Method, device and system for generating rainfall simulation data and medium

Abstract

The invention discloses a rainfall simulation data generation method, device and system and a medium. The method comprises the following steps: constructing a rainfall simulation model for simulating a rainfall event, and obtaining a parameter boundary condition and a target function of the rainfall simulation model in a target range; obtaining rainfall statistical data of a specified time dimension collected by a plurality of observation stations in the target range, and generating rainfall sequence data; inputting the rainfall sequence data into a rainfall simulation model, and calculating to obtain model optimal parameters corresponding to each observation station according to the target function and the parameter boundary conditions; and generating rainfall simulation data of each observation station in a specified time dimension within a preset time length by using the model optimal parameter corresponding to each observation station. By providing a general rainfall simulation model and an optimal solution mode for regions with different rainfall characteristics, different sites can generate corresponding rainfall simulation data based on model optimal parameters obtained by general solution, and the generation efficiency is improved while the accuracy of the simulation data is ensured.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a precipitation simulation data generation method, device, system and medium. Background Art

[0002] In the insurance field, some insurance products involve the assessment of rainstorm disaster risks, such as residential catastrophe insurance, agricultural insurance, motor vehicle insurance, etc. In the assessment of regional rainstorm disaster risks, more attention is paid to extreme precipitation conditions, and rainstorm disasters are usually reliably estimated through rainstorm catastrophe models. However, due to the limitations of the record length, completeness, temporal resolution or spatial coverage of historical precipitation data, historical precipitation data often does not meet the minimum requirements for reliable estimation of catastrophe models. Therefore, relying solely on historical data cannot take into account risks that exceed historical extreme levels, and it is difficult to ensure the accuracy of loss risk assessment of insurance programs. Therefore, it is necessary to reserve precipitation simulation data.

[0003] In the actual application of precipitation simulation generation methods, it is often necessary to make reasonable assumptions about model parameters based on the precipitation characteristics of different regions. However, due to the large differences in distance from the sea in different regions of the country, complex and diverse climates, and great differences in dry and wet conditions in different regions, these factors all have an impact on model parameters, which in turn affects the accuracy of simulation data. At present, there is still no mature solution for how to generate precipitation simulation data over a large area in a universal way, making it difficult to ensure both the accuracy and generation efficiency of precipitation simulation data. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the object of the present invention is to provide a precipitation simulation data generation method, device, system and medium that can be applied to financial technology or other related fields. Its main purpose is to provide a universal precipitation simulation data generation method to improve the generation efficiency of precipitation simulation data while ensuring the accuracy of simulation data.

[0005] The technical solution of the present invention is as follows:

[0006] A first aspect of the present invention provides a method for generating precipitation simulation data, comprising:

[0007] Constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and an objective function of the precipitation simulation model within a target range;

[0008] Obtaining precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data;

[0009] Inputting the precipitation sequence data into the precipitation simulation model, and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions;

[0010] The optimal model parameters corresponding to each observation station are used to generate precipitation simulation data for each observation station in a specified time dimension within a preset time length.

[0011] In one embodiment, before constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and an objective function of the precipitation simulation model within a target range, the method further includes:

[0012] Conduct statistical analysis on precipitation characteristics in the areas where multiple observation stations are located within the target range;

[0013] The parameter boundary conditions of the precipitation model within a target range are determined according to the statistical analysis results of the precipitation characteristics.

[0014] In one embodiment, the precipitation characteristics include an average waiting time between adjacent storm sources, an average interval between rain clusters, an average number of rain clusters, an average duration of rain clusters, and an average precipitation intensity of rain clusters.

[0015] In one embodiment, the objective function of the precipitation simulation model is a weighted average of Euclidean distances of multiple statistical indicators of real precipitation sequence data and simulated precipitation sequence data.

[0016] In one embodiment, obtaining precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data includes:

[0017] Obtaining precipitation data collected by multiple observation stations within the target range;

[0018] Aggregating and counting the precipitation data collected at each station according to the specified time dimension to obtain precipitation statistical data of the specified time dimension;

[0019] After sorting the precipitation statistical data of the specified time dimension of each observation station by time, the precipitation series data of each observation station are generated.

[0020] In one embodiment, the step of inputting the precipitation sequence data into the precipitation simulation model and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions includes:

[0021] Inputting precipitation sequence data of each observation station into the precipitation simulation model;

[0022] Under the constraints of the parameter boundary conditions and the objective function, the optimal model parameters corresponding to each observation site when the value of the objective function is minimized are solved through an optimization algorithm.

[0023] In one embodiment, the optimization algorithm is a PSO optimization algorithm.

[0024] A second aspect of the present invention provides a precipitation simulation data generating device, comprising:

[0025] A model building module, used to build a precipitation simulation model for simulating precipitation events and obtain parameter boundary conditions and objective functions of the precipitation simulation model within a target range;

[0026] A data acquisition module, used to acquire precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range, and generate precipitation sequence data;

[0027] An optimal solution module, used for inputting the precipitation sequence data into the precipitation simulation model, and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions;

[0028] The simulation generation module is used to generate precipitation simulation data of each observation station in a specified time dimension within a preset time length using the optimal model parameters corresponding to each observation station.

[0029] A third aspect of the present invention provides a precipitation simulation data generation system, the system comprising at least one processor; and

[0030] a memory communicatively connected to the at least one processor; wherein,

[0031] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned precipitation simulation data generation method.

[0032] A fourth aspect of the present invention provides a non-volatile computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by one or more processors, the one or more processors can execute the above-mentioned precipitation simulation data generation method.

[0033] Beneficial effects: The present invention discloses a precipitation simulation data generation method, device, system and medium. Compared with the prior art, the embodiment of the present invention constructs a precipitation simulation model for simulating precipitation events and obtains the parameter boundary conditions and objective function of the precipitation simulation model within the target range; obtains precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data; inputs the precipitation sequence data into the precipitation simulation model, calculates the model optimal parameters corresponding to each observation station according to the objective function and the parameter boundary conditions; uses the model optimal parameters corresponding to each observation station to generate precipitation simulation data of the specified time dimension of each observation station within a preset time length. By providing precipitation simulation models and optimal solution methods for regions with different precipitation characteristics, different stations can generate corresponding precipitation simulation data based on the model optimal parameters obtained by the general solution, ensuring the accuracy of the simulation data while improving the generation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0035] Figure 1 A flow chart of a method for generating precipitation simulation data provided by an embodiment of the present invention;

[0036] Figure 2 Another flow chart of the precipitation simulation data generation method provided by an embodiment of the present invention;

[0037] Figure 3 A flow chart of step S200 in the precipitation simulation data generation method provided in an embodiment of the present invention;

[0038] Figure 4 A flow chart of step S300 in the precipitation simulation data generation method provided in an embodiment of the present invention;

[0039] Figure 5 A schematic diagram of functional modules of a precipitation simulation data generating device provided by an embodiment of the present invention;

[0040] Figure 6 A schematic diagram of the hardware structure of a precipitation simulation data generation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The embodiments of the present invention are described below in conjunction with the accompanying drawings.

[0042] In the insurance field, some insurance products involve the assessment of rainstorm disaster risks, such as residential catastrophe insurance, agricultural insurance, motor vehicle insurance, etc. In the assessment of regional rainstorm disaster risks, more attention is paid to extreme precipitation conditions, and rainstorm disasters are usually reliably estimated through rainstorm catastrophe models. However, due to the limitations of the record length, completeness, temporal resolution or spatial coverage of historical precipitation data, historical precipitation data often does not meet the minimum requirements for reliable estimation of catastrophe models. Therefore, relying solely on historical data cannot take into account risks that exceed historical extreme levels, and it is difficult to ensure the accuracy of loss risk assessment of insurance programs. Therefore, it is necessary to reserve precipitation simulation data.

[0043] In the actual application of precipitation simulation generation methods, it is often necessary to make reasonable assumptions about model parameters based on the precipitation characteristics of different regions. However, due to the large differences in distance from the sea in different regions of the country, complex and diverse climates, and great differences in dry and wet conditions in different regions, these factors all have an impact on model parameters, which in turn affects the accuracy of simulation data. At present, there is still no mature solution for how to generate precipitation simulation data over a large area in a universal way, making it difficult to ensure both the accuracy and generation efficiency of precipitation simulation data.

[0044] In order to solve the above problems, the present invention proposes a method for generating precipitation simulation data. Figure 1 , Figure 1 The present invention provides a flowchart of an embodiment of the precipitation simulation data generation method. The system architecture of the precipitation simulation data generation method provided in this embodiment may include a first terminal device, a second terminal device, a third terminal device, a network and a server. The network is used to provide a medium for a communication link between the first terminal device, the second terminal device, the third terminal device and the server. The network may include various connection types, such as wired and / or wireless communication links, etc.

[0045] The user may use the first terminal device, the second terminal device, and the third terminal device to interact with the server through the network to receive or send messages, etc. Various communication client applications may be installed on the first terminal device, the second terminal device, and the third terminal device, such as knowledge reading applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only as examples).

[0046] The first terminal device, the second terminal device, and the third terminal device may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0047] The server may be a server that provides various services, such as a background management server that provides support for the content browsed by the user using the first terminal device, the second terminal device, and the third terminal device (for example only). The background management server may analyze and process the received data such as the user request, and feed back the processing results (such as web pages, information, or data obtained or generated according to the user request) to the terminal device. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server for a distributed system, or a server combined with a blockchain.

[0048] It should be noted that the precipitation simulation data generation provided by the embodiment of the present invention can generally be performed by the first terminal device, the second terminal device or the third terminal device. Accordingly, the precipitation simulation data generation provided by the embodiment of the present invention can also be set in the first terminal device, the second terminal device or the third terminal device.

[0049] Alternatively, the precipitation simulation data generation method provided in the embodiment of the present invention may also be generally executed by a server. Accordingly, the precipitation simulation data generation device provided in the embodiment of the present invention may generally be arranged in a server. The precipitation simulation data generation method provided in the embodiment of the present invention may also be executed by a server or server cluster that is different from the server and can communicate with the first terminal device, the second terminal device, the third terminal device and / or the server. Accordingly, the precipitation simulation data generation device provided in the embodiment of the present invention may also be arranged in a server or server cluster that is different from the server and can communicate with the first terminal device, the second terminal device, the third terminal device and / or the server.

[0050] It should be understood that the numbers of the above terminal devices, networks and servers are only illustrative. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0051] like Figure 1 As shown, the precipitation simulation data generation method provided in this embodiment specifically includes the following steps:

[0052] S100: constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and an objective function of the precipitation simulation model within a target range.

[0053] In this embodiment, a corresponding precipitation simulation model is constructed for the simulation of precipitation events, and the Neyman Scott Rectangular Pulses (NSRP) model in the point process method is specifically used for simulation. The NSRP model is a mathematical model for simulating random processes, which converts the physical process of precipitation into a mathematical model for simulating rainfall patterns. The model is based on a series of rectangular pulse sequences with random intensity, duration and frequency. These pulses can represent rainfall events, and long-term simulation forecasts of precipitation can be performed, so that precipitation random event set data can be generated. In the NSRP model, precipitation is regarded as a randomly occurring event with random intensity and duration; precipitation events are modeled as a series of rectangular pulses, each of which has a specific intensity and duration; model parameters include the frequency of precipitation events, the number of pulses, the intensity and duration of pulses, etc., which can be determined by statistical analysis; the model can take into account the seasonal changes in precipitation by setting different parameter sets for different seasons or months; it can be used for the analysis of extreme precipitation events to help assess the risks of extreme events such as floods; based on the above characteristics, the NSRP model can be used to predict precipitation patterns, and generate precipitation simulation data by simulating the randomness of precipitation efficiently and reliably.

[0054] After constructing the precipitation simulation model, this embodiment also obtains the pre-set parameter boundary conditions and objective functions of the precipitation simulation model within the target range. The target range is a large geographical range including multiple observation sites with different precipitation characteristics, which can be flexibly set according to needs, such as all provinces and cities in the country, designated provinces and cities, designated regions, etc., which are not limited in this embodiment. By providing a common precipitation simulation model and its parameter boundary conditions and objective functions for regions with different precipitation characteristics within the target range, a set of common model solving methods is provided for regions with different precipitation characteristics, without the need to make separate parameter assumptions based on regional precipitation characteristics, thereby reducing system errors caused by parameter assumptions.

[0055] In one embodiment, Figure 2 As shown, before step S100, the method further includes:

[0056] S101, performing statistical analysis on precipitation characteristics of the areas where multiple observation stations are located within the target range;

[0057] S102: Determine parameter boundary conditions of the precipitation model within a target range according to the statistical analysis results of the precipitation characteristics.

[0058] In this embodiment, starting from the model assumption, a statistical analysis is performed based on the precipitation characteristics of the areas where multiple observation stations are located within the target range to determine the parameter boundary conditions of the model within the target range. In the NSRP model, precipitation events are assumed to be generated by a series of independent storm processes that obey the Poisson distribution. Each precipitation event can be regarded as the superposition result of multiple rain group precipitations generated by the storm source. In the NSRP model assumption, the generation of precipitation events can be regarded as the following four steps:

[0059] 1. According to meteorological knowledge, a series of storm processes will occur within a certain period of time. Assuming that the storm source obeys the Poisson distribution with parameter λ, λ can be regarded as the storm occurrence rate;

[0060] 2. Each storm source will produce an indefinite number of rain clusters, assuming that the number of rain clusters follows a Poisson distribution with parameter v;

[0061] 3. The difference in precipitation level generated by each rain cell is determined by the duration of the rain cell and the intensity of the rain cell, where it is assumed that the duration of the rain cell and the intensity of the rain cell follow an exponential distribution with parameters η and ξ, respectively.

[0062] 4. The precipitation at each moment is equal to the sum of the precipitation of all active rain cells at that moment.

[0063] According to the above precipitation event generation process, the key parameters of the NSRP model can be determined to include λ, β, v, η, ξ. The precipitation characteristics of the areas where multiple observation stations are located are statistically analyzed. The precipitation characteristics are highly correlated with the above key parameters, including the average waiting time of adjacent storm sources (λ -1 ), the average interval between rain clusters (β -1 ), the average number of rain clusters (v), the average duration of rain clusters (η -1 ) and the average precipitation intensity of the rain cluster (ξ -1 ), after statistics of various precipitation characteristics in all regions within the target range, the parameter boundary conditions of the precipitation simulation model applicable to the current target range are determined according to the distribution characteristics of the precipitation characteristics. Preferably, the present embodiment adopts the following parameter boundary conditions: 20≤λ≤1000, 1.01≤β≤50, 1≤v≤150, 0.15≤η≤5, 0.5≤ξ≤2000. The present embodiment performs descriptive statistical analysis on the collected precipitation characteristic data, calculates basic statistics such as mean, median, variance, standard deviation, etc., and can determine the range of the corresponding parameters through the maximum and minimum values ​​of each precipitation characteristic in the statistical results, or can also select a specified probability distribution range as the parameter boundary based on the probability distribution of the precipitation characteristics, which is not limited in the present embodiment.

[0064] S200, obtaining precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range, and generating precipitation sequence data.

[0065] In this embodiment, the multiple observation sites within the target range are specifically meteorological observation sites, which refer to places used to collect and record meteorological data. Meteorological site observation is currently the primary way to obtain precipitation data in a certain area. Taking the meteorological observatory observation station as an example, the meteorological observatory observation station measures and records the precipitation of the day every day, usually in millimeters (mm). These sites are usually established and operated by meteorological departments, research institutions or other relevant organizations. The types of meteorological observation sites include: meteorological stations, meteorological balloon observation stations, meteorological radar stations, meteorological satellite receiving stations and meteorological ocean observation stations. Among them, the meteorological station is a special institution established by the meteorological bureau or meteorological department to observe and forecast the weather, usually located in the center of a city or region. The meteorological balloon observation station is a site for launching meteorological balloons to obtain observation data of the upper atmosphere, usually located in a relatively open area, such as a plain or a village. A meteorological radar station is a site used to detect precipitation, measure precipitation intensity and track meteorological systems. The radar station is usually located in highlands or mountains to obtain a wider coverage. Meteorological satellite receiving stations are sites used to receive, process and analyze data transmitted by meteorological satellites. These sites are usually located in areas away from urban light pollution to ensure the reception of clear satellite images. Meteorological oceanographic observation stations are buoys or platforms located on the ocean that collect marine meteorological data such as sea surface temperature, ocean wind speed and wave height.

[0066] The specified time dimension specifically refers to the time scale at which precipitation data needs to be simulated and generated. For example, the daily scale refers to the scale or time period at which precipitation observations are observed, analyzed, and represented in units of one day. That is, daily-scale precipitation statistical data corresponds to daily precipitation data. Daily precipitation data is obtained and temporally associated precipitation sequence data is generated so that corresponding daily precipitation simulation data can be generated through a precipitation simulation model. Similarly, precipitation statistical data at other time scales such as weekly precipitation data can also be collected as needed to meet the precipitation simulation data generation needs in different scenarios. This embodiment is not limited to this.

[0067] In one embodiment, Figure 3 As shown, step S200 includes:

[0068] S201, obtaining precipitation data collected by multiple observation stations within the target range;

[0069] S202, performing aggregation statistics on the precipitation data collected at each site according to the specified time dimension to obtain precipitation statistical data of the specified time dimension;

[0070] S203, sorting the precipitation statistical data of the specified time dimension of each observation station by time, and generating precipitation sequence data of each observation station.

[0071] In this embodiment, precipitation data collected by multiple observation stations within the target range are obtained, and the precipitation data are aggregated and counted according to the required time dimension to obtain precipitation statistical data of each observation station in the specified dimension. Through the aggregate statistics of the specified time dimension (such as day, week, month, year), the characteristics and laws of precipitation on different time scales can be analyzed. After sorting the precipitation statistical data by time, precipitation sequence data with time correlation can be generated for each observation station. The generated precipitation sequence data is helpful to identify precipitation patterns and periodicity, such as seasonal changes or long-term trends, etc., and can better provide data basis for precipitation simulation models and improve the reliability of precipitation simulation data.

[0072] S300: inputting the precipitation sequence data into the precipitation simulation model, and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions.

[0073] In this embodiment, according to the NSRP model assumption, a repeated precipitation process can be simulated and generated. After obtaining the precipitation sequence data obtained by statistics of each observation station according to the specified time dimension, such as daily precipitation data, it is input into the precipitation simulation model. The model accepts the given precipitation sequence data, and fits the model parameters under the constraints of the objective function and parameter boundary conditions, so that the statistical indicators of the simulation results are close to the statistical indicators of the original precipitation sequence, that is, the model parameter fitting is converted into an optimization problem solution. By defining the objective function, the optimal model parameters corresponding to each observation station are found within the parameter boundary conditions, and the final precipitation simulation model of the corresponding observation station can be stored. That is, in this embodiment, there is no need to make parameter assumptions separately according to the regional precipitation characteristics. Through the parameter boundary conditions and the objective function, a universal precipitation simulation model and an optimal solution method are provided for regions with different precipitation characteristics, so that different stations can obtain the optimal model parameters based on their own precipitation sequence data through universal solutions, reduce the system errors caused by parameter assumptions, ensure the accuracy of simulation data, and improve generation efficiency.

[0074] In one embodiment, Figure 4 As shown, step S300 includes:

[0075] S301, inputting precipitation sequence data of each observation station into the precipitation simulation model;

[0076] S302: Under the constraints of the parameter boundary conditions and the objective function, the optimal model parameters corresponding to each observation site when the value of the objective function is minimized are solved by an optimization algorithm.

[0077] In this embodiment, the precipitation sequence data of each observation station is input into the precipitation simulation model, and the optimal solution that minimizes the objective function under the constraint of parameter boundary conditions is solved through the defined objective function. The specific objective function is the weighted average of the Euclidean distances of multiple statistical indicators of the real precipitation sequence data and the simulated precipitation sequence data, which can be expressed as:

[0078]

[0079] Among them, multiple statistical indicators are: μ represents the mean precipitation, σ 1d ,σ 2d ,σ 3d ,σ 4d Respectively represent the variance of the total precipitation for 1 day, 2 days, 3 days, and 4 days, ρ 1d ,ρ 2d ,ρ 3d are the lagged autocorrelation coefficients of the total precipitation for 1 day, 2 days, and 3 days, respectively, 1d represents the probability of drought, represents the probability of two consecutive rainy seasons, represents the probability of two consecutive dry seasons, γ 1d Represents skewness.

[0080] Based on the boundary conditions and objective functions in the model assumptions, the optimization algorithm is used to solve. For any given precipitation sequence data, the corresponding NSRP model parameters that minimize the value of the objective function can be obtained. Therefore, the rainfall sequence data collected from each observation station is used as input, and the optimal model parameters corresponding to each observation station can be obtained through the optimization algorithm. The optimal precipitation simulation model for different regions is obtained through a unified model assumption and optimal solution method. There is no need to make parameter assumptions based on regional precipitation characteristics, which reduces the systematic errors caused by parameter assumptions. At the same time, the optimization solution is convenient for adjusting the weight of the optimization function according to the precipitation statistical characteristics that need to be highlighted. For example, increasing the mean weight will cause the generated precipitation simulation data to be consistent with the mean of historical precipitation data.

[0081] In specific implementation, the optimization algorithm can adopt the PSO optimization algorithm. The PSO (particle swarm optimization) algorithm is an optimization algorithm based on swarm intelligence. The PSO algorithm is simple to implement, does not require specific knowledge of the problem, and is easy to understand and implement; it has high fitness and can handle various types of optimization problems, including linear, nonlinear, constrained and unconstrained problems; it has a fast convergence speed and can usually quickly find high-quality solutions in the solution space. Therefore, the PSO algorithm, with its advantages of simple implementation, few parameters, high fitness, and fast convergence speed, can be applied to the optimization solution of the parameters of the precipitation simulation model, which can effectively improve the processing efficiency. Of course, in other embodiments, optimization algorithms with the same function can also be used, such as genetic algorithms, simulated annealing algorithms, ant colony algorithms, etc., which are not limited in this embodiment.

[0082] S400, using the optimal model parameters corresponding to each observation site, generating precipitation simulation data of each observation site in a specified time dimension within a preset time length.

[0083] In this embodiment, after using the precipitation sequence data of each observation station as input and solving the corresponding model optimal parameters, the model optimal parameters corresponding to each observation station can be stored as the model finally used to generate simulation data, and precipitation simulation data of each observation station in a specified time dimension within a preset time length is generated according to demand. For example, if the precipitation sequence data input to the precipitation simulation model is daily precipitation data, then after solving the model optimal parameters, the daily precipitation data of the preset time length can be generated accordingly to form a daily precipitation simulation data set within the target range. The specific preset time length can be adjusted arbitrarily according to demand, such as 1 year, 5 years, 10 years, etc.

[0084] This embodiment can generate daily precipitation simulation data of any length of time, which can better reproduce the statistical characteristics of historical precipitation events and simulate extreme situations that exceed historical precipitation. Therefore, the daily precipitation random simulation data set generated by this embodiment can be used as a supplement to historical data. On the one hand, it can be used to assess the risk losses of rainstorms and flood disasters in different regions of the country, and provide underwriting suggestions for underwriting risk control; on the other hand, it is also suitable for the construction of rainstorm catastrophe models and flood catastrophe models, providing pricing references for the formulation of catastrophe insurance plans.

[0085] It should be noted that there is not necessarily a certain order between the above-mentioned steps. A person skilled in the art can understand, based on the description of the embodiments of the present invention, that in different embodiments, the above-mentioned steps may have different execution orders, that is, they may be executed in parallel, may be executed interchangeably, and so on.

[0086] Another embodiment of the present invention provides a precipitation simulation data generating device, such as Figure 5 As shown, the device 1 comprises:

[0087] A model building module 11 is used to build a precipitation simulation model for simulating precipitation events and obtain parameter boundary conditions and objective functions of the precipitation simulation model within a target range;

[0088] A data acquisition module 12 is used to acquire precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data;

[0089] The optimal solution module 13 is used to input the precipitation sequence data into the precipitation simulation model, and calculate the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions;

[0090] The simulation generation module 14 is used to generate precipitation simulation data of each observation station at a specified time dimension within a preset time length using the optimal model parameters corresponding to each observation station.

[0091] The module referred to in the present invention refers to a series of computer program instruction segments that can complete specific functions, which are more suitable for describing the precipitation simulation data generation and execution process than programs. For the specific implementation methods of each module, please refer to the corresponding method embodiments above, which will not be repeated here.

[0092] In one embodiment, the device 1 comprises:

[0093] A characteristic statistics module is used to perform statistical analysis on precipitation characteristics of the areas where multiple observation stations are located within the target range;

[0094] The boundary determination module is used to determine the parameter boundary conditions of the precipitation model within the target range according to the statistical analysis results of the precipitation characteristics.

[0095] In one embodiment, the precipitation characteristics include an average waiting time between adjacent storm sources, an average interval between rain clusters, an average number of rain clusters, an average duration of rain clusters, and an average precipitation intensity of rain clusters.

[0096] In one embodiment, the objective function of the precipitation simulation model is a weighted average of Euclidean distances of multiple statistical indicators of real precipitation sequence data and simulated precipitation sequence data.

[0097] In one embodiment, the data acquisition module 12 includes:

[0098] An acquisition unit, used to acquire precipitation data collected by multiple observation stations within the target range;

[0099] A statistical unit, used to aggregate and count the precipitation data collected at each station according to the specified time dimension to obtain precipitation statistical data of the specified time dimension;

[0100] The sorting unit is used to sort the precipitation statistical data of the specified time dimension of each observation station by time and generate precipitation sequence data of each observation station.

[0101] In one embodiment, the optimal solution module 13 includes:

[0102] An input unit, used to input the precipitation sequence data of each observation station into the precipitation simulation model;

[0103] The solving unit is used to solve the optimal model parameters corresponding to each observation station when the value of the objective function is minimized through an optimization algorithm under the constraints of the parameter boundary conditions and the objective function.

[0104] In one embodiment, the optimization algorithm is a PSO optimization algorithm.

[0105] Another embodiment of the present invention provides a precipitation simulation data generation system, such as Figure 6 As shown, the system 10 includes:

[0106] One or more processors 110 and memory 120, Figure 6 A processor 110 is used as an example for description. The processor 110 and the memory 120 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0107] The processor 110 is used to complete various control logics of the system 10, and it can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine) or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or any combination of these components. In addition, the processor 110 can also be any traditional processor, microprocessor or state machine. The processor 110 can also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a DSP and / or any other such configuration.

[0108] The memory 120, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions corresponding to the precipitation simulation data generation method in the embodiment of the present invention. The processor 110 executes various functional applications and data processing of the system 10 by running the non-volatile software programs, instructions and units stored in the memory 120, that is, the precipitation simulation data generation method in the above method embodiment is implemented.

[0109] The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required by at least one function; the data storage area may store data created according to the use of the system 10, etc. In addition, the memory 120 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 120 may optionally include a memory remotely arranged relative to the processor 110, and these remote memories may be connected to the system 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0110] One or more units are stored in the memory 120, and when executed by one or more processors 110, the precipitation simulation data generation method in any of the above method embodiments is executed, for example, the following steps are executed:

[0111] Constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and an objective function of the precipitation simulation model within a target range;

[0112] Obtaining precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data;

[0113] Inputting the precipitation sequence data into the precipitation simulation model, and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions;

[0114] The optimal model parameters corresponding to each observation station are used to generate precipitation simulation data for each observation station in a specified time dimension within a preset time length.

[0115] In one embodiment, before constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and an objective function of the precipitation simulation model within a target range, the method further includes:

[0116] Conduct statistical analysis on precipitation characteristics in the areas where multiple observation stations are located within the target range;

[0117] The parameter boundary conditions of the precipitation model within a target range are determined according to the statistical analysis results of the precipitation characteristics.

[0118] In one embodiment, the precipitation characteristics include an average waiting time between adjacent storm sources, an average interval between rain clusters, an average number of rain clusters, an average duration of rain clusters, and an average precipitation intensity of rain clusters.

[0119] In one embodiment, the objective function of the precipitation simulation model is a weighted average of Euclidean distances of multiple statistical indicators of real precipitation sequence data and simulated precipitation sequence data.

[0120] In one embodiment, obtaining precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data includes:

[0121] Obtaining precipitation data collected by multiple observation stations within the target range;

[0122] Aggregating and counting the precipitation data collected at each station according to the specified time dimension to obtain precipitation statistical data of the specified time dimension;

[0123] After sorting the precipitation statistical data of the specified time dimension of each observation station by time, the precipitation series data of each observation station are generated.

[0124] In one embodiment, the step of inputting the precipitation sequence data into the precipitation simulation model and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions includes:

[0125] Inputting precipitation sequence data of each observation station into the precipitation simulation model;

[0126] Under the constraints of the parameter boundary conditions and the objective function, the optimal model parameters corresponding to each observation site when the value of the objective function is minimized are solved through an optimization algorithm.

[0127] In one embodiment, the optimization algorithm is a PSO optimization algorithm.

[0128] An embodiment of the present invention provides a non-volatile computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by one or more processors, the precipitation simulation data generation method in any of the above method embodiments is executed, for example, the following steps are executed:

[0129] Constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and an objective function of the precipitation simulation model within a target range;

[0130] Obtaining precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data;

[0131] Inputting the precipitation sequence data into the precipitation simulation model, and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions;

[0132] The optimal model parameters corresponding to each observation station are used to generate precipitation simulation data for each observation station in a specified time dimension within a preset time length.

[0133] In one embodiment, before constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and an objective function of the precipitation simulation model within a target range, the method further includes:

[0134] Conduct statistical analysis on precipitation characteristics in the areas where multiple observation stations are located within the target range;

[0135] The parameter boundary conditions of the precipitation model within a target range are determined according to the statistical analysis results of the precipitation characteristics.

[0136] In one embodiment, the precipitation characteristics include an average waiting time between adjacent storm sources, an average interval between rain clusters, an average number of rain clusters, an average duration of rain clusters, and an average precipitation intensity of rain clusters.

[0137] In one embodiment, the objective function of the precipitation simulation model is a weighted average of Euclidean distances of multiple statistical indicators of real precipitation sequence data and simulated precipitation sequence data.

[0138] In one embodiment, obtaining precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data includes:

[0139] Obtaining precipitation data collected by multiple observation stations within the target range;

[0140] Aggregating and counting the precipitation data collected at each station according to the specified time dimension to obtain precipitation statistical data of the specified time dimension;

[0141] After sorting the precipitation statistical data of the specified time dimension of each observation station by time, the precipitation series data of each observation station are generated.

[0142] In one embodiment, the step of inputting the precipitation sequence data into the precipitation simulation model and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions includes:

[0143] Inputting precipitation sequence data of each observation station into the precipitation simulation model;

[0144] Under the constraints of the parameter boundary conditions and the objective function, the optimal model parameters corresponding to each observation site when the value of the objective function is minimized are solved through an optimization algorithm.

[0145] In one embodiment, the optimization algorithm is a PSO optimization algorithm.

[0146] As an example, non-volatile storage media can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) as external cache memory. By way of illustration and not limitation, RAM can be obtained in many forms such as synchronous RAM (SRAM), dynamic RAM, (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The disclosed memory components or memories of the operating environment described herein are intended to include one or more of these and / or any other suitable types of memory.

[0147] In summary, the present invention discloses a precipitation simulation data generation method, device, system and medium, the method includes: constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and objective functions of the precipitation simulation model within a target range; obtaining precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data; inputting the precipitation sequence data into the precipitation simulation model, calculating the model optimal parameters corresponding to each observation station according to the objective function and parameter boundary conditions; using the model optimal parameters corresponding to each observation station to generate precipitation simulation data of a specified time dimension within a preset time length for each observation station. By providing precipitation simulation models and optimal solution methods for regions with different precipitation characteristics, different stations can generate corresponding precipitation simulation data based on the model optimal parameters obtained by the general solution, ensuring the accuracy of the simulation data while improving the generation efficiency.

[0148] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium, and the computer program can include the processes of the above-mentioned method embodiments when executed. The storage medium can be a memory, a disk, a floppy disk, a flash memory, an optical storage device, etc.

[0149] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for generating precipitation simulation data, characterized in that: include: Constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and an objective function of the precipitation simulation model within a target range; Obtaining precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data; Inputting the precipitation sequence data into the precipitation simulation model, and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions; The optimal model parameters corresponding to each observation station are used to generate precipitation simulation data for each observation station in a specified time dimension within a preset time length.

2. The precipitation simulation data generation method according to claim 1, characterized in that: Before constructing a precipitation simulation model for simulating precipitation events and obtaining parameter boundary conditions and an objective function of the precipitation simulation model within a target range, the method further includes: Conduct statistical analysis on precipitation characteristics in the areas where multiple observation stations are located within the target range; The parameter boundary conditions of the precipitation model within a target range are determined according to the statistical analysis results of the precipitation characteristics.

3. The precipitation simulation data generation method according to claim 2, characterized in that: The precipitation characteristics include the average waiting time between adjacent storm sources, the average interval between rain clusters, the average number of rain clusters, the average duration of rain clusters, and the average precipitation intensity of rain clusters.

4. The method for generating precipitation simulation data according to any one of claims 1 to 3, characterized in that: The objective function of the precipitation simulation model is the weighted average of the Euclidean distances of multiple statistical indicators of the real precipitation sequence data and the simulated precipitation sequence data.

5. The precipitation simulation data generation method according to claim 1, characterized in that: The obtaining of precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range to generate precipitation sequence data includes: Obtaining precipitation data collected by multiple observation stations within the target range; Aggregating and counting the precipitation data collected at each station according to the specified time dimension to obtain precipitation statistical data of the specified time dimension; After sorting the precipitation statistical data of the specified time dimension of each observation station by time, the precipitation series data of each observation station are generated.

6. The precipitation simulation data generation method according to claim 1, characterized in that: The step of inputting the precipitation sequence data into the precipitation simulation model and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions includes: Inputting precipitation sequence data of each observation station into the precipitation simulation model; Under the constraints of the parameter boundary conditions and the objective function, the optimal model parameters corresponding to each observation site when the value of the objective function is minimized are solved through an optimization algorithm.

7. The precipitation simulation data generation method according to claim 6, characterized in that: The optimization algorithm is a PSO optimization algorithm.

8. A precipitation simulation data generating device, characterized in that: include: A model building module, used to build a precipitation simulation model for simulating precipitation events and obtain parameter boundary conditions and objective functions of the precipitation simulation model within a target range; A data acquisition module, used to acquire precipitation statistical data of a specified time dimension collected by multiple observation stations within the target range, and generate precipitation sequence data; An optimal solution module, used for inputting the precipitation sequence data into the precipitation simulation model, and calculating the optimal model parameters corresponding to each observation station according to the objective function and parameter boundary conditions; The simulation generation module is used to generate precipitation simulation data of each observation station in a specified time dimension within a preset time length using the optimal model parameters corresponding to each observation station.

9. A precipitation simulation data generation system, characterized in that: The system includes at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the precipitation simulation data generating method according to any one of claims 1 to 7.

10. A non-volatile computer-readable storage medium, characterized in that: The non-volatile computer-readable storage medium stores computer-executable instructions, which, when executed by one or more processors, enable the one or more processors to execute the precipitation simulation data generation method according to any one of claims 1 to 7.

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