CN117454122A - Mountain torrent disaster rainfall early warning analysis method and device based on fixed-point fixed-surface relation - Google Patents
Mountain torrent disaster rainfall early warning analysis method and device based on fixed-point fixed-surface relation Download PDFInfo
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Abstract
The application relates to a mountain torrent disaster rainfall early warning analysis method and device based on a fixed-point fixed-surface relation, which are applied to the technical field of data processing, and the method comprises the following steps: acquiring region information and a target division level of a region to be analyzed, wherein the region information comprises a region area and a region shape; determining a region center point and a region surface of a region to be analyzed based on the region area, the region shape and the target division level; acquiring the point rainfall capacity of a central point of the area, and calculating a point rainfall correlation value based on the point rainfall capacity; acquiring site rainfall of a measurement site in an area, and calculating a surface rainfall correlation value based on the site rainfall, wherein the starting time of site rainfall acquisition is the same as the starting time of point rainfall acquisition; calculating to obtain a point-surface reduction parameter based on the point rainfall correlation value and the surface rainfall correlation value; and generating an early warning index contour map of the area to be analyzed based on the point-surface reduction parameters. The method has the effect of fast and accurately carrying out early warning analysis.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a mountain torrent disaster rainfall early warning analysis method and device based on a fixed-point fixed-surface relationship.
Background
The flood disaster prevention is a difficult and weak link of flood prevention and disaster reduction work in China, the area of a hilly area in China accounts for about 2/3 of the area of the country, and the flood disaster caused by heavy rainfall is frequently repeated, so that a large number of casualties are caused.
The mountain torrent disaster early warning index is a result in mountain torrent disaster prevention and treatment projects, and the analysis and calculation of the early warning index is a complex process. The existing early warning indexes are all surface rainfall, the monitoring rainfall of the station is spot rainfall, the two forms are not matched, and the existing early warning index calculation needs to do a large amount of earlier work, such as field measurement, local storm flood data collection and the like, and the calculation method is complex and complicated, and relates to complex drainage basin design, calculation of storm flood and calculation of water level flow relation, and the rechecking difficulty of the early warning indexes is high, so that early warning analysis is difficult to quickly and accurately perform.
Disclosure of Invention
In order to quickly and accurately perform early warning analysis, the application provides a mountain torrent disaster rainfall early warning analysis method and device based on fixed-point and fixed-surface relations.
In a first aspect, the application provides a mountain torrent disaster rainfall early warning analysis method based on a fixed-point fixed-surface relationship, which adopts the following technical scheme:
a mountain torrent disaster rainfall early warning analysis method based on a fixed-point fixed-surface relation comprises the following steps:
acquiring region information and a target division level of a region to be analyzed, wherein the region information comprises a region area and a region shape;
determining a region center point and a region surface of the region to be analyzed based on the region area, the region shape and the target division level;
acquiring the point rainfall capacity of the central point of the area, and calculating a point rainfall related value based on the point rainfall capacity;
acquiring the site rainfall of a measurement site in the regional surface, and calculating a surface rainfall related value based on the site rainfall, wherein the starting time of the site rainfall acquisition is the same as the starting time of the site rainfall acquisition;
calculating a point-surface reduction parameter based on the point rainfall correlation value and the surface rainfall correlation value;
and generating an early warning index contour map of the area to be analyzed based on the point-surface reduction parameters.
By adopting the technical scheme, the whole area to be analyzed is divided into a plurality of area faces and an area center point according to the area information of the area to be analyzed and the target division level, namely, the center points of the plurality of area faces are the same, then, the point rainfall correlation value of the area center point and the surface rainfall correlation value of each area face are determined based on historical rainfall data, the calculated point-surface reduction parameters of a fixed-point surface method can be obtained by using the point rainfall correlation values and the surface rainfall correlation values, a fixed-point surface relation is established, and when analysis is carried out, the analyzed position is in the area to be analyzed, the deviation of the surface rainfall is calculated by using the point-surface relation, the accuracy is high, so that the demand of early warning analysis is fast and accurate.
Optionally, the determining the region center point and the region face of the region to be analyzed based on the region area, the region shape and the target division level includes:
acquiring a measuring station of the central position of the area to be analyzed, and taking the measuring station of the central position as an area central point;
determining the dividing shape of the area to be analyzed based on the area shape and a preset dividing rule;
and determining an increase distance based on the area and the target division level, and carrying out concentric division based on the increase distance and the area center point to generate a plurality of area faces with different areas.
Optionally, the obtaining the point rainfall amount of the central point of the area, and calculating the point rainfall-related value based on the point rainfall amount includes:
acquiring historical rainfall of a preset calculation period;
selecting from the historical rainfall according to a preset value period based on the preset calculation period to generate a plurality of maximum rainfall observation arrays, wherein each preset value period corresponds to one maximum rainfall observation array;
and calculating a point rainfall correlation value for the time period maximum rainfall in each maximum rainfall observation sequence based on the probability density function of the P-III type curve.
Optionally, the selecting the historical rainfall according to the preset value period based on the preset calculation period includes:
arranging the historical rainfall of the target service years in the preset calculation years according to the acquisition time to generate a rainfall sequence;
and selecting the maximum value of the preset value period in the rainfall sequence according to the preset value period.
Optionally, the acquiring the site rainfall of the measurement site in the regional area, and calculating the surface rainfall-related value based on the site rainfall includes:
calculating an average value of the station rainfall in the preset value period, and taking the average value as the surface rainfall;
dividing the surface rainfall according to the preset calculation period and the preset value period to generate a plurality of surface rainfall observation series;
and calculating the surface rainfall correlation value of the surface rainfall in each surface rainfall observation sequence based on the probability density function of the P-III type curve.
Optionally, the calculating the point-surface reduction parameter based on the point rainfall correlation value and the surface rainfall correlation value includes:
calculating a point-surface reduction coefficient of the point rainfall frequency domain and the surface rainfall correlation value in each rainfall surface;
acquiring the rainfall surface area, and based on the reduction coefficient and a fitting formulaCalculating to obtain a point-surface reduction parameter, wherein eta is a point-surface reduction coefficient, and A is the rainfall surface area; C. n is a point-plane reduction parameter.
Optionally, the generating the early warning index contour map of the area to be analyzed based on the point-plane reduction parameter includes:
acquiring position information of a target analysis object in the area to be analyzed and a surface rainfall early warning index of the target analysis object;
determining the area surface of the target analysis object based on the position information;
acquiring a point-surface reduction parameter of the area surface, and taking the point-surface reduction parameter of the area surface as a target point-surface reduction parameter;
calculating the object point rainfall of the target analysis object based on the surface rainfall early warning index and the target point surface reduction parameter;
and drawing and generating an early warning index contour map based on all the object rainfall in the area to be analyzed.
In a second aspect, the application provides a mountain torrent disaster rainfall early warning analysis device based on a fixed-point fixed-surface relationship, which adopts the following technical scheme:
mountain torrent disaster rainfall early warning analysis device based on fixed point surface relation includes:
the device comprises a region information acquisition module, a target division level acquisition module and a target division level acquisition module, wherein the region information acquisition module is used for acquiring region information of a region to be analyzed, and the region information comprises a region area and a region shape;
the region point surface determining module is used for determining a region center point and a region surface of the region to be analyzed based on the region area, the region shape and the target division level;
the spot rainfall numerical calculation module is used for acquiring the spot rainfall capacity of the central point of the area and calculating a spot rainfall related value based on the spot rainfall capacity;
the surface rain value calculation module is used for acquiring the site rainfall of the measurement site in the regional surface and calculating a surface rainfall related value based on the site rainfall, wherein the starting time of the site rainfall acquisition is the same as the starting time of the point rainfall acquisition;
the discount parameter calculation module is used for calculating a point-surface discount parameter based on the point rainfall correlation value and the surface rainfall correlation value;
and the early warning line drawing generation module is used for generating an early warning index contour drawing of the area to be analyzed based on the point-surface reduction parameters.
By adopting the technical scheme, the whole area to be analyzed is divided into a plurality of area faces and an area center point according to the area information of the area to be analyzed and the target division level, namely, the center points of the plurality of area faces are the same, then, the point rainfall correlation value of the area center point and the surface rainfall correlation value of each area face are determined based on historical rainfall data, the calculated point-surface reduction parameters of a fixed-point surface method can be obtained by using the point rainfall correlation values and the surface rainfall correlation values, a fixed-point surface relation is established, and when analysis is carried out, the analyzed position is in the area to be analyzed, the deviation of the surface rainfall is calculated by using the point-surface relation, the accuracy is high, so that the demand of early warning analysis is fast and accurate.
In a third aspect, the present application provides an electronic device, which adopts the following technical scheme:
an electronic device comprising a processor coupled with a memory;
the processor is configured to execute a computer program stored in the memory, so that the electronic device executes the computer program of the mountain torrent disaster rainfall early warning analysis method based on the fixed-point fixed-surface relationship according to any one of the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the mountain torrent disaster rainfall early warning analysis method based on the fixed-point fixed-surface relationship according to any one of the first aspects.
Drawings
Fig. 1 is a flow chart of a mountain torrent disaster rainfall early warning analysis method based on a fixed-point fixed-surface relationship provided by an embodiment of the application.
Fig. 2 is a block diagram of an exemplary area division manner according to an embodiment of the present application.
Fig. 3 is a block diagram of a mountain torrent disaster rainfall early warning analysis device based on a fixed-point and fixed-surface relationship according to an embodiment of the present application.
Fig. 4 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
The embodiment of the application provides a mountain torrent disaster rainfall early warning analysis method based on a fixed-point fixed-surface relationship, which can be executed by electronic equipment, wherein the electronic equipment can be a server or terminal equipment, and the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud computing service. The terminal device may be, but is not limited to, a smart phone, a tablet computer, a desktop computer, etc.
Fig. 1 is a flow chart of a mountain torrent disaster rainfall early warning analysis method based on a fixed-point fixed-surface relationship provided by an embodiment of the application.
As shown in fig. 1, the main flow of the method is described as follows (steps S101 to S106):
step S101, obtaining region information and a target division level of a region to be analyzed, wherein the region information includes a region area and a region shape.
In this embodiment, a large area is divided into a plurality of areas to be analyzed according to the characteristics of the heavy rain, for example, the areas to be analyzed are obtained by dividing the same characteristics of the heavy rain in the areas to be analyzed into an a area to be analyzed and a B area to be analyzed, wherein the characteristics of the heavy rain in the area to be analyzed are high-strength and high-rainfall in a long time, the characteristics of the heavy rain in the area to be analyzed are rainfall in a short time and high-strength, and the target division level is to divide the area to be analyzed into several levels, for example, divide the area to be analyzed into two levels, divide the area to be analyzed into five levels, divide the area to be analyzed into ten levels, and the like.
Step S102, determining a region center point and a region face of the region to be analyzed based on the region area, the region shape and the target division level.
Aiming at step S102, acquiring a measurement site of the central position of the area to be analyzed, and taking the measurement site of the central position as an area central point; determining the division shape of the area to be analyzed based on the area shape and a preset division rule; and determining a growing distance based on the area of the region and the target classification level, and carrying out concentric classification based on the growing distance and the region center point to generate a plurality of region faces with different areas.
In this embodiment, a plurality of measurement sites for observing rainfall are set in each area to be analyzed, a measurement site at a central position in the whole area to be analyzed is selected as an area central point, area surface division is performed based on the area central point, selection is performed in a preset division rule according to an area shape, a division shape used when the area to be analyzed is divided is determined, and then a growth interval is determined according to an area of the area and a target division level, namely how to perform growth division in particular when the division is performed. The preset dividing rule is provided with a plurality of dividing shapes, after the area shape is obtained, the area shape is compared with the dividing shapes in the preset dividing rule, one dividing shape with the highest similarity with the area shape is selected as the dividing shape for subsequent dividing, the dividing shape comprises but is not limited to a circle, an ellipse, a square and a rectangle, but in any shape, the dividing is carried out by taking the center point of the area as the center or the center, namely, the dividing area is increased layer by taking the concentric circle or the concentric ellipse.
For example, as shown in fig. 2, the division shape is a circle, the target division level is 3, and the area of the region is 225 square kilometers, and then when the division is performed, the division is performed by dividing the division into three concentric circles with the center point of the region as the center and the increment interval of five kilometers, and the largest one of the region faces can include the whole region to be analyzed, namely, the three concentric circles are a region face a with a radius of 5 kilometers, a region face B with a radius of 10 kilometers and a region face C with a radius of 15 kilometers. The specific growth interval needs to be determined according to the actual area and the target division level, and the growth pitch needs to be increased in equal proportion or equal value according to a certain rule when division is performed, which is not particularly limited herein.
And step S103, obtaining the point rainfall capacity of the central point of the area, and calculating a point rainfall related value based on the point rainfall capacity.
Aiming at step S103, obtaining historical rainfall of a preset calculation period; selecting in the historical rainfall according to a preset value period based on a preset calculation period to generate a plurality of maximum rainfall observation arrays, wherein each preset value period corresponds to one maximum rainfall observation array; and calculating a point rainfall correlation value for the time period maximum rainfall in each maximum rainfall observation array based on the probability density function of the P-III type curve.
Further, the historical rainfall of the target service years in the preset calculation years is arranged according to the acquisition time, and a rainfall sequence is generated; and selecting the maximum value of the preset value period in the rainfall sequence according to the preset value period.
In this embodiment, the preset calculation period is generally set to be about 30 years, each measurement station will collect rainfall at each time at the beginning of establishment, and a skip bucket rainfall collection mode is generally adopted during collection, so that rainfall is collected and recorded during rainfall, when a maximum rainfall observation sequence of a regional center point is created, firstly rainfall data of about 30 years are sequentially selected from a large amount of rainfall data, the selected rainfall data is used as historical rainfall, each year of about 30 years is used as a target year, then the historical rainfall of the target year is arranged according to collection time, a rainfall sequence is generated, namely, the rainfall sequence is generated by sequentially arranging according to collection time, collection day and collection month according to the time of occurrence, selecting a maximum value of each period in a rainfall sequence according to a preset value time period, wherein the preset value time period is 10 periods of 10min, 30min, 60min, 1h, 3h, 6h, 12h, 24h, 1d and 3d, namely the annual maximum rainfall of a measurement site corresponding to a regional center point in 10 periods of 10min, 30min, 60min, 1h, 3h, 6h, 12h, 24h, 1d and 3d, after the maximum rainfall of 30 years is selected, dividing the maximum rainfall of 30 years into a plurality of maximum rainfall observation arrays according to the preset value time period, and since 10 maximum rainfall observation arrays are provided in the preset value time period, 10 maximum rainfall observation arrays are provided, and 30 maximum rainfall of 30 years of the value time period are provided in each maximum rainfall observation array. It should be noted that 60min and 1h may be distinguished from 60min to 60min, for example, the start time is 1 point 05, the end time is 2 points 05,1h is the whole time to another whole time, for example, the start time is 1 point, the end time is 2 points, 24h and 1d may be distinguished from 24h to 24h, for example, the start time is 12 noon, the end time is 12 noon the next day, 1d is a fixed start time at 8 noon, 8 points in the next day is a fixed end time, and 3d is three consecutive 1d.
Then calculating the point rainfall correlation value of each maximum rainfall in each maximum rainfall observation array, wherein the point rainfall correlation value is calculated according to a probability density function of a P-III type curve, and the probability density function of the P-III type curve is thatWherein->For->Gamma function (L)>In the shape of a P-III distribution,on the scale of the P-III distribution, +.>Calculating a point rainfall frequency according to a probability density function of a P-III curve for the position parameters distributed by P-III, and calculating a point rainfall related value according to a frequency fitting line principle after the point rainfall frequency is obtained, wherein the point rainfall related value comprises a mean value and a Cv value of rainfall of a central point of a region, and the parameters are->、/>、/>And statistical parameters->、/>And->Has the following relationship: />And calculate +.>And->The value of (2) is 3.5, the parameter +.>、/>、/>And (3) carrying out the calculation by taking the calculated point rainfall correlation value into a probability density function of the P-III type curve.
Step S104, acquiring the site rainfall of the measurement site in the regional surface, and calculating a surface rainfall correlation value based on the site rainfall, wherein the starting time of the site rainfall acquisition is the same as the starting time of the point rainfall acquisition.
Aiming at step S104, calculating the average value of the station rainfall in a preset value period, and taking the average value as the surface rainfall; dividing the surface rainfall according to a preset calculation period and a preset value period to generate a plurality of surface rainfall observation sequence; and calculating the surface rainfall correlation value of the surface rainfall in each surface rainfall observation array based on the probability density function of the P-III type curve.
In this embodiment, when the calculation of the surface rainfall correlation value is performed, a similar calculation manner to the calculation of the spot rainfall correlation value is adopted, and the calculation of the surface rainfall correlation value is to calculate the rainfall correlation value in each area surface, and how many area surfaces are in one area, then each area surface corresponds to the same number of surface rainfall correlation values as the spot rainfall correlation value, for example, three area surfaces, and the number of spot rainfall correlation values is 10, and then each area surface of the three area surfaces has 10 correlation values.
Firstly, selecting again in 30 years rainfall data selected when calculating the rainfall correlation value according to a selection mode of the rainfall correlation value, determining measuring stations in the regional surface, selecting all measuring stations in the regional surface according to the preset value period, wherein the starting time of the station rainfall acquisition is the same as the starting time of the point rainfall acquisition, for example, the starting time of the point rainfall in 10min is 1 point to 1 point for 10 minutes, the starting time of the station rainfall in 10min is 1 point to 1 point for 10 minutes, and the same, all preset value periods need to be kept consistent, so that the obtained station rainfall is the same as the value period when the maximum rainfall is selected, and all station rainfall obtained by selecting each regional surface is averaged by adopting an arithmetic average method or a Thiessen polygon method, and the average value is taken as the surface rainfall. As known, the above-mentioned rainfall data of about 30 years is selected, and the area to be analyzed is divided into a plurality of area surfaces according to the target division hierarchy, each area surface has one surface rainfall in each value period of each year, the surface rainfall corresponds to the maximum rainfall, the surface rainfall of 30 years is divided into a plurality of surface rainfall observation sequence according to the preset value period, and since 10 surface rainfall observation sequence is provided in the preset value period, 10 surface rainfall observation sequence is provided, each surface rainfall observation sequence has 30 maximum rainfall of 30 years of the value period, and each area surface has 10 surface rainfall observation sequence.
The surface rainfall correlation value adopts the same calculation mode as the point rainfall correlation value, namely the surface rainfall correlation value is calculated according to a P-III type curve probability density function, and the P-III type curve probability density function is thatWherein->For->Gamma function (L)>In the shape of a P-III distribution,on the scale of the P-III distribution, +.>Calculating the surface rainfall frequency according to the probability density function of the P-III curve for the position parameters distributed by P-III, and calculating a surface rainfall correlation value according to the frequency fitting line principle after the surface rainfall frequency is obtained, wherein the surface rainfall correlation value comprises the average value and Cv value of rainfall of the regional surface, wherein the parameters are->、/>、/>And statistical parameters->、/>And->Has the following relationship: />And calculate +.>And->The value of (2) is 3.5, the parameter +.>、/>、/>And (3) carrying out the calculation by taking the rainfall correlation value into a probability density function of the P-III type curve.
Before calculation, according to basic geospatial data processed by collecting national basic geographic information 1:5 ten thousand Digital Elevation Model (DEM) and Digital Line Graph (DLG), the method is superior to 2.5m resolution domestic digital orthophoto Data (DOM), chinese mountain torrent forecast and early warning partitions, hydrologic partitions, small river basin data, climate background data and the like, then using point rainfall frequency to establish a rainfall frequency curve of each preset value period, using point rainfall frequency to establish a surface rainfall frequency curve of each preset value period and each area surface, analyzing according to the collected data and the point rainfall frequency curve and the surface rainfall frequency curve, analyzing whether a change trend of frequency rainfall corresponding to average value, cv and rainfall frequency along with history is consistent with surrounding areas or not, comparing each item of data of the area surface with each item of data of an area center point, wherein each item of data of the area surface should be smaller than each item of the area center point. It should be noted that the content of the analysis needs to be increased according to the actual requirement, and the specific content of the analysis is not particularly limited herein.
Step S105, calculating to obtain the point-surface reduction parameter based on the point rainfall correlation value and the surface rainfall correlation value.
Aiming at step S105, calculating a point-surface reduction coefficient of a point rainfall frequency domain and a surface rainfall correlation value in each rainfall surface; acquiring rainfall surface area based on reduction coefficient and fitting formulaCalculating to obtain a point-surface reduction parameter, wherein eta is a point-surface reduction coefficient, and A is the rainfall surface area; C. n is a point-plane reduction parameter.
In this embodiment, the point-to-surface reduction coefficient is a ratio of the average value of each area surface to the average value of the center point of each area surface, and the calculated point-to-surface reduction coefficient is brought into a fitting formula, so that the point-to-surface reduction parameter of each area surface is calculated.
And S106, generating an early warning index contour map of the area to be analyzed based on the point-plane reduction parameters.
Aiming at step S106, acquiring the position information of a target analysis object in the area to be analyzed and the surface rainfall early warning index of the target analysis object; determining the area surface of the target analysis object based on the position information; acquiring a point-surface reduction parameter of the area surface, and taking the point-surface reduction parameter of the area surface as a target point-surface reduction parameter; calculating the object point rainfall of the target analysis object based on the surface rainfall early warning index and the target point surface reduction parameter; and drawing and generating an early warning index contour map based on all the object point rainfall in the area to be analyzed.
In this embodiment, when the calculation is performed, the target analysis object is a certain town, a certain town or a certain administrative village in the area to be analyzed, the calculation of the early warning index is performed on the target analysis object in advance, the early warning index of the surface rainfall of the target analysis object is obtained, the early warning index of the surface rainfall of the target analysis object is the early warning index of the surface rainfall of the target analysis object, then the area surface of the target analysis object, which is specifically affected by the disaster, is determined according to the position information of the target analysis object, the object point rainfall of the target analysis object is calculated according to the point surface reduction coefficient and the surface rainfall early warning index of the area surface, the object point rainfall of all the target analysis objects in the area to be analyzed is calculated, so that a plurality of object point rainfall is obtained, and the drawing of the early warning index contour map is performed by using all the object point rainfall, so that the mountain flood disaster of the target analysis object is early warned according to the early warning index contour map.
Fig. 3 is a block diagram of a mountain torrent disaster rainfall early warning analysis device 200 based on a fixed-point and fixed-surface relationship according to an embodiment of the present application.
As shown in fig. 3, the mountain torrent disaster rainfall early warning analysis device 200 based on the fixed-point fixed-surface relationship mainly includes:
a region information obtaining module 201, configured to obtain region information of a region to be analyzed and a target division level, where the region information includes a region area and a region shape;
the region point surface determining module 202 is configured to determine a region center point and a region surface of the region to be analyzed based on the region area, the region shape and the target division level;
the spot rain value calculation module 203 is configured to obtain a spot rainfall amount of a central point of the area, and calculate a spot rainfall correlation value based on the spot rainfall amount;
the surface rain value calculation module 204 is configured to obtain a site rainfall of a measurement site in a regional surface, and calculate a surface rainfall correlation value based on the site rainfall, where a start time of site rainfall acquisition is the same as a start time of point rainfall acquisition;
the reduction parameter calculation module 205 is configured to calculate a point-surface reduction parameter based on the point rainfall correlation value and the surface rainfall correlation value;
and the early warning line drawing generation module 206 is used for generating an early warning index contour drawing of the area to be analyzed based on the point-plane reduction parameters.
As an optional implementation manner of this embodiment, the area point-plane determining module 202 is specifically configured to obtain a measurement site of a central position of an area to be analyzed, and use the measurement site of the central position as an area central point; determining the division shape of the area to be analyzed based on the area shape and a preset division rule; and determining a growing distance based on the area of the region and the target classification level, and carrying out concentric classification based on the growing distance and the region center point to generate a plurality of region faces with different areas.
As an alternative implementation manner of this embodiment, the raining numerical calculation module 203 includes:
the historical rainfall obtaining module is used for obtaining historical rainfall of a preset calculation period;
the historical rainfall selecting module is used for selecting in the historical rainfall according to a preset value period based on a preset calculation period to generate a plurality of maximum rainfall observation arrays, wherein each preset value period corresponds to one maximum rainfall observation array;
and the rainfall frequency calculation module is used for calculating a point rainfall correlation value for the time period maximum rainfall in each maximum rainfall observation array based on the probability density function of the P-III type curve.
In this optional embodiment, the historical rainfall selecting module is specifically configured to arrange historical rainfall of a target service year in a preset calculation period according to the collection time, so as to generate a rainfall sequence; and selecting the maximum value of the preset value period in the rainfall sequence according to the preset value period.
As an optional implementation manner of this embodiment, the face rain numerical calculation module 204 is specifically configured to calculate an average value of the station rainfall in a preset value period, and take the average value as the face rainfall; dividing the surface rainfall according to a preset calculation period and a preset value period to generate a plurality of surface rainfall observation sequence; and calculating the surface rainfall correlation value of the surface rainfall in each surface rainfall observation array based on the probability density function of the P-III type curve.
As an optional implementation manner of this embodiment, the reduction parameter calculation module 205 is specifically configured to calculate a point-surface reduction coefficient of a point rainfall frequency domain and a surface rainfall correlation value in each rainfall surface; acquiring rainfall surface area based on reduction coefficient and fitting formulaCalculating to obtain a point-surface reduction parameter, wherein eta is a point-surface reduction coefficient, and A is the rainfall surface area; C. n is a point-plane reduction parameter.
As an optional implementation manner of this embodiment, the early warning line drawing generating module 206 is specifically configured to obtain location information of a target analysis object and a surface rainfall early warning indicator of the target analysis object in the area to be analyzed; determining the area surface of the target analysis object based on the position information; acquiring a point-surface reduction parameter of the area surface, and taking the point-surface reduction parameter of the area surface as a target point-surface reduction parameter; calculating the object point rainfall of the target analysis object based on the surface rainfall early warning index and the target point surface reduction parameter; and drawing and generating an early warning index contour map based on all the object point rainfall in the area to be analyzed.
In one example, a module in any of the above apparatuses may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (application specific integratedcircuit, ASIC), or one or more digital signal processors (digital signal processor, DSP), or one or more field programmable gate arrays (field programmable gate array, FPGA), or a combination of at least two of these integrated circuit forms.
For another example, when a module in an apparatus may be implemented in the form of a scheduler of processing elements, the processing elements may be general-purpose processors, such as a central processing unit (central processing unit, CPU) or other processor that may invoke a program. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus and modules described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
Fig. 4 is a block diagram of an electronic device 300 according to an embodiment of the present application.
As shown in fig. 4, the electronic device 300 includes a processor 301 and a memory 302, and may further include an information input/information output (I/O) interface 303, one or more of a communication component 304, and a communication bus 305.
The processor 301 is configured to control the overall operation of the electronic device 300, so as to complete all or part of the steps of the mountain torrent disaster rainfall early warning analysis method based on the fixed-point fixed-surface relationship; the memory 302 is used to store various types of data to support operation at the electronic device 300, which may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The Memory 302 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as one or more of static random access Memory (Static Random Access Memory, SRAM), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The I/O interface 303 provides an interface between the processor 301 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 304 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the corresponding communication component 104 may thus comprise: wi-Fi part, bluetooth part, NFC part.
The electronic device 300 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (Digital Signal Processor, abbreviated as DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the mountain torrent disaster rainfall pre-warning analysis method based on the fixed-point and fixed-surface relationship as given in the above embodiments.
Communication bus 305 may include a pathway to transfer information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The communication bus 305 may be divided into an address bus, a data bus, a control bus, and the like.
The electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like, and may also be a server, and the like.
The application also provides a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and the steps of the mountain torrent disaster rainfall early warning analysis method based on the fixed-point fixed-surface relation are realized when the computer program is executed by a processor.
The computer readable storage medium may include: a U-disk, a removable hard disk, a read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the application referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or their equivalents is possible without departing from the spirit of the application. Such as the above-mentioned features and the technical features having similar functions (but not limited to) applied for in this application are replaced with each other.
Claims (10)
1. The mountain torrent disaster rainfall early warning analysis method based on the fixed-point fixed-surface relation is characterized by comprising the following steps of:
acquiring region information and a target division level of a region to be analyzed, wherein the region information comprises a region area and a region shape;
determining a region center point and a region surface of the region to be analyzed based on the region area, the region shape and the target division level;
acquiring the point rainfall capacity of the central point of the area, and calculating a point rainfall related value based on the point rainfall capacity;
acquiring the site rainfall of a measurement site in the regional surface, and calculating a surface rainfall related value based on the site rainfall, wherein the starting time of the site rainfall acquisition is the same as the starting time of the site rainfall acquisition;
calculating a point-surface reduction parameter based on the point rainfall correlation value and the surface rainfall correlation value;
and generating an early warning index contour map of the area to be analyzed based on the point-surface reduction parameters.
2. The method of claim 1, wherein the determining a region center point and a region face of the region to be analyzed based on the region area, the region shape, and the target division level comprises:
acquiring a measuring station of the central position of the area to be analyzed, and taking the measuring station of the central position as an area central point;
determining the dividing shape of the area to be analyzed based on the area shape and a preset dividing rule;
and determining an increase distance based on the area and the target division level, and carrying out concentric division based on the increase distance and the area center point to generate a plurality of area faces with different areas.
3. The method of claim 1, wherein the obtaining a point rainfall amount for the center point of the area, and calculating a point rainfall-related value based on the point rainfall amount comprises:
acquiring historical rainfall of a preset calculation period;
selecting from the historical rainfall according to a preset value period based on the preset calculation period to generate a plurality of maximum rainfall observation arrays, wherein each preset value period corresponds to one maximum rainfall observation array;
and calculating a point rainfall correlation value for the time period maximum rainfall in each maximum rainfall observation sequence based on the probability density function of the P-III type curve.
4. A method according to claim 3, wherein said selecting among said historical rainfall according to said preset value period based on said preset calculation period comprises:
arranging the historical rainfall of the target service years in the preset calculation years according to the acquisition time to generate a rainfall sequence;
and selecting the maximum value of the preset value period in the rainfall sequence according to the preset value period.
5. The method of claim 4, wherein the obtaining the site rainfall for the measurement sites in the regional facets, and calculating the surface rainfall-related value based on the site rainfall comprises:
calculating an average value of the station rainfall in the preset value period, and taking the average value as the surface rainfall;
dividing the surface rainfall according to the preset calculation period and the preset value period to generate a plurality of surface rainfall observation series;
and calculating the surface rainfall correlation value of the surface rainfall in each surface rainfall observation sequence based on the probability density function of the P-III type curve.
6. The method of claim 1, wherein calculating a point-to-surface reduction parameter based on the point rainfall correlation value and the surface rainfall correlation value comprises:
calculating a point-surface reduction coefficient of the point rainfall frequency domain and the surface rainfall correlation value in each rainfall surface;
acquiring the rainfall surface area, and based on the reduction coefficient and a fitting formulaCalculating to obtain a point-surface reduction parameter, wherein eta is a point-surface reduction coefficient, and A is the rainfall surface area; C. n is a point-plane reduction parameter.
7. The method of claim 1, wherein generating an early warning indicator contour map of the area to be analyzed based on the point-to-surface reduction parameter comprises:
acquiring position information of a target analysis object in the area to be analyzed and a surface rainfall early warning index of the target analysis object;
determining the area surface of the target analysis object based on the position information;
acquiring a point-surface reduction parameter of the area surface, and taking the point-surface reduction parameter of the area surface as a target point-surface reduction parameter;
calculating the object point rainfall of the target analysis object based on the surface rainfall early warning index and the target point surface reduction parameter;
and drawing and generating an early warning index contour map based on all the object rainfall in the area to be analyzed.
8. Mountain torrent disaster rainfall early warning analysis device based on fixed point surface relation, characterized by comprising:
the device comprises a region information acquisition module, a target division level acquisition module and a target division level acquisition module, wherein the region information acquisition module is used for acquiring region information of a region to be analyzed, and the region information comprises a region area and a region shape;
the region point surface determining module is used for determining a region center point and a region surface of the region to be analyzed based on the region area, the region shape and the target division level;
the spot rainfall numerical calculation module is used for acquiring the spot rainfall capacity of the central point of the area and calculating a spot rainfall related value based on the spot rainfall capacity;
the surface rain value calculation module is used for acquiring the site rainfall of the measurement site in the regional surface and calculating a surface rainfall related value based on the site rainfall, wherein the starting time of the site rainfall acquisition is the same as the starting time of the point rainfall acquisition;
the discount parameter calculation module is used for calculating a point-surface discount parameter based on the point rainfall correlation value and the surface rainfall correlation value;
and the early warning line drawing generation module is used for generating an early warning index contour drawing of the area to be analyzed based on the point-surface reduction parameters.
9. An electronic device comprising a processor coupled to a memory;
the processor is configured to execute a computer program stored in the memory to cause the electronic device to perform the method of any one of claims 1 to 7.
10. A computer readable storage medium comprising a computer program or instructions which, when run on a computer, cause the computer to perform the method of any of claims 1 to 7.
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