Rainfall interpolation method, device, computer equipment and storage medium

Abstract

The embodiment of the present invention discloses a rainfall interpolation method, device, computer equipment and storage medium. The method comprises: grouping the research object area composed of all rain gauges to obtain a spatial area; judging whether the rainfall data in the spatial area has spatial heterogeneity; if the rainfall data in the spatial area has spatial heterogeneity, interpolating the rainfall data using a local model, local variables or local parameters; if the rainfall data in the spatial area does not have spatial heterogeneity, interpolating the rainfall data using a global model. By implementing the method of the embodiment of the present invention, the error between the interpolation and the actual rainfall can be reduced, and the interpolation is not easily disturbed by the confounding effect, thereby improving the accuracy of the interpolation.

Description

Technical Field

[0001] The invention relates to an interpolation method, and more specifically to a rainfall interpolation method, a device, a computer device and a storage medium. Background Art

[0002] The accuracy of rainfall data plays an extremely important role in accurately predicting the probability of landslides. The rainfall of the current research unit, i.e., grid unit / slope unit, is obtained by interpolating the rainfall of rain gauges in the region. When the error of rainfall interpolation is large, its prediction ability will be affected.

[0003] Existing rainfall interpolation methods can generally be divided into three categories: global interpolation, local interpolation, and mixed interpolation. Although local interpolation methods predict rainfall at unknown points by fitting rainfall at some known points in the region, such as Thiessen polygon method, inverse distance weighted method, Kriging interpolation method, radial basis function method, etc., all known points are used in their theoretical and practical models. That is, whether the global or local interpolation method uses a single interpolation model for all known points, the interpolation results generated will be within the control range of the model smoothing, which conceals the heterogeneity of the object, and the interpolation is easily disturbed by the confounding effect, and even leads to wrong conclusions.

[0004] Therefore, it is necessary to design a new method to reduce the error between the interpolation and the actual rainfall, and the interpolation is not easily disturbed by the confounding effect, so as to improve the accuracy of the interpolation. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the prior art and provide a rainfall interpolation method, device, computer equipment and storage medium.

[0006] To achieve the above object, the present invention adopts the following technical scheme: a rainfall interpolation method, comprising:

[0007] The study area consisting of all rainfall stations is grouped to obtain spatial regions;

[0008] Determining whether there is spatial heterogeneity in rainfall data within the spatial region;

[0009] If the rainfall data in the spatial region has spatial heterogeneity, the rainfall data is interpolated using a local model, a local variable or a local parameter;

[0010] If the rainfall data in the spatial region does not have spatial heterogeneity, a global model is used to interpolate the rainfall data.

[0011] A further technical solution is: the research object area composed of all rainfall stations is grouped to obtain a spatial area, including:

[0012] Thiessen polygons are used to determine the control area of ​​each rainfall station to obtain polygons, and all polygons constitute the research object area;

[0013] determining the rainfall intensity of the unique rain gauge contained within the polygon to obtain the rainfall intensity for each polygon;

[0014] The research object regions are grouped to obtain spatial regions.

[0015] A further technical solution is: the research object area composed of all rainfall stations is grouped to obtain a spatial area, including:

[0016] The research object regions are grouped based on expert knowledge or classification algorithms to obtain spatial regions.

[0017] A further technical solution is: the determining whether the rainfall data in the spatial region has spatial heterogeneity comprises:

[0018] Calculate spatial differentiation value;

[0019] Determining whether the spatial differentiation value meets the set requirements;

[0020] If the spatial differentiation value meets the set requirements, then there is no spatial differentiation in the rainfall data within the spatial area;

[0021] If the spatial differentiation value does not meet the set requirement, then the rainfall data in the spatial area has spatial differentiation.

[0022] Its further technical solution is: the spatial differentiation value is obtained by Obtained, where h = 1, ..., L is the grouping of variable Y or factor X, N represents the number of units in the spatial region, SSW represents the sum of the variances of rainfall within the group, SST represents the total variance of rainfall in the spatial region, σ is the standard deviation, and q is the spatial differentiation value.

[0023] A further technical solution is: the use of a global model to interpolate the rainfall data comprises:

[0024] The rainfall data is interpolated by combining a single interpolation model, global variables and global parameters.

[0025] A further technical solution is as follows: the global model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model; the local model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model.

[0026] The present invention also provides a rainfall interpolation device, comprising:

[0027] The grouping unit is used to group the research object area composed of all rainfall stations to obtain the spatial area;

[0028] A judging unit, used for judging whether the rainfall data in the spatial region has spatial heterogeneity;

[0029] A first interpolation unit is used to interpolate the rainfall data using a local model, a local variable or a local parameter if the rainfall data in the spatial region has spatial heterogeneity;

[0030] The second interpolation unit is used to interpolate the rainfall data using a global model if there is no spatial heterogeneity in the rainfall data within the spatial region.

[0031] The present invention further provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor implements the above method when executing the computer program.

[0032] The present invention also provides a storage medium, wherein the storage medium stores a computer program, and the computer program can implement the above method when executed by a processor.

[0033] Compared with the prior art, the beneficial effect of the present invention is as follows: the present invention groups the research object area composed of all rainfall stations according to the control area, that is, the spatial position, determines the spatial heterogeneity of rainfall data in the spatial area, and interpolates according to the determination result by combining the global model with the local model, thereby reducing the error between the interpolation and the actual rainfall, and the interpolation is not easily disturbed by the confounding effect, thereby improving the accuracy of the interpolation.

[0034] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0036] Figure 1 A schematic diagram of an application scenario of a rainfall interpolation method provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of a flow chart of a rainfall interpolation method provided by an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of a sub-process of a rainfall interpolation method provided by an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of a sub-process of a rainfall interpolation method provided by an embodiment of the present invention;

[0040] Figure 5 A schematic block diagram of a rainfall interpolation device provided in an embodiment of the present invention;

[0041] Figure 6 A schematic block diagram of a grouping unit of a rainfall interpolation device provided in an embodiment of the present invention;

[0042] Figure 7 A schematic block diagram of a judgment unit of a rainfall interpolation device provided in an embodiment of the present invention;

[0043] Figure 8 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0046] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0047] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0048] See also Figure 1 and Figure 2 , Figure 1 A schematic diagram of an application scenario of the rainfall interpolation method provided in an embodiment of the present invention. Figure 2A schematic flow chart of a rainfall interpolation method provided in an embodiment of the present invention. The rainfall interpolation method is applied in a server. The server exchanges data with the equipment of each rain gauge station, groups the research object area composed of all rain gauge stations to obtain spatial regions according to different spatial positions, determines the spatial heterogeneity in the spatial region according to the control area of ​​each rain gauge station, and interpolates rainfall using a geographic detector.

[0049] Figure 2 FIG. 1 is a flow chart of a rainfall interpolation method provided by an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S140.

[0050] S110. Group the research object area consisting of all rainfall stations to obtain spatial areas.

[0051] In this embodiment, the principle for grouping the research object regions is that the variance within the group is minimal and the variance between groups is maximal, and multiple non-adjacent regions may appear in the same group.

[0052] The research object area composed of all rainfall stations refers to the entire research area, and the entire research area is grouped according to the control area and spatial location.

[0053] In one embodiment, see Figure 3 , the above-mentioned step S110 may include steps S111 to S113.

[0054] S111. Use Thiessen polygons to determine the control area of ​​each rain gauge to obtain polygons, and all polygons constitute the research object area.

[0055] In this embodiment, the polygon refers to the area controlled by the rain gauge station.

[0056] Specifically, all adjacent meteorological stations, i.e. rain gauges, are connected into triangles, and perpendicular bisectors are drawn for the sides of these triangles. Then, several perpendicular bisectors around each meteorological station form a polygon, thereby determining the control area of ​​each rain gauge.

[0057] S112. Determine the rainfall intensity of the only rain gauge included in the polygon to obtain the rainfall intensity of each polygon.

[0058] In this embodiment, there is a rain gauge station in each polygon, and the rainfall intensity of the polygon is composed of the rainfall data of the rain gauge station.

[0059] S113. Group the research object areas to obtain spatial areas.

[0060] In this embodiment, the spatial region refers to the region of each group. The grouped rainfall data includes the rainfall intensity of the corresponding polygon.

[0061] In another embodiment, the above step S110 may include:

[0062] The research object regions are grouped based on expert knowledge or classification algorithms to obtain spatial regions.

[0063] In this embodiment, the classification algorithm includes k-means or a method of equal division after sorting.

[0064] S120: Determine whether the rainfall data in the spatial area has spatial heterogeneity.

[0065] Due to the uneven spatial distribution of economic, social, topographical, and climatic characteristics, geographic data must consider spatial heterogeneity. Spatial heterogeneity refers to the difference between attribute values ​​or phenomena in different spatial locations that exceeds random variation. The cause of spatial heterogeneity may be different mechanisms or different dominant factors in different spatial locations. The existing interpolation method conceals the heterogeneity of the object, such as different mechanisms or the same mechanism but different driving factors, or the same mechanism and factor but different parameters, which is called heterogeneity.

[0066] The spatial heterogeneity of rainfall data in each spatial area is judged and interpolated.

[0067] In one embodiment, see Figure 4 , the above-mentioned step S120 may include steps S121 to S124.

[0068] S121. Calculate spatial differentiation value.

[0069] In this embodiment, the spatial differentiation value refers to a value used to detect the extent to which a factor X explains the spatial differentiation of the variable Y.

[0070] Specifically, the spatial differentiation value is obtained by Obtained, where h = 1, ..., L is the grouping of variable Y or factor X, N represents the number of units in the spatial region, SSW represents the sum of the variances of rainfall within the group, SST represents the total variance of rainfall in the spatial region, σ is the standard deviation, and q is the spatial differentiation value.

[0071] The value range of q is [0, 1]. The larger the q value, the more obvious the spatial differentiation of Y. If the grouping is generated by the independent variable X, the larger the q value, the more consistent the spatial distribution of X and Y, and the stronger the explanatory power of the independent variable X for the attribute Y, and vice versa. The q value means that X explains 100q% of Y.

[0072] S122, judging whether the spatial differentiation value meets the set requirements;

[0073] S123. If the spatial differentiation value meets the set requirement, then the rainfall data in the spatial area does not have spatial differentiation;

[0074] S124: If the spatial differentiation value does not meet the set requirement, then the rainfall data in the spatial area has spatial differentiation.

[0075] The spatial differentiation value is [0, 1]. The larger the spatial differentiation value is, the more obvious the spatial differentiation of rainfall data in the spatial area is. Therefore, a limit can be set. When the spatial differentiation value exceeds the limit, it indicates that the spatial differentiation value meets the set requirements. Otherwise, the spatial differentiation value does not meet the set requirements.

[0076] For example, Y is the rainfall at the rain gauge station, and X is at least one of the elevation and the distance to the river. When the combined area of ​​the Thiessen polygons of the rain gauge station, that is, the q value calculated by the spatial area, reaches a significant value, the polygons of the rain gauge station are grouped until the variance of the rainfall within the group is the smallest and the variance between groups is the largest. The same group can appear in multiple non-adjacent areas.

[0077] S130, if the rainfall data in the spatial region has spatial heterogeneity, interpolating the rainfall data using a local model, a local variable or a local parameter;

[0078] S140: If the rainfall data in the spatial region does not have spatial heterogeneity, interpolate the rainfall data using a global model.

[0079] In this embodiment, if there is no heterogeneity in the data, that is, the q value is not significant, the rainfall data is interpolated by combining a single interpolation model, global variables, and global parameters.

[0080] In this embodiment, the global model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model; the local model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model.

[0081] In this embodiment, the local model can be the same model as the global model, or it can be other models. For example, when performing co-kriging interpolation, its variation function model can select the one with the highest predictive power among the circular model, spherical model, exponential model and Gaussian model under different rainfall distributions. In this way, the internal heterogeneity of the data set will not be offset by each other, resulting in a large error between the interpolation and the actual rainfall.

[0082] The semivariogram models provided by the Kriging algorithm include Gaussian, linear, spherical, damped sine and exponential models. The spherical model is better for interpolating meteorological elements. It takes into account both the randomness and the correlation of reservoir parameters, and gives the best linear unbiased interpolation under the condition of minimum interpolation variance, and also gives the interpolation variance.

[0083] The above-mentioned rainfall interpolation method obtains the spatial area by grouping the research object area composed of all rain gauges according to the control area, that is, the spatial position, and determines the spatial heterogeneity of the rainfall data in the spatial area. Based on the determination results, the global model and the local model are combined for interpolation to reduce the error between the interpolation and the actual rainfall. The interpolation is not easily disturbed by the confounding effect, thereby improving the accuracy of the interpolation.

[0084] Figure 5 is a schematic block diagram of a rainfall interpolation device 300 provided in an embodiment of the present invention. Figure 5 As shown, corresponding to the above rainfall interpolation method, the present invention also provides a rainfall interpolation device 300. The rainfall interpolation device 300 includes a unit for executing the above rainfall interpolation method, and the device can be configured in a server. Figure 5 The rainfall interpolation device 300 includes a grouping unit 301, a judging unit 302, a first interpolation unit 303 and a second interpolation unit 304.

[0085] The grouping unit 301 is used to group the research object area composed of all rainfall stations to obtain a spatial area; the judgment unit 302 is used to judge whether the rainfall data in the spatial area has spatial heterogeneity; the first interpolation unit 303 is used to interpolate the rainfall data using a local model, local variables or local parameters if the rainfall data in the spatial area has spatial heterogeneity; the second interpolation unit 304 is used to interpolate the rainfall data using a global model if the rainfall data in the spatial area does not have spatial heterogeneity.

[0086] In one embodiment, if Figure 6 As shown, the grouping unit 301 includes a polygon determination subunit 3011 , a strength determination subunit 3012 , and a region grouping subunit 3013 .

[0087] The polygon determination subunit 3011 is used to determine the control area of ​​each rain gauge by using Thiessen polygons to obtain polygons, and all polygons constitute the research object area; the intensity determination subunit 3012 is used to determine the rainfall intensity of the only rain gauge contained in the polygon to obtain the rainfall intensity of each polygon; the area grouping subunit 3013 is used to group the research object area to obtain the spatial area.

[0088] In one embodiment, the grouping unit 301 is used to group the research object regions based on expert knowledge or classification algorithms to obtain spatial regions.

[0089] In one embodiment, if Figure 7 As shown, the judgment unit 302 includes a differentiation value calculation subunit 3021 and a differentiation value judgment subunit 3022.

[0090] The differentiation value calculation subunit 3021 is used to calculate the spatial differentiation value; the differentiation value judgment subunit 3022 is used to judge whether the spatial differentiation value meets the set requirements; if the spatial differentiation value meets the set requirements, the rainfall data in the spatial area does not have spatial differentiation; if the spatial differentiation value does not meet the set requirements, the rainfall data in the spatial area has spatial differentiation.

[0091] In one embodiment, the second interpolation unit 304 is used to interpolate the rainfall data by combining a single interpolation model, global variables, and global parameters.

[0092] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned rainfall interpolation device 300 and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.

[0093] The rainfall interpolation device 300 can be implemented in the form of a computer program. Figure 8 Runs on the computer device shown.

[0094] See also Figure 8 , Figure 8 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0095] See also Figure 8 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0096] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 may execute a rainfall interpolation method.

[0097] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0098] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a rainfall interpolation method.

[0099] The network interface 505 is used to communicate with other devices over the network. Figure 8 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0100] The processor 502 is used to run the computer program 5032 stored in the memory to implement the following steps:

[0101] The research object area composed of all rainfall stations is grouped to obtain a spatial area; it is determined whether the rainfall data in the spatial area has spatial heterogeneity; if the rainfall data in the spatial area has spatial heterogeneity, a local model, a local variable or a local parameter is used to interpolate the rainfall data; if the rainfall data in the spatial area does not have spatial heterogeneity, a global model is used to interpolate the rainfall data.

[0102] In one embodiment, when the processor 502 implements the step of grouping the research object area composed of all rainfall stations to obtain the spatial area, the processor 502 specifically implements the following steps:

[0103] Thiessen polygons are used to determine the control area of ​​each rain gauge to obtain polygons, and all polygons constitute the research object area; the rainfall intensity of the only rain gauge contained in the polygon is determined to obtain the rainfall intensity of each polygon; the research object area is grouped to obtain the spatial area.

[0104] In one embodiment, when the processor 502 implements the step of grouping the research object area composed of all rainfall stations to obtain the spatial area, the processor 502 specifically implements the following steps:

[0105] The research object regions are grouped based on expert knowledge or classification algorithms to obtain spatial regions.

[0106] In one embodiment, when the processor 502 implements the step of determining whether the rainfall data in the spatial region has spatial heterogeneity, the processor 502 specifically implements the following steps:

[0107] Calculate the spatial differentiation value; determine whether the spatial differentiation value meets the set requirements; if the spatial differentiation value meets the set requirements, the rainfall data in the spatial area does not have spatial differentiation; if the spatial differentiation value does not meet the set requirements, the rainfall data in the spatial area has spatial differentiation.

[0108] The spatial differentiation value is obtained by Obtained, where h = 1, ..., L is the grouping of variable Y or factor X, N represents the number of units in the spatial region, SSW represents the sum of the variances of rainfall within the group, SST represents the total variance of rainfall in the spatial region, σ is the standard deviation, and q is the spatial differentiation value.

[0109] In one embodiment, when the processor 502 implements the step of interpolating the rainfall data using the global model, the processor 502 specifically implements the following steps:

[0110] The rainfall data is interpolated by combining a single interpolation model, global variables and global parameters.

[0111] Among them, the global model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model; the local model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model.

[0112] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0113] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0114] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

[0115] The research object area composed of all rainfall stations is grouped to obtain a spatial area; it is determined whether the rainfall data in the spatial area has spatial heterogeneity; if the rainfall data in the spatial area has spatial heterogeneity, a local model, a local variable or a local parameter is used to interpolate the rainfall data; if the rainfall data in the spatial area does not have spatial heterogeneity, a global model is used to interpolate the rainfall data.

[0116] In one embodiment, when the processor executes the computer program to implement the step of grouping the research object area composed of all rainfall stations to obtain the spatial area, the processor specifically implements the following steps:

[0117] Thiessen polygons are used to determine the control area of ​​each rain gauge to obtain polygons, and all polygons constitute the research object area; the rainfall intensity of the only rain gauge contained in the polygon is determined to obtain the rainfall intensity of each polygon; the research object area is grouped to obtain the spatial area.

[0118] In one embodiment, when the processor executes the computer program to implement the step of grouping the research object area composed of all rainfall stations to obtain the spatial area, the processor specifically implements the following steps:

[0119] The research object regions are grouped based on expert knowledge or classification algorithms to obtain spatial regions.

[0120] In one embodiment, when the processor executes the computer program to implement the step of determining whether the rainfall data in the spatial region has spatial heterogeneity, the processor specifically implements the following steps:

[0121] Calculate the spatial differentiation value; determine whether the spatial differentiation value meets the set requirements; if the spatial differentiation value meets the set requirements, the rainfall data in the spatial area does not have spatial differentiation; if the spatial differentiation value does not meet the set requirements, the rainfall data in the spatial area has spatial differentiation.

[0122] The spatial differentiation value is obtained by Obtained, where h = 1, ..., L is the grouping of variable Y or factor X, N represents the number of units in the spatial region, SSW represents the sum of the variances of rainfall within the group, SST represents the total variance of rainfall in the spatial region, σ is the standard deviation, and q is the spatial differentiation value.

[0123] In one embodiment, when the processor executes the computer program to implement the step of interpolating the rainfall data using a global model, the processor specifically implements the following steps:

[0124] The rainfall data is interpolated by combining a single interpolation model, global variables and global parameters.

[0125] Among them, the global model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model; the local model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model.

[0126] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0127] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0128] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0129] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0130] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a terminal, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0131] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A rainfall interpolation method, characterized in that: include: The study area consisting of all rainfall stations is grouped to obtain spatial regions; Determining whether there is spatial heterogeneity in rainfall data within the spatial region; If the rainfall data in the spatial region has spatial heterogeneity, the rainfall data is interpolated using a local model, a local variable or a local parameter; If there is no spatial heterogeneity in the rainfall data within the spatial region, interpolating the rainfall data using a global model; The adopting a global model to interpolate the rainfall data comprises: The rainfall data is interpolated by combining a single interpolation model, global variables and global parameters; The global model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model; the local model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model; When performing co-kriging interpolation, the variogram model can select the one with the highest predictive power among the circular model, spherical model, exponential model and Gaussian model under different rainfall distributions; The principle of grouping the research object area is to minimize the variance within the group and maximize the variance between groups, and multiple non-adjacent areas can appear in the same group; the entire research area is grouped according to the control area and spatial location; The determining whether the rainfall data in the spatial region has spatial heterogeneity includes: Calculate spatial differentiation value; Determining whether the spatial differentiation value meets the set requirements; If the spatial differentiation value meets the set requirements, then there is no spatial differentiation in the rainfall data within the spatial area; If the spatial differentiation value does not meet the set requirements, then the rainfall data in the spatial area has spatial differentiation; the spatial differentiation value is determined by Obtained, where h = 1, ..., L is the grouping of variable Y or factor X, N represents the number of units in the spatial region, SSW represents the sum of the variances of rainfall within the group, SST represents the total variance of rainfall in the spatial region, σ is the standard deviation, and q is the spatial differentiation value.

2. The rainfall interpolation method according to claim 1, characterized in that: The research object area consisting of all rainfall stations is grouped to obtain spatial areas, including: Thiessen polygons are used to determine the control area of ​​each rainfall station to obtain polygons, and all polygons constitute the research object area; determining the rainfall intensity of the unique rain gauge contained within the polygon to obtain the rainfall intensity for each polygon; The research object regions are grouped to obtain spatial regions.

3. The rainfall interpolation method according to claim 1, characterized in that: The research object area consisting of all rainfall stations is grouped to obtain spatial areas, including: The research object regions are grouped based on expert knowledge or classification algorithms to obtain spatial regions.

4. A rainfall interpolation device, characterized in that: include: The grouping unit is used to group the research object area composed of all rainfall stations to obtain the spatial area; A judging unit, used for judging whether the rainfall data in the spatial region has spatial heterogeneity; A first interpolation unit is used to interpolate the rainfall data using a local model, a local variable or a local parameter if the rainfall data in the spatial region has spatial heterogeneity; A second interpolation unit is used to interpolate the rainfall data using a global model if there is no spatial heterogeneity in the rainfall data in the spatial region; interpolate the rainfall data using a single interpolation model, a global variable, and a global parameter combination; The global model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model; the local model includes at least one of a circular model, a spherical model, an exponential model and a Gaussian model; When performing co-kriging interpolation, the variogram model can select the one with the highest predictive power among the circular model, spherical model, exponential model and Gaussian model under different rainfall distributions; The principle of grouping the research object areas is to minimize the variance within the group and maximize the variance between groups, and multiple non-adjacent areas can appear in the same group; The entire study area is grouped according to the control area and spatial location; The judgment unit includes a differentiation value calculation subunit and a differentiation value judgment subunit; The differentiation value calculation subunit is used to calculate the spatial differentiation value; the differentiation value judgment subunit is used to judge whether the spatial differentiation value meets the set requirements; If the spatial differentiation value meets the set requirements, then there is no spatial differentiation in the rainfall data within the spatial area; If the spatial differentiation value does not meet the set requirements, then the rainfall data in the spatial area has spatial differentiation; If the spatial differentiation value does not meet the set requirements, then the rainfall data in the spatial area has spatial differentiation; the spatial differentiation value is determined by Obtained, where h = 1, ..., L is the grouping of variable Y or factor X, N represents the number of units in the spatial region, SSW represents the sum of the variances of rainfall within the group, SST represents the total variance of rainfall in the spatial region, σ is the standard deviation, and q is the spatial differentiation value.

5. A computer device, characterized in that: The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 3 when executing the computer program.

6. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 3 can be implemented.

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