CN116128690A - Carbon emission cost value calculation method, device, equipment and medium - Google Patents
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Abstract
The application discloses a carbon emission cost value calculation method, device, equipment and medium, relates to the field of intelligent technical control, and comprises the following steps: performing normal inspection treatment on the carbon emission data to obtain carbon emission parameters; acquiring sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening out target weighting parameters; determining a control limit value based on the carbon emission sampling information and the target weighting parameter, and integrating the control limit value, the sample size, the target weighting parameter and the carbon emission sampling information to obtain a carbon emission amount calculation set; and calculating the carbon emission calculation set to obtain the expected time length and the unit prediction cost, determining the unit time prediction total cost based on the expected time length and the prediction cost, and calculating the unit time prediction total cost to obtain the current actual total cost value. According to the method and the device, the calculation cost of the carbon emission cost value can be reduced, the calculation accuracy of the carbon emission cost value is improved, and measures are taken in advance to control excessive emission.
Description
Technical Field
The invention relates to the field of intelligent technical control, in particular to a method, a device, equipment and a medium for calculating a carbon emission cost value.
Background
The carbon emission in the high energy consumption industry has the characteristics of high growth speed and large emission, and is an important field of carbon emission control. The discharge amount tracking and carbon transaction system of enterprises in succession in each country, and the carbon footprint also becomes one of the scales of products or services in cross-country purchasing. The method establishes a modern energy system with high energy efficiency, green and low carbon, suppresses the blind development of enterprises with high emission and low energy efficiency, and has important significance for achieving the double-carbon target in China. And a reasonable carbon emission tracking system is a basic driving force for encouraging enterprises and industries to use energy-saving products and clean energy sources, realize carbon emission tracking and participate in a carbon market. The conventional carbon emission tracking mode is based on electric quantity, water resources and fossil fuel tracking, and calculates carbon emission according to the energy consumption value, so that the granularity is low, the fluctuation sensitivity is poor, an industry standard value is generally adopted as a judgment standard, an analysis process cannot be combined with the historical actual production and emission conditions of enterprises, the analysis process is not linked with cost factors, and human factors or external factors causing excessive carbon emission cannot be timely judged and improved. The quality management technology is mainly applied to quality control in the manufacturing process of products in the past, has remarkable advantages in tracking and controlling important nodes in the manufacturing process, and the application field is also expanding due to the continuous improvement of the functions of the data acquisition and computing system in recent years. In the prior art, the quality management technology is only used for tracking the process quantity change of a single or a plurality of specific parameters, but in the actual application, the prediction process is very complex due to the time-by-time change of the process quantity, the real characteristics of the actual carbon emission tracking process are difficult to match, and the false alarm rate is higher.
From the above, how to reduce the cost of calculating the carbon emission cost value, improve the accuracy of calculating the carbon emission cost value, and take measures in advance to control excessive emission is a problem to be solved in the art.
Disclosure of Invention
In view of the above, the present invention aims to provide a method, a device, an apparatus and a medium for calculating a carbon emission cost value, which can reduce the cost of calculating the carbon emission cost value, improve the accuracy of calculating the carbon emission cost value, and take measures in advance to control excessive emission. The specific scheme is as follows:
in a first aspect, the present application discloses a method for calculating a carbon emission cost value, comprising:
acquiring carbon emission data, and performing normal inspection treatment on the carbon emission data to obtain carbon emission parameters;
acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters;
determining a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrating the control limit value, the sample amount, the target weighting parameter and the carbon emission amount sampling information to obtain a carbon emission amount calculation set;
And respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation set according to the carbon emission amount parameters to obtain an expected time length and unit prediction cost, determining unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost to obtain a current actual total cost value.
Optionally, after the acquiring the carbon emission data, the method further includes:
performing time sequence calculation on the carbon emission data to obtain a carbon emission time sequence;
and performing sequence autocorrelation test on the carbon emission time sequence, and if the carbon emission time sequence has autocorrelation, calculating the carbon emission time sequence by using a generalized differential transformation method to obtain the carbon emission time sequence without autocorrelation.
Optionally, the calculating the carbon emission sampling information based on the sample amount, and screening the target weighting parameters from all the weighting parameters includes:
calculating a preset carbon emission statistic and a carbon emission variable average value in real time based on the sample size to obtain carbon emission sampling information; wherein the carbon emission sampling information comprises a current carbon emission statistic and a current carbon emission variable average value;
And acquiring the service requirement, analyzing the service requirement to obtain a screening condition, and screening out target weighting parameters from all weighting parameters according to the screening condition.
Optionally, the determining the control limit value of the carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter includes:
calculating the ratio between the sample size and the preset carbon emission amount test frequency to obtain a sampling frequency; calculating the carbon emission sampling information, the target weighting parameter and the sampling frequency to obtain a control limit value of a carbon emission calculation model; wherein the control limits include a weighted moving average control limit and a Huhattan control limit.
Optionally, the integrating the control limit value, the sample size, the target weighting parameter, and the carbon emission sampling information includes:
judging whether the current carbon emission statistic is smaller than the weighted moving average control limit value or whether the current carbon emission variable average value is smaller than the Huhatt control limit value;
and if the current carbon emission statistic is smaller than the weighted moving average control limit value, and or if the current carbon emission variable average value is smaller than the Huhattan control limit value, integrating the control limit value, the sample size, the target weighting parameter, the sampling frequency and the carbon emission sampling information.
Optionally, the calculating the time length calculation and the calculating the prediction cost of the carbon emission amount calculation set according to the carbon emission amount parameter respectively to obtain an expected time length and a unit prediction cost includes:
acquiring a carbon emission time period, and calculating the time length of the carbon emission time period and the carbon emission quantity parameter to obtain an expected time length;
calculating a preset upper limit of carbon emission based on the expected time length and calling a preset field loss function to obtain a first unit prediction cost;
determining a sampling cost component value from the carbon emission parameters, and calculating a second unit prediction cost based on the expected time length and the sampling cost component value;
determining false alarm cost from the carbon emission parameters, and calculating a third unit prediction cost based on the expected time length, the false alarm cost and a preset false alarm rate;
and calculating the sum among the first unit prediction cost, the second unit prediction cost and the third unit prediction cost to obtain unit prediction cost.
Optionally, after the obtaining the current actual total cost value, the method further includes:
Storing the current actual total cost value into a local database, and repeating the steps to obtain the actual total cost value at the next moment;
and judging whether the actual total cost value at the next moment is larger than the current actual total cost value, if the actual total cost value at the next moment is larger than the current actual total cost value, reserving the current actual total cost value, and if the actual total cost value at the next moment is not larger than the current actual total cost value, replacing the current actual total cost value with the actual total cost value at the next moment.
In a second aspect, the present application discloses a carbon emission cost value calculation apparatus including:
the carbon emission data acquisition module is used for acquiring carbon emission data and carrying out normal inspection processing on the carbon emission data so as to obtain carbon emission parameters;
the target weighting parameter screening module is used for acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters;
a carbon emission amount calculation set determining module, configured to determine a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, calculate the initial total cost value based on the control limit value, and integrate the control limit value, the sample amount, the target weighting parameter, and the carbon emission amount sampling information to obtain a carbon emission amount calculation set;
And the actual total cost value calculation module is used for respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation collection according to the carbon emission amount parameter so as to obtain an expected time length and unit prediction cost, determining the unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost so as to obtain the current actual total cost value.
In a third aspect, the present application discloses an electronic device comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the aforementioned carbon emission cost value calculation method.
In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the previously disclosed carbon emission cost value calculation method.
It can be seen that the present application provides a method for calculating a carbon emission cost value, including obtaining carbon emission data, and performing normal inspection processing on the carbon emission data to obtain a carbon emission parameter; acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters; determining a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrating the control limit value, the sample amount, the target weighting parameter and the carbon emission amount sampling information to obtain a carbon emission amount calculation set; and respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation set according to the carbon emission amount parameters to obtain an expected time length and unit prediction cost, determining unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost to obtain a current actual total cost value. According to the method, the exponential weighted moving average model is combined with the Huhatt control model to realize carbon emission tracking and management of a high-energy-consumption enterprise, the factors such as carbon emission cost control, operation cost, carbon emission limit and response time are comprehensively considered, the advantages of the model are combined, the carbon emission process quantity is subtle and sudden random change is creatively tracked through statistical probability, a comprehensive quality management method is formed and used for tracking carbon emission of the high-energy-consumption enterprise in environmental quality management, so that measures are timely taken to control excessive emission, the excessive emission is facilitated to be identified in an early stage, whether the emission quantity is in the high risk exceeding emission limit or in the high risk exceeding limit is evaluated, the influence of emission on the environment and the related cost are tracked and measured, key tracking equipment and parameters are identified, accordingly reasonable control of measures can be adopted in advance, the expected total cost is reduced to the minimum, economic loss of the high-energy-consumption enterprise due to excessive carbon emission is greatly reduced, and the current and future carbon emission trend of the high-energy-consumption enterprise is facilitated to be evaluated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for calculating a cost value of carbon emissions disclosed herein;
FIG. 2 is a flow chart of a method for calculating a cost value of carbon emissions disclosed herein;
FIG. 3 is a flowchart showing a method for calculating a cost value of carbon emission disclosed in the present application;
FIG. 4 is a schematic diagram of a device for calculating the cost value of carbon emissions disclosed in the present application;
fig. 5 is a block diagram of an electronic device provided in the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The carbon emission in the high energy consumption industry has the characteristics of high growth speed and large emission, and is an important field of carbon emission control. The discharge amount tracking and carbon transaction system of enterprises in succession in each country, and the carbon footprint also becomes one of the scales of products or services in cross-country purchasing. The method establishes a modern energy system with high energy efficiency, green and low carbon, suppresses the blind development of enterprises with high emission and low energy efficiency, and has important significance for achieving the double-carbon target in China. And a reasonable carbon emission tracking system is a basic driving force for encouraging enterprises and industries to use energy-saving products and clean energy sources, realize carbon emission tracking and participate in a carbon market. The conventional carbon emission tracking mode is based on electric quantity, water resources and fossil fuel tracking, and calculates carbon emission according to the energy consumption value, so that the granularity is low, the fluctuation sensitivity is poor, an industry standard value is generally adopted as a judgment standard, an analysis process cannot be combined with the historical actual production and emission conditions of enterprises, the analysis process is not linked with cost factors, and human factors or external factors causing excessive carbon emission cannot be timely judged and improved. The quality management technology is mainly applied to quality control in the manufacturing process of products in the past, has remarkable advantages in tracking and controlling important nodes in the manufacturing process, and the application field is also expanding due to the continuous improvement of the functions of the data acquisition and computing system in recent years. In the prior art, the quality management technology is only used for tracking the process quantity change of a single or a plurality of specific parameters, but in the actual application, the prediction process is very complex due to the time-by-time change of the process quantity, the real characteristics of the actual carbon emission tracking process are difficult to match, and the false alarm rate is higher. From the above, how to reduce the cost of calculating the carbon emission cost value, improve the accuracy of calculating the carbon emission cost value, and take measures in advance to control excessive emission is a problem to be solved in the art.
Referring to fig. 1, the embodiment of the invention discloses a method for calculating a carbon emission cost value, which specifically includes:
step S11: and acquiring carbon emission data, and performing normal inspection processing on the carbon emission data to obtain carbon emission parameters.
In this embodiment, carbon emission data is obtained, time series calculation is performed on the carbon emission data to obtain a carbon emission time series, a sequence autocorrelation test is performed on the carbon emission time series, if the carbon emission time series has autocorrelation, a generalized differential transformation method is used to calculate the carbon emission time series to obtain the carbon emission time series without autocorrelation, and then normal test and normal distribution processing are performed on the carbon emission data without autocorrelation to obtain a carbon emission parameter. Wherein, the sequence autocorrelation test is: checking the degree of correlation of the carbon emission time series with itself lagging for a certain continuous time period; the generalized differential transformation method is as follows: the time series of carbon emission is converted into a corresponding differential form, the sequence correlation is eliminated, and then the estimation is carried out by using a common least square method.
In this embodiment, the autocorrelation of the carbon emission variable x (i.e., the carbon emission variable x in the carbon emission data) as the object of study affects the effectiveness of the combination method, so that the autocorrelation of the data should be checked before calculation.
The direct fitting test has strong applicability, can test the first-order or higher-order autocorrelation, can provide the specific form of autocorrelation and estimation value of parameter at the same time, the specific steps are as follows:
calculating a time series arrangement x of a given sample carbon emission variable x i Residual of (2)
For residual sequences
And carrying out regression fitting of different forms of legal persons by using a least square method. Such as:
wherein v is i Representing random error ρ i Representing regression coefficients. Significance testing of each of the above combinations to obtain a carbon emission variable sequence x i What form of autocorrelation exists. If the carbon emission variable sequence x i The generalized differential transformation is used if there is an autocorrelation until there is no autocorrelation.
The carbon emission variable x should be normal and independent distribution when using the present combination method. Slight or moderate violations of the normalization are assumed to not affect the validity of the present combination method, but the normalization test and normalization distribution process of the data is tenIf necessary, a chartaro-wilk test was performed using spss (Statistical Product Service Solutions), with 95% confidence intervals set in the statistics column, and the normalization of the data was checked in combination with histogram trend. Let the data to be checked be x 1 ＜x 2 ＜...＜x n The statistics used for the test are:
wherein x is (i) Representing the i-order statistics, x is the mean value of the samples, and the normal coefficient b i The calculation is as follows:
wherein V is the statistic x to be tested i (i=1, 2,) n), C is the vector norm, c= ||v -1 m is, according to the normal probability map of X value inquiry, judge that the data belongs to normal distribution, therefore need to carry on Box-Cox conversion (a generalized power transformation method) to change the distance variable of non-normal distribution into the distance variable of normal distribution, the transformation formula is:
the logarithmic transformation is performed taking λ=0 respectively,
square root conversion is carried out, reciprocal conversion is carried out on lambda= -1, the Charpy-Weierce test is carried out on three conversion results, and the conversion result with the strongest normalization is selected for carrying out the calculation of the combined model. The inverse transformation is carried out after the calculation of the model, and the inverse transformation formula is as follows:
step S12: and acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters.
In this embodiment, after a preset sample size and a weighting parameter are obtained, the carbon emission statistic and the average value of the carbon emission statistic are calculated in real time based on the sample size, so as to obtain carbon emission sampling information; wherein the carbon emission sampling information comprises a current carbon emission statistic and a current carbon emission variable average value; and acquiring the service requirement, analyzing the service requirement to obtain a screening condition, and screening out target weighting parameters from all weighting parameters according to the screening condition.
Step S13: and determining a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrating the control limit value, the sample amount, the target weighting parameter and the carbon emission amount sampling information to obtain a carbon emission amount calculation set.
In the embodiment, calculating the ratio between the sample size and the preset carbon emission test frequency to obtain the sampling frequency; calculating the carbon emission sampling information, the target weighting parameter and the sampling frequency to obtain a control limit value of a carbon emission calculation model; wherein the control limits include a weighted moving average control limit and a Huhattan control limit.
In this embodiment, the specific procedure for obtaining the carbon emission amount calculation set is as follows: judging whether the current carbon emission statistic is smaller than the weighted moving average control limit value or whether the current carbon emission variable average value is smaller than the Huhatt control limit value; and if the current carbon emission statistic is smaller than the weighted moving average control limit value, and or if the current carbon emission variable average value is smaller than the Huhatt control limit value, combining the control limit value, the sample size, the target weighting parameter, the sampling frequency and the carbon emission sampling information to obtain a carbon emission amount calculation set.
In the calculation model, x is a sustainable carbon emission variable (subjected to the correlation and normal distribution treatment) from a certain high-energy-consumption enterprise, and the average value under the inherent state is x 0 Standard deviation is sigma 0 . A non-random factor will change the average value x of the carbon emission variable x under the natural state 0 Making the average value x in the fluctuation state 1 。
x 1 ＝x 0 +εσ 0
Wherein ε is a carbon emission change coefficient and is caused by a non-random factor, that is, ε=0 when the emission process is in an inherent state, ε is subjected to Rayleigh distribution (the expression form of the process transfer distribution is more suitable for the actual situation), and the average value is
The probability of the non-random factor is following the homogeneous poisson process, and the average value of the probability is theta 1 . Non-random factors may be reduced efficiency due to equipment failure, personnel mishandling, etc. that may cause excessive carbon emissions. f (f) i For a carbon emission statistic to be tracked in the present combined method, x i Is the average value of sustainable carbon emission variables (which may be inherent or fluctuating for tracking quantity and thus different from x) 0 ) The calculation method is as follows:
f i ＝ax i +(1-a)f i-1
where a is the weighting parameter of the present combining method, (0) <a<1)，f i The initial value of (i.e., i=0) is the average value x in the state of inherent carbon emission 0 (i.e.) f 0 ＝x 0 . If the variable f i Exceeding the control limit U of a number-weighted moving average model limitH (upper control limit of exponentially weighted moving average model), and/or x i The present value of (2) exceeds the control limit U of the Huhattan control model method limitX (upper control limit of Huhattan control model), the method will signal the fluctuation state. The signal of the fluctuation state indicates that the occurrence of abnormal carbon emission is caused by the existence of non-random factorsGrowing, it is necessary to pay attention to and examine the root cause of such a situation. If the signal of the fluctuation state indicates that there is a non-random factor causing an abnormal increase in the carbon emission amount, it is necessary to pay attention to and examine the root cause of the situation. If f i 、x i If the control limit value is not exceeded, the discharge amount is in an inherent state without concern. C (C) total For the expected total cost per unit time (i.e., S) due to carbon emissions during operation of the present method, S represents the carbon emissions test frequency, and S is the highest frequency for carbon emissions test at the business. n is the sample size, n max Is the maximum allowable sample size that the enterprise where the method is intended to consider. Sampling frequency q=n/s. R is R false For the false alarm rate of the combination method, R 0 Is the false alarm rate allowed by the combination method. U is the upper limit of the allowable carbon emission of the enterprise, and the carbon emission should not exceed U carbon The goal of the tracking model approach cost control is to ensure S, n during the run-time of the approach max 、R false 、U carbon Meeting the requirements, such that the expected total cost C due to carbon emissions total Lowest. C (C) total The objective function formula of (2) is as follows:
wherein the probability density function f ε (epsilon) is calculated from the Rayleigh distribution as follows:
wherein C is carbon (ε) means the total cost of carbon emissions generated during the operation when ε is the change in the carbon emission change coefficient during the carbon emission, and the total cost per unit time C of an operation period for any given change in the carbon emission change coefficient during the emission carbon (epsilon) expected cost C exp (epsilon) and expected time length of operation period T exp The effect of (ε) is calculated as follows: c (C) carbon (ε)＝C exp (ε)/T exp (ε)。
Step S14: and respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation set according to the carbon emission amount parameters to obtain an expected time length and unit prediction cost, determining unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost to obtain a current actual total cost value.
In the embodiment, carbon emission data is acquired, and normal inspection processing is performed on the carbon emission data to obtain carbon emission parameters; acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters; determining a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrating the control limit value, the sample amount, the target weighting parameter and the carbon emission amount sampling information to obtain a carbon emission amount calculation set; and respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation set according to the carbon emission amount parameters to obtain an expected time length and unit prediction cost, determining unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost to obtain a current actual total cost value. According to the method, the exponential weighted moving average model is combined with the Huhatt control model to realize carbon emission tracking and management of a high-energy-consumption enterprise, the factors such as carbon emission cost control, operation cost, carbon emission limit and response time are comprehensively considered, the advantages of the model are combined, the carbon emission process quantity is subtle and sudden random change is creatively tracked through statistical probability, a comprehensive quality management method is formed and used for tracking carbon emission of the high-energy-consumption enterprise in environmental quality management, so that measures are timely taken to control excessive emission, the excessive emission is facilitated to be identified in an early stage, whether the emission quantity is in the high risk exceeding emission limit or in the high risk exceeding limit is evaluated, the influence of emission on the environment and the related cost are tracked and measured, key tracking equipment and parameters are identified, accordingly reasonable control of measures can be adopted in advance, the expected total cost is reduced to the minimum, economic loss of the high-energy-consumption enterprise due to excessive carbon emission is greatly reduced, and the current and future carbon emission trend of the high-energy-consumption enterprise is facilitated to be evaluated.
Referring to fig. 2, the embodiment of the invention discloses a method for calculating a carbon emission cost value, which specifically includes:
step S21: and acquiring carbon emission data, and performing normal inspection processing on the carbon emission data to obtain carbon emission parameters.
Step S22: and acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters.
Step S23: and determining a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrating the control limit value, the sample amount, the target weighting parameter and the carbon emission amount sampling information to obtain a carbon emission amount calculation set.
Step S24: and respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation set according to the carbon emission amount parameters to obtain an expected time length and unit prediction cost, determining unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost to obtain a current actual total cost value.
In this embodiment, the specific process of determining the expected time length and the unit prediction cost is: acquiring a carbon emission time period, and calculating the time length of the carbon emission time period and the carbon emission quantity parameter to obtain an expected time length; calculating a preset upper limit of carbon emission based on the expected time length and calling a preset field loss function to obtain a first unit prediction cost; determining a sampling cost component value from the carbon emission parameters, and calculating a second unit prediction cost based on the expected time length and the sampling cost component value; determining false alarm cost from the carbon emission parameters, and calculating a third unit prediction cost based on the expected time length, the false alarm cost and a preset false alarm rate; and calculating the sum among the first unit prediction cost, the second unit prediction cost and the third unit prediction cost to obtain unit prediction cost.
The length of time within an operational period refers to the period of time from the start of the emission process to the attention and investigation of non-random causes. Consists of four time period random variables, which are respectively: period of intrinsic state (m 1 ) Wave time period (m 2 ) A time (m) for extracting and analyzing a carbon emission data sample (size n) 3 ) And a time length (m) from the fluctuation state to the attention and the investigation of a non-random cause 4 ). An average period of time of an intrinsic state (i.e. the average time between non-random reasons) m 1 The calculation method is as follows:
m 1 ＝1/θ 1 a non-random cause occurs between two consecutive natural states, creating a fluctuating time period, the offset value of size epsilon for any given carbon emission process, being the expected time of occurrence of a process transition (carbon emission change factor epsilon) between two adjacent samples. I.e. fluctuation time period m 2 The calculation mode of (a) is as follows: m is m 2 ＝θ 1 q 2 Time of sample analysis m/12-q/2 3 The calculation formula is as follows: m is m 3 ＝m c N, where n is the sample size, m c For estimating and testing the time of observation data of carbon emission samples, the time length m from the generation of a fluctuation state to the attention and the investigation of a non-random cause 4 And the method is only related to the investigation capability of the enterprise, and is directly acquired without calculation. The above summary is available: t (T) exp (ε)＝1/θ 1 +θ 1 q 2 /12-q/2+m c ·n+m 4 。
Cost per unit expected C exp (ε) including the expected cost per unit time E due to carbon emissions 1 (i.eFirst unit predicted cost), cost of operation of the method sampling and estimating cost E of carbon emission data 2 (i.e. second unit predicted cost), cost E of checking false alarms 3 (i.e., third unit prediction cost). For a given E 1 Value, expected cost per unit time E due to carbon emissions of one operating cycle 1 I.e. the cost due to epsilon variations in the carbon emission process, can be calculated from the field loss function:
E 1 ＝[T exp (ε)-1/θ 1 ]·F·(σ 0 2 +ε 2 σ 0 2 )
F＝E F /(U carbon -x 0 )
wherein 1/θ 1 Is the length of time of the intrinsic state, T exp (ε)-1/θ 1 The time length of the fluctuation state due to the variation of epsilon, F is a cost factor based on the upper limit of carbon emission U carbon Related cost component E F (average penalty cost due to enterprise excess of specified carbon emissions). Operating cost sampling of method and estimating expected cost E of carbon emission data 2 From a fixed sampling cost component P 1 And a variable sampling cost component P 2 Is calculated by the following formula:
wherein the cost E of false alarm is checked 3 Can be 1/theta according to the time length of the inherent state 1 False alarm rate R of the combination method false And cost P of checking false alarms 4 Calculation by Markov chain to determine the expected cost E of investigating false alarms in an operational cycle 3 ，E 3 ＝P 4 /(θ 1 ·R false )。
During an operation period, an enterprise fixed sampling cost component P 1 And a variable sampling cost component P 2 Detecting and parsing a cost P of non-random causes 3 Cost P of checking false alarms 4 The method is related to the actual situation of the enterprise and can be directly obtained. Thus, by combining all cost components E 1 、E 2 、E 3 And P 3 Adding to obtain C exp (ε)，
To sum up, for ε, C carbon Any given value of (ε), the total cost per unit time of the operating cycle due to carbon emissions can be as follows: c (C) carbon (ε)＝C exp (ε)/T exp (ε), finally, C can be calculated total ，
Step S25: and storing the current actual total cost value into a local database, repeating the steps to obtain an actual total cost value at the next moment, judging whether the actual total cost value at the next moment is larger than the current actual total cost value, if the actual total cost value at the next moment is larger than the current actual total cost value, reserving the current actual total cost value, and if the actual total cost value at the next moment is not larger than the current actual total cost value, replacing the current actual total cost value with the actual total cost value at the next moment.
The method for tracking and managing the carbon emission of the high-energy-consumption enterprise by combining the exponential weighted moving average model and the Huhattan control model in the technical field of quality management provided by the application aims at: the average value x of persistent carbon emission variables is tracked from time to time during the operation of the process i (for tracking the amount, may be in an intrinsic state, or may fluctuate state) and tracking the carbon emission statistic f i In the course of ensuring S, n max 、R false 、U carbon Meeting the requirements, such that the expected total cost C due to carbon emissions total Lowest. Method operation step figure 3Firstly, acquiring carbon emission data, and performing correlation check, normal check and normal distribution treatment on the carbon emission data to obtain carbon emission parameters; then the weighting parameters (i.e., θ 1 、S、U carbon 、ε、R 0 、E F 、P 1 、P 2 、P 3 、P 4 ) Setting x 0 ＝0，σ 0 =0, calculate the initial change amount C total Setting n=1, then setting the step length as the sample size n, and performing time-by-time monitoring calculation; then, q is calculated and the constraint condition R is satisfied false ≤R 0 Target weighting parameters are selected from all weighting parameters, and then S, U is selected carbon Adjusting, and then combining the sample size, the target weighting parameter, the carbon emission sampling information and the actual carbon emission cost information to obtain a cost calculation set, and calculating an upper limit of the carbon emission and a current total cost value according to the cost calculation set; and then storing the current cost value into a local database, repeating the steps to obtain the total cost value at the next moment, judging whether the total cost value at the next moment is larger than the current cost value, if the total cost value at the next moment is larger than the current cost value, reserving the current cost value, and if the total cost value at the next moment is not larger than the current cost value, replacing the current cost value with the total cost value at the next moment.
In the embodiment, carbon emission data is acquired, and normal inspection processing is performed on the carbon emission data to obtain carbon emission parameters; acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters; determining a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrating the control limit value, the sample amount, the target weighting parameter and the carbon emission amount sampling information to obtain a carbon emission amount calculation set; and respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation set according to the carbon emission amount parameters to obtain an expected time length and unit prediction cost, determining unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost to obtain a current actual total cost value. According to the method, the exponential weighted moving average model is combined with the Huhatt control model to realize carbon emission tracking and management of a high-energy-consumption enterprise, the factors such as carbon emission cost control, operation cost, carbon emission limit and response time are comprehensively considered, the advantages of the model are combined, the carbon emission process quantity is subtle and sudden random change is creatively tracked through statistical probability, a comprehensive quality management method is formed and used for tracking carbon emission of the high-energy-consumption enterprise in environmental quality management, so that measures are timely taken to control excessive emission, the excessive emission is facilitated to be identified in an early stage, whether the emission quantity is in the high risk exceeding emission limit or in the high risk exceeding limit is evaluated, the influence of emission on the environment and the related cost are tracked and measured, key tracking equipment and parameters are identified, accordingly reasonable control of measures can be adopted in advance, the expected total cost is reduced to the minimum, economic loss of the high-energy-consumption enterprise due to excessive carbon emission is greatly reduced, and the current and future carbon emission trend of the high-energy-consumption enterprise is facilitated to be evaluated.
Referring to fig. 4, the embodiment of the invention discloses a carbon emission cost value calculating device, which specifically may include:
a carbon emission data acquisition module 11 for acquiring carbon emission data and performing normal inspection processing on the carbon emission data to obtain carbon emission parameters;
the target weighting parameter screening module 12 is configured to obtain a preset sample size and weighting parameters, calculate carbon emission sampling information based on the sample size, and screen target weighting parameters from all the weighting parameters;
a carbon emission amount calculation set determining module 13, configured to determine a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrate the control limit value, the sample amount, the target weighting parameter, and the carbon emission amount sampling information to obtain a carbon emission amount calculation set;
the actual total cost value calculation module 14 is configured to calculate a time length and a predicted cost for the carbon emission amount calculation set according to the carbon emission amount parameter, so as to obtain an expected time length and a predicted cost per unit, determine a predicted total cost per unit time based on the expected time length and the predicted cost, and call a preset function to calculate the predicted total cost per unit time, so as to obtain a current actual total cost value.
In the embodiment, carbon emission data is acquired, and normal inspection processing is performed on the carbon emission data to obtain carbon emission parameters; acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters; determining a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrating the control limit value, the sample amount, the target weighting parameter and the carbon emission amount sampling information to obtain a carbon emission amount calculation set; and respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation set according to the carbon emission amount parameters to obtain an expected time length and unit prediction cost, determining unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost to obtain a current actual total cost value. According to the method, the exponential weighted moving average model is combined with the Huhatt control model to realize carbon emission tracking and management of a high-energy-consumption enterprise, the factors such as carbon emission cost control, operation cost, carbon emission limit and response time are comprehensively considered, the advantages of the model are combined, the carbon emission process quantity is subtle and sudden random change is creatively tracked through statistical probability, a comprehensive quality management method is formed and used for tracking carbon emission of the high-energy-consumption enterprise in environmental quality management, so that measures are timely taken to control excessive emission, the excessive emission is facilitated to be identified in an early stage, whether the emission quantity is in the high risk exceeding emission limit or in the high risk exceeding limit is evaluated, the influence of emission on the environment and the related cost are tracked and measured, key tracking equipment and parameters are identified, accordingly reasonable control of measures can be adopted in advance, the expected total cost is reduced to the minimum, economic loss of the high-energy-consumption enterprise due to excessive carbon emission is greatly reduced, and the current and future carbon emission trend of the high-energy-consumption enterprise is facilitated to be evaluated.
In some specific embodiments, the carbon emission data acquisition module 11 may specifically include:
the carbon emission time sequence determining module is used for performing time sequence calculation on the carbon emission data to obtain a carbon emission time sequence;
and the sequence autocorrelation checking module is used for carrying out sequence autocorrelation checking on the carbon emission time sequence, and if the carbon emission time sequence has autocorrelation, calculating the carbon emission time sequence by using a generalized differential transformation method so as to obtain the carbon emission time sequence without autocorrelation.
In some specific embodiments, the target weighted parameter screening module 12 may specifically include:
the carbon emission sampling information determining module is used for calculating a preset carbon emission statistic and a carbon emission variable average value in real time based on the sample size so as to obtain carbon emission sampling information; wherein the carbon emission sampling information comprises a current carbon emission statistic and a current carbon emission variable average value;
and the target weighting parameter determining module is used for acquiring the service requirement, analyzing the service requirement to obtain screening conditions, and screening target weighting parameters from all weighting parameters according to the screening conditions.
In some specific embodiments, the carbon emission amount calculation set determination module 13 may specifically include:
the control limit value determining module is used for calculating the ratio between the sample size and the preset carbon emission amount test frequency to obtain the sampling frequency; calculating the carbon emission sampling information, the target weighting parameter and the sampling frequency to obtain a control limit value of a carbon emission calculation model; wherein the control limits include a weighted moving average control limit and a Huhattan control limit.
In some specific embodiments, the carbon emission amount calculation set determination module 13 may specifically include:
the judging module is used for judging whether the current carbon emission statistic is smaller than the weighted moving average control limit value or not and or judging whether the current carbon emission variable average value is smaller than the Huhatt control limit value or not;
and the information integration module is used for integrating the control limit value, the sample size, the target weighting parameter, the sampling frequency and the carbon emission sampling information if the current carbon emission statistic is smaller than the weighted moving average control limit value and/or if the current carbon emission variable average value is smaller than the Huhattan control limit value.
In some specific embodiments, the actual total cost value calculating module 14 may specifically include:
the expected time length determining module is used for acquiring a carbon emission time period, and calculating the time length of the carbon emission time period and the carbon emission quantity parameter to obtain an expected time length;
the first unit prediction cost determining module is used for calculating a preset upper limit of carbon emission based on the expected time length and calling a preset field loss function so as to obtain first unit prediction cost;
a second unit prediction cost determination module configured to determine a sampling cost component value from the carbon emission parameters, and calculate a second unit prediction cost based on the expected time length and the sampling cost component value;
the third unit prediction cost determining module is used for determining false alarm cost from the carbon emission quantity parameter, and calculating the third unit prediction cost based on the expected time length, the false alarm cost and a preset false alarm rate;
and the unit prediction cost determining module is used for calculating the sum among the first unit prediction cost, the second unit prediction cost and the third unit prediction cost to obtain the unit prediction cost.
In some specific embodiments, the actual total cost value calculating module 14 may specifically include:
the actual total cost value determining module at the next moment is used for storing the current actual total cost value into a local database and repeating the steps to obtain the actual total cost value at the next moment;
and the actual total cost value replacing module is used for judging whether the actual total cost value at the next moment is larger than the current actual total cost value, if the actual total cost value at the next moment is larger than the current actual total cost value, the current actual total cost value is reserved, and if the actual total cost value at the next moment is not larger than the current actual total cost value, the current actual total cost value is replaced by the actual total cost value at the next moment.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is used for storing a computer program that is loaded and executed by the processor 21 to implement the relevant steps in the carbon emission amount cost value calculation method performed by the electronic device as disclosed in any of the foregoing embodiments.
In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon include an operating system 221, a computer program 222, and data 223, and the storage may be temporary storage or permanent storage.
The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and the computer program 222, so as to implement the operation and processing of the data 223 in the memory 22 by the processor 21, which may be Windows, unix, linux or the like. The computer program 222 may further include a computer program that can be used to perform other specific works in addition to the computer program that can be used to perform the carbon emission amount cost value calculation method performed by the electronic device 20 disclosed in any of the foregoing embodiments. The data 223 may include, in addition to data received by the carbon emission cost value calculation device and transmitted from an external device, data collected by the own input/output interface 25, and the like.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Further, the embodiment of the application also discloses a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program realizes the steps of the carbon emission amount cost value calculation method disclosed in any embodiment when being loaded and executed by a processor.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description of the method, the device, the equipment and the storage medium for calculating the cost value of the carbon emission provided by the invention applies specific examples to illustrate the principle and the implementation of the invention, and the description of the examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. A carbon emission cost value calculation method, characterized by comprising:
acquiring carbon emission data, and performing normal inspection treatment on the carbon emission data to obtain carbon emission parameters;
acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters;
determining a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrating the control limit value, the sample amount, the target weighting parameter and the carbon emission amount sampling information to obtain a carbon emission amount calculation set;
And respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation set according to the carbon emission amount parameters to obtain an expected time length and unit prediction cost, determining unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost to obtain a current actual total cost value.
2. The carbon emission amount cost value calculation method according to claim 1, characterized by further comprising, after the acquiring of the carbon emission amount data:
performing time sequence calculation on the carbon emission data to obtain a carbon emission time sequence;
and performing sequence autocorrelation test on the carbon emission time sequence, and if the carbon emission time sequence has autocorrelation, calculating the carbon emission time sequence by using a generalized differential transformation method to obtain the carbon emission time sequence without autocorrelation.
3. The carbon emission amount cost value calculation method according to claim 1, wherein the calculating the carbon emission amount sampling information based on the sample amount, and screening out target weighting parameters from among all weighting parameters, comprises:
Calculating a preset carbon emission statistic and a carbon emission variable average value in real time based on the sample size to obtain carbon emission sampling information; wherein the carbon emission sampling information comprises a current carbon emission statistic and a current carbon emission variable average value;
and acquiring the service requirement, analyzing the service requirement to obtain a screening condition, and screening out target weighting parameters from all weighting parameters according to the screening condition.
4. The carbon emission amount cost value calculation method according to claim 3, wherein the determining a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter includes:
calculating the ratio between the sample size and the preset carbon emission amount test frequency to obtain a sampling frequency; calculating the carbon emission sampling information, the target weighting parameter and the sampling frequency to obtain a control limit value of a carbon emission calculation model; wherein the control limits include a weighted moving average control limit and a Huhattan control limit.
5. The method of calculating a carbon emission cost value according to claim 4, wherein integrating the control limit value, the sample size, the target weighting parameter, and the carbon emission sampling information includes:
Judging whether the current carbon emission statistic is smaller than the weighted moving average control limit value or whether the current carbon emission variable average value is smaller than the Huhatt control limit value;
and if the current carbon emission statistic is smaller than the weighted moving average control limit value, and or if the current carbon emission variable average value is smaller than the Huhattan control limit value, integrating the control limit value, the sample size, the target weighting parameter, the sampling frequency and the carbon emission sampling information.
6. The carbon emission amount cost value calculation method according to claim 1, wherein the calculating the carbon emission amount calculation set according to the carbon emission amount parameter for a time length calculation and a prediction cost calculation to obtain an expected time length and a unit prediction cost, respectively, includes:
acquiring a carbon emission time period, and calculating the time length of the carbon emission time period and the carbon emission quantity parameter to obtain an expected time length;
calculating a preset upper limit of carbon emission based on the expected time length and calling a preset field loss function to obtain a first unit prediction cost;
Determining a sampling cost component value from the carbon emission parameters, and calculating a second unit prediction cost based on the expected time length and the sampling cost component value;
determining false alarm cost from the carbon emission parameters, and calculating a third unit prediction cost based on the expected time length, the false alarm cost and a preset false alarm rate;
and calculating the sum among the first unit prediction cost, the second unit prediction cost and the third unit prediction cost to obtain unit prediction cost.
7. The method of calculating a carbon emission cost value according to any one of claims 1 to 6, further comprising, after the obtaining the current actual total cost value:
storing the current actual total cost value into a local database, and repeating the steps to obtain the actual total cost value at the next moment;
and judging whether the actual total cost value at the next moment is larger than the current actual total cost value, if the actual total cost value at the next moment is larger than the current actual total cost value, reserving the current actual total cost value, and if the actual total cost value at the next moment is not larger than the current actual total cost value, replacing the current actual total cost value with the actual total cost value at the next moment.
8. A carbon emission cost value calculation apparatus, characterized by comprising:
the carbon emission data acquisition module is used for acquiring carbon emission data and carrying out normal inspection processing on the carbon emission data so as to obtain carbon emission parameters;
the target weighting parameter screening module is used for acquiring preset sample size and weighting parameters, calculating carbon emission sampling information based on the sample size, and screening target weighting parameters from all the weighting parameters;
a carbon emission amount calculation set determining module, configured to determine a control limit value of a carbon emission amount calculation model based on the carbon emission amount sampling information and the target weighting parameter, and integrate the control limit value, the sample amount, the target weighting parameter, and the carbon emission amount sampling information to obtain a carbon emission amount calculation set;
and the actual total cost value calculation module is used for respectively carrying out time length calculation and prediction cost calculation on the carbon emission amount calculation collection according to the carbon emission amount parameter so as to obtain an expected time length and unit prediction cost, determining the unit time prediction total cost based on the expected time length and the prediction cost, and calling a preset function to calculate the unit time prediction total cost so as to obtain the current actual total cost value.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to realize the carbon emission amount cost value calculation method according to any one of claims 1 to 7.
10. A computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the carbon emission cost value calculation method according to any one of claims 1 to 7.
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| CN116703185A (en) * | 2023-08-09 | 2023-09-05 | 杭州泽天春来科技有限公司 | Carbon emission analysis device and method for traffic carrier |
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