CN111122402B - Pollutant road condition map generation method and system based on discrete monitoring point data - Google Patents
Pollutant road condition map generation method and system based on discrete monitoring point data Download PDFInfo
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Abstract
The invention relates to a pollutant road condition graph generating method and system based on discrete monitoring point data. And then, carrying out second spatial interpolation on the intermediate interpolation point set, namely further increasing the number of data points in the intermediate interpolation point set to generate a final interpolation point set, and then generating a more detailed pollutant road condition map according to the final interpolation point set, wherein the details are richer, the visualization effect is good, and the user can observe the map more conveniently and visually.
Description
Technical Field
The invention relates to the field of pollutant monitoring, in particular to a pollutant road condition map generating method and system based on discrete monitoring point data.
Background
With the continuous improvement of the living standard of people, people pay more and more attention to the condition of the air quality of the living environment. In order to monitor the change condition of urban air quality more accurately and in real time, more and more air monitoring stations are deployed in cities. However, even if the discrete monitoring point data with the relatively high resolution is fed back by the monitoring means, the visualization effect of the road condition map of the air quality data, i.e. the road condition map of the pollutants, generated according to the discrete monitoring point data with the relatively high resolution is poor, and the user is not favorable to intuitively know the surrounding air quality condition.
Therefore, how to generate a pollutant road condition map with a good visualization effect based on discrete monitoring point data is a technical problem to be solved urgently in the industry.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and provides a pollutant road condition map generation method and system based on discrete monitoring point data.
The invention discloses a pollutant road condition map generation method based on discrete monitoring point data, which adopts the technical scheme as follows:
s1, processing the discrete monitoring point data according to preset conditions, and respectively generating a sparse point set and a non-sparse point set;
s2, expanding the sparse point set into a sparse interpolation point set after carrying out first spatial interpolation processing on the sparse point set by using a spatial interpolation algorithm of a linear function;
s3, fusing the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set;
and S4, performing second spatial interpolation on the intermediate interpolation point set by using a spatial interpolation algorithm of a nonlinear function to obtain a final interpolation point set, and generating a pollutant road condition map according to the final interpolation point set.
The pollutant road condition map generation method based on the discrete monitoring point data has the following beneficial effects:
processing discrete monitoring point data returned by discrete pollutant monitoring points which are non-uniformly distributed in space according to preset conditions to generate a sparse point set and a non-sparse point set, performing first-time spatial interpolation on the sparse point set, increasing the number of data points in the sparse point set to form a sparse interpolation point set, and fusing the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set. And then, carrying out second spatial interpolation on the intermediate interpolation point set, namely further increasing the number of data points in the intermediate interpolation point set to generate a final interpolation point set, and then generating a more detailed pollutant road condition map according to the final interpolation point set, wherein the details are richer, the visualization effect is good, and the user can observe the map more conveniently and visually.
On the basis of the scheme, the pollutant road condition map generating method based on the discrete monitoring point data can be further improved as follows.
Further, the preset conditions are specifically as follows: sequentially acquiring actual monitoring distances between two adjacent data points in the discrete monitoring point data according to the sequence, judging whether the actual monitoring distances are larger than a preset distance threshold value, and if so, adding the two adjacent data points into the sparse point set in a point set pair mode; and if not, adding the two adjacent data points into the non-sparse point set in a point set pair mode.
The beneficial effect of adopting the further scheme is that: whether the actual monitoring distance between two adjacent data points in the discrete monitoring point data is larger than a preset distance threshold value or not is judged in sequence, and the actual monitoring distance is added into a sparse point set or a non-sparse point set in a data pair mode, so that the method is simple and effective, the calculated amount is small, and the guarantee is provided for updating the pollutant road condition graph in real time.
Further, S2 specifically is: and after the first spatial interpolation processing is carried out on the sparse point set by using a linear function spatial interpolation algorithm, a plurality of extended data points are inserted between two data points of each point set pair of the sparse point set to form the sparse interpolation point set.
The beneficial effect of adopting the further scheme is that: through the first time of spatial interpolation processing, a plurality of expanded data points are inserted between two data points of each point set pair of the sparse point set, the number of the data points in the sparse point set is increased, and the generated pollutant road condition graph is guaranteed to have rich details.
Further, S3 specifically is: and fusing the point set pairs in the non-sparse point set and the corresponding point set pairs in the sparse interpolation point set, and deleting repeated data points to form the intermediate interpolation point set.
The beneficial effect of adopting the further scheme is that: the method specifically explains the fusion mode of the sparse interpolation point set and the non-sparse point set.
Further, the spatial interpolation algorithm of the nonlinear function is a spatial interpolation algorithm of a third-order spline curve.
The technical scheme of the pollutant road condition map generation system based on the discrete monitoring point data comprises the following steps:
the system comprises a point set generation module, an interpolation module, a fusion module and a generation module;
the point set generation module processes the discrete monitoring point data according to preset conditions and respectively generates a sparse point set and a non-sparse point set;
the interpolation module performs first spatial interpolation processing on the sparse point set by using a spatial interpolation algorithm of a linear function and then expands the sparse point set into a sparse interpolation point set;
the fusion module fuses the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set;
the interpolation module further performs second spatial interpolation on the intermediate interpolation point set by using a spatial interpolation algorithm of a nonlinear function to obtain a final interpolation point set;
and the generating module generates a pollutant road condition map according to the final interpolation point set.
The pollutant road condition map generating system based on the discrete monitoring point data has the following beneficial effects:
the point set generating module processes discrete monitoring point data returned by discrete pollutant monitoring points which are non-uniformly distributed in space according to preset conditions to generate a sparse point set and a non-sparse point set, the interpolation module performs first-time spatial interpolation on the sparse point set and then increases the number of data points in the sparse point set to form a sparse interpolation point set, and the fusion module fuses the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set. And finally, a generation module generates a more detailed pollutant road condition map according to the final interpolation point set, so that the details are richer, the visualization effect is good, and the user can observe the map more conveniently and visually.
On the basis of the scheme, the pollutant road condition map generating system based on the discrete monitoring point data can be further improved as follows.
Further, the preset conditions are specifically as follows: sequentially acquiring actual monitoring distances between two adjacent data points in the discrete monitoring point data according to the sequence, judging whether the actual monitoring distances are larger than a preset distance threshold value, and if so, adding the two adjacent data points to the sparse point set in a point set pair mode; if not, adding the two adjacent data points into the non-sparse point set in a point set pair mode.
The beneficial effect of adopting the further scheme is that: whether the actual monitoring distance between two adjacent data points in the discrete monitoring point data is larger than a preset distance threshold value or not is judged in sequence, and the actual monitoring distance is added into a sparse point set or a non-sparse point set in a data pair mode, so that the method is simple and effective, the calculated amount is small, and the guarantee is provided for updating the pollutant road condition graph in real time.
Further, still include: the interpolation module performs first spatial interpolation processing on the sparse point set by using a spatial interpolation algorithm of a linear function, and then inserts a plurality of extended data points between two data points of each point set pair of the sparse point set to form the sparse interpolation point set.
The beneficial effect of adopting the further scheme is that: through the first time of spatial interpolation processing, a plurality of expanded data points are inserted between two data points of each point set pair of the sparse point set, the number of the data points in the sparse point set is increased, and the generated pollutant road condition graph is guaranteed to have rich details.
Further, still include: and the fusion module fuses each point set pair in the non-sparse point set and the corresponding point set pair in the sparse interpolation point set and deletes repeated data points to form the intermediate interpolation point set.
The beneficial effect of adopting the further scheme is that: the fusion mode that the fusion module fuses the sparse interpolation point set and the non-sparse point set is specifically explained.
Further, the spatial interpolation algorithm of the nonlinear function is a spatial interpolation algorithm of a third-order spline curve.
Drawings
Fig. 1 is a schematic flow chart of a method for generating a road condition map of pollutants based on discrete monitoring point data according to an embodiment of the present invention;
fig. 2 is a schematic flow frame diagram of a method for generating a road condition map of pollutants based on discrete monitoring point data according to an embodiment of the present invention;
FIG. 3 is a road condition map of pollutants generated directly based on data of discrete monitoring points;
FIG. 4 is a road condition diagram of pollutants generated by processing discrete monitoring point data directly by using a spatial interpolation method of a nonlinear function;
fig. 5 is a pollutant road map generated by the pollutant road map generating method based on discrete monitoring point data according to the present invention;
fig. 6 is a schematic structural diagram of a system for generating a road condition map of pollutants based on discrete monitoring point data according to an embodiment of the present invention;
Detailed Description
As shown in fig. 1 and fig. 2, a method for generating a road condition map of pollutants based on discrete monitoring point data according to an embodiment of the present invention includes the following steps:
s1, processing the discrete monitoring point data according to preset conditions, and respectively generating a sparse point set and a non-sparse point set;
s2, expanding the sparse point set into a sparse interpolation point set after carrying out first spatial interpolation processing on the sparse point set by using a spatial interpolation algorithm of a linear function;
s3, fusing the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set;
and S4, performing secondary spatial interpolation on the intermediate interpolation point set by using a spatial interpolation algorithm of a nonlinear function to obtain a final interpolation point set, and generating a pollutant road condition map according to the final interpolation point set.
Processing discrete monitoring point data returned by discrete pollutant monitoring points which are non-uniformly distributed in space according to preset conditions to generate a sparse point set and a non-sparse point set, performing first-time spatial interpolation on the sparse point set, increasing the number of data points in the sparse point set to form a sparse interpolation point set, and fusing the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set. And then, carrying out second spatial interpolation on the intermediate interpolation point set, namely further increasing the number of data points in the intermediate interpolation point set to generate a final interpolation point set, and then generating a more detailed pollutant road condition map according to the final interpolation point set, wherein the details are richer, the visualization effect is good, and the user can observe the map more conveniently and visually.
Wherein the pollutant can affect air quality to PM2.5 value and SO2Values, etc., the discrete contaminant monitoring points that are spatially non-uniformly distributed are: for example, N air quality monitoring sites are deployed in a city such as beijing, where N is a positive integer, and each site is respectively marked as a first air quality monitoring site and a second air quality monitoring site … … nth air quality detection site, and since the N air quality monitoring sites are dispersed at each position of beijing, returned air quality data form discrete monitoring point data.
The cloud images are displayed in different colors according to different ranges of data, so that a user can observe the cloud images conveniently, and the generation of the cloud images according to the data is the prior art and is not described herein again.
Preferably, in the above technical solution, the preset condition is specifically: sequentially acquiring actual monitoring distances between two adjacent data points in the discrete monitoring point data according to the sequence, judging whether the actual monitoring distances are larger than a preset distance threshold value, and if so, adding the two adjacent data points to the sparse point set in a point set pair mode; and if not, adding the two adjacent data points into the non-sparse point set in a point set pair mode.
Whether the actual monitoring distance between two adjacent data points in the discrete monitoring point data is larger than a preset distance threshold value or not is judged in sequence, and the actual monitoring distance is added into a sparse point set or a non-sparse point set in a data pair mode, so that the method is simple and effective, the calculated amount is small, and the guarantee is provided for updating the pollutant road condition graph in real time.
Continuing to use the above example to describe the actual monitoring distance, assuming that two adjacent data points in the discrete monitoring point data are respectively returned by the first air quality monitoring station and the second air quality monitoring station, the actual monitoring distance refers to the distance between the first air quality monitoring station and the second air quality monitoring station, if the actual monitoring distance is 2000 meters and the preset distance threshold is 1000 meters, because the actual monitoring distance is greater than the preset distance threshold, the two adjacent data points are added into the sparse point set in a point set pair mode, and if the preset distance threshold is 2000 meters, the two adjacent data points are added into the non-sparse point set.
Preferably, in the above technical solution, S2 specifically is: and after the first spatial interpolation processing is carried out on the sparse point set by using a linear function spatial interpolation algorithm, a plurality of extended data points are inserted between two data points of each point set pair of the sparse point set to form the sparse interpolation point set.
Through the first time of spatial interpolation processing, a plurality of expanded data points are generated between two data points of each point set pair of the sparse point set, the number of the data points in the sparse point set is increased, and the generated pollutant road condition graph is guaranteed to have rich details.
Preferably, in the above technical solution, S3 specifically is: and fusing the point set pairs in the non-sparse point set and the corresponding point set pairs in the sparse interpolation point set, and deleting repeated data points to form the intermediate interpolation point set. The method specifically explains the fusion mode of the sparse interpolation point set and the non-sparse point set.
Preferably, in the above technical solution, the spatial interpolation algorithm of the nonlinear function is a spatial interpolation algorithm of a third-order spline curve.
The following describes a method for generating a road map of pollutants based on discrete monitoring point data in this application in more detail by way of an example:
taking PM2.5 in air quality as an example, suppose 5 air quality monitoring sites are deployed in a certain area, each site is respectively marked as a first air quality monitoring site, a second air quality monitoring site, a third air quality monitoring site, a fourth air quality monitoring site and a fifth air quality monitoring site, and the returned data points are d1、d2、d3、d4And d5I.e. discrete checkpoint data as d1、d2、d3、d4And d5In this orderSequencing, if the distance between the first air quality monitoring station and the second air quality monitoring station is d1And d2The actual monitoring distance between the first air quality monitoring station and the second air quality monitoring station is 2000 m, and the distance between the second air quality monitoring station and the third air quality monitoring station is d2And d3The actual monitoring distance between the monitoring stations is 500 m, and the distance between the third air quality monitoring station and the fourth air quality monitoring station is d3And d4The actual monitoring distance between the stations is 2000 m, and the distance between the fourth air quality monitoring station and the fifth air quality monitoring station is d4And d5The actual monitored distance between is 2000 meters, then:
1) according to d1、d2、d3、d4And d5The road map of the directly generated pollutants is shown in FIG. 3, and the road map of the pollutants is sequentially represented by d from top to bottom in FIG. 31、d2、d3、d4And d5And the generated corresponding local pollutant road condition map has no data points in the blank area, and the details are less, so that the user can not observe the map intuitively.
2) If the space interpolation method of the nonlinear function is directly utilized, for example, the space interpolation method of the third-order spline curve is utilized for d1、d2、d3、d4And d5The road map of the pollutants formed after the treatment is shown in fig. 4, although the road map has more details than those in fig. 3, the road map is still not convenient for the user to visually observe.
If the preset distance threshold is 1000 m, d is the minimum distance1And d2Actual monitored distance between, d3And d4The actual monitoring distances between are all larger than the preset distance threshold value, d2And d3Actual monitored distance between, d4And d5All the actual monitoring distances between the two are smaller than the preset distance threshold value, d is added1And d2、d3And d4Adding sparse point sets in a data pair form respectively: i.e. d1—d2And d3—d4D is mixing2And d3、d4And d5Adding non-sparse point sets in the form of data pairs respectivelyNamely: d is a radical of2—d3And d4—d5Wherein, the point set is paired with d1—d2Denotes d1To d2Range, point set pair d3—d4Denotes d3To d4By analogy, the discrete detection data is marked as D _ input, the sparse point set is marked as D _ sparse, the non-sparse point set is marked as D _ dense, a middle variable D _ left is set, the preset distance threshold is marked as S, and the following logic can be used for realizing:
initialization:
D_left＝D_input；
while D_left≠[]：
two adjacent data points are sequentially selected from D _ left, and are marked as Di、di+1；
Calculating di，di+1Actual monitored distance between, labeled: dist _ di_di+1；
if dist_di_di+1＞S：
Then d will bei、d i+1Adding a sparse point set D _ sparse in a point set pair form;
delete D from D _ leftiPoint;
else：
will di、d i+1Adding non-sparse point set D _ dense in point set pair form
Point D1 is deleted from D _ left;
removing the weight of the same data point in the non-sparse point set D _ dense;
and outputting a sparse point set D _ sparse and a sparse point set D _ dense.
Wherein, i is a positive integer and 0< i <5, when there are N air quality monitoring stations, 0< i < N.
Then, after the first spatial interpolation is performed on the sparse point set by using a linear function spatial interpolation algorithm, a plurality of extended data points are inserted between two data points of each point set pair of the sparse point set, and the condition that d is the position of d is assumed1—d2Between which 2 extended data points are inserted, respectively denoted d11And d12At d3—d4Also generates 2 extended data points, labeled d respectively31And d32At this time, the generated sparse interpolation point set is: d1—d11—d12—d2And d3—d31—d32—d4I.e. with d respectively1And d2Is head and tail, and d is added in the middle11And d12Respectively by d3And d4Is head and tail, and d is added in the middle31And d32At this time, the range included in the sparse interpolation point set is d1—d11—d12—d2And d3—d31—d32—d4D in a non-sparse point set2—d3And d4—d5Respectively fusing the point set pairs corresponding to the sparse interpolation point set to form complete d1To d5Due to d during fusion2、d3、d4The three data points coincide, and the duplicate removal is performed on the data points, and the obtained intermediate interpolation point set is as follows: d1—d11—d12—d2—d3—d31—d32—d4—d5Then, a spatial interpolation algorithm of a nonlinear function is used for carrying out second spatial interpolation on the intermediate interpolation point set, and d is the value of1—d11、d11—d12Insert again the augmented data points between, for example: at d1—d11Is inserted between111At d is11—d12Is inserted between112At d12—d2Is inserted between122At d2—d3Is inserted between22At d3—d31Is inserted between30At d31—d32Is inserted between d311At d32—d4Is inserted between322At d4—d5Is inserted between40Then, the final set of interpolation points generated is: d1—d111—d11—d112—d12—d122—d2—d22—d3—d30—d31—d311—d32—d322—d4—d40—d5Fig. 5 shows a pollutant road map generated according to the final interpolation point set, and due to the existence of more data points, the generated pollutant road map has richer details, which is convenient for a user to visually observe.
That is to say, the application provides an interpolation method of a double-layer composite structure consisting of approximate interpolation and fine interpolation, the sparse degree of the distribution of discrete air quality monitoring points is used as a judgment basis, when the monitoring points are distributed in a sparse area, a linear function-based spatial interpolation method is adopted to reasonably estimate the air quality result between the two points, namely, the first spatial interpolation is carried out, the estimation result and other acquisition points are used as input, and a nonlinear function-based spatial interpolation method is used for fine interpolation, namely, the second spatial interpolation is carried out, so that a pollutant road condition map with richer details is obtained.
In addition, the updating frequency of the pollutant cloud picture can be set, for example, the updating frequency is set to be 1 time/minute, 2 times/minute and the like, and the pollutant cloud picture is updated by implementation, so that the observation by a user is facilitated.
As shown in fig. 6, a system 200 for generating a road condition map of pollutants based on discrete monitoring point data according to an embodiment of the present invention includes a point set generating module 210, an interpolation module 220, a fusion module 230, and a generating module 240;
the point set generating module 210 processes the discrete monitoring point data according to preset conditions, and respectively generates a sparse point set and a non-sparse point set;
the interpolation module 220 performs a first spatial interpolation process on the sparse point set by using a spatial interpolation algorithm of a linear function and then expands the sparse point set into a sparse interpolation point set;
the fusion module 230 fuses the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set;
the interpolation module 220 further performs a second spatial interpolation on the intermediate interpolation point set by using a spatial interpolation algorithm of a nonlinear function to obtain a final interpolation point set;
the generating module 240 generates a road condition map of the pollutants according to the final interpolation point set.
The point set generating module 210 processes discrete monitoring point data returned by discrete pollutant monitoring points which are non-uniformly distributed in space according to preset conditions to generate a sparse point set and a non-sparse point set, the interpolation module 220 performs first spatial interpolation on the sparse point set to increase the number of data points in the sparse point set to form a sparse interpolation point set, and the fusion module 230 fuses the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set. Then, the interpolation module 220 performs a second spatial interpolation on the intermediate interpolation point set, which is equivalent to further increasing the number of data points in the intermediate interpolation point set to generate a final interpolation point set, and finally, the generation module 240 generates a more detailed pollutant road map according to the final interpolation point set.
Preferably, in the above technical solution, the preset condition is specifically: sequentially acquiring actual monitoring distances between two adjacent data points in the discrete monitoring point data according to the sequence, judging whether the actual monitoring distances are larger than a preset distance, and if so, adding the two adjacent data points to the sparse point set in a point set pair mode; and if not, adding the two adjacent data points into the non-sparse point set in a point set pair mode.
Whether the actual monitoring distance between two adjacent data points in the discrete monitoring point data is larger than the preset distance or not is judged in sequence, and the actual monitoring distance is added into a sparse point set or a non-sparse point set in a data pair mode, so that the method is simple and effective, the calculated amount is small, and the guarantee is provided for updating the pollutant road condition diagram in real time.
Preferably, in the above technical solution, the method further comprises: the interpolation module 220 performs a first spatial interpolation process on the sparse point set by using a spatial interpolation algorithm of a linear function, and then inserts a plurality of extended data points between two data points of each point set pair of the sparse point set to form the sparse interpolation point set.
Through the first time of spatial interpolation processing, a plurality of expanded data points are generated between two data points of each point set pair of the sparse point set, the number of the data points in the sparse point set is increased, and the generated pollutant road condition graph is guaranteed to have rich details.
Preferably, in the above technical solution, the method further comprises: the fusion module 230 fuses pairs of points in the non-sparse point set with corresponding pairs of points in the sparse interpolation point set and deletes repeated data points to form the intermediate interpolation point set. The fusion mode of the fusion module 230 for fusing the sparse interpolation point set and the non-sparse point set is illustrated.
Preferably, in the above technical solution, the spatial interpolation algorithm of the nonlinear function is a spatial interpolation algorithm of a third-order spline curve.
Preferably, in the above technical solution, the system further comprises an updating module, and the updating module is configured to set an updating frequency for updating the cloud image of the pollutant.
The above steps for realizing the corresponding functions of each parameter and each unit module in the system 200 for generating a road condition map of pollutants based on discrete monitoring point data of the present invention can refer to each parameter and step in the above embodiment of the method for generating a road condition map of pollutants based on discrete monitoring point data, which are not described herein again.
In the present invention, the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specified otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A pollutant road condition map generation method based on discrete monitoring point data is characterized by comprising the following steps:
s1, processing the discrete monitoring point data according to preset conditions, and respectively generating a sparse point set and a non-sparse point set;
s2, expanding the sparse point set into a sparse interpolation point set after carrying out first spatial interpolation processing on the sparse point set by using a spatial interpolation algorithm of a linear function;
s3, fusing the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set;
and S4, performing second spatial interpolation on the intermediate interpolation point set by using a spatial interpolation algorithm of a nonlinear function to obtain a final interpolation point set, and generating a pollutant road condition map according to the final interpolation point set.
2. The method for generating the road condition map of the pollutants based on the data of the discrete monitoring points as claimed in claim 1, wherein the preset conditions are specifically as follows: sequentially acquiring actual monitoring distances between two adjacent data points in the discrete monitoring point data according to the sequence, judging whether the actual monitoring distances are larger than a preset distance threshold value, and if so, adding the two adjacent data points to the sparse point set in a point set pair mode; and if not, adding the two adjacent data points into the non-sparse point set in a point set pair mode.
3. The method for generating the road condition map of the pollutants based on the discrete monitoring point data as claimed in claim 2, wherein S2 is specifically: and after the first spatial interpolation processing is carried out on the sparse point set by using a linear function spatial interpolation algorithm, a plurality of extended data points are inserted between two data points of each point set pair of the sparse point set to form the sparse interpolation point set.
4. The method for generating the road condition map of the pollutants based on the discrete monitoring point data as claimed in claim 3, wherein S3 is specifically as follows: and fusing each point set pair in the non-sparse point set and each corresponding point set pair in the sparse interpolation point set, and deleting repeated data points to form the intermediate interpolation point set.
5. The method as claimed in claim 4, wherein the spatial interpolation algorithm of the nonlinear function is a spatial interpolation algorithm of a third-order spline curve.
6. A pollutant road condition map generating system based on discrete monitoring point data is characterized by comprising a point set generating module, an interpolation module, a fusion module and a generating module;
the point set generation module processes the discrete monitoring point data according to preset conditions and respectively generates a sparse point set and a non-sparse point set;
the interpolation module performs first spatial interpolation processing on the sparse point set by using a spatial interpolation algorithm of a linear function and then expands the sparse point set into a sparse interpolation point set;
the fusion module fuses the sparse interpolation point set and the non-sparse point set to form an intermediate interpolation point set;
the interpolation module further performs second spatial interpolation on the intermediate interpolation point set by using a spatial interpolation algorithm of a nonlinear function to obtain a final interpolation point set;
and the generating module generates a pollutant road condition map according to the final interpolation point set.
7. The system according to claim 6, wherein the preset conditions are specifically as follows: sequentially acquiring actual monitoring distances between two adjacent data points in the discrete monitoring point data according to the sequence, judging whether the actual monitoring distances are larger than a preset distance threshold value, and if so, adding the two adjacent data points to the sparse point set in a point set pair mode; and if not, adding the two adjacent data points into the non-sparse point set in a point set pair mode.
8. The system of claim 7, further comprising: the interpolation module performs first spatial interpolation processing on the sparse point set by using a spatial interpolation algorithm of a linear function, and then inserts a plurality of extended data points between two data points of each point set pair of the sparse point set to form the sparse interpolation point set.
9. The system of claim 8, further comprising: and the fusion module fuses each point set pair in the non-sparse point set and each corresponding point set pair in the sparse interpolation point set and deletes repeated data points to form the intermediate interpolation point set.
10. The system of claim 9, wherein the spatial interpolation algorithm of the nonlinear function is a spatial interpolation algorithm of a third-order spline curve.
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