CN104181276A - Unmanned plane-based enterprise carbon emission detection method - Google Patents
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Abstract
The invention discloses an unmanned plane-based enterprise carbon emission detection method, and according to the method, an unmanned plane is used as a carrier platform for inspection of distribution state of factory buildings and chimneys, the position with the largest pollution gas concentration at a corresponding height can be determined. On the basis, an unmanned plane movement route can be planned and optimized by use of Dijkstra algorithm and cubic spline difference value. The method comprises the following steps: (1) obtaining plant planar graph data; (2) according to a plant planar graph, drawing Voronoi diagram; (3) on the basis of an improved Gauss model, respectively detecting points needed to be traversed when the unmanned plane detects a high self-supporting chimney and points needed to be traversed when the unmanned plane detects a low self-supporting chimney; (4) using the Dijkstra algorithm, preliminarily planning an unmanned plane detection path; and (5) using the cubic spline difference value and seqential quadratic programming for path optimization to determine an optimal unmanned plane detection path. The method can guarantee that the unmanned plane achieves carbon emission detection of multiple emission sources of a plant under the condition of the optimal path, and has the advantages of being low in cost, real-time and flexible and the like.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle sensor detection, in particular to an enterprise carbon emission detection method based on an unmanned aerial vehicle, and the method is an enterprise carbon emission detection method combining a Gaussian emission model and unmanned aerial vehicle path optimization.
Background
In the aspect of carbon emission detection, a typical method at present is to place a sensor at a proper position of a production enterprise, collect relevant data of carbon emission, and then calculate and summarize the data. However, the detection environment has multiple factors such as height, temperature and the like, so that the complexity and uncertainty of the traditional fixed-point sampling method are increased, the method cannot track the pollution source, the concentration measurement is not accurate, the method is easy to counterfeit, and an ideal effect is difficult to obtain. Meanwhile, a considerable part of the existing carbon pollution amount data is only obtained through theoretical calculation, the accuracy is poor, and the conclusion of the pollution degree is difficult to directly draw. For this reason, various solutions have been proposed by government agencies and research institutions at home and abroad. For example, multiple high towers are built, and carbon emission is monitored in real time; or an energy consumption and carbon emission online monitoring system is established by relying on the Internet, various sensors are arranged to collect information such as electric quantity, temperature and humidity, and the information is transmitted to a remote environment information collection server through the Internet in a standard format, so that an energy and carbon emission database is established; the system also comprises a satellite which is planned to emit and can draw the emission details of carbon dioxide gas in the atmosphere, and is used for measuring the emission amount and the specific position of absorbed carbon dioxide on the earth surface, wherein the monitoring object is the carbon dioxide emission within the range of 5-10 kilometers of the ground, and the concentration of the carbon dioxide is recorded every other week; and the infrared spectrum technology is used for telemetering the polluted gas under the condition of a mobile platform, and various exploration researches such as a bright spectrum method, a background information statistical method and the like are carried out on an infrared spectrum background information suppression algorithm of the polluted gas. However, the above schemes all have certain defects, such as huge investment, overlong construction period, limited detection range, difficult realization of algorithms and the like, are in planning and experimental stages, and are difficult to obtain ideal effects in the near term.
Be applied to city field carbon emission with unmanned aerial vehicle and detect's advantage lies in: as an outstanding mobile platform, unmanned aerial vehicle can carry the direct pollution source department of check out test set, can all sample and then detect relevant index such as pollution degree at high, low latitude. The method is far beyond high tower detection in coverage range, is better than ground data acquisition in effect accuracy, and is far lower than a special satellite scheme in construction period and inherent cost. The method aims at the real-time detection of carbon emission of enterprises, provides that an unmanned aerial vehicle is used as a carrier platform, the distribution conditions of factory buildings and chimneys are investigated, and the position with the maximum concentration of the polluted gas at the corresponding height is determined. On the basis, the Dijkstra algorithm and the cubic spline difference value are adopted, and planning and optimization of the motion route of the unmanned aerial vehicle are achieved. The unmanned aerial vehicle is adopted to discharge the polluted gas in factories, is a real-time, effective and safe method, and can realize a real-time and flexible detection scheme with lower cost.
In conclusion, the increasingly serious high carbon emission poses serious threat to the sustainable development of the society, and how to accurately measure the carbon emission is a key factor for implementing effective control. The carbon emission detection based on the unmanned aerial vehicle can construct an effective mobile platform, reduce man-made interference, realize a real-time and flexible detection scheme with lower cost, and is an effective mode realized within a predictable time.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the enterprise carbon emission detection method based on the unmanned aerial vehicle, and the planning and optimization of the movement route of the unmanned aerial vehicle are realized by combining a Gaussian emission model, so that the detection precision is improved. The unmanned aerial vehicle is adopted to discharge the polluted gas in factories, is a real-time, effective and safe method, and can realize a real-time and flexible detection scheme with lower cost.
The invention discloses an enterprise carbon emission detection method based on an unmanned aerial vehicle platform, which comprises the steps of calculating the emission source intensity based on a Gaussian diffusion model and directly calculating the maximum concentration measurement corresponding to a target emission source under the height under the condition of knowing the flight height of an airplaneLocation of the measuring point based onDijkstraAlgorithm bindingVoronoiAnd planning the unmanned aerial vehicle path. Specifically, the method comprises the following steps: determining the coordinates and relative positions of buildings inside the plant according to the plane of the plant, deriving a plan view of the plant based on specific dataVoronoiA drawing; based on the gaussian diffusion model, calculate the inside every chimney emission source's of mill maximum concentration department, use this as the point that the basis needs to detect of unmanned aerial vehicle, at unmanned aerial vehicle route planning in-process, divide into high self-reliance chimney and low self-reliance chimney to the inside chimney of mill, wherein the height of low self-reliance chimney is imitative with other factory building heights of mill, so when detecting the emission of low self-reliance chimney, unmanned aerial vehicle barrier problem is considered in the unmanned aerial vehicle route planning algorithm that this patent designed, when detecting the emission of high self-reliance chimney, unmanned aerial vehicle route planning algorithm that this patent designed need not consider unmanned aerial vehicle barrier problem. Modifying the points generated based on the plant plan after determining the points to be detected inside the plant for the droneVoronoiDiagram based onDijkstraAnd (3) planning the unmanned aerial vehicle path by using the algorithm, and then correcting the shortest path cusp by using the third difference value and the sequence second planning to determine the optimal detection path of the unmanned aerial vehicle.
The technical scheme of the invention is realized as follows:
an enterprise carbon emission detection method based on an unmanned aerial vehicle comprises the following steps:
the method comprises the following steps: acquiring plant plan data;
step two: drawing from a plant floor planVoronoiFigure (a).
VoronoiThe figure refers to a set of discrete growing points which are different from each other on a two-dimensional Euclidean planeP={P 1 ,P 2 ,…..P j …….P n }The region given by equation (1) is then the growth pointP j Is/are as followsVoronoiPolygonal and all growing pointsP 1 ,P 2 ,…..P j …….P n Is/are as followsVoronoiThe set of polygons forms a set of discrete pointsPIs/are as followsVoronoiFigure (a).
Step three: respectively determining points to be detected by the unmanned aerial vehicle when the emission of the high self-supporting chimney is detected and points to be detected by the unmanned aerial vehicle when the emission of the low self-supporting chimney is detected according to an improved Gaussian model algorithm;
the improved gaussian model is described as follows: this patent relies on classic gaussian model, and gaussian model is applicable to the atmospheric condition of homogeneity to and the ground is wide and flat area, the diffusion mode of point source. Although the sizes of chimneys, diffusing pipes, ventilation openings and the like discharging a large amount of pollutants are different, the chimneys, the diffusing pipes, the ventilation openings and the like can be regarded as point sources, such as a formula (2):
wherein:Qis strongly, i.e. the amount of pollutant discharged per unit time
σ y As a lateral diffusion coefficient, in that the contaminants areyStandard deviation of directional distribution
σ z As longitudinal coefficient, contamination inzIs tied to the standard deviation of that distribution
uMeasuring average wind speed of nearby area for air outlet
HIs the effective height of the air outlet (chimney)
xIs discharged from the point to the bottom of the pollution sourceDistance of measuring point at wind direction
yDistance from central axis of smoke to measuring point in right-angle horizontal direction
zHeight from the surface to the measuring point
For the emission sources such as the low self-standing chimney, it is known that one emission source corresponds to one maximum concentration point on the same height plane, and the concentration of the maximum concentration point is higher than the carbon emission concentration of the surrounding concentration measurement point, so that the measurement data of the sampling point near the effective source point where the downwind direction is located is less affected by the surrounding emission sources, and the measurement is easier.
The parameters of wind speed, source intensity, effective height and the like in the model are different for each emission source, and the maximum concentration point is known to be downwind, namelyy=0,x-hShould be related to equation (3)xPartial derivatives of (a):
wherein, when the order is given,
when in useC=0,y=0At this time, thehAndxthe relationship (A) is known to be that the distance from the maximum concentration point to the source point is found in the height planexFormula (4):
for emission sources such as low self-standing chimneys, considering barrier factors, the optimal detection point is obtained by the formula (4) under the condition of a known height plane, and the position of the maximum concentration measurement point corresponding to the target emission source is obtained, so that a flying target is provided for the unmanned aerial vehicle.
For an emission source such as a high free-standing chimney, because the problem of a barrier does not need to be considered, an optimal concentration measurement point should be located at a source point on a plane with a high height higher than an effective source of the emission source, which is a target of the unmanned aerial vehicle when detecting the high free-standing chimney.
Step four: by usingDijkstraAn algorithm is used for carrying out primary planning on a path detected by the unmanned aerial vehicle;
this patent adoptsVoronoiDrawing combinationDijkstraAnd (4) carrying out unmanned plane path planning by an algorithm. The main idea is as follows: when the drone flies along each edge, each edge will have a weight, mainly the shortest path and obstacle avoidance issues. The obstacle avoidance cost is the reciprocal of the distance from the obstacle to the edge, and the cost of flying over the edge is smaller as the obstacle is farther away from the edge. The fuel cost is proportional to the length of the path traversed, the fuel cost of a particular edgeJ path The length of the edge can be usedLAnd (5) obtaining the product through quantification, as shown in the formula (5). Risk cost of one edgeJ threat Can be calculated by the formula (6) whereinAx+By+C=0Is an equation of an edge of the corresponding edge in a two-dimensional plane,(s) ((s))X 0 ，Y 0 ）Is the coordinate of the center point of the corresponding obstacle.
In thatDijkstraIn the algorithm, the algorithm is carried out,Voronoitotal cost of an edge in a graphJLike formula (7)
Wherein,αis a weight coefficient, and has a value ranging from 0 to 1. Tong (Chinese character of 'tong')Often 0.5 is taken, and other cases need to be specifically analyzed in combination with actual situations.
Step five: and optimizing the path by adopting the cubic spline difference and the sequence quadratic programming on the basis of the step four.
Because the connecting part in the path planning forms a sharp point, the unreachable sharp point in the shortest path needs to be corrected by adopting a cubic spline difference value so as to meet the condition of the unmanned aerial vehicle during actual flight, a suboptimal path is obtained, and in the correction process, the optimal solution is optimized by using Sequential Quadratic Programming (SQP).
Starting point of unmanned aerial vehicle and first barVoronoiThe middle point of the edge is two nodes of a cubic spline curve, and the middle node is inserted into the intersection position of the two edges and is defined as（x，y）. The three nodes are connected by a cubic spline curve, the direction of the initial part is determined by the initial course of the designated unmanned aerial vehicle, and the direction of the final part is determined by the first nodeVoronoiThe edges are tangent, so that the requirement of the initial course of the unmanned aerial vehicle is met.
Then, by changing（x，y）Seeking for each segmentVoronoiAnd the optimal solution of the spline curve limited by the minimum turning radius of the unmanned aerial vehicle is satisfied. The Sequential Quadratic Programming (SQP) problem is described as:
by changing（x，y）So that:
simultaneously, the following requirements are met:R-R min >0wherein:Ris the radius of curvature of each point on the spline curve,R min is the minimum turning radius determined by the self dynamic limit of the unmanned aerial vehicle, the minimum turning radius is as the formula (9),Rthe radius of curvature at each point on the spline curve is as in equation (10):
in the formula,V min the minimum flying speed of the unmanned aerial vehicle,n ymax and the unmanned aerial vehicle is overloaded maximally normally.y ， ，y ，， The first and second derivatives of each point of the spline curve are shown.
The unmanned aerial vehicle detects the step of low self-supporting chimney:
the method comprises the following steps: on the basis of FIG. 2, modifyVoronoiDetermining points needing to be detected by the unmanned aerial vehicle by using a formula (4) according to the flying height of the unmanned aerial vehicle, judging whether the coordinates of the points have obstacles or not, if so, selecting obstacle-free points in the downwind direction, and performing a second step; if no obstacle exists, performing a second step;
step two: connecting the point which is determined in the first step and needs to be traversed by the unmanned aerial vehicle with the point nearest to the pointVoronoiTwo vertices of an edge, the original to which the two vertices are connected being deleted simultaneouslyVoronoiAnd (7) edge. The edge whose end point tends to be infinite is deleted. At this time, the measurement point is downwind from the chimney because: due to the obstacles of factory buildings, the unmanned aerial vehicle sometimes cannot fly to the position right above the low chimney, and the unmanned aerial vehicle is modifiedVoronoiAs shown in fig. 3.
Step three: by usingDijsktraThe algorithm performs unmanned aerial vehicle path planning to obtain a rough path planning scheme, and the result is shown in fig. 4 and 5.
Step four: and based on the result of the third step, carrying out path optimization by adopting a cubic spline difference value and sequential quadratic programming.
The optimal detection point of the high chimney is far higher than the height of a factory building, so that the obstacle avoidance problem does not need to be considered, and the position right above the effective source height of the chimney is detected. The method comprises the following steps of unmanned detection of a high self-supporting chimney:
the method comprises the following steps: using shortest path as optimization targetDijkstraThe algorithm can directly acquire the flight path of the unmanned aerial vehicle.
Step two: and (4) based on the result of the step one, performing path optimization by adopting a cubic spline difference value and sequential quadratic programming.
Advantageous effects
The method can reach the position near a factory chimney which is not easy to contact by people by utilizing a mobile platform of the unmanned aerial vehicle to accurately monitor the carbon concentration of the factory exhaust gas in a short distance, and respectively determines the optimal detection points of a high self-supporting chimney and a low self-supporting chimney based on an improved Gaussian model in the process of monitoring the carbon emission of an enterprise, so that the unmanned aerial vehicle path planning has practical significance in the application level. Meanwhile, the unmanned aerial vehicle is used for detecting the carbon emission of enterprises, compared with the fixed sensor, the unmanned aerial vehicle can complete the spot check of the carbon emission with lower cost, and one unmanned aerial vehicle detection formation can be recycled by cities across the country.
Drawings
FIG. 1 is a process flow diagram of an embodiment of the present invention;
FIG. 2 plan view of a plant andVoronoicombining the graphs;
modified in FIG. 3VoronoiA drawing;
fig. 4 shows a result of the unmanned aerial vehicle path planning for the high self-supporting chimney;
fig. 5 shows the result of the unmanned aerial vehicle path planning for the low self-standing chimney;
FIG. 6 shows a result of optimization of unmanned aerial vehicle path planning for the high self-supporting chimney;
fig. 7 shows the result of the optimization of the unmanned aerial vehicle path planning for the low self-standing chimney.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
The data used in this embodiment is shown in table 1, and includes coordinates of the center of the factory building inside the factory, coordinates of the center point of the high self-standing chimney, and coordinates of the center point of the low self-standing chimney. The plants are distributed in a planar area of 200 x 200 units, each chimney being in operation.
TABLE 1 plant floor plan coordinates
The embodiment is an unmanned aerial vehicle path planning scheme for unmanned aerial vehicle carbon emission detection based on an improved Gaussian model, and the software environment isWINDOWS 7The system, simulation environment isMATLAB2010The process is shown in fig. 1, and includes the following steps:
the method comprises the following steps: make a study in accordance with Table 1VoronoiFIG. 2;
step two: when the discharge amount of the high self-supporting chimney is detected, the point which needs to be detected by the unmanned aerial vehicle is determined, and because the unmanned aerial vehicle needs to fly through the position right above the high chimney when the high self-supporting chimney is detected, the point which needs to be detected by the unmanned aerial vehicle is shown in table 2;
TABLE 2 coordinates to traverse when unmanned detection of high chimney points
| Serial number | 1 | 2 | 3 | 4 | 5 |
| Coordinate point | (17,49) | (20,78) | (50,116) | (130,141) | (150,178) |
Step three: based on an improved Gaussian model algorithm, in the known height plane, the distance between the maximum concentration point of the corresponding unmanned aerial vehicle flying height and a source point is obtained by using a formula (4), whether a barrier exists at the determined detection point is judged, if so, the fourth step is executed, and if not, the fifth step is executed;
step four: taking the point determined in the step three as a starting point, and searching a point capable of avoiding the obstacle in the downwind direction;
step five: the points determined in the third step and the fourth step are shown in table 3, and the points which are determined in table 3 and need to be traversed by the unmanned aerial vehicle are connected with the points which are closest to the pointsVoronoiTwo vertices of an edge, the original to which the two vertices are connected being deleted simultaneouslyVoronoiAnd (7) edge. The edge whose end point tends to be infinite is deleted. At this time, the measurement point is downwind from the chimney because: due to the obstacles of factory buildings, the unmanned aerial vehicle sometimes cannot fly to the position right above the low chimney, and the unmanned aerial vehicle is modifiedVoronoiAs shown in fig. 3.
Table 3 coordinates to be traversed when the unmanned aerial vehicle detects a low chimney point
| Serial number | 1 | 2 | 3 | 4 | 5 | 6 |
| Coordinate point | (80,49) | (85,90) | (94,101) | (114,133) | (125,150) | (130,165) |
Step six: according toDijkstraAn algorithm, which is to calculate the cost of flying through each edge by using a formula (7) respectively, so as to calculate a path with the minimum optimal cost, and obtain a path when the unmanned aerial vehicle detects the discharge amount of the high self-standing chimney and a path when the unmanned aerial vehicle detects the low self-standing chimney, which are respectively shown in fig. 4 and fig. 5;
step seven: on the basis of the obtained path in the step six, the constraint conditions of a formula (9) and a formula (10) are simultaneously met by using a formula (8), and the path optimization is carried out by adopting cubic spline difference and sequential quadratic programming to obtain the optimization results shown in the figures 6 and 7, and the optimization results of the unmanned aerial vehicle path for detecting the high self-supporting chimney and the unmanned aerial vehicle path for detecting the low self-supporting chimney are respectively and correspondingly obtained;
in conclusion, for the enterprise carbon emission detection of the unmanned aerial vehicle, the detection path optimization algorithm based on the improved gaussian model is feasible and effective, can detect the emission of each factory emission source while ensuring accurate obstacle avoidance, and is a technical scheme for effectively detecting the carbon emission of the factory in real time.
Claims (2)
1. The method uses the unmanned aerial vehicle as a mobile platform and an equipment carrier, researches the carbon emission detection technology of enterprises, can be used for carrying out accurate carbon emission monitoring in a wider range on the air of an enterprise factory area, and transmits data to a ground server in an online or offline mode.
2. The enterprise carbon emission detection method based on the unmanned aerial vehicle is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: the control of the flight position of the unmanned aerial vehicle by a ground station is realized by depending on a mobile platform of the unmanned aerial vehicle, and a small mechanical device is designed to collect gas and assist a sensor to collect data;
step two: based on a Gaussian diffusion model, establishing a functional relation between the maximum concentration point and the measured height in each height plane in the downwind measuring points of the gas emission source, and calculating the distance between the maximum concentration point and the central point of the chimney at different heights according to the functional relation;
step three: when detecting the discharge amount of a high chimney, the most effective detection point is the effective source height right above the chimney; when the low chimney emission is detected, the unmanned aerial vehicle cannot reach the position right above the chimney due to the influence of the obstacles, and the detection point is required to be at a certain point below the plane where the effective source of the chimney is located;
step four: constructing a Voronoi diagram threatening distribution by taking a corresponding chimney and a central point of a factory building according to the known plane distribution condition of the factory;
step five: adding points which need to be traversed by the unmanned aerial vehicle into the path generated in the step four, searching the path in the graph by utilizing a Dijkstra algorithm to form a rough shortest path, wherein the rough shortest path has the main defect of containing a non-flyable sharp corner;
step six: on the basis of the rough shortest path, searching an optimal path of the unmanned aerial vehicle, and realizing rapid and accurate detection of a specified place;
step seven: the unmanned aerial vehicle carries various gas sensors, real-time detection of carbon concentration in the air is carried out on a target site along a planned path, data are transmitted back to the ground server, carbon emission concentration data are calculated in real time, and whether the standard is exceeded or not is judged.
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| CN104863631A (en) * | 2015-04-22 | 2015-08-26 | 南华大学 | Optimization method for reducing harm of uranium ore gaseous effluents to surrounding environment |
| CN104951658A (en) * | 2015-06-20 | 2015-09-30 | 西安科技大学 | Carbon emission metering method based on urban planning |
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| CN106198305A (en) * | 2016-09-13 | 2016-12-07 | 成都创慧科达科技有限公司 | A kind of greenhouse gas emissions detection device |
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| WO2020018867A1 (en) * | 2018-07-20 | 2020-01-23 | Schlumberger Technology Corporation | Optimized multi-stage intermittent fugitive emission detection |
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| CN108827722A (en) * | 2018-09-13 | 2018-11-16 | 潘小乐 | It is a kind of for estimating the UAV system and method for the overhead in line source strength of industrial area PM |
| CN110308244A (en) * | 2019-06-26 | 2019-10-08 | 深圳市宇驰检测技术股份有限公司 | Air monitering method for early warning, system and the storage medium of unmanned plane |
| CN110308244B (en) * | 2019-06-26 | 2022-03-04 | 深圳市宇驰检测技术股份有限公司 | Air monitoring and early warning method and system of unmanned aerial vehicle and storage medium |
| CN112363408A (en) * | 2020-08-28 | 2021-02-12 | 西安羚控电子科技有限公司 | Method for constructing unmanned aerial vehicle air route virtual simulation model |
| CN113467507A (en) * | 2021-06-28 | 2021-10-01 | 中交遥感载荷(江苏)科技有限公司 | Atmospheric environment sampling method based on unmanned aerial vehicle system and unmanned aerial vehicle system thereof |
| CN113467507B (en) * | 2021-06-28 | 2024-02-23 | 中交遥感载荷(江苏)科技有限公司 | Atmospheric environment sampling method based on unmanned aerial vehicle system and unmanned aerial vehicle system |
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