CN110967461A - Method for realizing dynamic distribution of river water quality based on GIS technology - Google Patents
Method for realizing dynamic distribution of river water quality based on GIS technology Download PDFInfo
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Abstract
The invention discloses a method for realizing dynamic distribution of river water quality based on a GIS technology, which comprises the following steps: data acquisition, spatialization processing, objectification processing, layer data superposition, layer data classification, event table construction and assignment, dynamic segmentation and rendering, and finally a rendering map reflecting river water quality dynamic distribution is obtained. The method realizes the dynamic linear reference segmentation and assignment of the river under the condition of limited water environment monitoring data, thereby performing multi-stage seamless dynamic rendering on the basis of not interrupting the river and supporting automatic updating and processing of the whole process.
Description
Technical Field
The invention relates to the technical field of environmental protection information management, in particular to a method for realizing dynamic distribution of river water quality based on a GIS technology.
Background
A Geographic Information System (GIS) is a computer technology system for processing and managing geospatial information, and can support acquisition, management, and analysis of geospatial data, and a dynamic segmentation technology provided by the GIS system is generally used in the traffic field in many cases. The mileage peg and the monitoring information are utilized on the road to dynamically render, and road conditions such as road congestion, high-speed and heavy fog road sections and the like can be visually checked.
At present, with the development of socioeconomic in China, the water environment quality is greatly influenced, and the data related to the water environment quality also has the spatial distribution characteristic, which determines that the GIS can play an important role in the field. However, because the water environment monitoring sites are few and the data acquisition amount is small, data such as mileposts in road data are lacked, and the road data is not provided with so many monitoring sections (only a few monitoring sections may be on one river), the dynamic segmentation technology provided by the GIS software cannot be directly used, and the GIS dynamic segmentation technology is applied to visually and accurately express the water quality of the river.
Therefore, a flexible, open and high-applicability technical method is needed to be established to support information expression of water environment monitoring data, render and display dynamic distribution of river water quality is realized, and automatic updating and processing of data in a technical framework are realized.
Disclosure of Invention
The invention provides a method for realizing dynamic distribution of river water quality based on a GIS technology, which can effectively realize rendering and display of the dynamic distribution of the river water quality.
The specific technical scheme is as follows:
a method for realizing dynamic distribution of river water quality based on a GIS technology comprises the following steps:
(1) data acquisition: collecting attribute data of all rivers in an area to be treated, attribute data of all water quality monitoring sites and real-time water quality monitoring data of all actual water quality monitoring sites;
(2) and (3) spatialization treatment: reading the attribute data of the water quality monitoring stations obtained in the step (1) by using GIS software, and performing spatialization treatment on the attribute data of the water quality monitoring stations, so that each water quality monitoring station in the area is represented by one point, and point-like layer data consisting of all the water quality monitoring stations in the area is obtained;
(3) objectification processing: reading the attribute data of the rivers obtained in the step (1) by using GIS software to form planar layer data, extracting a river center line of each river from the planar layer data, and performing objectification treatment on the river center line, so that each river in the area is represented by one line, the connectivity of the rivers is ensured, and linear layer data consisting of all the rivers in the area is obtained;
(4) and (3) layer data superposition: correcting the dot-shaped layer data obtained in the step (2) and the linear layer data obtained in the step (3) by using satellite remote sensing data, ensuring that a water quality monitoring site falls on a river central line through spatial position correction and spatial superposition analysis, and removing layer data corresponding to a river which does not contain the water quality monitoring site to obtain river layer data A at least containing one water quality monitoring site attribute data;
(5) and (4) layer data classification: processing the river layer data A obtained in the step (4) by using a space analysis method, dividing the linear river in the river layer data A at the position of the water quality monitoring site, wherein the divided positions all contain the water quality monitoring site and attribute data thereof, and generating new river layer data B with a plurality of line segments;
classifying the river layer data B, classifying the river line segment and layer data thereof which only have one water quality monitoring site and attribute data thereof in the segmented line segments into I-class image layer data, and classifying the river line segment and layer data thereof which both have the water quality monitoring site and attribute data thereof at the head and tail into II-class image layer data;
(6) and (3) construction and assignment of an event table: setting a linear reference parameter value M for each river line segment in the river map layer data B, setting M to be N, wherein N represents the total number of segments of each river line segment, and N is more than or equal to 50; reading the real-time water quality monitoring data of the actual water quality monitoring station obtained in the step (1) and creating a riverID information, M start value MbegM end value MendAnd an event table for Val;
the assignment is carried out in the following two cases:
(6-1) for class I map data: importing river ID information of each river line segment in the I-class graph layer data into an event table, and setting Mbeg＝0，MendAssigning value to each river line segment to obtain an event table E1；
The assignment formula is: valI＝S
In the formula, ValIRepresenting the real-time water quality monitoring data service value assigned to each river, and S representing the real-time water quality monitoring data service value of an actual water quality monitoring station in each river;
(6-2) for class II map data: importing river ID information of each river line segment in the II-class graph data into an event table, and setting Mbeg＝W-1，MendCopying the river ID information into N pieces according to the total number of the segments of the river line segment, and assigning values to the N small line segments formed after the river line segment is segmented to obtain an event table E2；
The assignment formula is: valII＝Sw+[(Sw+1-Sw)/N]×W；
In the formula, ValIIRepresenting the real-time water quality monitoring data service value assigned to each small line segment; swRepresenting the real-time water quality monitoring data service value of each small line segment actual upstream water quality monitoring station; sw+1Representing the real-time water quality monitoring data service value of each small line segment of the actual downstream water quality monitoring station; n represents the total number of the segments of each river line segment, and N is more than or equal to 50; w represents the serial number of the small line segment, from 1 to N;
event table E obtained in step (6-1)1And the event table E obtained in the step (6-2)2Merging to obtain a total event table E;
(7) dynamic segmentation: according to M in the event table EbegAnd MendAnd setting a linear reference parameter value M in the river layer data B, and utilizing a GIS linear reference dynamic segmentation technology to divide river line segments in the river layer data B into M values according to the M valuesbegAnd MendDynamically segmenting into small line segments in an event table;
(8) rendering: and continuously and dynamically rendering real-time water quality monitoring data of the rivers with at least one water quality monitoring site in the region in a seamless manner according to the assigned real-time water quality monitoring data service value of each river line segment, and finally obtaining a rendering graph reflecting the dynamic distribution of the river water quality.
The point-like layer data and the linear layer data are displayed in the GIS software in the form of map pictures, and the layer data can be obtained from databases provided by regional hydrology and water conservancy departments and environmental protection departments. The river line segment is a line segment formed by the segmentation treatment in the step (5) of a river, at least one end of the line segment comprises a water quality monitoring station, at most two ends of the line segment comprise water quality monitoring stations, and no water quality monitoring station exists in the line segment; the two divided line segments are divided into two sections which contain water quality monitoring stations. The small line segment is a small segment formed by dynamic segmentation on the basis of the river line segment, and the rendering in the step (8) is to perform color rendering by taking the small line segment or the river line segment with a water quality monitoring site at only one end as a rendering unit. The lengths of the small segments in one river segment are equal, while the lengths of the small segments between different river segments are not necessarily the same.
The attribute data described above includes names of river and water quality monitoring sites and corresponding geographical location information data. Specifically, in the step (1), the attribute data of the river at least includes a river number and a river name; the attribute data of the water quality monitoring station at least comprises a water quality monitoring station number, a water quality monitoring station name and a water quality monitoring station longitude and latitude.
Further, in the step (2), the spatialization processing method includes:
and (2) storing the attribute data of the water quality monitoring station obtained in the step (1) in a GIS software in the form of an Excel file, and converting the Excel file into spatialized point-like layer data by using the GIS software according to longitude and latitude information in the attribute data.
Further, in the step (3), the method of the objectification processing is:
converting the planar layer data into side line set data by using GIS software, and generating central line set data of each river in the area according to the parallel relation between lines to obtain linear layer data of all rivers in the area; and then splitting and merging the linear layer data to enable the linear layer data to correspond to an actual river object, so as to obtain river-objectified linear layer data consisting of all rivers in the area.
The method adopted by the invention is not influenced by the specific water quality monitoring index types, and other water quality monitoring indexes except the indexes mentioned by the invention are also applicable theoretically.
Preferably, in the step (1), the real-time water quality monitoring data of the water quality monitoring station includes: conventional water quality indexes and comprehensive water quality indexes;
the conventional water quality indexes comprise: water temperature, pH, dissolved oxygen, conductivity, turbidity, oxidation-reduction potential;
the water quality comprehensive indexes comprise: permanganate index, chemical oxygen demand, total organic carbon, ammonia nitrogen, total phosphorus, total nitrogen, petroleum, volatile phenol, anionic surfactant, fluoride, cyanide, chloride, nitrate, sulfate, metal ion.
Further, in the step (4), an image definition space reference method is adopted to correct the space position, so that the space position of the water quality monitoring site in the point-shaped layer data is correctly matched with the space position of the actual water quality monitoring site in the satellite remote sensing data, the space position of the river in the linear layer data is correctly matched with the space position of the actual river in the satellite remote sensing data, and then the corrected data is analyzed by using a space superposition analysis method to extract the linear layer data capable of covering the point-shaped layer data.
The total number of the segments needs to be set according to the distance of the river segment with the longest distance, the length of each small segment is guaranteed not to be too long, the rendering effect is guaranteed to be preferable, and in the step (7-b2), N is 50-200.
Preferably, in the step (8), the rendering value of the continuous dynamic rendering is 50-500, so as to ensure the rendering effect.
Compared with the prior art, the invention has the following beneficial effects:
(1) the method realizes the dynamic linear reference segmentation and assignment of the river under the condition of limited water environment monitoring data, thereby performing multi-stage seamless dynamic rendering on the basis of not interrupting the river and supporting automatic updating and processing of the whole process.
(2) On the basis of adopting a GIS technology, the invention integrates computer programming, graphics, surveying and mapping internal processing, drawing technology and the like, establishes a set of flexible, open and high-applicability technical method to support information expression of water environment monitoring data, realizes rendering and display of river water quality dynamic distribution, and realizes automatic updating and processing of data in a technical framework.
(3) The whole dynamic segmentation process can automatically read data, automatically segment and automatically calculate monitoring data of each segment by packaging a Python script as a tool, thereby realizing the full-automatic operation of the whole segmentation process and relating to two drawing templates: the dynamic data monitoring value drawing template and the river water quality dynamic segmentation drawing template realize the butt joint integration with a service system, support the dynamic update of monitoring section data and store all historical data by the system. After the spatial data have the temporal information, the temporal function can be started on the GIS desktop, and the client can check all historical data through the temporal playing function, so that temporal query and dynamic playing are performed in the map.
Drawings
FIG. 1 is a flow chart of the method for realizing dynamic distribution of river water quality based on GIS technology.
Fig. 2 is a rendering graph of dynamic distribution of river water quality index conductivity in application example 1.
FIG. 3 is a rendering graph of the dynamic distribution of the river water quality index ammonia nitrogen in application example 1.
Detailed Description
The present invention will be further described with reference to the following specific examples, which are only illustrative of the present invention, but the scope of the present invention is not limited thereto.
Example 1
A method for realizing dynamic distribution of river water quality based on a GIS technology comprises the following specific steps:
(1) data acquisition: collecting attribute data of all rivers in an area to be treated, attribute data of all water quality monitoring sites and real-time water quality monitoring data of all actual water quality monitoring sites;
the attribute data of the river is a river number and a river name; the attribute data of the water quality monitoring station comprises a water quality monitoring station number, a water quality monitoring station name and a water quality monitoring station longitude and latitude;
the real-time water quality monitoring data of the water quality monitoring station comprises water quality conventional indexes and water quality comprehensive indexes;
the conventional water quality indexes are as follows: water temperature, pH, dissolved oxygen, conductivity, turbidity, oxidation-reduction potential;
the comprehensive water quality indexes are as follows: permanganate index, chemical oxygen demand, total organic carbon, ammonia nitrogen, total phosphorus, total nitrogen, petroleum, volatile phenol, anionic surfactant, fluoride, cyanide, chloride, nitrate, sulfate, metal ions;
(2) and (3) spatialization treatment: reading the attribute data of the water quality monitoring stations obtained in the step (1) by using GIS software, and performing spatialization treatment on the attribute data of the water quality monitoring stations, so that each water quality monitoring station in the area is represented by one point, and point-like layer data consisting of all the water quality monitoring stations in the area is obtained;
the spatialization processing method comprises the following steps: storing the attribute data of the water quality monitoring station obtained in the step (1) in a GIS (geographic information system) in the form of an Excel file, and converting the Excel file into spatialized point-like layer data by utilizing the GIS according to longitude and latitude information in the attribute data;
(3) objectification processing: reading the attribute data of the rivers obtained in the step (1) by using GIS software to form planar layer data, extracting a river center line of each river from the planar layer data, and performing objectification treatment on the river center line, so that each river in the area is represented by one line, the connectivity of the rivers is ensured, and linear layer data consisting of all the rivers in the area is obtained;
the objectification processing method comprises the following steps: converting the planar layer data into side line set data by using GIS software, and generating central line set data of each river in the area according to the parallel relation between lines to obtain linear layer data of all rivers in the area; then, the linear layer data are split and merged, so that the linear layer data correspond to an actual river object, and river-objectified linear layer data consisting of all rivers in the area are obtained;
(4) and (3) layer data superposition: correcting the space position by using the dot-shaped layer data obtained in the satellite remote sensing data correction step (2) and the linear layer data obtained in the step (3) by using an image definition space reference method, so that the space position of a water quality monitoring site in the dot-shaped layer data is correctly matched with the space position of an actual water quality monitoring site in the satellite remote sensing data, the space position of a river in the linear layer data is correctly matched with the space position of an actual river in the satellite remote sensing data, analyzing the corrected data by using a space superposition analysis method, and extracting the linear layer data capable of covering the dot-shaped layer data, thereby removing the layer data corresponding to the river not containing the water quality monitoring site, and obtaining river layer data A at least containing one water quality monitoring site attribute data;
(5) and (4) layer data classification: processing the river layer data A obtained in the step (4) by using a space analysis method, dividing the linear river in the river layer data A at the position of the water quality monitoring site, wherein the divided positions all contain the water quality monitoring site and attribute data thereof, and generating new river layer data B with a plurality of line segments;
classifying the river layer data B, classifying the river line segment and layer data thereof which only have one water quality monitoring site and attribute data thereof in the segmented line segments into I-class image layer data, and classifying the river line segment and layer data thereof which both have the water quality monitoring site and attribute data thereof at the head and tail into II-class image layer data;
(6) and (3) construction and assignment of an event table: setting a linear reference parameter value M for each river line segment in the river map layer data B, setting M to be N, wherein N represents the total number of segments of each river line segment, and N is more than or equal to 50; reading the real-time water quality monitoring data of the actual water quality monitoring station obtained in the step (1), and creating a river ID information and an M initial value MbegM end value MendAnd an event table for Val;
the assignment is carried out in the following two cases:
(6-1) for class I map data: importing river ID information of each river line segment in the I-class graph layer data into an event table, and setting Mbeg＝0，MendAssigning value to each river line segment to obtain an event table E1；
The assignment formula is: valI＝S
In the formula, ValIRepresenting the real-time water quality monitoring data service value assigned to each river, and S representing the real-time water quality monitoring data service value of an actual water quality monitoring station in each river;
(6-2) for class II map data: importing river ID information of each river line segment in the II-class graph data into an event table, and setting Mbeg＝W-1，MendCopying the river ID information into N pieces according to the total number of the segments of the river line segment, and assigning values to the N small line segments formed after the river line segment is segmented to obtain an event table E2；
The assignment formula is: valII＝Sw+[(Sw+1-Sw)/N]×W；
In the formula, ValIIRepresenting the real-time water quality monitoring data service value assigned to each small line segment; swRepresenting the real-time water quality monitoring data service value of each small line segment actual upstream water quality monitoring station; sw+1Representing the real-time water quality monitoring data service value of each small line segment of the actual downstream water quality monitoring station; n represents the total number of the segments of each river line segment, and N is more than or equal to 50; w represents the serial number of the small line segment, from 1 to N;
event table E obtained in step (6-1)1And the event table E obtained in the step (6-2)2To carry out the combinationAnd obtaining a total event table E;
(7) dynamic segmentation: according to M in the event table EbegAnd MendAnd setting a linear reference parameter value M in the river layer data B, and utilizing a GIS linear reference dynamic segmentation technology to divide river line segments in the river layer data B into M values according to the M valuesbegAnd MendDynamically segmenting into small line segments in an event table;
(8) rendering: and continuously and dynamically rendering real-time water quality monitoring data of the rivers with at least one water quality monitoring site in the region in a seamless manner according to the assigned real-time water quality monitoring data service value of each river line segment, and finally obtaining a rendering graph reflecting the dynamic distribution of the river water quality.
Application example 1
(1) Taking a certain grade city of Hangjia lake plain as an example, collecting river attribute data and water quality monitoring station attribute data of the area, wherein the river data relates to 3433 surface objects, and the water quality monitoring stations relate to 73 sites; collecting real-time water quality monitoring data of all water quality monitoring stations;
the river attribute data is: river number and river name;
the attribute data of the water quality monitoring station are as follows: the water quality monitoring station number, the water quality monitoring station name and the water quality monitoring station longitude and latitude;
the real-time water quality monitoring data of the water quality monitoring site comprises two types of water quality conventional indexes and water quality comprehensive indexes;
wherein, the conventional indexes of water quality are as follows: water temperature, pH, dissolved oxygen, conductivity, turbidity, and oxidation-reduction potential;
the comprehensive indexes comprise: permanganate index, chemical oxygen demand, total organic carbon, ammonia nitrogen, total phosphorus, total nitrogen, petroleum species, volatile phenols, anionic surfactants, fluorides, cyanides, chlorides, nitrates, sulfates, and metal ions.
(2) Reading attribute data of water quality monitoring stations by using GIS software, storing the acquired attribute data of the 73 water quality monitoring stations in the GIS software in an Excel file form, representing each water quality monitoring station in an area by using the GIS software according to longitude and latitude information in the attribute data, and converting the Excel file into punctiform layer data consisting of the 73 water quality monitoring stations which are spatially divided in the area.
(3) Reading river attribute data by using GIS software to form planar layer data, converting the planar layer data into side line set data, generating central line set data of each river in an area according to the parallel relation between lines to obtain linear layer data of all rivers in the area, wherein the linear layer data of all the rivers in the area relate to 13669 river linear objects; and then, the linear layer data is split and merged, so that the linear layer data corresponds to the actual river objects, and river-objectified linear layer data consisting of all rivers in the area is obtained, and 4042 river linear objects are involved.
(4) Correcting the dot-shaped layer data obtained in the step (2) and the linear layer data obtained in the step (3) by using satellite remote sensing data, ensuring that a water quality monitoring site falls on a river central line through spatial position correction and spatial superposition analysis, and removing layer data corresponding to a river not containing the water quality monitoring site to obtain river layer data A at least containing one water quality monitoring site attribute data and containing 109 river linear objects;
(5) classifying the river layer data A obtained in the step (4), wherein the I-type river layer data containing only one water quality monitoring site attribute data relates to 70 river linear objects, and the II-type river layer data containing two or more water quality monitoring site attribute data relates to 39 river linear objects;
(6) and (3) construction and assignment of an event table: setting a linear reference parameter value M for each river line segment in the river layer data B, and setting M to be N, wherein N represents the total number of segments of each river line segment, and N is 200; reading the real-time water quality monitoring data of the actual water quality monitoring station obtained in the step (1), and creating a river ID information and an M initial value MbegM end value MendAnd an event table for Val;
the assignment is carried out in the following two cases:
(6-1) for class I map data: guiding river ID information of each river line segment in I-type image layer dataEnter the event table and set Mbeg＝0，MendAssigning value to each river line segment to obtain an event table E1；
The assignment formula is: valI＝S
In the formula, ValIRepresenting the real-time water quality monitoring data service value assigned to each river, and S representing the real-time water quality monitoring data service value of an actual water quality monitoring station in each river;
(6-2) for class II map data: importing river ID information of each river line segment in the II-class graph data into an event table, and setting Mbeg＝W-1，MendCopying the river ID information into N pieces according to the total number of the segments of the river line segment, and assigning values to the N small line segments formed after the river line segment is segmented to obtain an event table E2；
The assignment formula is: valII＝Sw+[(Sw+1-Sw)/N]×W；
In the formula, ValIIRepresenting the real-time water quality monitoring data service value assigned to each small line segment; swRepresenting the real-time water quality monitoring data service value of each small line segment actual upstream water quality monitoring station; sw+1Representing the real-time water quality monitoring data service value of each small line segment of the actual downstream water quality monitoring station; n represents the total number of the segments of each river line segment, and N is more than or equal to 50; w represents the serial number of the small line segment, from 1 to N;
event table E obtained in step (6-1)1And the event table E obtained in the step (6-2)2Merging to obtain a total event table E;
(7) dynamic segmentation: according to M in the event table EbegAnd MendAnd setting a linear reference parameter value M in the river layer data B, and utilizing a GIS linear reference dynamic segmentation technology to divide river line segments in the river layer data B into M values according to the M valuesbegAnd MendDynamically segmenting into small line segments in an event table;
(8) rendering: according to the size of the real-time water quality monitoring data service value after each river line segment is assigned, continuous dynamic rendering of real-time water quality monitoring data is carried out on rivers with at least one water quality monitoring site in the region, the rendering value of the continuous dynamic rendering is 500, and finally rendering graphs reflecting dynamic distribution of river water quality are obtained (for example, conventional water quality index conductivity and comprehensive index ammonia nitrogen are taken as examples, as shown in fig. 1 and fig. 2).
Claims (8)
1. A method for realizing dynamic distribution of river water quality based on a GIS technology is characterized by comprising the following steps:
(1) data acquisition: collecting attribute data of all rivers in an area to be treated, attribute data of all water quality monitoring sites and real-time water quality monitoring data of all actual water quality monitoring sites;
(2) and (3) spatialization treatment: reading the attribute data of the water quality monitoring stations obtained in the step (1) by using GIS software, and performing spatialization treatment on the attribute data of the water quality monitoring stations, so that each water quality monitoring station in the area is represented by one point, and point-like layer data consisting of all the water quality monitoring stations in the area is obtained;
(3) objectification processing: reading the attribute data of the rivers obtained in the step (1) by using GIS software to form planar layer data, extracting a river center line of each river from the planar layer data, and performing objectification treatment on the river center line, so that each river in the area is represented by one line, the connectivity of the rivers is ensured, and linear layer data consisting of all the rivers in the area is obtained;
(4) and (3) layer data superposition: correcting the dot-shaped layer data obtained in the step (2) and the linear layer data obtained in the step (3) by using satellite remote sensing data, ensuring that a water quality monitoring site falls on a river central line through spatial position correction and spatial superposition analysis, and removing layer data corresponding to a river which does not contain the water quality monitoring site to obtain river layer data A at least containing one water quality monitoring site attribute data;
(5) and (4) layer data classification: processing the river layer data A obtained in the step (4) by using a space analysis method, dividing the linear river in the river layer data A at the position of the water quality monitoring site, wherein the divided positions all contain the water quality monitoring site and attribute data thereof, and generating new river layer data B with a plurality of line segments;
classifying the river layer data B, classifying the river line segment and layer data thereof which only have one water quality monitoring site and attribute data thereof in the segmented line segments into I-class image layer data, and classifying the river line segment and layer data thereof which both have the water quality monitoring site and attribute data thereof at the head and tail into II-class image layer data;
(6) and (3) construction and assignment of an event table: setting a linear reference parameter value M for each river line segment in the river map layer data B, setting M to be N, wherein N represents the total number of segments of each river line segment, and N is more than or equal to 50; reading the real-time water quality monitoring data of the actual water quality monitoring station obtained in the step (1), and creating a river ID information and an M initial value MbegM end value MendAnd an event table for Val;
the assignment is carried out in the following two cases:
(6-1) for class I map data: importing river ID information of each river line segment in the I-class graph layer data into an event table, and setting Mbeg＝0，MendAssigning value to each river line segment to obtain an event table E1；
The assignment formula is: valI＝S
In the formula, ValIRepresenting the real-time water quality monitoring data service value assigned to each river, and S representing the real-time water quality monitoring data service value of an actual water quality monitoring station in each river;
(6-2) for class II map data: importing river ID information of each river line segment in the II-class graph data into an event table, and setting Mbeg＝W-1，MendCopying the river ID information into N pieces according to the total number of the segments of the river line segment, and assigning values to the N small line segments formed after the river line segment is segmented to obtain an event table E2；
The assignment formula is: valII＝Sw+[(Sw+1-Sw)/N]×W；
In the formula, ValIIRepresenting the real-time water quality monitoring data service value assigned to each small line segment; swRepresenting actual upstream water quality monitoring of each small line segmentReal-time water quality monitoring data service value of the station; sw+1Representing the real-time water quality monitoring data service value of each small line segment of the actual downstream water quality monitoring station; n represents the total number of the segments of each river line segment, and N is more than or equal to 50; w represents the serial number of the small line segment, from 1 to N;
event table E obtained in step (6-1)1And the event table E obtained in the step (6-2)2Merging to obtain a total event table E;
(7) dynamic segmentation: according to M in the event table EbegAnd MendAnd setting a linear reference parameter value M in the river layer data B, and utilizing a GIS linear reference dynamic segmentation technology to divide river line segments in the river layer data B into M values according to the M valuesbegAnd MendDynamically segmenting into small line segments in an event table;
(8) rendering: and continuously and dynamically rendering real-time water quality monitoring data of the rivers with at least one water quality monitoring site in the region in a seamless manner according to the assigned real-time water quality monitoring data service value of each river line segment, and finally obtaining a rendering graph reflecting the dynamic distribution of the river water quality.
2. The method for realizing dynamic distribution of river water quality based on the GIS technology according to claim 1, wherein in the step (1), the attribute data of the river at least comprises a river number and a river name; the attribute data of the water quality monitoring station at least comprises a water quality monitoring station number, a water quality monitoring station name and a water quality monitoring station longitude and latitude.
3. The method for realizing dynamic distribution of river water quality based on the GIS technology according to claim 2, wherein in the step (2), the spatialization treatment method comprises the following steps:
and (2) storing the attribute data of the water quality monitoring station obtained in the step (1) in a GIS software in the form of an Excel file, and converting the Excel file into spatialized point-like layer data by using the GIS software according to longitude and latitude information in the attribute data.
4. The method for realizing dynamic distribution of river water quality based on the GIS technology according to claim 2, wherein in the step (3), the objective treatment method comprises:
converting the planar layer data into side line set data by using GIS software, and generating central line set data of each river in the area according to the parallel relation between lines to obtain linear layer data of all rivers in the area; and then splitting and merging the linear layer data to enable the linear layer data to correspond to an actual river object, so as to obtain river-objectified linear layer data consisting of all rivers in the area.
5. The method for realizing the dynamic distribution of the river water quality based on the GIS technology according to claim 1, wherein in the step (1), the real-time water quality monitoring data of the water quality monitoring station comprises the following steps: conventional water quality indexes and comprehensive water quality indexes;
the conventional water quality indexes comprise: water temperature, pH, dissolved oxygen, conductivity, turbidity, oxidation-reduction potential;
the water quality comprehensive indexes comprise: permanganate index, chemical oxygen demand, total organic carbon, ammonia nitrogen, total phosphorus, total nitrogen, petroleum, volatile phenol, anionic surfactant, fluoride, cyanide, chloride, nitrate, sulfate, metal ion.
6. The method for realizing dynamic distribution of river water quality based on the GIS technology according to claim 1, wherein in the step (4), the spatial position correction is performed by adopting an image definition spatial reference method, so that the spatial position of the water quality monitoring site in the point-like layer data is correctly matched with the spatial position of the actual water quality monitoring site in the satellite remote sensing data, the spatial position of the river in the linear layer data is correctly matched with the spatial position of the actual river in the satellite remote sensing data, and the corrected data is analyzed by using a spatial superposition analysis method to extract the linear layer data capable of covering the point-like layer data.
7. The method for realizing river water quality dynamic distribution based on the GIS technology as claimed in claim 1, wherein in the step (7-b2), N is 50-200.
8. The method for realizing river water quality dynamic distribution based on the GIS technology according to claim 1, wherein in the step (8), the rendering value of the continuous dynamic rendering is 50-500.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113127524A (en) * | 2021-03-26 | 2021-07-16 | 深圳市震有软件科技有限公司 | Dynamic segmentation method, mobile terminal and storage medium |
| CN113658311A (en) * | 2021-08-09 | 2021-11-16 | 力合科技（湖南）股份有限公司 | Water quality dynamic rendering method, system, equipment and computer readable storage medium |
| CN116089519A (en) * | 2022-11-29 | 2023-05-09 | 中国环境科学研究院 | Pollution tracing path visualization platform based on GIS |
| CN116151777A (en) * | 2023-04-20 | 2023-05-23 | 深圳奥雅设计股份有限公司 | Intelligent automatic rendering method and system for landscape garden plan |
| CN116991806A (en) * | 2023-09-04 | 2023-11-03 | 爬山虎科技股份有限公司 | Automatic soil data superposition analysis method based on spatial data |
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100043489A (en) * | 2008-10-20 | 2010-04-29 | 한국건설기술연구원 | River network making method using the digital elevation model |
| CN102184487A (en) * | 2011-05-17 | 2011-09-14 | 华东师范大学 | Pollution source management system based on geographic information system (GIS) technology and one-dimensional water quality model and operation method thereof |
| CN102708520A (en) * | 2012-05-10 | 2012-10-03 | 南京国图信息产业股份有限公司 | Section gradient interpolation technology for water quality multi-index dynamic simulation |
| CN103218485A (en) * | 2013-04-03 | 2013-07-24 | 郑州大学 | Method for calculating small watershed environmental capacity under support of GIS (Geographic Information System) technology |
| CN104392100A (en) * | 2014-10-29 | 2015-03-04 | 南京南瑞集团公司 | Pollution source diffusion early-warning method based on water quality on-line monitoring system |
| CN106204692A (en) * | 2016-07-13 | 2016-12-07 | 国家海洋信息中心 | A kind of ocean based on GIS platform sectional drawing method for drafting |
| CN107091911A (en) * | 2017-04-17 | 2017-08-25 | 郑州大学环境技术咨询工程有限公司 | A kind of water quality prediction of river method |
| CN107831285A (en) * | 2017-01-19 | 2018-03-23 | 江苏省金威测绘服务中心 | A kind of dystrophication monitoring system and its method based on Internet of Things |
| CN107943880A (en) * | 2017-11-15 | 2018-04-20 | 国网四川省电力公司经济技术研究院 | A kind of susceptibility of geological hazards based on analytic hierarchy process (AHP) improves appraisal procedure |
-
2019
- 2019-12-19 CN CN201911316175.2A patent/CN110967461B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100043489A (en) * | 2008-10-20 | 2010-04-29 | 한국건설기술연구원 | River network making method using the digital elevation model |
| CN102184487A (en) * | 2011-05-17 | 2011-09-14 | 华东师范大学 | Pollution source management system based on geographic information system (GIS) technology and one-dimensional water quality model and operation method thereof |
| CN102708520A (en) * | 2012-05-10 | 2012-10-03 | 南京国图信息产业股份有限公司 | Section gradient interpolation technology for water quality multi-index dynamic simulation |
| CN103218485A (en) * | 2013-04-03 | 2013-07-24 | 郑州大学 | Method for calculating small watershed environmental capacity under support of GIS (Geographic Information System) technology |
| CN104392100A (en) * | 2014-10-29 | 2015-03-04 | 南京南瑞集团公司 | Pollution source diffusion early-warning method based on water quality on-line monitoring system |
| CN106204692A (en) * | 2016-07-13 | 2016-12-07 | 国家海洋信息中心 | A kind of ocean based on GIS platform sectional drawing method for drafting |
| CN107831285A (en) * | 2017-01-19 | 2018-03-23 | 江苏省金威测绘服务中心 | A kind of dystrophication monitoring system and its method based on Internet of Things |
| CN107091911A (en) * | 2017-04-17 | 2017-08-25 | 郑州大学环境技术咨询工程有限公司 | A kind of water quality prediction of river method |
| CN107943880A (en) * | 2017-11-15 | 2018-04-20 | 国网四川省电力公司经济技术研究院 | A kind of susceptibility of geological hazards based on analytic hierarchy process (AHP) improves appraisal procedure |
Non-Patent Citations (2)
| Title |
|---|
| 孙启宏等: "利用动态分段技术进行河流一维水质扩散模拟", 《环境科学研究》 * |
| 董艳平等: "基于ＧＩＳ的山西省传统村落空间分布特征研究", 《太原理工大学学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113127524A (en) * | 2021-03-26 | 2021-07-16 | 深圳市震有软件科技有限公司 | Dynamic segmentation method, mobile terminal and storage medium |
| CN113658311A (en) * | 2021-08-09 | 2021-11-16 | 力合科技（湖南）股份有限公司 | Water quality dynamic rendering method, system, equipment and computer readable storage medium |
| CN116089519A (en) * | 2022-11-29 | 2023-05-09 | 中国环境科学研究院 | Pollution tracing path visualization platform based on GIS |
| CN116089519B (en) * | 2022-11-29 | 2023-10-10 | 中国环境科学研究院 | Pollution tracing path visualization platform based on GIS |
| CN116151777A (en) * | 2023-04-20 | 2023-05-23 | 深圳奥雅设计股份有限公司 | Intelligent automatic rendering method and system for landscape garden plan |
| CN116151777B (en) * | 2023-04-20 | 2023-07-14 | 深圳奥雅设计股份有限公司 | Intelligent automatic rendering method and system for landscape garden plan |
| CN116991806A (en) * | 2023-09-04 | 2023-11-03 | 爬山虎科技股份有限公司 | Automatic soil data superposition analysis method based on spatial data |
| CN117351373A (en) * | 2023-12-04 | 2024-01-05 | 厦门大学 | Intelligent fusion processing method for estuary and bay water quality remote sensing products |
| CN117351373B (en) * | 2023-12-04 | 2024-03-12 | 厦门大学 | Intelligent fusion processing method for estuary and bay water quality remote sensing products |
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