flow efficiency corresponding to a preset time period in the future; The target water flow efficiency is generated based on the current water flow efficiency and the predicted water flow efficiency.

6. The method according to claim 1, wherein The target water system connectivity result includes the current water system connectivity result and the future water system connectivity result within a preset time period; The calculating of the target water resource supply and demand matching degree corresponding to the target water network based on the target water system connectivity result includes: Calculating the current water resource supply and demand matching degree corresponding to the target water network based on the current water system connectivity result; Calculating the future water resource supply and demand matching degree corresponding to the target water network based on the future water system connectivity result; Based on the current water resource supply-demand matching degree and the future water resource supply-demand matching degree, the target water resource supply-demand matching degree corresponding to the target water network is determined.

7. The method according to claim 6, characterized in that The calculating the future water resource supply and demand matching degree corresponding to the target water network based on the future water system connectivity result includes: Acquire future time characteristic data of a preset future time period corresponding to the target area, and future water resource demand corresponding to the target area; Calculating the future water resource supply corresponding to the target water network based on the water body characteristics corresponding to each of the water bodies, future meteorological observation data, and current hydrological monitoring data; Calculating the future spatial supply-demand matching degree and the future temporal supply-demand matching degree corresponding to the target water network based on the future time characteristic data, the future water resource demand, the future water resource supply, and the future water system connectivity result; Based on the future spatial supply-demand matching degree and the future temporal supply-demand matching degree, the future water resource supply-demand matching degree corresponding to the target water network is determined.

8. The method according to claim 7, characterized in that The calculating of the future spatial supply-demand matching degree and the future temporal supply-demand matching degree corresponding to the target water network based on the future time characteristic data, the future water resource demand, the future water resource supply, and the future water system connectivity result includes: Each water-using area in the target water network is used as a regional node, and the water flow channels between the water-using areas are used as edges. A weight is assigned to each edge according to the future water system connectivity result to construct a basic graph structure; According to the time step, based on the future time characteristic data, the future water resource demand, and the future water resource supply, the state of each regional node and the weight of each edge in the basic graph structure are adjusted to generate multiple spatiotemporal graphs based on time series; Construct a multi-layer spatiotemporal graph convolutional network, and perform graph convolution operations on the spatial node features corresponding to each regional node in the basic graph structure; Performing a convolution operation on each of the spatiotemporal graphs using a temporal convolution layer to obtain time node features corresponding to each regional node in the basic graph structure; Calculating the future spatial supply-demand matching degree corresponding to the target water network based on the characteristics of each of the spatial nodes; Based on the characteristics of each time node, the future time supply and demand matching degree corresponding to the target water network is calculated.

9. A water network pattern planning device, characterized in that: The device comprises: An acquisition module is used to acquire at least one water body corresponding to a target area and water body characteristics corresponding to each of the water bodies; the water body characteristics include water body location pattern characteristics, water body morphology characteristics, water body structure characteristics and water body flow characteristics; A first calculation module is configured to calculate a target water system connectivity result of a target water network corresponding to the target area based on the water body characteristics corresponding to each of the water bodies; the target water network is composed of each of the water bodies; A second calculation module is used to calculate the target water resource supply and demand matching degree corresponding to the target water network based on the target water system connectivity result; An adjustment module is used to plan and adjust the pattern of the target water network based on the target water resource supply and demand matching degree.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the water network pattern planning method according to any one of claims 1 to 8 by executing the computer instructions.

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