Carbon emission budget management method based on multi-information analysis

Abstract

The invention discloses a carbon emission budget management method based on multi-information analysis, relates to the technical field of carbon emission management, and solves the technical problem of low efficiency of carbon emission budget management caused by difficult accurate distribution of carbon emission budget according to the production efficiency of an enterprise in the prior art. According to the invention, the production information of each enterprise in the park is obtained; calculating the carbon emission of each enterprise based on the electricity consumption in the production information; determining a carbon emission priority coefficient of each enterprise based on the carbon emission and the production information; and distributing the carbon emission budget of each enterprise based on the carbon emission priority coefficient. According to the method, the total carbon emission budget of the park is divided into the first carbon emission budget and the second carbon emission budget according to the proportion, and the second carbon emission budget is used for allocating the carbon emission budget for the enterprise with the high carbon emission priority, so that the production requirements of the corresponding enterprise are met, and the management efficiency of the carbon emission budget is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission management, and specifically is a carbon emission budget management method based on multi-information analysis. Background Art

[0002] As the energy crisis and climate issues become more and more serious, achieving low-carbon sustainable development and reducing carbon emissions have become a global consensus. Therefore, it is very necessary to build a clean and low-carbon smart transportation carbon emissions monitoring system to achieve low-carbon development in cities.

[0003] Existing technologies obtain the energy consumption of enterprises in the park and allocate and manage carbon emission budgets based on energy consumption; however, the production efficiency of enterprises will affect carbon emissions. For example, under the same output value, the lower the electricity consumption of an enterprise means lower carbon emissions. Existing technologies make it difficult to accurately allocate carbon emission budgets based on the production efficiency of an enterprise, resulting in low accuracy in carbon emission budget allocation and low efficiency in carbon emission budget management.

[0004] The present invention provides a carbon emission budget management method based on multi-information analysis to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a carbon emission budget management method based on multi-information analysis, which is used to solve the technical problem that the production efficiency of an enterprise will affect the carbon emissions, and the prior art is difficult to accurately allocate the carbon emission budget according to the production efficiency of the enterprise, resulting in low accuracy of carbon emission budget allocation and low efficiency of carbon emission budget management.

[0006] To achieve the above objectives, the first aspect of the present invention provides a carbon emission budget management method based on multi-information analysis, comprising:

[0007] S1: Obtain production information of each enterprise in the park; the production information includes output value and electricity consumption;

[0008] S2: Calculate the carbon emissions of each enterprise based on the electricity consumption in the production information;

[0009] S3: Determine the carbon emission priority coefficient of each enterprise based on carbon emissions and production information;

[0010] S4: Allocate carbon emission budgets to each enterprise based on carbon emission priority coefficients;

[0011] S5: inputting the carbon emissions of each enterprise for several consecutive periods into the carbon emissions prediction model to obtain the carbon emissions prediction value; wherein the carbon emissions prediction model is obtained by training the artificial intelligence model;

[0012] S6: Issue early warning based on the predicted carbon emission value of each enterprise and the corresponding carbon emission budget to obtain early warning information; wherein the early warning information includes whether the carbon emission has not exceeded the standard or has exceeded the standard.

[0013] Preferably, the step of calculating the carbon emissions of each enterprise based on the electricity consumption in the production information includes:

[0014] Extract the electricity consumption from the production information of each enterprise in the park; calculate the carbon emission TPFi of the i-th enterprise through the formula TPFi=YDLi×EF; where YDLi is the electricity consumption of the i-th enterprise, EF is the carbon emission coefficient; i=1, 2,…, n, n is the total number of enterprises in the park.

[0015] Preferably, the step of determining the carbon emission priority coefficient of each enterprise based on carbon emissions and production information includes:

[0016] A1: Extract carbon emissions and production information of each enterprise;

[0017] A2: Calculate the production efficiency ratio of each enterprise based on production information;

[0018] A3: The carbon emission priority coefficient TPXi of the ith enterprise is calculated by the formula TPXi=a×CNXi+b×TPFi; wherein CNXi is the production capacity efficiency ratio of the ith enterprise, and a and b are both proportional coefficients greater than 0.

[0019] Preferably, the calculation of the production capacity efficiency ratio of each enterprise based on the production information includes:

[0020] The power consumption and output value in the production information of each enterprise are extracted to obtain the contribution factor of each enterprise; the production capacity efficiency ratio CNXi of the ith enterprise is calculated by the formula CNXi=(GYi×CZi) / YDLi; among them, GYi is the contribution factor of the ith enterprise, and CZi is the output value of the ith enterprise.

[0021] The present invention obtains the contribution factor of each enterprise by extracting the electricity consumption and output value from the production information of each enterprise, and calculates the enterprise's capacity efficiency ratio through a formula based on the electricity consumption, output value and contribution factor; the calculated capacity efficiency ratio can accurately reflect the energy utilization of the enterprise, making it convenient to subsequently determine the enterprise's carbon emission priority coefficient based on the capacity efficiency ratio.

[0022] Preferably, obtaining the contribution factor of each enterprise includes:

[0023] B1: Obtain the tax payment of the enterprise in several consecutive periods and the average tax payment of the industry in which the enterprise is located in several consecutive periods, and perform linear fitting to obtain the curve fi(t) and curve gi(t) respectively; respectively calculate the historical violation rate of carbon emission budget of each enterprise and the number of employees of each enterprise;

[0024] B2: By formula Calculate the cumulative difference Fi of the tax paid by the i-th enterprise; where t = t1, t2, ..., tm, tm is the total number of consecutive cycles;

[0025] B3: Determine whether the cumulative difference in tax payment Fi is less than 0; if so, set the contribution factor of the i-th enterprise to 1; if not, use the formula Calculate the contribution factor GYi of the ith enterprise; where YGSi represents the number of employees of the ith enterprise, WGLi represents the historical violation rate of the carbon emission budget of the ith enterprise; ln() is a logarithmic function with a natural constant as the base.

[0026] The present invention obtains the number of employees, tax payments and historical violation rate of carbon emission budget of each enterprise, and obtains the corresponding cumulative difference in tax payments by subtracting the integral value of the average tax payments of the industry in which the enterprise is located in the corresponding several consecutive periods from the integral value of the tax payments of each enterprise in several consecutive periods; the contribution factor of the enterprise whose cumulative difference in tax payments is less than 0 is set to 1; when the cumulative difference in tax payments of the enterprise is not less than 0, the corresponding contribution factor is calculated by inputting the number of employees and the historical violation rate of carbon emission budget of the enterprise into the formula; the number of employees and the tax payment comprehensively consider the contribution of the enterprise to the employment rate and fiscal tax payment, and the historical violation rate of carbon emission budget can accurately reflect the degree of completion of the execution of the carbon emission budget by the enterprise, which is conducive to improving the accuracy of allocating the carbon emission budget of the enterprise.

[0027] It is worth noting that the more employees a company has, the higher the amount of tax it pays, and the lower its historical carbon emission budget violation rate, the greater its contribution factor will be, that is, its production capacity efficiency ratio will be higher, which is conducive to allocating more carbon emission budgets.

[0028] Preferably, the separately counting of the historical violation rate of carbon emission budget of each enterprise includes:

[0029] The total number of cycles for each enterprise to implement the carbon emission budget and the number of times the actual carbon emissions exceed the carbon emission budget set for the corresponding cycle are obtained, and the number of times the actual carbon emissions exceed the carbon emission budget set for the corresponding cycle is marked as the total number of violations; the total number of violations is divided by the total number of cycles for implementing the carbon emission budget to obtain the historical violation rate of the carbon emission budget of each enterprise.

[0030] Preferably, the allocating carbon emission budgets to each enterprise based on the carbon emission priority coefficient includes:

[0031] T1: Extract the carbon emission priority coefficient of each enterprise in the park; obtain the total carbon emission budget of the park;

[0032] T2: Divide the carbon emission priority coefficient of each enterprise by the sum of the carbon emission priority coefficients of all enterprises in the park to obtain the allocation ratio of each enterprise;

[0033] T3: Divide the total carbon emission budget of the park into the first carbon emission budget and the second carbon emission budget in proportion;

[0034] T4: Multiply the first carbon emission budget by the allocation ratio of each enterprise to obtain the carbon emission budget of each enterprise;

[0035] T5: Dynamically adjust the carbon emission budget of each enterprise based on the second carbon emission budget.

[0036] The present invention obtains the allocation ratio of each enterprise by dividing the carbon emission priority coefficient of each enterprise by the sum of the carbon emission priority coefficients of all enterprises in the park; multiplies the first carbon emission budget by the allocation ratio of each enterprise to obtain the carbon emission budget of each enterprise; ensures that enterprises with higher carbon emission priorities can be allocated more carbon emission budgets. Since enterprises with higher carbon emission priorities often have higher production efficiency, correspondingly allocating more carbon emission budgets can reduce the overall carbon emissions of the park, which is conducive to improving the efficiency of managing the carbon emission budget.

[0037] Preferably, the dynamically adjusting the carbon emission budget of each enterprise based on the second carbon emission budget includes:

[0038] Z1: Determine whether the carbon emission priority coefficient is greater than the preset priority threshold; if yes, the carbon emission priority of the corresponding enterprise is marked as high priority; if no, the carbon emission priority of the corresponding enterprise is marked as low priority;

[0039] Z2: When the carbon emissions of an enterprise with a high carbon emission priority is greater than the corresponding carbon emission budget, a corresponding amount of carbon emission budget is allocated from the second carbon emission budget so that the carbon emission budget of the corresponding enterprise meets the production demand;

[0040] Z3: When the carbon emissions of an enterprise with a low carbon emission priority is greater than the corresponding carbon emission budget, the carbon emission budget of the corresponding enterprise remains unchanged.

[0041] The present invention divides the total carbon emission budget of the park into a first carbon emission budget and a second carbon emission budget in proportion, and determines the carbon emission priority of each enterprise according to a preset priority threshold and a carbon emission priority coefficient. The second carbon emission budget is used to allocate carbon emission budgets to enterprises with a high carbon emission priority, thereby meeting the production needs of the corresponding enterprises, which is conducive to improving the efficiency of managing the carbon emission budget.

[0042] Preferably, the carbon emission prediction model is obtained by training an artificial intelligence model, including:

[0043] Extract the carbon emissions of each enterprise for several consecutive cycles and integrate them into several groups of training data and test data; use the training data to train the artificial intelligence model; use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a carbon emissions prediction model whose input is the carbon emissions of each enterprise for several recent consecutive cycles and whose output is the carbon emissions of each enterprise in the predicted cycle; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0044] The present invention extracts the carbon emissions of several consecutive periods of each enterprise for training the artificial intelligence model. After the training is completed, a carbon emissions prediction model is obtained. By inputting the carbon emissions of several consecutive periods of each enterprise into the carbon emissions prediction model, the carbon emissions of each enterprise in the prediction period are obtained. This is conducive to estimating the carbon emissions in advance and determining whether it exceeds the carbon emission budget of the corresponding enterprise, which is convenient for the enterprise to adjust the production plan in advance, thereby helping to reduce the efficiency of carbon emission budget management.

[0045] Preferably, the early warning based on the predicted carbon emission value of each enterprise and the corresponding carbon emission budget includes:

[0046] Extract the predicted carbon emissions value and the corresponding carbon emissions budget of each enterprise; determine whether the predicted carbon emissions value is greater than the corresponding carbon emissions budget; if yes, mark the warning information as carbon emissions have exceeded the standard; if not, mark the warning information as carbon emissions have not exceeded the standard.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The present invention obtains the allocation ratio of each enterprise by dividing the carbon emission priority coefficient of each enterprise by the sum of the carbon emission priority coefficients of all enterprises in the park; multiplies the first carbon emission budget by the allocation ratio of each enterprise to obtain the carbon emission budget of each enterprise; ensures that enterprises with higher carbon emission priorities can be allocated more carbon emission budgets. Since enterprises with higher carbon emission priorities often have higher production efficiency, allocating more carbon emission budgets accordingly can reduce the overall carbon emissions of the park, which is conducive to improving the efficiency of managing the carbon emission budget.

[0049] 2. The present invention divides the total carbon emission budget of the park into a first carbon emission budget and a second carbon emission budget in proportion, and determines the carbon emission priority of each enterprise according to a preset priority threshold and a carbon emission priority coefficient. The second carbon emission budget is used to allocate carbon emission budgets to enterprises with high carbon emission priorities, so as to meet the production needs of the corresponding enterprises, so that the adjusted carbon emission budget will not affect the normal production of the enterprises, which is conducive to improving the accuracy of allocating carbon emission budgets and improving the efficiency of managing carbon emission budgets.

[0050] 3. The present invention extracts the carbon emissions of several consecutive periods of each enterprise for training the artificial intelligence model, and obtains the carbon emissions prediction model after the training is completed. The carbon emissions of several consecutive periods of each enterprise are input into the carbon emissions prediction model to obtain the carbon emissions of each enterprise in the prediction period. This is conducive to estimating the carbon emissions in advance and determining whether it exceeds the carbon emission budget of the corresponding enterprise, facilitating the enterprise to adjust the production plan in advance, and is conducive to improving the compliance rate of the carbon emission budget, thereby helping to reduce the efficiency of carbon emission budget management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 is an overall flow chart of the carbon emission budget management method of the present invention;

[0053] Figure 2 A flow chart for determining the carbon emission priority coefficient of each enterprise in the present invention;

[0054] Figure 3 This is a flow chart for allocating carbon emission budgets to various enterprises in the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1-Figure 3 The first aspect of the present invention provides a carbon emission budget management method based on multi-information analysis, including:

[0057] S1: Obtain production information of each enterprise in the park; the production information includes output value and electricity consumption;

[0058] S2: Calculate the carbon emissions of each enterprise based on the electricity consumption in the production information;

[0059] S3: Determine the carbon emission priority coefficient of each enterprise based on carbon emissions and production information;

[0060] S4: Allocate carbon emission budgets to each enterprise based on carbon emission priority coefficients;

[0061] S5: inputting the carbon emissions of each enterprise for several consecutive periods into the carbon emissions prediction model to obtain the carbon emissions prediction value; wherein the carbon emissions prediction model is obtained by training the artificial intelligence model;

[0062] S6: Issue early warning based on the predicted carbon emission value of each enterprise and the corresponding carbon emission budget to obtain early warning information; wherein the early warning information includes whether the carbon emission has not exceeded the standard or has exceeded the standard.

[0063] In this embodiment, the carbon emissions of each enterprise are calculated based on the electricity consumption in the production information, including:

[0064] Extract the electricity consumption from the production information of each enterprise in the park; calculate the carbon emission TPFi of the i-th enterprise through the formula TPFi=YDLi×EF; where YDLi is the electricity consumption of the i-th enterprise, EF is the carbon emission coefficient; i=1, 2,…, n, n is the total number of enterprises in the park.

[0065] Exemplarily, the electricity consumption of the first enterprise is set to YDL1=400kWh, and the carbon emission factor EF=0.5568kg / kWh; the carbon emission of the first enterprise is calculated to be TPF1=222.72kg through the formula.

[0066] In this embodiment, the carbon emission priority coefficient of each enterprise is determined based on the carbon emission and production information, including:

[0067] A1: Extract carbon emissions and production information of each enterprise;

[0068] A2: Calculate the production efficiency ratio of each enterprise based on production information;

[0069] A3: The carbon emission priority coefficient TPXi of the ith enterprise is calculated by the formula TPXi=a×CNXi+b×TPFi; wherein CNXi is the production capacity efficiency ratio of the ith enterprise, a and b are both proportional coefficients greater than 0, and the specific values ​​of a and b are set by experts in related industries based on experience.

[0070] Exemplarily, set a=10kwh / 10,000 yuan, b=0.1 / kg; CNX1 of the first enterprise=190,950 yuan / kwh, and the carbon emissions of the first enterprise TPF1=222.72 kg; the carbon emission priority coefficient TPX1 of the first enterprise is calculated by the formula to be 41.367.

[0071] In this embodiment, the production capacity efficiency ratio of each enterprise is calculated based on the production information, including:

[0072] The power consumption and output value in the production information of each enterprise are extracted to obtain the contribution factor of each enterprise; the production capacity efficiency ratio CNXi of the ith enterprise is calculated by the formula CNXi=(GYi×CZi) / YDLi; among them, GYi is the contribution factor of the ith enterprise, and CZi is the output value of the ith enterprise.

[0073] Exemplarily, the output value of the first enterprise is set to CZ1=5.7 million yuan, the electricity consumption of the first enterprise is set to YDL1=400 kwh, and the contribution factor of the first enterprise is set to GY1=1.34; the production capacity efficiency ratio of the first enterprise is calculated by the formula to be CNX1=190,950 yuan / kwh.

[0074] The present invention obtains the contribution factor of each enterprise by extracting the electricity consumption and output value from the production information of each enterprise, and calculates the enterprise's capacity efficiency ratio through a formula based on the electricity consumption, output value and contribution factor; the calculated capacity efficiency ratio can accurately reflect the energy utilization of the enterprise, making it convenient to subsequently determine the enterprise's carbon emission priority coefficient based on the capacity efficiency ratio.

[0075] In this embodiment, obtaining the contribution factor of each enterprise includes:

[0076] B1: Obtain the tax payment of the enterprise in several consecutive periods and the average tax payment of the industry in which the enterprise is located in several consecutive periods, and perform linear fitting to obtain the curve fi(t) and curve gi(t) respectively; respectively calculate the historical violation rate of carbon emission budget of each enterprise and the number of employees of each enterprise;

[0077] B2: By formula Calculate the cumulative difference Fi of the tax paid by the i-th enterprise; where t = t1, t2, ..., tm, tm is the total number of consecutive cycles;

[0078] B3: Determine whether the cumulative difference in tax payment Fi is less than 0; if so, set the contribution factor of the i-th enterprise to 1; if not, use the formula Calculate the contribution factor GYi of the ith enterprise; where YGSi represents the number of employees of the ith enterprise, WGLi represents the historical violation rate of the carbon emission budget of the ith enterprise; ln() is a logarithmic function with a natural constant as the base.

[0079] For example, the number of employees of the first enterprise is set to 53, the historical violation rate of carbon emission budget is 2.33%, the cumulative difference of tax payment of the first enterprise is F1 = 300,000 yuan, and the cumulative value of tax payment of the first enterprise is Ten thousand yuan; since the cumulative difference in tax payments F1 is greater than 0, the contribution factor of the first enterprise calculated by the formula is GY1≈1.34.

[0080] The present invention obtains the number of employees, tax payments and historical violation rate of carbon emission budget of each enterprise, and obtains the corresponding cumulative difference in tax payments by subtracting the integral value of the average tax payments of the industry in which the enterprise is located in the corresponding several consecutive periods from the integral value of the tax payments of each enterprise in several consecutive periods; the contribution factor of the enterprise whose cumulative difference in tax payments is less than 0 is set to 1; when the cumulative difference in tax payments of the enterprise is not less than 0, the corresponding contribution factor is calculated by inputting the number of employees and the historical violation rate of carbon emission budget of the enterprise into the formula; the number of employees and the tax payment comprehensively consider the contribution of the enterprise to the employment rate and fiscal tax payment, and the historical violation rate of carbon emission budget can accurately reflect the degree of completion of the execution of the carbon emission budget by the enterprise, which is conducive to improving the accuracy of allocating the carbon emission budget of the enterprise.

[0081] It is worth noting that the more employees a company has, the higher the amount of tax it pays, and the lower its historical carbon emission budget violation rate, the greater its contribution factor will be, that is, its production capacity efficiency ratio will be higher, which is conducive to allocating more carbon emission budgets.

[0082] In this embodiment, the historical violation rate of carbon emission budget of each enterprise is counted separately, including:

[0083] The total number of cycles for each enterprise to implement the carbon emission budget and the number of times the actual carbon emissions exceed the carbon emission budget set for the corresponding cycle are obtained, and the number of times the actual carbon emissions exceed the carbon emission budget set for the corresponding cycle is marked as the total number of violations; the total number of violations is divided by the total number of cycles for implementing the carbon emission budget to obtain the historical violation rate of the carbon emission budget of each enterprise.

[0084] Exemplarily, the total number of cycles for the first enterprise to execute the carbon emission budget is set to 43 times, the number of times the actual carbon emissions exceed the carbon emission budget set for the corresponding cycle is 1 time, and the number of times the actual carbon emissions exceed the carbon emission budget set for the corresponding cycle is marked as the total number of violations; the total number of violations is divided by the total number of cycles for executing the carbon emission budget, and the historical violation rate of the carbon emission budget of the first enterprise is obtained to be 2.33%.

[0085] In this embodiment, the carbon emission budget of each enterprise is allocated based on the carbon emission priority coefficient, including:

[0086] T1: Extract the carbon emission priority coefficient of each enterprise in the park; obtain the total carbon emission budget of the park as 3,000 tons / year;

[0087] T2: Divide the carbon emission priority coefficient of each enterprise by the sum of the carbon emission priority coefficients of all enterprises in the park, and get the allocation ratio of each enterprise:

[0088] T3: 80% of the total carbon emission budget of the park is divided into the first carbon emission budget, and 20% is divided into the second carbon emission budget;

[0089] T4: By formula Calculate the corresponding carbon emission budget TPYi; where YPY 第一 For the first carbon emissions budget;

[0090] T5: Dynamically adjust the carbon emission budget of each enterprise based on the second carbon emission budget.

[0091] Furthermore, the first carbon emission budget YPY is set 第一 =7000kg, the carbon emission priority coefficient TPX1 of the first enterprise = 41.367, the sum of the carbon emission priority coefficients of all enterprises in the park The allocation ratio of the first enterprise is 5.57%; calculated by the formula, the carbon emission budget of the first enterprise is 389.9 kg.

[0092] The present invention obtains the allocation ratio of each enterprise by dividing the carbon emission priority coefficient of each enterprise by the sum of the carbon emission priority coefficients of all enterprises in the park; multiplies the first carbon emission budget by the allocation ratio of each enterprise to obtain the carbon emission budget of each enterprise; ensures that enterprises with higher carbon emission priorities can be allocated more carbon emission budgets. Since enterprises with higher carbon emission priorities often have higher production efficiency, correspondingly allocating more carbon emission budgets can reduce the overall carbon emissions of the park, which is conducive to improving the efficiency of managing the carbon emission budget.

[0093] In this embodiment, the carbon emission budget of each enterprise is dynamically adjusted based on the second carbon emission budget, including:

[0094] Z1: Determine whether the carbon emission priority coefficient is greater than the preset priority threshold; if yes, the carbon emission priority of the corresponding enterprise is marked as high priority; if no, the carbon emission priority of the corresponding enterprise is marked as low priority;

[0095] Z2: When the carbon emissions of an enterprise with a high carbon emission priority is greater than the corresponding carbon emission budget, a corresponding amount of carbon emission budget is allocated from the second carbon emission budget so that the carbon emission budget of the corresponding enterprise meets the production demand;

[0096] Z3: When the carbon emissions of an enterprise with a low carbon emission priority is greater than the corresponding carbon emission budget, the carbon emission budget of the corresponding enterprise remains unchanged.

[0097] Exemplarily, the carbon emission priority coefficient TPX1 of the first enterprise is set to 41.367, and the preset priority threshold is 40; since the carbon emission priority coefficient of the first enterprise is greater than the preset priority threshold, the carbon emission priority of the first enterprise is marked as high priority; assuming that the actual carbon emissions of the first enterprise are 450kg, and the corresponding carbon emission budget is 389.9kg, 60.1kg of the carbon emission budget is allocated from the second carbon emission budget so that the carbon emission budget of the first enterprise meets production needs.

[0098] The present invention divides the total carbon emission budget of the park into a first carbon emission budget and a second carbon emission budget in proportion, and determines the carbon emission priority of each enterprise according to a preset priority threshold and a carbon emission priority coefficient. The second carbon emission budget is used to allocate carbon emission budgets to enterprises with a high carbon emission priority, thereby meeting the production needs of the corresponding enterprises, which is conducive to improving the efficiency of managing the carbon emission budget.

[0099] In this embodiment, the carbon emission prediction model is obtained by training an artificial intelligence model, including:

[0100] The carbon emissions of several consecutive cycles of each enterprise are extracted and integrated into several groups of original data, 80% of the original data are used as training data and 20% as test data; the training data are used to train the artificial intelligence model; the test data is used to test the trained artificial intelligence model, and the artificial intelligence model is adjusted according to the test results; finally, a carbon emissions prediction model is obtained, whose input is the carbon emissions of several recent consecutive cycles of each enterprise, and whose output is the carbon emissions of each enterprise in the predicted cycle; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0101] The present invention extracts the carbon emissions of several consecutive periods of each enterprise for training the artificial intelligence model. After the training is completed, a carbon emissions prediction model is obtained. By inputting the carbon emissions of several consecutive periods of each enterprise into the carbon emissions prediction model, the carbon emissions of each enterprise in the prediction period are obtained. This is conducive to estimating the carbon emissions in advance and judging whether it exceeds the carbon emission budget of the corresponding enterprise, facilitating the enterprise to adjust the production plan in advance, and helping to improve the carbon emission budget compliance rate, thereby helping to reduce the efficiency of carbon emission budget management.

[0102] In this embodiment, early warning is performed based on the predicted carbon emission value of each enterprise and the corresponding carbon emission budget, including:

[0103] Extract the predicted carbon emissions value and the corresponding carbon emissions budget of each enterprise; determine whether the predicted carbon emissions value is greater than the corresponding carbon emissions budget; if yes, mark the warning information as carbon emissions have exceeded the standard; if not, mark the warning information as carbon emissions have not exceeded the standard.

[0104] Exemplarily, the predicted carbon emissions value of the first enterprise is set to 360 kg, and the corresponding carbon emissions budget is 389.9 kg; because the predicted carbon emissions value of the first enterprise is less than the corresponding carbon emissions budget, the warning information is marked as carbon emissions are within the standard.

[0105] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0106] Working principle of the present invention:

[0107] The present invention obtains production information of each enterprise in the park; calculates the carbon emissions of each enterprise based on the electricity consumption in the production information; determines the carbon emission priority coefficient of each enterprise based on the carbon emissions and production information; allocates the carbon emission budget of each enterprise based on the carbon emission priority coefficient; inputs the carbon emissions of each enterprise for several consecutive periods into a carbon emission prediction model to obtain a carbon emission prediction value; and issues an early warning based on the carbon emission prediction value of each enterprise and the corresponding carbon emission budget to obtain early warning information.

[0108] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A carbon emission budget management method based on multi-information analysis, characterized in that: S1: Obtain production information of each enterprise in the park; the production information includes output value and electricity consumption; S2: Calculate the carbon emissions of each enterprise based on the electricity consumption in the production information; S3: Determine the carbon emission priority coefficient of each enterprise based on carbon emissions and production information; S4: Allocate carbon emission budgets to each enterprise based on carbon emission priority coefficients; S5: inputting the carbon emissions of each enterprise for several consecutive periods into the carbon emissions prediction model to obtain the carbon emissions prediction value; wherein the carbon emissions prediction model is obtained by training the artificial intelligence model; S6: Issue early warning based on the predicted carbon emission value of each enterprise and the corresponding carbon emission budget to obtain early warning information; wherein the early warning information includes whether the carbon emission has not exceeded the standard or has exceeded the standard.

2. A carbon emission budget management method based on multi-information analysis according to claim 1, characterized in that: The carbon emissions of each enterprise are calculated based on the electricity consumption in the production information, including: The electricity consumption in the production information of each enterprise in the park is extracted; the carbon emissions of the ith enterprise are obtained by calculating the product of the electricity consumption and the carbon emission coefficient; where i = 1, 2, ..., n, and n is the total number of enterprises in the park.

3. A carbon emission budget management method based on multi-information analysis according to claim 1 or 2, characterized in that: The carbon emission priority coefficient of each enterprise is determined based on carbon emissions and production information, including: A1: Extract carbon emissions and production information of each enterprise; A2: Calculate the production efficiency ratio of each enterprise based on production information; A3: The carbon emission priority coefficient of the corresponding enterprise is obtained by multiplying the production capacity efficiency ratio and carbon emissions by the corresponding proportional coefficients and adding the products.

4. The carbon emission budget management method based on multi-information analysis according to claim 3 is characterized in that: The calculation of the production capacity efficiency ratio of each enterprise based on production information includes: The power consumption and output value in the production information of each enterprise are extracted to obtain the contribution factor of each enterprise; the capacity efficiency ratio of each enterprise is calculated by multiplying the contribution factor of each enterprise by the corresponding output value and dividing it by the corresponding power consumption.

5. The carbon emission budget management method based on multi-information analysis according to claim 4 is characterized in that: The obtaining of the contribution factor of each enterprise includes: B1: Obtain the tax payment of the enterprise in several consecutive periods and the average tax payment of the industry in which the enterprise is located in several consecutive periods, and perform linear fitting to obtain the curve fi(t) and curve gi(t) respectively; respectively calculate the historical violation rate of carbon emission budget of each enterprise and the number of employees of each enterprise; B2: By formula Calculate the cumulative difference Fi of the tax paid by the i-th enterprise; where t = t1, t2, ..., tm, tm is the total number of consecutive cycles; B3: Determine whether the cumulative difference in tax payment Fi is less than 0; if so, set the contribution factor of the i-th enterprise to 1; if not, use the formula Calculate the contribution factor GYi of the ith enterprise; where YGSi represents the number of employees of the ith enterprise, WGLi represents the historical violation rate of the carbon emission budget of the ith enterprise; ln() is a logarithmic function with a natural constant as the base.

6. The carbon emission budget management method based on multi-information analysis according to claim 5 is characterized in that: The above statistics on the historical violation rate of carbon emission budget of each enterprise include: The total number of cycles for each enterprise to implement the carbon emission budget and the number of times the actual carbon emissions exceed the carbon emission budget set for the corresponding cycle are obtained, and the number of times the actual carbon emissions exceed the carbon emission budget set for the corresponding cycle is marked as the total number of violations; the total number of violations is divided by the total number of cycles for implementing the carbon emission budget to obtain the historical violation rate of the carbon emission budget of each enterprise.

7. The carbon emission budget management method based on multi-information analysis according to claim 1 is characterized in that: The allocation of carbon emission budgets to each enterprise based on the carbon emission priority coefficient includes: T1: Extract the carbon emission priority coefficient of each enterprise in the park; obtain the total carbon emission budget of the park; T2: Divide the carbon emission priority coefficient of each enterprise by the sum of the carbon emission priority coefficients of all enterprises in the park to obtain the allocation ratio of each enterprise; T3: Divide the total carbon emission budget of the park into the first carbon emission budget and the second carbon emission budget in proportion; T4: Multiply the first carbon emission budget by the allocation ratio of each enterprise to obtain the carbon emission budget of each enterprise; T5: Dynamically adjust the carbon emission budget of each enterprise based on the second carbon emission budget.

8. The carbon emission budget management method based on multi-information analysis according to claim 7 is characterized in that: The dynamically adjusting the carbon emission budget of each enterprise based on the second carbon emission budget includes: Z1: Determine whether the carbon emission priority coefficient is greater than the preset priority threshold; if yes, the carbon emission priority of the corresponding enterprise is marked as high priority; if no, the carbon emission priority of the corresponding enterprise is marked as low priority; Z2: When the carbon emissions of an enterprise with a high carbon emission priority is greater than the corresponding carbon emission budget, a corresponding amount of carbon emission budget is allocated from the second carbon emission budget so that the carbon emission budget of the corresponding enterprise meets the production demand; Z3: When the carbon emissions of an enterprise with a low carbon emission priority is greater than the corresponding carbon emission budget, the carbon emission budget of the corresponding enterprise remains unchanged.

9. The carbon emission budget management method based on multi-information analysis according to claim 1 is characterized in that: The carbon emission prediction model is obtained through artificial intelligence model training, including: Extract the carbon emissions of each enterprise for several consecutive cycles and integrate them into several groups of training data and test data; use the training data to train the artificial intelligence model; use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a carbon emissions prediction model whose input is the carbon emissions of each enterprise for several recent consecutive cycles and whose output is the carbon emissions of each enterprise in the predicted cycle; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

10. The carbon emission budget management method based on multi-information analysis according to claim 1 is characterized in that: The early warning based on the predicted carbon emission value of each enterprise and the corresponding carbon emission budget includes: Extract the predicted carbon emissions value and the corresponding carbon emissions budget of each enterprise; determine whether the predicted carbon emissions value is greater than the corresponding carbon emissions budget; if yes, mark the warning information as carbon emissions have exceeded the standard; if not, mark the warning information as carbon emissions have not exceeded the standard.

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