CN115271218A

CN115271218A - Carbon emission prediction method, device, equipment and medium based on electric carbon factor

Info

Publication number: CN115271218A
Application number: CN202210916296.6A
Authority: CN
Inventors: 奚增辉; 杨奕辉; 王卫斌; 屈志坚; 朱云龙
Original assignee: State Grid Shanghai Electric Power Co Ltd
Current assignee: State Grid Shanghai Electric Power Co Ltd
Priority date: 2022-08-01
Filing date: 2022-08-01
Publication date: 2022-11-01

Abstract

The invention discloses a method, a device, equipment and a medium for predicting carbon emission based on an electrical carbon factor. According to the method, the normal distribution of the electrical carbon factor and the normal distribution of the factor weight are obtained for each object to be determined, the estimated electrical carbon factor is determined according to the normal distribution of the electrical carbon factor, the normal distribution of the factor weight, the extraction number of the preset factor and the extraction number of the preset weight, and further the target electrical carbon factor of the target object is determined according to the reference electrical carbon factor and each estimated electrical carbon factor, so that the accurate determination of the electrical carbon factor is realized, the estimated carbon emission of the target object is further determined according to the determined target electrical carbon factor, the accurate determination of the carbon emission is realized, the problems of low accuracy, low efficiency and high cost in artificial carbon emission analysis in the prior art are solved, and the influence of the extraction value on the estimated electrical carbon factor is avoided through the extraction number of the preset factor and the extraction number of the preset weight, so that the accuracy of the electrical carbon factor is improved.

Description

Carbon emission prediction method, device, equipment and medium based on electric carbon factor

Technical Field

The invention relates to the technical field of carbon emission, in particular to a method, a device, equipment and a medium for predicting carbon emission based on an electrical carbon factor.

Background

With the increase of economy and the progress of society, people pay more and more attention to the harmonious development of people and nature, and the requirement of coexistence of environmental protection and sustainable development reaches a new height. Today, the aspects of each enterprise's production involve energy consumption and consequent carbon emissions. Research shows that carbon-related gases such as carbon dioxide have significant influence on global climate, and the influence can cause various worldwide hidden dangers such as extreme weather occurrence in various regions, sea level rise and the like. In dealing with carbon-related climate problems, significant labor and material costs are required.

In order to achieve the most effective improvement of energy problems, it is desirable to reduce carbon emissions and maximize carbon emissions that are consumed for neutralization. Therefore, it is necessary to analyze the carbon emissions of each industry or each enterprise to make a carbon reduction plan for each industry or each enterprise. In the prior art, the carbon emission of each industry or each enterprise generally needs to be analyzed manually, however, the manual analysis method cannot obtain accurate carbon emission, and is low in efficiency and high in cost of manpower and material resources.

In the process of implementing the invention, at least the following technical problems are found in the prior art: the analyzed carbon emission has low accuracy, low analysis efficiency and high analysis cost.

Disclosure of Invention

The invention provides a method, a device, equipment and a medium for predicting carbon emission based on an electrical carbon factor, which aim to solve the problems of low accuracy, low efficiency and high cost in the prior art of artificially analyzing the carbon emission.

According to an aspect of the present invention, there is provided a method for predicting carbon emissions based on an electrical carbon factor, including:

acquiring the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of each object to be determined, and determining the pre-estimated electrical carbon factor of the object to be determined based on the normal distribution of the electrical carbon factor, the normal distribution of the factor weight, the extraction number of preset factors and the extraction number of preset weights;

acquiring reference electrical carbon factors corresponding to at least two reference objects respectively, and determining a target electrical carbon factor corresponding to a target object in the objects to be determined based on each reference electrical carbon factor and each estimated electrical carbon factor;

and determining the predicted carbon emission amount corresponding to the target object based on the target electric carbon factor corresponding to the target object and the actual electricity consumption of the target object.

Optionally, the method further includes:

for each object to be determined, determining a first sub-object and a second sub-object corresponding to the object to be determined;

acquiring the electric carbon factor weight corresponding to each first sub-object and the sub-electric carbon factor corresponding to each second sub-object;

and constructing factor weight normal distribution of the object to be determined based on each electric carbon factor weight, and constructing electric carbon factor normal distribution of the object to be determined based on each sub-electric carbon factor.

Optionally, the constructing a normal distribution of the factor weight of the object to be determined based on each of the electrical carbon factor weights, and constructing a normal distribution of the electrical carbon factor of the object to be determined based on each of the sub-electrical carbon factors, includes:

calculating a weight mean value and a weight variance corresponding to the object to be determined based on each electric carbon factor weight, and constructing a factor weight normal distribution corresponding to the object to be determined according to the weight mean value and the weight variance;

and calculating the mean value and the variance of the electrical carbon factors corresponding to the object to be determined based on the sub-electrical carbon factors, and constructing the normal distribution of the electrical carbon factors corresponding to the object to be determined according to the mean value and the variance of the electrical carbon factors.

Optionally, the determining the estimated electrical carbon factor of the object to be determined based on the electrical carbon factor normal distribution, the factor weight normal distribution, the preset factor extraction number and the preset weight extraction number includes:

determining an initial weight and an initial electrical carbon factor;

determining at least one current random weight based on the factor weight normal distribution and the preset weight extraction quantity, and determining at least one target random weight and the quantity of the target random weights in each current random weight according to each current random weight, the initial weight and a preset weight threshold;

determining each target random factor based on the number of the target random weights, the normal distribution of the electrical carbon factors and the extraction number of the preset factors, wherein the number of the target random factors is the same as the number of the target random weights;

and determining the estimated electric carbon factor of the object to be determined based on each target random weight, each target random factor and the initial electric carbon factor.

The determining at least one current random weight based on the factor weight normal distribution and the preset weight extraction number comprises:

determining a preset extraction turn;

and aiming at each extraction in the preset extraction turns, extracting the preset weight extraction quantity of weights to be screened from the factor weight normal distribution, and determining the current random weight based on each weight to be screened.

The extracting the preset weights in the factor weight normal distribution to extract a number of weights to be screened, and determining the current random weight based on each weight to be screened includes:

determining each preset slave node, and determining the single-round weight extraction quantity of each preset slave node based on the quantity of the preset slave nodes and the preset weight extraction quantity;

extracting the single round of weights from the factor weight normal distribution and extracting a number of weights to be screened through each preset slave node;

and determining the current random weight based on the weights to be screened extracted from the preset slave nodes.

Determining each target random factor based on the number of the target random weights, the normal distribution of the electrical carbon factors and the number of the preset factor extractions, including:

determining factor extraction turns based on the number of the target random weights, and determining the single-turn factor extraction number of each preset slave node based on the number of the preset slave nodes and the preset factor extraction number;

and aiming at each round of extraction in the factor extraction rounds, extracting the factors to be screened in the single round of factor extraction quantity from the electrical carbon factor normal distribution through each preset slave node, and determining the target random factor corresponding to the current round based on the factors to be screened extracted from each preset slave node.

According to another aspect of the present invention, there is provided an electrical carbon factor-based carbon emission amount prediction apparatus including:

the pre-estimated factor determination module is used for acquiring the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of each object to be determined, and determining the pre-estimated electrical carbon factor of the object to be determined based on the normal distribution of the electrical carbon factor, the normal distribution of the factor weight, the extraction number of preset factors and the extraction number of preset weights;

the target factor determination module is used for acquiring reference electrical carbon factors corresponding to at least two reference objects respectively, and determining a target electrical carbon factor corresponding to a target object in each object to be determined based on each reference electrical carbon factor and each estimated electrical carbon factor;

and the carbon emission calculation module is used for determining the predicted carbon emission corresponding to the target object based on the target electric carbon factor corresponding to the target object and the actual power consumption of the target object.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method of predicting an amount of carbon emissions based on an electrical carbon factor according to any of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the method for predicting carbon emission based on an electrical carbon factor according to any one of the embodiments of the present invention when the computer instructions are executed.

According to the technical scheme, the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of each object to be determined are obtained, the estimated electrical carbon factor of the object to be determined is determined according to the normal distribution of the electrical carbon factor, the normal distribution of the factor weight, the extraction quantity of the preset factor and the extraction quantity of the preset weight, the target electrical carbon factor of the target object is determined according to the reference electrical carbon factor of the reference object and each estimated electrical carbon factor, the accurate determination of the electrical carbon factor is achieved, the estimated carbon emission of the target object is further determined according to the determined target electrical carbon factor and the actual power consumption, the accurate determination of the carbon emission is achieved, manual analysis is not needed, the problems that the accuracy of manually analyzing the carbon emission is low, the efficiency is low and the cost is high in the prior art are solved, the situation that the extraction number is few in the extraction number, the extreme value is extracted is avoided through the extraction quantity of the preset factor and the extraction quantity of the preset weight, the accuracy of the estimated electrical carbon factor is guaranteed, and the carbon emission of the object of the electrical carbon factor can be predicted.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flowchart of a method for predicting carbon emissions based on an electrical carbon factor according to an embodiment of the present invention;

fig. 2 is a schematic flowchart of a method for predicting carbon emission based on an electrical carbon factor according to a second embodiment of the present invention;

fig. 3 is a schematic flowchart of a method for predicting carbon emission based on an electrical carbon factor according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of a carbon emission prediction apparatus based on an electrical carbon factor according to a fourth embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.

Detailed Description

In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a schematic flowchart of a method for predicting carbon emission based on an electrical carbon factor according to an embodiment of the present invention, where the method is applicable to determining an electrical carbon factor of an enterprise with other unknown electrical carbon factors according to an enterprise with known electrical carbon factors, and then predicting the carbon emission of the enterprise according to the electrical carbon factor, or determining an electrical carbon factor of an industry with other unknown electrical carbon factors according to an industry with known electrical carbon factors, and then predicting the carbon emission of the industry according to the electrical carbon factors, and the method may be implemented by an electrical carbon factor-based carbon emission prediction apparatus, which may be implemented in a form of hardware and/or software, and the electrical carbon factor-based carbon emission prediction apparatus may be configured in an electronic device, such as a computer, a mobile phone, an intelligent tablet, or a server. As shown in fig. 1, the method includes:

s110, acquiring normal distribution and factor weight normal distribution of the electrical carbon factors of the objects to be determined aiming at each object to be determined, and determining the estimated electrical carbon factor of the objects to be determined based on the normal distribution of the electrical carbon factors, the normal distribution of the factor weight, the extraction number of preset factors and the extraction number of preset weights.

In the present embodiment, the electrical carbon factor may be a ratio of carbon emission of electricity to carbon emission of all energy, wherein the carbon emission of all energy may include carbon emission of water, electricity, natural gas, gas and other energy sources. Illustratively, the electrical carbon factor may be expressed by the following formula:

ρ = carbon emission amount by electricity/carbon emission amount by energy;

the object to be determined can be an industry of the electric carbon factor of the unknown industry, or can also be an enterprise of the electric carbon factor of the unknown enterprise. If the object to be determined is the industry of the electric carbon factor of the unknown industry, such as the fuel production industry, the automobile production industry, the chemical production industry and the like, the target electric carbon factor of one industry can be determined in the industry of the electric carbon factor of the unknown industry by determining the estimated electric carbon factor of the industry of the electric carbon factor of each unknown industry. If the object to be determined is an enterprise with unknown enterprise electrical carbon factors, the target electrical carbon factor of a certain industry can be determined in the enterprise with unknown enterprise electrical carbon factors by determining the estimated electrical carbon factors of the enterprise with unknown enterprise electrical carbon factors.

In this embodiment, the normal distribution of electrical carbon factors may be a pre-estimated normal distribution of electrical carbon factors corresponding to each sub-object in the object to be determined. The normal distribution of the factor weight may be a pre-estimated normal distribution of the electrical carbon factor weight corresponding to each sub-object in the object to be determined.

For example, the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of each reference object may be determined, and the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of the object to be determined are obtained based on the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of the reference object. Or the normal distribution of the electrical carbon factor of the object to be determined can be determined according to the energy consumption data of the sub-object with known energy consumption data in the object to be determined, and the normal distribution of the factor weight can be determined according to the generated data of the sub-object with known production data in the object to be determined.

Specifically, a preset factor extraction number value can be extracted from the normal distribution of the electrical carbon factor according to the preset factor extraction number, and a factor average value can be obtained according to the extracted value; extracting preset weight extraction number values from the factor weight normal distribution according to the preset weight extraction number, and obtaining a weight average value according to the extracted factor weight; and further multiplying the factor average value by the weight average value, judging whether the sum of all the obtained weight average values reaches a preset weight threshold value, if not, returning to the step of continuously executing extraction values from the normal distribution of the electrical carbon factor according to the preset factor extraction number to obtain the factor average value, and extracting values from the normal distribution of the factor weight according to the preset weight extraction number to obtain the weight average value until the sum of all the obtained weight average values reaches the preset weight threshold value, and at the moment, taking the sum of the multiplication results of all the weight average values and the factor average value as the estimated electrical carbon factor. The preset weight threshold may be 1, that is, the sum of all the obtained weight averages needs to be as less than 1 as possible.

It should be noted that the estimated electrical carbon factor corresponding to the object to be determined is not the accurate electrical carbon factor of the object to be determined, and the estimated electrical carbon factor is obtained based on sampling, so the estimated electrical carbon factor can be used as an estimated value of the electrical carbon factor for the object to be determined, and further, subsequent processing is required to obtain the accurate electrical carbon factor.

In a specific embodiment, the method for determining the estimated electrical carbon factor of the object to be determined based on the electrical carbon factor normal distribution, the factor weight normal distribution, the preset factor extraction number and the preset weight extraction number comprises the following steps:

step 1, determining an initial weight and an initial electrical carbon factor;

step 2, determining at least one current random weight based on the factor weight normal distribution and the preset weight extraction quantity, and determining at least one target random weight and the quantity of the target random weights in each current random weight according to each current random weight, the initial weight and the preset weight threshold;

step 3, determining each target random factor based on the number of the target random weights, the normal distribution of the electrical carbon factors and the extraction number of preset factors, wherein the number of the target random factors is the same as the number of the target random weights;

and 4, determining the estimated electrical carbon factor of the object to be determined based on the target random weights, the target random factors and the initial electrical carbon factor.

Wherein the initial weight and the initial electrical carbon factor may be 0. In step 2, at least one current random weight is determined based on the factor weight normal distribution and the preset weight extraction number, and the at least one current random weight may be: and further, the steps of extracting the preset weight extraction number values from the factor weight normal distribution and taking the average value of all the extracted values as the current random weight are repeated, and taking the average value of all the extracted values as the current random weight to obtain a plurality of current random weights.

Optionally, in step 2, the at least one current random weight is determined based on the factor-weight normal distribution and the preset weight extraction number, and the at least one current random weight may be: determining a preset extraction turn; and aiming at each extraction in the preset extraction turns, extracting preset weights in the factor weight normal distribution to extract a number of weights to be screened, and determining the current random weight based on each weight to be screened.

The preset number of rounds of extraction may be used to specify the number of current random weights, that is, one current random weight may be obtained in each round of extraction. In each round of extraction, the average value of the extracted preset weight extraction quantity of the weights to be screened is the current random weight obtained in the current round.

It should be noted that the benefit of obtaining each current random weight based on the preset round of extraction is: the method has the advantages that whether the weight extraction needs to be stopped or not is judged after the current random weight is obtained every time, all the current random weights are obtained once to screen out the target random weight, the method is suitable for node task deployment in the distributed cluster, all the extracted current random weights can be returned to the main node by each node once, the current random weights do not need to be returned to the main node after the extraction is completed every time, logical judgment is not needed, and the processing efficiency is improved.

Further, in step 2, according to each current random weight, the initial weight and the preset weight threshold, at least one target random weight and the number of target random weights are determined in each current random weight, which may be: and sequentially adding each target random weight on the basis of the initial weight according to the generation sequence of each current random weight from morning to evening until the added result is greater than or equal to a preset weight threshold, determining the added current random weight as the target random weight, and obtaining the number of the target random weights.

For example, according to the generation sequence of the current random weight values from morning to evening, the current random weights are respectively 0.1, 0.2, 0.4, 0.2, 0.3, 0.4, 0.1, 0.4 and 0.2; when the initial weight is 0 and then 0+0.1+0.2+0.4+0.2+0.3=1.2, the target random weights are determined to be 0.1, 0.2, 0.4, 0.2 and 0.3 respectively, and the number of the target random weights is 5.

The step 2 realizes the determination of the random weights of the targets, and further, in the step 3, the random factors of the targets can be determined according to the number of the random weights of the targets. Specifically, a preset factor extraction number value may be extracted from the electrical carbon factor normal distribution, the extracted average value may be used as a current random factor, and the operation of extracting the preset factor extraction number value from the electrical carbon factor normal distribution is repeatedly performed until the number of the current random factors is equal to the number of the target random weights, at which time each current random factor may be used as a target random factor.

Furthermore, weighting calculation can be carried out on each target random factor and each target random weight to obtain the estimated electrical carbon factor. Such as, for example,

wherein the content of the first and second substances,

… are each a random weight for each object,

… is the estimated electrical carbon factor, respectively.

It should be noted that, by adopting the above steps 1-3, the advantages of determining the plurality of current random weights first, and then determining the target random weight and the number of the target random weights from the plurality of current random weights are as follows: the computer can finish the extraction of the weight once, and the judgment of whether the weight exceeds a preset weight threshold value is not needed to be carried out once every extraction, so that the determination efficiency of each pre-estimated electro-carbon factor is improved. For example, if the weight extraction task is deployed on the nodes in the distributed cluster, each node may execute a simple extraction logic, and the master node may intercept each target random factor through each current random factor returned by each node without determining the preset weight threshold, thereby improving the processing efficiency of each node.

In addition, the advantage of setting the preset factor extraction number and the preset weight extraction number in this embodiment is that the estimated electrical carbon factor can be determined by the mean value of a large number of extracted factors and the mean value of a large number of weights, so that the influence of extraction extreme values on the accuracy of the estimated electrical carbon factor when only one value is extracted in each round is avoided, and the accuracy of the estimated electrical carbon factor is improved.

And S120, obtaining reference electric carbon factors respectively corresponding to the at least two reference objects, and determining a target electric carbon factor corresponding to a target object in each object to be determined based on each reference electric carbon factor and each estimated electric carbon factor.

The reference object can be an industry with known industry electrical carbon factors, or an enterprise with known enterprise electrical carbon factors. It should be noted that the object to be determined and the reference object belong to objects at the same level, if the object to be determined is an industry, the reference object is also an industry, and if the object to be determined is an enterprise, the reference object is also an enterprise.

Specifically, after obtaining the reference electrical carbon factors corresponding to the at least two reference objects, the target electrical carbon factor corresponding to the target object may be determined in each object to be determined according to each reference electrical carbon factor and each estimated electrical carbon factor.

For example, one object may be randomly selected from each object to be determined as a target object, an average value of reference electrical carbon factors between any two reference objects is calculated based on the reference electrical carbon factors corresponding to the reference objects, and further, according to a difference between each average value and an estimated electrical carbon factor of the target object, the average value with the minimum difference is used as the target electrical carbon factor of the target object.

In a specific embodiment, determining a target electrical carbon factor corresponding to a target object in each object to be determined based on each reference electrical carbon factor and an estimated electrical carbon factor corresponding to each object to be determined respectively includes: determining a reference mean factor based on each reference electrical carbon factor; determining a target object in each object to be determined according to the reference mean value factor and the estimated electric carbon factor corresponding to each object to be determined; and taking the reference mean value factor as a target electric carbon factor corresponding to the target object.

The reference mean factor may be a mean value between all reference electrical carbon factors, or may also be a mean value between any two reference electrical carbon factors. Specifically, after the reference mean factor is calculated, the object to be determined, for which the estimated electrical carbon factor is closest to the reference mean factor, may be determined as the target object, and the reference mean factor may be determined as the target electrical carbon factor of the target object; or, a target object can be randomly selected from the objects to be determined, a reference mean factor closest to the estimated electrical carbon factor is determined in each reference mean factor according to the estimated electrical carbon factor of the target object, and the reference mean factor is determined as the target electrical carbon factor corresponding to the target object.

Calculating a reference mean factor, and determining the target object according to the reference mean factor and each estimated electric carbon factor has the advantages that: the difference between each object to be determined and the reference object, such as enterprise difference or industry difference, can be determined by referring to the mean value factor and each estimated electric carbon factor, and then the object to be determined with the smallest difference is determined as the target object, so that the electric carbon factors of other industries strongly related to the industry are predicted based on the electric carbon factors of the industry, or the electric carbon factors of other enterprises strongly related to the industry are predicted based on the electric carbon factors of the enterprise, and the accuracy of the predicted electric carbon factors is ensured.

Optionally, determining a target object in each object to be determined according to the reference mean factor and the estimated electrical carbon factor corresponding to each object to be determined, including: calculating the difference value of the electric carbon factor corresponding to each object to be determined respectively based on the reference mean value factor and the estimated electric carbon factor corresponding to each object to be determined respectively; and determining the object to be determined with the minimum electrical carbon factor difference value as a target object according to the electrical carbon factor difference value corresponding to each object to be determined.

The difference value of the electrical carbon factor corresponding to the object to be determined may be an absolute value of a difference value between the reference mean factor and the estimated electrical carbon factor corresponding to the object to be determined. Specifically, after the electrical carbon factor difference value of each object to be determined is calculated, the object to be determined with the smallest electrical carbon factor difference value is determined as the target object, and then the reference mean value factor is used as the target electrical carbon factor of the target object.

The target object is determined according to the electric carbon factor difference values, accurate determination of the target object is achieved, electric carbon factors of other industries or enterprises which are closest to the industry or enterprise of the known electric carbon factor can be determined according to the industry or enterprise of the known electric carbon factor, accuracy of estimating the electric carbon factors of the industries or enterprises is guaranteed, prediction of the electric carbon factor of another industry which has a large difference with the industry according to the electric carbon factor of one industry is avoided, and the electric carbon factor of the food processing industry can be predicted according to the electric carbon factor of the equipment manufacturing industry.

It should be noted that, according to the method for predicting carbon emission based on an electrical carbon factor provided in this embodiment, a target electrical carbon factor of an object to be determined can be determined every time the method is executed.

And S130, determining the predicted carbon emission amount corresponding to the target object based on the target electric carbon factor corresponding to the target object and the actual electricity consumption amount of the target object.

Specifically, after the target electrical carbon factor of the target object is obtained, the actual power consumption of the target object may be obtained, and then the predicted carbon emission of the target object may be determined according to the actual power consumption and the target electrical carbon factor.

For example, the actual power consumption of the target object in a set time period, such as the actual power consumption of the last 3 months, may be obtained, and the actual power consumption may be divided by the target electrical carbon factor to obtain the predicted carbon emission of the target object.

According to the technical scheme, the normal distribution of the electrical carbon factors and the normal distribution of the factor weights of the objects to be determined are obtained for each object to be determined, the estimated electrical carbon factor of the object to be determined is determined according to the normal distribution of the electrical carbon factors, the normal distribution of the factor weights, the extraction number of the preset factor and the extraction number of the preset weight, the target electrical carbon factor of the target object is further determined according to the reference electrical carbon factor of the reference object and each estimated electrical carbon factor, accurate determination of the electrical carbon factor is achieved, further, the estimated carbon emission of the target object is determined according to the determined target electrical carbon factor and the actual power consumption, accurate determination of the carbon emission is achieved, manual analysis is not needed, the problems that in the prior art, in the manual analysis of the carbon emission, accuracy is low, efficiency is low, and cost is high are solved, the situation that extraction to an extreme value occurs due to small extraction times is avoided through the extraction number of the preset factor and the extraction number of the preset weight, the accuracy of the estimated electrical carbon factor is guaranteed, and the accuracy of the electrical carbon emission of the electrical carbon factor is further guaranteed, and the carbon emission of the object of an unknown electrical carbon factor can be predicted.

Example two

Fig. 2 is a schematic flow chart of a method for predicting carbon emission based on an electrical carbon factor according to a second embodiment of the present invention, and this embodiment exemplarily illustrates, on the basis of the foregoing embodiments, extracting a preset weight in a factor weight normal distribution, extracting a number of weights to be filtered, and determining a current random weight based on each weight to be filtered. As shown in fig. 2, the method includes:

s210, acquiring the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of each object to be determined.

S220, determining an initial weight and an initial electric carbon factor, determining a preset extraction turn, determining each preset slave node aiming at each extraction turn in the preset extraction turn, and determining the single-turn weight extraction number of each preset slave node based on the number of the preset slave nodes and the preset weight extraction number.

Wherein the preset slave node may be a slave node in a distributed cluster.

Determining the single-round weight extraction number of each preset slave node based on the number of the preset slave nodes and the preset weight extraction number, wherein the number may be: and taking the ratio of the preset weight extraction quantity to the number of the preset slave nodes as the single-round weight extraction quantity of each preset slave node. Namely, the preset weight extraction quantity is uniformly distributed into each preset slave node, so that each preset slave node respectively extracts a single round of weight extraction quantity weights to be screened, and the sum of the quantity of all the preset slave nodes extracted weights to be screened is the preset weight extraction quantity.

Illustratively, the preset weight extraction number is 10000, and the preset slave node number is 10, then the preset slave node single-round weight extraction number is 1000.

And S230, extracting a single round of weights from the factor weight normal distribution through each preset slave node, extracting a number of weights to be screened, and determining the current random weight based on the weights to be screened extracted from each preset slave node.

Specifically, each preset slave node may extract a single round of weights from the factor weight normal distribution to extract a number of weights to be filtered, and send each extracted weight to be filtered to the master node. Further, the master node may determine the current random weight according to all weights to be filtered.

Optionally, in each extraction round in the preset extraction round, each preset slave node may perform extraction simultaneously, so as to obtain a single round of weight extraction quantity of weights to be filtered simultaneously, so as to further determine the current random weight corresponding to the current round. And repeating the above mode to obtain the current random weight corresponding to each round. That is, the number of current random weights is equal to the preset round of decimation.

It should be noted that, after completing the extraction of the weights to be filtered of all the preset extraction rounds, each preset slave node may send the weights to be filtered of all the rounds to the master node in a number sequence or an array. The master node may determine the weight to be screened corresponding to each turn according to the sequence of each numerical value in the received sequence or array. The benefit of presetting the weights to be filtered for all rounds sent by the slave node at one time is that: the data transmission times between the main node and the main node are reduced, and the determination efficiency of the estimated electric carbon factor is further improved.

S240, determining at least one target random weight and the number of the target random weights in each current random weight according to each current random weight, the initial weight and a preset weight threshold value.

And S250, determining each target random factor based on the number of the target random weights, the normal distribution of the electrical carbon factors and the extraction number of preset factors, wherein the number of the target random factors is the same as the number of the target random weights.

Specifically, after the extraction of the factor weight is completed, the extraction of the electrical carbon factor may be performed. Specifically, the number of target random factors to be extracted should be equal to the number of target random weights.

Illustratively, determining each target random factor based on the number of target random weights, the normal distribution of the electrical carbon factor, and the number of preset factor extractions includes: determining factor extraction turns based on the number of the target random weights, and determining the single-turn factor extraction number of each preset slave node based on the number of the preset slave nodes and the preset factor extraction number; aiming at each round of extraction in the factor extraction rounds, extracting a single round of factor extraction quantity factors to be screened from the normal distribution of the electrical carbon factor through each preset slave node, and determining a target random factor corresponding to the current round based on the factors to be screened extracted from each preset slave node.

Specifically, the ratio between the preset factor extraction number and the number of the preset slave nodes may be determined as the single-round factor extraction number of each preset slave node; that is, the preset factors to be extracted in each round can be extracted by the number of the preset factors to be extracted, and the preset factors are distributed to each preset slave node for extraction. If the preset factor extraction number is 5000 and the preset slave node number is 10, the single-round factor extraction number of each preset slave node is 500, and each preset slave node obtains 500 factors to be screened in each round of extraction.

Furthermore, each preset slave node can send the factors to be screened of all rounds to the master node once after the factors to be screened of all rounds are extracted. The master node may determine the target random factor for each round for the average of the factors to be screened for each round.

In the above embodiment, the preset factors are cooperatively extracted through the preset slave nodes to extract the number of the factors to be screened, so that the extraction efficiency of the preset factor extraction number of the factors to be screened in each turn is improved, and the determination efficiency of the estimated electrical carbon factor is improved.

S260, determining an estimated electrical carbon factor of the object to be determined based on the target random weights, the target random factors and the initial electrical carbon factor.

S270, obtaining reference electric carbon factors corresponding to at least two reference objects respectively, determining a target electric carbon factor corresponding to a target object in each object to be determined based on each reference electric carbon factor and each estimated electric carbon factor, and determining an estimated carbon emission corresponding to the target object based on the target electric carbon factor corresponding to the target object and the actual power consumption of the target object.

According to the technical scheme, the single-round weight extraction quantity of the weights to be screened is extracted in each round through each preset slave node, the task that the preset weight extraction quantity of the weights to be screened is required to be obtained in each round is deployed to each preset slave node, the extraction efficiency of the weights to be screened is greatly improved, the influence of extreme values on the estimated electric carbon factor is avoided by sampling the preset weight extraction quantity in a large quantity, the influence of the large quantity of samples on the determination efficiency of the estimated electric carbon factor is reduced, and the determination efficiency of the estimated electric carbon factor is improved.

EXAMPLE III

Fig. 3 is a schematic flow chart of a method for predicting carbon emission based on an electrical carbon factor according to a third embodiment of the present invention, and this embodiment exemplifies a process of constructing a normal distribution of factor weight and a normal distribution of an electrical carbon factor based on the above embodiments. As shown in fig. 3, the method includes:

s310, aiming at each object to be determined, and determining a first sub-object and a second sub-object corresponding to the object to be determined.

Wherein the first sub-object may be a sub-object of known yield data. For example, if the object to be determined is an industry, the first sub-object is an enterprise in the industry, where the enterprise can acquire output data; if the object to be determined is an enterprise, the first sub-object is a unit which can acquire output data in the enterprise. The production data may be a total production value of the first sub-object over a set period of time, such as a total production value over 12 months.

The second sub-object may be a sub-object of known energy data. Illustratively, if the object to be determined is an industry, the second sub-object is an enterprise which can acquire the energy consumption data in the industry; and if the object to be determined is an enterprise, the second sub-object is a unit which can acquire the energy data in the enterprise. The energy consumption data may be usage data of the second sub-object for various energy sources in a set time period, such as electricity consumption, water consumption, and gas consumption in 12 months.

S320, acquiring electric carbon factor weights corresponding to the first sub-objects and sub-electric carbon factors corresponding to the second sub-objects, constructing normal distribution of the electric carbon factors of the object to be determined based on the electric carbon factor weights, and constructing normal distribution of the electric carbon factors of the object to be determined based on the sub-electric carbon factors.

In this embodiment, after determining each first sub-object and each second sub-object in the object to be determined, the electrical carbon factor weight of each first sub-object and the electrical carbon factor of each second sub-object may be obtained. The weight of the electric carbon factor of each first sub-object and the sub-electric carbon factor of each second sub-object can be preset; alternatively, the calculation may be performed separately from the production data and the energy data.

In a specific embodiment, obtaining the electric carbon factor weight corresponding to each first sub-object and the sub-electric carbon factor corresponding to each second sub-object includes: acquiring output data corresponding to each first sub-object, and determining the electric carbon factor weight corresponding to each first sub-object based on the output data corresponding to each first sub-object; and acquiring energy application data corresponding to the second sub-objects respectively, and determining the corresponding sub-electric carbon factors of the second sub-objects respectively based on the energy application data corresponding to the second sub-objects respectively.

Specifically, for each first sub-object, the electrical carbon factor weight of the first sub-object may be calculated according to the output data of the first sub-object and the output data of the object to be determined to which the first sub-object belongs. For example, see the following equation:

wherein alpha is _i Is the electrical carbon factor weight of the ith first sub-object.

Specifically, for each second sub-object, the carbon emission of the usage amount corresponding to each energy usage type in the energy usage data can be calculated according to the usage amount of each energy usage type in the energy usage data of the second sub-object and the carbon emission conversion rate (which can be checked according to the published conversion standard) corresponding to each energy usage type, and then the carbon emission of the electricity usage is divided by the carbon emission of all the energy usage types to obtain the sub-electrical carbon factor of the second sub-object.

It should be noted that the first sub-object capable of calculating the electric carbon factor weight and the second sub-object capable of calculating the electric carbon factor may be the same sub-object or different sub-objects, and the first sub-object and the second sub-object are selected according to whether each sub-object in the object to be determined has the production data and the energy data.

In the embodiment, the weight of the electric carbon factor corresponding to the first sub-object is calculated through the output data of the first sub-object, and the sub-electric carbon factor corresponding to the second sub-object is calculated through the energy consumption data of the second sub-object, so that the accurate determination of the weight of the electric carbon factor and the sub-electric carbon factor is realized, and the accurate determination of the estimated electric carbon factor is further realized, and the estimated electric carbon factor is enabled to be close to the actual condition of the industry as much as possible.

Further, in a specific embodiment, the constructing of the normal distribution of the factor weight of the object to be determined based on the weight of each electrical carbon factor, and the constructing of the normal distribution of the electrical carbon factor of the object to be determined based on each sub-electrical carbon factor may be: calculating a weight mean value and a weight variance corresponding to the object to be determined based on the weight of each electric carbon factor, and constructing a factor weight normal distribution corresponding to the object to be determined according to the weight mean value and the weight variance; and calculating the mean value and the variance of the electrical carbon factors corresponding to the object to be determined based on the sub electrical carbon factors, and constructing the normal distribution of the electrical carbon factors corresponding to the object to be determined according to the mean value and the variance of the electrical carbon factors.

Wherein the weight mean may be an average of all the electric carbon factor weights, and the weight variance may be a variance of all the electric carbon factor weights. Illustratively, the calculation formula of the weight mean and the weight variance is as follows:

wherein the content of the first and second substances,

is the weight mean value, alpha, corresponding to the w-th object to be determined _wi The electric carbon factor weight corresponding to the ith first sub-object in the w-th object to be determined, k is the number of the first sub-objects in the w-th object to be determined,

is the weight variance corresponding to the w-th object to be determined. The factor weight normal distribution constructed based on the weight mean and the weight variance may be

The electric carbon factor mean may be a mean of all the sub-electric carbon factors, and the electric carbon factor variance may be a variance of all the sub-electric carbon factors. For example, the calculation formula of the mean value and the variance of the electrical carbon factor is as follows:

wherein the content of the first and second substances,

is the mean value of the electrical carbon factor, rho, corresponding to the w-th object to be determined _wi The sub-electric carbon factor corresponding to the ith second sub-object in the w-th object to be determined, m is the number of the second sub-objects in the w-th object to be determined,

is the electrical carbon factor variance corresponding to the w-th object to be determined. The normal distribution of the electrical carbon factor constructed based on the mean value and the variance of the electrical carbon factor can be

In the above embodiment, the factor weight normal distribution and the electrical carbon factor normal distribution of each object to be determined may be respectively constructed.

The normal distribution of the weight of the factors is constructed through the weight mean value and the weight variance of the weight of the electric carbon factor corresponding to each first sub-object, and the normal distribution of the electric carbon factor is constructed through the weight mean value and the weight variance of the electric carbon factor corresponding to each second sub-object, so that the normal distribution of the weight of the factors and the normal distribution of the electric carbon factor are close to the actual condition of the object to be determined as far as possible, and the accuracy of the pre-estimated electric carbon factor is improved.

S330, acquiring the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of each object to be determined, and determining the pre-estimated electrical carbon factor of the object to be determined based on the normal distribution of the electrical carbon factor, the normal distribution of the factor weight, the extraction number of preset factors and the extraction number of preset weights.

S340, obtaining reference electric carbon factors corresponding to the at least two reference objects respectively, and determining a target electric carbon factor corresponding to a target object in the objects to be determined based on each reference electric carbon factor and each estimated electric carbon factor.

And S350, determining the predicted carbon emission amount corresponding to the target object based on the target electric carbon factor corresponding to the target object and the actual electricity consumption of the target object.

According to the technical scheme, the factor weight normal distribution is constructed through the electric carbon factor weights respectively corresponding to the first sub-objects of the object to be determined, the electric carbon factor normal distribution is constructed through the sub-electric carbon factors respectively corresponding to the second sub-objects of the object to be determined, the factor weight normal distribution and the electric carbon factor normal distribution can be close to the actual use condition of the object to be determined as far as possible, and therefore the accuracy of the pre-estimated electric carbon factor is improved.

It should be noted that, taking an example that the object to be determined and the target object are industries, the method for predicting carbon emission based on an electrical carbon factor provided in this embodiment has no requirement on the number and the list of enterprises with known total production values in the industries, and has no requirement on the number and the list of enterprises with known energy consumption data in the industries, so that the applicability is wide. Moreover, if an enterprise is a composite enterprise, namely belongs to multiple industries at the same time, the energy consumption data or the total production value of the enterprise can be reused, and the electric carbon factor weight or the sub-electric carbon factor of the enterprise can be used for predicting the electric carbon factors of the multiple industries.

Example four

Fig. 4 is a schematic structural diagram of a carbon emission prediction apparatus based on an electrical carbon factor according to a fourth embodiment of the present invention. As shown in FIG. 4, the apparatus includes an estimation factor determination module 410, a target factor determination module 420, and a carbon emissions calculation module 430.

The pre-estimation factor determination module 410 is configured to obtain, for each object to be determined, electrical carbon factor normal distribution and factor weight normal distribution of the object to be determined, and determine a pre-estimation electrical carbon factor of the object to be determined based on the electrical carbon factor normal distribution, the factor weight normal distribution, a preset factor extraction number and a preset weight extraction number;

a target factor determining module 420, configured to obtain reference electrical carbon factors corresponding to at least two reference objects, and determine a target electrical carbon factor corresponding to a target object in each object to be determined based on each reference electrical carbon factor and each estimated electrical carbon factor;

and a carbon emission calculation module 430, configured to determine an expected carbon emission corresponding to the target object based on a target electrical carbon factor corresponding to the target object and an actual power consumption of the target object.

According to the technical scheme of the embodiment, the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of each object to be determined are obtained, the estimated electrical carbon factor of the object to be determined is determined according to the normal distribution of the electrical carbon factor, the normal distribution of the factor weight, the extraction number of the preset factor and the extraction number of the preset weight, the target electrical carbon factor of the target object is determined according to the reference electrical carbon factor of the reference object and each estimated electrical carbon factor, the accurate determination of the electrical carbon factor is achieved, the estimated carbon emission of the target object is further determined according to the determined target electrical carbon factor and the actual power consumption, the accurate determination of the carbon emission is achieved, manual analysis is not needed, the problems that the accuracy of manually analyzing the carbon emission is low, the efficiency is low and the cost is high in the prior art are solved, in addition, the condition that the extraction number is low due to the extraction number is avoided through the extraction number of the preset factor and the extraction number of the preset weight, the accuracy of the estimated electrical carbon factor is guaranteed, and the accuracy of the electrical carbon emission of the object of unknown electrical carbon factor can be predicted.

On the basis of the above embodiment, optionally, the apparatus further includes a sub-object determining unit and a normal distribution constructing unit, wherein:

the sub-object determining unit is used for determining a first sub-object and a second sub-object corresponding to the object to be determined for each object to be determined; acquiring the electric carbon factor weight corresponding to each first sub-object and the sub-electric carbon factor corresponding to each second sub-object;

the normal distribution construction unit is configured to construct a factor weight normal distribution of the object to be determined based on each electrical carbon factor weight, and construct an electrical carbon factor normal distribution of the object to be determined based on each sub-electrical carbon factor.

On the basis of the foregoing embodiment, optionally, the normal distribution building unit is specifically configured to:

calculating a weight mean value and a weight variance corresponding to the object to be determined based on each electric carbon factor weight, and constructing a factor weight normal distribution corresponding to the object to be determined according to the weight mean value and the weight variance; and calculating the mean value and the variance of the electrical carbon factors corresponding to the object to be determined based on the sub-electrical carbon factors, and constructing the normal distribution of the electrical carbon factors corresponding to the object to be determined according to the mean value and the variance of the electrical carbon factors.

On the basis of the foregoing embodiment, optionally, the estimation factor determining module 410 includes an initial information determining unit, a weight extracting unit, a factor extracting unit, and an estimation factor calculating unit, where:

the initial information determination unit is used for determining an initial weight and an initial electrical carbon factor;

the weight extraction unit is configured to determine at least one current random weight based on the factor weight normal distribution and the preset weight extraction number, and determine at least one target random weight and the number of the target random weights in each current random weight according to each current random weight, the initial weight and a preset weight threshold;

the factor extraction unit is used for determining each target random factor based on the number of the target random weights, the normal distribution of the electrical carbon factors and the preset factor extraction number, wherein the number of the target random factors is the same as the number of the target random weights;

the estimated factor calculation unit is used for determining the estimated electric carbon factor of the object to be determined based on each target random weight, each target random factor and the initial electric carbon factor.

On the basis of the foregoing embodiment, optionally, the weight extracting unit is further configured to:

determining a preset extraction turn; and aiming at each extraction in the preset extraction turns, extracting the preset weight extraction quantity of weights to be screened from the factor weight normal distribution, and determining the current random weight based on each weight to be screened.

determining each preset slave node, and determining the single-round weight extraction quantity of each preset slave node based on the quantity of the preset slave nodes and the preset weight extraction quantity; extracting the single round of weights from the factor weight normal distribution and extracting a number of weights to be screened through each preset slave node; and determining the current random weight based on the weights to be screened extracted from the preset slave nodes.

On the basis of the foregoing embodiment, optionally, the factor extracting unit is further configured to:

determining factor extraction turns based on the number of the target random weights, and determining the single-turn factor extraction number of each preset slave node based on the number of the preset slave nodes and the preset factor extraction number; and aiming at each round of extraction in the factor extraction rounds, extracting the factors to be screened in the single round of factor extraction quantity from the electrical carbon factor normal distribution through each preset slave node, and determining the target random factor corresponding to the current round based on the factors to be screened extracted from each preset slave node.

The carbon emission prediction device based on the electrical carbon factor provided by the embodiment of the invention can execute the carbon emission prediction method based on the electrical carbon factor provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

EXAMPLE five

Fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 may also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as a carbon emission prediction method based on an electrical carbon factor.

In some embodiments, the electrical carbon factor-based carbon emission prediction method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above-described electrical carbon factor-based carbon emission amount prediction method may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the electrical carbon factor-based carbon emission prediction method by any other suitable means (e.g., by way of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

The computer program for implementing the electrical carbon factor-based carbon emission prediction method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

Example six

An embodiment of the present invention further provides a computer-readable storage medium, in which computer instructions are stored, and the computer instructions are configured to cause a processor to execute a method for predicting carbon emission based on an electrical carbon factor, where the method includes:

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting carbon emission based on an electrical carbon factor is characterized by comprising the following steps:

2. The method of claim 1, further comprising:

3. The method according to claim 2, wherein the constructing a normal distribution of the factor weight of the object to be determined based on each of the electrical carbon factor weights and the constructing a normal distribution of the electrical carbon factor of the object to be determined based on each of the sub-electrical carbon factors comprises:

4. The method according to claim 1, wherein the determining the estimated electrical carbon factor of the object to be determined based on the electrical carbon factor normal distribution, the factor weight normal distribution, the preset factor extraction number and the preset weight extraction number comprises:

determining an initial weight and an initial electrical carbon factor;

5. The method according to claim 4, wherein the determining at least one current random weight based on the factor weight normal distribution and the preset weight extraction number comprises:

determining a preset extraction turn;

6. The method according to claim 5, wherein the extracting the preset weight extraction number of weights to be filtered in the factor weight normal distribution, and the determining the current random weight based on each weight to be filtered comprises:

7. The method of claim 6, wherein determining each target stochastic factor based on the number of target stochastic weights, the normal distribution of electrical carbon factors, and the number of pre-set factor extractions comprises:

8. An electrical carbon factor-based carbon emission amount prediction apparatus, comprising:

the pre-estimation factor determination module is used for acquiring the normal distribution of the electrical carbon factor and the normal distribution of the factor weight of each object to be determined, and determining the pre-estimation electrical carbon factor of the object to be determined based on the normal distribution of the electrical carbon factor, the normal distribution of the factor weight, the extraction number of preset factors and the extraction number of preset weights;

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of predicting electrical carbon factor-based carbon emissions of any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a processor to implement the method for predicting an amount of carbon emissions based on an electrical carbon factor of any one of claims 1 to 7 when executed.