CN114444025A - Rainfall interpolation model determination method, rainfall interpolation method and rainfall interpolation device - Google Patents
Rainfall interpolation model determination method, rainfall interpolation method and rainfall interpolation device Download PDFInfo
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Abstract
The invention discloses a rainfall interpolation model determining method, a rainfall interpolation method and a rainfall interpolation device. The rainfall interpolation model determining method comprises the following steps: acquiring an initial rainfall interpolation model and a basic condition and a constraint condition of the initial rainfall interpolation model; acquiring a multidimensional rainfall data set, and determining interpolation parameters of the initial rainfall interpolation model based on the multidimensional rainfall data set, the basic conditions and the constraint conditions; wherein the multi-dimensional rainfall data set comprises rainfall time, rainfall location and rainfall; and adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model. By the technical scheme disclosed by the invention, the time sequence and the spatial information of rainfall are fully utilized, the space-time interpolation calculation is realized, and the rainfall spatial distribution and rainfall estimation precision are further improved.
Description
Technical Field
The invention relates to the technical field of rainfall estimation, in particular to a rainfall interpolation model determining method, a rainfall interpolation method and a rainfall interpolation device.
Background
The rainfall data has an important role in national emergency decision management of natural disasters such as flood prevention, drought resistance and the like, accurate rainfall estimation and spatial distribution determination can provide scientific and accurate data for emergency decision of natural disasters, the emergency management level is improved, and social benefits are remarkable.
The existing rainfall spatial interpolation method is based on the geographical position relationship of interpolation points and rainfall stations, and can interpolate rainfall data by utilizing the interpolation method, wherein the existing rainfall spatial interpolation method is not only integral interpolation (such as inner boundary difference and trend surface analysis) but also local interpolation (such as a Critical method and a Thiessen polygon). However, these methods only consider the spatial relationship, and reduce the rainfall calculation accuracy.
Disclosure of Invention
The invention provides a rainfall interpolation model determining method, a rainfall interpolation method and a rainfall interpolation device, which are used for fully utilizing time sequence and space information of rainfall to realize space-time interpolation calculation so as to improve rainfall space distribution and rainfall estimation precision.
According to an aspect of the present invention, there is provided a rainfall interpolation model determining method, including:
acquiring an initial rainfall interpolation model and a basic condition and a constraint condition of the initial rainfall interpolation model;
acquiring a multi-dimensional rainfall data set, and determining interpolation parameters of the initial rainfall interpolation model based on the multi-dimensional rainfall data set, the basic conditions and the constraint conditions; wherein the multi-dimensional rainfall data set comprises rainfall time, rainfall location and rainfall;
and adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model.
Optionally, the constraint conditions of the initial rainfall interpolation model include a rainfall expectation constraint condition and a rainfall spatiotemporal distance constraint condition.
Optionally, the initial rainfall interpolation model includes a three-dimensional interpolation model obtained by improving a two-dimensional kriging model.
Optionally, the obtaining a multidimensional rainfall data set, and determining an interpolation parameter of the initial rainfall interpolation model based on the multidimensional rainfall data set, the base condition, and the constraint condition includes:
constructing an initial discrete variation model; wherein the initial discrete variation model is a three-dimensional model;
determining a semi-variogram mean value of the initial discrete variogram model based on the multi-dimensional rainfall data set, and determining a semi-variogram of the initial discrete variogram model based on the semi-variogram mean value;
determining interpolation parameters of the initial rainfall interpolation model based on the semi-variogram, the base condition and the constraint condition.
According to another aspect of the present invention, there is provided a rainfall interpolation method, including:
acquiring rainfall data to be interpolated; the rainfall data to be interpolated comprises rainfall time, rainfall position and rainfall;
inputting the rainfall data to be interpolated into a target rainfall interpolation model to generate interpolated target rainfall data; the target rainfall interpolation model is constructed based on the rainfall interpolation model determination method in any embodiment of the invention.
Optionally, the method further includes:
and performing rainfall interpolation evaluation on the rainfall data after interpolation and the rainfall data to be interpolated by using the average absolute error MAE and the root mean square error RMSE.
According to another aspect of the present invention, there is provided a rainfall interpolation model determining apparatus including:
the system comprises an initial rainfall interpolation model acquisition module, a rainfall interpolation model generation module and a rainfall interpolation model generation module, wherein the initial rainfall interpolation model acquisition module is used for acquiring an initial rainfall interpolation model and basic conditions and constraint conditions of the initial rainfall interpolation model;
the interpolation parameter determination module is used for acquiring a multi-dimensional rainfall data set and determining interpolation parameters of the initial rainfall interpolation model based on the multi-dimensional rainfall data set, the basic conditions and the constraint conditions; wherein the multi-dimensional rainfall data set comprises rainfall time, rainfall location and rainfall;
and the target rainfall interpolation model determining module is used for adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model.
According to another aspect of the present invention, there is provided a rainfall interpolation device, including:
the rainfall data acquisition module to be interpolated is used for acquiring rainfall data to be interpolated; the rainfall data to be interpolated comprises rainfall time, rainfall position and rainfall;
the rainfall data generation module is used for inputting the rainfall data to be interpolated into a target rainfall interpolation model and generating interpolated target rainfall data; the target rainfall interpolation model is constructed based on the rainfall interpolation model determination method in any embodiment of the invention.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the rainfall interpolation model determination method and/or the rainfall interpolation method according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the rainfall interpolation model determination method and/or the rainfall interpolation method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, an initial rainfall interpolation model and the basic conditions and the constraint conditions of the initial rainfall interpolation model are obtained; acquiring a multi-dimensional rainfall data set, and determining interpolation parameters of an initial rainfall interpolation model based on the multi-dimensional rainfall data set, basic conditions and constraint conditions; the multi-dimensional rainfall data set comprises rainfall time, rainfall position and rainfall; and adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model. According to the technical scheme, the rainfall data is interpolated by utilizing the multi-dimensional rainfall data, the common kriging interpolation method is extended to the time-space-based kriging interpolation method, the problems that interpolation results are inaccurate and incomplete due to the fact that only a space kriging interpolation method is adopted in the prior art are solved, time-space interpolation calculation is achieved, and then the rainfall spatial distribution and rainfall estimation precision are improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for determining a rainfall interpolation model according to an embodiment of the present invention;
fig. 2 is a flowchart of a rainfall interpolation method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a rainfall interpolation model determining device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a rainfall interpolation device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the rainfall interpolation model determination method and the rainfall interpolation method according to the embodiment of the present invention;
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is to be understood that the terms "target," "initial," and the like in the description and claims of the invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a method for determining a rainfall interpolation model according to an embodiment of the present invention, where the embodiment is applicable to a situation of performing rainfall interpolation on rainfall station data, and the method may be performed by a rainfall interpolation model determining device, and the rainfall interpolation model determining device may be implemented in a hardware and/or software manner, and may be configured in a mobile phone, a tablet computer, a desktop computer, and the like. As shown in fig. 1, the method includes:
s110, obtaining an initial rainfall interpolation model and basic conditions and constraint conditions of the initial rainfall interpolation model.
S120, acquiring a multi-dimensional rainfall data set, and determining interpolation parameters of the initial rainfall interpolation model based on the multi-dimensional rainfall data set, the basic conditions and the constraint conditions.
S130, adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model.
In the embodiment of the invention, in order to prevent natural disasters such as flood prevention, rainfall data needs to be acquired to determine emergency decisions. In practical applications, rainfall data is usually acquired by collecting data of rainfall stations. However, random loss of the acquired rainfall data is often caused by some reasons, and if the rainfall data is lost too much, the problem that interpolation cannot be performed easily occurs.
In order to solve the above technical problem, the rainfall data may be interpolated by an interpolation method. Therefore, it is necessary to interpolate the rainfall data acquired by the rainfall station. Commonly used interpolation methods include kriging interpolation. However, the existing kriging interpolation method only considers the spatial relationship, and reduces the rainfall calculation accuracy. In order to improve the rainfall calculation accuracy, the technical scheme of the embodiment proposes to consider time information, interpolate rainfall data by utilizing multi-dimensional rainfall data, and extend the common kriging interpolation method to a spatio-temporal-based kriging interpolation method so as to optimize the above problems, thereby obtaining more comprehensive rainfall data.
In this embodiment, the initial rainfall interpolation model may be a three-dimensional interpolation model improved based on a two-dimensional kriging interpolation model considering spatial dimensions. The three-dimensional interpolation model adds time dimension information on the basis of original space dimension information, so that the interpolation result of the interpolation model is more accurate and comprehensive.
In this embodiment, the initial rainfall interpolation model may be an initial equation containing interpolation parameters to be determined. Specifically, the expression of the initial equation may be:
Z*(i0,t0)＝∑λZ(i,t)；
where Z (i0, t0) represents an estimated value of the rainfall at the time t0 at the i0 position, Z (i, t) represents the rainfall at the time t based on the i position acquired by the rainfall station, and λ represents an interpolation parameter.
It should be noted that, since the rainfall data is similar in space-time, the rainfall data at a certain space-time position can be approximated by linear weighting of the surrounding rainfall data.
In order to make the initial equation have a solution, that is, the interpolation parameter may be solved, the technical solution of this embodiment also sets a basic condition for the initial rainfall interpolation model in advance. It can be explained that the base conditions can be understood as the second order stationary assumption of the kriging method.
Specifically, the basic conditions of the initial rainfall interpolation model include a rainfall expectation constraint condition and a rainfall spatiotemporal distance constraint condition. Alternatively, the rainfall expectation constraint may be interpreted as that the spatiotemporal rainfall is expected to be constant. For example, the expression of the constraint condition may be:
E[Z(i,t)]＝μ；
where E denotes expectation, Z (i, t) denotes rainfall data for position i and time t, and μ denotes a constant (fixed value).
Alternatively, the rainfall spatiotemporal distance constraint may be interpreted as the spatiotemporal covariance only depends on the spatiotemporal distance. For example, the expression of the constraint condition may be:
Cov[Z(i+h,t+k),Z(i,t)]＝Cov[Z(h,k),Z(0,0)]＝C(h,k)；
where Cov represents a space-time covariance, h represents a spatial position change amount, and k represents a time change amount.
Of course, in order to make the above initial equation have an optimal solution, a constraint condition is also set on the initial rainfall interpolation model on the basis of the above. Specifically, the expression of the constraint includes:
min Var(Z*-Z)；
∑λ＝1；
wherein min Var (Z x-Z) indicates that the variance between the interpolated rainfall and the actual rainfall is minimum;
Σ λ ═ 1 denotes that the sum of the interpolation parameters is 1.
Further, after determining the basic conditions and the constraint conditions of the initial rainfall interpolation model, acquiring a multi-dimensional rainfall data set, and performing interpolation parameter solution on the initial rainfall interpolation model based on the conditions and the rainfall data set.
It should be noted that the multi-dimensional rainfall data set in this embodiment includes data information of three dimensions, namely rainfall time, rainfall position, and rainfall amount, and the specific multi-dimensional rainfall data set may include the rainfall amount in the rainfall station range in the preset time sequence range before the current time. Therefore, a more accurate and comprehensive rainfall interpolation model can be obtained, and the interpolated rainfall data can be more accurate and comprehensive.
In this embodiment, the method for determining the interpolation parameter of the initial rainfall interpolation model based on the multidimensional rainfall data set, the basic condition and the constraint condition may include: constructing an initial discrete variation model; wherein the initial discrete variation model is a three-dimensional model; determining a half variation function average value of the initial discrete variation model based on the multi-dimensional rainfall data set, and determining a half variation function of the initial discrete variation model based on the half variation function average value; and determining an interpolation parameter of the initial rainfall interpolation model based on the half-variation function, the basic condition and the constraint condition. Specifically, according to a multi-dimensional rainfall data set, respectively calculating the half variation function average values of the initial rainfall interpolation model in the true north, the true east and the time dimension directions; further, a space ellipsoid is constructed, the anisotropy coefficients of all directions are respectively solved, and the time dimension anisotropy is converted into isotropy; and further, selecting an optimal half-variation function by adopting a binary weighted regression mode, and solving an initial rainfall interpolation model meeting constraint conditions and basic conditions based on the half-variation function so as to obtain an interpolation parameter lambda of the rainfall interpolation model.
Further, the interpolation parameter lambda is substituted into an initial equation of the initial rainfall interpolation model to obtain a target rainfall interpolation model. According to the technical scheme of the embodiment of the invention, an initial rainfall interpolation model and the basic conditions and the constraint conditions of the initial rainfall interpolation model are obtained; acquiring a multidimensional rainfall data set, and determining interpolation parameters of an initial rainfall interpolation model based on the multidimensional rainfall data set, basic conditions and constraint conditions; the multi-dimensional rainfall data set comprises rainfall time, rainfall position and rainfall; and adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model.
According to the technical scheme, the rainfall data is interpolated by utilizing the multi-dimensional rainfall data, the common kriging interpolation method is extended to the time-space-based kriging interpolation method, the problems that interpolation results are inaccurate and incomplete due to the fact that only a space kriging interpolation method is adopted in the prior art are solved, time-space interpolation calculation is achieved, and then the rainfall spatial distribution and rainfall estimation precision are improved.
Example two
Fig. 2 is a flowchart of a rainfall interpolation method according to a second embodiment of the present invention, where a target rainfall interpolation model used in this embodiment is constructed based on the rainfall interpolation model determination method described in the above embodiment. Specifically, the embodiment is applicable to the case of performing rainfall interpolation on rainfall station data, and the method may be performed by a rainfall interpolation method device, which may be implemented in a form of hardware and/or software, and may be configured in a mobile phone, a tablet computer, a desktop computer, and the like. As shown in fig. 2, the method includes:
s210, acquiring rainfall data to be interpolated; the rainfall data to be interpolated comprises rainfall time, rainfall position and rainfall.
In the embodiment of the invention, the rainfall data to be interpolated can be interpreted as rainfall data collected based on a rainfall station.
Specifically, the rainfall data includes the rainfall in the rainfall station range in a preset time sequence range before the current time.
S220, inputting the rainfall data to be interpolated into a target rainfall interpolation model, and generating interpolated target rainfall data.
In the embodiment of the invention, the rainfall data to be differenced is input into a target rainfall interpolation model to obtain an interpolation result output by the model, namely the interpolated target rainfall data.
It should be noted that the target rainfall data includes the rainfall to be interpolated in the rainfall data to be interpolated and the rainfall obtained by the new interpolation, and the rainfall obtained by the new interpolation is located around the rainfall to be interpolated.
Illustratively, the rainfall of the rainfall station in a preset time sequence before the current time is obtained and input into a target rainfall interpolation model, so as to obtain interpolated target rainfall data. The target rainfall data comprises the rainfall interpolated at the current moment and the rainfall obtained by the new difference, and the position corresponding to the rainfall can be other positions except the position corresponding to the rainfall to be interpolated in the rainfall station.
According to the technical scheme of the embodiment of the invention, the rainfall data to be interpolated is interpolated by utilizing the multi-dimensional rainfall data, and a common kriging interpolation method is expanded to a time-space-based kriging interpolation method, so that the problems of inaccurate and incomplete interpolation results caused by only adopting a space kriging interpolation method in the prior art are solved, the time-space interpolation calculation is realized, and the precision of rainfall spatial distribution and rainfall estimation is further improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a rainfall interpolation model determining device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: an initial rainfall interpolation model obtaining module 310, an interpolation parameter determining module 320 and a target rainfall interpolation model determining module 330; wherein the content of the first and second substances,
an initial rainfall interpolation model obtaining module 310, configured to obtain an initial rainfall interpolation model, and a basic condition and a constraint condition of the initial rainfall interpolation model;
an interpolation parameter determining module 320, configured to obtain a multi-dimensional rainfall data set, and determine an interpolation parameter of the initial rainfall interpolation model based on the multi-dimensional rainfall data set, the basic condition, and the constraint condition; wherein the multi-dimensional rainfall data set comprises rainfall time, rainfall location and rainfall;
a target rainfall interpolation model determining module 330, configured to adjust the initial rainfall interpolation model based on the interpolation parameter, to obtain a target rainfall interpolation model.
Optionally, the constraints of the initial rainfall interpolation model include a rainfall expectation constraint and a rainfall spatiotemporal distance constraint.
Optionally, the initial rainfall interpolation model includes a three-dimensional interpolation model obtained by improving a two-dimensional kriging model.
Optionally, the interpolation parameter determining module includes:
an initial discrete variation model component unit, configured to construct an initial discrete variation model; wherein the initial discrete variation model is a three-dimensional model;
a semi-variant function determining unit, configured to determine a semi-variant function average value of the initial discrete variant model based on the multi-dimensional rainfall data set, and determine a semi-variant function of the initial discrete variant model based on the semi-variant function average value;
an interpolation parameter determination unit configured to determine an interpolation parameter of the initial rainfall interpolation model based on the semi-variogram, the base condition, and the constraint condition.
The rainfall interpolation model determining device provided by the embodiment of the invention can execute the rainfall interpolation model determining method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the executing method.
Example four
Fig. 4 is a schematic structural diagram of a rainfall interpolation device according to a fourth embodiment of the present invention. As shown in fig. 4, the apparatus includes: a rainfall data acquisition module 410 to be interpolated and a rainfall data generation module 420; wherein the content of the first and second substances,
a rainfall data to be interpolated acquisition module 410, configured to acquire rainfall data to be interpolated; the rainfall data to be interpolated comprises rainfall time, rainfall position and rainfall;
a rainfall data generation module 420, configured to input the rainfall data to be interpolated to a target rainfall interpolation model, and generate interpolated target rainfall data; the target rainfall interpolation model is constructed based on any rainfall interpolation model determination method in the embodiment of the invention.
Optionally, the device module includes:
and the evaluation module is used for carrying out rainfall interpolation evaluation on the rainfall data after interpolation and the rainfall data to be interpolated by utilizing the average absolute error MAE and the root mean square error RMSE.
The rainfall interpolation device provided by the embodiment of the invention can execute the rainfall interpolation method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 may also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the method obtaining an initial rainfall interpolation model, and the base conditions and constraints of the initial rainfall interpolation model; acquiring a multi-dimensional rainfall data set, and determining interpolation parameters of the initial rainfall interpolation model based on the multi-dimensional rainfall data set, the basic conditions and the constraint conditions; wherein the multi-dimensional rainfall data set comprises rainfall time, rainfall location and rainfall; and adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model. For example, acquiring rainfall data to be interpolated; the rainfall data to be interpolated comprises rainfall time, rainfall position and rainfall; and inputting the rainfall data to be interpolated into a target rainfall interpolation model to generate interpolated target rainfall data.
In some embodiments, the method obtains an initial rainfall interpolation model, and base conditions and constraint conditions of the initial rainfall interpolation model; acquiring a multidimensional rainfall data set, and determining interpolation parameters of the initial rainfall interpolation model based on the multidimensional rainfall data set, the basic conditions and the constraint conditions; wherein the multi-dimensional rainfall data set comprises rainfall time, rainfall location and rainfall; and adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model. Acquiring rainfall data to be interpolated; the rainfall data to be interpolated comprises rainfall time, rainfall position and rainfall; and inputting the rainfall data to be interpolated into a target rainfall interpolation model to generate interpolated target rainfall data. May be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method rainfall interpolation model determination method and the rainfall interpolation method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method rainfall interpolation model determination method and the rainfall interpolation method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A rainfall interpolation model determining method is characterized by comprising the following steps:
acquiring an initial rainfall interpolation model and a basic condition and a constraint condition of the initial rainfall interpolation model;
acquiring a multi-dimensional rainfall data set, and determining interpolation parameters of the initial rainfall interpolation model based on the multi-dimensional rainfall data set, the basic conditions and the constraint conditions; wherein the multi-dimensional rainfall data set comprises rainfall time, rainfall location and rainfall;
and adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model.
2. The method of claim 1, wherein the constraints of the initial rainfall interpolation model include a rainfall expectation constraint and a rainfall spatiotemporal distance constraint.
3. The method of claim 1, wherein the initial rainfall interpolation model comprises a three-dimensional interpolation model refined by a two-dimensional kriging model.
4. The method of claim 1, wherein obtaining a multi-dimensional rainfall data set and determining interpolation parameters for the initial rainfall interpolation model based on the multi-dimensional rainfall data set, the base conditions, and the constraints comprises:
constructing an initial discrete variation model; wherein the initial discrete variation model is a three-dimensional model;
determining a semi-variogram mean value of the initial discrete variogram model based on the multi-dimensional rainfall data set, and determining a semi-variogram of the initial discrete variogram model based on the semi-variogram mean value;
determining interpolation parameters of the initial rainfall interpolation model based on the semi-variogram, the base condition and the constraint condition.
5. A rainfall interpolation method, comprising:
acquiring rainfall data to be interpolated; the rainfall data to be interpolated comprises rainfall time, rainfall position and rainfall;
inputting the rainfall data to be interpolated into a target rainfall interpolation model to generate interpolated target rainfall data; wherein the target rainfall interpolation model is constructed based on the rainfall interpolation model determination method according to any one of claims 1 to 4.
6. The method of claim 5, further comprising:
and performing rainfall interpolation evaluation on the rainfall data after interpolation and the rainfall data to be interpolated by using the average absolute error MAE and the root mean square error RMSE.
7. A rainfall interpolation model determination device, comprising:
the system comprises an initial rainfall interpolation model acquisition module, a rainfall interpolation model generation module and a rainfall interpolation model generation module, wherein the initial rainfall interpolation model acquisition module is used for acquiring an initial rainfall interpolation model and basic conditions and constraint conditions of the initial rainfall interpolation model;
the interpolation parameter determination module is used for acquiring a multi-dimensional rainfall data set and determining interpolation parameters of the initial rainfall interpolation model based on the multi-dimensional rainfall data set, the basic conditions and the constraint conditions; wherein the multi-dimensional rainfall data set comprises rainfall time, rainfall location and rainfall;
and the target rainfall interpolation model determining module is used for adjusting the initial rainfall interpolation model based on the interpolation parameters to obtain a target rainfall interpolation model.
8. A rainfall interpolation device, comprising:
the rainfall data acquisition module to be interpolated is used for acquiring rainfall data to be interpolated; the rainfall data to be interpolated comprises rainfall time, rainfall position and rainfall;
the rainfall data generation module is used for inputting the rainfall data to be interpolated into a target rainfall interpolation model and generating interpolated target rainfall data; wherein the target rainfall interpolation model is constructed based on the rainfall interpolation model determination method according to any one of claims 1 to 4.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the rainfall interpolation model determination method of any one of claims 1-4 and/or the rainfall interpolation method of any one of claims 5-6.
10. A computer-readable storage medium, having stored thereon computer instructions for causing a processor, when executed, to implement the rainfall interpolation model determining method of any one of claims 1-4 and/or the rainfall interpolation method of any one of claims 5-6.
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