CN120257673B - Optimization method and device for rainfall station network, electronic equipment and computer program product - Google Patents
Optimization method and device for rainfall station network, electronic equipment and computer program productInfo
- Publication number
- CN120257673B CN120257673B CN202510744366.8A CN202510744366A CN120257673B CN 120257673 B CN120257673 B CN 120257673B CN 202510744366 A CN202510744366 A CN 202510744366A CN 120257673 B CN120257673 B CN 120257673B
- Authority
- CN
- China
- Prior art keywords
- site
- sub
- basin
- rainfall
- station
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本申请涉及一种雨量站网的优化方法、装置、电子设备和计算机程序产品。该方法包括：通过结合各原始站点降雨数据的不确定性、各原始站点所在子流域的山洪灾害风险等级，以及，各原始站点与前一个选择的站点的信息重叠量对站点进行选择，并且在信息增益较小时停止进行站点选择。本申请提供的方案，能够降低冗余度、减少信息重叠，在提升站网整体的信息获取效率的同时保证关键区域的监测覆盖率提高，有效提升山洪灾害监测预警能力。
The present application relates to a method, apparatus, electronic device, and computer program product for optimizing a rainfall station network. The method comprises selecting stations by combining the uncertainty of rainfall data from each original station, the flash flood disaster risk level of the sub-basin where each original station is located, and the amount of information overlap between each original station and the previously selected station, and stopping station selection when the information gain is low. The solution provided by the present application can reduce redundancy and information overlap, thereby improving the overall information acquisition efficiency of the station network while ensuring increased monitoring coverage in key areas, effectively enhancing the flash flood disaster monitoring and early warning capabilities.
Description
Technical Field
The application relates to the technical field of station network layout, in particular to a method and a device for optimizing a rainfall station network, electronic equipment and a computer program product.
Background
Short-time strong rainfall is a main cause for inducing mountain torrents, and accurate acquisition of rainfall data is important for early warning. The rainfall in mountain areas is obvious due to the spatial heterogeneity, and the rationality of the layout of the rainfall station network directly influences the data precision and early warning aging. Therefore, how to optimize the layout scheme of the rainfall monitoring station network and improve the monitoring and early warning capability of the mountain torrent disasters becomes a technical problem to be solved urgently by the technicians in the field.
Disclosure of Invention
In order to solve or partially solve the problems existing in the related art, the application provides an optimization method, an optimization device, electronic equipment and a computer program product of a rainfall station network, which can scientifically optimize a rainfall monitoring station network layout scheme and improve mountain torrent disaster monitoring and early warning capability.
The first aspect of the application provides an optimization method of a rainfall station network, comprising the following steps:
s1, dividing a region to be optimized into a plurality of sub-watercourses, selecting an initial site from original sites according to uncertainty of rainfall data of each original site in the region to be optimized and a torrent disaster risk level of the sub-watercourse where each original site is located, and taking the initial site as a reference site;
S2, selecting a subsequent site from the rest original sites according to the uncertainty of rainfall data of the rest original sites, the mountain torrent disaster risk level of the sub-basin where the rest original sites are located and the information overlapping amount of the rest original sites and the reference site;
s3, calculating information gain generated after the subsequent station is increased, if the information gain is larger than a first set value, reserving the subsequent station, taking the subsequent station as a reference station, and repeatedly executing the S2-S3 until the information gain is smaller than or equal to the first set value;
S4, forming the initial site and all stored subsequent sites into a rainfall station network after the area to be optimized is optimized.
Further, in the method described above, after the forming the initial site and all the saved subsequent sites into the rainfall station network after the optimization of the to-be-optimized area, the method includes:
correcting the optimized rainfall station network in each sub-drainage basin according to the number of stations required by each sub-drainage basin in the plurality of sub-drainage basins, wherein the number of stations required by each sub-drainage basin is determined according to the mountain torrent disaster risk level of each sub-drainage basin and the area of each sub-drainage basin.
Further, in the above method, the correcting the optimized rainfall station network in each sub-drainage basin according to the number of stations required by each sub-drainage basin in the plurality of sub-drainage basins includes:
If the target sub-domains with the optimized rainfall station network have the station number smaller than the required station number exist in the plurality of sub-domains, stations are added in the rainfall station network with the optimized target sub-domains according to the station number required by the target sub-domains.
Further, in the method described above, adding the sites in the rainfall station network after optimization of the target sub-drainage basin according to the number of sites required by the target sub-drainage basin includes:
Adding sites in a target area of the target sub-basin according to the number of sites required by the target sub-basin;
The target area comprises an area, upstream of a mountain torrent disaster dangerous area of the target sub-basin, of which the space representative difference is larger than a second set value, of an area where the optimized rainfall station network is located in the target sub-basin, and an area, in which the information redundancy degree is smaller than a third set value, of the target sub-basin, wherein the space representative difference comprises an elevation difference.
Further, in the above method, the selecting an initial site from the original sites according to the uncertainty of rainfall data of each original site in the region to be optimized and the mountain torrent disaster risk level of the sub-basin where each original site is located includes:
Measuring the uncertainty of the rainfall data of each original site by calculating the edge information entropy of the rainfall data of each original site in the region to be optimized, wherein the larger the edge information entropy is, the higher the uncertainty of the rainfall data of the corresponding original site is;
Determining the mountain torrent disaster risk level of the sub-watershed where each original site is located according to disaster information of each sub-watershed, wherein the disaster information comprises at least one of disaster factors, disaster-tolerant environments and disaster-bearing bodies;
And calculating the edge information entropy of rainfall data of each original site, taking the product of the edge information entropy and the mountain torrent disaster risk level of the sub-basin where each original site is located as first data of each original site, and selecting the site with the highest first data in the original sites as the initial site.
Further, in the method, selecting a subsequent site from the remaining original sites according to the uncertainty of rainfall data of the remaining original sites, the mountain torrent disaster risk level of the sub-basin where the remaining original sites are located, and the information overlapping amount of the remaining original sites and the reference site includes:
Determining the mountain torrent disaster risk level of the sub-basin where each original site is located according to disaster information of each sub-basin, calculating the product of the edge information entropy of the rainfall data of each original site and the mountain torrent disaster risk level of the sub-basin where each original site is located as first data of each original site, wherein the larger the edge information entropy is, the higher the uncertainty of the rainfall data of the corresponding original site is, and the disaster information comprises at least one of disaster factors, disaster-causing environments and disaster-bearing bodies;
determining the information overlapping amount of the rest original stations and the reference station according to the mutual information between the rest original stations and the reference station;
and calculating the first data of each residual original site, and selecting the site with the highest second data in the residual original sites as the subsequent site, wherein the difference of the information overlapping amount corresponding to each residual original site is used as the second data of each residual original site.
Further, in the method described above, the calculating increases the gain of information generated after the subsequent station includes:
Before the subsequent stations are added, calculating first comprehensive edge information entropy of rainfall data of the initial station and all current subsequent stations; after the subsequent stations are added, calculating second comprehensive edge information entropy of rainfall data of the initial station and all current subsequent stations;
And determining the difference between the second comprehensive edge information entropy and the first comprehensive edge information entropy to increase the information gain generated after the subsequent station.
The second aspect of the present application provides an optimization apparatus for a rainfall station network, including:
S1, dividing an area to be optimized into a plurality of sub-domains, selecting an initial site from the original sites according to uncertainty of rainfall data of each original site in the area to be optimized and a mountain torrent disaster risk level of the sub-domain where each original site is located, and taking the initial site as a reference site;
S2, selecting a subsequent site from the rest original sites according to the uncertainty of rainfall data of the rest original sites, the mountain torrent disaster risk level of the sub-basin where the rest original sites are located and the information overlapping amount of the rest original sites and the reference site, S3, calculating information gain generated after the subsequent site is increased, if the information gain is larger than a first set value, reserving the subsequent site, taking the subsequent site as the reference site, and repeatedly executing S2-S3 until the information gain is smaller than or equal to the first set value;
And the networking module is configured to execute S4, and the initial site and all the stored subsequent sites form a rainfall station network after the region to be optimized is optimized.
A third aspect of the present application provides an electronic apparatus, comprising:
Processor, and
A memory having executable code stored thereon which, when executed by the processor, causes the processor to perform the method as described above.
A fourth aspect of the application provides a computer program product comprising computer instructions which, when executed by a processor, implement a method as described above.
The technical scheme provided by the application can comprise the following beneficial results:
According to the technical scheme, a region to be optimized is divided into a plurality of sub-watercourses, an initial site is selected from the original sites according to the uncertainty of rainfall data of all original sites in the region to be optimized and the flood disaster risk level of the sub-watercourses where all original sites are located, the initial site is used as a reference site, then a subsequent site is selected from the remaining original sites according to the uncertainty of rainfall data of all remaining original sites, the flood disaster risk level of the sub-watercourses where all original sites are located and the information overlapping amount of all remaining original sites and the reference site, the information gain generated after the subsequent site is increased is calculated, if the information gain is larger than a first set value, the subsequent site is used as the reference site, the step of selecting the subsequent site is repeatedly executed until the information gain is smaller than or equal to the first set value, and the initial site and all the subsequent sites form a rainfall station network after the region to be optimized. By combining uncertainty of rainfall data of each original site, the mountain torrent disaster risk level of the sub-basin where each original site is located and information overlapping amount of each original site and a previously selected site to select the site, and stopping selecting the site when the information gain is smaller, redundancy can be reduced, information overlapping is reduced, monitoring coverage rate of a key area is improved while overall information acquisition efficiency of a site network is improved, and mountain torrent disaster monitoring and early warning capability is effectively improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
FIG. 1 is a flow diagram of a method of optimizing a rain gauge network according to an embodiment of the present application;
FIG. 2 is a schematic diagram of the configuration of an optimization device for a rain gauge network according to an embodiment of the present application;
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While embodiments of the present application are illustrated in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the present application may employ terms "first," "second," "third," etc. to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, the second information may also be referred to as second information, and similarly, the second information may also be referred to as second information, without departing from the scope of the application. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Short-time strong rainfall is a main cause for inducing mountain torrents, and accurate acquisition of rainfall data is important for early warning. The rainfall in mountain areas is obvious due to the spatial heterogeneity, and the rationality of the layout of the rainfall station network directly influences the data precision and early warning aging.
At present, the common rainfall station network planning optimization method comprises an information entropy theory, a geological statistics method, a cone method, a station extraction method, a spatial interpolation method and the like, however, the method is insufficient in consideration of single factors or considered disaster risk factors, for example, the information entropy method is mainly used for considering only site information and redundancy, disaster risk spatial distribution is ignored, the spatial interpolation method is mainly used for paying attention to single factors of rainfall field precision, and the disaster risk prevention and control requirements are not effectively combined.
Therefore, how to optimize the layout scheme of the rainfall monitoring station network and improve the monitoring and early warning capability of the mountain torrent disasters becomes a technical problem to be solved urgently by the technicians in the field.
In order to solve the problems, the embodiment of the application provides an optimization method, an optimization device, electronic equipment and a computer program product for a rainfall station network, which can reduce redundancy and information overlapping, ensure the improvement of monitoring coverage rate of a key area while improving the overall information acquisition efficiency of the station network, and effectively improve the monitoring and early warning capability of mountain torrents.
The following describes the technical scheme of the embodiment of the present application in detail with reference to the accompanying drawings.
Fig. 1 is a flow chart of an optimization method of a rainfall station network according to an embodiment of the present application. Referring to fig. 1, the optimization method of the rainfall station network in the embodiment of the application specifically includes the following steps:
S1, dividing a region to be optimized into a plurality of sub-watersheds, selecting an initial site from the original sites according to the uncertainty of rainfall data of each original site in the region to be optimized and the mountain torrent disaster risk level of the sub-watershed where each original site is located, and taking the initial site as a reference site.
The area to be optimized refers to an area needing to be optimized for the rainfall station network, and specifically may be a certain drainage basin needing to be optimized for the rainfall station network. Any area needing to be optimized for the rainfall station network can be selected as the area to be optimized in the embodiment, and the area is not limited herein.
The original site refers to a rainfall site where the area to be optimized exists before optimization. According to the embodiment of the application, further optimization is performed on the basis of the existing rainfall station of the area to be optimized.
The region to be optimized is divided into a plurality of sub-watershed, and specifically, the region to be optimized can be divided into a plurality of sub-watershed according to topography, landform, area size or watershed length, which is not limited in this embodiment.
Further, according to the uncertainty of rainfall data of each original site in the area to be optimized and the mountain torrent disaster risk level of the sub-basin where each original site is located, selecting an initial site from the original sites, and taking the initial site as a reference site.
And collecting rainfall data of each original site in the area to be optimized in a historical time period, and calculating uncertainty of the rainfall data of each original site in the area to be optimized. Specifically, the rainfall data of each original site in the historical time period can be discretized, and then the uncertainty of the rainfall data of each original site is measured by calculating the edge information entropy of the rainfall data of each original site in the region to be optimized.
In some embodiments, the rainfall data of each original site in the historical time period is converted into discrete variables by adopting an equal-width method and a bottom limit function method, and the conversion formula is as follows:
Wherein X represents the original value of rainfall data, X r is the discrete value of the data sequence, a is the adjustable coefficient of interval width or rounding function, and the value a is determined by experimental trial calculation when the rainfall, water level and flow data are discrete.
The edge information entropy H (X) of each original site reflects the uncertainty of rainfall data of each original site X, and the calculation formula is as follows:
where p (X i) is the probability of the original site X rainfall value X i and n is the total number of discrete states. The greater the entropy of the edge information, the greater the uncertainty of the rainfall data of the corresponding original site, and the greater the potential information contribution thereof.
In some embodiments, data such as a digital elevation model (Digital Elevation Model, DEM) of the area to be optimized, a domestic total production value (Gross Domestic Product, GDP), population in the dangerous area determined by mountain torrent disaster investigation and evaluation, current flood control capacity and the like can be collected to assist in optimizing the rainfall station network.
And determining the torrent disaster risk level of the sub-basin where each original site is located according to the disaster information of each sub-basin. The disaster information comprises at least one of disaster factors, disaster-tolerant environments and disaster-bearing bodies.
The disaster-causing factors are driving factors for directly triggering or aggravating disasters, such as rainfall intensity, terrain gradient and the like, reflect the triggering mechanism and energy intensity of the disasters, the disaster-causing environment refers to background conditions for disaster formation and development, such as geological conditions, vegetation coverage rate, climate characteristics and the like, determine the occurrence possibility and spatial distribution of the disasters, and represent objects exposed to disaster risks and vulnerability thereof, such as population density, economic asset value, infrastructure distribution and the like, and measure the possible loss scale caused by the disasters.
More specifically, the comprehensive risk assessment of the small river basin can be firstly carried out, namely, a multi-index comprehensive assessment method is adopted to construct the comprehensive risk assessment index of the mountain torrent disaster, wherein the comprehensive risk assessment index comprises disaster factors, a disaster-pregnant environment and a disaster-bearing body. For example, 1h maximum rainfall, 6h maximum rainfall, elevation, gradient, GDP and dangerous area population are selected as the mountain torrent disaster comprehensive risk evaluation indexes. And dividing the region to be optimized into regions with different risk grades by using a natural breakpoint method or cluster analysis, for example, four grade regions with high risk, medium risk and low risk.
And then carrying out risk space analysis, namely extracting area ratio data of pixels with different risk levels in each sub-drainage basin, and adopting a highest risk level with the area ratio of the pixels in the sub-drainage basin exceeding a specific threshold as a judgment rule, wherein the threshold is determined by factors such as drainage basin characteristics, dangerous area distribution and the like, so as to determine the comprehensive risk level of the sub-drainage basin.
Specifically, extracting the area occupation ratio data of the pixels with different risk levels in each sub-drainage basin refers to calculating the ratio of the area of each risk level pixel in each sub-drainage basin to the total area of the corresponding sub-drainage basin. For example, the area of the low-risk pixel in a certain sub-drainage basin is 0, the area of the medium-risk pixel is 91.8, the area of the high-risk pixel is 8.2, and the area of the high-risk pixel is 0, so that the area ratio of the low-risk pixel is 0, the area ratio of the medium-risk pixel is 91.8, the area ratio of the high-risk pixel is 8.2, and the area ratio of the high-risk pixel is 0.
The adoption of the highest risk level with the pixel area ratio exceeding a specific threshold value in a sub-drainage basin as a judging rule means that the specific threshold value is preset, and if the pixel area ratio of one or more risk levels in a certain sub-drainage basin exceeds the specific threshold value, the highest risk level in the sub-drainage basin is selected as the highest risk level of the sub-drainage basin. The risk threshold may be set according to practical situations, for example, set to 20%, which is not limited in this embodiment. For example, the area ratio of the low-risk pixels in a certain sub-basin is 15.9%, the area ratio of the medium-risk pixels is 14.3%, the area ratio of the high-risk pixels is 49.2%, the area ratio of the high-risk pixels is 20.6%, and the risk threshold is set to 20%, wherein the area ratio of the high-risk pixels and the area ratio of the high-risk pixels are both over 20%, and the highest risk level, namely the high risk, is selected as the highest risk level of the sub-basin.
And calculating the comprehensive risk weight of each sub-basin according to the highest risk level of each sub-basin so as to measure the torrent disaster risk level of the sub-basin where each original site is located through the comprehensive risk weight of each sub-basin.
The comprehensive risk weight omega R(Xk) of each sub-basin is calculated as follows:
Wherein R (X k) is a torrent disaster risk weight factor of a sub-basin where the original site X k is located, the corresponding value of R (X k) can be set according to practical situations, the higher the risk level of the sub-basin is, the larger the corresponding value of R (X k) is, and alpha is a risk adjustment coefficient which can be determined through experimental trial calculation.
And calculating the edge information entropy of rainfall data of each original site, taking the product of the edge information entropy and the mountain torrent disaster risk level of the sub-basin where each original site is located as first data of each original site, and selecting the site with the highest first data in the original sites as the initial site.
More specifically, the product of the edge information entropy of rainfall data of each original site and the comprehensive risk weight of the sub-basin where each original site is located can be calculated as the first data X 1 of each original site, and the site with the highest first data X 1 in the original sites is selected as the initial site, where the calculation formula is as follows:
Omega R(Xi) is the comprehensive risk weight of the sub-basin where the original site X i is located, and H (X i) is the edge information entropy of the rainfall data of the original site.
Therefore, the site with the highest rainfall data uncertainty and the mountain torrent disaster risk level of the sub-basin where each original site is located can be selected as the initial site, and the information entropy theory and the mountain torrent disaster risk characteristics are comprehensively considered, so that the method provided by the embodiment can be suitable for the basins with different topographic features and climatic conditions, and has good popularization and application values.
Further, the initial station is taken as a reference station.
S2, selecting a subsequent site from the rest original sites according to the uncertainty of rainfall data of the rest original sites, the mountain torrent disaster risk level of the sub-basin where the rest original sites are located and the information overlapping amount of the rest original sites and the reference site.
After the reference site is obtained, a subsequent site is selected according to the reference site, and the optimization of the site network is completed.
Specifically, uncertainty of rainfall data of all the remaining original sites and mountain torrent disaster risk levels of sub-domains where all the remaining original sites are located are calculated. The step of calculating the uncertainty of the rainfall data of each remaining original site is the same as the step of calculating the uncertainty of the rainfall data of each original site in the above embodiment, and the step of calculating the risk level of the mountain torrent disaster in the sub-basin where each remaining original site is located is the same as the step of calculating the risk level of the mountain torrent disaster in the sub-basin where each original site is located in the above embodiment.
The method comprises the steps of calculating the edge information entropy of rainfall data of each original station, measuring the uncertainty of the rainfall data of each original station, determining the mountain torrent disaster risk level of the sub-basin where each original station is located according to disaster information of each sub-basin, calculating the product of the edge information entropy of the rainfall data of each original station and the mountain torrent disaster risk level of the sub-basin where each original station is located as first data of each original station, and the calculation mode can be described in the embodiment more specifically and is not repeated herein.
And then calculating the information overlapping amount of each original station and the reference station, wherein the information overlapping amount of each original station and the reference station refers to the information redundancy of each original station and the reference station, and the information redundancy is usually expressed by mutual information.
Based on this, in the embodiment of the present application, the information overlapping amount of each remaining original station and the reference station is determined according to the mutual information between each remaining original station and the reference station.
Mutual information I (X; Y) measures the shared information quantity between the rest original site X and the reference site Y, and the calculation formula is as follows:
Wherein H (X) represents the edge information entropy of the rainfall data of the rest original site X, H (Y) represents the edge information entropy of the rainfall data of the reference site Y, and H (X, Y) represents the joint information entropy of the rest original site X and the reference site Y.
The smaller the mutual information value, the less information overlap between sites, and more independent information can be provided. In optimizing a site network, it is generally preferable to select a site combination with lower mutual information to achieve maximization of information gain and minimization of redundancy.
The joint information entropy H (X, Y) is used for evaluating uncertainty of rainfall data joint distribution of the rest original site X and the reference site Y, and a calculation formula is as follows:
Wherein, p (X i,yi) represents the joint probability of the X value X i and the Y value Y i. The joint information entropy represents the overall uncertainty of the multi-site combination.
It should be noted that the total correlation is an extended form of mutual information, and is used to characterize information dependencies among multiple sites. For a multi-site system, the total correlation can be calculated by a multi-dimensional mutual information approximation method to evaluate the information redundancy degree of the whole site network. As the amount of duplicate information, i.e., redundancy, between the multidimensional random variables. The mathematical definition of the total correlation is:
above X 1,X2,…,Xn is a multidimensional random variable, corresponding to C (X 1,X2,…,Xn) represents information redundancy.
In the station network optimization, the maximum total correlation value is, and the greater the information redundancy degree of the whole station network is.
For a station network of a plurality of stations, the complexity of directly calculating the high-dimensional combined information entropy is high, and the combined entropy of the multi-dimensional discrete random variables is required to be fused into the information entropy of the one-dimensional discrete random variables, so that the combined information entropy of the multi-dimensional discrete random variables is obtained.
The joint entropy of the multi-dimensional discrete random variables is expressed as follows:
In some embodiments, mutual information between the remaining original stations and the reference station may be determined as an amount of information overlap between the remaining original stations and the reference station. In other embodiments, the information overlapping amount T of each remaining original station and the reference station may also be calculated according to mutual information between each remaining original station and the reference station.
The calculation formula is as follows:
s is an original site set, S is the number of sites in the set, and beta is the adjustment coefficient of balance information quantity and redundancy, and can be determined through experimental trial calculation.
Further, the first data of each remaining original site is calculated, the difference between the information overlapping amounts corresponding to each remaining original site is used as the second data of each remaining original site, and the site with the highest second data in the remaining original sites is selected as the subsequent site.
The method includes the steps of calculating edge information entropy of rainfall data of all remaining original sites, measuring uncertainty of the rainfall data of all remaining original sites, determining mountain torrent disaster risk levels of the sub-domains where all remaining original sites are located according to disaster information of all sub-domains, calculating the product of the edge information entropy of the rainfall data of all remaining original sites and the mountain torrent disaster risk levels of the sub-domains where all remaining original sites are located as first data of all remaining original sites, determining information overlapping amounts of all remaining original sites and reference sites according to mutual information between all remaining original sites and the reference sites, calculating first data of all remaining original sites, and enabling the steps corresponding to differences of the information overlapping amounts corresponding to all remaining original sites to be represented by a risk weighting MIMR optimization model.
The expression of the risk weighting MIMR optimization model is:
J R(Xk) is a risk weighted site evaluation function value, that is, second data, I (X k) is an information amount (usually expressed by edge entropy) of a remaining original site X k, ω R(Xk) is a comprehensive risk weight of a sub-basin where the remaining original site X k is located, R (X i,Xk) is an information redundancy (usually expressed by mutual information) between the remaining original site X k and a reference site X i, S is an original site set, s|is the number of sites in the set, β is an adjustment coefficient of balance information amount and redundancy, and can be determined through experimental trial calculation.
And selecting the site with the highest second data in the rest original sites as the expression corresponding to the subsequent site, wherein the expression comprises the following steps:
Wherein X next represents a subsequent site determined according to the above steps.
In the above embodiment, according to the first data of each remaining original site, the subsequent site is selected according to the difference of the information overlapping amount corresponding to each remaining original site, on one hand, the information entropy theory and the torrential flood disaster risk characteristics are comprehensively considered, so that the method provided by the embodiment can be suitable for the watershed with different terrain features and climatic conditions, has good popularization and application values, on the other hand, the site with smaller information overlapping amount with the reference site can be selected as the subsequent site, the redundancy can be reduced, the information overlapping is reduced, the monitoring coverage rate of the key area is ensured to be improved while the overall information acquisition efficiency of the site network is improved, and the torrential flood disaster monitoring and early warning capability is effectively improved.
And S3, calculating information gain generated after the subsequent station is added, detecting whether the information gain is larger than a first set value, if so, reserving the subsequent station, taking the subsequent station as a reference station, and repeatedly executing S2-S3 until the information gain is smaller than or equal to the first set value.
The gain of information generated after adding the subsequent stations is calculated. The method comprises the steps of calculating first comprehensive edge information entropy of rainfall data of an initial station and all current subsequent stations before adding the subsequent stations, calculating second comprehensive edge information entropy of rainfall data of the initial station and all current subsequent stations after adding the subsequent stations, and determining difference between the second comprehensive edge information entropy and the first comprehensive edge information entropy to increase information gain generated after adding the subsequent stations.
The calculation formula is as follows:
Where Δh is the first comprehensive edge information entropy, and H (S ∈xnext }) represents the second comprehensive edge information entropy.
When ΔH is greater than the first set value, the selected subsequent station can provide more new information, which is beneficial to monitoring rainfall, the subsequent station can be reserved, then the subsequent station is used as a reference station and S2-S3 is repeatedly executed, and the new subsequent station is selected again, so that the method is repeated.
And stopping site selection when the delta H is smaller than or equal to the first set value, wherein the fact that the selected subsequent sites cannot provide enough new information when the delta H is smaller is that if the sites continue to be selected, information redundancy can be caused, the overall information acquisition efficiency of the site network is reduced, and meanwhile, the subsequent sites obtained when the delta H is smaller than or equal to the first set value are deleted.
The first setting value can be dynamically adjusted according to the characteristics of the river basin and the scale of the station network, and the embodiment is not limited.
S4, forming the initial site and all stored subsequent sites into a rainfall station network after the area to be optimized is optimized.
And combining the initial site and all the stored subsequent sites to obtain the rainfall station network after the optimization of the area to be optimized.
In a specific embodiment, the region to be optimized includes the original sites 1-10, and the site 5 is selected as the initial site according to the steps of the above embodiment, and the site selection steps are as follows:
Site 5 is the initial site, and site 5 is the reference site. Station 1 is selected as the subsequent station according to the steps of the above embodiment. And calculating the information gain after the station 1 is added, wherein the information gain is larger than a first set value, and storing the station 1.
Site 1 was used as a reference site. Station 6 is selected as a subsequent station according to the steps of the above embodiment. And calculating the information gain after the station 6 is added, wherein the information gain is larger than a first set value, and storing the station 6.
Station 6 is referred to as the reference station. Station 9 is selected as a subsequent station according to the steps of the above embodiment. And calculating the information gain after the station 9 is added, wherein the information gain is larger than a first set value, and storing the station 9.
Station 9 is taken as a reference station. Station 7 is selected as a subsequent station according to the steps of the above embodiment. And calculating the information gain after the station 7 is added, wherein the information gain is larger than a first set value, and storing the station 7.
Station 7 is taken as a reference station. Station 8 is selected as a subsequent station according to the steps of the above embodiment. And calculating the information gain after adding the station 8, wherein the information gain is smaller than a first set value, deleting the station 8 and stopping selecting the station.
The stations selected in this way include station 5, station 1, station 6, station 9 and station 7. And forming a rainfall station network after the optimization of the area to be optimized by the station 5, the station 1, the station 6, the station 9 and the station 7.
In the above embodiment, the site is selected by combining the uncertainty of rainfall data of each original site, the mountain torrent disaster risk level of the sub-basin where each original site is located, and the information overlapping amount of each original site and the previously selected site, and the site selection is stopped when the information gain is smaller. The system can reduce redundancy and information overlapping, improves the monitoring coverage rate of a key area while improving the overall information acquisition efficiency of the station network, and effectively improves the monitoring and early warning capacity of mountain torrent disasters.
Meanwhile, multidimensional mountain torrent disaster risk prevention and control factors such as an information entropy theory, mountain torrent disaster risk, dangerous area characteristics and the like can be effectively integrated, scientific optimization layout of a mountain torrent disaster rainfall station network is realized, monitoring and early warning capability of the mountain torrent disaster is improved, and casualties and property loss are reduced.
As an optional embodiment, after the steps of the above embodiment form the rainfall station network after the optimization of the to-be-optimized area by the initial station and all the saved subsequent stations, the steps may further include:
Correcting the optimized rainfall station network in each sub-drainage basin according to the number of stations required by each sub-drainage basin in the plurality of sub-drainage basins, wherein the number of stations required by each sub-drainage basin is determined according to the mountain torrent disaster risk level of each sub-drainage basin and the area of each sub-drainage basin.
In particular, sub-watershed with different risk classes, with different density criteria, typically very high risk areas no less than 30 square kilometers per station, high risk areas no less than 50 square kilometers per station, medium risk areas no less than 70 square kilometers per station, low risk areas no less than 100 square kilometers per station. Therefore, in the embodiment of the application, the number of stations required by each sub-drainage basin in a plurality of sub-drainage basins can be determined according to the mountain torrent disaster risk level of each sub-drainage basin and the area of each sub-drainage basin and by combining the density standard corresponding to the region to be optimized.
For example, if the density criteria is that the extremely high risk area is not lower than 30 square kilometers per station, the high risk area is not lower than 50 square kilometers per station, the medium risk area is not lower than 70 square kilometers per station, the low risk area is not lower than 100 square kilometers per station, then the number of required stations for a sub-basin of 322.46 square kilometers is 7, the number of required stations for a sub-basin of 91.11 square kilometers is extremely high, and the number of required stations for a sub-basin is 4.
And then correcting the optimized rainfall station network in each sub-drainage basin according to the number of stations required by each sub-drainage basin in the plurality of sub-drainage basins. The specific correction mode is that if the number of stations of the optimized rainfall station network is smaller than the number of required stations in the plurality of sub-streams, stations are added in the rainfall station network after the optimization of the target sub-streams according to the number of stations required by the target sub-streams.
That is, the number of stations in the optimized rainfall station network in the target sub-basin is supplemented to be the same as the number of stations required by the target sub-basin. For example, the number of stations in the optimized rainfall station network in the target subbasin is 3, and the number of stations required by the target subbasin is 5, so that 2 stations are required to be added in the rainfall station network of the target subbasin.
Further, sites are added to a target area of the target sub-basin according to the number of sites required by the target sub-basin, wherein the target area comprises an area, upstream of a mountain torrent disaster dangerous area of the target sub-basin, with a space representative difference of an area where an optimized rainfall station network in the target sub-basin is located, larger than a second set value, and an area, in the target sub-basin, with information redundancy degree smaller than a third set value, and the space representative difference comprises an elevation difference.
Specifically, the hydrological response relation between the site and the nearby associated mountain torrent disaster dangerous areas is analyzed, the target area is determined, and factors such as elevation difference, converging paths, hydrological response time, rainfall spatial correlation and the like are mainly considered, so that the site can be ensured to provide effective early warning for the dangerous areas. The specific strategies comprise:
aiming at sub-drainage basins with unqualified density and insufficient coverage of a dangerous area, a site is additionally arranged at the optimal position of a water collecting area at the upstream of the dangerous area based on terrain analysis and a converging path;
analyzing site space representativeness by using geographic weighted regression, and arranging a newly added site at a position with the largest space representativeness difference so as to maximize information gain;
and optimizing site layout positions aiming at areas with dense distribution of dangerous areas by combining rainfall spatial correlation and confluence time analysis, so that a single site can simultaneously serve the early warning requirements of a plurality of dangerous areas.
By analyzing the position relation and upstream water collecting characteristics of the early warning protection objects, key factors such as elevation difference, collecting paths and the like are considered, so that the important areas are ensured to obtain enough early warning reaction time, and the early warning accuracy is improved.
Taking the river basin A as a region to be optimized, taking optimization layout of a mountain torrent rainfall station network of the river basin A as an example, and specifically explaining the method of the embodiment:
Collecting the 52 automatic rainfall monitoring sites (i.e. original sites) in the A river basin from 1 st 2019 to 12 nd 31 st 2023, and dividing the A river basin into 18 sub-river basins. And converting continuous rainfall data into discrete variables by adopting an equal-width interval method and a bottom limit function method, and comparing the discrete variables by a plurality of experimental tests to obtain interval width parameters a=3 mm.
And calculating statistics such as the sum of edge information entropy, joint information entropy, total mutual information, total relativity and the like of the original site taking 18 sub-watershed as a unit, and particularly showing in table 1. Where sub-basin 16 has no original site and therefore no calculation results.
TABLE 1
| Sub-basin | Number of sites | Sum of edge entropy | Joint entropy | Total mutual information | Total correlation |
| 1 | 3 | 10.8 | 6.7 | 4.0 | 0.4 |
| 2 | 3 | 9.2 | 5.8 | 3.4 | 0.4 |
| 3 | 2 | 7.1 | 5.5 | 1.6 | 0.2 |
| 4 | 1 | 3.5 | 3.5 | 0.0 | 0.0 |
| 5 | 1 | 3.6 | 3.6 | 0.0 | 0.0 |
| 6 | 4 | 14.4 | 7.2 | 7.2 | 0.5 |
| 7 | 4 | 13.9 | 7.1 | 6.8 | 0.5 |
| 8 | 4 | 12.6 | 6.9 | 5.7 | 0.5 |
| 9 | 1 | 3.5 | 3.5 | 0.0 | 0.0 |
| 10 | 6 | 21.3 | 7.2 | 14.1 | 0.7 |
| 11 | 8 | 28.6 | 7.7 | 20.9 | 0.7 |
| 12 | 2 | 7.0 | 5.4 | 1.6 | 0.2 |
| 13 | 3 | 10.8 | 6.5 | 4.3 | 0.4 |
| 14 | 2 | 7.2 | 5.4 | 1.8 | 0.3 |
| 15 | 2 | 7.1 | 5.6 | 1.5 | 0.2 |
| 16 | 0 | —— | —— | —— | —— |
| 17 | 2 | 7.2 | 5.4 | 1.8 | 0.3 |
| 18 | 4 | 14.3 | 6.8 | 7.5 | 0.5 |
And selecting 1h maximum rainfall, 6h maximum rainfall, elevation, gradient, GDP (GDP), and dangerous area population as comprehensive risk evaluation indexes of the mountain torrent disasters, and obtaining each index weight and 18 sub-river mountain torrent disasters comprehensive risk indexes by using a hierarchical analysis method. Based on the torrential flood disaster comprehensive risk index, the torrential flood disaster comprehensive risk index is divided into risk areas with different grades by adopting a natural breakpoint method. The comprehensive index ranges of the mountain torrent disaster risks corresponding to the low risk, the medium risk, the high risk and the extremely high risk grades are respectively [8.55,9.03 ] [9.03,9.49 ] [9.49,9.80 ] [9.80,10.24].
And counting pixel area occupation ratios of different risk grades in each sub-river basin according to the comprehensive risk assessment result layer of the mountain torrent disasters in the 18 sub-river basins of the A-river basin. According to factors such as characteristics of the river basin and distribution of dangerous areas, the embodiment adopts the highest risk level with the pixel area ratio in the river basin exceeding 20% as a rule for judging the comprehensive risk level of the mountain torrent disaster of the whole river basin, so that 1 low risk, 5 medium risk, 6 high risk and 6 extremely high risk in the 18 comprehensive risk levels of the river basin are obtained.
In this embodiment, risk weight factors corresponding to low, medium, high and extremely high risks are respectively assigned to 0.2, 0.4, 0.6 and 0.8, and the risk adjustment coefficient α is calculated to be 1.35, so that comprehensive risk weights corresponding to low, medium, high and extremely high levels of each sub-drainage basin are respectively 1.27, 1.54, 1.81 and 2.08. See in particular table 2.
TABLE 2
And constructing a risk weighting MIMR optimization model which takes the maximization of the information quantity and the minimization of the redundancy into consideration. In this embodiment, the balance coefficient β is 0.5.
The sites are gradually selected based on the risk weighting MIMR optimization model, and the information gain is calculated until the adaptive termination condition is reached.
The original site with the greatest risk weighting information entropy (i.e., first data) is first selected as the initial site, and then the subsequent sites are progressively selected based on the risk weighting MIMR optimization model. After each selection of a new station, the information gain Δh is calculated. In this embodiment station selection is stopped when the information gain ah is less than the threshold value 0.2.
According to the rainfall site density standards of different risk level areas, an extremely high risk area is not lower than 30 square kilometers per station, a high risk area is not lower than 50 square kilometers per station, a medium risk area is not lower than 70 square kilometers per station, a low risk area is not lower than 100 square kilometers per station, the existing site network density of 18 sub-domains of the A-basin is initially evaluated, sub-domains with the density not meeting the requirement are identified, 10 sub-domains in total cannot reach the minimum site density required by the corresponding risk level, and all the sub-domains belong to the high risk or extremely high risk area, and the specific see table 3.
TABLE 3 Table 3
| Sub-basin | Comprehensive risk level | Area (square kilometer) | The number of required sites | Number of existing sites | Lack of site count |
| 6 | High risk | 322.46 | 7 | 4 | 3 |
| 8 | Extremely high risk | 185.87 | 7 | 4 | 3 |
| 9 | High risk | 57.27 | 2 | 1 | 1 |
| 11 | Extremely high risk | 359.85 | 12 | 8 | 4 |
| 12 | Extremely high risk | 91.11 | 4 | 2 | 2 |
| 13 | Extremely high risk | 95.16 | 4 | 3 | 1 |
| 14 | High risk | 115.77 | 3 | 2 | 1 |
| 15 | Extremely high risk | 167.42 | 6 | 2 | 4 |
| 16 | High risk | 67.00 | 2 | 0 | 2 |
| 17 | High risk | 122.82 | 3 | 2 | 1 |
Based on the preliminary optimization evaluation of the station network, the corresponding optimization strategies are adopted for different situations by combining the hydrological response relation between the rainfall station and the nearby associated mountain torrent disaster dangerous areas.
The densities of the sites of the sub-watershed 9, 13 and 17 are slightly higher than the standards of the corresponding risk levels, but the absolute areas are not large, the existing sites well cover the torrential flood disaster dangerous areas, and the information redundancy of the sub-watershed 13 and 17 is relatively low. The monitoring benefit and the construction cost are comprehensively weighed, the current situation is recommended to be maintained, and the rainfall monitoring site is not newly added.
For the rest 7 sub-watersheds (6,8,11,12,14,15,16), analysis shows that the sub-watersheds are required to be additionally provided with rainfall monitoring stations, and the arrangement of the rainfall stations of each sub-watershed is adjusted as follows:
the sub-river basin 6 is newly added with 3 stations, which are mainly arranged in the source areas of the western A area and the southern B area and are encrypted at the dense positions of the danger areas at the upstream of the B area, and the C river basin is provided with X rainfall stations which are arranged at the downstream of the three danger areas and are recommended to be adjusted to the upstream of the danger areas;
The sub-river basin 8 is provided with 3 stations which are respectively arranged at the blank position of the downstream station network in the D area and the river source position at the upstream of the dangerous area in the E area;
the sub-basin 11 needs to be newly added with 4 stations, the newly added stations are distributed in the edge and elevation difference areas of the north, the west and the like, and in addition, F and G reservoir rainfall stations recommend to be adjusted to the upstream of the danger area;
a sub-river basin 12, which needs to be newly added with 1 station and is arranged in the source area of the H area and upstream of the dangerous area;
the sub-river basin 14 needs to be newly added with 1 station, is arranged at the upstream of the dangerous area of the I area, and is also suggested to be adjusted to the upstream area of the dangerous area by the J rainfall station;
A sub-river basin 15, wherein 4 stations are required to be added, and the newly added stations are arranged in source areas of the K area and the L area and a low redundancy area at the downstream of the river basin;
and the sub-basin 16 is newly added with 2 stations and is arranged at the upstream of the dangerous areas of the basin sources of the M area and the N area.
And finally obtaining the station network layout of the A river basin after optimizing according to the distribution adjustment condition of the sub-river basin stations, wherein the station network layout is newly added with 18 stations in the optimization, and the station network layout is suggested to adjust 4 stations of the positions. After optimization, the monitoring density of all the sub-watershed basically meets or exceeds the minimum requirement of the corresponding risk level. The specific effects are shown in table 4:
TABLE 4 Table 4
Corresponding to the embodiment of the application function implementation method, the application also provides an optimization device, electronic equipment, a computer program product and corresponding embodiments of the rainfall station network.
Fig. 3 is a schematic structural view of an optimizing apparatus for a rainfall station network according to an embodiment of the present application.
Referring to fig. 3, the optimization device for the rainfall station network includes:
S1, dividing an area to be optimized into a plurality of sub-domains, selecting an initial site from the original sites according to uncertainty of rainfall data of each original site in the area to be optimized and a mountain torrent disaster risk level of the sub-domain where each original site is located, and taking the initial site as a reference site;
The subsequent site selection module 110 is configured to execute S2-S3, wherein the subsequent site is selected from the remaining original sites according to the uncertainty of rainfall data of the remaining original sites, the mountain torrent disaster risk level of the sub-basin where the remaining original sites are located and the information overlapping amount of the remaining original sites and the reference sites;
The networking module 120 is configured to execute S4, and form the initial site and all the saved subsequent sites into a rainfall station network after the region to be optimized is optimized.
As an optional implementation manner, the optimization device of the rainfall station network further includes:
The correction module is used for correcting the optimized rainfall station network in each sub-drainage basin according to the number of stations required by each sub-drainage basin in the plurality of sub-drainage basins, wherein the number of stations required by each sub-drainage basin is determined according to the mountain torrent disaster risk level of each sub-drainage basin and the area of each sub-drainage basin.
As an alternative embodiment, the above correction module is specifically configured to:
If the optimized rainfall station network has the target sub-drainage basin with the station number smaller than the required station number in the plurality of sub-drainage basins, stations are added in the rainfall station network with the optimized target sub-drainage basin according to the station number required by the target sub-drainage basin.
As an alternative embodiment, the above correction module is specifically configured to:
adding sites in a target area of the target sub-basin according to the number of sites required by the target sub-basin;
The target area comprises an area, upstream of the mountain torrent disaster dangerous area of the target sub-river basin, with a space representative difference of the area where the optimized rainfall station network is located in the target sub-river basin being larger than a second set value, and an area, in the target sub-river basin, with information redundancy degree being smaller than a third set value, wherein the space representative difference comprises an elevation difference.
As an alternative embodiment, the initial station selection module 100 is specifically configured to:
Measuring the uncertainty of the rainfall data of each original site by calculating the edge information entropy of the rainfall data of each original site in the region to be optimized, wherein the larger the edge information entropy is, the higher the uncertainty of the rainfall data of the corresponding original site is;
determining the mountain torrent disaster risk level of the sub-river basin where each original site is located according to disaster information of each sub-river basin, wherein the disaster information comprises at least one of disaster factors, disaster-tolerant environments and disaster-bearing bodies;
And calculating the edge information entropy of rainfall data of each original site, taking the product of the edge information entropy and the mountain torrent disaster risk level of the sub-basin where each original site is located as first data of each original site, and selecting the site with the highest first data in the original sites as the initial site.
As an alternative embodiment, the subsequent station selection module 110 is specifically configured to:
Determining the mountain torrent disaster risk level of the sub-basin where each original site is located according to disaster information of each sub-basin, calculating the product of the edge information entropy of the rainfall data of each original site and the mountain torrent disaster risk level of the sub-basin where each original site is located as first data of each original site, wherein the larger the edge information entropy is, the higher the uncertainty of the rainfall data of the corresponding original site is;
Determining the information overlapping amount of the rest original stations and the reference station according to the mutual information between the rest original stations and the reference station;
and calculating the first data of each residual original site, and selecting the site with the highest second data in the residual original sites as the subsequent site, wherein the difference of the information overlapping amount corresponding to each residual original site is used as the second data of each residual original site.
As an alternative embodiment, the subsequent station selection module 110 is specifically configured to:
before adding the subsequent stations, calculating first comprehensive edge information entropy of rainfall data of the initial station and all the current subsequent stations; after the subsequent stations are added, calculating second comprehensive edge information entropy of rainfall data of the initial station and all the current subsequent stations;
And determining the difference between the second comprehensive edge information entropy and the first comprehensive edge information entropy to increase the information gain generated after the subsequent stations.
The specific manner in which the respective modules perform the operations in the apparatus of the above embodiments has been described in detail in the embodiments related to the method, and will not be described in detail herein.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Referring to fig. 3, the electronic device 1000 includes a memory 1010 and a processor 1020.
The Processor 1020 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Memory 1010 may include various types of storage units, such as system memory, read Only Memory (ROM), and persistent storage. Where the ROM may store static data or instructions that are required by the processor 1020 or other modules of the computer. The persistent storage may be a readable and writable storage. The persistent storage may be a non-volatile memory device that does not lose stored instructions and data even after the computer is powered down. In some embodiments, the persistent storage device employs a mass storage device (e.g., magnetic or optical disk, flash memory) as the persistent storage device. In other embodiments, the persistent storage may be a removable storage device (e.g., diskette, optical drive). The system memory may be a read-write memory device or a volatile read-write memory device, such as dynamic random access memory. The system memory may store instructions and data that are required by some or all of the processors at runtime. Furthermore, memory 1010 may comprise any combination of computer-readable storage media including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic disks, and/or optical disks may also be employed. In some implementations, memory 1010 may include readable and/or writable removable storage devices such as Compact Discs (CDs), digital versatile discs (e.g., DVD-ROMs, dual-layer DVD-ROMs), blu-ray discs read only, super-density discs, flash memory cards (e.g., SD cards, min SD cards, micro-SD cards, etc.), magnetic floppy disks, and the like. The computer readable storage medium does not contain a carrier wave or an instantaneous electronic signal transmitted by wireless or wired transmission.
The memory 1010 has stored thereon executable code that, when processed by the processor 1020, can cause the processor 1020 to perform some or all of the methods described above.
Furthermore, the method according to the application may also be implemented as a computer program or computer program product comprising computer program code instructions for performing part or all of the steps of the above-described method of the application.
Or the application may also be embodied as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) having stored thereon executable code (or a computer program or computer instruction code) that, when executed by a processor of an electronic device (or a server, etc.), causes the processor to perform some or all of the steps of a method according to the application as described above.
The application also provides a computer program product comprising computer instructions which, when executed by a processor, implement a method as described above.
The foregoing description of embodiments of the application has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510744366.8A CN120257673B (en) | 2025-06-05 | 2025-06-05 | Optimization method and device for rainfall station network, electronic equipment and computer program product |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510744366.8A CN120257673B (en) | 2025-06-05 | 2025-06-05 | Optimization method and device for rainfall station network, electronic equipment and computer program product |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN120257673A CN120257673A (en) | 2025-07-04 |
| CN120257673B true CN120257673B (en) | 2025-08-15 |
Family
ID=96185612
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510744366.8A Active CN120257673B (en) | 2025-06-05 | 2025-06-05 | Optimization method and device for rainfall station network, electronic equipment and computer program product |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN120257673B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115545344A (en) * | 2022-11-07 | 2022-12-30 | 长江水利委员会长江科学院 | Optimization method of rainfall station network layout in small watersheds of mountain torrents |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BR112016009470A2 (en) * | 2013-10-28 | 2018-03-20 | Intel Corp | computer program methods, systems and products for using a distributed associative memory base to determine correlations and data convergences thereon |
| CN107315722B (en) * | 2017-07-03 | 2020-01-21 | 南京大学 | Hydrological station network optimization method based on the coupling of kriging and information entropy theory |
| CN110428586B (en) * | 2019-07-08 | 2021-01-05 | 大连理工大学 | Early warning method for rural basal flood based on early rainfall and upstream and downstream topological relation |
| CN116992676A (en) * | 2023-08-08 | 2023-11-03 | 浙江省水利河口研究院(浙江省海洋规划设计研究院) | Ground rainfall station network optimization design method assisted by multi-source satellite precipitation observation |
| CN118863138A (en) * | 2024-07-02 | 2024-10-29 | 河海大学 | A spatial optimization method for rainfall station network to serve the needs of flash flood disaster prevention and control |
| CN118780092B (en) * | 2024-09-10 | 2025-01-28 | 长江三峡集团实业发展(北京)有限公司 | A technical method, system and device for optimizing the layout of a ground rainfall station network |
| CN119886840B (en) * | 2025-03-25 | 2025-06-10 | 浙江省水利河口研究院(浙江省海洋规划设计研究院) | Hierarchical and regional differential risk early warning method for mountain torrents in small and medium-sized river basins |
-
2025
- 2025-06-05 CN CN202510744366.8A patent/CN120257673B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115545344A (en) * | 2022-11-07 | 2022-12-30 | 长江水利委员会长江科学院 | Optimization method of rainfall station network layout in small watersheds of mountain torrents |
Non-Patent Citations (1)
| Title |
|---|
| 珠江流域 (云南省)雨量站网分析;袁树堂 等;人民珠江;20080229(第1期);正文第1-2节 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN120257673A (en) | 2025-07-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ali et al. | Spatial–temporal characterization of rainfall in Pakistan during the past half-century (1961–2020) | |
| CN110837925B (en) | Urban waterlogging prediction method and device | |
| Jalili Pirani et al. | Geostatistical and deterministic methods for rainfall interpolation in the Zayandeh Rud basin, Iran | |
| Otieno et al. | Influence of rain gauge density on interpolation method selection | |
| Caldera et al. | A comparison of methods of estimating missing daily rainfall data | |
| Rajsekhar et al. | Hydrologic drought atlas for Texas | |
| Katipoğlu et al. | Spatio-temporal analysis of meteorological and hydrological droughts in the Euphrates Basin, Turkey | |
| Feki et al. | Geostatistically based optimization of a rainfall monitoring network extension: case of the climatically heterogeneous Tunisia | |
| Sahu et al. | Density-based spatial clustering of application with noise approach for regionalisation and its effect on hierarchical clustering | |
| Bajamgnigni Gbambie et al. | Added value of alternative information in interpolated precipitation datasets for hydrology | |
| Hasan et al. | Assessment of probability distributions and analysis of the minimum storage draft rate in the equatorial region | |
| Kumari et al. | Interpolation methods for groundwater quality assessment in tank cascade landscape: A Study of Ulagalla Cascade, Sri Lanka | |
| Zhu et al. | Regression modeling of streamflow, baseflow, and runoff using geographic information systems | |
| Chebbi et al. | Optimal extension of rain gauge monitoring network for rainfall intensity and erosivity index interpolation | |
| CN120257673B (en) | Optimization method and device for rainfall station network, electronic equipment and computer program product | |
| Gnanaprakkasam et al. | Evaluation of regional flood quantiles at ungauged sites by employing nonlinearity-based clustering approaches | |
| CN119692532A (en) | A green space optimization method and related device based on urban waterlogging risk assessment model | |
| CN117633539B (en) | Underground water drought identification method and device for uneven site distribution | |
| Livne et al. | Components of uncertainty in primary production model: the study of DEM, classification and location error | |
| CN118350656A (en) | Photovoltaic power prediction method and system based on morphological clustering and multi-level residual network | |
| Kanooni et al. | Comparison of sensitivity of interpolation methods by rain gauge network density | |
| GOVAY et al. | Comparative analysis of different techniques for spatial interpolation of rainfall datasets in Duhok Governorate | |
| Feizizadeh et al. | A spatially explicit approach for sensitivity and uncertainty analysis of GIS-multicriteria landslide susceptibility mapping | |
| Chen et al. | Gridded rainfall erosivity (2014–2022) in mainland China using 1 min precipitation data from densely distributed weather stations | |
| Chen et al. | Gridded rainfall erosivity (2014–2022) in mainland China using 1 min precipitation data from densely distributed weather stations |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |