CN116340850B - Carbon emission monitoring method and system in building process - Google Patents

Carbon emission monitoring method and system in building process Download PDF

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CN116340850B
CN116340850B CN202310613790.XA CN202310613790A CN116340850B CN 116340850 B CN116340850 B CN 116340850B CN 202310613790 A CN202310613790 A CN 202310613790A CN 116340850 B CN116340850 B CN 116340850B
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monitoring
conversion rate
block
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CN116340850A (en
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李静原
朱杰
李璟
李然
姚林林
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Beijing Intelligent Building Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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    • G06F16/285—Clustering or classification
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Abstract

The invention provides a method and a system for monitoring carbon emission in a building process, which relate to the technical field of carbon emission monitoring, and the method comprises the following steps: collecting an energy operation data set of a first building project, wherein the energy operation data set is stored in a partition block; acquiring an energy conversion rate set, wherein the number of the energy conversion rate set corresponds to the number of the blocks; clustering the energy conversion rate set to obtain a clustering result; merging the block sets according to the clustering result, and outputting a carbon-row distributed block chain; building a plurality of carbon row monitoring channels; according to the carbon-emission monitoring channels, carbon-emission monitoring results are fitted, and first early warning information is generated according to the fitting results, so that the technical problems that in the prior art, most of carbon emission monitoring and early warning are only directly carried out, conversion analysis is not carried out on carbon emission energy, the monitoring and early warning significance is low, the effectiveness of the monitoring and early warning is insufficient, and the monitoring and early warning effect is poor are solved.

Description

Carbon emission monitoring method and system in building process
Technical Field
The invention relates to the technical field of carbon emission monitoring, in particular to a method and a system for monitoring carbon emission in a building process.
Background
In recent years, the global warming problem has become one of important global crisis, and the emission of greenhouse gases is a recognized main cause of global warming, so that the "low carbon" is imperative in order to solve the global warming problem, the emission of greenhouse gases is directly related to the consumption of energy, and the emission of greenhouse gases is essentially applied to various energy sources in the building construction process, so that the quantity of greenhouse gases is generated, and the method has important practical significance for improving the carbon emission in the building process.
At present, most of the prior art only directly monitors and pre-warns carbon emission, and does not perform conversion analysis on carbon emission energy, so that the significance of monitoring and pre-warning is not great, the effectiveness of monitoring and pre-warning is insufficient, and the monitoring and pre-warning effect is poor.
Disclosure of Invention
The invention provides a method and a system for monitoring carbon emission in a building process, which are used for solving the technical problems that in the prior art, most of the carbon emission is monitored and early-warned directly, the carbon emission energy is not converted and analyzed, so that the meaning of the monitoring and early-warning is not great, the effectiveness of the monitoring and early-warning is insufficient, and the monitoring and early-warning effect is poor.
According to a first aspect of the present invention, there is provided a method of monitoring carbon emissions in a building process, comprising: collecting an energy operation data set of a first building project, wherein the energy operation data set is stored in blocks, and each block stores data corresponding to an energy type; performing energy conversion analysis on each block according to the energy operation data set to obtain an energy conversion rate set, wherein the number of the energy conversion rate sets corresponds to the number of the blocks; clustering the energy conversion rate set to obtain a clustering result; merging the block sets according to the clustering result, and outputting a carbon-row distributed block chain; establishing a plurality of carbon row monitoring channels, wherein the plurality of carbon row monitoring channels comprise a plurality of monitoring abnormality models, and the plurality of monitoring abnormality models are respectively output by model training on the carbon row distribution block chain; and fitting carbon row monitoring results according to the plurality of carbon row monitoring channels, and generating first early warning information according to the fitting results.
According to a second aspect of the present invention there is provided a carbon emission monitoring system in a construction process comprising: the data block storage module is used for collecting an energy operation data set of the first building project, the energy operation data set is stored in blocks, and each block stores data corresponding to one energy type; the energy conversion analysis module is used for carrying out energy conversion analysis on each block according to the energy operation data set to obtain energy conversion rate sets, wherein the number of the energy conversion rate sets corresponds to the number of the blocks; the cluster analysis module is used for obtaining a clustering result by clustering the energy conversion rate set; the block merging module is used for merging the block sets according to the clustering result and outputting a carbon-row distributed block chain; the carbon row monitoring channel construction module is used for constructing a plurality of carbon row monitoring channels, wherein the plurality of carbon row monitoring channels comprise a plurality of monitoring abnormal models, and the plurality of monitoring abnormal models are respectively output through model training on the carbon row distribution block chain; and the monitoring result fitting module is used for fitting the carbon-row monitoring results according to the plurality of carbon-row monitoring channels and generating first early warning information according to the fitting results.
According to the carbon emission monitoring method in the building process, the following beneficial effects can be achieved:
1. in this embodiment, a block-stored energy operation data set is obtained according to an energy type, and then energy conversion analysis is performed on each block to obtain an energy conversion rate set, after clustering is performed on the energy conversion rate set, a carbon row distribution block chain is output, a plurality of carbon row monitoring channels are built, carbon row monitoring results are fitted according to the plurality of carbon row monitoring channels, and first early warning information is generated according to the fitting results, so that the carbon row monitoring effect is improved, and the technical effects of timeliness and effectiveness of monitoring early warning are guaranteed.
2. Through carrying out carbon row intensity analysis to each block, obtain a plurality of higher a plurality of sign blocks of carbon emission to carry out the primary conversion of energy, the analysis of secondary conversion to a plurality of sign blocks and obtain the energy conversion and combine, be convenient for carry out carbon row monitoring to the sign block pertinently, reach when reducing the monitoring complexity, promote carbon row monitoring efficiency, improve the technical effect of the validity of carbon row monitoring.
3. By constructing a plurality of carbon-emission monitoring channels, corresponding monitoring anomaly models are arranged for blocks corresponding to different energy conversion rates, so that the technical effects of improving anomaly monitoring precision and accuracy are achieved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for monitoring carbon emission in a building process according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of acquiring an energy conversion rate set according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a first warning message generation process according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a carbon emission monitoring system in a building process according to an embodiment of the present invention.
Reference numerals illustrate: the system comprises a data block storage module 11, an energy conversion analysis module 12, a cluster analysis module 13, a block merging module 14, a carbon bank monitoring channel construction module 15 and a monitoring result fitting module 16.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In order to solve the technical problems that in the prior art, the monitoring and early warning of carbon emission are directly carried out, and conversion analysis is not carried out on carbon emission energy, so that the monitoring and early warning significance is low, the effectiveness of the monitoring and early warning is insufficient, and the monitoring and early warning effect is poor, the inventor of the invention obtains the carbon emission monitoring method and system in the building process through creative labor.
Example 1
Fig. 1 is a diagram of a method for monitoring carbon emission in a building process according to an embodiment of the present invention, as shown in fig. 1, where the method includes:
step S100: collecting an energy operation data set of a first building project, wherein the energy operation data set is stored in blocks, and each block stores data corresponding to an energy type;
specifically, the first building project generally refers to any building project of any type, such as a building project, an indoor decoration project and the like, when the building project is performed, building materials such as cement, glass, steel and the like are used, when the building materials are produced, different energy sources such as combustion energy sources consumed in steel smelting are consumed, meanwhile, various construction equipment such as a cement mixer, a cutting machine and the like are also used in the building process, different energy sources can be consumed in the operation of the construction equipment, all equipment or materials consuming energy sources in the construction process of the first building project are collected, the quantity of the consumed energy sources is counted, the energy sources are stored in blocks according to the consumed energy source types, energy source operation data are obtained, the consumed energy source types comprise energy sources such as combustion coal, petroleum, natural gas and electricity, the energy source operation data are stored in each block, for example, data corresponding to one energy source type are stored in each block, and data corresponding to the consumed energy source type are stored in each block, and data are provided for the subsequent carbon emission monitoring.
Step S200: performing energy conversion analysis on each block according to the energy operation data set to obtain an energy conversion rate set, wherein the number of the energy conversion rate sets corresponds to the number of the blocks;
as shown in fig. 2, step S200 of the embodiment of the present invention includes:
step S210: performing carbon emission index calculation on the energy operation data set, and outputting carbon emission intensity;
step S220: positioning according to the carbon row strength to obtain a plurality of identification blocks with carbon row strength greater than or equal to preset carbon row strength;
step S230: performing energy conversion analysis based on the preset carbon row intensity on the plurality of identification blocks to obtain a primary conversion block and a secondary conversion block;
step S240: performing energy conversion rate analysis according to the primary conversion block, and outputting a primary conversion rate set;
step S250: performing energy conversion rate analysis according to the secondary conversion block, and outputting a secondary conversion rate set;
step S260: and obtaining an energy conversion rate set according to the primary conversion rate set and the secondary conversion rate set.
The step S240 of the embodiment of the present invention includes:
step S241: obtaining a converted object and a primary conversion object of the primary conversion block;
step S242: carbon emission data acquisition is carried out on the process of the converted object and the primary converted object, so that carbon emission data of the converted object and carbon emission data of the primary converted object are obtained;
step S243: and inputting the carbon number data of the converted object and the carbon number data of the primary conversion object into a primary conversion rate analysis model, and outputting a primary conversion rate set.
The step S250 of the embodiment of the present invention includes:
step S251: obtaining a converted object, a primary converted object and a secondary converted object of the secondary conversion block;
step S252: carbon emission data acquisition is carried out on the processes of the converted object, the primary conversion object and the secondary conversion object to obtain first carbon emission data and second carbon emission data;
step S253: and inputting the first carbon bank data and the second carbon bank data into a secondary conversion rate analysis model, and outputting a secondary conversion rate set.
The step S253 of the embodiment of the present invention further includes:
step S2531: introducing a loss function to perform loss analysis on the first carbon bank data and the second carbon bank data to obtain conversion loss rate;
step S2532: and generating a loss network layer based on the conversion loss rate, and optimizing the model layer of the secondary conversion rate analysis model by using the loss network layer.
In particular, the energy conversion analysis may be understood as energy replacement, such as replacing fossil energy with clean energy, but in practical applications, some of the energy cannot be directly replaced and secondary conversion is required, so the energy conversion analysis includes primary conversion analysis and secondary conversion analysis, resulting in an acquired energy conversion set, the number of which corresponds to the number of blocks, that is, one block corresponds to one energy conversion set, and the energy conversion set includes a primary energy conversion set and a secondary energy conversion set.
Specifically, the carbon emission index calculation is performed on the energy operation data set, so that various equipment and materials consume different types of energy sources in the building process, the carbon dioxide isothermal chamber gas is discharged in each energy source consumption process, the carbon emission amount of different energy sources is different, the consumption amount of various energy sources is obtained, the carbon emission index can be calculated by multiplying the consumption amount by the greenhouse gas amount discharged by the energy sources in unit quantity, the carbon emission index calculation result is taken as the carbon emission intensity, and one block corresponds to one carbon emission intensity. The preset carbon-row strength is set according to the actual situation, and the allowable highest carbon-row strength can be obtained according to the regulations of the related departments, that is, if the carbon-row strength is greater than or equal to the preset carbon-row strength, the carbon-row strength and the preset carbon-row strength are compared, a plurality of blocks with the carbon-row strength greater than or equal to the preset carbon-row strength are obtained and marked, so that a plurality of identification blocks are obtained, and the subsequent targeted management of the blocks with higher carbon-row strength is facilitated.
Performing energy conversion analysis based on the preset carbon emission intensity on the plurality of identification blocks, wherein the energy conversion analysis process is to judge whether the energy type used by the identification blocks can be replaced by other energy (energy type with less carbon emission), such as using hydrogen energy to replace coal and petroleum for combustion, namely primary conversion; however, in the process of replacing energy, part of the energy can not be directly replaced, for example, if the used electric energy is generated by fossil energy, the electric energy can not be directly replaced by clean energy, and the clean energy needs to be converted into electric energy, for example, solar energy is converted into electric energy by photovoltaic or photo-thermal power generation equipment, which is the secondary conversion. Each block contains data of an energy type, based on the analysis, a plurality of identification blocks can be divided into a primary conversion block and a secondary conversion block, that is, the primary conversion block refers to a block which only carries out direct replacement of primary energy, and the secondary conversion block refers to a block which needs to replace energy to carry out corresponding energy conversion, and then the converted energy is replaced, that is, the block which needs to carry out two interval conversion.
And then, according to the primary conversion block, the energy conversion rate analysis is performed, the primary conversion rate set is output, according to the secondary conversion block, the energy conversion rate analysis is performed, the secondary conversion rate set is output, the conversion rate refers to the reduction degree of the replacement energy source to the carbon emission, in a simple way, the energy source with a small carbon emission is used for replacing the energy source with a large carbon emission, all the carbon emission cannot be completely eliminated in the process, only the carbon emission of the replaced energy source is reduced, in particular, the carbon emission generated by the replacement energy source can be obtained, then, the ratio of the carbon emission generated by the replacement energy source to the carbon emission of the initial energy source is subtracted by 1 to obtain a calculation result as the energy conversion rate, the greater the energy conversion rate is, the better the effect of the energy source replacement on the carbon reduction is, the primary conversion rate set and the secondary conversion rate set are obtained based on the conversion rate, and the primary conversion rate set and the secondary conversion rate set are combined into the energy conversion rate set.
Specifically, the process of obtaining a set of primary conversions is as follows: the method comprises the steps of obtaining a converted object and a primary conversion object of a primary conversion block, wherein the converted object is an initial energy source with higher carbon emission, the primary conversion object is an energy source with lower carbon emission for replacing the initial energy source, further, carbon emission data acquisition is carried out on the process of the converted object and the primary conversion object to obtain carbon emission data of the converted object and carbon emission data of the primary conversion object, the carbon emission data is carbon emission generated in the energy source application process, the carbon emission data of the converted object and the carbon emission data of the primary conversion object are input into a primary conversion rate analysis model, a primary conversion rate set is output, the primary conversion rate analysis model is a model with a simple calculation formula embedded inside, for example, the primary conversion rate is obtained by subtracting the ratio between the carbon emission data of the primary conversion object and the carbon emission data of the primary conversion object through the primary conversion rate analysis model, the primary conversion rate set is obtained by embedding the primary conversion rate analysis model in a calculation mode according to practical conditions, and the primary conversion rate set is obtained.
Specifically, the procedure for obtaining the secondary conversion set is as follows: the method comprises the steps of obtaining a converted object, a primary converted object and a secondary converted object of the secondary conversion block, namely, the secondary conversion process comprises a primary intermediate conversion process, the converted object is an initial energy source which needs to be replaced, the primary converted object is an initial replacement energy source, the secondary converted object is an energy source which is generated by the conversion of the initial replacement energy source and is used for replacing the initial energy source, for example, a process of replacing fossil energy with solar energy with higher carbon emission is performed, the fossil energy source is the converted object, the solar energy is the primary converted object, but the solar energy cannot directly replace the fossil energy source, the solar energy is required to be converted into electric energy for use, and the electric energy is the secondary converted object. And further, collecting carbon emission data of the process of the object to be converted, the primary conversion object and the secondary conversion object to obtain first carbon emission data and second carbon emission data, wherein the first carbon emission data refers to carbon emission data generated by the object to be converted, and the second carbon emission data refers to the sum of the carbon emission data generated by the primary conversion object converted into the secondary conversion object and the carbon emission data generated by the secondary conversion object in the application process. And further inputting the first carbon bank data and the second carbon bank data into a secondary conversion rate analysis model, and outputting a secondary conversion rate set, wherein the secondary conversion rate analysis model has the same model structure as the primary conversion rate analysis model.
Specifically, a loss function is introduced to perform loss analysis on the first carbon emission data and the second carbon emission data to obtain conversion loss rate, in short, the collected first carbon emission data and the collected second carbon emission data have errors, so that data collection results are inaccurate, for example, carbon emission generated by energy combustion is obtained through converted heat energy, but partial energy is released into air in the process of releasing greenhouse gases by the energy, the partial energy also needs to be detected through carbon emission, the loss analysis is performed on the first carbon emission data and the second carbon emission data based on the partial energy, the conversion loss rate is obtained through calculation of errors existing in the carbon emission collection process, the loss function can be understood as a formula for calculating the errors, a loss network layer is generated according to the conversion loss rate, the secondary conversion rate is subjected to analysis by the loss network layer, the conversion rate is subjected to model analysis, the conversion rate is optimized, the conversion rate is corrected through the secondary conversion rate is corrected, and the accuracy is improved.
Step S300: clustering the energy conversion rate set to obtain a clustering result;
the step S300 of the embodiment of the present invention includes:
step S310: setting a preset optimizing K value interval, wherein K is a positive integer greater than or equal to 2 and used for determining the quantity in the clustering result;
step S320: optimizing the preset optimizing K value interval by using a particle swarm algorithm to obtain a first K value;
step S330: and carrying out K-means optimization on the energy conversion rate set according to the first K value to obtain the clustering result.
Specifically, the energy conversion rate set is clustered through a particle swarm algorithm, and the energy conversion rates which are relatively close to each other are clustered together to obtain a clustering result, wherein the clustering result is specifically obtained through the following steps:
setting a preset optimizing K value interval, wherein K is a positive integer greater than or equal to 2 and is used for determining the quantity in the clustering result, in short, clustering is to classify data in an energy conversion rate set, K is the number of categories which can be separated, and the preset optimizing K value interval is the constraint range of the number of categories and can be determined by combining historical experience and actual conditions. And then optimizing the preset optimizing K value interval by using a particle swarm algorithm to obtain a first K value, namely taking the energy conversion rate set as a search space, namely taking the energy conversion rates as centroids according to the first K value randomly, then dividing other energy conversion rates into centroids closest to K data clusters by calculating distances between the other energy conversion rates and the K energy conversion rates to obtain the clustering result, wherein the differences between the energy conversion rates in the optimal data combinations are minimum, namely the data are closest, so as to obtain the number of the optimal data combinations, the number of the optimal data combinations is taken as a first K value, the first K value belongs to the preset optimizing K value interval, and K-means optimizing is carried out on the energy conversion rate set according to the first K value. The accuracy of the clustering result can be improved by the particle swarm algorithm.
Step S400: merging the block sets according to the clustering result, and outputting a carbon-row distributed block chain;
specifically, the clustering result includes K data clusters, where the K data clusters are K block sets, each data cluster includes a plurality of energy conversion rates, each energy conversion rate corresponds to a block, based on which, blocks corresponding to the plurality of energy conversion rates in the same data cluster are connected together, and a carbon-row distributed blockchain can be obtained, and one data cluster corresponds to one carbon-row distributed blockchain.
Step S500: establishing a plurality of carbon row monitoring channels, wherein the plurality of carbon row monitoring channels comprise a plurality of monitoring abnormality models, and the plurality of monitoring abnormality models are respectively output by model training on the carbon row distribution block chain;
step S600: and fitting carbon row monitoring results according to the plurality of carbon row monitoring channels, and generating first early warning information according to the fitting results.
As shown in fig. 3, step S600 of the embodiment of the present invention includes;
step S610: configuring abnormal monitoring precision according to the carbon row distribution block chain;
step S620: training based on the abnormality monitoring precision and a historical carbon bank conversion data set corresponding to each block in the carbon bank distribution block chain to generate a plurality of monitoring abnormality models, wherein the monitoring abnormality models are used for outputting indexes of the carbon bank conversion rate abnormality of each block;
step S630: performing weight fitting according to the index of the marked carbon bank conversion rate abnormality to obtain a first abnormality index;
step S640: and when the first abnormality index exceeds a preset abnormality index, generating first early warning information.
Specifically, a plurality of carbon row monitoring channels are built, wherein the plurality of carbon row monitoring channels comprise a plurality of monitoring anomaly models, in short, the energy conversion rates corresponding to different carbon row distribution block chains are different, different energy conversion rates need to be provided with different monitoring anomaly models, one carbon row monitoring channel corresponds to one carbon row distribution block chain and a plurality of monitoring anomaly models through building the plurality of carbon row monitoring channels, that is, the carbon row distribution block chain is input into the corresponding carbon row monitoring channel, the energy conversion rates of a plurality of blocks on the carbon row distribution block chain are input into the monitoring anomaly models corresponding to each block, and the index of the energy conversion rate anomaly of each block is obtained, so that the carbon row distribution block chain can be monitored simultaneously, and the monitoring accuracy can be improved. The plurality of monitoring anomaly models are respectively output through model training on the carbon bank distribution block chain, and can specifically acquire historical carbon bank conversion data of the carbon bank distribution block chain for training and testing. And further inputting the carbon row distribution block chain into a plurality of corresponding carbon row monitoring channels, and outputting carbon row monitoring results, wherein the carbon row monitoring results refer to abnormality indexes corresponding to the carbon row distribution block chain respectively.
Specifically, according to the carbon-row distribution blockchain, the abnormality monitoring precision is configured, the abnormality monitoring precision refers to a monitoring unit of the energy conversion rate, the carbon-row distribution blockchain is obtained by combining blocks together based on the energy conversion rate of each block, that is, the energy conversion rates of a plurality of blocks included in one carbon-row distribution blockchain, therefore, different monitoring units can be set according to different energy conversion rates corresponding to different blocks, for example, for a block with a larger energy conversion rate, the energy conversion effect is better, the reduced carbon emission amount is more, the monitoring unit can be set to be larger, for example, the abnormality determination is performed when the variation degree of the energy conversion rate exceeds 5%, meanwhile, the carbon reduction effect is relatively poorer for a block with a smaller variation degree as an index of the conversion rate, for example, the monitoring unit can be set to be smaller, for example, the abnormality determination is performed when the variation degree of the energy conversion rate exceeds 1%, and the abnormality monitoring precision can be set according to the specific self-setting of the actual abnormality monitoring precision.
And training based on the abnormality monitoring precision and historical carbon bank conversion data sets corresponding to all blocks in the carbon bank distribution block chain, wherein the historical carbon bank conversion data sets are carbon bank conversion data in the past period, historical abnormality indexes are configured for the historical carbon bank conversion data sets corresponding to all the blocks by analyzing the historical carbon bank conversion data sets corresponding to all the blocks, the historical carbon bank conversion data sets corresponding to all the blocks and the corresponding historical abnormality indexes are used as construction data, a plurality of monitoring abnormality models are constructed by the construction data, the plurality of monitoring abnormality models are neural network models in machine learning, the input is energy conversion rate corresponding to all the blocks on the carbon bank distribution block chain, and the output is the conversion rate abnormality index. The method specifically comprises the steps of dividing the construction data into a training set and a testing set, training the monitoring abnormal model to be converged by using the data in the training set, and then testing the accuracy of the monitoring abnormal model by using the data in the testing set, so that a plurality of monitoring abnormal models with the accuracy meeting the requirement are obtained.
Through a plurality of monitoring abnormality models, indexes of abnormal carbon-row conversion rate of each block identification are output, one carbon-row distribution block chain can obtain a plurality of indexes, further, weight calculation is carried out on the indexes of abnormal carbon-row conversion rate of the identification on one carbon-row distribution block chain, and then a first abnormality index can be obtained, wherein the first abnormality index generally refers to the abnormality index of each carbon-row distribution block chain, when the first abnormality index exceeds a preset abnormality index, the preset abnormality index can be set by itself according to actual conditions, and also can be based on the size of the first abnormality index, first early warning information corresponding to different abnormality grades is generated, the first early warning information is sent to staff, the staff is reminded to take measures in time, the stability of energy conversion rate is guaranteed, the carbon-row monitoring effect is improved, and meanwhile, the technical effect of monitoring early warning effectiveness is improved.
Based on the analysis, the invention provides a carbon emission monitoring method in the building process, which has the following beneficial effects:
1. in this embodiment, a block-stored energy operation data set is obtained according to an energy type, and then energy conversion analysis is performed on each block to obtain an energy conversion rate set, after clustering is performed on the energy conversion rate set, a carbon row distribution block chain is output, a plurality of carbon row monitoring channels are built, carbon row monitoring results are fitted according to the plurality of carbon row monitoring channels, and first early warning information is generated according to the fitting results, so that the carbon row monitoring effect is improved, and the technical effects of timeliness and effectiveness of monitoring early warning are guaranteed.
2. Through carrying out carbon row intensity analysis to each block, obtain a plurality of higher a plurality of sign blocks of carbon emission to carry out the primary conversion of energy, the analysis of secondary conversion to a plurality of sign blocks and obtain the energy conversion and combine, be convenient for carry out carbon row monitoring to the sign block pertinently, reach when reducing the monitoring complexity, promote carbon row monitoring efficiency, improve the technical effect of the validity of carbon row monitoring.
3. By constructing a plurality of carbon-emission monitoring channels, corresponding monitoring anomaly models are arranged for blocks corresponding to different energy conversion rates, so that the technical effects of improving anomaly monitoring precision and accuracy are achieved.
Example two
Based on the same inventive concept as the carbon emission monitoring method in the building process in the foregoing embodiment, as shown in fig. 4, the present invention further provides a carbon emission monitoring system in the building process, the system comprising:
the data block storage module 11 is used for collecting an energy operation data set of the first building project, the energy operation data set is stored in blocks, and data corresponding to one energy type is stored in each block;
the energy conversion analysis module 12 is configured to perform energy conversion analysis on each block according to the energy operation data set, so as to obtain an energy conversion rate set, where the number of the energy conversion rate sets corresponds to the number of the blocks;
the cluster analysis module 13 is used for obtaining a clustering result by clustering the energy conversion rate set;
the block merging module 14 is configured to merge the block sets according to the clustering result, and output a carbon-row distributed block chain;
the carbon-row monitoring channel construction module 15 is used for constructing a plurality of carbon-row monitoring channels, wherein the plurality of carbon-row monitoring channels comprise a plurality of monitoring anomaly models, and the plurality of monitoring anomaly models are respectively output through model training on the carbon-row distribution block chain;
the monitoring result fitting module 16 is configured to fit the monitoring results of the carbon-row according to the plurality of carbon-row monitoring channels, and generate first early warning information according to the fitting results.
Further, the energy conversion analysis module 12 is further configured to:
performing carbon emission index calculation on the energy operation data set, and outputting carbon emission intensity;
positioning according to the carbon row strength to obtain a plurality of identification blocks with carbon row strength greater than or equal to preset carbon row strength;
performing energy conversion analysis based on the preset carbon row intensity on the plurality of identification blocks to obtain a primary conversion block and a secondary conversion block;
performing energy conversion rate analysis according to the primary conversion block, and outputting a primary conversion rate set;
performing energy conversion rate analysis according to the secondary conversion block, and outputting a secondary conversion rate set;
and obtaining an energy conversion rate set according to the primary conversion rate set and the secondary conversion rate set.
Further, the energy conversion analysis module 12 is further configured to:
obtaining a converted object and a primary conversion object of the primary conversion block;
carbon emission data acquisition is carried out on the process of the converted object and the primary converted object, so that carbon emission data of the converted object and carbon emission data of the primary converted object are obtained;
and inputting the carbon number data of the converted object and the carbon number data of the primary conversion object into a primary conversion rate analysis model, and outputting a primary conversion rate set.
Further, the energy conversion analysis module 12 is further configured to:
obtaining a converted object, a primary converted object and a secondary converted object of the secondary conversion block;
carbon emission data acquisition is carried out on the processes of the converted object, the primary conversion object and the secondary conversion object to obtain first carbon emission data and second carbon emission data;
and inputting the first carbon bank data and the second carbon bank data into a secondary conversion rate analysis model, and outputting a secondary conversion rate set.
Further, the energy conversion analysis module 12 is further configured to:
introducing a loss function to perform loss analysis on the first carbon bank data and the second carbon bank data to obtain conversion loss rate;
and generating a loss network layer based on the conversion loss rate, and optimizing the model layer of the secondary conversion rate analysis model by using the loss network layer.
Further, the cluster analysis module 13 is further configured to:
setting a preset optimizing K value interval, wherein K is a positive integer greater than or equal to 2 and used for determining the quantity in the clustering result;
optimizing the preset optimizing K value interval by using a particle swarm algorithm to obtain a first K value;
and carrying out K-means optimization on the energy conversion rate set according to the first K value to obtain the clustering result.
Further, the monitoring result fitting module 16 is further configured to:
configuring abnormal monitoring precision according to the carbon row distribution block chain;
training based on the abnormality monitoring precision and a historical carbon bank conversion data set corresponding to each block in the carbon bank distribution block chain to generate a plurality of monitoring abnormality models, wherein the monitoring abnormality models are used for outputting indexes of the carbon bank conversion rate abnormality of each block;
performing weight fitting according to the index of the marked carbon bank conversion rate abnormality to obtain a first abnormality index;
and when the first abnormality index exceeds a preset abnormality index, generating first early warning information.
The specific example of the carbon emission monitoring method in the building process in the first embodiment is also applicable to the carbon emission monitoring system in the building process in the present embodiment, and the carbon emission monitoring system in the building process in the present embodiment is clearly known to those skilled in the art from the foregoing detailed description of the carbon emission monitoring method in the building process, so that the details thereof will not be described herein for brevity.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (7)

1. A method for monitoring carbon emissions during a building process, the method comprising:
collecting an energy operation data set of a first building project, wherein the energy operation data set is stored in blocks, and each block stores data corresponding to an energy type;
performing energy conversion analysis on each block according to the energy operation data set to obtain an energy conversion rate set, wherein the number of the energy conversion rate sets corresponds to the number of the blocks;
clustering the energy conversion rate set to obtain a clustering result;
merging the block sets according to the clustering result, and outputting a carbon-row distributed block chain;
establishing a plurality of carbon row monitoring channels, wherein the plurality of carbon row monitoring channels comprise a plurality of monitoring abnormality models, and the plurality of monitoring abnormality models are respectively output by model training on the carbon row distribution block chain;
performing carbon row monitoring result fitting according to the plurality of carbon row monitoring channels, and generating first early warning information according to fitting results;
the energy conversion analysis is carried out on each block according to the energy operation data set, and the method comprises the following steps:
performing carbon emission index calculation on the energy operation data set, and outputting carbon emission intensity;
positioning according to the carbon row strength to obtain a plurality of identification blocks with carbon row strength greater than or equal to preset carbon row strength;
performing energy conversion analysis based on the preset carbon row intensity on the plurality of identification blocks to obtain a primary conversion block and a secondary conversion block;
performing energy conversion rate analysis according to the primary conversion block, and outputting a primary conversion rate set;
performing energy conversion rate analysis according to the secondary conversion block, and outputting a secondary conversion rate set;
and obtaining an energy conversion rate set according to the primary conversion rate set and the secondary conversion rate set.
2. The method of claim 1, wherein the energy conversion analysis is performed based on the primary conversion zone, outputting a primary conversion set, the method comprising:
obtaining a converted object and a primary conversion object of the primary conversion block;
carbon emission data acquisition is carried out on the process of the converted object and the primary converted object, so that carbon emission data of the converted object and carbon emission data of the primary converted object are obtained;
and inputting the carbon number data of the converted object and the carbon number data of the primary conversion object into a primary conversion rate analysis model, and outputting a primary conversion rate set.
3. The method of claim 1, wherein the energy conversion analysis is performed based on the secondary conversion zone, outputting a set of secondary conversions, the method comprising:
obtaining a converted object, a primary converted object and a secondary converted object of the secondary conversion block;
carbon emission data acquisition is carried out on the processes of the converted object, the primary conversion object and the secondary conversion object to obtain first carbon emission data and second carbon emission data;
and inputting the first carbon bank data and the second carbon bank data into a secondary conversion rate analysis model, and outputting a secondary conversion rate set.
4. A method as claimed in claim 3, wherein the method further comprises:
introducing a loss function to perform loss analysis on the first carbon bank data and the second carbon bank data to obtain conversion loss rate;
and generating a loss network layer based on the conversion loss rate, and optimizing the model layer of the secondary conversion rate analysis model by using the loss network layer.
5. The method of claim 1, wherein the clustering result is obtained by clustering the set of energy conversions, the method comprising:
setting a preset optimizing K value interval, wherein K is a positive integer greater than or equal to 2 and used for determining the quantity in the clustering result;
optimizing the preset optimizing K value interval by using a particle swarm algorithm to obtain a first K value;
and carrying out K-means optimization on the energy conversion rate set according to the first K value to obtain the clustering result.
6. The method of claim 1, wherein the method further comprises:
configuring abnormal monitoring precision according to the carbon row distribution block chain;
training based on the abnormality monitoring precision and a historical carbon bank conversion data set corresponding to each block in the carbon bank distribution block chain to generate a plurality of monitoring abnormality models, wherein the monitoring abnormality models are used for outputting indexes of the carbon bank conversion rate abnormality of each block;
performing weight fitting according to the index of the marked carbon bank conversion rate abnormality to obtain a first abnormality index;
and when the first abnormality index exceeds a preset abnormality index, generating first early warning information.
7. A system for monitoring carbon emissions during a building process, the system comprising:
the data block storage module is used for collecting an energy operation data set of the first building project, the energy operation data set is stored in blocks, and each block stores data corresponding to one energy type;
the energy conversion analysis module is used for carrying out energy conversion analysis on each block according to the energy operation data set to obtain energy conversion rate sets, wherein the number of the energy conversion rate sets corresponds to the number of the blocks;
the cluster analysis module is used for obtaining a clustering result by clustering the energy conversion rate set;
the block merging module is used for merging the block sets according to the clustering result and outputting a carbon-row distributed block chain;
the carbon row monitoring channel construction module is used for constructing a plurality of carbon row monitoring channels, wherein the plurality of carbon row monitoring channels comprise a plurality of monitoring abnormal models, and the plurality of monitoring abnormal models are respectively output through model training on the carbon row distribution block chain;
the monitoring result fitting module is used for fitting carbon row monitoring results according to the plurality of carbon row monitoring channels and generating first early warning information according to the fitting results;
the energy conversion analysis module comprises:
performing carbon emission index calculation on the energy operation data set, and outputting carbon emission intensity;
positioning according to the carbon row strength to obtain a plurality of identification blocks with carbon row strength greater than or equal to preset carbon row strength;
performing energy conversion analysis based on the preset carbon row intensity on the plurality of identification blocks to obtain a primary conversion block and a secondary conversion block;
performing energy conversion rate analysis according to the primary conversion block, and outputting a primary conversion rate set;
performing energy conversion rate analysis according to the secondary conversion block, and outputting a secondary conversion rate set;
and obtaining an energy conversion rate set according to the primary conversion rate set and the secondary conversion rate set.
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