CN115759488A - Carbon emission monitoring and early warning analysis system and method based on edge calculation - Google Patents
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Abstract
The invention relates to a carbon emission monitoring and early warning analysis system and a method thereof based on edge computing, belonging to the technical field of carbon emission analysis, and comprising an edge monitoring station and a cloud computing center, wherein the edge monitoring station is provided with a data acquisition module, an edge computing module and a data transmission module and is used for acquiring data, performing primary site analysis and simultaneously packaging and uploading the data to the cloud computing center; the edge calculation module is used for constructing a station carbon emission analysis model; the cloud computing center is used for estimating and predicting urban carbon emission and generating an early warning report; the station carbon emission analysis model comprises a regression model and a BP neural network; the data acquisition module comprises a carbon dioxide concentration sensor and a meteorological observation sensor. According to the invention, monitoring sites based on edge computing are set for urban carbon emission concentration points, the prediction analysis of the carbon emission of the monitoring sites is completed from data collected in real time, and then an early warning report is generated through a cloud computing center.
Description
Technical Field
The invention belongs to the technical field of carbon emission analysis, and particularly relates to a carbon emission monitoring and early warning analysis system and method based on edge calculation.
Background
With the continuous advance of urbanization and the continuous migration of labor population, the urban energy consumption is increased, the environmental pollution is increased and the carbon emission is increased, and the climate change caused by the emission of carbon dioxide which is one of greenhouse gases is not small.
At present, a series of measures are taken in the aspect of energy conservation and emission reduction aiming at the urban carbon emission problem, so that the original energy structure, emission factors and pollution control measures are changed greatly. The carbon emission is tracked by installing an online continuous emission monitoring system in a large industrial enterprise, but most of the carbon emission of small and medium-sized enterprises is ignored, the small enterprises do not install the monitoring system, the emission characteristics of the small and medium-sized enterprises are difficult to accurately capture in time, and the daily life and the behavior activities of urban residents are not estimated. Therefore, based on the fact that the data source is insufficient and the prediction angles are different, real-time monitoring and early warning of urban carbon emission are not timely enough, and the prediction effect is poor.
In view of the above, it is desirable to provide a carbon emission monitoring and early warning analysis system and method based on edge calculation, which perform real-time data acquisition from a carbon emission source, analyze a mapping relationship between influencing factors and carbon emission, and more accurately monitor and early warn urban carbon emission.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a carbon emission monitoring and early warning analysis system and a method thereof based on edge computing.
The purpose of the invention can be realized by the following technical scheme:
a carbon emission monitoring and early warning analysis system based on edge computing comprises an edge monitoring station and a cloud computing center, wherein the edge monitoring station is connected with the cloud computing center in a wired or wireless mode;
the edge monitoring station is provided with a data acquisition module, an edge computing module and a data transmission module, and is used for acquiring data, performing primary site analysis and uploading data to a cloud computing center in a packaging manner;
the edge calculation module is used for constructing a station carbon emission analysis model;
and the cloud computing center is used for estimating and predicting urban carbon emission and generating an early warning report.
As a preferred technical scheme of the invention, the station carbon emission analysis model comprises a regression model and a BP neural network;
the regression model is used for screening carbon emission influence factor variables;
and the BP neural network is used for outputting the predicted value of the carbon emission of the station.
As a preferred technical scheme of the invention, the data acquisition module comprises a carbon dioxide concentration sensor and a meteorological observation sensor; the early warning report includes a site category carbon emission ranking and a regional carbon emission ranking.
As a preferred technical scheme of the present invention, the present invention further includes a carbon emission monitoring and early warning analysis method based on edge calculation, which is applied to the carbon emission early warning analysis system described above, and includes the following steps:
s1, analyzing an urban carbon source, and screening out the most representative edge monitoring station position;
s2, establishing an edge monitoring station and debugging a communication network;
s3, the edge monitoring station performs station analysis through the collected data;
s4, uploading the site analysis result to a cloud computing center;
and S5, the cloud computing center carries out urban carbon emission estimation according to the station analysis result and generates an early warning report.
Further, in step S1, the most representative edge monitoring station locations are specifically: traffic jam stations, community stations, office park stations and industrial plant stations.
Further, in step S3, the site analysis specifically includes:
s31, screening influence factors of carbon emission;
s32, building a BP neural network;
and S33, predicting the carbon emission of the station.
Further, in step S31, the carbon emission influencing factors specifically include: consumption factors, production factors, and combination factors.
Further, the screening of the carbon emission influencing factors specifically comprises the following steps:
s311, standardizing data;
s312, constructing a linear regression model of the carbon emission influence factors;
and S313, determining a final selection variable.
Further, the construction of the BP neural network specifically comprises the following steps:
s321, normalizing the sample data;
s322, establishing a BP neural network structure;
and S323, training the BP neural network.
Further, step S5 specifically includes the following steps: the cloud computing center sets two types according to the emission characteristics and the regional division respectively, and the first type estimates the emission of other monitoring sites in the whole city without the monitoring sites according to the data of the monitoring sites with the same characteristics; the second type estimates the carbon emission of each area according to the result of geographical region division; and generating early warning reports according to the site category carbon emission ranking and the regional carbon emission ranking.
The invention has the beneficial effects that:
the monitoring station based on edge computing is arranged for an urban carbon emission concentration point, a mapping relation between each influence factor and carbon emission is obtained by combining the actually acquired data and possible factors influencing the carbon emission, the carbon emission of the monitoring station is predicted and analyzed, and then an early warning report is generated through a cloud computing center.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of an early warning analysis model according to the present invention;
FIG. 2 is a schematic diagram of the steps of the early warning analysis method of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, characteristics and effects according to the present invention will be made with reference to the accompanying drawings and preferred embodiments.
According to a first aspect of the present application, an embodiment of the present application provides a carbon emission monitoring and early warning analysis system based on edge computing, as shown in fig. 1, including an edge monitoring station and a cloud computing center, where the edge monitoring station is connected with the cloud computing center in a wired or wireless manner.
The edge monitoring station is provided with a data acquisition module, an edge computing module and a data transmission module, and is used for acquiring data, performing primary site analysis and uploading an analysis result to a cloud computing center; the data acquisition module comprises a carbon dioxide concentration sensor and a meteorological observation sensor.
In this embodiment, the carbon dioxide concentration sensor is used for collecting a real-time carbon dioxide concentration value, and the meteorological observation sensor is used for collecting ambient temperature, relative humidity, wind speed, wind direction, atmospheric pressure and optical rainfall corresponding to a time period.
The edge calculation module is used for constructing a station carbon emission analysis model;
the cloud computing center is used for urban carbon emission estimation and prediction and generating early warning reports, and the early warning reports comprise site category carbon emission sequencing and regional carbon emission sequencing.
The station carbon emission analysis model comprises a regression model and a BP neural network; the regression model is used for screening carbon emission influence factor variables; and the BP neural network is used for outputting the predicted value of the carbon emission of the station.
In this embodiment, the regression model adopts a Lasso (Least absolute sum shrinkage) model to complete the screening of the carbon emission influencing factors, and then the screened influencing factors are used as the input of the BP neural network, and the carbon emission predicted value is used as the output.
The invention is based on edge cloud computing, processes the real-time data collected by the sensor at the edge of the network, only transmits the analysis result to the cloud computing center, greatly reduces the pressure of network bandwidth and the power consumption of the data center, processes the data near the data collection position, does not need to request the response of the cloud computing center through the network, greatly reduces the system delay and enhances the service response capability.
According to a second aspect of the present application, an embodiment of the present application provides a carbon emission monitoring and early warning analysis method based on edge calculation, as shown in fig. 2, including the following steps:
s1, analyzing urban carbon sources, and screening out the most representative edge monitoring station positions according to key areas of carbon emission, wherein the key areas of carbon emission specifically comprise a traffic carbon emission area, an artificial active carbon emission area and an industrial waste gas carbon emission area, the artificial active carbon emission area comprises an active square, a residential area and an office park, and the industrial waste gas carbon emission area comprises an urban industrial factory; the edge monitoring stations sequentially screened out corresponding to the key areas are traffic jam stations in peak periods, cell stations, office park stations and industrial factory stations.
In this embodiment, the number of the edge monitoring stations can be set to 3 to 5 according to the classification characteristics, data are acquired through the same type of characteristic stations, an estimation mean value is provided for a cloud computing center in a later period, and estimation accuracy is further improved.
S2, establishing an edge monitoring station and debugging a communication network; the method comprises the steps of establishing an edge monitoring site after screening the geographic position of the site in the whole city, specifically comprising a data acquisition module, an edge calculation module and a data transmission module, wherein the data acquisition module is divided into a carbon dioxide concentration sensor and a meteorological observation sensor.
In this embodiment, the data acquisition module transmits the acquired carbon dioxide concentration value and the meteorological data to the edge calculation module in real time, and since the drastic change of the meteorological conditions may affect the monitoring value of the carbon dioxide concentration, the edge calculation module may select the carbon dioxide concentration value according to the corresponding stored meteorological data when analyzing the acquired data, and only selects the monitoring value with stable meteorological conditions.
S3, the edge monitoring station analyzes the station through the collected data, and the specific analysis process is as follows:
s31, screening carbon emission influence factors, wherein the carbon emission influence factors specifically comprise consumption factors, production factors and comprehensive factors; the consumption factors comprise the dominant income of citizens, the average consumption expenditure of citizens, the average social wage and the like; the production factors comprise production material price, production energy price, production process time, product demand and the like; the comprehensive factors comprise the number of the permanent population, epidemic situation limiting time factors, meteorological condition factors, local financial emission reduction expenditure and the like.
In this embodiment, the screening of the carbon emission influencing factors is completed through the Lasso model, and 11 variables influencing carbon emission are selected, including income that can be controlled by citizensx 1 Consumption expenditure of citizensx 2 Social average payrollx 3 Production material pricex 4 Producing energyPricex 5 Production process timex 6 Product demand, and method of producing the samex 7 Number of permanent populationx 8 Epidemic situation time-limiting factorx 9 Weather condition factorsx 10 And local financial emission reduction expenditurex 11 。
Specifically, the Lasso model screening step comprises the following steps:
s311, data standardization: because the units of each influence factor are not consistent, the center standardization processing needs to be performed on the original data, so that the features of different dimensions have the same measurement scale, and the specifically standardized data are as follows:
wherein,
as the original data, it is the original data,
is the average of the raw data and is,
is the variance;
s312, construction of carbon emission Y t Influence factor linear regression model:
wherein,
is a regression coefficient of the influencing factor,
are a series of interference terms that follow a standard normal distribution,
a constant.
S313, determining a final selected variable: subjecting the normalized data to Lasso solution, and subjecting the data to Lasso solutionnAll Lasso solutions are obtained through the secondary iteration, and the best model is determined by using an akage pool information criterion. The Chi-chi model describes the accuracy of the model by adding a penalty term to a likelihood function, and the optimal model can be determined from a series of different models according to the Chi-chi minimum value A, namely
Wherein,
is a parameter number and
，nfor the number of observations (i.e. the number of iterations),Rfor the sum of the squares of the residuals, the calculation method is:
wherein,
in the form of an actual value of the value,
are estimated values.
The model fit is best when the akabane information value a reaches a minimum value during the iteration. And selecting the iteration result as a variable screening basis, and selecting a final variable.
S32, constructing a BP neural network, and specifically comprising the following steps:
s321, normalization processing of sample data: the data were mapped to [0,1] using a zero mean normalization approach.
S322, establishing a BP neural network structure: the variables screened out by the Lasso regression model are used as input layer nodes of the BP neural network, the output layer nodes are carbon emission, and the calculation formula of the number of hidden layer nodes is as follows:
in the formula,Nto input the number of layer nodes (i.e. the number of filter variables),Mis the number of nodes of the output layer, constant
The range is [0, 10].
S323, BP neural network training: error target is set to 10 -8 And training the neural network.
S33, predicting site carbon emission: and inputting the test sample into the constructed BP neural network, predicting the carbon emission, and performing inverse normalization processing on an output result to obtain a carbon emission predicted value.
S4, uploading the site analysis result to a cloud computing center;
s5, the cloud computing center estimates the urban carbon emission according to the station analysis result and generates an early warning report: after receiving the analysis results of all the sites, filling the data into corresponding feature classifications in sequence, wherein the specific classification comprises two parts: the first type is classified according to emission characteristics, including traffic emission, daily life emission, office park emission and industrial production emission, the acquisition of the data is to estimate the emission of other monitoring stations not arranged in the whole city according to the data of the monitoring stations with the same characteristics, and generate the ranking of the station type carbon emission; and the second category is divided according to regions, and estimates the carbon emission of the whole market according to the result of geographical region division, thereby generating a regional carbon emission ranking. And generating an early warning report according to the site category carbon emission sequence and the regional carbon emission sequence, and making an emission reduction plan of the city through the early warning report.
According to the method, monitoring sites based on edge computing are set for urban carbon emission concentration points, real-time collection of carbon dioxide concentration and prediction and analysis of site carbon emission are completed, and then the carbon emission values of the whole city are estimated in a classified mode through a cloud computing center. In the process of site prediction analysis, screening carbon emission influence factors is completed by adopting a Lasso model, more key factors are obtained, and the BP neural network training is completed according to the key factors, so that the carbon emission prediction is completed.
According to the method, the mapping relation between each influence factor and the carbon emission is obtained by combining the actually acquired data and the possible factors influencing the carbon emission, the constructed carbon emission prediction model has a better prediction effect, the future carbon emission can be predicted through historical data, and early warning and guidance are provided for carbon emission reduction measures.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The utility model provides a carbon emission monitoring early warning analytic system based on edge calculation which characterized in that: the system comprises an edge monitoring station and a cloud computing center, wherein the edge monitoring station is connected with the cloud computing center in a wired or wireless mode;
the edge monitoring station is provided with a data acquisition module, an edge computing module and a data transmission module, and is used for acquiring data, performing primary site analysis and simultaneously packaging and uploading the data to a cloud computing center;
the edge calculation module is used for constructing a station carbon emission analysis model;
and the cloud computing center is used for estimating and predicting urban carbon emission and generating an early warning report.
2. The system of claim 1, wherein the system comprises: the station carbon emission analysis model comprises a regression model and a BP neural network; the regression model is used for screening carbon emission influence factor variables; and the BP neural network is used for outputting the predicted value of the carbon emission of the station.
3. The system for monitoring, pre-warning and analyzing carbon emission based on edge calculation as claimed in claim 1, wherein: the data acquisition module comprises a carbon dioxide concentration sensor and a meteorological observation sensor; the early warning report includes a site category carbon emission ranking and a regional carbon emission ranking.
4. A carbon emission monitoring and early warning analysis method based on edge calculation, which is applied to the carbon emission early warning analysis system as claimed in any one of claims 1 to 3, and is characterized in that: the method comprises the following steps:
s1, analyzing a city carbon source, and screening out the most representative edge monitoring station position;
s2, establishing an edge monitoring station and debugging a communication network;
s3, the edge monitoring station performs station analysis through the collected data;
s4, uploading the site analysis result to a cloud computing center;
and S5, the cloud computing center carries out urban carbon emission estimation according to the station analysis result and generates an early warning report.
5. The carbon emission monitoring and early warning analysis method based on edge calculation as claimed in claim 4, wherein: in step S1, the most representative edge monitoring station positions are specifically: traffic jam stations, community stations, office park stations and industrial plant stations.
6. The carbon emission monitoring and early warning analysis method based on edge calculation as claimed in claim 4, wherein: in step S3, the site analysis specifically includes:
s31, screening influence factors of carbon emission;
s32, building a BP neural network;
and S33, predicting the carbon emission of the site.
7. The carbon emission monitoring and early warning analysis method based on edge calculation as claimed in claim 6, wherein: in step S31, the carbon emission influencing factors specifically include: consumption factors, production factors, and combination factors.
8. The carbon emission monitoring and early warning analysis method based on edge calculation as claimed in claim 7, wherein: the screening of the carbon emission influence factors specifically comprises the following steps:
s311, standardizing data;
s312, constructing a linear regression model of the carbon emission influence factors;
and S313, determining the final selected variable.
9. The carbon emission monitoring and early warning analysis method based on edge calculation as claimed in claim 8, wherein: the BP neural network construction specifically comprises the following steps:
s321, normalizing the sample data;
s322, establishing a BP neural network structure;
and S323, training the BP neural network.
10. The carbon emission monitoring and early warning analysis method based on edge calculation as claimed in claim 4, wherein: the step S5 specifically includes the following steps: the cloud computing center sets two types according to the emission characteristics and the regional division respectively, and the first type estimates the emission of other monitoring sites in the whole city without the monitoring sites according to the data of the monitoring sites with the same characteristics; the second type estimates the carbon emission of each area according to the result of geographical region division; and generating early warning reports according to the site category carbon emission ranking and the regional carbon emission ranking.
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| CN116933983A (en) * | 2023-09-19 | 2023-10-24 | 江西财经大学 | Low-carbon emission data monitoring system and method |
| CN116933983B (en) * | 2023-09-19 | 2024-01-23 | 江西财经大学 | Low-carbon emission data monitoring system and method |
| CN118037078A (en) * | 2024-04-12 | 2024-05-14 | 国网浙江省电力有限公司湖州供电公司 | Substation carbon emission calculation data migration method |
| CN118627764A (en) * | 2024-08-14 | 2024-09-10 | 南京江行联加智能科技有限公司 | Cloud edge cooperative large model-based enterprise carbon emission monitoring system |
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