CN104408094A - Method for rapidly generating real-time environment monitoring special topic picture based on user location - Google Patents

Abstract

The invention discloses a method for quickly generating special pictures for real-time environment monitoring based on user positions. This method is as follows: 1) The server performs spatial interpolation on the environmental monitoring data to obtain a grid layer, and then generates a PNG grid image according to the grid layer; 2) The client sends an environment query request, and the server generates a rectangle of the requested area according to the request Coordinate range; 3) The server queries the grid image database to obtain all PNG grid images in the requested area according to the time information, thematic map type, and rectangular coordinate range in the request; then splicing and clipping with the rectangular range to obtain an environmental monitoring Thematic pictures are returned to the client for display. The invention enables the user to quickly and conveniently understand the surrounding environment from the space-time position.

Description

A rapid generation method of real-time environmental monitoring thematic pictures based on user location

technical field

The present invention provides real-time environmental monitoring data thematic picture rapid generation technology based on user location information, and specifically relates to a method of providing users with environmental quality monitoring within a certain area around their current location in the form of visualized pictures based on the location information of the user's mobile terminal Method for thematic map information.

Background technique

Environmental problems are becoming more and more serious, especially air pollution and noise pollution in cities, which have become one of the most serious environmental problems and are directly related to people's health. Making the public obtain environment-related information will not only help the public understand the environmental conditions they live in, but also help improve their environmental awareness. And because people have innate spatial vision capabilities, maps have become the main form of public scientific data visualization. On the basis of the basic base map, adding thematic data, the map that intuitively shows the spatial distribution of a certain attribute of the theme is the thematic map. The environmental thematic map can display the real-time geographic visualization results of environmental monitoring data. It is not only easy for the public to understand, but also has visual appeal. It can also effectively help people deal with information overload and obtain the pollution status of the living environment intuitively and quickly, which has great application value.

Location based service (LBS for short) is to first determine the geographical location of the mobile terminal held by the user through positioning technology, and then use wireless communication technology and GIS technology to provide information services related to the location to the mobile terminal of the user. Location-based services can be divided into two categories: one type provides services based on user queries; the other type directly pushes services to users, such as marketing information. LBS includes positioning technology, mobile Internet technology and geographic information technology. There are many existing mobile positioning technologies, the most widely used are GPS positioning, network positioning and joint positioning technology. Mobile transmission technologies include GPRS, CDMA, WCDMA, TDS-CDMA, WIFI, WIMAX, satellite communication technology and so on. Geographic information technology is to analyze, process, make thematic maps and release spatial data. With the rapid development of Android and IOS mobile operating systems, LBS has become more and more widely used in various industries.

Under existing technical conditions, location-based real-time environmental quality monitoring services, such as air pollution indicators, temperature, precipitation, etc., only provide users with the actual or simulated values monitored at spatial coordinate points, or the overall average value of the area. It is not easy for the user to understand the real environmental quality around the current geographical location if it is presented to the user in the form of a numerical value.

Contents of the invention

In view of the problems existing in the prior art, the purpose of the present invention is to propose a method based on the time and location information of the user's mobile terminal, to realize the real-time environmental monitoring data thematic map within the range of any geographical location to quickly generate pictures and return them to the user, and to provide the user with time-space-based The real-time environmental quality monitoring information of the information enables users to quickly and conveniently understand the surrounding environmental conditions from the spatial and temporal position.

The steps of the present invention include:

1) The data preprocessing environment regularly acquires real-time environmental monitoring data, and saves the environmental monitoring data and its spatio-temporal information to the real-time environmental monitoring database for storage;

2) Using the collaborative kriging spatial interpolation method, the various real-time monitoring data with space-time information obtained in step 1) and the background grid data such as terrain, temperature, and traffic flow are subjected to spatial interpolation to obtain a grid layer, and saved to Spatio-temporal raster database;

3) step 2) the raster layer obtained by interpolation, set the color scheme, then generate a PNG grid picture of 256x256 pixels in GIS software according to a certain scale, and save the spatio-temporal information and the grid picture together to the grid picture database;

4) When the user requests, use the mobile terminal device to locate, and upload the coordinate position, distance range, time information and thematic map type as request input parameters to the WEB server;

5) After the WEB server receives the request, it calculates with the coordinate point as the center and the distance range specified by the user as the unit, and generates a rectangular coordinate range of the request area;

6) According to the time information submitted in step 4), the type of thematic map and the rectangular coordinate range generated in step 5), the server performs a space-time intersection query with the grid image data information generated in step 3) to obtain all PNG grid images in the area requested by the user ;

7) splicing the PNG grid picture of the user request area obtained in step 6), and clipping with the rectangular range of step 5) to obtain the environmental monitoring thematic picture required by the user request, after marking the legend and time information, etc. on the picture, It is returned to the user client through HTTP protocol for display.

Compared with prior art, positive effect of the present invention is:

The invention proposes a method based on the time and location information of the user's mobile terminal to realize the real-time environmental monitoring data thematic map in any geographic location to quickly generate pictures and return them to the user, and provide the user with real-time environmental quality monitoring information based on spatio-temporal information.

In terms of implementation technology, the WEB service environment and data preprocessing environment are used to separate, and complex and time-consuming computing tasks such as real-time environmental monitoring data acquisition, spatiotemporal grid data interpolation, and spatiotemporal grid image generation are executed asynchronously in the background. The WEB service program only performs spatio-temporal network image query and image splicing and cutting tasks, reducing the computing load of the server, and quickly responding to user requests for real-time environmental monitoring data thematic images, so that users can easily and quickly understand the surrounding environment from the spatial-temporal position environmental conditions.

Description of drawings

The attached figure is a flow chart of the rapid generation of the real-time environmental monitoring thematic pictures based on the user's location.

Detailed ways

The present invention involves two data service environments: a data preprocessing environment and a WEB service environment, wherein the data preprocessing environment realizes: real-time monitoring data timing acquisition and storage, real-time monitoring data space interpolation, real-time monitoring grid data grid image generation , real-time monitoring of steps such as space-time storage of data grid pictures; WEB service environment is responsible for receiving client service requests, parsing request parameters for rectangular range calculation, real-time monitoring of data grid picture space-time intersecting retrieval, picture splicing and clipping and other functions.

Below in conjunction with accompanying drawing and air pollutant data, the present invention is described in further detail as an example:

Data Preprocessing Environment

The first step, real-time monitoring data timing acquisition and storage: use Python language to write a data crawling service program, crawl the original air pollutant monitoring data from the Internet atmospheric environment monitoring website every 20 minutes, and use Python regular expression re module Parsing, extracting monitoring time information (dateh), monitoring point latitude and longitude coordinates (x, y), monitoring point name (city), monitoring point number (name), monitoring index value (SO ₂ , O ₃ , NO _x , CO, PM2.5, PM10), etc., combine the data into json format data and store it in the mongodb database.

Json data format is:

{

"_id":ObjectId("52cb6af9af56137081000000"),

"co": "2.3",

"no2": "74",

"so2": "59",

"o3": "7",

"pm10": "101",

"pm25": "147",

"x": "116.417",

"y": "39.929",

"dateh": "2014010710",

"city": "beijing",

"city0": "Beijing",

"name": "East Fourth"

}

The second step, real-time monitoring data spatial interpolation: When the data crawling of all monitoring websites in the first step is completed, all the latest inserted real-time monitoring data are extracted from the mongodb database, for example, all data of monitoring time dateh="2014010710", Remove abnormal records with no value.

Since the coordinates of the real-time monitoring data are longitude and latitude geographic coordinates, it is necessary to convert the geographic coordinate system into a planar projection coordinate system, and the Albers coordinate system is adopted in China. Coordinate projection transformation was carried out using the Python+OGR package. After the projection conversion is completed, use the geoR module and constrainedKriging module in the open source R statistical software, and use the collaborative Kriging space interpolation method to calculate the values of each monitoring indicator (SO ₂ , O ₃ , NO _x , CO, PM2.5 , PM10) and terrain, temperature, traffic flow and other background raster data for spatial interpolation, the resolution of the interpolated raster data depends on the actual range, interpolation effect and distribution of monitoring points, etc., adjust according to the actual situation, we use 90m The resolution interpolates the value of each monitoring indicator to obtain raster data.

The third step, real-time monitoring raster data grid image generation:

We generate a grid image for the interpolated raster data in step 2), and the scale of the grid image is selected according to the resolution and coverage of the raster data; we choose a scale of 1:250,000 and use the Python+GDAL software package PNG grid image generation. The grid image calculation process is as follows:

In the existing geographic information system environment, 1 inch is equal to 0.254 meters, which is equal to 96 pixels;

1: The actual grid resolution corresponding to the 250,000 scale is 250,000*(0.254/96)=66.15 meters;

The calculation method of the total number of grid images of the entire interpolated raster data is as follows, where right, left, top, and bottom are the spatial range of the raster data, and the unit is meter:

The number of pixels on the X axis is: X=round(abs(right–left)/66.15)

The number of pixels on the Y axis is: Y=round(abs(top–bottom)/66.15)

The total number of grid images of the entire interpolated raster data at a scale of 1:250,000 is:

T=round(X/256)*round(Y/256)

The calculation formula of any grid image T _i,j (X>i>=0, Y>j>=0) corresponding to the spatial range of raster data minX _i , minY _j , maxX _i , maxY _j is:

minX _i =left+(256*66.15)*i, where X>i>=0

maxX _i ＝left+(256*66.15)*(i+1), where X>i>=0

minY _j = bottom+(256*66.15)*j, where Y>j>=0

maxY _j = bottom+(256*66.15)*(j+1), where Y>j>=0

After the grid image is generated, store the image and metadata information in the grid image database, where the image metadata information includes: monitoring data type and collection time, grid data range right, left, top, bottom, grid offset Shift indices i and j, and grid width and height.

WEB server environment

The fourth step, client parameter collection and submission: on the mobile terminal device, the longitude-latitude format coordinate position obtained by the GPS automatic positioning module, the current time, the request distance (m) set by the user, the thematic map type (such as PM _2.5 , O ₃ etc.) and other request information are encoded into XML format parameters and submitted to the server through HTTP requests.

The fifth step, client parameter analysis and calculation:

After the server receives the XML parameters submitted by the client, it first parses the XML data, obtains relevant parameters, and uses the Python+OGR software package to perform coordinate projection conversion of the coordinate position in the latitude and longitude format into the coordinates Px, y of the Albers coordinate system. Calculate the coordinate point and the distance range W (in meters) specified by the user to form a rectangular range minX, minY, maxX, maxY, and the calculation formula is:

minX＝ _Px -W

maxX＝ _Px +W

minY＝P _y –W

maxY=P _y +W

Then convert the rectangular range minX, minY, maxX, maxY (in meters) into the grid offset index range, the specific calculation formula is:

tileX _min = round((minX-left)/256)

tileX _max = round((maxX-left)/256)

tileY _min = round((minY-left)/256)

tileY _max = round((maxY-left)/256)

Step 6. Slice spatiotemporal attribute intersection query:

Use the grid offset index range (tileX _min , tileY _min , tileX _max , tileY _max ) corresponding to the rectangular range requested by the user calculated in step 5), time, thematic map type and the grid image database stored in step 3). Space-time intersection query. If the offset index of the grid is within the grid offset index range of the user requested range, the spatial intersection condition is met.

The seventh step, thematic map slice splicing and cutting;

Use the grid image data obtained in step 6) to query, use the PIL image processing library of Python to paste, stitch and crop the image, save it as image data in PNG format, and return it to the client with an HTTP response request through the WEB server for display.

Claims (7)

1. the real time environment based on customer location monitors a thematic picture rapid generation, the steps include:

1) server timing acquisition real time environment Monitoring Data, is saved in real time environment monitor database by environmental monitoring data and space time information thereof;

2) space interpolation is carried out to environmental monitoring data and obtain raster map layer, be saved in space-time grid data storehouse;

3) arrange Color scheme to described raster map layer, then chi generates PNG graticule mesh picture in GIS software according to a certain percentage, and space time information is saved in graticule mesh picture database together with graticule mesh picture;

4) subscription client sends environment inquiry request to server, and described environment inquiry request comprises the coordinate position of subscription client, distance range, time and thematic map type;

5) after server receives this environment inquiry request, centered by coordinate position, the distance range of specifying is that unit calculates, and generates the rectangular coordinates scope in request region;

6) temporal information, thematic map type and the described rectangular coordinates scope of server environmentally in inquiry request, from described graticule mesh picture database, inquiry obtains all PNG graticule mesh pictures of asking region;

7) the PNG graticule mesh picture of server to request region splices, and is undertaken cutting out by described rectangular extent and obtain an environmental monitoring special topic picture and turn back to subscription client and show.

2. the method for claim 1, it is characterized in that described generation PNG graticule mesh picture, method space time information being saved in together with graticule mesh picture graticule mesh picture database is:

21) according to spatial dimension and the engineer's scale of the raster data arranged, the graticule mesh picture total quantity of whole interpolation raster data is calculated;

22) each graticule mesh picture T is calculated _i,jthe spatial dimension of corresponding raster data, cuts raster data;

23) after graticule mesh picture has generated, picture and metadata information are stored into graticule mesh picture database, and wherein picture metadata information comprises: Monitoring Data type and acquisition time, the scope of raster data, graticule mesh offset index i and j, graticule mesh wide and high.

3. method as claimed in claim 2, it is characterized in that, the graticule mesh offset index scope of described server corresponding to request region, time, thematic map type are carried out space-time to described graticule mesh picture database and are intersected inquiry, obtain all PNG graticule mesh pictures of asking region.

4. method as claimed in claim 2, is characterized in that, described server adopts Cokriging spatial interpolation methods, carries out space interpolation obtain raster map layer to environmental monitoring data.

5. the method as described in claim 1 or 2 or 3, is characterized in that, described server, in the environmental monitoring special topic picture subscript note legend returned and temporal information, then turns back to subscription client with http protocol and shows.

6. the method as described in claim 1 or 2 or 3, it is characterized in that, described server timing crawls protoatmosphere pollutant monitoring data from atmosphere environment supervision website, internet, uses Python regular expression re module to resolve, extracts monitoring time information, monitoring point latitude and longitude coordinates, monitoring point title, monitoring point is numbered, various monitoring index value, then data assemblies is become json formatted data, be stored in mongodb database.

7. the method as described in claim 1 or 2 or 3, is characterized in that, environment inquiry request information coding is that XML format parameter is submitted to server by HTTP request by described subscription client.