Design method and device for multi-source precipitation monitoring station network based on complex network theory

Abstract

The application discloses a multi-source precipitation monitoring station network design method and device based on complex network theory, and relates to the field of precipitation monitoring station network design. The method comprises the following steps: obtaining multi-source precipitation data of a research area; dividing the research area into grids according to a preset precision, determining candidate stations according to the divided grids, determining edges between the candidate stations according to the correlation between the multi-source precipitation data of the candidate stations, and constructing a precipitation network of the research area; calculating the weighted betweenness of each candidate station in the precipitation network according to a weighted betweenness index calculation method; determining the minimum number of precipitation monitoring stations in the research area; sorting the weighted betweenness of each candidate station, and selecting at least the minimum number of candidate stations from the sorting result as precipitation monitoring stations. The application adopts multi-source precipitation data, optimally designs the precipitation monitoring station network layout points based on the complex network theory, and improves the accuracy of the precipitation monitoring station network layout.

Description

Technical Field

[0001] This application relates to the field of precipitation monitoring network optimization design, and in particular to a design method and device for multi-source precipitation monitoring network based on complex network theory. Background Technology

[0002] The hydrological cycle is a complex physical process with strong nonlinear characteristics, exhibiting complex spatiotemporal variations. It is influenced by numerous factors, including human activities, topography, climate, and soil conditions. Precipitation is a crucial component of the hydrological cycle, displaying significant spatiotemporal variability. In hydrology, it serves as vital input data for hydrological models, used in water resource management, reservoir scheduling, and flood forecasting. Optimizing the deployment of a multi-source, high-precision precipitation monitoring system is the first step in studying hydrological systems. Therefore, accurately deploying a precipitation monitoring network is of paramount importance. Summary of the Invention

[0003] The purpose of this application is to provide a design method and device for a multi-source precipitation monitoring network based on complex network theory. It uses multi-source precipitation data and optimizes the layout of precipitation monitoring station sites based on complex network theory, thereby improving the accuracy of precipitation monitoring station network layout.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] Firstly, this application provides a design method for a multi-source precipitation monitoring network based on complex network theory, including:

[0006] Acquire multi-source precipitation data for the study area;

[0007] Within the study area, a grid is divided according to a preset precision, and candidate sites are determined based on the divided grid.

[0008] The edges between candidate stations are determined based on the correlation between the multi-source precipitation data of each candidate station.

[0009] A precipitation network for the study area is constructed by using each candidate site as a network node and the edges connecting each candidate site as network edges.

[0010] The weighted betweenness index was calculated for each candidate station in the precipitation network.

[0011] Determine the minimum number of precipitation monitoring stations within the study area;

[0012] The weighted betweenness factors of each candidate station are sorted, and at least the M candidate stations with the largest weights are selected as precipitation monitoring stations from the sorting results; M is the minimum number of precipitation monitoring stations in the study area.

[0013] Secondly, this application provides a design device for a multi-source precipitation monitoring network based on complex network theory, comprising:

[0014] The data acquisition module is used to acquire multi-source precipitation data for the study area;

[0015] The network construction module is used to divide the study area into grids with a preset precision, determine candidate stations based on the divided grids, determine the edges between candidate stations based on the correlation between the multi-source precipitation data of each candidate station, and construct the precipitation network of the study area with each candidate station as a network node and the edges between each candidate station as network edges.

[0016] The weighted betweenness factor calculation module is used to calculate the weighted betweenness factor of each candidate station in the precipitation network according to the weighted betweenness factor index calculation method.

[0017] The precipitation monitoring network design module is used to determine the minimum number of precipitation monitoring stations in the study area; to sort the weighted betweenness coefficients of each candidate station, and to select at least M candidate stations with the largest weights from the sorting results as precipitation monitoring stations; M is the minimum number of precipitation monitoring stations in the study area.

[0018] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described design method for a multi-source precipitation monitoring network based on complex network theory.

[0019] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described design method for a multi-source precipitation monitoring network based on complex network theory.

[0020] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0021] This application provides a method and apparatus for designing a multi-source precipitation monitoring network based on complex network theory. The method includes: acquiring multi-source precipitation data for a study area; dividing the study area into grids with a preset precision; determining candidate stations based on the grid division; determining the connections between candidate stations based on the correlation between their multi-source precipitation data; constructing a precipitation network for the study area; calculating the weighted betweenness index of each candidate station in the precipitation network using a weighted betweenness index calculation method; determining the minimum number of precipitation monitoring stations in the study area; ranking the weighted betweenness indices of each candidate station; and selecting at least the minimum number of candidate stations from the ranking results as precipitation monitoring stations. This application uses multi-source precipitation data and optimizes the layout of the precipitation monitoring network based on complex network theory, improving the accuracy of the precipitation monitoring network layout. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is an application environment diagram of a multi-source precipitation monitoring station network design method based on complex network theory in one embodiment of this application;

[0024] Figure 2 A flowchart illustrating a design method for a multi-source precipitation monitoring network based on complex network theory, provided in an embodiment of this application;

[0025] Figure 3 A schematic diagram illustrating the technical concept of a multi-source precipitation monitoring network design method based on complex network theory, provided in an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the grid division of the study area provided in one embodiment of this application;

[0027] Figure 5 A schematic diagram showing the weighted betweenness index ranking results of candidate sites according to an embodiment of this application;

[0028] Figure 6 A functional module diagram of a multi-source precipitation monitoring station network design device based on complex network theory is provided in one embodiment of this application;

[0029] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] The multi-source precipitation monitoring network design method based on complex network theory provided in this application can be applied to, for example... Figure 1 The application environment is shown. The terminal communicates with the server via a network. The data storage system stores the data the server needs to process. The data storage system can be set up independently, integrated into the server, or placed in the cloud or on another server. The terminal can send multi-source precipitation data of the study area to the server. After receiving the multi-source precipitation data, the server divides the study area into grids according to a preset precision, determines candidate stations based on the grid division, determines the edges between candidate stations based on the correlation between their multi-source precipitation data, constructs a precipitation network for the study area using each candidate station as a network node and the edges between candidate stations as network edges, calculates the weighted betweenness index of each candidate station in the precipitation network, determines the minimum number of precipitation monitoring stations in the study area, sorts the weighted betweenness indices of each candidate station, and selects at least M candidate stations with the highest weighted betweenness indices from the sorting results as precipitation monitoring stations. The server can provide feedback on the obtained precipitation monitoring station deployment plan. In addition, in some embodiments, the multi-source precipitation monitoring network design method based on complex network theory can also be implemented by a server or a terminal. For example, the terminal can directly design a multi-source precipitation monitoring network based on complex network theory for the multi-source precipitation data of the study area, or the server can obtain the multi-source precipitation data of the study area from the data storage system and design a multi-source precipitation monitoring network based on complex network theory.

[0033] The terminal can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. The server can be a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0034] In one exemplary embodiment, such as Figure 2 and Figure 3 As shown, a design method for a multi-source precipitation monitoring network based on complex network theory is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 The following steps, from step 101 to step 107, will be used as an example to illustrate the process.

[0035] Step 101: Obtain multi-source precipitation data for the study area. Multi-source precipitation data includes precipitation data monitored by ground rain gauges and precipitation data monitored by satellite remote sensing products. The multi-source precipitation data consists of daily precipitation at all stations.

[0036] Step 102, within the study area, according to a preset precision (e.g.) ) Perform grid division, and determine candidate sites based on the divided grid. For example... Figure 4 As shown, one grid represents one candidate site.

[0037] Step 103: Determine the edges between candidate stations based on the correlation between the multi-source precipitation data of each candidate station.

[0038] Step 104: Construct a precipitation network for the study area using each candidate site as a network node and the edges connecting each candidate site as network edges.

[0039] Step 105: Calculate the weighted betweenness index of each candidate station in the precipitation network according to the weighted betweenness index calculation method.

[0040] Step 106: Determine the minimum number of precipitation monitoring stations within the study area.

[0041] Step 107: Sort the weighted betweenness indices of each candidate station (e.g., sort by criteria from high to low), and select at least M candidate stations with the highest weights from the sorting results as precipitation monitoring stations; M is the minimum number of precipitation monitoring stations in the study area.

[0042] By implementing steps 101 to 107 above, and leveraging multi-source monitoring technologies such as radar and remote sensing, the sources of precipitation data have become increasingly diverse, leading to significant progress in rainfall observation and data products. High spatiotemporal resolution rainfall products, as a supplement to ground-based rainfall monitoring stations, can be used to assist in the refined characterization of rainfall spatiotemporal distribution patterns and scenarios. Complex network theory, derived from graph theory, has unique advantages in studying the complexity of hydrological systems. Complex networks use nodes to represent the elements within the network system and edges to represent the relationships between these elements. Nodes and edges in complex networks often exhibit uneven distribution, corresponding to the spatial unevenness in station distribution, independence, and observation coverage in the real physical world. In the context of hydrological systems, nodes in complex networks can represent the key variables in hydrological research; edges and their weights describe the correlation and strength between variables; and the direction of the edges represents the causal relationship, with undirected networks only describing mutual influence. Therefore, the evolution of complex networks can correspondingly describe the dynamic changes of hydrological systems. Treating the stations in a precipitation monitoring network as nodes and the correlations between stations as edges, network optimization can be viewed as structural optimization of a complex network. Leveraging the similarity between the monitoring network's topology and complex networks, optimization indices based on local station data and system-wide metrics are constructed. This allows for the analysis of the network structure characteristics of the hydrological monitoring system and the assessment of node (precipitation monitoring station) importance and the analysis of inter-node connectivity. Assuming there are n precipitation monitoring stations in the studied watershed for observing the required precipitation data, the monitoring points... The monitored precipitation data is 'm' represents the total number of days in the precipitation sequence (daily-scale data). Monitoring stations are used as nodes in a complex network. The correlation between hydrological processes at different monitoring stations is used as the criterion for determining whether there are connections between nodes. If a node... , The correlation between them is greater than a given threshold. If two nodes are connected, then an edge is considered to exist between them. This application utilizes complex network theory to fully extract effective information from multi-source precipitation monitoring data, optimize the layout of the precipitation monitoring station network, and uses the weighted degree betweenness index (WDB) in complex network theory to comprehensively consider the global importance, propagation influence, and cumulative contribution effect of nodes. This facilitates the selection of more suitable precipitation monitoring locations, improves the accuracy of precipitation monitoring station network layout, and is beneficial for the application of precipitation monitoring station network optimization results in flood forecasting.

[0043] In another exemplary embodiment of this application, only a simple undirected, unweighted network, i.e., a pair of nodes, is considered. There can only be one edge between them, and it is not allowed to... Self-organizing types exist. This type of network can be represented by a symmetric adjacency matrix:

[0044]

[0045] in, Then it represents the first The and the first There is one edge between each node, and 0 means there is no edge. This represents the set of edges; edges can be used to characterize the similar evolutionary or mutated characteristics between different nodes, and therefore can be quantified using similarity indices, such as the Pearson correlation coefficient, synchronicity index, or mutual information measure. In this application, mutual information measure is used to construct the adjacency matrix, with a threshold of 95%. Mutual information represents a measure of the amount of information one random variable contains about another, and also refers to the reduction in uncertainty of the original random variable given information about another random variable; it describes the information redundancy and transmission between two variables. Mutual Information Joint distribution of two random variables Multiplication and integral distribution The relative entropy between them is defined as follows:

[0046]

[0047] Where P and Q represent the total number of possible values ​​for variables X and Y, respectively.

[0048] Therefore, step 103, which determines the connections between candidate stations based on the correlation between multi-source precipitation data from each candidate station, specifically includes:

[0049] (1) Obtain the multi-source precipitation sequence for each candidate station.

[0050] (2) Calculate the mutual information measure between each pair of candidate stations based on the multi-source precipitation sequence of each candidate station.

[0051] (3) Two candidate sites with mutual information measures greater than the preset value are considered to have a connection relationship.

[0052] In another exemplary embodiment of this application, in step 105, the formula for calculating the weighted betweenness factor of the candidate site is:

[0053]

[0054] in, ; ; ;

[0055] In the formula, Indicates candidate sites The weighted betweenness factor is used to evaluate candidate sites. In terms of importance within the system, the higher a site's WDB value, the greater its importance within the site network system. The representative betweenness, i.e., the number of candidate sites visited. Connect to candidate sites and candidate sites Shortest path number With candidate sites and candidate sites Total number of shortest paths between The ratio indicates the propagation and impact capacity of hydrological processes; Representatives and candidate sites Directly connected candidate sites The cumulative effect of influence or contribution; Representative candidate sites (node The degree of comparison with candidate sites The number of connected edges indicates the influence capacity of a hydrological node; Representatives and candidate sites The number of connected edges; Indicates candidate sites and candidate sites Whether there is an edge between them, a value of 1 indicates that there is 1 edge, and a value of 0 indicates that there is no edge; N represents the total number of candidate sites.

[0056] In another exemplary embodiment of this application, step 106, determining the minimum number of precipitation monitoring stations within the study area, specifically includes:

[0057] The minimum number of precipitation monitoring stations in the study area was determined based on the area, topographic features (plains, coastal areas, mountains, hills, etc.) and the standards of the World Meteorological Organization (WMO).

[0058] In another exemplary embodiment of this application, precipitation data from selected candidate stations and precipitation data monitored by surface rain gauges are merged, and training and validation sets are created to construct a distributed hydrological model (SWAT). The distributed hydrological model is then trained and validated using the training and validation sets. Corresponding precipitation stations are included in their respective sub-basin zones (a sub-basin zone within the study area), and the average precipitation value is calculated. For sub-basins that do not contain precipitation stations, the average precipitation value of the nearest sub-basin is selected to replace the precipitation value for that sub-basin. The average precipitation data within each sub-basin is input into the distributed hydrological model to obtain flow prediction data. Then, based on the measured flow data from hydrological stations, and according to relevant indicators (such as NSE coefficient, KGE coefficient, and R...), the predicted flow data is calculated. 2 The coefficient is used to evaluate the runoff simulation results (flow prediction data). The expression for the relevant index is:

[0059]

[0060] KGE=

[0061] ,

[0062]

[0063] in, It is the observed flow rate at time t; It is the predicted flow rate at time t; It is the total number of all moments; It is the average of the observed flow rates at all times during the observation period; It is the average of the predicted flow rate at all times during the observation period; Represents the correlation coefficient; It is the standard deviation of the flow rate observations; It is the standard deviation of the traffic forecast; It is the mean of the flow observations; It is the mean of the traffic forecast values; KGE The closer the value is to 1, the better the output of the distributed hydrological model, which proves that the multi-source precipitation monitoring network deployed using the design method of this application has high accuracy.

[0064] Therefore, the multi-source precipitation monitoring network design method based on complex network theory also includes:

[0065] (1) Construct a distributed hydrological model within the study area.

[0066] (2) Input the multi-source precipitation data corresponding to each precipitation monitoring station into the distributed hydrological model to obtain the predicted flow value in the study area.

[0067] (3) Calculate the evaluation index based on the predicted flow value and the corresponding measured flow data of the hydrological station in the study area. The measured flow data of the hydrological station is the daily runoff of the hydrological station.

[0068] (4) Verify the effectiveness of the precipitation monitoring station design based on the evaluation indicators.

[0069] Comparing the runoff prediction results under the two scenarios of the optimal combination of stations designed in this application and the combination of all candidate stations, it can be concluded that the design method of this application has better runoff prediction performance at different hydrological stations.

[0070] This application takes the precipitation station network (including 1615 grid stations and 35 surface rain gauge stations) and three important hydrological stations (Baihe Station, Huangjiagang Station, and Huangzhuang Station) in the Hanjiang River Basin as examples. The total length of the daily value time series of precipitation and runoff is 11 years (2000-2010), which verifies the rationality and effectiveness of the multi-source precipitation monitoring station network design method based on complex network theory.

[0071] Obtain satellite remote sensing precipitation data for the Han River basin, and in accordance with... Resolution was used to extract all candidate grid points within the study area, totaling 1615 grid points, such as... Figure 4 As shown. Following the objective function of maximizing the weighted betweenness index (WDB), the importance of the precipitation station network consisting of 1615 grid candidate stations was ranked, as follows: Figure 5 As shown in Table 1, based on the World Meteorological Organization (WMO) standards for the minimum number of precipitation monitoring stations required for different terrain features (plains, coastal areas, mountains, hills, etc.), and considering the actual area and topography of this study area (mainly hills and plains), the minimum number of precipitation monitoring stations is approximately 20%–40% of the total number of grid points. These stations are then sorted from highest to lowest according to their WDB index values ​​to obtain the corresponding set of stations. Figure 5 The distribution shows that the sites are mainly concentrated in the central part of the study area.

[0072] Table 1. World Meteorological Organization (WMO) Recommended Standards for Station Network Layout

[0073]

[0074] Based on measured precipitation data from 35 surface rain gauges and measured flow data from hydrological stations, a SWAT distributed hydrological model was constructed to simulate runoff in the study area. The stations were sorted from highest to lowest according to their WDB (Warning-Depth) index, and the runoff simulation results were compared under three scenarios: all stations, the top 20% of stations, and the top 40% of stations. For each scenario, precipitation data was used as the model input. First, the selected station and surface rain gauge data were merged into a new dataset (forming a multi-source precipitation dataset). According to the model's sub-basin division, the average precipitation data from each station falling within a sub-basin was used as the sub-basin precipitation input. If no station fell within a sub-basin, the nearest average sub-basin precipitation was used as a substitute. The runoff simulation results of the hydrological model... The results for NSE and KGE are shown in Table 2.

[0075] Table 2 Comparison of runoff simulation results between the optimized scheme and all site schemes

[0076]

[0077] Based on Table 2, it can be seen that for the three site deployment schemes, the optimized 20% and 40% site scenarios have better evaluation indicators than the unoptimized all site scenario (All), reflected in higher [performance / indicators]. The coefficients, NSE coefficient, and KGE coefficient were determined. The results show that the multi-source precipitation monitoring network design method based on complex network theory proposed in this application is reasonable and reliable in terms of runoff simulation performance.

[0078] Applying complex network theory and multi-source precipitation data to the optimization and evaluation of precipitation monitoring station networks yields the following significant results:

[0079] 1. The weighted degree betweenness index (WDB) in complex network theory comprehensively considers the global importance of nodes, their propagation influence, and their cumulative contribution, which is beneficial for selecting more suitable precipitation monitoring sites.

[0080] 2. A distributed hydrological model SWAT was constructed, and the multi-source precipitation data after optimization were assigned to each sub-basin and applied to subsequent runoff simulation, further verifying the rationality and reliability of the station optimization layout results.

[0081] In summary, this application uses multi-source precipitation data, optimizes the layout of precipitation monitoring station network based on complex network theory, processes the optimized precipitation data using a distributed hydrological model, and verifies the results in runoff simulation at a watershed control station. This approach is beneficial for the application of precipitation station network optimization results in flood forecasting.

[0082] This application also provides an application scenario in which the above-mentioned multi-source precipitation monitoring network design method based on complex network theory is applied. Specifically, the multi-source precipitation monitoring network design method based on complex network theory provided in this embodiment can be applied in a precipitation monitoring network design scenario. This scenario includes a data acquisition stage and a design stage; the data acquisition stage is used to collect multi-source precipitation data within the study area; the design stage is used to design a multi-source precipitation monitoring network based on complex network theory based on the collected multi-source precipitation data. The multi-source precipitation monitoring network design method based on complex network theory provided in this embodiment belongs to the design stage.

[0083] Based on the same inventive concept, this application also provides a device for designing a multi-source precipitation monitoring network based on complex network theory to implement the aforementioned design method for multi-source precipitation monitoring network based on complex network theory. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for designing a multi-source precipitation monitoring network based on complex network theory provided below can be found in the limitations of the design method for multi-source precipitation monitoring network based on complex network theory described above, and will not be repeated here.

[0084] In one exemplary embodiment, such as Figure 6 As shown, a multi-source precipitation monitoring network design device based on complex network theory is provided, comprising:

[0085] The data acquisition module M1 is used to acquire multi-source precipitation data for the study area.

[0086] The network construction module M2 is used to divide the study area into grids with a preset precision, determine candidate stations based on the divided grids, determine the edges between candidate stations based on the correlation between the multi-source precipitation data of each candidate station, and construct the precipitation network of the study area with each candidate station as a network node and the edges between each candidate station as network edges.

[0087] The weighted betweenness factor calculation module M3 is used to calculate the weighted betweenness factor of each candidate station in the precipitation network according to the weighted betweenness factor index calculation method.

[0088] The precipitation monitoring station network design module M4 is used to determine the minimum number of precipitation monitoring stations in the study area; to sort the weighted betweenness coefficients of each candidate station, and to select at least M candidate stations with the largest weights from the sorting results as precipitation monitoring stations; M is the minimum number of precipitation monitoring stations in the study area.

[0089] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores design data for a multi-source precipitation monitoring station network based on complex network theory. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a design method for a multi-source precipitation monitoring station network based on complex network theory.

[0090] Those skilled in the art will understand that Figure 7 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0091] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0092] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0093] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0094] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0095] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for designing a multi-source precipitation monitoring station network based on complex network theory, characterized in that, The method comprises the following steps: obtaining multi-source precipitation data of a study area; dividing the study area into grids according to a preset accuracy, and determining candidate sites according to the divided grids; determining edges between the candidate sites according to the correlation between the multi-source precipitation data of the candidate sites; constructing a precipitation network of the study area by taking the candidate sites as network nodes and the edges between the candidate sites as network edges; calculating the weighted betweenness of each candidate site in the precipitation network according to a weighted betweenness calculation method; determining the minimum number of precipitation monitoring stations in the study area; sorting the weighted betweenness of each candidate site, and selecting at least the largest M candidate sites from the sorting result as precipitation monitoring stations; M is the minimum number of precipitation monitoring stations in the study area; wherein the calculation formula of the weighted betweenness of each candidate site is: wherein ; ; ; wherein, denotes the weighted betweenness of candidate site ; denotes the shortest path number between candidate site and candidate site ; denotes the ratio of the shortest path number between candidate site and candidate site and the total shortest path number between candidate site and candidate site ; denotes the cumulative effect of the influence or contribution of candidate site directly connected to candidate site ; denotes the number of edges connected to candidate site ; denotes the number of edges connected to candidate site ; denotes whether there is an edge between candidate site and candidate site , with a value of 1 indicating one edge and a value of 0 indicating no edge; N denotes the total number of candidate sites. 2.The method of claim 1, wherein, determining edges between the candidate sites according to the correlation between the multi-source precipitation data of the candidate sites, specifically comprising: obtaining a multi-source precipitation sequence of each candidate site; calculating the mutual information measure between each two candidate sites according to the multi-source precipitation sequence of each candidate site; regarding two candidate sites with a mutual information measure greater than a preset value as having an edge relationship. 3.The method of claim 1, wherein, determining the minimum number of precipitation monitoring stations in the study area, specifically comprising: determining the minimum number of precipitation monitoring stations in the study area according to the area, terrain characteristics of the study area, and the specification standard of the World Meteorological Organization. 4.The method of claim 1, wherein, The multi-source precipitation monitoring station network design method based on the complex network theory further comprises: constructing a distributed hydrological model in the study area; inputting the multi-source precipitation data corresponding to each precipitation monitoring station into the distributed hydrological model to obtain a flow prediction value in the study area; calculating an evaluation index according to the flow prediction value in the study area and the measured flow data of the corresponding hydrological station; verifying the effectiveness of the precipitation monitoring station design according to the evaluation index.

5. The method of claim 4, wherein the method is characterized by: The evaluation index includes: NSE coefficient, KGE coefficient and R 2 coefficient.

6. A device for designing a multi-source precipitation monitoring station network based on complex network theory, characterized in that, The method comprises the following steps: a data acquisition module for obtaining multi-source precipitation data of a study area; a network construction module for dividing the study area into grids according to a preset accuracy, determining candidate sites according to the divided grids, determining edges between the candidate sites according to the correlation between the multi-source precipitation data of the candidate sites, and constructing a precipitation network of the study area by taking the candidate sites as network nodes and the edges between the candidate sites as network edges; a weighted betweenness calculation module for calculating the weighted betweenness of each candidate site in the precipitation network according to a weighted betweenness calculation method; wherein the calculation formula of the weighted betweenness of each candidate site is: wherein ; ; ; wherein, denotes the weighted betweenness of candidate site ; denotes the shortest path number between candidate site and candidate site ; denotes the ratio of the shortest path number between candidate site and candidate site and the total shortest path number between candidate site and candidate site ; denotes the cumulative effect of the influence or contribution of candidate site directly connected to candidate site ; denotes the number of edges connected to candidate site ; denotes the number of edges connected to candidate site ; denotes whether there is an edge between candidate site and candidate site , with a value of 1 indicating one edge and a value of 0 indicating no edge; N denotes the total number of candidate sites. a precipitation monitoring station network design module for determining the minimum number of precipitation monitoring stations in the study area, sorting the weighted betweenness of each candidate site, and selecting at least the largest M candidate sites from the sorting result as precipitation monitoring stations; M is the minimum number of precipitation monitoring stations in the study area.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the multi-source precipitation monitoring station network design method based on the complex network theory according to any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the method for designing a multi-source precipitation monitoring station network based on complex network theory according to any one of claims 1-5.

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