CN110889199B - Layout optimization method of port atmospheric particulate matter concentration online detector - Google Patents
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Abstract
The invention discloses a layout optimization method of a port atmospheric particulate matter concentration online detector, which can be used for online real-time continuous detection of concentrations of PM2.5, PM10 and TSP in a port atmospheric environment. The method comprises the following steps: carrying out port plane grid division; detecting the concentration of atmospheric particulate matters in a vertical space of a port grid; constructing a worst inferior curved surface of the port space with the most serious particulate pollution; optimizing the space aggregation of the port grids; and selecting the inferior curved surface of one mesh in each determined aggregation mesh set as an optimal layout scheme of the on-line detector. According to the correlation between the particulate matter concentrations in the adjacent grid spaces, the invention carries out aggregation optimization on the grids of the port, comprehensively considers the spatial relationship of different grids on the worst-case curved surface with the most particulate matter pollution, and determines the optimal layout position of the on-line detector. The detection result effectiveness can be ensured while the distribution is reduced, and the input and output benefits of port pollution monitoring are improved.
Description
Technical Field
The invention relates to a layout optimization method of a port atmospheric particulate matter concentration online detector, which mainly considers the land area range in a port and does not consider the atmospheric particulate matter concentration detection in the water area range, and belongs to the field of traffic environmental pollution and prevention and control.
Background
The reasonable arrangement of the on-line detectors is the basis for effectively detecting the atmospheric pollution, is the premise of correctly reflecting the atmospheric environment condition of the port, and is an important way for improving the reliability of the atmospheric environment detection result of the port. Due to the influence factors such as the complexity of the sources of the particles, the diversity of the arrangement of the port functional areas, the particularity of the operation machinery and the operation process, the atmosphere pollution condition in the port space is complex and variable. For the layout of the online detectors, when point location selection is inappropriate, detection data cannot fully reflect the actual atmospheric pollution condition of the port, and even the selection of a port dust pollution treatment scheme is influenced.
The inventor finds that the arrangement point of the online detector capable of reflecting the real pollution condition of the atmospheric environment of the port can be obtained by detecting the concentration data of the particulate matters in the three-dimensional space of the port through the unmanned aerial vehicle, the whole pollution condition in the port space is effectively detected, the incompleteness of the conventional ground detection is avoided, and the input and output benefits of port online detection equipment are improved.
Disclosure of Invention
The invention aims to provide a layout optimization method of a port environment particulate matter concentration online detector, which can fully reflect the online real-time continuous detection of the concentrations of PM2.5, PM10 and TSP in a port atmospheric environment on the basis of the actual port atmospheric pollution condition of a detection result.
In order to solve the technical problems, the invention adopts the technical scheme that:
a layout optimization method for a port atmospheric particulate matter concentration online detector is characterized by comprising the following steps:
1) and (3) carrying out plane meshing on the port: carrying out grid division on a port plane according to a port general plane layout diagram;
2) detecting the concentration of atmospheric particulate matters in a vertical space of a port grid: detecting the concentration of the particulate matters at L different height positions in a space with the height of H meters from the ground to the vertical upper part of the center position of each grid by using a particulate matter concentration detector to obtain L particulate matter concentration data in the vertical space above the grid, wherein L is more than or equal to 2;
3) constructing the worst inferior curved surface of the particulate pollution in the port space: determining the most serious height of the particulate matter pollution in each grid, and joining the planes with the most serious particulate matter pollution severity of all grids to form a worst curved surface with the most serious particulate matter pollution in a port space;
4) and (3) space aggregation optimization of the port grid: performing correlation analysis according to the particle concentration data detected in different grid vertical spaces of the port to determine a converged grid set;
5) determining an optimal layout scheme of the online detector: and selecting the inferior curved surface of one mesh in each determined aggregation mesh set as an optimal layout scheme of the on-line detector.
The method for determining the optimal layout scheme of the online detector in the step 5) comprises the following steps:
51) determining the coordinate (X) of the central point of each aggregation grid set on the inferior curved surface with the highest concentration of particulate matters in the port space c (O n ),Y c (O n ) In the formula O) n N is the nth aggregated grid set, where N is 1,2, …, and N is the total number of aggregated grid sets;
52) respectively calculating each aggregation grid set O n All grids in and aggregate grid set O n Center point coordinate (X) c (O n ),Y c (O n ) Distance of (c)
Comprises the following steps:
in the formula, a n ＝1,2,…,A n ；A n For the nth aggregated mesh set O n The number of contained grids;
53) determining each aggregation grid set O n All of A in n Individual grids and grid sets O n Center point coordinate (X) c (O n ),Y c (O n ) Maximum value of distance of)
Comprises the following steps:
54) determining each aggregation grid set O n Each grid a in n Corrected value of particulate matter pollution severity on inferior curved surface in upper vertical space
55) And taking the grid with the maximum corrected value of the particulate matter pollution severity in all grids in the aggregation grid set as the arrangement point of the online detector.
L ═ H/b, where b is the height spacing.
And (3) detecting the concentration of the atmospheric particulate matters in the port grid vertical space in the step 2) by adopting an unmanned aerial vehicle.
When unmanned aerial vehicle detects, according to port historical meteorological information, the wind speed, temperature and humidity index of comprehensive consideration are confirmed, the best date of detection of unmanned aerial vehicle.
The method for determining the optimal detection date of the unmanned aerial vehicle comprises the following steps:
21) acquiring historical meteorological data of a port, and calculating data of average wind speed, average temperature and average humidity of each week in local past K years;
22) constructing a weekly average wind speed set, an average temperature set and an average humidity set according to the weekly average wind speed, temperature and humidity data;
23) measuring and calculating the weekly wind speed index f s (i) Temperature index f t (i) And a humidity index f h (i)，
Wherein mins (i) is the lowest wind speed value in the weekly average wind speed set, maxs (i) is the highest wind speed value in the weekly average wind speed set, mint (i) is the lowest temperature value in the weekly average temperature set, maxt (i) is the highest temperature value in the weekly average temperature set, minh (i) is the lowest humidity value in the weekly average humidity set, and maxs (i) is the highest humidity value in the weekly average humidity set;
24) determining B according to the wind speed index, the temperature index and the humidity index t (i) The corresponding best detection week of the day week drone,
wherein, B t (i) For the optimal detection meteorological conditions after comprehensively considering the wind speed, the temperature and the humidity, MaxV (i) is the maximum value of the wind speed index, the temperature index and the humidity index, lambda, of the comprehensive consideration of the whole year for 52 weeks 1 ,λ 2 ,λ 3 Is a weight coefficient, and λ 1 +λ 2 +λ 3 ＝1；
25) And selecting a day as the unmanned aerial vehicle detection date in the best detection week.
The method for dividing the plane grids of the port in the step 1) comprises the following steps:
11) acquiring a layout diagram of a total plane of a port area, and determining longitude and latitude coordinates of boundary points of the total plane;
12) in the layout diagram of the total plane of the harbor area, setting an X axis with the lowest point of latitude, setting a Y axis with the lowest point of longitude, and establishing a rectangular plane coordinate system of the layout diagram of the total plane of the harbor area by combining the size data of the total plane of the harbor area;
13) in the constructed rectangular plane coordinate system, dividing the port into M grids on the abscissa and the ordinate respectively by taking the width w as an interval, numbering all grids in the total plane of the port from the position closest to the zero point of the X axis as 1, and numbering the grids in sequence by a clockwise spiral rule until all grids have special numbers M, wherein M is 1,2, … and M; the clockwise spiral rule means that after adjacent grids are numbered from the grid with the number 1 along the Y-axis direction until the grid boundary which can be numbered is reached, the adjacent grids are numbered in the clockwise direction to the X-axis direction until the grid boundary which can be numbered is reached, and then the adjacent grids are numbered in the clockwise direction to the Y-axis direction until all grids are numbered;
14) determining the position of the central point of each grid in the rectangular coordinate system, and recording as (X) c (m),Y c (m))。
The method for detecting the concentration of the atmospheric particulates in the vertical space of the harbor grid in the step 2) comprises the following steps:
21) by using the unmanned aerial vehicle carrying the particulate matter concentration detector, the concentration of PM2.5 detected at the position of every b meters in the space from the ground to the H meters above the vertical at the central position of each grid is PM 2.5 (m, l, d), concentration of PM10 being PM 10 (m, l, d) and the concentration of TSP is TSP (m, l, d). Wherein L is a detection position number from the ground to a position vertically above the ground within a height space of H meters, and the height interval of each detection within the height range of H meters is b meters, so that L is 1,2, … and L from the position b meters above the ground; d is the number of the unmanned aerial vehicle detection times, D is 1,2, …, D is the total detection times; PM (particulate matter) 2.5 (m, l, d) refers to the concentration of particulate matters with the dynamic equivalent diameter of less than or equal to 2.5 micrometers in the position l numbered by the central height of the grid m and detected by the unmanned aerial vehicle in the d-th flight; PM (particulate matter) 10 (m, l, d) refers to the concentration of particulate matters with the dynamic equivalent diameter of less than or equal to 10 micrometers in the position l numbered by the central height of the grid m and detected by the unmanned aerial vehicle in the d-th flight; TSP (m, l, d) refers to the concentration of particles with the height number of l in the center of the grid m, and the dynamic equivalent diameter of the unmanned aerial vehicle detected in the d-th flight is less than or equal to 100 micrometers;
22) according to the concentration data of the particulate matters detected by the unmanned aerial vehicle in each grid vertical space, obtaining the average concentrations of PM2.5, PM10 and TSP with different heights; the average concentration of PM2.5 detected at different heights is
An average concentration of PM10 of
Average concentration of TSP of
23) Determining the particle concentration data sets of different grid spaces, wherein the concentration set of PM2.5 is
Concentration of PM10 is
Concentration of TSP is set to
The method for constructing the worst particle concentration curved surface in the step 3) comprises the following steps:
31) determining the maximum value of the particle concentration of the space above different grids of the port and constructing a set Z (m) with the highest particle concentration:
concentration maximum of PM2.5 in the vertically above space
Concentration maximum value of PM10
Maximum concentration value TSP of TSP max (m)＝MaxTSP(M)；
Constructing the set with the highest concentration of particulate matters at the m position of the grid
32) Measuring and calculating the concentration index of each grid particulate matter:
concentration index f of PM2.5 PM2.5 (m, l) concentration index of PM10 is f PM10 (m, l), TSP concentration index is f TSP (m, l) are respectively:
33) determine the maximum value of each grid concentration index:
maximum value of PM2.5 concentration index
Maximum value of PM10 concentration index
Maximum value of TSP concentration index
In the formula, Max [ ] each represents the maximum value in the vector;
34) determining the maximum value of each grid particulate matter concentration index:
the maximum value of the particulate matter concentration index is:
35) calculating a correlation coefficient between the concentration coefficient of the particulate matters in each grid and the maximum value of the concentration coefficient:
the correlation coefficients of the concentration coefficients of PM2.5, PM10 and TSP and the maximum value of the concentration coefficients are as follows:
36) determining the pollution severity of different heights according to the correlation coefficient of the particulate matter concentration data of each grid, and obtaining a particulate matter pollution severity set of each grid:
the particulate concentration contamination severity is P (m, l), and the particulate contamination severity set P (m) for grid m is:
β 1 ,β 2 ,β 3 is a weight coefficient, and β 1 +β 2 +β 3 ＝1；
37) Determining the maximum value of the contamination severity in the vertical space above each grid:
maximum value of contamination severity P max (m)＝MaxP(m)；
38) And connecting the planes with the maximum grid particle pollution severity to form an inferior curved surface with the most serious particle pollution in the port space.
The spatial aggregation optimization method of the port grids in the step 4) comprises the following steps:
41) calculating the correlation degree of the particulate matter concentration between adjacent grids:
correlation degree Co of PM2.5 PM2.5 (n, q), degree of correlation Co of PM10 PM10 Correlation Co of (n, q) and TSP TSP (n, q) is:
wherein q is a grid adjacent to grid n;
42) calculating the comprehensive correlation coefficient of the particulate matter concentration between adjacent grids:
particle concentration comprehensive correlation coefficient CT (n, q):
wherein,
is a weight coefficient, and
43) if the comprehensive correlation coefficient CT (n, q) of the particle concentration in the grid n and the adjacent grid q is more than or equal to epsilon, dividing the adjacent grid q into the aggregation subset O of the grid n n (ii) a Where epsilon is a preset correlation threshold.
Compared with the prior art, the invention has the beneficial effects that:
1) according to the invention, the port plane is subdivided into different grids, and the unmanned aerial vehicle is used for detecting the concentration of the particulate matters in different grid vertical spaces of the port, so that compared with the existing online detector only fixed on the ground, the device can more fully master the concentration characteristics of the particulate matters in the port.
2) According to the characteristics of the concentration of the particulate matters in each grid space, the inferior curved surface with the most serious particulate matter pollution in the port space is determined, and only the local plane area with the most serious particulate matter pollution can be judged through the online detector fixed on the ground.
3) According to the correlation between the particulate matter concentrations in the adjacent grid spaces, the invention carries out aggregation optimization on the grids of the port, comprehensively considers the spatial relationship of different grids on the worst-case curved surface with the most particulate matter pollution, and determines the optimal layout position of the on-line detector. The problem that the existing online detector layout lacks quantitative judgment basis can be solved, the optimal distribution position is selected in a targeted manner according to the pollution concentration characteristics of port particulate matters, the distribution can be reduced, meanwhile, the effectiveness of detection results is guaranteed, and the input-output benefits of port pollution monitoring are improved.
Drawings
FIG. 1 is a flow chart;
FIG. 2 is a schematic diagram of a plane meshing of a port;
FIG. 3 is a schematic diagram of a grid association optimization adjustment set;
fig. 4 is a schematic diagram of an optimal layout scheme of an online detector.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
the layout optimization method of the port atmospheric particulate matter concentration online detector can be used for online real-time continuous detection of the concentrations of PM2.5, PM10 and TSP in the port atmospheric environment, and can really master the pollution condition of the port atmospheric environment while saving the construction cost.
As shown in FIG. 1, the method of the present invention comprises the following steps:
firstly, carrying out grid division on the port according to a port general plane layout diagram, and numbering according to a clockwise spiral rule;
secondly, detecting the concentration of the particulate matters in each grid vertical space by using an unmanned aerial vehicle carrying a particulate matter concentration detector;
further, determining the worst inferior curved surface of the port space with the most serious pollution of the particulate matters according to the characteristics of the particulate matter concentration of each grid space;
meanwhile, carrying out aggregation optimization on grids of the port according to the correlation between the concentrations of the particulate matters in the adjacent grid spaces;
and finally, comprehensively considering the spatial relationship of different grids on the inferior curved surface with the most serious particulate pollution, and determining the optimal layout position of the online detector.
A. The method for dividing the plane grid of the port comprises the following steps:
A1) acquiring a layout diagram of a total plane of a port area, and determining longitude and latitude coordinates of boundary points of the total plane;
A2) as shown in fig. 2, in the layout diagram of the total plane of the harbor district, an X axis is set with the lowest point of latitude, a Y axis is set with the lowest point of longitude, and a rectangular plane coordinate system of the layout diagram of the total plane of the harbor district is established by combining the dimensional data of the total plane of the harbor district;
A3) in the constructed rectangular plane coordinate system, the port is divided into M grids on the abscissa and the ordinate respectively by taking the width w as an interval, all grids in the total plane of the port are numbered from the position closest to the zero point of the X axis as 1, and the other grids are sequentially numbered by a clockwise spiral rule until all grids have special numbers M, wherein M is 1,2, … and M. The clockwise spiral rule means that after the grids numbered 1 are numbered along the Y-axis direction until the grids which can be numbered are bounded, the clockwise direction is converted into the X-axis direction to number the adjacent grids until the grids which can be numbered are bounded, and then the clockwise direction is converted into the Y-axis direction to carry out the same numbering until all the grids are numbered completely. As shown in fig. 2, when there is no special requirement, according to the experience of the inventor, the interval width w of the grid division may take a value of 50 meters;
A4) determining the position of the central point of each grid in the rectangular coordinate system, and recording as (X) c (m),Y c (m))。
B. The method for determining the unmanned aerial vehicle detection date comprises the following steps:
B1) acquiring historical meteorological data of a port, and calculating the average wind speed, average temperature and average humidity data of each week in the local past K years, wherein the average wind speed is 1,2, … and 52 week in the K (K is 1,2, … and K) year as an example
Average temperature of
Average humidity of
Where s (i, j, k) is the average wind speed at day j of the ith week of the kth year, t (i, j, k) is the average temperature at day j of the ith week of the kth year, and h (i, j, k) is the average humidity at day j of the ith week of the kth year. According to the experience of the inventor, historical meteorological data can be acquired by a local meteorological administration, and the recommended historical year K is 3.
B2) Constructing a weekly average wind speed set according to the weekly average wind speed, temperature and humidity data
Mean temperature set
And average humidity set
B3) Measuring and calculating the weekly wind speed index f s (i) Temperature index f t (i) And a humidity index f h (i)，
Wherein mins (i) is the lowest wind speed value in the weekly average wind speed set, maxs (i) is the highest wind speed value in the weekly average wind speed set, mint (i) is the lowest temperature value in the weekly average temperature set, maxt (i) is the highest temperature value in the weekly average temperature set, minh (i) is the lowest humidity value in the weekly average humidity set, and maxs (i) is the highest humidity value in the weekly average humidity set.
B4) From the wind speed index, the temperature index and the humidity index, B can be determined t (i) The corresponding best detection week of the day week drone,
wherein, B t (i) For the optimal detection meteorological conditions after comprehensively considering the wind speed, the temperature and the humidity, MaxV (i) comprehensively considers the maximum values of the wind speed index, the temperature index and the humidity index, lambda, for 52 weeks all year round 1 ,λ 2 ,λ 3 Is a weight coefficient, and λ 1 +λ 2 +λ 3 ＝1。
When there is no particular requirement, λ may be set according to the experience of the inventors 1 ＝0.3,λ 2 ＝0.3,λ 3 ＝0.4。
B5) One day (port normal operation) was selected as the unmanned inspection date in the best inspection week.
C. The method for detecting the concentration of the atmospheric particulate matters in the vertical space of the port grid comprises the following steps:
C1) by using the unmanned aerial vehicle carrying the particulate matter concentration detector, the concentration of PM2.5 detected at the position of every b meters in the space from the ground to the H meters above the vertical at the central position of each grid is PM 2.5 (m, l, d), concentration of PM10 being PM 10 (m, l, d) and the concentration of TSP is TSP (m, l, d). Wherein l is the number of the detection position in the space of height H meters from the ground to the vertical upper part, the height interval of each detection in the range of height H meters in the invention is b meters, therefore, l is 1,2 from the position b meters above the ground,…, L; d is the total times of unmanned detection, D is the number of unmanned detection, and D is 1,2, …, D; PM (particulate matter) 2.5 (m, l, d) is the concentration of particulate matters with the dynamic equivalent diameter of less than or equal to 2.5 microns detected in the d-th flight at the position with the central height number of the grid m as l; PM (particulate matter) 10 (m, l, d) is the concentration of particulate matters with the dynamic equivalent diameter of less than or equal to 10 microns detected in the d flight at the position with the central height number of the grid m as the position l; TSP (m, l) refers to the concentration of particles with the height number of l at the center of the grid m, and the kinetic equivalent diameter of the unmanned aerial vehicle detected in the d-th flight is less than or equal to 100 micrometers. When there is no special requirement, according to the inventor's experience, the height H detected by the drone above the port ground is preferably set to be in the range of H120 m, and the height interval b m detected by the drone may be set to be b5 m, so that L H/b 24, starting from a position 5 m above the ground, L1, 2, …, 24. According to the experience of the inventor, the number D of times of unmanned detection can be set to 3, and D can be recorded as 1,2 and 3.
C2) From the unmanned detected particulate matter concentration data in each grid vertical space, the average concentrations of PM2.5, PM10, and TSP at different heights can be obtained. Taking grid m as an example, the average concentration of PM2.5 detected at different heights is
An average concentration of PM10 of
Average concentration of TSP of
C3) Determining particulate matter concentration data sets of different grid spaces, taking grid m as an example, taking PM2.5 concentration set as a concentration set
Concentration of PM10 is
Concentration of TSP is set to
D. The method for constructing the worst curved surface of the particulate matter concentration comprises the following steps:
D1) the maximum value of the particle concentration in the space above different grids of the harbor is determined and a set Z (m) with the highest particle concentration is constructed. Taking grid m as an example, the maximum concentration of PM2.5 in the vertically upper space
Concentration maximum of PM10
Highest TSP concentration value TSP max (m) ═ maxstp (m). Thereby constructing the set with the highest concentration of the particles at the grid m
D2) Measuring and calculating the concentration index of the particulate matter in each grid, taking grid m as an example, the concentration index f of PM2.5 PM2.5 (m, l) concentration index of PM10 is f PM10 (m, l), TSP concentration index is f TSP (m, l) are respectively:
D3) determining the maximum value of each grid concentration index, taking grid m as an example, the maximum value of PM2.5 concentration index
Maximum value of PM10 concentration index
Maximum value of TSP concentration index
Max [ ] in the present invention each represents the maximum value in the vector.
D4) Determining the maximum value of the particulate matter concentration index of each grid, taking grid m as an example, the maximum value of the particulate matter concentration index is as follows:
D5) calculating a correlation coefficient between the concentration coefficient of the particulate matter of each grid and the maximum value of the concentration coefficient, taking the concentration of the particulate matter detected at a position which is numbered l vertically above the center of the grid m as an example, the correlation coefficients of the concentration coefficients of PM2.5, PM10 and TSP and the maximum value of the concentration coefficients are as follows:
D6) and determining the pollution severity of different heights according to the correlation coefficient of the particulate matter concentration data of each grid, and obtaining a particulate matter pollution severity set of each grid. Taking the concentration of the particulate matter detected at the position numbered l vertically above the center of the grid m as an example, the contamination severity of the particulate matter concentration is P (m, l), and the collection P (m) of the contamination severity of the particulate matter in the grid m is respectively:
β 1 ,β 2 ,β 3 is a weight coefficient, and β 1 +β 2 +β 3 1. According to the experience of the inventor, the local authorities can take the following values when no special requirements exist: beta is a beta 1 ＝0.2,β 2 ＝0.3,β 3 ＝0.5。
D7) Determining the maximum pollution severity value in the vertical space above each grid, taking grid m as an example, the maximum pollution severity value P max (m)＝MaxP(m)；
D8) And connecting the planes with the maximum grid particle pollution severity to form an inferior curved surface with the most serious particle pollution in the port space.
E. The space aggregation optimization method of the port grid comprises the following steps:
E1) starting from grid number 1, the correlation degree of the particulate matter concentration between the adjacent grids is calculated. Taking the grid m and the adjacent grid q as an example, the correlation Co of PM2.5 PM2.5 (m, q), degree of correlation Co of PM10 PM10 Correlation Co of (m, q) and TSP TSP (m, q) is:
E2) calculating a comprehensive correlation coefficient of the particulate matter concentration between adjacent grids, taking grid m and adjacent grid q as an example, and calculating a comprehensive correlation coefficient CT (m, q) of the particulate matter concentration;
wherein,
is a weight coefficient, and
when there is no particular requirement, it can be set according to the experience of the inventor
E3) If the comprehensive correlation coefficient CT (m, q) of the particle concentration in the grid m and the adjacent grid q is more than or equal to epsilon, dividing the adjacent grid q into the aggregation subset O of the grid m m . Where epsilon is a preset correlation threshold, epsilon can be set to 0.85 according to the inventor's experience. As shown in FIG. 3, the new aggregation subset O m There are 7 different grid sets, including all 73 grids in a port. The number of each grid includes the original number of the grid and the number of the aggregation subset, as shown in grid number 3(60)The original grid numbered 60 in FIG. 1 belongs to O 3 A subset.
E4) And so on until all grids of the port are aggregated and optimized into different associated grid sets, as shown in fig. 3;
F. the method for determining the optimal layout scheme of the online detector comprises the following steps:
F1) determining the coordinates of the central points of all the associated grid sets on the inferior curved surface with the highest concentration of the particulate matters in the port space to aggregate the subsets O m For example, the coordinates of the center point of the graph formed by all the grids in the aggregate subset are (X) c (O m ),Y c (O m )). With O m The graph formed by all the grids in the 6 th subset is taken as an example, and the coordinates of the center point are shown in fig. 4.
F2) Separately computing aggregate subsets O m All A grids in the set and the aggregation subset O m Center point coordinate (X) c (O m ),Y c (O m ) ) to aggregate the subsets O m The middle grid a (a is 1,2, …, a) is taken as an example, and is a distance from the center coordinate of the aggregation subset
Comprises the following steps:
F3) determining an aggregated subset O m All A grids in the set and the aggregation subset O m Center point coordinate (X) c (O m ),Y c (O m ) Maximum value of distance of)
Is composed of
F4) Comprehensively considering the corrected value of the pollution severity of the particles in the inferior curved surface with the highest concentration of the particles in all grid spaces in the aggregation subset to obtain the aggregation subset O m For example, on grid aCorrected value of particulate matter pollution severity on inferior curved surface in square vertical space
F5) And taking the grid with the maximum corrected value of the particulate matter pollution severity in all grids in the aggregation subset as the arrangement point of the online detector.
F6) The above steps are repeated for each aggregate subset to obtain the optimal arrangement of all on-line detectors in the port space, as shown in fig. 4.
Claims (9)
1. A layout optimization method for a port atmospheric particulate matter concentration online detector is characterized by comprising the following steps:
1) carrying out plane meshing on the port: carrying out grid division on a port plane according to a port general plane layout diagram;
2) detecting the concentration of atmospheric particulate matters in a vertical space of a port grid: detecting the concentration of the particulate matters at L different height positions in a space with the height of H meters from the ground to the vertical upper part of the center position of each grid by using a particulate matter concentration detector to obtain L particulate matter concentration data in the vertical space above the grid, wherein L is more than or equal to 2;
3) constructing the worst inferior curved surface of the particulate pollution in the port space: determining the most serious height of the particulate matter pollution in each grid, and joining the planes with the most serious particulate matter pollution severity of all grids to form a worst curved surface with the most serious particulate matter pollution in a port space;
4) and (3) space aggregation optimization of the port grid: performing correlation analysis according to the particle concentration data detected in different grid vertical spaces of the port to determine a converged grid set;
5) determining an optimal layout scheme of the online detector: selecting the inferior curved surface of one grid in each determined aggregation grid set as an optimal layout scheme of the on-line detector;
the method for determining the optimal layout scheme of the online detector in the step 5) comprises the following steps:
51) on the inferior curved surface with the highest concentration of particulate matters in the port space,determining coordinates (X) of a center point of each aggregated mesh set c (O n ),Y c (O n ) In the formula, O) n N is the nth aggregation grid set, N is 1,2, …, and N is the total number of aggregation grid sets;
52) respectively calculating each aggregation grid set O n All grids in and aggregate grid set O n Center point coordinate (X) c (O n ),Y c (O n ) Distance of (c)
Comprises the following steps:
in the formula, a n ＝1,2,…,A n ；A n For the nth aggregated mesh set O n The number of contained grids;
53) determining each aggregation grid set O n All of A in n Individual grid and grid set O n Center point coordinate (X) c (O n ),Y c (O n ) Maximum value of distance of)
Comprises the following steps:
54) determining each aggregation grid set O n Each mesh a in n Corrected value of particulate matter pollution severity on inferior curved surface in upper vertical space
In the formula, P max (a n ) Is a grid a n The maximum value of the severity of particulate contamination of (a);
55) and taking the grid with the maximum corrected value of the particulate matter pollution severity in all grids in the aggregation grid set as the arrangement point of the online detector.
2. The layout optimization method of claim 1, wherein L ═ H/b, where b is the height spacing.
3. The layout optimization method according to claim 1, characterized in that the port grid vertical space atmospheric particulate matter concentration detection of step 2) is performed by using an unmanned aerial vehicle.
4. The layout optimization method according to claim 3, wherein when unmanned aerial vehicles are detected, the optimal detection date of the unmanned aerial vehicles is determined according to historical meteorological information of ports and comprehensive consideration of wind speed, temperature and humidity indexes.
5. The layout optimization method according to claim 4, wherein the determination method of the optimal detection date of the unmanned aerial vehicle is as follows:
21) acquiring historical meteorological data of a port, and calculating data of average wind speed, average temperature and average humidity of each week in local past K years;
22) constructing a weekly average wind speed set, an average temperature set and an average humidity set according to the weekly average wind speed, temperature and humidity data;
23) measuring and calculating the weekly wind speed index f s (i) Temperature index f t (i) And a humidity index f h (i)，
Wherein mins (i) is the value with the lowest wind speed in the weekly set of average wind speeds, maxs (i) is the value with the highest wind speed in the weekly set of average wind speeds, mint (i) is the value with the lowest temperature in the weekly set of average temperatures, maxt (i) is the value with the highest temperature in the weekly set of average temperatures, minh (i) is the value with the lowest humidity in the weekly set of average humidities, and maxs (i) is the value with the highest humidity in the weekly set of average humidities;
24) determining B according to the wind speed index, the temperature index and the humidity index t (i) Corresponding to the best detection week of the unmanned aerial vehicle of week i,
wherein, B t (i) For the optimal detection meteorological conditions after comprehensively considering the wind speed, the temperature and the humidity, MaxV (i) comprehensively considers the maximum values of the wind speed index, the temperature index and the humidity index, lambda, for 52 weeks all year round 1 ,λ 2 ,λ 3 Is a weight coefficient, and λ 1 +λ 2 +λ 3 ＝1；
25) And selecting a day as the unmanned aerial vehicle detection date in the best detection week.
6. The layout optimization method according to claim 1, wherein the method for the grid division of the port plane in step 1) is as follows:
11) acquiring a layout diagram of a total plane of a port area, and determining longitude and latitude coordinates of boundary points of the total plane;
12) in the layout diagram of the total plane of the harbor area, setting an X axis with the lowest point of latitude, setting a Y axis with the lowest point of longitude, and establishing a rectangular plane coordinate system of the layout diagram of the total plane of the harbor area by combining the size data of the total plane of the harbor area;
13) in the constructed rectangular plane coordinate system, dividing the port into M grids on the abscissa and the ordinate respectively by taking the width w as an interval, numbering all grids in the total plane of the port from the position closest to the zero point of the X axis as 1, and numbering the grids in sequence by a clockwise spiral rule until all grids have special numbers M, wherein M is 1,2, … and M; the clockwise spiral rule means that after adjacent grids are numbered from the grid with the number 1 along the Y-axis direction until the grid boundary which can be numbered is reached, the adjacent grids are numbered in the clockwise direction to the X-axis direction until the grid boundary which can be numbered is reached, and then the adjacent grids are numbered in the clockwise direction to the Y-axis direction until all grids are numbered;
14) determining the position of the central point of each grid in the rectangular coordinate system, and recording as (X) c (m),Y c (m))。
7. The layout optimization method of claim 1, wherein the method for detecting the concentration of the atmospheric particulates in the vertical space of the harbor grid in the step 2) comprises the following steps:
21) by using the unmanned aerial vehicle carrying the particulate matter concentration detector, the concentration of PM2.5 detected at the position of every b meters in the space from the ground to the H meters above the vertical at the central position of each grid is PM 2.5 (m, l, d), concentration of PM10 being PM 10 (m, l, d) and the concentration of the TSP is TSP (m, l, d); wherein L is a detection position number from the ground to a position vertically above the ground within a height space of H meters, and the height interval of each detection within the height range of H meters is b meters, so that L is 1,2, …, L from the position b meters above the ground; d is the number of the unmanned aerial vehicle detection times, D is 1,2, …, D is the total detection times; PM (particulate matter) 2.5 (m, l, d) is the concentration of particulate matters with the dynamic equivalent diameter of less than or equal to 2.5 microns detected in the d-th flight at the position with the central height number of the grid m as l; PM (particulate matter) 10 (m, l, d) is the concentration of particulate matters with the dynamic equivalent diameter of less than or equal to 10 microns detected in the d flight at the position with the central height number of the grid m as the position l; TSP (m, l, d) refers to the concentration of particles with the number of l at the center height of the grid m, and the kinetic equivalent diameter of the unmanned aerial vehicle detected in the d-th flight is less than or equal to 100 micrometers;
22) according to the particle concentration data detected by the unmanned aerial vehicle in each grid vertical space, obtaining the average concentrations of PM2.5, PM10 and TSP with different heights; the average concentration of PM2.5 detected at different heights is
An average concentration of PM10 of
Has an average TSP concentration of
23) Determining the particle concentration data sets of different grid spaces, wherein the concentration set of PM2.5 is
Concentration of PM10 is
Concentration of TSP is set to
8. The layout optimization method according to claim 7, wherein the method for constructing the worst curved surface with the particulate matter concentration in the step 3) comprises the following steps:
31) determining the maximum value of the particle concentration of the space above different grids of the port and constructing a set Z (m) with the highest particle concentration:
concentration maximum of PM2.5 in the vertically above space
Concentration maximum of PM10
Maximum concentration value TSP of TSP max (m)＝MaxTSP(M)；
Constructing the set with the highest concentration of particulate matters at the m position of the grid
32) Measuring and calculating the concentration index of each grid particulate matter:
concentration index f of PM2.5 PM2.5 (m, l) concentration index of PM10 is f PM10 (m, l) a TSP concentration index of f TSP (m, l) is dividedRespectively, the following steps:
33) determine the maximum value of each grid concentration index:
maximum value of PM2.5 concentration index
Maximum value of PM10 concentration index
Maximum value of TSP concentration index
In the formula, Max [ ] all represent the maximum value in the vector;
34) determining the maximum value of each grid particulate matter concentration index:
the maximum value of the particulate matter concentration index is:
35) calculating a correlation coefficient between each grid particle concentration coefficient and the maximum value of the concentration coefficient:
the correlation coefficients of the concentration coefficients of PM2.5, PM10 and TSP and the maximum value of the concentration coefficients are as follows:
36) determining the pollution severity of different heights according to the correlation coefficient of the particulate matter concentration data of each grid, and obtaining a particulate matter pollution severity set of each grid:
the particulate concentration contamination severity is P (m, l), and the particulate contamination severity set P (m) for grid m is:
β 1 ,β 2 ,β 3 is a weight coefficient, and β 1 +β 2 +β 3 ＝1；
37) Determining the maximum value of the contamination severity in the vertical space above each grid:
maximum value of contamination severity P max (m)＝MaxP(m)；
38) And connecting all planes with the maximum grid particle pollution severity to form the worst curved surface with the most serious particle pollution in the port space.
9. The layout optimization method of claim 8, wherein the spatial aggregation optimization method for the harbor grids in the step 4) is:
41) calculating the correlation degree of the particulate matter concentration between adjacent grids:
correlation degree Co of PM2.5 PM2.5 (n, q), degree of correlation Co of PM10 PM10 Correlation Co of (n, q) and TSP TSP (n, q) is:
wherein q is a grid adjacent to grid n;
42) calculating the comprehensive correlation coefficient of the particulate matter concentration between adjacent grids:
particle concentration comprehensive correlation coefficient CT (n, q):
wherein,
is a weight coefficient, and
43) if the comprehensive correlation coefficient CT (n, q) of the particle concentration in the grid n and the adjacent grid q is more than or equal to epsilon, dividing the adjacent grid q into the aggregation subset O of the grid n n (ii) a Where epsilon is a preset correlation threshold.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104483248A (en) * | 2014-12-30 | 2015-04-01 | 中科宇图天下科技有限公司 | Regional particulate matter stereoscopic monitoring system and method |
| CN104865353A (en) * | 2015-06-01 | 2015-08-26 | 上海交通大学 | Atmospheric pollution data acquisition method for industrial park based on unmanned aerial vehicle |
| CN108648127A (en) * | 2018-04-28 | 2018-10-12 | 北京航空航天大学 | A kind of urban air pollution hot spot region locking means |
| CN109765335A (en) * | 2018-12-25 | 2019-05-17 | 北京英视睿达科技有限公司 | Method, control device and the electronic equipment of monitoring point are set in monitoring region |
| CN110084418A (en) * | 2019-04-21 | 2019-08-02 | 北京工业大学 | A kind of monitoring point optimization distribution method of burst atmosphere pollution accident emergency monitoring |
-
2019
- 2019-11-01 CN CN201911063520.6A patent/CN110889199B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104483248A (en) * | 2014-12-30 | 2015-04-01 | 中科宇图天下科技有限公司 | Regional particulate matter stereoscopic monitoring system and method |
| CN104865353A (en) * | 2015-06-01 | 2015-08-26 | 上海交通大学 | Atmospheric pollution data acquisition method for industrial park based on unmanned aerial vehicle |
| CN108648127A (en) * | 2018-04-28 | 2018-10-12 | 北京航空航天大学 | A kind of urban air pollution hot spot region locking means |
| CN109765335A (en) * | 2018-12-25 | 2019-05-17 | 北京英视睿达科技有限公司 | Method, control device and the electronic equipment of monitoring point are set in monitoring region |
| CN110084418A (en) * | 2019-04-21 | 2019-08-02 | 北京工业大学 | A kind of monitoring point optimization distribution method of burst atmosphere pollution accident emergency monitoring |
Non-Patent Citations (2)
| Title |
|---|
| Investigating the Influence of the Implementation of an Energy Development Plan on Air Quality Using WRF-CAMx Modeling Tools: A Case Study of Shandong Province in China;Ruoting Luo 等;《Atmosphere》;20191029;全文 * |
| 基于无人机平台的细颗粒物三维分布监测;鲁斯嘉等;《科学技术与工程》;20170331;第17卷(第8期);全文 * |
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