CN116559369A - Sky-ground three-dimensional space-time carbon monitoring selection point and fusion carbon checking-based monitoring method - Google Patents
Sky-ground three-dimensional space-time carbon monitoring selection point and fusion carbon checking-based monitoring method Download PDFInfo
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Abstract
The invention provides a sky-ground three-dimensional space-time carbon monitoring point selection and fusion carbon checking-based monitoring method. The carbon dioxide monitoring method based on the multi-time space scale can make up for the defect of sensor precision by means of data cleaning and fusion, and the problem of high uncertainty of indirect carbon check data. The carbon emission condition of the region under a time period is quantitatively and accurately mastered, and meanwhile, the fused data are screened by means of indirect carbon check, so that the reliability of the data is ensured.
Description
Technical Field
The invention belongs to the field of industrial environment monitoring, and particularly relates to a space and time three-dimensional carbon emission monitoring system formed by carbon monitoring satellites, unmanned aerial vehicle air carbon monitoring, ground carbon monitoring, real-time ground monitoring, short-time scale air monitoring and long-time scale satellite monitoring.
Background
Carbon dioxide emissions are greenhouse gases often produced in human life production, and with the increase of global warming, human beings have indirectly jeopardized ecological balance of carbon dioxide emitted in nature. The monitoring of carbon dioxide emission in the area is helpful for people to clearly and intuitively understand the total carbon emission, and meanwhile, the accurate measurement and detection are carried out for the next step of carbon emission reduction.
In the prior art, the monitoring of carbon dioxide is mostly focused on the research of the monitoring equipment level, and the improvement of the hardware angle for improving the monitoring precision of a carbon dioxide sensor is mostly focused on. Few researches consider the deployment angle, such as a carbon dioxide detection node disclosed in China patent 201220243181.7, and consider the mode of carrying out multi-sensor deployment on carbon dioxide in an area to form a node so as to achieve the purpose of monitoring the carbon dioxide in the area. However, the method mainly focuses on carbon dioxide monitoring in forestry, only considers measurement of a single dimension of the ground, and does not consider measurement accuracy and result confidence. Therefore, a carbon dioxide monitoring method with multiple space-time angles, taking the reliability of the results into consideration and improving the measurement accuracy is urgently needed.
Disclosure of Invention
Aiming at the defects, the invention provides a space-earth three-dimensional area carbon emission method based on multiple time-space scales, which can provide theoretical basis for ground deployment of a carbon monitoring sensor, determination of an air monitoring track and a carbon satellite monitoring center and improve accuracy and reliability of a final data result.
In order to achieve the above purpose, the method for monitoring the sky-ground-based three-dimensional space-time carbon monitoring selection point and the fused carbon check comprises the following steps:
the method comprises the following steps:
(1) Pre-deploying a ground sensor aiming at a selected area to obtain ground carbon emission data under n evenly distributed point positions, wherein the point positions are all ground carbon measurement point positions;
(2) Aiming at the selected area, comprehensively examining the carbon emission characteristics and network transmission benefits, selecting the pre-deployment position of the ground sensor by adopting a particle swarm optimization algorithm, calibrating n ground carbon measurement points, wherein g is the ground carbon measurement points 1 、g 2 、g 3 …g n Representing, selecting the carbon emission characteristics of the reference area of the flight route of the unmanned aerial vehicle, and selecting the carbon satellite monitoring center position to obtain the short time scale t of the unmanned aerial vehicle s Lower airborne carbon concentration data and upper carbon satellite long time t l Carbon data of scale collection;
(2.1) determining a monitoring area boundary line based on the ground carbon emission data obtained in the step (1), optimizing the ground sensor pre-deployment azimuth according to a particle swarm optimization algorithm, and simultaneously determining the carbon satellite monitoring center position to obtain the short time scale t of the unmanned aerial vehicle s Air carbon concentration data;
(2.2) based on the preliminary measurement ground carbon emission data obtained in the step (1), determining an aerial monitoring track of the unmanned aerial vehicle to obtain a long-time t of the zenith carbon satellite l Carbon data of scale collection;
(3) According to the real-time ground carbon emission data obtained in the step (1), the unmanned aerial vehicle obtained in the step (2) has a short time scale t s Lower aerial carbon concentration data and upper carbon satellite long time t l Cleaning and fusing the carbon data acquired by the scale;
(4) Cleaning and fusing the three measurement data obtained in the step (3) into multi-element heterogeneous data to obtain multi-time-space-scale regional carbon monitoring data;
(5) And (3) based on the cleaning and fusion results in the step (4), using an emission factor method to carry out indirect carbon emission check comparison on the monitoring area, and judging whether the carbon emission data processing result is credible.
In the invention, in the step (2.1), aiming at the collected ground carbon emission data, the determination of the pre-deployment position of the ground sensor is performed by utilizing a perception quality model of an improved Gaussian model and a weight model of a key area, and meanwhile, the determination of a carbon satellite monitoring center is performed, wherein the model expression is as follows:
wherein: f (A) is a perception model, X A And X V Random variables representing sensor readings associated with A and V positions, respectively, by X when deploying a sensor target at A A Observing the representation X as accurately as possible V The method comprises the steps of carrying out a first treatment on the surface of the (2.1.1) each variable x at the set position pεS p Obeying Gaussian distribution, and estimating mean value and covariance distribution of non-monitoring positions by utilizing ground carbon monitoring points of a Gaussian process at a pre-deployment position of a ground sensor; for non-Gaussian phenomenon, splitting a time axis into smaller frames, and regarding the distribution of each frame as Gaussian distribution, wherein a Gaussian process consisting of n Gaussian distribution variables consists of a mean vector M epsilon R n Sum covariance matrix Σ e R n*n A representation; radial Basis Functions (RBFs) estimate covariance between two undeployed locations by distance between them; assume that at a given surface sensor, the surface carbon is monitored at point location X A In the case of (1), X V The conditional mean and covariance of the value distribution of (2) conform to a gaussian distribution given by:
wherein:is the average sensor reading at A, taking into account that the sensor reading is subject to drift or disturbance in the actual environment, perceptually adding the variance σ in equations (2) and (3) n 2 ；
(2.1.2) monitoring importance I of each region I ε S (region set) to be monitored i Defined as a weighted sum of the concentration profile conditions, and a weighted sum of statistical data obtained from the carbon concentration profile over a period of one month:
wherein:c is the statistical time, and T is the total statistical period;
the carbon monitoring sensor deployment objective is defined to obtain a deployment scenario i that provides for ensuring perceived quality and connectivity of the network; in combination with equation (4), the optimization problem can be defined as follows:
Fit(A)＝ω 1 max(Q-F(A)，0)+ω 2 max(I(A)) (a)
F(A)≥Q (b)
∑ q∈Γ(p) g pq -∑ q∈Γ(p) g qp ＝R，p∈S (c)
∑ q∈Γ(c) g qc ＝mR，q∈S (d)
wherein: equation (a) is an optimization problem fitness value Fit (a) consisting of perceived quality and key region monitoring weights, ω 1 And omega 2 Respectively isOptimization weights of the corresponding models, and ω 1 +ω 2 =1; constraining the sensing quality F (a) to be greater than the sensing quality threshold Q, equations (b) and (c) ensuring that each deployed sensor generates one data unit in the network and all sensor readings are aggregated to the sink node; assuming each sensor reports R bytes of data at even intervals, without loss of generality, g is utilized pq Representing traffic from nodes p to q, a set of transmission neighbor nodes is defined as Γ (p) = { q e S, where d pq ＜r}，g qp Is the traffic from node q to p. r is a communication radius, and is determined according to specific hardware capacity; c is the sink node Γ (c) = { q∈s, where d cq ＜r}，g qc And the flow from the node q to the sink node c is represented, S is the regional set to which the carbon monitoring sensors are deployed, and m is the number of the carbon monitoring sensors.
In the invention, aiming at the collected carbon emission data in the step (2.2), an improved ant colony algorithm is utilized to determine an unmanned aerial vehicle carbon emission monitoring flight path;
building a terrain elevation matrixWherein: a is height, m and n are plane coordinate positions respectively;
a threat source model is established by adopting a disc mode, a small circle area represents an absolute killing area, and the outer side of a large circle is called a safety area; when the uncertain region flies, a certain killing probability exists, and the killing probability P m The definition is as follows:
taking the distance from the starting point to the target point and the flight distance of the unmanned aerial vehicle into consideration, using J L To represent voyage distance costs; setting n nodes in the track planning, and enabling the unmanned aerial vehicle to fly at a constant speed in the track planning process, so that the distance cost J L Expressed as:
wherein: delta is a proportionality coefficient;
cost of altitude J H Expressed as:
J H ＝c 2 h (7)
according to the established model, a certain weight is given to each threat by combining with the actual situation, and then the indicated comprehensive cost function is constructed; the comprehensive cost function W is:
wherein: v is the weight of the radar threat cost, v is the weight of the range cost, and phi is the weight of the altitude cost; through a state transition probability formulaSelecting the next node by ants;
wherein: allowed m Is the sensor node set which is not passed by ant m, τ ij (t)，η ij (t) pheromone concentration function and heuristic function, η, of nodes i to j respectively ij (t)＝1/d ij (t) α represents a pheromone concentration influence factor, and β represents a heuristic function influence factor;
the update pheromone rule is as follows:
τ ij (t+1)＝(1+ρ)τ ij (t)+Δτ ij (t) (10)
wherein: ρ is the pheromone volatilization amount, and ρ ε [0, 1); Δτ ij (t) the path pheromone increment when nodes i to j are selected for all ants,the path pheromone increment when selecting nodes i to j for ant m, Q is the pheromone intensity, L m The path length of ant m;
in order to accelerate the convergence rate in the initial stage and avoid the rapid local convergence in the later stage, the pheromone concentration influence factor alpha is corrected as follows;
α(k)＝α 0 (1+e -Sk )，0≤k≤K (13)
wherein: alpha 0 S is a constant, K is the number of iterations, and K is the total number of iterations;
limiting a pheromone threshold value in order to avoid that the concentration of the pheromone on each path is too high, so that relatively quick local convergence is caused;
wherein: Γ represents a threshold value, n is the total number of ants, L m Is the path length of the mth ant, ρ represents the pheromone volatilization coefficient, ρ ε [0,1 ].
In the present invention, the step (5) includes the following steps:
(5.1): based on the boundary line of the monitoring area obtained in the step (2.1), indirectly carbon checking the area according to the carbon emission factor by using an emission factor method aiming at the energy use condition to obtain carbon checking data;
(5.2): and (3) carrying out fusion in the step (4) and judgment of a cleaning result based on the carbon check data obtained in the step (5.1).
In the invention, in the step (5.1), the indirect carbon check is performed on the area by using an emission factor method. The model is as follows:
wherein: e (E) c Is an indirect measurement of total carbon emissions, CEF i Is the carbon emission factor of different energy units, EC i Representing different energy consumption; the carbon emissions indirect accounting is performed for the electricity usage, gas usage, steam usage, and warm air usage of the selected area.
In the invention, in the step (5.2), the comparison and judgment are carried out on the carbon emission fusion data result according to the set numerical range of the confidence coefficient of the indirect carbon check data, and the confidence coefficient calculation model is as follows:
wherein: confidence of C as a whole, C i Confidence for a single measurement object.
In the invention, the indirect carbon emission check carbon emission factor selection standard in the step (5.1) is ISO14067 international standard.
The invention has the beneficial effects that:
the method comprises the steps of firstly carrying out preliminary carbon emission ground monitoring on a selected area, then carrying out carbon emission ground monitoring on selected points and deployment optimization by means of emission results, judging flight tracks of unmanned aerial vehicles and determining carbon satellite monitoring center points, carrying out carbon emission monitoring on the selected points by means of carbon satellites, aerial unmanned aerial vehicles and ground monitoring sensors, and finally carrying out cleaning and fusion treatment on measured multi-element heterogeneous data, and judging by means of indirect carbon verification as fusion results. The carbon dioxide monitoring method based on the multi-time space scale can make up for the defect of sensor precision by means of data cleaning and fusion, and the problem of high uncertainty of indirect carbon check data. The carbon emission condition of the region under a time period is quantitatively and accurately mastered, and meanwhile, the fused data are screened by means of indirect carbon check, so that the reliability of the data is ensured.
Drawings
Fig. 1 is a schematic flow chart of a monitoring method based on sky-earth three-dimensional space-time carbon monitoring selection and fused carbon check.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The following described embodiments are illustrative only and are not to be construed as limiting the invention.
Example 1:
the invention provides a sky-ground three-dimensional space-time carbon monitoring point selection and fusion carbon check-based monitoring method aiming at a three-dimensional space-space region with multiple space-time scales, ground deployment of a carbon monitoring sensor, determination of an air monitoring track, carbon satellite monitoring center selection, monitoring data accuracy and processed data confidence.
In order that the invention may be more clearly understood, a brief description will be provided herein, the invention comprising two basic steps: step one, selecting monitoring points; and step two, carrying out confidence judgment on the data after cleaning and fusion. Specifically, fig. 1 is a schematic flow chart of a monitoring method based on sky-ground three-dimensional space-time carbon monitoring and point selection and fused carbon checking in an embodiment of the invention, which comprises the following steps:
s1: pre-collecting carbon dioxide data of the selected area in an average distribution mode;
s2: and (2) processing data according to the data obtained in the step (S1), wherein the step (S2) for selecting a measurement area and optimizing a monitoring point position comprises the following steps:
s21: based on the S1 preliminary measurement data, determining a boundary line of a monitoring area, optimizing the deployment azimuth of the ground sensor according to an improved particle swarm optimization algorithm, and simultaneously determining a carbon satellite monitoring center;
s22: and based on the S1 preliminary measurement data, determining an air monitoring track of the unmanned aerial vehicle.
In the step S21, for the collected carbon data, the ground sensor deployment position is determined by using a perception quality model based on a gaussian model and a weight model of a key region, and the model expression is as follows:
the perceived quality model is related to the spatial distribution of the sensor network nodes in a 2-dimensional spaceThe deployment device monitors the environmental parameters. Because of the cost constraint of sensor deployment m, suppose a is the selected deployment region set and V is a set of unmonitored reference locations. X is X A And X V Is a random variable representing sensor readings associated with a and V positions. By X when deploying a sensor target at A A Observing the representation X as accurately as possible V The perceptual model F (a) can be represented by a cross-correlation form as:
assume that each variable x at position p ε S p And (3) obeying Gaussian distribution, and estimating the mean value and covariance distribution of the non-monitoring position by utilizing the observed value of the Gaussian process at the sensor deployment position. For non-Gaussian phenomenon, the time axis can be split into smaller frames, and the distribution of each frame is respectively regarded as Gaussian distribution, and the Gaussian process consisting of n Gaussian distribution variables is composed of a mean vector M epsilon R n Sum covariance matrix Σ e R n*n And (3) representing. Radial Basis Functions (RBFs) estimate covariance between two undeployed locations by the distance between them. Assume that at a given sensor observation X A In the case of (1), X V The conditional mean and covariance of the value distribution of (2) conform to a gaussian distribution given by:
wherein the method comprises the steps ofIs the average sensor reading at a. Taking into account that the sensor readings are subject to drift or disturbance in the actual environment, perceptually adding the variance sigma in equations (2) and (3) n 2 。
And establishing a weight model for monitoring the key region by considering the key region for monitoring the carbon dioxide concentration. In the carbon concentration monitoring, high carbon emission areas (high carbon emission enterprise users and carbon sink areas) are focused, and in a campus, because each user enterprise has different energy equipment and different energy utilization forms, the carbon emission amount of each user has larger difference, and under the certain limit of the deployment cost of monitoring equipment, the carbon emission amount of each user needs to be focused on in order to meet the requirement of subsequent high carbon emission enterprise emission reduction. The high carbon emissions/sink region is weighted according to carbon emissions/sink. Different regions have different carbon emission characteristics, and the important high carbon emission regions are weighted according to the average carbon emission estimation value.
Monitoring importance I of each region I e S (region set) to be monitored i Defined as a weighted sum of the concentration profile conditions, and a weighted sum of statistical data obtained from the carbon concentration profile in one month period:
in the method, in the process of the invention,and c is the statistical time, and T is the total statistical period.
The carbon monitoring sensor deployment objective may be defined as obtaining a deployment scenario I that provides for ensuring perceived quality
And connectivity of the network. In combination with the above model, the optimization problem can be defined as follows:
Fit(A)＝ω 1 max(Q-F(A)，0)+ω 2 max(I(A)) (a)
F(A)≥Q (b)
∑ q∈Γ(p) g pq -∑ q∈Γ(p) g qp ＝R，p∈S (c)
∑ q∈Γ(c) g qc ＝mR，q∈S (d)
equation (a) is an optimization problem fitness value Fit (a) consisting of perceived quality and key region monitoring weights, ω 1 And omega 2 Respectively optimizing weights of corresponding models, and omega 1 +ω 2 =1. Equations (b) constrain the sensing quality F (a) to be greater than the sensing quality threshold Q, equations (b) and (c) ensure that each deployed sensor generates one data unit in the network and all sensor readings are aggregated to the sink node. Assuming each sensor reports R bytes of data at even intervals, without loss of generality, g is utilized pq Representing traffic from nodes p to q, a set of transmission neighbor nodes is defined as Γ (p) = { q e S, where d pq ＜r}，g qp Is the traffic from node q to p. r is the communication radius, which is determined according to the specific hardware capability. c is the sink node Γ (c) = { q∈s, where d cq ＜r}，g qc And the flow from the node q to the sink node c is represented, S is the regional set to which the carbon monitoring sensors are deployed, and m is the number of the carbon monitoring sensors.
Building a terrain elevation matrixa is height, m, n is plane coordinate position. A threat source model is built in a disc mode, a small circle area represents an absolute killing area, and the outer side of a large circle is called a safety area. When the uncertain region flies, a certain killing probability exists, and the killing probability P m The definition is as follows:
considering unmanned aerial vehicle from starting pointDistance to target point and flight distance per se, using J L To represent the range cost. Setting n nodes in the track planning, and enabling the unmanned aerial vehicle to fly at a constant speed in the track planning process, so that the distance cost J L Can be expressed as:
where δ is a scaling factor.
Cost of altitude J H Can be expressed as:
J H ＝c 2 h (7)
and (3) according to the established model, a certain weight is given to each threat by combining the actual situation, and then, a comprehensive cost function shown in a formula (7) is constructed. Where μ is the weight of the radar threat cost, v is the weight of the range cost, and φ is the weight of the altitude cost. The weight coefficient can be correspondingly adjusted according to different task requirements, and the performance index is mainly inspected in the flight task process, so that the weight coefficient value of the performance index is relatively larger. The comprehensive cost function W is shown in formula (8):
and adopting an improved ant colony algorithm to carry out track planning in a three-dimensional space, wherein the method mainly comprises the following steps:
step 1: initializing parameters in an algorithm, determining position information of a task starting point and a task ending point in an environment, placing ants on the starting point, and giving a main direction of movement;
step 2: each ant searches for the next node according to the state transition probability formula (9);
step 3: after the ants move to the next node, updating the pheromone according to the local updating of the pheromone (10);
step 4: judging whether the ants complete one-time path construction, if not, turning to step 2;
step 5: and (3) updating the global pheromone according to the formula (13), judging whether the termination condition set by the algorithm is met, outputting a result if the termination condition meets the requirement, and otherwise, turning to the step (2).
Through a state transition probability formulaThe ant selects the next node.
allowed m Is the sensor node set which is not passed by ant m, τ ij (t)，η ij (t) pheromone concentration function and heuristic function, η, of nodes i to j respectively ij (t)＝1/d ij (t), alpha represents a pheromone concentration influence factor, and beta represents a heuristic function influence factor. For ant m, τ ij The larger the pheromone value (t), d ij The smaller the distance between two nodes, the probability of selectionThe larger.
In the process of searching the path, ants release corresponding pheromones on the path after going to the next sensor node or passing through all the sensor nodes. In order to prevent heuristic information from being covered due to excessive accumulation of pheromones under multiple iterations, the ant colony algorithm introduces rules of the volatilization of the pheromones. While the optimization process is continued, the pheromones are volatilized and rearranged. The update pheromone rule is as follows:
τ ij (t+1)＝(1+ρ)τ ij (t)+Δτ ij (t) (10)
ρ is the pheromone volatilization amount, and ρ ε [0,1 ]. Δτ ij (t) the path pheromone increment when nodes i to j are selected for all ants,the path pheromone increment when selecting nodes i to j for ant m, Q is the pheromone intensity, L m Is the path length of ant m.
In the ant colony algorithm, a pheromone influence factor alpha is used for measuring the proportion of the pheromone, and alpha remains unchanged to influence the convergence of the algorithm. In order to accelerate the convergence rate in the initial stage and avoid the rapid local convergence in the later stage, the influence factor alpha is corrected as follows.
α(k)＝α 0 (1+e -Sk )，0≤k≤K (13)
Algorithms place limitations on the pheromone threshold in order to avoid that the concentration of the pheromone on each path is too great, resulting in a relatively fast local convergence.
Γ represents a threshold value, n is the total number of ants, L m Is the path length of the mth ant, ρ represents the pheromone volatilization coefficient ρ ε [0,1 ].
And S4, cleaning and fusing the multi-element heterogeneous data of the measured data to obtain multi-time-space regional carbon monitoring data.
S5: and (3) according to the boundary of the monitoring area divided in the step (S21), performing indirect carbon check on the area by using an emission factor method on the energy consumption condition in the area:
wherein E is c Is an indirect measurement of total carbon emissions, CEF i Is the carbon emission factor of different energy units, EC i Representing different energy consumption. Primarily for the charge of a selected areaAnd (3) performing indirect accounting of carbon emission by using fuel gas, steam and heating.
Calculating the confidence of the indirect carbon check according to the confidence of the indirect carbon check data in the steps:
where C is the confidence of the whole, C i Confidence for a single measurement object.
Setting upper and lower limits of regional carbon emission according to the total carbon check confidence, and judging E d Whether or not in section [ E c -(1-C)×E c ,E c +(1-C)×E c ]And judging whether the result of the fusion data is credible or not according to the result. The monitoring method based on sky-ground three-dimensional space-time carbon monitoring selection and fused carbon check can realize the optimized deployment of the sky-ground three-dimensional carbon sensor, accurately and reliably monitor the carbon dioxide emission of a monitoring area on the basis, and provide a full theoretical basis for effectively monitoring the carbon dioxide emission and reducing the comprehensive carbon emission.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limited in use. Modifications of the technical method or equivalent substitutions of some technical features may be made to the above-mentioned method by those skilled in the art, and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and the scope of the present invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The monitoring method based on sky-ground three-dimensional space-time carbon monitoring and point selection and fused carbon checking is characterized by comprising the following steps of:
(1) Pre-deploying a ground sensor aiming at a selected area to obtain ground carbon emission data under n evenly distributed point positions, wherein the point positions are all ground carbon measurement point positions;
(2) Aiming at the selected area, comprehensively examining the carbon emission characteristics and network transmission benefits, selecting the pre-deployment position of the ground sensor by adopting a particle swarm optimization algorithm, calibrating n ground carbon measurement points, wherein g is the ground carbon measurement points 1 、g 2 、g 3 …g n Representing, selecting the carbon emission characteristics of the reference area of the flight route of the unmanned aerial vehicle, and selecting the carbon satellite monitoring center position to obtain the short time scale t of the unmanned aerial vehicle s Lower airborne carbon concentration data and upper carbon satellite long time t l Carbon data of scale collection;
(2.1) determining a monitoring area boundary line based on the ground carbon emission data obtained in the step (1), optimizing the ground sensor pre-deployment azimuth according to a particle swarm optimization algorithm, and simultaneously determining the carbon satellite monitoring center position to obtain the short time scale t of the unmanned aerial vehicle s Air carbon concentration data;
(2.2) based on the preliminary measurement ground carbon emission data obtained in the step (1), determining an aerial monitoring track of the unmanned aerial vehicle to obtain a long-time t of the zenith carbon satellite l Carbon data of scale collection;
(3) According to the real-time ground carbon emission data obtained in the step (1), the unmanned aerial vehicle obtained in the step (2) has a short time scale t s Lower aerial carbon concentration data and upper carbon satellite long time t l Cleaning and fusing the carbon data acquired by the scale;
(4) Cleaning and fusing the three measurement data obtained in the step (3) into multi-element heterogeneous data to obtain multi-time-space-scale regional carbon monitoring data;
(5) And (3) based on the cleaning and fusion results in the step (4), using an emission factor method to carry out indirect carbon emission check comparison on the monitoring area, and judging whether the carbon emission data processing result is credible.
2. The monitoring method based on sky-ground three-dimensional space-time carbon monitoring selection and fusion carbon verification according to claim 1, wherein in the step (2.1), aiming at the collected ground carbon emission data, the determination of the pre-deployment position of a ground sensor is performed by using a perception quality model of an improved Gaussian model and a key area weight model, and simultaneously, the determination of a carbon satellite monitoring center is performed, and the model expression is as follows:
wherein: f (A) is a perception model, X A And X V Random variables representing sensor readings associated with A and V positions, respectively, by X when deploying a sensor target at A A Observing the representation X as accurately as possible V The method comprises the steps of carrying out a first treatment on the surface of the (2.1.1) each variable x at the set position pεS p Obeying Gaussian distribution, and estimating mean value and covariance distribution of non-monitoring positions by utilizing ground carbon monitoring points of a Gaussian process at a pre-deployment position of a ground sensor; for non-Gaussian phenomenon, splitting a time axis into smaller frames, and regarding the distribution of each frame as Gaussian distribution, wherein a Gaussian process consisting of n Gaussian distribution variables consists of a mean vector M epsilon R n Sum covariance matrix Σ e R n*n A representation; radial Basis Functions (RBFs) estimate covariance between two undeployed locations by distance between them; assume that at a given surface sensor, the surface carbon is monitored at point location X A In the case of (1), X V The conditional mean and covariance of the value distribution of (2) conform to a gaussian distribution given by:
wherein:is the average sensor reading at A, taking into account that the sensor reading is subject to drift or disturbance in the actual environment, and is perceived in equations (2) and (3)Adding variance sigma n 2 ；
(2.1.2) monitoring importance I of each region I ε S (region set) to be monitored i Defined as a weighted sum of the concentration profile conditions, and a weighted sum of statistical data obtained from the carbon concentration profile over a period of one month:
wherein:c is the statistical time, and T is the total statistical period;
the carbon monitoring sensor deployment objective is defined to obtain a deployment scenario i that provides for ensuring perceived quality and connectivity of the network; in combination with equation (4), the optimization problem can be defined as follows:
Fit(A)＝ω 1 max(Q-F(A)，0)+ω 2 max(I(A)) (a)
F(A)≥Q (b)
∑ q∈Γ(p) g pq -∑ q∈Γ(p) g qp ＝R，p∈S (c)
∑ q∈Γ(c) g qc ＝mR，q∈S (d)
wherein: equation (a) is an optimization problem fitness value Fit (a) consisting of perceived quality and key region monitoring weights, ω 1 And omega 2 Respectively optimizing weights of corresponding models, and omega 1 +ω 2 =1; constraining the sensing quality F (a) to be greater than the sensing quality threshold Q, equations (b) and (c) ensuring that each deployed sensor generates one data unit in the network and all sensor readings are aggregated to the sink node; assuming each sensor reports R bytes of data at even intervals, without loss of generality, g is utilized pq Representing traffic from nodes p to q, a set of transmission neighbor nodes is defined as Γ (p) = { q e S, where d pq ＜r}，g qp Is the traffic from node q to p. r is a communication radius, and is determined according to specific hardware capacity; c is the sink node Γ (c) = { q∈s, where d cq ＜r}，g qc And the flow from the node q to the sink node c is represented, S is the regional set to which the carbon monitoring sensors are deployed, and m is the number of the carbon monitoring sensors.
3. The monitoring method based on sky-ground three-dimensional space-time carbon monitoring selection and fusion carbon check according to claim 1, wherein in the step (2.2), aiming at the collected carbon emission data, an improved ant colony algorithm is utilized to determine an unmanned aerial vehicle carbon emission monitoring flight path;
building a terrain elevation matrixWherein: a is height, m and n are plane coordinate positions respectively;
a threat source model is established by adopting a disc mode, a small circle area represents an absolute killing area, and the outer side of a large circle is called a safety area; when the uncertain region flies, a certain killing probability exists, and the killing probability P m The definition is as follows:
taking the distance from the starting point to the target point and the flight distance of the unmanned aerial vehicle into consideration, using J L To represent voyage distance costs; setting n nodes in the track planning, and enabling the unmanned aerial vehicle to fly at a constant speed in the track planning process, so that the distance cost J L Expressed as:
wherein: delta is a proportionality coefficient;
cost of altitude J H Expressed as:
J H ＝c 2 h (7)
according to the established model, a certain weight is given to each threat by combining with the actual situation, and then the indicated comprehensive cost function is constructed; the comprehensive cost function W is:
wherein: μ is the weight of the radar threat cost, v is the weight of the range cost, and φ is the weight of the altitude cost;
through a state transition probability formulaSelecting the next node by ants;
wherein: allowed m Is the sensor node set which is not passed by ant m, τ ij (t)，η ij (t) pheromone concentration function and heuristic function, η, of nodes i to j respectively ij (t)＝1/d ij (t) α represents a pheromone concentration influence factor, and β represents a heuristic function influence factor;
the update pheromone rule is as follows:
τ ij (t+1)＝(1+ρ)τ ij (t)+Δτ ij (t) (10)
wherein:ρ is the pheromone volatilization amount, and ρ ε [0, 1); Δτ ij (t) the path pheromone increment when nodes i to j are selected for all ants,the path pheromone increment when selecting nodes i to j for ant m, Q is the pheromone intensity, L m The path length of ant m;
in order to accelerate the convergence rate in the initial stage and avoid the rapid local convergence in the later stage, the pheromone concentration influence factor alpha is corrected as follows;
α(k)＝α 0 (1+e -Sk )，0≤k≤K (13)
wherein: alpha 0 S is a constant, K is the number of iterations, and K is the total number of iterations;
limiting a pheromone threshold value in order to avoid that the concentration of the pheromone on each path is too high, so that relatively quick local convergence is caused;
wherein: Γ represents a threshold value, n is the total number of ants, L m Is the path length of the mth ant, ρ represents the pheromone volatilization coefficient, ρ ε [0,1 ].
4. The method for monitoring the sky-earth three-dimensional space-time carbon monitoring selection point and the fused carbon check according to claim 1, wherein the step (5) comprises the following steps:
(5.1): based on the boundary line of the monitoring area obtained in the step (2.1), indirectly carbon checking the area according to the carbon emission factor by using an emission factor method aiming at the energy use condition to obtain carbon checking data;
(5.2): and (3) carrying out fusion in the step (4) and judgment of a cleaning result based on the carbon check data obtained in the step (5.1).
5. The method for monitoring the carbon selection and fusion check based on sky-earth three-dimensional space-time in accordance with claim 4, wherein the step (5.1) uses an emission factor method to indirectly check the carbon in the region. The model is as follows:
wherein: e (E) c Is an indirect measurement of total carbon emissions, CEF i Is the carbon emission factor of different energy units, EC i Representing different energy consumption; the carbon emissions indirect accounting is performed for the electricity usage, gas usage, steam usage, and warm air usage of the selected area.
6. The method for monitoring carbon monitoring selection points and fused carbon verification based on sky-earth three-dimensional space-time according to claim 5, wherein in the step (5.2), the comparison and judgment are performed on the carbon emission fused data result according to the set numerical range of the confidence coefficient of the indirect carbon verification data, and the confidence coefficient calculation model is as follows:
wherein: confidence of C as a whole, C i Confidence for a single measurement object.
7. The method for monitoring the carbon check based on sky-earth three-dimensional space-time carbon monitoring and the fused carbon check according to claim 5, wherein the indirect carbon emission check carbon emission factor selection standard in the step (5.1) is an international standard ISO 14067.
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