CN115508916A - Starry sky landscape forecasting method - Google Patents
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Abstract
The invention relates to the technical field of weather forecasting, and aims to provide a starry sky landscape forecasting method. The invention discloses a starry sky landscape forecasting method, which comprises the following steps: acquiring a meteorological grid containing geographical height data in a target area, and acquiring a total aerial cloud amount forecast value of all grid points in the target area according to the current meteorological grid; obtaining the ground visibility forecast value and PM of the target area 10 Forecast values and PM 2.5 A forecast value; high altitude total cloud amount forecast value based on any grid point in meteorological grid, ground visibility forecast value and PM of target area 10 Forecast values and PM 2.5 Forecasting values to obtain the values of the corresponding meteorological elements; obtaining a starry sky appreciation total value according to the values of all meteorological elements; and obtaining the viewing grade of the visibility degree of the sky landscape at night according to the total score of the sky viewing degree. The starry sky landscape forecasting result has the characteristics of meshing and refining, and meanwhile, the forecasting accuracy is high.
Description
Technical Field
The invention relates to the technical field of weather forecast, in particular to a starry sky landscape forecasting method which is used for knowing starry sky viewing conditions.
Background
The meteorological landscape is formed naturally under special meteorological conditions in cooperation with certain geographic environment and astronomical conditions. The method has the advantages that the meteorological landscape is effectively excavated and developed, good tourism and popular science effects can be generated, the meteorological service chain can be prolonged, and the popularity of the area can be improved. In recent years, the need for weather services is not limited to the acquisition of basic weather element data such as temperature, air pressure, humidity, and wind direction, but more weather services are expected to be available to help people enjoy weather and improve quality of life. The weather landscape forecasting service is developed, tourists can timely know weather landscape information of scenic spots, optimal travel time and tour routes are planned, and experience quality is improved; the scenic spot can dig scenic spot meteorological landscape resource potentiality deeply, develop characteristic tourism project, dispose scenic spot resource scientifically. The weather landscape forecast service developed from these trending points is receiving more and more attention.
The starry sky is an astronomical phenomenon and a meteorological landscape, each place is provided with a large group of enthusiasts fond of starry sky, and in recent years, a tourism project taking the starry sky as a theme is prosperous. Although starry sky observation mainly belongs to astronomical activities, the largest determining factor for seeing starry sky is the meteorological condition, so the starry sky observation condition can also be classified as meteorological landscape. However, with the development of human cities, air pollution and the flooding of artificial light sources make it not suitable to view starry sky anywhere. In China, qinghai-Tibet plateau, inner Mongolia grassland and Gobi desert are hot places for viewing starry sky, but the places are remote and difficult to be removed. The starry sky observation condition forecast makes the observation starry sky no longer exclusive for researchers or star observers, and ordinary people without the experience of observing stars can also obtain the opportunity of appreciating the bright stars according to the forecast, so that the ordinary people can conveniently select a better time to observe stars.
However, no technically reliable starry sky landscape forecasting method appears in the prior art.
Disclosure of Invention
The invention aims to solve the technical problems at least to a certain extent, and provides a starry sky landscape forecasting method.
The technical scheme adopted by the invention is as follows:
the invention provides a starry sky landscape forecasting method, which comprises the following steps:
acquiring a meteorological grid containing geographical height data in a target area, and acquiring a total aerial cloud amount forecast value of all grid points in the target area according to the current meteorological grid;
obtaining the ground visibility forecast value and PM of the target area 10 Forecast values and PM 2.5 A forecast value;
high altitude total cloud amount forecast value based on any grid point in meteorological grid, ground visibility forecast value and PM of target area 10 Forecast values and PM 2.5 Forecasting values to obtain the values of the corresponding meteorological elements;
obtaining a starry sky appreciation total value according to the values of all meteorological elements;
and obtaining the viewing grade of the visibility degree of the sky landscape at night according to the total value of the sky viewing degree.
The method comprises the steps of obtaining the forecast value of the total aerial cloud cover of all grid points of a target area, and the forecast value of the ground visibility and PM of the target area 10 Forecast values and PM 2.5 And forecasting values to obtain the viewing grade of the visibility degree of the sky landscape at night, so that the occurrence grade of the sky landscape is forecasted, the sky landscape is forecasted through the meteorological grid, the star landscape forecasting result has the characteristics of meshing and refining, and meanwhile, the forecasting accuracy is improved.
In one possible design, obtaining a predicted value of ground visibility for a target area includes:
obtaining a visibility historical observation value and a ground meteorological element historical observation value of the ground of a target area, wherein the ground meteorological element historical observation value comprises air temperature, air pressure, relative humidity, air volume and/or precipitation;
establishing a visibility and meteorological element forecasting relation model based on a BP neural network according to the visibility historical observation value and the ground meteorological element historical observation value;
and inputting the real-time meteorological element observed value into the visibility and meteorological element forecasting relation model to obtain a ground visibility forecast value of the target area.
In one possible design, the total starry sky appreciation score is:
wherein Sc is the total value of the star sky appreciation, and Sc p Is a score, alpha, of each meteorological element p Are weight coefficients.
In one possible design, the weighting factors for each meteorological element are shown in Table 1:
TABLE 1 Meteorological element weight coefficient Table
The score and the judgment conditions of each meteorological element are shown in table 2:
TABLE 2 Meteorological element score and judgment condition Table
In one possible design, the viewing grade of the visibility degree of the sky landscape at night is divided into four grades; wherein when Sc is more than or equal to 60, the ornamental grade is I grade; when Sc is more than or equal to 30 and less than 60, the ornamental grade is II grade; when 0-plus Sc-plus-30 is adopted, the ornamental grade is grade III; when Sc =0, the ornamental rating is IV.
In one possible design, acquiring a meteorological grid containing geographic altitude data of a target area, and obtaining a total aerial cloud amount forecast value of all grid points of the target area according to the current meteorological grid, wherein the total aerial cloud amount forecast value comprises the following steps:
establishing a meteorological grid of a target area, and acquiring cloud data of multiple vertical layers of the target area;
acquiring geographic altitude data of a target area, and acquiring a geographic grid of the target area according to the geographic altitude data of the target area;
inserting the geographic height data in the geographic grid of the target area into the meteorological grid of the target area to obtain the meteorological grid of the target area containing the geographic height data;
comparing the potential height value corresponding to each meteorological layer in the vertical direction on the grid point with the geographical height value corresponding to the grid point for any grid point in the meteorological grids to obtain all meteorological layers above the geographical height value corresponding to the grid point, then calculating the maximum value of the cloud cover in all meteorological layers above the geographical height corresponding to the grid point, and taking the maximum value of the cloud cover as the high-altitude total cloud cover forecast value of the grid point; and repeating the steps until the forecast values of the total high-altitude cloud cover of all grid points in the target area are obtained.
In one possible design, establishing a meteorological grid of a target area, and acquiring cloud cover data of vertical multiple layers of the target area comprises:
acquiring a relative humidity value and a potential height value of a plurality of layers vertical to a target area;
establishing a meteorological grid of a target area;
and obtaining the cloud amount data of the vertical layers on the meteorological grid of the target area according to the relative humidity value and the potential height value of the vertical layers of the target area.
In one possible design, the cloud cover data of the vertical multilayer on the meteorological grid of the target area is obtained through a Slingo cloud cover calculation formula, wherein the Slingo cloud cover calculation formula is as follows:
wherein N is the cloud number, H k Relative humidity of the kth meteorological layer, H ck Is the relative humidity threshold of the kth meteorological layer.
In one possible design, when the geographic height data in the geographic grid of the target area is inserted into the meteorological grid of the target area, a bilinear interpolation method is adopted for implementation;
inserting the geographic height data in the geographic grid of the target area into the meteorological grid of the target area by adopting a bilinear interpolation method to obtain the meteorological grid of the target area containing the geographic height data, wherein the method comprises the following steps:
acquiring geographic height values respectively corresponding to four geographic grid points around a specified meteorological grid point in a meteorological grid of a target area;
and obtaining the geographic height values of the designated meteorological grid points according to the geographic height values respectively corresponding to the four geographic grid points around the designated meteorological grid points, and so on to obtain the geographic height values of all the meteorological grid points in the meteorological grid of the target area and obtain the meteorological grid of the target area containing the geographic height data.
In one possible design, the coordinates of a designated meteorological grid point are set to (x, y), and the coordinates of four geographical grid points around the designated meteorological grid point are set to (x, y), respectively 1 ,y 1 )、(x 1 ,y 2 )、(x 2 ,y 1 )、(x 2 ,y 2 ) Designating four geographical grid points around the meteorological grid point as f (x) corresponding to the geographical height values 1 ,y 1 )、f(x 1 ,y 2 )、f(x 2 ,y 1 )、f(x 2 ,y 2 ) (ii) a Obtaining the geographic height values of the designated meteorological grid points according to the geographic height values respectively corresponding to the four geographic grid points around the designated meteorological grid points, wherein the method comprises the following steps:
according to geographical grid points (x) 1 ,y 1 ) Corresponding geographic height value f (x) 1 ,y 1 ) And geographical grid points (x) 2 ,y 1 ) Corresponding geographical height value f (x) 2 ,y 1 ) Obtaining meteorological grid points (x, y) 1 ) Of (d) is determined by the geographic height value f (x, y) 1 ) (ii) a Wherein the meteorological grid points (x, y) 1 ) Is determined by the geographic height value f (x, y) 1 ) Comprises the following steps:
according to a geographical grid point (x) 1 ,y 2 ) Corresponding geographic height value f (x) 1 ,y 2 ) And geographical grid points (x) 2 ,y 2 ) Corresponding geographical height value f (x) 2 ,y 2 ) Obtaining the meteorological grid points (x, y) 2 ) Is determined by the geographic height value f (x, y) 2 ) (ii) a Wherein the meteorological grid points (x, y) 2 ) Is determined by the geographic height value f (x, y) 2 ) Comprises the following steps:
from meteorological grid points (x, y) 1 ) Is determined by the geographic height value f (x, y) 1 ) And meteorological grid points (x, y) 2 ) Is determined by the geographic height value f (x, y) 2 ) Obtaining a geographic height value f (x, y) of a designated meteorological grid point (x, y); wherein the geographic height value f (x, y) of the designated meteorological grid point (x, y) point is:
drawings
FIG. 1 is a flow chart of a starry sky landscape forecasting method according to the present invention;
FIG. 2 is a schematic diagram of a designated meteorological grid point and four geographical grid points thereabout in accordance with the present invention;
FIG. 3 is a schematic illustration of clouds in different altitudes of the present invention;
fig. 4 is a block diagram of an electronic device according to the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific embodiments.
It should be understood that in some embodiments, the functions/acts may occur out of the order in which the figures occur. For example, two figures shown in succession may in fact be executed substantially concurrently or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
Example 1:
the first aspect of the present embodiment provides a starry sky landscape forecasting method, which may be, but not limited to, executed by a Computer device or a virtual machine with certain computing resources, for example, executed by a Personal Computer (PC, which refers to a multipurpose Computer with a size, price and performance suitable for Personal use, a desktop Computer, a notebook Computer, a mini-notebook Computer, a tablet Computer, a super notebook, and the like, belonging to a Personal Computer), a smart phone, a Personal digital assistant (PAD), a wearable device, and the like, or executed by a virtual machine Hypervisor, so as to obtain a level of viewing the starry sky landscape at night that day.
As shown in fig. 1, a starry sky landscape forecasting method may include, but is not limited to, the following steps:
s1, acquiring a meteorological grid containing geographical height data in a target area, and acquiring a total high-altitude cloud amount forecast value of all grid points in the target area according to the current meteorological grid;
in step S1, acquiring a weather grid including geographic altitude data in a target area, and obtaining a forecast value of total amount of high altitude clouds of all grid points in the target area according to a current weather grid, including:
s101, establishing a meteorological grid of a target area, and acquiring cloud cover data of multiple vertical layers of the target area;
in step S101, a meteorological grid of a target area is established, and cloud data of multiple vertical layers of the target area is obtained, including:
A1. acquiring relative humidity values and potential height values of multiple layers of a target area; it should be noted that the data is obtained through a multi-time forecasting mode, which is the prior art and is not described herein again;
A2. establishing a meteorological grid of a target area;
A3. and obtaining the cloud amount data of the vertical layers on the meteorological grid of the target area according to the relative humidity value and the potential height value of the vertical layers of the target area.
In step SA3, the cloud cover data of the vertical multiple layers on the meteorological grid of the target area is obtained by a Slingo cloud cover calculation formula, which is:
wherein N is cloud number, H k Is the relative humidity of the kth meteorological layer, H ck Is the relative humidity threshold of the kth meteorological layer.
It should be noted that, in the current meteorological numerical model, the atmosphere can be divided into a plurality of meteorological layers in the vertical direction from the ground to the top of the atmospheric layer, in this embodiment, the atmosphere is divided into 51 meteorological layers in different heights to perform analog calculation at different heights, specifically, the kth meteorological layer refers to the kth meteorological layer in the vertical direction from bottom to top, and if the kth meteorological layer belongs to the upper layer, H is the distance between the kth meteorological layer and the atmospheric layer ck Is 0.8, the cloud layer of the kth meteorological layer is high cloud at this moment; if the kth meteorological layer belongs to the middle layer, H ck 0.65, the cloud layer of the kth meteorological layer is a middle cloud; if the kth meteorological layer belongs to the lower layer, H ck Is 0.8, the cloud layer of the kth meteorological layer is low cloud at this time.
It should be further noted that, in this embodiment, the upper, middle and lower layers of the vertical multi-layered weather layer are defined by 500hPa and 700hPa (hPa is the unit of pressure, the pressure increases closer to the ground, in a mid-latitude area, about 1000hPa near the ground, the top of the atmospheric layer is about 0hPa, the pressure at a potential height of 5500 m is about 500hPa, and the pressure at a potential height of 3000 m is about 700 hPa). Specifically, in this embodiment, a weather layer having an atmospheric pressure of 700hPa or more is set as a lower layer, a weather layer having an atmospheric pressure of 500hPa or less is set as an upper layer, and a weather layer having an atmospheric pressure of 700 to 500hPa is set as an intermediate layer. For example, the meteorological layer with the air pressure of 850hPa is larger than 700hPa, so the meteorological layer with the air pressure of 850hPa belongs to the lower layer and corresponds to H ck Is 0.8; if the relative humidity of the meteorological layer is 0.8, the cloud number N =0 of the meteorological layer; if the relative humidity of the meteorological layer is 0.9, the cloud amount N = [ (0.9-0.8)/(1-0.8) ] corresponding to the meteorological layer] 2 =0.25, i.e. cloud number of the meteorological layer is 025; if the relative humidity of the meteorological layer is 0.7, which is lower than the critical value, the cloud amount of the meteorological layer is 0.
S102, acquiring geographic height data of a target area, and acquiring a geographic grid of the target area according to the geographic height data of the target area;
s103, inserting the geographic height data in the geographic grid of the target area into the meteorological grid of the target area to obtain the meteorological grid of the target area containing the geographic height data;
in step S103, when the geographic altitude data in the geographic grid of the target area is inserted into the meteorological grid of the target area, the bilinear interpolation method is used to implement the geographic altitude data;
inserting the geographic height data in the geographic grid of the target area into the meteorological grid of the target area by adopting a bilinear interpolation method to obtain the meteorological grid of the target area containing the geographic height data, wherein the method comprises the following steps:
B1. as shown in FIG. 2, four geographic grid points (x, y) surrounding a designated meteorological grid point (x, y) in a meteorological grid for a target area are acquired 1 ,y 1 )、(x 1 ,y 2 )、(x 2 ,y 1 )、(x 2 ,y 2 ) Respectively corresponding geographic height values f (x) 1 ,y 1 )、f(x 1 ,y 2 )、f(x 2 ,y 1 )、f(x 2 ,y 2 )；
B2. And obtaining the geographic height values of the designated meteorological grid points according to the geographic height values corresponding to the four geographic grid points around the designated meteorological grid points, and so on to obtain the geographic height values of all the meteorological grid points in the meteorological grid of the target area, so that the geographic height data in the geographic grid of the target area is inserted into the meteorological grid of the target area, and the meteorological grid of the target area containing the geographic height data is obtained.
In step B2, obtaining the geographic height values of the designated meteorological grid points according to the geographic height values corresponding to the four geographic grid points around the designated meteorological grid point, including:
B101. according to geographical grid points (x) 1 ,y 1 ) Corresponding geographic height valuef(x 1 ,y 1 ) And geographical grid points (x) 2 ,y 1 ) Corresponding geographic height value f (x) 2 ,y 1 ) Obtaining meteorological grid points (x, y) 1 ) Is determined by the geographic height value f (x, y) 1 ) (ii) a Wherein the meteorological grid points (x, y) 1 ) Is determined by the geographic height value f (x, y) 1 ) Comprises the following steps:
B102. according to a geographical grid point (x) 1 ,y 2 ) Corresponding geographical height value f (x) 1 ,y 2 ) And geographical grid points (x) 2 ,y 2 ) Corresponding geographical height value f (x) 2 ,y 2 ) Obtaining the meteorological grid points (x, y) 2 ) Is determined by the geographic height value f (x, y) 2 ) (ii) a Wherein the meteorological grid points (x, y) 2 ) Is determined by the geographic height value f (x, y) 2 ) Comprises the following steps:
B103. from meteorological grid points (x, y) 1 ) Of (d) is determined by the geographic height value f (x, y) 1 ) And meteorological grid points (x, y) 2 ) Of (d) is determined by the geographic height value f (x, y) 2 ) Obtaining a geographic height value f (x, y) of a designated meteorological grid point (x, y); wherein the geographic height value f (x, y) of the designated meteorological grid point (x, y) point is:
and (4) repeatedly executing the steps B101-B103 until the geographic height values of all the weather grid points in the weather grid of the target area are obtained, and further obtaining the weather grid of the target area containing the geographic height data.
S104, comparing the potential height value corresponding to each meteorological layer in the vertical direction on any grid point in the meteorological grids with the geographical height value corresponding to the grid point to obtain all meteorological layers above the geographical height value corresponding to the grid point, wherein if the potential height value of any meteorological layer is greater than the geographical height value corresponding to the grid point, the meteorological layer is judged to be above the geographical height corresponding to the grid point; then calculating the maximum value of the cloud cover in all meteorological layers above the geographical height corresponding to the grid point, and taking the maximum value of the cloud cover as the forecast value of the total high-altitude cloud cover of the grid point; and repeating the steps until the forecast values of the total high-altitude cloud cover of all grid points in the target area are obtained.
Specifically, as shown in fig. 3, for point a, the k2 meteorological layer is the meteorological layer closest to the geographical height of point a, the maximum value of the cloud amount in the k2 meteorological layer and all meteorological layers above the k2 meteorological layer is calculated, and if the maximum value of the cloud amount is Cmax, cmax is the predicted value of the total high-altitude cloud amount of point a; similarly, for point B, the maximum value of cloud cover in k1 layer and all meteorological layers above is the predicted value of total high-altitude cloud cover of point B; for point C, the maximum value of cloud cover in k layer and all the meteorological layers above is the predicted value of total cloud cover at high altitude of point C.
S2, acquiring ground visibility forecast value and PM of target area 10 Forecast values and PM 2.5 Forecasting a value;
it should be noted that the forecast value, visibility and PM of the total cloud cover in the high altitude 10 Value sum PM 2.5 The values are four meteorological elements that mainly affect the visibility of the starry sky.
In step S2, obtaining a ground visibility forecast value of the target area includes:
s201, obtaining a visibility historical observation value and a ground meteorological element historical observation value of the ground of a target area, wherein the ground meteorological element historical observation value comprises air temperature, air pressure, relative humidity, air quantity and/or precipitation;
s202, establishing a visibility and meteorological element forecasting relation model based on a Back Propagation (BP) neural network according to the visibility historical observation value and the ground meteorological element historical observation value;
s203, inputting the real-time meteorological element observed value into the visibility and meteorological element forecasting relation model to obtain a ground visibility forecast value of the target area.
In this embodiment, the predicted ground visibility value and PM in the target area 10 Forecast values and PM 2.5 The forecast value can also be obtained through a weather forecast system, wherein the weather forecast system can be but is not limited to adopt the existing "day information intelligent forecast system" (abbreviated as "day information" system), is a component of a Chongqing intelligent weather "four-day" system developed by Chongqing city weather bureau and has the function of high-resolution weather numerical forecast.
S3, forecasting values of total aerial clouds based on any grid point in meteorological grids, and forecasting values of ground visibility and PM of target area 10 Forecast values and PM 2.5 Forecasting values to obtain the values of the corresponding meteorological elements;
s4, obtaining a starry sky appreciation total value according to the values of all meteorological elements;
in the step S4, the total value of the starry sky ornamental value is as follows:
wherein Sc is the total value of the star sky appreciation, and Sc p Is a score, alpha, of each meteorological element p Are weight coefficients. It should be noted that the expression of the total star sky appreciation value indicates that Sc is 0 when the value of any meteorological element is 0, and Sc is the sum of the values of the four meteorological elements when the values of all meteorological elements are not 0.
Specifically, in the present embodiment, the weighting factor of each meteorological element is shown in table 1:
TABLE 1 Meteorological element weight coefficient Table
In this embodiment, the score and the determination condition of each meteorological element are shown in table 2:
TABLE 2 Meteorological element score and judgment condition Table
Here, the forecast value of total cloud cover in high altitude C is taken as an example, such as C>30%, then the score Sc of the meteorological element is predicted as the total cloud amount at high altitude p Is 0, as much as 20%<C is less than or equal to 30 percent, the score Sc of the meteorological element is predicted as the total cloud cover in high altitude p At 30, the score Sc of the corresponding meteorological element can be obtained from the judgment condition of each meteorological element in Table 2 p 。
And S5, obtaining the viewing grade of the visibility degree of the sky landscape at night according to the total value of the sky viewing degree.
In this embodiment, in step S5, the viewing level of the sky landscape at night on the day is divided into four levels; wherein when Sc is more than or equal to 60, the ornamental grade is I grade; when Sc is more than or equal to 30 and less than 60, the ornamental grade is II grade; when 0 & lt Sc & lt 30 & gt is provided, the ornamental grade is grade III; when Sc =0, the appreciation grade is IV grade, which is represented graphically as follows:
TABLE 3 starry sky view grade table
| Starry sky view grade | I stage | Stage II | Grade III | Grade IV |
| Total fraction Sc | Sc≥60 | 30≤Sc<60 | 0<Sc<30 | Sc＝0 |
In this embodiment, the total high-altitude cloud amount forecast values of all grid points in the target area, the ground visibility forecast value of the target area, and the PM are obtained 10 Forecast values and PM 2.5 And forecasting values to obtain the viewing grade of the visibility degree of the sky landscape at night, so that the occurrence grade forecast of the sky landscape is realized, the star landscape is forecasted through a meteorological grid, the star landscape forecasting result has the characteristics of meshing and refining, and meanwhile, the forecasting accuracy is improved.
Example 2:
the embodiment provides a starry sky landscape forecasting system, which is used for realizing the starry sky landscape forecasting method in the embodiment 1; the starry sky landscape forecasting system comprises:
the meteorological element acquisition module is used for acquiring meteorological grids containing geographic height data in a target area and obtaining a total aerial cloud amount forecast value of all grid points in the target area according to the current meteorological grids; and is also used for acquiring ground visibility forecast value and PM of a target area 10 Forecast values and PM 2.5 A forecast value;
a meteorological element value calculation module which is in communication connection with the meteorological element acquisition module and is used for predicting the total amount of high-altitude clouds based on any grid point in the meteorological grid and the ground visibility prediction value and PM of the target area 10 Forecast values and PM 2.5 Forecasting values to obtain the values of the corresponding meteorological elements;
the starry sky ornamental degree total score calculating module is in communication connection with the meteorological element score calculating module and is used for obtaining the starry sky ornamental degree total score according to the scores of all meteorological elements;
and the starry sky landscape ornamental level calculating module is in communication connection with the starry sky ornamental degree total score calculating module and is used for obtaining the ornamental level of the starry sky landscape visibility degree at night.
Example 3:
on the basis of embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a notebook computer, or a desktop computer, etc. The electronic device may be referred to as a terminal, a portable terminal, a desktop terminal, etc., and as shown in fig. 4, the electronic device includes:
a memory for storing computer program instructions; and the number of the first and second groups,
a processor for executing the computer program instructions to perform the operations of the starry sky landscape forecasting method of any of embodiment 1.
In particular, the processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen.
In some embodiments, the terminal may further optionally include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected by a bus or signal lines. Various peripheral devices may be connected to communication interface 303 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 304, a display screen 305, and a power source 306.
The communication interface 303 may be used to connect at least one peripheral device related to I/O (Input/Output) to the processor 301 and the memory 302. In some embodiments, processor 301, memory 302, and communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302 and the communication interface 303 may be implemented on a single chip or circuit board, which is not limited by the embodiment.
The Radio Frequency circuit 304 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 304 communicates with communication networks and other communication devices via electromagnetic signals.
The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof.
The power supply 306 is used to power various components in the electronic device.
Example 4:
on the basis of any embodiment of embodiments 1 to 3, the present embodiment discloses a computer-readable storage medium for storing computer-readable computer program instructions configured to, when executed, perform the operations of the starry sky landscape forecasting method according to embodiment 1.
It should be noted that the functions described herein, if implemented in software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above can be implemented by a general purpose computing device, they can be centralized in a single computing device or distributed over a network of multiple computing devices, and they can alternatively be implemented by program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: modifications of the technical solutions described in the embodiments or equivalent replacements of some technical features may still be made. And such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Finally, it should be noted that the present invention is not limited to the above alternative embodiments, and that various other forms of products can be obtained by anyone in light of the present invention. The above detailed description should not be taken as limiting the scope of the invention, which is defined in the claims, and which the description is intended to be interpreted accordingly.
Claims (10)
1. A starry sky landscape forecasting method is characterized by comprising the following steps: the method comprises the following steps:
acquiring a meteorological grid containing geographical height data in a target area, and acquiring a total aerial cloud amount forecast value of all grid points in the target area according to the current meteorological grid;
obtaining the ground visibility forecast value and PM of the target area 10 Forecast values and PM 2.5 A forecast value;
high-altitude total cloud amount forecast value based on any grid point in meteorological grid, ground visibility forecast value and PM of target area 10 Forecast values and PM 2.5 Forecasting values to obtain the values of the corresponding meteorological elements;
obtaining a starry sky appreciation total value according to all meteorological element values;
and obtaining the viewing grade of the visibility degree of the sky landscape at night according to the total score of the sky viewing degree.
2. The starry sky landscape forecasting method according to claim 1, characterized in that: the method for acquiring the ground visibility forecast value of the target area comprises the following steps:
obtaining a visibility historical observation value of the ground of a target area and a ground meteorological element historical observation value, wherein the ground meteorological element historical observation value comprises air temperature, air pressure, relative humidity, air quantity and/or precipitation;
establishing a visibility and meteorological element forecasting relation model based on a BP neural network according to the visibility historical observation value and the ground meteorological element historical observation value;
and inputting the real-time meteorological element observed value into the visibility and meteorological element forecasting relation model to obtain a ground visibility forecast value of the target area.
3. The starry sky landscape forecasting method according to claim 1, characterized in that: the starry sky ornamental value is as follows:
wherein Sc is the total value of the star sky appreciation, and Sc p Is a score, alpha, of each meteorological element p Are the weight coefficients.
4. The starry sky landscape forecasting method according to claim 3, characterized in that: the weighting factor of each meteorological element is shown in table 1:
TABLE 1 Meteorological element weight coefficient Table
The score and judgment conditions of each meteorological element are shown in table 2:
TABLE 2 Meteorological element score and judgment condition Table
5. The starry sky landscape forecasting method according to claim 4, characterized in that: dividing the viewing grade of the visibility degree of the sky landscape at night on the day into four grades; wherein when the Sc is more than or equal to 60, the ornamental grade is I grade; when Sc is more than or equal to 30 and less than 60, the ornamental grade is II grade; when 0-plus Sc-plus-30 is adopted, the ornamental grade is grade III; when Sc =0, the ornamental rating is IV.
6. The starry sky landscape forecasting method according to claim 1, characterized in that: acquiring a meteorological grid containing geographical altitude data in a target area, and acquiring a total aerial cloud amount forecast value of all grid points in the target area according to the current meteorological grid, wherein the total aerial cloud amount forecast value comprises the following steps:
establishing a meteorological grid of a target area, and acquiring cloud data of multiple vertical layers of the target area;
acquiring geographic altitude data of a target area, and acquiring a geographic grid of the target area according to the geographic altitude data of the target area;
inserting the geographic height data in the geographic grid of the target area into the meteorological grid of the target area to obtain the meteorological grid of the target area containing the geographic height data;
comparing the potential height value corresponding to each meteorological layer in the vertical direction on the grid point with the geographical height value corresponding to the grid point for any grid point in the meteorological grids to obtain all meteorological layers above the geographical height value corresponding to the grid point, then calculating the maximum value of the cloud cover in all meteorological layers above the geographical height corresponding to the grid point, and taking the maximum value of the cloud cover as the high-altitude total cloud cover forecast value of the grid point; and repeating the steps until the forecast values of the total high-altitude cloud cover of all grid points in the target area are obtained.
7. The starry sky landscape forecasting method according to claim 6, characterized in that: establishing a meteorological grid of a target area, and acquiring cloud data of multiple layers of the target area, wherein the method comprises the following steps:
acquiring relative humidity values and potential height values of multiple layers of a target area;
establishing a meteorological grid of a target area;
and obtaining cloud cover data of the vertical layers on the meteorological grid of the target area according to the relative humidity value and the potential height value of the vertical layers of the target area.
8. The starry sky landscape forecasting method according to claim 7, wherein: the cloud amount data of the vertical multiple layers on the meteorological grid of the target area is obtained through a Slingo cloud amount calculation formula, wherein the Slingo cloud amount calculation formula is as follows:
wherein N is the cloud number, H k Is the relative humidity of the kth meteorological layer, H ck Is the relative humidity threshold of the kth meteorological layer.
9. The starry sky landscape forecasting method according to claim 6, characterized in that: when the geographic height data in the geographic grid of the target area is inserted into the meteorological grid of the target area, a bilinear interpolation method is adopted for implementation;
inserting the geographic height data in the geographic grid of the target area into the meteorological grid of the target area by adopting a bilinear interpolation method to obtain the meteorological grid containing the geographic height data in the target area, wherein the method comprises the following steps:
acquiring geographic height values respectively corresponding to four geographic grid points around a designated meteorological grid point in a meteorological grid of a target area;
and obtaining the geographic height values of the designated meteorological grid points according to the geographic height values corresponding to the four geographic grid points around the designated meteorological grid points, and so on, obtaining the geographic height values of all the meteorological grid points in the meteorological grid of the target area, and obtaining the meteorological grid of the target area containing the geographic height data.
10. The starry sky landscape forecasting method according to claim 9, characterized in that: the coordinates of a designated meteorological grid point are set to be (x, y), and the coordinates of four geographical grid points around the designated meteorological grid point are respectively set to be (x) 1 ,y 1 )、(x 1 ,y 2 )、(x 2 ,y 1 )、(x 2 ,y 2 ) Designating four geographical grid points around the meteorological grid point as f (x) corresponding to the geographical height values 1 ,y 1 )、f(x 1 ,y 2 )、f(x 2 ,y 1 )、f(x 2 ,y 2 ) (ii) a Obtaining the geographic height values of the designated meteorological grid points according to the geographic height values respectively corresponding to the four geographic grid points around the designated meteorological grid points, wherein the geographic height values comprise:
according to a geographical grid point (x) 1 ,y 1 ) Corresponding geographical height value f (x) 1 ,y 1 ) And a geographical grid point (x) 2 ,y 1 ) Corresponding geographical height value f (x) 2 ,y 1 ) Obtaining the meteorological grid points (x, y) 1 ) Ground ofPhysical height value f (x, y) 1 ) (ii) a Wherein the meteorological grid points (x, y) 1 ) Of (d) is determined by the geographic height value f (x, y) 1 ) Comprises the following steps:
according to a geographical grid point (x) 1 ,y 2 ) Corresponding geographic height value f (x) 1 ,y 2 ) And geographical grid points (x) 2 ,y 2 ) Corresponding geographical height value f (x) 2 ,y 2 ) Obtaining meteorological grid points (x, y) 2 ) Is determined by the geographic height value f (x, y) 2 ) (ii) a Wherein the meteorological grid points (x, y) 2 ) Is determined by the geographic height value f (x, y) 2 ) Comprises the following steps:
according to meteorological grid points (x, y) 1 ) Is determined by the geographic height value f (x, y) 1 ) And meteorological grid points (x, y) 2 ) Of (d) is determined by the geographic height value f (x, y) 2 ) Obtaining a geographic height value f (x, y) of a designated meteorological grid point (x, y); wherein the geographic height value f (x, y) of the designated meteorological grid point (x, y) point is:
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| CN106919645A (en) * | 2017-01-17 | 2017-07-04 | 广西师范学院 | The sight spot meteorological element Intelligent fine Forecasting Methodology at the big scenic spot of complex landform |
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