CN113553532B - Comprehensive display system based on meteorological data analysis and rendering - Google Patents
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Abstract
The invention provides a comprehensive display system based on meteorological data analysis and rendering, which belongs to the technical field of meteorological data and comprises a server end and a browser end, wherein the server end acquires latest wind field data regularly and converts the latest wind field data into png files to be provided for rendering display calling at the front end of the browser end, and aims to solve the following problems in the prior art: 1. the data can not be updated regularly, and the timed task is not set to execute the data updating operation; 2. the data file of the meteorological data for the transmission between the webs is json, and for the data, the json file is too large, so that the pressure of network transmission is increased; 3. meteorological data shows that most of wind field data is only rendered in a single color to identify the size, and professional people tend to look at the wind vector more.
Description
Technical Field
The invention belongs to the technical field of meteorological data, and particularly relates to a comprehensive display system based on meteorological data analysis and rendering.
Background
At present, a data file of meteorological data for transmission between webs is json, the json file is too large for the data, the pressure of network transmission is increased, most of the prior art is not provided with a timing task to acquire and process the meteorological data, and the meteorological data can be effectively ensured to be real-time and available after being updated for multiple times every day; meanwhile, most of the current meteorological data show that the size of the wind field data is only marked by single color rendering, and for professionals, the weather data are more prone to seeing the wind vector.
Disclosure of Invention
The invention aims to provide a comprehensive display system based on meteorological data analysis and rendering, and aims to solve the following problems in the prior art: 1. the data can not be updated regularly, and the timed task is not set to execute the data updating operation; 2. the data file of the meteorological data for the transmission between the webs is json, and for the data, the json file is too large, so that the pressure of network transmission is increased; 3. meteorological data shows that most of wind field data is only rendered in a single color to identify the size, and professional people tend to look at the wind vector more.
In order to achieve the purpose, the invention provides the following technical scheme:
a comprehensive display system based on meteorological data analysis and rendering comprises a server end and a browser end, wherein the server end acquires latest wind field data regularly and converts the latest wind field data into png files to be provided for a browser end front-end rendering display call, and the comprehensive display system specifically comprises the following steps:
step S1, data download: the server sets a timing task execution data downloading script, and downloads the required data regularly, wherein the data downloading script comprises the following steps:
step S11, time acquisition: acquiring the current time, and determining the resolution and the data range to organize the download link;
step S12, downloading grib data: the server side downloads the grib data in the u direction and the v direction respectively through the download link;
step S2, data processing: the server side processes the downloaded data, and the data processing comprises the following steps:
step S21, data simplification: calling the ecCodes to extract byte data of the simplified processing grib data file to obtain a scattered u and v single-quantity intermediate data file;
step S22, data merging: merging two grib data in the u direction and the v direction into a json file;
step S23, png: the method comprises the following steps that a script calls a js file to process json to generate png, the png file is named according to dates, and since data of seven days in the future are generated each time, the current newly generated png is used for replacing the previous predicted data to enable the data to always keep real-time performance, wherein the conversion into the png comprises the following steps:
acquiring data values in the directions of S231, u and v: acquiring the maximum value and the minimum value in data in u and v directions;
step S232, generating a png value: each pixel value in the png stores a corresponding numerical value, and each png pixel value is calculated by calculating the proportion of the maximum value and the minimum value in the data in the u and v directions to the data in the u and v directions in the current pixel, so that a png file is generated for a browser to call;
step S3, layer generation: the method comprises the following steps that a browser terminal renders wind field data based on a leaf frame to generate a leaf layer, and the layer generation comprises the following steps:
step S31, request data: the browser end acquires the generated png data;
step S32, data analysis: the browser end reversely analyzes the data according to the rule generated by the data;
step S33, rendering the layer: acquiring a current screen display range, selecting corresponding png pixel value data according to the display range, and further calculating wind speed and wind direction data; color rendering: the browser side invokes a bilinear interpolation method to interpolate data in the current screen range, each data renders a layer according to a color bar interval, different numerical values are expressed by different colors, and then a rendered layer is generated; and (3) numerical rendering: obtaining the zoom level and the display range of the current map, calculating the distance between each numerical value, and drawing a corresponding wind speed value on a canvas; and (3) vector rendering: obtaining the zoom level and the display range of the current map, calculating the distance between each wind vector, obtaining wind vector icons of different levels, rotating the wind vector icons according to the wind direction, and sequentially drawing wind vector information of different positions through canvas; particle rendering: creating particles randomly, obtaining the wind speed and the wind direction of each particle, changing the next moment, reducing the transparency of the particles at the previous moment, calculating the position of the particles at the current moment, drawing a line, shortening the life cycle of the particles, regenerating new particles when the life cycle of the particles is finished, repeating the steps continuously, and rendering a particle field;
step S4, query data: clicking a certain point on a map, calculating a specific pixel value on a png file corresponding to the point according to the longitude and latitude, calculating corresponding data values in u and v directions according to data of a pixel value R, G channel, and further calculating the wind speed of the point;
step S5, end of rendering: and after the data is compared with the rendering result, determining the rendering effect, and finishing the rendering effect display.
As a preferable aspect of the present invention, in step S12, u in the grib data represents a wind in a longitudinal direction, and v represents a wind in a latitudinal direction;
(a) when u is positive, it represents the western style;
(b) when v is positive, it indicates a south wind.
As a preferred embodiment of the present invention, the grid data calculates the wind direction according to the formula var dir =180+ math.atan2(u, v) × 180/math.pi, and the grid data calculates the wind speed according to the formula var speed = math.sqrt (u × u + v) ().
As a preferred solution of the present invention, the file obtained by downloading the grib data is in a grib2 format, the grib2 format is a special binary format code, and the ecCodes uses its command grib _ dump to convert the data into json.
As a preferable embodiment of the present invention, in step S232, the png value is generated by: the numerical value in the u direction corresponds to an R channel, the numerical value in the v direction corresponds to a G channel, parameters of each channel in RGB are in the range of 0 to 255, and the numerical value of the RG channel on the current numerical value is obtained by multiplying the ratio of the current value in each direction minus the minimum value and the maximum value minus the minimum value by 255 and taking the integral.
As a preferable aspect of the present invention, in step S33, after the browser end acquires the interpolation data, the browser end generates two layer display wind field information of a numerical value representing the wind speed and the wind vector according to the difference between the current map zoom level and the display range.
As a preferable aspect of the present invention, in step S33, after the browser side acquires the interpolation data, the browser side generates a particle layer indicating a flow direction of wind, and the length of the particle indicates a wind speed.
As a preferred scheme of the invention, the comprehensive display system based on meteorological data analysis and rendering is applied to the fields of oceans, urban emergency, agriculture, fishery, disaster prevention and reduction.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, a client renders wind field data based on a leaf frame, generates a leaf layer, acquires png data at a corresponding moment, analyzes the png data to obtain regular grid data, interpolates the obtained detailed data according to a bilinear interpolation method, sets a color band, renders the layer based on the data, selects different colors of the color band to represent according to the difference of wind speeds, and can set the transparency of the layer to be superposed with map data so as to more clearly display the wind speed condition.
2. In the invention, the downloaded file is in a grib2 format, the grib2 format is a special binary format code, the ecodes are installed and use the grib _ dump command to convert the data into json, the converted json file is large, and the converted json file is converted into png which can effectively compress the file size and transmit the data in a secret manner. U corresponds to R channel, V corresponds to G channel, and the equal proportion obtains the numerical value. And finally generating the png file for web calling. Wherein the json data is about 90K after being converted into png by about 9M, and the network transmission pressure is greatly reduced.
3. In the invention, if the json file is converted into the png file, the png file bearing the same data size is smaller, the transmission is facilitated, png can be generated according to a user-defined rule, the significance of the png bearing the data is encrypted, the data analyzed reversely according to the user-defined rule at the front end, meanwhile, professionals are more used to identify the data in a wind vector mode, non-professionals are likely to be more inclined to view values or render colors and the like more intuitively, the scheme of rendering different values according to color bands is provided in the prior art, and wind vectors, values and particles are added on the basis of the rendering, so that wind field data are displayed more intuitively in various forms, and the wind field data are convenient for all people to view and use.
4. According to the method, the meteorological data acquire the predicted data of seven days in the future once, the script for acquiring the predicted data is executed at regular time every day, all the time every day is named for png according to a fixed naming mode, the predicted data of the previous day is deleted, and the predicted data of the current day is stored.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a block diagram of a data download process of the present invention;
FIG. 3 is a block flow diagram of data processing in the present invention;
FIG. 4 is a block diagram of the process of converting to png in the present invention.
In the figure: s1, downloading data; s11, time acquisition; s12, downloading grib data; s2, processing data; s21, simplifying data; s22, merging data; s23, converting into png; s231, acquiring data values in u and v directions; s232, generating a png numerical value; s3, generating a layer; s31, requesting data; s32, analyzing data; s33, rendering the layer; s4, inquiring data; and S5, ending the rendering.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Referring to fig. 1-4, the present invention provides the following technical solutions:
a comprehensive display system based on meteorological data analysis and rendering comprises a server end and a browser end, wherein the server end acquires latest wind field data regularly and converts the latest wind field data into a png file to be provided for a browser end front-end rendering display call, and the comprehensive display system specifically comprises the following steps:
step S1, data download: the server sets a timing task execution data downloading script, and downloads the required data regularly, wherein the data downloading script comprises the following steps:
step S11, time acquisition: acquiring the current time, and determining the resolution and the data range to organize the download link;
step S12, downloading grib data: the server side downloads the grib data in the u direction and the v direction respectively through the download link;
step S2, data processing: the server side processes the downloaded data, and the data processing comprises the following steps:
step S21, data simplification: calling the ecCodes to extract byte data of the simplified processing grib data file to obtain a scattered u and v single-quantity intermediate data file;
step S22, data merging: merging two grib data in the u direction and the v direction into a json file;
step S23, png: the method comprises the following steps that a script calls a js file to process json to generate png, the png file is named according to dates, and since data of seven days in the future are generated each time, the current newly generated png is used for replacing the previous predicted data to enable the data to always keep real-time performance, wherein the png conversion step comprises the following steps:
acquiring data values in the directions of S231, u and v: acquiring the maximum value and the minimum value in data in u and v directions;
step S232, generating a png value: each pixel value in the png stores a corresponding numerical value, and each png pixel value is calculated by calculating the proportion of the maximum value and the minimum value in the data in the u and v directions to the data in the u and v directions in the current pixel, so that a png file is generated for a browser to call;
step S3, layer generation: the method comprises the following steps that a browser end renders wind field data based on a leaf frame to generate a leaf layer, and the layer generation comprises the following steps:
step S31, request data: the browser end acquires the generated png data;
step S32, data analysis: the browser end reversely analyzes the data according to the rule generated by the data;
step S33, rendering the layer: acquiring a current screen display range, selecting corresponding png pixel value data according to the display range, and further calculating wind speed and wind direction data; color rendering: the browser side invokes a bilinear interpolation method to interpolate data in the current screen range, each data renders a layer according to a color bar interval, different numerical values are expressed by different colors, and then a rendered layer is generated; and (3) numerical rendering: obtaining the zoom level and the display range of the current map, calculating the distance between each numerical value, and drawing a corresponding wind speed value on a canvas; and (3) vector rendering: obtaining the zoom level and the display range of the current map, calculating the distance between each wind vector, obtaining wind vector icons of different levels, rotating the wind vector icons according to the wind direction, and sequentially drawing wind vector information of different positions through canvas; particle rendering: creating particles randomly, obtaining the wind speed and the wind direction of each particle, changing the next moment, reducing the transparency of the particles at the previous moment, calculating the position of the particles at the current moment, drawing a line, shortening the life cycle of the particles, regenerating new particles when the life cycle of the particles is finished, repeating the steps continuously, and rendering a particle field;
step S4, query data: clicking a certain point on a map, calculating a specific pixel value on a png file corresponding to the point according to the longitude and latitude, calculating corresponding data values in u and v directions according to data of a pixel value R, G channel, and further calculating the wind speed of the point;
step S5, end of rendering: and after the data is compared with the rendering result, determining the rendering effect, and finishing the rendering effect display.
Specifically, in step S12, u in the grib data represents a wind in the longitudinal direction, and v represents a wind in the latitudinal direction;
(a) when u is positive, it represents the western style;
(b) when v is positive, it indicates a south wind.
Specifically, the grib data calculates the wind direction according to the formula var dir =180+ math.atan2(u, v) × 180/math.pi, and the grib data calculates the wind speed according to the formula var speed = math.sqrt (u × u + v) ().
Specifically, the file obtained by downloading the grib data is in a grib2 format, the grib2 format is a special binary format code, and the installation ecodes uses the grib _ dump command to convert the data into json.
Specifically, in step S232, the png value is generated in the following manner: the numerical value in the u direction corresponds to an R channel, the numerical value in the v direction corresponds to a G channel, parameters of each channel in RGB are in the range of 0 to 255, and the numerical value of the RG channel on the current numerical value is obtained by multiplying the ratio of the current value in each direction minus the minimum value and the maximum value minus the minimum value by 255 and taking the integral.
Specifically, in step S33, after the browser end acquires the interpolation data, the browser end generates two layers of presentation wind field information, which are a wind vector and a numerical value indicating the magnitude of the wind speed, according to the difference between the current map zoom level and the display range.
Specifically, in step S33, after the browser side acquires the interpolation data, the browser side generates a particle layer indicating the flow direction of the wind, and the length of the particle indicates the wind speed.
Specifically, the comprehensive display system based on meteorological data analysis and rendering is applied to the fields of oceans, urban emergency, agriculture, fishery, disaster prevention and reduction.
The working principle and the using process of the invention are as follows: the method comprises the steps that a server side acquires latest wind field data at regular time and converts the latest wind field data into png files to be provided for a browser side to render, display and call, the server side acquires current time, determines resolution and a data range to organize a download link, downloads the grib data in the u direction and the v direction respectively through the download link, extracts byte data of a simplified processing grib data file through the download link, obtains a scattered u-v single-quantity intermediate data file, combines the two grib data in the u direction and the v direction into a json file, enables a script to call the js file to process the json to generate png, the png file is named according to dates, and since data of seven days in the future can be generated each time, the data always keeps real-time by replacing the predicted data with the png which is newly generated at present, wherein when the png is generated, the maximum value of the data in the u direction and the v direction is acquired at first, each pixel value in png stores a corresponding numerical value, the numerical value in u direction corresponds to R channel, the numerical value in v direction corresponds to G channel, because the parameter of each channel in RGB is in the range of 0-255, the numerical value of RG channel in current numerical value is obtained by using the ratio of current value minus minimum value and maximum value minus minimum value in each direction to multiply 255 for integer equal proportion, png file is finally generated for web calling, then wind field data is rendered based on a LEAFLET frame through a browser end to generate a LEAFLET layer, the generated png data is obtained, data is reversely analyzed according to the rule generated by the data, the data in current screen range is interpolated by calling a bilinear interpolation method, each data renders the layer according to a color band interval, different numerical values are expressed by different colors, further a rendered layer is generated, the browser end obtains the interpolated data and then has different zoom levels and display ranges according to the current map, generating two layers of a wind vector and a numerical value representing the wind speed to display wind field information, and simultaneously generating a particle layer to represent the flowing direction of wind, wherein the length of the particle represents the wind speed.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The utility model provides a synthesize display system based on meteorological data is analyzed and is rendered, includes server end and browser end, its characterized in that: the server side acquires the latest wind field data regularly and converts the latest wind field data into a png file to be provided for rendering, displaying and calling at the front end of the browser side, and the method specifically comprises the following steps:
step S1, data download: the server sets a timing task execution data downloading script, and downloads the required data regularly, wherein the data downloading script comprises the following steps:
step S11, time acquisition: acquiring the current time, and determining the resolution and the data range to organize the download link;
step S12, downloading grib data: the server side downloads the grib data in the u direction and the v direction respectively through the download link;
step S2, data processing: the server side processes the downloaded data, and the data processing comprises the following steps:
step S21, data simplification: calling the ecCodes to extract byte data of the simplified processing grib data file to obtain a scattered u and v single-quantity intermediate data file;
step S22, data merging: merging two grib data in the u direction and the v direction into a json file;
step S23, png: the method comprises the following steps that a script calls a js file to process json to generate png, the png file is named according to dates, and since data of seven days in the future are generated each time, the current newly generated png is used for replacing the previous predicted data to enable the data to always keep real-time performance, wherein the conversion into the png comprises the following steps:
acquiring data values in the directions of S231, u and v: acquiring the maximum value and the minimum value in data in u and v directions;
step S232, generating a png value: each pixel value in the png stores a corresponding numerical value, and each png pixel value is calculated by calculating the proportion of the maximum value and the minimum value in the data in the u and v directions to the data in the u and v directions in the current pixel, so that a png file is generated for a browser to call;
step S3, layer generation: the method comprises the following steps that a browser terminal renders wind field data based on a leaf frame to generate a leaf layer, and the layer generation comprises the following steps:
step S31, request data: the browser end acquires the generated png data;
step S32, data analysis: the browser end reversely analyzes the data according to the rule generated by the data;
step S33, rendering the layer: acquiring a current screen display range, selecting corresponding png pixel value data according to the display range, and further calculating wind speed and wind direction data; color rendering: the browser side invokes a bilinear interpolation method to interpolate data in the current screen range, each data renders a layer according to a color bar interval, different numerical values are expressed by different colors, and then a rendered layer is generated; and (3) numerical rendering: obtaining the zoom level and the display range of the current map, calculating the distance between each numerical value, and drawing a corresponding wind speed value on a canvas; and (3) vector rendering: obtaining the zoom level and the display range of the current map, calculating the distance between each wind vector, obtaining wind vector icons of different levels, rotating the wind vector icons according to the wind direction, and sequentially drawing wind vector information of different positions through canvas; particle rendering: creating particles randomly, obtaining the wind speed and the wind direction of each particle, changing the next moment, reducing the transparency of the particles at the previous moment, calculating the position of the particles at the current moment, drawing a line, shortening the life cycle of the particles, regenerating new particles when the life cycle of the particles is finished, repeating the steps continuously, and rendering a particle field;
step S4, query data: clicking a certain point on a map, calculating a specific pixel value on a png file corresponding to the point according to the longitude and latitude, calculating corresponding data values in u and v directions according to data of a pixel value R, G channel, and further calculating the wind speed of the point;
step S5, end of rendering: after the data is compared with the rendering result, determining a rendering effect, and finishing rendering effect display;
in step S12, u in the grib data represents a wind in the longitudinal direction, and v represents a wind in the latitudinal direction;
(a) when u is positive, it represents the western style;
(b) when v is positive, it indicates a south wind.
2. The integrated presentation system based on meteorological data analysis and rendering of claim 1, wherein: the grib data calculates wind direction according to the formula var dir =180+ Math.atan2(u, v) × 180/Math.PI, and calculates wind speed according to the formula var speed = Math.sqrt (u × u + v × v).
3. The integrated presentation system based on meteorological data analysis and rendering of claim 2, wherein: the file obtained by downloading the grib data is in a grib2 format, the grib2 format is a special binary format code, and the installation ecCodes uses the command grib _ dump to convert the data into json.
4. The integrated presentation system based on meteorological data analysis and rendering of claim 1, wherein: in step S232, the png value is generated by: the numerical value in the u direction corresponds to an R channel, the numerical value in the v direction corresponds to a G channel, parameters of each channel in RGB are in the range of 0 to 255, and the numerical value of the RG channel on the current numerical value is obtained by multiplying the ratio of the current value in each direction minus the minimum value and the maximum value minus the minimum value by 255 and taking the integral.
5. The integrated presentation system based on meteorological data analysis and rendering of claim 1, wherein: in step S33, after the browser end obtains the interpolation data, the browser end generates two layers of presentation wind field information, i.e., a wind vector and a numerical value representing the wind speed, according to the difference between the current map zoom level and the display range.
6. The integrated presentation system based on meteorological data analysis and rendering of claim 5, wherein: in step S33, after the browser side acquires the interpolation data, the browser side generates a particle layer indicating the flow direction of the wind, and the length of the particle indicates the wind speed.
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