Tobacco baking carbon emission intelligent detection device and carbon emission accounting method

Abstract

The invention relates to the technical field of data analysis, in particular to a tobacco baking carbon emission intelligent detection device and a carbon emission accounting method, and the tobacco baking carbon emission intelligent detection device comprises a data communication module, a data storage module, a data calculation and analysis module, a carbon emission accounting module, a data division module and an evaluation module. According to the invention, a feature sub-index solution set and a non-feature sub-index solution set are calculated through the data calculation and analysis module, and the influence degree of feature sub-values and non-feature sub-values on a total processing value is specifically analyzed, so that the process flow can be conveniently analyzed and adjusted in the later period; the carbon emission component of fuel combustion and the carbon emission component of tobacco respiration are obtained through calculation of an accounting analysis module formula, the accounting method is scientific and reasonable, and the accounting process is efficient; and the evaluation module is used for respectively judging and analyzing the complete carbon emission, the complete fuel consumption and the complete material consumption and giving corresponding prompts, so that the intelligent analysis effect of tobacco baking is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an intelligent detection device for carbon emissions from tobacco baking and a carbon emissions accounting method. Background Art

[0002] In the important process of tobacco baking, since coal has been used as the main fuel for heating for a long time, a large amount of carbon emissions will inevitably be generated. Coal, as a traditional energy source, will release a variety of greenhouse gases during the combustion process, among which carbon dioxide emissions are particularly prominent. At the same time, during the baking process, tobacco leaves continue to breathe due to their own physiological characteristics, thereby releasing a certain amount of carbon dioxide and energy. When considering the large-scale tobacco planting and the simultaneous baking operations in many baking rooms, the carbon emissions generated by the breathing of tobacco leaves cannot be underestimated.

[0003] In the prior art, there is still a lack of effective detection means and accurate accounting methods for carbon emissions during tobacco baking, making it difficult to achieve precise management and effective control of carbon emissions during tobacco baking. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the background technology and to propose an intelligent detection device for carbon emissions from tobacco baking and a carbon emissions accounting method.

[0005] In order to achieve the above-mentioned object, the present invention adopts the following technical scheme: an intelligent detection device for carbon emission of tobacco baking and a carbon emission accounting method, comprising a data communication module, a data storage module, a data calculation and analysis module, a carbon emission accounting module, a data division module and an evaluation module;

[0006] The data communication module is responsible for data transmission between modules. The communication module and each module are connected in a one-way manner.

[0007] A data storage module is used to store historical processing data sets, wherein the historical processing data sets of a unified batch include historical carbon emissions, historical fuel losses, historical material losses, baking time, dry-bulb temperature, and wet-bulb temperature; the historical carbon emissions are marked as the total processing value, the historical fuel losses and the historical material losses are marked as the first characteristic sub-value and the second characteristic sub-value, respectively, the historical baking time, the historical dry-bulb temperature, and the historical wet-bulb temperature are marked as the first non-characteristic sub-value, the second non-characteristic sub-value, and the third non-characteristic sub-value, respectively, and a total historical processing data set is established based on different batches of historical processing data sets;

[0008] The data calculation and analysis module is used to establish a standard range set based on the total set of historical processing data and determine whether the historical processing data set is the target data set, obtain the total reference coefficient, characteristic sub-reference coefficient and non-characteristic sub-reference coefficient of the target data set, and construct a reference set, obtain the characteristic reference sequence based on the reference set and establish an accounting model;

[0009] A carbon emission accounting module is used to obtain the total carbon emission and calculate the fuel emission component. The total carbon emission includes the fuel emission component and the tobacco breathing emission component.

[0010] A data division module is used to divide tobacco baking time periods;

[0011] The evaluation module is used to evaluate the baking results and give corresponding prompts.

[0012] In the above-mentioned tobacco baking carbon emission intelligent detection device and carbon emission accounting method, the method for obtaining the characteristic reference sequence is:

[0013] The total processing value in the target processing data set is calculated by ratio with the average carbon emission to obtain the total reference coefficient ZONG; the several characteristic sub-values ​​are calculated by ratio with the corresponding average amount to obtain several first characteristic sub-reference coefficients, second sub-reference coefficients, first non-characteristic sub-reference coefficients, second characteristic sub-reference coefficients and third characteristic sub-reference coefficients; a reference set ZIk is constructed according to the several characteristic sub-reference coefficients and the several non-characteristic sub-reference coefficients, where k is the element number of the reference set, i.e., the total number of the several characteristic sub-reference coefficients and the several non-characteristic sub-reference coefficients, k∈[1,5];

[0014] By comparing the sub-coefficient reference formula Calculate the year-on-year sub-reference coefficient TBk; obtain the minimum year-on-year sub-reference coefficient TBkmin in the target data set, and mark the characteristic sub-reference coefficient and non-characteristic sub-reference coefficient corresponding to the minimum year-on-year sub-reference coefficient TBkmin as key characteristic sub-reference coefficient and key non-characteristic sub-reference coefficient respectively;

[0015] Characteristic sub-reference coefficients and non-characteristic sub-reference coefficients with the same name are divided into similar characteristic sub-reference coefficients and similar non-characteristic sub-reference coefficients; the number of occurrences of key characteristic sub-reference coefficients and key non-characteristic sub-reference coefficients corresponding to several similar characteristic sub-reference coefficients and similar non-characteristic sub-reference coefficients are counted respectively to obtain the total number of key characteristic sub-reference coefficients and the total number of key non-characteristic sub-reference coefficients; the total number of key characteristic sub-reference coefficients and the total number of key non-characteristic sub-reference coefficients are sorted from large to small to obtain a characteristic key characteristic sub-reference coefficient sequence and a non-characteristic key characteristic sub-reference coefficient sequence.

[0016] In the above-mentioned tobacco baking carbon emission intelligent detection device and carbon emission accounting method, according to the element sorting order of the characteristic key characteristic sub-reference coefficient sequence and the non-characteristic key characteristic sub-reference coefficient sequence, the corresponding characteristic sub-reference coefficients and non-characteristic sub-reference coefficients are sorted respectively to obtain the characteristic sub-reference sequence {Ti} and the non-characteristic sub-reference sequence {FTj}

[0017] In the above-mentioned tobacco baking carbon emission intelligent detection device and carbon emission accounting method, according to the sub-indicator processing formula:

[0018]

[0019] The total index J is calculated; where A represents the characteristic sub-index corresponding to the characteristic sub-value, and B represents the non-characteristic sub-index corresponding to the non-characteristic sub-value; the characteristic sub-index solution Ai and the non-characteristic sub-index solution Bi are obtained;

[0020] The solution set of accounting indicators should satisfy the limited range J∈ZONG×Y; Y is the standard coefficient range; the characteristic sub-indicator solution Ai satisfies A1≥A2, and the non-characteristic sub-indicator solution Bi satisfies B1≥B2≥B3.

[0021] In the above-mentioned tobacco baking carbon emission intelligent detection device and carbon emission accounting method, according to the calculation formula Calculate the carbon emission component TF(x); where TP1 represents the total carbon emission, x is the number of the required characteristic sub-indicator solution, x∈(1,2); TF(1) represents the fuel emission component, and TF(2) represents the tobacco respiratory emission component; the units of the total carbon emission TP2 and the carbon emission component TF(x) are both standard cubic meters.

[0022] In the above-mentioned tobacco baking carbon emission intelligent detection device and carbon emission accounting method, the method of dividing tobacco baking time periods is:

[0023] Obtain the historical fuel loss mean WSc, real-time fuel consumption M1, real-time material consumption M2 and real-time carbon emissions TP2, and use the fuel carbon value monitoring formula:

[0024]

[0025] The respiratory carbon release monitoring value E is calculated; the units of real-time fuel consumption M1 and real-time material consumption M2 are both kilograms, and the unit of real-time carbon emission TP2 is standard cubic meters.

[0026] In the above-mentioned intelligent detection device for carbon emissions from tobacco baking and carbon emissions accounting method, data analysis is performed on the carbon release monitoring value E. If the carbon release monitoring value E is not less than the first threshold value K1, it is determined that the tobacco is in the first time period; if the carbon release monitoring value E belongs to the threshold interval (K2, K1), it is determined that the tobacco is in the second time period; if the carbon release monitoring value E is not greater than the second threshold value K2, it is determined that the tobacco is in the third time period.

[0027] In the above-mentioned tobacco baking carbon emission intelligent detection device and carbon emission accounting method, the specific evaluation method is:

[0028] Obtain the total fuel consumption and the total material consumption, and generate a first normal label or a first abnormal label according to whether the total carbon emissions fall within the carbon emissions standard range;

[0029] And, generating a second normal label or a second abnormal label according to whether the total fuel consumption and the total material consumption belong to the corresponding standard range in the standard range set; and obtaining a judgment result;

[0030] In the above-mentioned tobacco baking carbon emission intelligent detection device and carbon emission accounting method, the judgment results are traversed, and if there is no abnormal tag, the baking process is judged to be normal; if there is a first abnormal tag, the baking process is judged to be abnormal;

[0031] If only the first abnormal label exists, the result of the traversal is checked to see if the second abnormal label exists. If the second abnormal label does not exist, it is prompted that the process needs to be adjusted, that is, the relevant settings of the non-characteristic sub-values ​​need to be adjusted;

[0032] If only the second abnormal tag exists, it is determined whether the total fuel consumption and the total material consumption are less than the minimum value of the corresponding standard range; if the total fuel consumption or the total material consumption is less than the minimum value of the corresponding standard range, it is prompted that the process needs to be adjusted; if the total fuel consumption or the total material consumption is greater than the maximum value of the corresponding standard range, it is prompted that the process is good and an optimization instruction is generated;

[0033] Update the corresponding standard range according to the optimization instructions;

[0034] If the first abnormal tag and the second abnormal tag exist, it is determined that the total fuel consumption and the total material consumption do not meet the standards and need to be adjusted.

[0035] Compared with the prior art, the advantages of the present invention are:

[0036] By calculating the solution set of characteristic sub-indicators and the solution set of non-characteristic sub-indicators, the influence of characteristic sub-values ​​and non-characteristic sub-values ​​on the total processing value is specifically analyzed, which facilitates the analysis and adjustment of the process flow in the later stage.

[0037] The carbon emissions from fuel combustion and tobacco breathing are calculated through accounting formulas. The accounting method is scientific and reasonable, and the accounting process is efficient.

[0038] By separately judging and analyzing the total carbon emissions, total fuel consumption and total material consumption, corresponding prompts are given according to the judgment results, thereby improving the intelligent analysis effect of tobacco baking. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a module flow chart of the present invention. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0041] In the description of the present invention, it is necessary to understand that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0042] Reference Figure 1 , an intelligent detection device for carbon emission from tobacco baking and a carbon emission accounting method, comprising a data communication module, a data storage module, a data acquisition module, a data analysis module, a carbon emission accounting module, a data division module and an evaluation module;

[0043] The data communication module is responsible for data transmission between modules. The communication module and each module are connected in a one-way manner.

[0044] A data storage module is used to store historical processing data sets, wherein a unified batch of historical processing data sets includes historical carbon emissions, historical fuel losses, historical material losses, baking time, dry-bulb temperature, and wet-bulb temperature; historical carbon emissions are marked as a total processing value, historical fuel losses and historical material losses are marked as a first characteristic sub-value and a second characteristic sub-value, respectively, and historical baking time, historical dry-bulb temperature, and historical wet-bulb temperature are marked as a first non-characteristic sub-value, a second non-characteristic sub-value, and a third non-characteristic sub-value, respectively, and a total set of historical processing data sets is established based on different batches of historical processing data sets; characteristic sub-values ​​represent characteristic items that have a direct relationship with the total processing value, and non-characteristic sub-values ​​represent non-characteristic items that have an indirect relationship with the total processing value;

[0045] A data calculation and analysis module is used to establish a standard range set based on the total set of historical processing data and determine whether the historical processing data set is a target data set;

[0046] The method for establishing standard data is as follows: obtain a number of historical carbon emissions in the total historical data set, calculate the average of the historical carbon emissions to obtain the average carbon emissions, calculate the average of the historical fuel losses to obtain the average fuel losses, and calculate the average of the historical material losses to obtain the average material losses; obtain the average baking amount, the average dry-bulb temperature, and the average wet-bulb temperature according to the same method, and establish a standard value set based on the five numerical items of the average carbon emissions, the average fuel losses, the average material losses, the average baking amount, the average dry-bulb temperature, and the average wet-bulb temperature;

[0047] Multiply all the numerical items in the standard numerical set by the minimum and maximum values ​​of the standard coefficient range respectively and combine them to obtain the corresponding carbon emission standard range, fuel loss standard range, material loss standard range, baking time standard range, dry bulb temperature standard range and wet bulb temperature standard range and establish a standard range set; the standard coefficient range can be (0.9-1.1);

[0048] The method for determining whether a historical data set is a target data set is as follows: comparing the total processing value in the historical processing data set with the corresponding carbon emission standard range in the standard range set; if the total processing value belongs to the standard range set, marking the historical processing data set as the target data set;

[0049] If the total processing value does not belong to the standard range set, then determine whether several characteristic sub-values ​​and non-characteristic sub-values ​​in the historical processing data set belong to the standard range corresponding to the standard range set, and if the characteristic sub-value or the non-characteristic sub-value belongs to the corresponding standard range, generate a confirmation label; if the characteristic sub-value or the non-characteristic sub-value does not belong to the corresponding standard range, generate a negative label; and generate a judgment result;

[0050] Traverse the judgment results; if the judgment result has a negative label, mark the historical processing data set as the target processing data set; if the judgment result does not have a negative label, mark the historical processing data set as an invalid processing data set;

[0051] Establishing a target data set based on a number of target processing data sets;

[0052] The total processing value in the target processing data set is calculated by ratio with the average carbon emission to obtain the total reference coefficient ZONG; the several characteristic sub-values ​​are calculated by ratio with the corresponding average amount to obtain several first characteristic sub-reference coefficients, second sub-reference coefficients, first non-characteristic sub-reference coefficients, second characteristic sub-reference coefficients and third characteristic sub-reference coefficients; a reference set ZIk is constructed according to the several characteristic sub-reference coefficients and the several non-characteristic sub-reference coefficients, where k is the element number of the reference set, i.e., the total number of the several characteristic sub-reference coefficients and the several non-characteristic sub-reference coefficients, k∈[1,5];

[0053] By comparing the sub-coefficient reference formula The year-on-year sub-reference coefficient TBk is calculated; the minimum year-on-year sub-reference coefficient TBkmin in the target data set is obtained, and the characteristic sub-reference coefficient and non-characteristic sub-reference coefficient corresponding to the minimum year-on-year sub-reference coefficient TBkmin are marked as key characteristic sub-reference coefficient and key non-characteristic sub-reference coefficient respectively; for example, in target data set 1, the total reference coefficient ZONG is 1.5, the first characteristic sub-reference coefficient and the second sub-reference coefficient are 1.2 and 1.5 respectively, and the first non-characteristic sub-reference coefficient, the second non-characteristic sub-reference coefficient and the third non-characteristic sub-reference coefficient are 1.1, 0.8, and 0.9 respectively, then the minimum year-on-year sub-reference coefficient TBkmin is Then the second characteristic sub-reference coefficient is marked as a key sub-reference coefficient;

[0054] The characteristic sub-reference coefficients and non-characteristic sub-reference coefficients with the same name are divided into the same type of characteristic sub-reference coefficients and the same type of non-characteristic sub-reference coefficients; the number of occurrences of the key characteristic sub-reference coefficients and the key non-characteristic sub-reference coefficients corresponding to the same type of characteristic sub-reference coefficients and the same type of non-characteristic sub-reference coefficients are counted respectively to obtain the total number of key characteristic sub-reference coefficients and the total number of key non-characteristic sub-reference coefficients, and the total number of key characteristic sub-reference coefficients and the total number of key non-characteristic sub-reference coefficients are sorted from large to small to obtain a characteristic key characteristic sub-reference coefficient sequence and a non-characteristic key characteristic sub-reference coefficient sequence;

[0055] For example: the key sub-reference coefficient in target data set 2 is the third non-characteristic sub-reference coefficient, the key sub-reference coefficient in target data set 3 is the second characteristic sub-reference coefficient, the key sub-reference coefficient in target data set 4 is the first characteristic sub-reference coefficient, and the key sub-reference coefficient in target data set 5 is the first non-characteristic sub-reference coefficient; then in target data 1 to target data 5, the total number of key sub-reference coefficients corresponding to the first characteristic sub-reference coefficient is 1, the total number of key sub-reference coefficients corresponding to the second characteristic sub-reference coefficient is 2, the total number of key sub-reference coefficients corresponding to the first non-characteristic sub-reference coefficient is 1, the total number of key sub-reference coefficients corresponding to the second non-characteristic sub-reference coefficient is 0, and the total number of key sub-reference coefficients corresponding to the third non-characteristic sub-reference coefficient is 1;

[0056] According to the element sorting order of the feature key feature sub-reference coefficient sequence and the non-feature key feature sub-reference coefficient sequence, the corresponding feature sub-reference coefficients and non-feature sub-reference coefficients are sorted respectively to obtain the feature sub-reference sequence {Ti} and the non-feature sub-reference sequence {FTj}; i is the number of the feature sub-value, i∈[1,2], and m is the total number of feature sub-values; {FTj} is the non-feature sub-reference coefficient sequence, j is the number of the non-feature sub-value, j∈[1,3], and m is the total number of non-feature sub-values;

[0057] According to the above sorting, the processed sub-value sequence is {second characteristic sub-value, first characteristic sub-value, first non-characteristic sub-value, third non-characteristic sub-value, second non-characteristic sub-value};

[0058] The method to establish the accounting model is:

[0059] According to the sub-indicator processing formula:

[0060]

[0061] The total index J is calculated; where A represents the characteristic sub-index corresponding to the characteristic sub-value, and B represents the non-characteristic sub-index corresponding to the non-characteristic sub-value; the characteristic sub-index solution Ai and the non-characteristic sub-index solution Bi are obtained;

[0062] The accounting indicator solution set should satisfy the limited range J∈ZONG×Y; Y is the standard coefficient range; the characteristic sub-indicator solution Ai satisfies A1≥A2, and the non-characteristic sub-indicator solution Bi satisfies B1≥B2≥B3; it should be noted that when the total number of key sub-reference coefficients corresponding to different sub-reference coefficients is the same, the sub-reference coefficients are randomly sorted and the sub-indicator values ​​corresponding to the two sub-reference coefficients are the same; by calculating the characteristic sub-indicator solution set and the non-characteristic sub-indicator solution set, the influence of the characteristic sub-value and the non-characteristic sub-value on the total processing value is specifically analyzed, which is convenient for the analysis and adjustment of the process flow in the later stage;

[0063] The carbon emission accounting module is used to obtain the total carbon emissions and calculate the fuel emission component. The total carbon emissions include the fuel emission component and the tobacco breathing emission component. According to the calculation formula Calculate the carbon emission component TF(x); where TP1 represents the total carbon emission, x is the number of the required characteristic sub-indicator solution, x∈(1,2); TF(1) represents the fuel emission component, and TF(2) represents the tobacco breathing emission component; the units of the total carbon emission TP2 and the carbon emission component TF(x) are both standard cubic meters;

[0064] Among them, the historical carbon emissions and the total carbon emissions TP1 are obtained by calculation through the carbon emission detection equipment set at the flue gas emission port. The calculation method is to obtain the real-time carbon concentration through the concentration sensor, obtain the real-time flow rate through the flow rate sensor, and obtain the real-time concentration by the real-time flow rate sensor through the integral multiplication method; the historical fuel loss is obtained by weighing the mass of the fuel before and after baking and performing the difference operation, and the unit is kilogram; the historical material loss is obtained by weighing the mass of the tobacco before and after baking and performing the difference operation, and the unit is kilogram;

[0065] The carbon emission components from fuel combustion and tobacco breathing are calculated through accounting formulas. The accounting method is scientific and reasonable, and the accounting process is efficient.

[0066] The data division module is used to divide the tobacco baking time period; obtain the historical fuel loss mean WSc, real-time fuel consumption M1, real-time material consumption M2 and real-time carbon emissions TP2, and use the fuel carbon value monitoring formula:

[0067]

[0068] The respiratory carbon release monitoring value E is calculated; the units of real-time fuel consumption M1 and real-time material consumption M2 are both kilograms, and the unit of real-time carbon emission TP2 is standard cubic meters; the respiratory carbon release monitoring value E represents the relationship between the carbon dioxide produced by material respiration and the amount of water loss. The greater the water loss, the weaker the respiration of the tobacco, the lower the amount of carbon dioxide produced, and the lower the carbon absorption release monitoring value E;

[0069] The carbon release monitoring value E is analyzed. If the carbon release monitoring value E is not less than the first threshold value K1, the tobacco is determined to be in the first period; if the carbon release monitoring value E belongs to the threshold interval (K2, K1), the tobacco is determined to be in the second period; if the carbon release monitoring value E is not greater than the second threshold value K2, the tobacco is determined to be in the third period; the first period corresponds to the yellowing period of the tobacco, the second period corresponds to the color fixing period, and the third period corresponds to the dry tendon period; the first threshold value K1 and the second threshold value K2 are both obtained according to the historical baking data;

[0070] By analyzing the carbon release monitoring value E, intelligent monitoring of carbon emissions during the entire baking process can be achieved, and precise management and effective control of carbon emissions from tobacco baking can be achieved;

[0071] Evaluation module: used to evaluate the baking results and give corresponding prompts;

[0072] The specific evaluation methods are:

[0073] Obtain the total fuel consumption and the total material consumption, and generate a first normal label or a first abnormal label according to whether the total carbon emissions fall within the carbon emissions standard range;

[0074] And, generating a second normal label or a second abnormal label according to whether the total fuel consumption and the total material consumption belong to the corresponding standard range in the standard range set; and obtaining a judgment result;

[0075] Traverse the judgment results, if there is no abnormal tag, it is determined that the baking process is normal; if there is a first abnormal tag, it is determined that the baking process is abnormal;

[0076] If only the first abnormal label exists, the result of the traversal is checked to see if the second abnormal label exists. If the second abnormal label does not exist, it is prompted that the process needs to be adjusted, that is, the relevant settings of the non-characteristic sub-values ​​need to be adjusted;

[0077] If only the second abnormal tag exists, it is determined whether the total fuel consumption and the total material consumption are less than the minimum value of the corresponding standard range; if the total fuel consumption or the total material consumption is less than the minimum value of the corresponding standard range, it is prompted that the process needs to be adjusted; if the total fuel consumption or the total material consumption is greater than the maximum value of the corresponding standard range, it is prompted that the process is good and an optimization instruction is generated;

[0078] Update the corresponding standard range according to the optimization instructions;

[0079] The specific updating method is to update the complete fuel consumption that is greater than the maximum value of the standard range of fuel loss to the average fuel loss, or to update the complete material consumption that is greater than the maximum value of the standard range of material loss to the average material loss; and to multiply and combine the updated average fuel loss or average material loss with the minimum and maximum values ​​of the standard coefficient range, respectively, to obtain the updated standard range of fuel loss or standard range of material loss;

[0080] If the first abnormal tag and the second abnormal tag exist, it is determined that the total fuel consumption and the total material consumption do not meet the standards and need to be adjusted.

[0081] By separately judging and analyzing the total carbon emissions, total fuel consumption and total material consumption, corresponding prompts are given according to the judgment results, thereby improving the intelligent analysis effect of tobacco baking.

[0082] It is further explained that the above-mentioned fixed connection should be understood in a broad sense unless otherwise clearly specified and limited. For example, it can be welding, gluing, or one-piece molding, etc., which are conventional means well known to those skilled in the art.

[0083] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent detection device for carbon emissions from tobacco baking and a carbon emissions accounting method, characterized in that: It includes data communication module, data storage module, data calculation and analysis module, carbon emission accounting module, data division module and evaluation module; The data communication module is responsible for data transmission between modules. The communication module and each module are connected in a one-way manner. A data storage module is used to store historical processing data sets, wherein the historical processing data sets of a unified batch include historical carbon emissions, historical fuel losses, historical material losses, baking time, dry-bulb temperature, and wet-bulb temperature; the historical carbon emissions are marked as the total processing value, the historical fuel losses and the historical material losses are marked as the first characteristic sub-value and the second characteristic sub-value, respectively, the historical baking time, the historical dry-bulb temperature, and the historical wet-bulb temperature are marked as the first non-characteristic sub-value, the second non-characteristic sub-value, and the third non-characteristic sub-value, respectively, and a total historical processing data set is established based on different batches of historical processing data sets; The data calculation and analysis module is used to establish a standard range set based on the total set of historical processing data and determine whether the historical processing data set is the target data set, obtain the total reference coefficient, characteristic sub-reference coefficient and non-characteristic sub-reference coefficient of the target data set, and construct a reference set, obtain the characteristic reference sequence based on the reference set and establish a calculation model; A carbon emission accounting module is used to obtain the total carbon emission and calculate the fuel emission component. The total carbon emission includes the fuel emission component and the tobacco breathing emission component. A data division module is used to divide tobacco baking time periods; The evaluation module is used to evaluate the baking results and give corresponding prompts.

2. An intelligent detection device and carbon emission calculation method for tobacco baking according to claim 1, characterized in that: The method to obtain the feature reference sequence is: The total processing value in the target processing data set is calculated by ratio with the average carbon emission to obtain the total reference coefficient Z0NG; the several characteristic sub-values ​​are calculated by ratio with the corresponding average to obtain several first characteristic sub-reference coefficients, second sub-reference coefficients, first non-characteristic sub-reference coefficients, second characteristic sub-reference coefficients and third characteristic sub-reference coefficients; the reference set ZIk is constructed according to the several characteristic sub-reference coefficients and the several non-characteristic sub-reference coefficients; By comparing the sub-coefficient reference formula Calculate the sub-reference coefficient TBk; Obtain the minimum year-on-year sub-reference coefficient TBkmin in the target data set, and mark the characteristic sub-reference coefficient and non-characteristic sub-reference coefficient corresponding to the minimum year-on-year sub-reference coefficient TBkmin as key characteristic sub-reference coefficient and key non-characteristic sub-reference coefficient respectively; Characteristic sub-reference coefficients and non-characteristic sub-reference coefficients with the same name are divided into similar characteristic sub-reference coefficients and similar non-characteristic sub-reference coefficients; the number of occurrences of key characteristic sub-reference coefficients and key non-characteristic sub-reference coefficients corresponding to several similar characteristic sub-reference coefficients and similar non-characteristic sub-reference coefficients are counted respectively to obtain the total number of key characteristic sub-reference coefficients and the total number of key non-characteristic sub-reference coefficients; the total number of key characteristic sub-reference coefficients and the total number of key non-characteristic sub-reference coefficients are sorted from large to small to obtain a characteristic key characteristic sub-reference coefficient sequence and a non-characteristic key characteristic sub-reference coefficient sequence.

3. An intelligent detection device and carbon emission calculation method for tobacco baking according to claim 2, characterized in that: According to the element sorting order of the characteristic key characteristic sub-reference coefficient sequence and the non-characteristic key characteristic sub-reference coefficient sequence, the corresponding characteristic sub-reference coefficients and non-characteristic sub-reference coefficients are sorted respectively to obtain the characteristic sub-reference sequence {Ti} and the non-characteristic sub-reference sequence {FTj}.

4. An intelligent detection device and carbon emission calculation method for tobacco baking according to claim 3, characterized in that: According to the sub-indicator processing formula: The total index J is calculated; where A represents the characteristic sub-index corresponding to the characteristic sub-value, and B represents the non-characteristic sub-index corresponding to the non-characteristic sub-value; the characteristic sub-index solution Ai and the non-characteristic sub-index solution Bi are obtained; The solution set of accounting indicators should satisfy the limited range J∈ZONG×Y; Y is the standard coefficient range; the characteristic sub-indicator solution Ai satisfies A1≥A2, and the non-characteristic sub-indicator solution Bi satisfies B1≥B2≥B3.

5. An intelligent detection device and carbon emission calculation method for tobacco baking according to claim 4, characterized in that: According to the calculation formula Calculate the carbon emission component TF(x); where TP1 represents the total carbon emission, x is the number of the required characteristic sub-indicator solution, x ∈(1,2); TF(1) represents the fuel emission component, and TF(2) represents the tobacco respiratory emission component; the units of the total carbon emission TP2 and the carbon emission component TF(x) are both standard cubic meters.

6. An intelligent detection device and carbon emission calculation method for tobacco baking according to claim 5, characterized in that: The method of dividing the tobacco baking period is: Obtain the historical fuel loss mean WSc, real-time fuel consumption M1, real-time material consumption M2 and real-time carbon emissions TP2, and use the fuel carbon value monitoring formula: The respiratory carbon release monitoring value E is calculated; the units of real-time fuel consumption M1 and real-time material consumption M2 are both kilograms, and the unit of real-time carbon emission TP2 is standard cubic meters.

7. An intelligent detection device and carbon emission calculation method for tobacco baking according to claim 6, characterized in that: Performing data analysis on the carbon release monitoring value E, if the carbon release monitoring value E is not less than the first threshold value K1, it is determined that the tobacco is in the first time period; if the carbon release monitoring value E belongs to the threshold interval (K2, K1), it is determined that the tobacco is in the second time period; If the carbon release monitoring value E is not greater than the second threshold value K2, it is determined that the tobacco is in the third time period.

8. An intelligent detection device and carbon emission calculation method for tobacco baking according to claim 7, characterized in that: The specific evaluation methods are: Obtain the total fuel consumption and the total material consumption, and generate a first normal label or a first abnormal label according to whether the total carbon emissions fall within the carbon emissions standard range; And, a second normal label or a second abnormal label is generated according to whether the total fuel consumption and the total material consumption belong to the corresponding standard range in the standard range set; and a judgment result is obtained.

9. An intelligent detection device and carbon emission calculation method for tobacco baking according to claim 8, characterized in that: The judgment results are traversed. If there is no abnormal label, the baking process is considered normal; If the first abnormal label exists, it is determined that the baking process is abnormal; If only the first abnormal label exists, the result of the traversal is checked to see if the second abnormal label exists. If the second abnormal label does not exist, it is prompted that the process needs to be adjusted, that is, the relevant settings of the non-characteristic sub-values ​​need to be adjusted; If only the second abnormal tag exists, it is determined whether the total fuel consumption and the total material consumption are less than the minimum value of the corresponding standard range; if the total fuel consumption or the total material consumption is less than the minimum value of the corresponding standard range, it is prompted that the process needs to be adjusted; if the total fuel consumption or the total material consumption is greater than the maximum value of the corresponding standard range, it is prompted that the process is good and an optimization instruction is generated; Update the corresponding standard range according to the optimization instructions; If the first abnormal tag and the second abnormal tag exist, it is determined that the total fuel consumption and the total material consumption do not meet the standards and need to be adjusted.

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